Techstrong TV – January 29, 2025

In response to the Soviet Union's launch of Sputnik, the United States has started a new space program called Mercury. You're watching Textron Gang. Hey everyone.

Happy Wednesday. Welcome to another edition of, uh, Textron Gang. You know, it's, it's no kidding around.

We've had our little Sputnik moment here, perhaps in ai. We're gonna discuss that. We've got some other AI stuff in as well as a little quantum.

It's AI and quantum. I feel like I'm living in the future. Um, who would've thought?

But we've got a lot of good, good stuff to go over to. We've got good people to go over it with. Let me introduce you to our gang lineup for today.

First of all, sporting her Valentine's colors in red and pink and white. It's our, uh, resident. Well, weren't you, you were just on somewhere in Asia.

Was it Bloomberg, Asia, or Middle East? I was released, yes, last week. Very cool.

It's our own. Lisa Martin. Hi, Lisa.

How are you? Hey, Alan. I am so good.

I love the Sputnik reference. I'm a space geek. I used to work for nasa, so, and I'm big into ai, and this is gonna be a really interesting show.

I, I agree with you. I'm, I'm into, I'm a space geek too. Um, going from California, we'll stop over in Colorado for just a quick check in with our very own Mitch Ashley.

Hey, Mitch. How are you? Me?

Very good. You know, one of the things I remember, you remember Edward Teller, who was, you know, part of the Manhattan Project. Sure.

Well, I don't think know things he said about Sputnik was, it's a greater defeat for our country than Pearl Harbor. That's how impactful Sputnik was when it happened. Yeah.

Interesting. Interesting. Mike, do you wanna remind people out there what Sputnik was?

Sputnik was an effort where the Russians put a satellite up in the space just before, uh, we got our act together around the moon program, and everybody flipped out, and suddenly we were needed to be on the moon before anything else, and during the Kennedy administration. And, um, I don't know, you know, I'm sure it was a big moment at the time, but then they, you know, looking back in retrospective, I'm not sure anybody's remembers it as bigger moment, but you Guys figured it well, some would, some would say we wouldn't have had a moon program had it not been for Sputnik. It, it's spurred the, the US to take a hard look and maybe win it all cost.

Some people say it might have cost the lives of a couple Gus Gris and a couple of the Apollo one astronauts, right? Because they were under such pressure to keep ahead of the Russians and the Soviets. The real, the real fear was it was a ballistic missile that put Nik up there, and now we can reach anywhere via space.

That's what really freaked people. I mean, about it. Sputnik itself was nothing more than a little ball that sent out a beep beep beep signal, right?

Uh, but we'll, we'll talk more about that. I was afraid to bring it up too early, and I did. And anyway, Mike Ard, welcome to our show today, thanks to the historical reference.

Let's move. Well, hey, Hey. I would, I would just say that if we're talking space, I'm going with Steve Miller and Space Cowboy.

That's where I think it's at. Yeah. Well, Well, that okay.

That, but some people call him Maurice, but that's okay. Let's, let's move on over to, uh, Amanda, our editor at Lodge, Amanda Ani down in San Angelo, Texas, which is not quite Houston, but, uh, we don't have a problem there, do we, Amanda? Nope.

Doing well, Happy to be here today. Okay. Just full of space references today.

Right. Um, anyway, so, look, going to talk deep, deep seek, because everyone's talking about it, but, we'll, we're gonna save that for our second block. Let's go to our first block.

Mike, you wanna kick it off? All right. Well, there's been some noise in the social system, and Mark Andreessen is out once again, banging the drum, but he put out a tweet saying that ultimately the goal of AI was to destroy wages to the point where we could then rebuild the economy.

That seems like a rather broad statement. I'm sure there's a lot of hyperbole involved, but, um, I'm gonna start with, uh, Lisa, is that kind of stuff helpful at the end of the day? Because it seems like it just sends everybody running for cover and it's, it's really kind of counterproductive and maybe we do need a national AI strategy, because otherwise it's left in the hands of people who don't seem to care what happens to anybody else.

Yeah, I thought it was a very bold statement, but that is Mark Andreessen. So I wasn't surprised by that. But, you know, we, we just came off of talking about the World Economic Forum.

That's, well, actually what I was talking Bloomberg Middle East talking about where, you know, the big fear is AI will take jobs, and I talk about that on the radio to help, um, lay people kind of get their concerns as squashed, like guess. There, there we're already seeing jobs replaced by automation. We haven't talked about wage impact yet.

We're seeing that, but we're also gonna see 170 million jobs created in the next five years according to the World Economic Forums feature of Jobs report, which shows positive gain of 78 million as 92 million jobs are replaced. So there's some positive messages coming out there, and I think what Andreessen puts out in terms of wages, I haven't heard that before. I thought it was a bit of a pessimistic statement.

And I don't know if that means we need AI regulations. I think that's, that's a topic that we could unpack on its own. We probably will.

But it was, um, something that I think will send shockwaves through Silicon Valley investors, wall Street and Washington. Well, I, I had trouble interpreting, well, trouble, I interpreted two ways that could mean that we, we, our future is Star Trek, where we don't have wages and, you know, people don't have to make a, make a living. Um, or it could be, uh, medieval England, you know, back, you know, and the, the s surfs and the, you know, the, the, the Lords and et cetera.

I mean, it could, it could be very draconian. It didn't make sense to me who's gonna buy anything if they don't have any wages, Resource based economy, Right? Am I gonna afford that Tesla from Elon if I don't have anything?

Yeah, that's, that's kind of like a, let's figure that part out. No, I, it just seemed, uh, it seemed like a very arrogant statement in my opinion. Uh, so, So the piece, and I don't think we're calling for more regulations as much as I just feel like maybe we need an actual strategy, because it seems like, you know, right now there isn't anybody who's got their hands on the steering wheel.

But, You know, given the deep seek stuff, forget a national strategy. This an all out race to the finish line, or whatever the perceived finish line is, and rules and strategies and processes and safeguards be damned, right? That's, that's what you're going to get out of, out of our government right now.

But look, there was a time where I really respected Mark and Jason, right? The work he did in building out Mosaic and then Netscape and everything when he was still in school. I mean, he in many ways gave us a web that we u the web that we see today and use today as a, as a venture capital, as a venture capitalist.

He's be, he's very successful, but he's come, he's become a b******e, right? He cares about nothing other than the return on his funds. And maybe that's what vulture capitalists are about.

And he's, he's the head vulture, right? And of course, he'd like to see a crush, wages to be more efficient, to get greater returns on his investments. And that's all the man cares about.

And curing favor with our president. And if that's what, that's what he's become, honestly, I pity him as mm-hmm. It makes me think of that movie.

Show me the money. Yeah. I mean, that's the first, you know, all the money in, in the world don't buy happiness and doesn't make you a good person.

And, and so, but let's, let's get to the, to the crux of the matter, now that I've unloaded that off my chest, um, is AI going to crush wages? Yeah, when it takes away jobs, it makes menial jobs. I'm reminded, I probably have told this story before my, my oldest son got into the Kelly School at Indiana University when he was applying to college.

And I went up there with him on a Friday. And, uh, first thing they did is they brought us into a class with a professor. And this was an economics professor.

And he started telling, telling the students about why minimum wage laws were a bad thing, because it prevents people from giving, it prevents employers from providing jobs to people because the jobs would be too expensive. And so instead, they're going to use machines or some automation or robots to replace people with those jobs. And I raised my hand and I said, I don't know what PhD you program you went to or what, but this is not a school for my son.

Because as an employer, I will tell you, if I have a technology, whether it's robots or AI or anything else that can take the place of a human more economically, I'm a fool not to do it. Right? They never get sick.

They never take a day off. They never steal, they never do things that, you know, some employees could do. And so, you know, creating keep, keep getting rid of minimum wages just to create busy work jobs, maybe that work for FDR and the Depression and the TVA, the Tennessee Valley Authority.

It might being as your historical commentary, you could tell people what that is, but it doesn't work in the, in the, in the real world. So if AI is gonna take jobs away, let it take the jobs away. 'cause as Lisa says, it's gonna create other jobs, and those other jobs are gonna be high paying good jobs.

Go. Henry Ford, when he took, when he made the assembly line, everyone said, oh my God, it's taken away the jobs of these craftsmen who were building the horseless carriages. And you know what, it gave rise to the middle class, right?

So let the chips forward. They may, this is economics 1 0 1. Yeah, some, some, some jobs are gonna be d you know, taken away.

Some jobs are not gonna be worth paying the kinda wages they pay now. But this is how the, the economy, it's like first growth forest, right? The old forest burns down, new trees come in.

This is how the economy and, and the job market renews itself. Yeah. And as you mentioned in the last show, um, people going to school should maybe think more about AI related degrees and certifications, because you're gonna be working with a lot more AI that's taking those jobs.

So there's gonna be people needing to manage the ai. I, I think there's a real concern that you're gonna have political unrest driven by people who don't understand what you just described, and are gonna wake up one morning and go, let me get this straight. I can't make a living.

So I think I'll go vote for this other fellow over here who's gonna tell me he's gonna restore order to the natural way it was before all this chaos ensued. And, you know, we could have a serious bro. So, so we have that already.

And we have that all. I was just going to say that, Mitch, we're going to have, look, but here's the reality of the way our politics works. Two months ago, people were ready to, had their pitchforks and torches out because the price of eggs were so high.

Well, this week, the price of eggs went back up. And all of a sudden those people with the pitchforks and torches are saying, my God, do you know what goes into pricing eggs here between global climate change, the bird flu and everything else? We've gotta give this thing a chance.

Our country is so g*****n tribalized that they will move heaven and earth and high water to make an excuse for their tribes shortcomings or successes, right? If the, if AI crushes job wages, the red are going to say it's the blue fault. The blue is gonna say it's the red fault.

And that's just the way it is in this country. I'm sorry. Well, mob rule might be coming this way because you know what happens?

Isn't that what January 6th was? And now they're pardoned. Let's not even go here.

I was gonna, I was gonna say also, Alan, thank you. You, you and I were in sync on that one is, you know, we, we've had this multiple times. Just one example is manufacturing, leaving the United States, right?

Business pushed manufacturing overseas, because it's more economical. It, you know, saves money. It helps you be more competitive.

Uh, it brings a few challenges with it, but it disrupts jobs, right? Yeah. And you can talk about this steel industry or, or manufacturing community.

And we as a country don't have a history of putting together programs to help people, like seriously help people that are displaced when big changes happen in, in our economy, except for the depression. That's probably really the only exception I can think of. Um, so it's a, it is a natural order.

And I, my, just my personal opinion, I think a lot of the unrest has been multiple sequence of that between manufacturing and steel, and then, uh, robotics and then the cloud. And now AI is the next thing. And, you know, is there, is there a, uh, tipping point or a flash point where, you know, all of a sudden everybody, there's a big uprise?

Hasn't happened yet, but possible. I I just think this is another cycle that we're going through. I, I, I, you know, and I was gonna bring it up, when we talk about deep seek later, unfortunately, the knee jerk reaction of too many of the quasi intellectuals or not, they're not intellectuals.

The people who are in power right now is, let's put up a wall to keep the barbarians out, whether it's immigrants, technology, jobs, factories, whatever. Let's put up a wall to keep the barbarians out. They, they only did, they were only able to accomplish this.

'cause they used our technology. They used our chips, they used our universities. They came in here with as criminals and DR and broad drugs.

And let's put up a wall and keep the barbarians out. It didn't work well for China 2000 or a thousand years ago. It won't work well for us.

This is you, you've gotta embrace change. You've gotta embrace new realities, adapt and thrive. You can't, you can't react by trying to move ti move time back.

You can't turn back time like Cher says. And that's, that's Shimmy's uh, shimmy's advice for you all today from the great philosopher. S**t.

Yeah, her worst. But Gods, if I could turn back time. So anyway, We got, we got a lot of music on this show today.

Yeah, We got a couple of music references there. Mitch may wanna break out the guitar, but, um, in a minute, I'm, I'm holding myself back. All right, let's take a break then.

We're gonna come back. And this is we, the main events coming on up. Uh, we're gonna talk about deep seek target for cyber attack poppycock.

We'll be right back. com is the leading resource for news analysis and education on challenges facing the cybersecurity industry. com covers all aspects of cybersecurity, including data security, DevSecOps, cloud security, application security, network security, security threats, and more.

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Home of security bloggers network. All right, folks, we're back. And yes, well, everybody's talking about this new AI model from China that's got everybody kind of bent outta shape.

And the latest is allegedly that, uh, that suffered a cyber attack, and they were limiting registrations for the most popular downloaded piece of software in recent memory. But some people argue that that may or may not be the case. But Amanda, I know you've been following this on the socials.

What are you hearing? What are you seeing? Oh, so many things.

So, um, I actually have a few comments fold up that I'm gonna share. And it kind of goes back to what we're talking in the first segment. Um, but, uh, so one of 'em says yes, and the irony is that Nvidia gave them the lesser hardware and leftovers.

But one thing I'm reminded is that the Chinese government has invested heavily in their infrastructure. And I think the United States will be doing that over the next four years. It will be a big race.

Uh, then somebody else said, um, they're now under the microscope, whether political or legitimate concerns, China is the new Russian spy. So in my opinion, we should be aware of that implication in our conversations. Um, and then, uh, I was reading that article that was, um, talking about, oh, here we have another popular app from China.

Um, and, you know, if you listen to the conspiracy theories out there, which sometimes turn out to be true, I'm just saying, um, if everybody was paying attention to TikTok, when it got, um, pulled down, I was on TikTok when it got pulled down. Um, and then it came back up the next day. And now everybody's saying that, um, the content's completely different.

The algorithm changed. There's nothing about fires, there's nothing about the drones, there's nothing about the border. So was China trying to spread descent there through that app?

So in that regard, my thought, like, the wheels are spinning in my brain, yes, it could be an attack. So deep seek suffered this attack. It could be an attack from somebody that, um, is trying to break 'em down because their competition.

But could it be it's their own attack because people are already on it and now their own attack got all this information. I don't know. I'm just saying.

So let me weigh in here. Mitch. I did you wanna go first?

Mitch, you go ahead. You go ahead. I'll jump in.

All right, so first of all, I think this is as simple as we live in a crazy world where all of a sudden you're the top app on the app store, and it's like that IBM commercial, 10 orders, 12 orders, 200 orders, 5,000 orders, 5 million orders. Oh my God. Oh my God, we succeeded beyond our dreams.

I think that's what happened here. I think that they just didn't have the infrastructure to set up account to set up account registrations, and it crashed their servers. It's a very, it happens all the time now, but the world we live in, and the stakes, you know, at, at play here right away we say, oh, it's a cyber attack.

It was worth down. Who, who's looking to bring these people down? The NSA what you, do you really think open AI launched a cyber attack on deep sea?

Come on. Who know? It's just conspiracy theorists.

I'm quoting. I'm just saying. Yeah, no, I did.

And there's a, you know, what they say about conspiracy theory? It's every toilet has one sitting on top of it. Okay, now who, who is going to attack them?

Right? It now you wanna get really conspiracy theories, or was the Chinese attacking themselves to make it look like the Americans attacked the Chinese? Back to my article that I wrote Sunday, I know that, you know, that I know that, you know, let's stop.

There was no financial gain here necessarily in, in, in this attack nation state. I don't think it's, it's not the u it's, it's a short term silliness to, to block that right now. Right?

That's, I I think it know, again, Sputnik is launching ballistic missiles on us, and oh my God, I better go dig my shelter out and make sure I got a lot of water saved up. It, it's all part of this fearmongering, keep the barbarians out. Stop.

It was, it's as simple. They crashed their servers. It wasn't a cyber attack.

And they're claiming it's a cyber attack to curry sympathy favor and patience from the audience. Because you know how we all are here? My internet doesn't work at 35,000 feet on the plane.

God dammit, I'm not flying that American airline anymore. So they don't want people to say that. It's easier for them to say, well, we can't get you registered.

'cause someone cyber attacked us must be those ugly Americans, right? Boris? Yes, Natasha.

Hey, did you hear about this new afternoon show? It's called As the AI World Turns. Exactly.

Exactly. Well, hey, to quote a, uh, a famous philosopher Madonna, don't cry for me, Argentina, who gives a crap, you know, you know, another attack on another website, who gives a Crap? I'll, I'll point.

That's Not really a story I don't think. I'll point out something. I don't know if you caught Pat Gelsinger having a last laugh, but he was yes.

Pointing at the fact that, uh, gee, isn't it a, an amazing achievement that somebody actually created all these AI models using cheap, inexpensive hardware? And he was like, Hmm. Well, so that brings up the real mean of the issue here.

Yes. Whether it was a cyber attack or just a, the stuff, you know, uh, over their, their machines couldn't handle the load. The real issue here is this Sputnik kind of reaction that we're seeing, right?

It, it created such, I I I'm too young for Sputnik, right? I wasn't born in, I was born in 1960, so that was already, I think after Sputnik. I do remember the early astronauts.

I certainly remember the moon landing. Um, that being said, from what I learned about it in school and being a history buff, is it created tremendous angst in the West and in the US specifically. And had it not been for that moment, and it really was just a moment, right?

The US never would have moved the way it moved in, in, in, you know, with NASA and the space program and it, and it was Sputnik. And, and then if you remember, the Russians were the first one to put Gregorian the first man in space mm-hmm. A couple months before the us John Glenn, Alan Shepherd.

So I think that's what we're looking at here, right? The fact of the matter is, stop trying to say, I, you know, I read people, even some of our fu and rather on LinkedIn today, saying, until I see this replicated on, on, on our reliable hardware, I won't believe a word that said stop. Maybe it's not a hundred percent, but 90% of what these people have done is true.

They might have found a a, a better mouse chap. That doesn't mean the game is over. It just means now we got a new mousetrap to play with, and can we take that build on it, innovate out, innovate, outcompete, outperform.

That's America. That's what we need to do. We chose to go to the moon, not because it's easy, because it's hard, right?

Here's what I like. Isn't that calling like about deep fake, is that it, it it actually has a unintended consequence. Wait, Mitchell, what did you call it?

I'm oh, deep fake. Sorry. That was Ian was Talk about, okay, moment.

So deep peak is, you know, I don't believe what the Chinese say about how much time it took and how much money, and who knows, could be true, could not be true. But the, I think the fact of it is it gets us off of this hardware infrastructure train of that's, you know, a bigger, better hammer solves all problems. And it's putting it back on the board to say, all right, if this is possible, whether they did it that way or not, or whether they had Nvidia chips or not, and let's go back to the drawing board and let's use a constrained model of trying to do this much faster, much cheaper.

And how can we do innovation down that path instead of just complaining we can't get enough chips from Nvidia. So I think there's actually a, a, it's sort of the, uh, the Star Wars effect. You remember that when Reagan announced that against the Russians about we're gonna be able to do all these things in space and, you know, attack Russia and defend ourselves.

And 90% of it was fake, but it was, you know, misinformation. In some ways, this is kind of that moment with deep seek for us, like to get us, it's, it's gotten us to react. We'll see if we forget about it by tomorrow, but it could have a, A lot of reaction, isn't it?

Calling to question though. Sorry, Mike. Um, you know, something that we've been hearing about for such a long time is just that hundreds of billions being invested in AI infrastructure and investors wanting to see where's the ROI, where's the proof in the pudding?

And we just saw a big commitment with Project Stargate, what, a week ago-ish committing 500 billion for AI infrastructure In the US that didn't age well. Did at least It didn't age well, not at all. But does it call into question the metas uh, the Microsofts who are saying, we're gonna be investing $60 billion each in AI infrastructure.

Does it call that into question? Can it be done? Is it, it's, you know, they're saying we can do this way faster, way cheaper.

Now there's rumors that they have access to 50,000 Nvidia chips that they can't disclose because of export rules. But does it call into question the validity of the, of the, some of those in the magnificent seven who are investing bill tens of billions of dollars in AI infrastructure? I I, I've got a few thoughts on that, if you don't mind.

2, 2, 2 streams of thought there. One is, look what they've done here with, and I, I've been reading some of the technical stuff and admittedly it's over my head, right? I don't claim to be an AI engineer or know the final workings of neural network and, and training and models and so forth.

However, what I have read is that what they've done here is really optimize for math, mathematics, and code. Now, will that allow it to branch out into more general reasoning, perhaps, but it's really focused around mathematics and code. And it may not do the kinds of things, but you know, for instance, they say it's more comparable to the four, oh, uh, chat GPT model, not the O one or even the O three, which is newer, that it wouldn't necessarily, they don't think it would necessarily scale, for lack of a better word, or compete with O one or O three.

So that this methodology of re reinforced training and so forth on, on cheaper hardware and less resources may work for this narrow type of use, which is a very useful use, right? But it may, you may still need to, to scale the heights, if you will. So of, of you know, of a AGI or whatever.

Mike one another. I actually, I'll come back to my next point. Go ahead.

You jump in. To me, that kind of sounds very similar to what we see in the car industry, right? There are very expensive Maseratis that might be built by open ai.

We're using their model, and then there's gonna be generic models that are next to free because we built them using open source software, and that's gonna be what the bulk of most people use would Be. Is that gonna limit the number of API calls to open ai? Absolutely.

But, And so be it, if that's the way it is, that's the, that's the market at work. But you mentioned the key point there, the open source, that's the real winner here. The real winner here is open source.

This thing was supposedly built using open source models, and it, and in and itself is, was released under an open source license, right? Which allows other people to, you know, Mitch and I have been through this with open source. Uh, it allows other people to use it to innovate off of it, to continue developing and, and seeing it in their own labs and seeing what happens there.

Can they replicate it? Can they improve upon it? For everyone who wants to put up the wall to keep the barbarians out, it it sand, the harder you squeeze it, the more it slips outta your fingers.

And when you release this kind of thing as open source, I mean, I'm trying to think of a movie that, that once it got out there, it was like you in everybody's hands. Covid you mean like covid? Well, COVID, well that's not a movie, but no, you know, another, another words, once it's out there, it's wildfire, right?

And, and you are not going to be able to build a wall around it and keep it out or, or what have you. It's open source. And that's, that's the real beauty of, of open source.

And, and so we're gonna have to see how this plays out. It, it may be a world, Mike, as you where, you know, open AI is a Cadillac or a Maserati, but a lot of people are happy driving a Chevy. Well, I think it's, it's myopic to only have a view of the goal is general ai, right?

And every, and that's the only thing that's valuable. I think it's this, this shows that there are multiple paths and sometimes good enough is good enough. Not everyone has to have the latest iPhone 17 pro max, like Alan and I do.

Some folks are happy to have the starter entry level, right? And, and, you know, is, is everybody gonna be able to use the highest end model that does the greatest latest thing? No.

A lot of people will be able to leverage a service that is much cheaper and is good enough for what they need that job to do. So I think it's myopic to think that there's one path we're all headed towards one destination. Yeah.

That's partly race. Well, well, but Mitchell, they call it the singularity. Come outta that, right?

We we're gonna get Velcro, we're gonna get Tang, we're gonna get all these other things that come out as part of the space race. But Mitch, they, they call it the singularity. Mike, I'm sorry, go ahead.

I know, I'm, I'm looking forward to when they try to quote unquote ban this. And then, you know, when we're chasing immigrants will be chasing smugglers of the deeps seek AI model. Yeah.

Let me check your chips. Bring it in plane. Lemme check, lemme check your chips.

I mean, that, that, but, but unfortunately, you know, I, I look, you know, I'm not a huge fan necessarily of Donald Trump, but did you hear what he said about this? Right? He said, wow, this, this is, this is, this has gotta give us reason to, to try harder to do more.

But the good news is, is ultimately it's gonna be better for everyone. 'cause it's gonna bring the price down. It's gonna make it more available.

That's the right attitude here. Where other people in our government are saying, this is the Chinese Communist Party attacking us. They've built it using the technology they stole from us.

They, you know, those are the keep build the wall, keep the barbarians out people, and no, we, I think you, you take this and say, okay, thank you. Thank you for waking us up. Thank you for showing us that.

Maybe there is another way. Let's, let's out-innovate them. Let's, let's build on this.

Let's go do a better, let's do what we do, build it better. This can be a great catalyst. Yeah, exactly.

That's, that's the added, this isn't the time to build your bomb shelter. We don't need any ducking cover drills. Let's go for it.

Absolutely. All right, Mitch, take us over the hill. Um, take us Over the hill.

Thank you. We're gonna take a break here on Text and Gang. Let's, let's, let's get outta this cutting edge stuff of AI and we'll talk about some quantum.

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Contact us today and tell your story to the world in the most powerful way with Textron Group. All right, folks, the next conversation is about Q Day. It's coming.

We don't know when, but the theory is, is that we're seeing rapid advances in quantum computing, and that's gonna break all our cryptographic algorithms. So we need to replace those algorithms, which is much easier to say than apparently do, because apparently some folks are saying it's gonna take years. And if we don't start now, we won't have it in time for when Q day arrives.

Uh, I don't know, Alan, you've been following this too, but there, first there was Google talking about a new quantum willow chip, and then there was, uh, some other startup talking about an even faster chip. So it seems like the faster these systems get, the more realistic they are. The sooner QA arrives.

What do you think? Well, let me first deal with the story at hand and then I'll, I'll go, I'm gonna do it backwards. I'm gonna go from that specific story to the more general quantum stuff.

I usually like to go general to specifics Socratic method, but that's the martini glass project, right? We'll, we'll go the other way. Um, this Palo Alto network announcement about post quantum cryptography, APIs available, congratulations, of course.

NIST put out post quantum algorithms for encryption. Now, about a year and a half ago, Digi Cert and some of the other cert providers have been using post quantum or have available post quantum algorithms for their certificates now for some time. So this sounded very much to me, like a me too.

And hey, they're talking about all this AI stuff. We don't wanna be just another AI story. Let's put out a quantum story.

Congratulations Palo Alto. Um, but to quantum in general, you're right, we have been seeing a steady drumbeat here. Uh, the willow chip you mentioned, uh, I know some of the folks who are at the, uh, not CES, the Supercomputing show in Atlanta.

There were two different quantum computers. One from A IBM, which is still the perceived leader in quantum, um, one from a startup. Google does have the, uh, the willow chip as you said, but also importantly, Google's, um, not deep seek and not deep fake DeepMind.

They bought DeepMind a while ago. Not deep thought either, right? But DeepMind uh, announced it, I don't know, maybe a week or two ago, um, that they were actually using AI on quantum computing, uh, issues and programs that made, made using quantum existing quantum chips, existing quantum technology, much more feasible and maybe pushing up than the, the day for Q day, right?

When quantum becomes more. Um, but it's funny, I, this isn't, I I, I'm fascinated with quantum computing 'cause I'm fascinated with quantum mechanics in general. I, I'm just not smart enough to grasp it.

Uh, but I read it and my head goes, um, I don't know if I can make that noise again. But anyway, um, it's coming. It's coming.

But when the more people I speak to, the more I realize it's probably not coming till 2030, maybe even later. 'cause there are significant hurdles, right? For most tests that you're going to use the computer for, you're gonna have to bifurcate what needs to be done at a quantum, on a quantum processor versus what needs to be done on traditional binary and then bring it back together, put it back, segregate, bifurcate it back out, bring it the, the, you know, just the, the, the, the blueprint, the, the, the process for what you're going to need to really run real world things.

Where Quantum's gonna be able to help you like that, I think is still a while away. Probably beyond my horizon is, is how I put it. No, I heard something kind of about that.

Alan, um, Dr. Bob Suiter, who is a mm-hmm. Uh, analyst at Futurum.

He covers Quantum. Yep. And he comes from like, longtime IBM and he worked on quantum and other things there.

So he is a real authority on this. He, he said something, and again, you know, I'm learning too, right? I'm no quantum expert by far.

You said that, um, the challenge with Quantum is it, it's not good at things that require a lot of data. So if you have problems that don't, that don't require a lot of data, I'm not sure why that's an issue. Um, it's really good for it.

So to your point about offloading, how do you, what kinds of things do you offload or handle other ways maybe in traditional, I mean, you need microservices to the umpteenth degree, right? And Wait a minute, wait a minute. What a, what about this Chinese Deep C quantum startup that found cheaper hardware in these, this No.

Did they got taken down by a cyber attack, Right? RIP. So, uh, my thoughts when I, when I was reading up on the quantum is, um, there's a lot of preparation on the cybersecurity front when we do hit the, you know, QA because all those people holding the keys that are gonna be easily encrypted at that point.

So what are your thoughts on the cyber aspect? So I, I think this is a case where we actually are out in front of things and that, you know, because NIST and I think Mitre was involved and private industry have been putting out kind of quantum proof algorithms now for well over a year. By the time you have Q Day, all the locks on all the doors and the safes and the banks and everything aren't gonna break, right?

There's a Y 2K, I don't, I, well, maybe the banks will have the resources to prioritize it, but I think there's large numbers of companies that are dependent upon encryption that are gonna wake up one more and discover, discover that the stuff that they had today that they thought was encrypted, was harvested five years ago and is now being decrypted and all kinds of embarrassing things. Well, that, that could happen, right? Stuff that was take like salted or hash salted, uh, information that is now undergo.

But, but that information, Mike has a fungible life. It was five years ago. It may very well be useless by now.

It's kinda like email addresses. Unless Q day comes a little sooner maybe, or, you know, there are things that companies do that they still don't want anybody to know About 10. Well, this is why there's such a big push to make the lives of, of encryption certificates shorter, right?

They wanna go from never ending to two years to a year to 90 days, to 30 days to two weeks, because they wanna force the system to adopt the latest and greatest, uh, encryption for your, for your encrypted, you know, certificates and stuff. That'll be quantum proof. And they're doing applications of, of certificate life of minutes and seconds, because that's how quickly they want to turn over.

I mean, I think that risk of the, the data being, you know, unencrypted later, there's all kinds of rumors about government storing and maybe bad guys storing encrypted information to be able to use it. The problem is, uh, quantum computing has to be at a price point that you can get to use it besides being the, I mean, government of the US or I mean, rumor, the NSA might have a quantum computer already. Yeah.

Just, but most, most people are not gonna have access to it for some time. I'm sure that room is the Chinese Communist Party does too. That's how they got into this deep seek thing.

I'm kidding. I'm kidding. I mean, the, the bottom isn't gonna fall out of the quantum market the day they announce they've got a quantum computer you can buy.

Yeah. It's gonna be a while before it's accessible to a lot more people. I'm not sure nation states care about the cost that much.

So Chime is just gonna build one. We're gonna build one. Oh, that's who can afford to do it.

I'm saying you and I and everybody else that, so be used for that. But that being said, you know, I, I'm gonna call this my Zein Pike theory, uh, um, right. I'd like the Thon Webb.

No, no, no. Zebulon Pike, right? Is don't underestimate the ability of some lone wolf genius who figures out a way to, to make quantum computing or figures out a cheaper, faster AI thing and launches himself into low earth orbit just as a Vulcan ship is going by.

And, and ha you know, boom, the next thing you know, we're part of the federation, um, this, that could very well be, I mean this, this right outta Star Trek law for all your zeis out there, right? The earth, you know, there's a, there's a, some sort of h caustic war between the powers that be and, and, you know, people had dispersed, kinda living in small groups, but, you know, some guy out in the, was it in Minnesota or somewhere he was, or Wyoming niche, I don't remember. He, he manages to, to break this, the speed of light barrier and, and achieve warp speed.

And, and, uh, and that's how he was in the set. Yeah. In this, uh, we might have similar kind of breakthroughs.

Yeah. Deep seek is sort of like that. We might have a sim there might be someone right now in, in, uh, Nebraska working on some quantum Siri, right?

It's, it's right next to their coal fusion reactor on the other side of the garage. Well, could be. Hey, this is the year for nuclear fusion.

That's what I'm waiting for. I've been waiting for that my whole life too. Mm-hmm.

Zin Cochrane. Zephyr Cochrane. Not Avil, not Pike.

Yeah. Captain Pi, Captain Pike. Right.

But it Zephyr Cochrane, that was the myth. Mm-hmm. Right.

See, we just ed the Warp Driver. We just saved you 4,672 emails. So there you go.

There you go. Thank you. I thank you.

Deep seek for that answer. I just got out. Exactly.

They filtered that out. But, um, anyway, it's been an interesting show here today. I hope you guys have had as much fun as I've had talking about some of this stuff.

The, these, these are great tasting. I'm glad we have this forum to talk about it. I'm gonna just do a quick plug for my own thing this Thursday, 2:30 PM Eastern Time.

I'm gonna be on LinkedIn live for my second episode of Shimmy. Says it's only about 10, 12 minutes, but I'm gonna be talking about Deep Seek. If you want to talk to me about it, come on to LinkedIn live.

com page where and LinkedIn Live will be there. And I'm welcome your questions and comments. I'll try to hit 'em if they're there.

If not, you could just listen to me. Um, Mitch, you are heading out to, uh, tech Field Day ai, right? Yeah.

AI Field Day in San Jose. Some great companies we're talking to about the latest steps that they're doing. You can watch it on Techstrong TV tomorrow and I guess today and tomorrow.

Good. Um, you have some great conversations. We have delegates that are asking all those good questions you'd love to ask, right?

Um, we have several vendors that are coming in, so join us there. It's also on technical day website, YouTube, YouTube channel on all the papers. YouTube channel, text channel.

It'll also be the Textron Gang for, uh, um, Thursday and Friday this week will be live from San Jose at Will Not Live, but recorded from San Jose Tech Field Day, AI Mitchell will be there, along with John Willis, Steven fst, some of our other friends. So stay tuned for that. Lisa, what do you've got going on?

I've got going on just more research on ai. One of the things I'm gonna be talking about on the radio is AI powered serendipity. Ever wonder when you're scrolling through like Netflix and it pops up something that you would never have, have thought was the right thing.

What is AI powered serendipity? How does it work? And why is it a good thing?

And something to also be a little bit concerned about. So that's something I'm gonna be digging into for this week. Very cool.

You'll have to come back and tell us. Yeah, that will be On, that will be on the next episode of Techstrong Gang that you're on. So we'll be coming back to That.

We're coming back to that. Perfect. I'm, I would just say, you know, um, are you gonna translate all that LinkedIn, shimmy says stuff into Chinese for our new AI overloads.

How's that Going? I don't have to. It gets done.

It gets done automatically. All right. On, on Pentium chips, by the way.

Oh, old school. Amanda, what do you, I know you've been up to your ears here. We should announce Amanda's recently, uh, assumed a new role here at Techstrong.

Amanda is now our senior managing editor for All Techstrong sites. So Congratulations to Amanda. Thank you.

Go Amanda. You go. All right.

It is, It is, it comes with great responsibility because, well, if anything goes wrong, the first call goes to Amanda. There. That's good.

That's good. Maybe you can get one of those Chi Chinese AI to help you. Um, just kidding.

All right, look, thank you. Wherever you are. Was it Raim Cochran.

Raim Cochrane. Yep. You know, I actually saw a thing at somewhere in Iowa.

There's a, it's the future birthplace of James T. Kirk, because maybe there's somewhere there. Anyway, we've had enough, we've gone into One episode.

Does he make his first appearance on Star Trep Cochran on the original series, they crash land on a planet, and he has a companion who's, who's cured him and keeps him alive. Immortally. And she becomes a, a human person.

'cause they're in love and they live happily ever after, but not forever, because now they're humid. Do you remember that one, Mitch? Oh, I do.

I do. And I think it was like the movie Star Trek seven or something when it was the New Generation Next Generation took over. When, when he, when he breaks the war, Barry.

Yes. Yes. I think it was the Sierra Mountains is where he was, or somewhere like that.

All good for all you Trekkies out there. Don't say we don't cover it here on Textron Gang. We'll be back tomorrow.

Trans aluminum. That's another invention. That's a great one.

So, yep. We'll be, we'll be back tomorrow, actually. Excuse me.

We'll be back tomorrow, uh, from Silicon Valley and, and Tech Field Day. So stay tuned for that. Stay tuned for, uh, we actually have Tech Field Day AI live on our show today, as well as some more great news and content on Techstrong tv.

Until then, though, this Alan Shimmel, on behalf of all our gang members here today. Good luck. We're out.

This is Textron tv. Hey guys, it's Alan Shimmel here for Techron tv. I've got a gentleman I want to introduce you to.

His name is Art Gilliland. Good morning. That's that right.

Art Gilland. You did it. Thank you very much to You.

Excellent. Art is the CEO of a company called Deline. We're going to find out all about Deline and about a new report they've recently released.

But before we do that, let's find out a little bit about art. Art. Give us kind of the, uh, the art story, if you will.

Uh, the quick two, uh, two second version of it. Uh, so Alan, thank you for having me on the show. I really appreciate it.

Uh, so hello folks. My name is Art Gilland. I'm the CEO of Delineate.

As Alan said, I've been in the security industry now for about 26 years or so. I, I like to say I tripped over it on accident at a startup back in the late nineties, uh, and then have been a part of the ride. Uh, Did most of us Yes, exactly.

We a we accidentally tripped Over. Same thing here. I've been in it since then and no one Oh, for security then that unheard of.

It Was, but it was not so cool then. But now think, of course, people are making movies about it and it's in the news all the time. So it's been a, it's been quite a ride for sure through, for me, through small companies, startups, as well as, uh, very large companies running, uh, the businesses at Symantec and hp.

And then, uh, a, a time as a CEO of a startup that got acquired by Cisco and now, uh, here at Deline. I love it. com.

And again, by access, Slammer, worm and all that excitement. Well, we were, we were operating data centers, and the next thing you know, we, we had to do manage firewalls and, and people started caring about what was kept in those data centers. And that's right.

Then my next company that I, I also did a lot of startups, was a full on InfoSec company, as we called it. Um, how long are you with De Linear Art? I've been here now, uh, almost four years.

It'll be four years in March. Uh, so I was part of TPG who owns us, uh, when we brought these companies together to, to create delineate in the identity security space. Ah, TPG.

We deal with them also on, uh, oh, do ai, a DevOps company also kinda a little bit of a roll up within it. That's right. Um, give a give if you wouldn't mind.

You know, let's talk about delineate. So it sounds like it, it's the, the product of a, a roll up of a couple of companies put together. Give us kind of the genesis there, if you'd the Yeah, so I mean, I think, uh, I'll, I'll give you a quick history if that's all right.

A little, uh, it's a little bit of a stroke, but I'll I'll do that. And so I think one of the, it de Linea was really formed at the, uh, at, at a conversation we were having around sort of what areas of security was I most interested in. Um, and I think the, the two areas in security that I think are the most going to be the most impactful, are the most interesting right now are identity, security and data security.

And the reason I think that is because if you look at what's happening for most companies today, they're in some part of the process of moving a lot of their infrastructure into cloud or SaaS computing. Um, you know, back, you know, when we started it was all walled gardens and you had firewalls and stopped everything, uh, kind of at the edge. But if you look at it today, most companies are basically renting infrastructure from someone else, whether that's the application you use for SaaS or the, the infrastructure from like a hoster or something.

And the reality is, is you don't control the policy in those. They, they attest to you what they're doing and you, and you try to set, uh, sort of boundaries for what they do. But the reality is the only place that you really are gonna be controlling the policy is on your users and what your users are allowed to do.

Uh, and then hopefully at some point on the actual information itself, no matter where that information is living. And so when I look out over the, you know, the horizon of where do I think the most interesting innovation and challenges, where are they gonna, where is it gonna occur? Um, that's kind of how this came about.

And so, uh, we started looking at the identity security space and saying where, uh, where are there opportunities? And, uh, the opportunity was really to build a platform for authorization. Um, and so that means like being able to set the rules, centralize and set the rules on what are users allowed to do in any environment, whether it's your own environment, whether it's your laptop, whether it's in the DevOps space, whether it's in cloud.

Um, and so Delania is building that platform for intelligent authorization, essentially to define what privileges, what entitlements are available to the users after they, uh, after they log in. So there's, there's authorization saying, okay, you, you can go here, but you can't go there. That's right.

But then, you know, a another piece of of this of course, though, is identification, right? Before I could let you hear there, I gotta make sure you really aren't, Yeah. So I should break identity into sort of three basic spaces, just, I mean, look simple.

Mm-hmm. But it helps me 'cause I, I think, I think simply about stuff. Um, so step one is authentication.

That is I am art and prove it. Right? And so in that world, you have obviously the Microsofts and the Pings and the Okta's and those folks Okta, that and Yeah.

And so my my view of that is that that world is gonna continue to commoditize, right? You're gonna look at Microsoft and Google kind of owning the I am art and I can prove it. There may be open source.

If you look at what happens right now in consumer, it's kind of a bring your own. I can authenticate to websites using my Google ID or my Facebook, and people still are using that. Yeah.

That shows zero off or whatever. Yeah. And so this, this ability to authenticate.

And so that's step one. Step two is then deciding after I've proven I'm art, what should art be allowed to do in my environment? And that's the area where we are focused.

Um, and then the next part is governance. So tell me everything Art has access to give me a workflow for making sure that I refresh those things. And so if you look at companies like SailPoint and savi and, and yeah, to a certain extent, uh, de Linea as well now too.

'cause uh, because of an acquisition, we made those, that whole workflow of defining and, and reporting on and governing sort of the privileges that people have, that's step three. And so if you look at those sort of three steps, we are very focused on the area that I think is most interesting, which is tell me what ARC should be allowed to do. Yep.

Yep. And, and, and, uh, and you're right that that is how the identity space is sort of broken down. You know, I've always felt that identity was kind of the, the killer app for cloud security, right?

'cause when we moved away from the, you know, moat and castle model to a cloud model, having big boxes on a perimeter that doesn't exist anymore was trying to, we've been talking About that for, uh, for like a couple decades, Alan, with the Jericho Forum and the sort of perimeter, Yo, you Still do pretty good. So ERs, But exactly now micro ones. But companies like Palo Alto and others have been continuing to just, uh, to accelerate.

And I think, you know, their view is there are some, there are digital boundaries, and I think that's still gonna be a really important part of the security landscape. But I do think identity is gonna become more and more critical in partnership with these kinds of technologies to really secure the environment. Look, You know, those technologies are, are they're traffic cops.

Yeah, that's right. Right. They, they could lower the gate, raise the gate, yep.

What information they use on when to lower the gate or how to lower the gate, could be the kind of stuff that Right. And that, that's the kind of stuff I imagine they could get from a delineate. But that's a great way of looking at it.

Yeah. Um, and it, it more true than ever. Uh, you know, for me, of course the big thing in identity and then authorization is non-human identity and authorization.

The explosion in that is humongous. It's uh, it's really, and then AI obviously just adds another layer of that kind of connectivity. Well, I just had this discussion with another vendor.

What about agents? Right? Everybody's talking about this is gonna be the year of AgTech ai.

Yep. You and I are gonna have dozens of agents doing our bidding and we'll have an agent orchestrator hopefully that manages alls it all. But quite frankly, how do I know arts agent is arts agent?

And that Arts Agent one is okay for this, but Arts Agent two isn't. And you know, just a another level of complexity that I'm sure you guys are already thinking about and how do we go there? Yeah.

I think with, when you're looking at the, when you look at sort of the, the explosion of what I'll call service accounts, uh, and you know, these were API connecting to another API and those things talking, and you see it in DevOps. You see it in the way we build applications. You know, when outside talks to inside through an API connection, there was already a lot of investment in trying to lock those down and find them all and secure them.

And that, that, if you look at sort of the environments that we serve today, there's the human privileged users, but then there's also these connect connections and service accounts and non-human connections. I think what is happening with AI is now you're giving that, uh, that intelligent quote unquote, uh, connection. You're connecting it to a bunch of stuff because that robot, we'll just call it a robot.

'cause I think it's easier that robot is actually needing a bunch of different information from different places. Typically, those API connections have very, very broad rights and they can get access and take actions and do things. And so, uh, there's going to be a lot of focus on how do you manage those robots?

How do you let, how do you make sure that those connections should happen, number one, and, and verify it. And then how do you control what they're allowed to do once they make that connection? So certain eight robots will connect to the same API, but they may only need to use a small little piece of it.

And I think that's mm-hmm. Me, that's the, that's the place where customers are really gonna focus on privilege management solutions like deline, which is, yeah, you can authenticate it, you can make sure that password is safe, but then how are you managing that rotation of the password? How do you make sure those connections are, uh, sort of rebooted that the passwords are not hardcoded in the robot's mind, that it actually is a token that gets changed a lot so that if the robot gets compromised, you can turn it off, uh, and you can stop its actions.

And then also how do you limit, just give it enough, just enough or just in time access. Um, and I think that's gonna be, that the pace of that and the speed of that is going to require system centralized management systems like ours to be able to help companies take advantage of it. Um, personally, I'm excited About it.

Zero trust, Euro trust just right isn't enough. There's, there's gonna be Exactly Layers and layers, But I'm excited about it 'cause it, for the first time I'm seeing sort of a pathway to having a security product be a business enabler with an ROI behind it versus just insurance. I mean, I think our Euro, Euro life and my life, we've been sort of glorified insurance salesman.

And I think now you can have a conversation that basically says we ROI calculators. Exactly. Sure you did too.

Right? But now we, we can tell a company, I'm gonna help you go faster. I'm gonna help you adopt this cool stuff.

'cause I'm gonna help you manage it a little better. Kind of like the brakes on a car. You can drive faster breaks.

I think we can do that. And you're gonna need it because it's just, as you said, the, it's not just people. When you start adding these agents and API and a APIs API traffic already accounts for majority of the traffic out there.

Right. Yeah. It's crazy.

Anyway, art, I wanted to move along. You guys recently released a report on, uh, identity and, and one of the topics was, you know, what do budgets look like? Yeah.

Yeah. I think, look, if you look at the, the overall sort of output of that, uh, report, there's not a lot of surprises, at least in, except for in one area. And I think, uh, the things that are not surprising, but, uh, but just reinforce what we see is this growth in budgets.

Um, for identity and particular, uh, companies are spending sort of 20 to 30% of their entire budget on identity solutions, whether that's the authentication piece or the authorization or the governance. They're spending a massive amount of their budget, and that's only growing. Um, and so we see a lot of, uh, data like that.

I think the other thing that's, uh, not surprising but interesting, um, is just how much, uh, security risk identity has posed. And sort of part of the reason the budgets are continuing to go up is just, uh, you know, still explosions of, uh, attacks on and sort of focus on, um, using identity as a, a way to get access. And so, you know, huge percentages like 80, you know, in the 80 percentage range of sort of identity based attacks.

Uh, and I think those, uh, areas are sort of reinforced again by this research. And you see a lot of research out there that is showing that. And so, you know, it's, it's one of those sort of, again, like a little bit like the insurance side.

You're like, great budgets are growing in security, uh, attacks are becoming, uh, more, uh, sort of more, uh, more, more relevant. Um, yeah. But again, I think the tho those things I think are, uh, are clear, they're reinforced, um, by, um, by the research for sure.

Here. I, I, I couldn't agree more with you. Um, if you don't mind me asking, when was the data for this report assembled by get how, how?

Yeah, so this was, this was, this was research done over sort of the November through December, uh, and early January term. So it's, it's there, It's fresh, It's an interview at somewhere 350 or so, uh, different IT security folks. And so this is, you know, this is 2025 data for sure.

Uh, and how we, and how we go there. Um, it's obviously there's that. Yep.

Go ahead. Mm-hmm. No, no.

I'm sorry. I'm waiting to stop you. Go art.

Yeah, no, I, I, I was gonna say, like, there's, there's that interesting information that we, that we see from, from the, the data. I think the part that is the most interesting in the research and the, the place that I spent, uh, a lot of time sort of trying to interpret and think about sort of what is it actually telling us is the, the, the massive numbers of companies that are thinking about using and being attacked by ai. And so, uh, that part of the report, I think, uh, is kind of an eyeopener, right?

And so the other ones I think people will read and go, yep, yep. That seems reasonable. I think the part that is the most impactful is, is the AI one.

I I as seems that's the story everywhere, right? The AI being most impactful. Yeah, but I mean, but, but here's the thing.

You know what, quite frankly we've been hearing when it comes to security budgets that a lot of organizations are saying, Hey, we've been on a binge for 10 years buying you shiny new trinkets, and, and our security is not demonstrably better than it was necessarily. Yep, that's right. And, and so the fact that they are still increasing their identity security spending this year says that may be true, but we know how important identity is.

And because of the rise of, let's call it machine identity, whether it's robots or APIs or what have or agents Yeah. Um, people are recognizing that we're gonna need to spend dollars on that. Yeah, no, I mean, look, I think what they're, what they're seeing is that they believe, uh, you know, in the high eighties, uh, that they have, uh, seen identity based attacks.

And so I think what's happened is they've done a good job of securing their endpoints. They've done a good job of securing the perimeters. And so they have a lot of technology that's in place that actually is doing its job.

And so because the adversary is a learning human, uh, environment or a marketplace or whatever you want to think about as the adversary, they've moved to the place that is, uh, that probably is the most vulnerable. And so, you know, people really aren't breaking in as much as they used to. They're basically just logging in.

And so they're stealing credentials. They're tricking the humans to give away credentials. They're looking at applications that, how did you proceed?

And so now they're just logging in. And so I think companies are starting to think about what do I do about that? Um, and then of course, you add AI on top of that, and now you have sort of a, a, an autonomous system to a certain extent attacking the most vulnerable place in the environment, which is the identity elements.

And so companies are, are putting a lot of resources there. Um, Yeah, we, we just, you know, we were, the day you and I recorded this happens to be National Data Privacy Day. I don't know if you're aware of that, Right?

You could actually go, you know, get a t-shirt down to the Hallmark store and get a card, something, right? But, but the fact, and, and so we were having a discussion in a few people on our Textron gang we're like, well, we need to be encrypting our data and, you know, and protecting our data. And that was the point I made art, is that people are brute forcing their way in, and they're going to steal all this salted dash, you know, hash data, and no, they get in right through the front door with your credentials, and you do have access to that data.

And until we, until we hit that ha square on, you know, everything else is kind of secondary. So that, that should be job one as we deliver data privacy type. Anyway.

Yeah, Go ahead. Go ahead. I was gonna say to your point, yeah, so I think to your point, um, the reality is, is there, there's a couple of levels of that security that need to happen.

One is obviously you wanna manage those passwords, you want to change them frequently. So if they do get stolen, it's hard to use. I think the other thing that I think companies are starting to realize is it's not just your admins and your super users, your privileged users, that you need to be thinking about sort of managing privilege in this way.

And it's, it's all your users. And so it, it's not to the same level of, of stringency that you would control your privilege, your admins, but you know, our Art Giland has access to our customer data. Art Giland has access to our financial systems.

You want to be managing that access in a more sort of privileged minded way. Uh, and so this expansion of authorization, expansion of privilege, um, and then you wanna watch what art does with threat detection and other kinds of identity based monitoring. And so a lot of the work that we've been doing is not only building the foundation to do the authorization, but also expanding capabilities into threat detection, into governance, and managing the sort of whole ecosystem.

'cause right now, a lot of those systems are separate in companies, and that makes it difficult. 'cause you're managing in different UIs, you're managing different data sets. You're sort of, sort of swivel chair trying to do it.

And in the world of ai, you, you kind of need the system to also be smart about it and respond fast. Um, and so a lot of our investment has been there. Fair enough.

Hey, art, we're about outta time. For people who want to get more, maybe download the report or get more information on the report, or maybe even just find out more about deline, where can they go? com.

Uh, you can get access to this report and a bunch of it, and then also a lot more inform Yep. Sure. Make well, it, it's on your background, so they should be able to read it from there.

Right. com. Yep.

com. And, uh, there's lots of information there, a bunch of reports, this report, and many others. Fantastic.

Hey, art, thanks for coming on here and, and telling us, talking a little bit about identity security and, and all. It's, it's a brave new world, right? Exactly.

But, but here's the good news. We're making progress. We really are.

We're here, we're here to find bad guys with you. So thank you very much, Alan. I appreciate that.

Exactly. We're here, we're here, we're here to spread the good news. We on Gilad, CEO Deline here on text drum tv.

We're gonna take a break. We'll be back in a moment. Funny.

'cause a lot of people, when they hear about Davos, this is actually what they imagine. They imagine the mountains, they imagine the fog. They imagine, you know, we got skiers in the background.

They don't actually, they haven't seen what we experienced, which was sunshine. I thought it was busier. And, you know, you being a first timer novice, you know, me being a wily experienced second year veteran, um, it was different.

The streets were busier, the energy was higher. Maybe it was all the excitement about, you know, what we think is gonna be several years of growth ahead. Maybe people just like seeing the sun.

Hey everyone, welcome to Davos. Daniel Newman here, back again and join, by the way, first time brought my bestie with me, Patrick Morehead. I know I have given this event, uh, world Economic Forum, a lot of grief, uh, prior, but I have to tell you, uh, when it comes to combination of meeting with senior leaders in tech, with governments and finance, this is the best event for them.

We're all in one place. We are literally on one street. Chuck, uh, thanks for coming on the show.

It's great to Be here. No, it, it is. This Weather's terrible too, huh?

Oh, no. They tell me. It's always Like this.

It's always like this. It's an incredible place. You can actually spend a lot of time talking to companies about next generation technologies, what's going on, the geopolitical dynamics that are happening around the world.

It's just a great convening. A lot of great people to spend time with. It's highly efficient.

It creates a level of energy when you have so many people across the world coming together to discuss both business as well as the social impact item. You and I get used to coming to tech event, and the focus is products. That's right.

What are we launching here? That's not why these CEOs are here. You're seeing the CEOs engaging with these countries, with these leaders, with these sort of policy makers, pundits.

It was all about like, how does the world absorb this AI movement? The six five is on the road with a view from Davos. Thank you for having me, and welcome to Davos, an amazing place in an interesting time.

Happy to See both of You. My friend here Talking tech. You're empowering women.

I don't know, I'm gonna do your whole speech for you, but welcome to The show. Very happy to be here, is my favorite thing to do. I Appreciate that.

Thank you. You probably say that to all the Folks. Ransomware attacks on the rise, 90% increase.

You can probably imagine what the cause of that is. Sure. It's startling.

Wait, Wait. We're supposed to say ai. You're, You are.

Okay, hold on. You missed your cue. No, AI is top of agenda for all governments.

They have to control the destiny with these amazing technology. And we had, for example, the Prime Minister of Belgium, the finance minister of Germany, the AI Minister of France. It was a good, I think, conversation as you saw about importance of Europe to understand the need for innovation and how they're gonna reshape.

I think their policies, I felt like it was more affirmation. Of course, they want less regulation, uh, particularly across doing commerce cross border. Yeah, I, I think that was really the tone with set with our rooftop conversation with, uh, chief Commercial Officer at IBM, Rob Thomas.

The thing I'm the most hopeful on as you look out the next four years, is less regulation. We want to be able to create growth, create jobs, do m and a, and I think the incoming administration gives us a lot of opportunity to do that. So really excited about that.

Growth solves every problem. Well, clearly there're gonna be changes, uh, you know, different view of, uh, climate and business. And, uh, hopefully it'll be positive.

One thing we're gonna see is greater government efficiency, but a big change, obviously coming. There's not a single government or business who doesn't agree that, that doing more with less energy is, is a good thing. And to hear The CEO finally sort of acknowledge it, and it was like a safe space.

What I like to say, and this might sound a little bit controversial, but it's a little bit like feeding vegetables. It's fine to the camera, feeding vegetables to your children. You know, we need to sort of cover up the decarbonization technologies with some returns and future proofing these legacy industries.

I think those industries are ready now. And what I hope is the younger generations are gonna embed those new technologies. AI is gonna demand more and more energy.

And I think a lot of the forecasts that we're reading of how much energy we will need over the next 10, 12 years are too low. I could see the demand and doubling over the next 12 years. Most of it's gonna come from the traditional sources, oil and gas, and even coal.

The only answer ultimately for humanity is fusion, because fusion produces no greenhouse gases and, uh, will be a real breakthrough. But how far off it is, and is it gonna be available on a huge scale that remains to be seen? But AI may help us solve a problem.

All Of the customers are saying, you know, especially in the IT departments, they're like, I have got to deliver all this innovation, but I also have to think about the, the long-term sustainability of both cost and right energy at compute. So how do I think about managing that? Interesting.

And so we're able to kind of bring all that metadata together as well and give those recommendations around. How do you think about where those workloads go? How do you think about fulfilling this demand?

So What we did, that's very different than GPUs. GPUs have a lot of external memory, and they will do part of the problem, bring stuff in from memory, and then do part of the problem and bring stuff in from memory. It's very slow, very energy intensive.

So we use about a third of the energy versus A GPU. 'cause what we do is we'll take hundreds or thousands of our chips, lay the problem out completely in those chips, so we don't touch any external memory. And it's like an assembly line.

We'll just go through that very quickly. So we take advantage of that double exponential, and we're the first to do that. There's only two types of companies in the world, one's that are gonna be AI forward.

What I, what I like to call is people that know how to take advantage of AI really well. They have high dexterity with AI and others that are gonna struggle for relevance. And, and so the first, the great ones, I think they, they wanna move fast, but they're being held back because of safety and security in ai.

So that's an area that we have really doubled down on at Cisco, Our report said that 67% of organizations said, yes, AI will have the biggest impact to my security posture, but only about 30% are doing anything about it. And that's where it's really important to consult with and partner with the cybersecurity professionals around you. 2025, really the, the March, and I don't think it was unrealistic March, that they wanted to start seeing return on investments from what the, the investments they're making into AI infrastructure, AI, SaaS, and everything in between.

On an aggregate basis. Our customers saying that they're gonna spend three times more around ai. So there's a lot of experimental thing that we see here in their, happened in past few years, how to do that so that we can maximize the return on investment, right?

So those are the, are the feedback that we have got from our customer. And as a result, we announced together with, uh, Nvidia a new framework, which is called the Lenovo AI Hybrid Advantage, right? As a framework of solution with an aim to help our customer to land AI use cases fast, uh, and with ease, Like I can be more efficient, I can get stuff done.

There's things that used to take me hours or I couldn't even do at all, that are, are very, very easy to get done. That actually helps companies retain 20 to 30% higher retention rates. That's money directly back into customer's hands.

Yeah. We came into Davos 2025, the World Economic Forum, really in a moment of change. It was really interesting because what I was wondering is, were people gonna be sort of a little bit nervous and uncomfortable with what HA is to come, or people gonna be feeling super optimistic?

There Was a optimism right now in the industry, and especially about the US economy. I think there's expectation. There's gonna be growth.

You know, the combination of availability of energy deregulation and technology innovation usually is a good combination. I think We kind of hit a pain point at the right time on resiliency, which is every company's trying to say, how do I have an infrastructure that can stand? The test of time is not gonna be exposed to threats.

I think we hit something with concerts. You'll see more on that in May. Looking forward to it.

Nice little, uh, tip there. We Made an announcement last week on a product called AI Defense that really helps take out the unpredictability from Ai. Christina, you had a pretty big few months.

Um, first of all, you, you, you raised a little money just a little bit. Just a little bit. Several hundred million at a, uh, 800 million.

Yeah, I said several hundred. It was effectively, but I didn't have it memorized. I think the correct word is, uh, almost a billion dollars.

Rumors are, we may have taped out a four nanometer chip that may be coming soon. I'm not gonna confirm or deny 10 20 customers large CapEx deployments in the tens of billion dollars. They soon will be consuming million GPUs.

They already are in the tens of thousands, in some cases, hundreds of thousands. We came, we saw, we conquered. I mean, we had so many great conversations, many memories.

Right? I'll leave you with this question 12 months from now. Will you be back here in Davos?

Yeah. I think my answer would be a conditional yes, probably match it, uh, next year closer to the experience that, that I would like to have and to be able to live better value, uh, to our audience and also, uh, our clients. Well, I think that's a great way to wrap it up.

I will plan to be back. We'll see what happens. The next year is gonna be and furious.

And of course, you can keep up with most of what's going on. We only pick the good stuff to talk about here on the six five. Thanks so much for joining us.

Thanks for coming to Davos with us. See you in 2025 on the next show. ai video series.

I'm your host, Mike Azar. Today we're talking with Amad Al, who's CTO for Mitra. And we're talking about how things like warehouses are being automated using various robotic platforms and software and all kinds of fun stuff.

But maybe we need to actually physically understand what it is we're trying to automate first, because, well, a lot of people are putting the cart before the horse. Ahman, welcome to the show. Thank you.

Great to be here. Lot. What is happening here with these types of projects?

Because on the one hand, I wonder, do we really wanna replicate what we already have? And is that what's working? And or people trying to jump ahead and re-engineer the entire thing without understanding what it is they're re-engineering.

And so maybe we do need some more hands-on experience with the process. I, I, I think there's a, um, an approach that we've seen before where people see a new emerging market that is, uh, interesting and, um, uh, they see an opportunity to create value. And so they jump in.

Um, when, uh, social media started, uh, taking, taking off, uh, you have a lot of entrance to say, Hey, it'd be really nice to have, you know, a social media platform. And a lot of apps being built trying to build that. Um, many of them failed.

Uh, some of them, uh, one may be by mistake, but a lot of the, uh, uh, the right approaches is to get customer insight. Meaning try to understand what you know, what your potential customers are trying to solve first. And that is hard to do by guessing, uh, especially from the outside.

If you haven't experienced, uh, when yourself, uh, if you take, uh, Instagram as an example, you know, people might have an idea about, I just wanna share a pictures. Great. See if, if it's a workflow that, that you actually enjoy yourself first and go, I wanna share pictures with friends and, you know, become a customer of that service first and see what works.

And then go ahead and say, okay, now I know what to do. I'm gonna go build an app that does something special. This is the same thing now applied in industrial robotics.

It's really a hot market right now. Uh, a lot of entrants are coming in and, uh, it's easy to go and say, I'm gonna build this nice looking, you know, uh, my robot, that kind of baby dances. And, uh, it looks great.

Wonderful. It's fun. I want to do that as an engineer.

Wonderful. But then you have to become a customer first. The reason, uh, we believe, you know, our approach is, um, uh, more applicable is we've been customers of material flow at industrial, um, uh, manufacturing, you know, Ian Rivian.

And it wasn't that we wanted something that looked nice or really cool, we had a real problem to solve. And we thought, oh, what really is important right now as a customer, what it would solve? My problem is a, an efficient flow of material that was, uh, easy to deploy, that had, uh, minimum number of integrations and complications.

After that, after that insight, I know what I, what would make my life great. I go and invest and build a new solution that does exactly that. And so the step of you actually doing, uh, becoming a customer is generally skipped.

People go, I know what to do. Yeah, this would be great. You're making assumptions.

And it's not a matter of, um, serving people. Say, Hey, tell me what's important. It's kind of a, a, a, a known wisdom that the customers will never tell you exactly what they want.

They, they'll tell you like, I, I need a faster horse. Like, maybe it's a car. And so you will not get that insight by just asking.

You have to physically become the customer. Uh, in the case of warehouse or industrial, um, or manufacturing, go to the floor. I mean, it's not really hard.

Go to the floor and say, okay, show me how it works. Become that person. Work, uh, in the factory, work in the warehouse, and see what are the friction points that with your skills as an engineer or whatever field that you're in, how can I solve this, uh, from my, from my perspective, um, that, that is the approach that, that we're pushing.

And as I understand it, it's not all that simple because a lot of the AI technologies that we're trying to embed into these industrial robots don't really understand physics just yet. And so they don't understand the movement and how much distance there is between different things and how much hitting something might hurt. And a lot of times a lot of these robotic projects get scrapped, and that costs millions of dollars simply because they didn't think through the physics of the thing.

So, um, what is your sense of, uh, what do we have to overcome here to kind of make the industrial robot smart enough to see and understand what it's looking at? Uh, it needs to, um, there are a few data points that need to be collected more than just saying, uh, I have a great, uh, generative AI can answer a few questions. Now I'm gonna take that and apply it to, uh, physical world.

And it's just gonna look, uh, maybe you really have to try it out and be ready to, uh, admit to the gap. Say, Hey, there is a gap here. I invested all this time.

There's a gap between the generative part or the thing that the AI is giving me, and what really needs to happen. Um, it could be that you have some wishful thinking, like, it, it really must work. It's really cool.

I, it'll, it'll actually work. But unless it is, um, uh, actually effective at, uh, giving you more productivity, you need to pivot. Uh, this is, you know, pivoting is a very, uh, well understood, uh, business process where people go, Hey, this, this didn't work.

You shouldn't be emotionally attached to it. This did not work. Let's move on to the next thing.

Or say, Hey, it needs more tweaking. Uh, get it done better. Uh, I think there's a little bit of wishful thinking, um, that, that comes with the deployments of AI that said, this will solve everything.

It, it's smart enough that that's it, we're done. We don't have to invent anything new. Um, it that, that has yet to be proven.

Um, it is not impossible, but I think the homework hasn't been done yet. Deploy the ai, see what the gap is, try to fill it, uh, try to fill to, you know, bridge this gap. Uh, that doesn't happen by just, um, building an algorithm, handing it over as like, Hey, this will solve everything.

You have to actually apply it. So where would I find the people who understand the warehouse or wherever I'm gonna put this industrial robot and have the AI skills? I mean, am I looking for a unicorn or is there, is this more of a team sport?

How does that come together? We're lucky in the, in the Bay area, we have, um, multiple disciplines, um, that come together that, uh, you know, feed each other and, um, uh, tech and software is, is prevalent in this area. And so when manufacturing started having California with Tesla, uh, it was a fresh look, uh, at, uh, you know, established practices.

Uh, I think the best, uh, uh, people, uh, and, um, uh, at least culture approach to solving these problems right now is to look at other disciplines, uh, how they solve things and apply it, let's say, uh, industrial automation. The best engineers have a good solid background in the problem, say warehousing, uh, manufacturing, uh, uh, uh, industrial automation. And they start to branch out.

It's like, how was this problem solved in, uh, computer science? So engineering or, uh, DevOps where you take that, uh, uh, innovation and apply it to the physical wheel. A very good example is, um, if you notice, uh, maybe 10, 15 years ago, we started not worrying about the search engine, not not showing up or your mail not arriving, because, you know, companies like Google and, and other providers, they kind of figured out how to get a service to reliably, uh, be available, extreme high availability.

That's their business. Um, they had to invent new processes, you know, things like containerization and Kubernetes and, you know, fault tolerate design. Logically, those things were solved.

Today, we don't really worry too much about opening your browser and, and getting a search result. It's, it's extremely reliable. And that was a, a, uh, an innovation in distributed computing and reliability that happened in that discipline, um, that can easily migrate into the physical world.

How do you ensure that a production line runs exactly like a stream of, you know, data, uh, is always available while you build in this fault tolerance? When one machine dies, the next machine takes over automatically and smoothly. Uh, the data is shared between machines.

It's a stupid extraction. These disciplines, um, uh, or innovations haven't migrated to the physical world, uh, fully. And there's a huge opportunity to apply these, uh, these techniques to the physical world where you can look at your material flow, just like data running in a, in a data center.

Um, if you apply the same principles, uh, to the physical world, the people who understand how this physical world operates today, um, uh, indoctrinated in, in a new way to do, you know, quick sorting, uh, uh, uh, fault tolerance, uh, high availability, take that and apply it to the physical world, and they're the best people to, uh, implement that as we go down this path. Am I gonna see kind of these humanoid robots capable of performing multiple tasks, or is it more likely we're gonna see, you know, a network of highly specialized robotic systems, devices, whatever it may be, that are working in concert with each other to accomplish a task? 'cause it seems to me it's difficult to teach something that's humanoid multiple job functions.

And so maybe we're better off just having a little, a lot of specialist things. There is a, um, hypothesis that a generalized human humanoid, uh, is exactly what we need forever. Uh, maybe it's a hypothesis, I believe, uh, at this point.

Um, a good example is the, the bot behind me. Uh, the problem we're trying to solve is, um, material flow in manufacturing. And most of what you're doing is moving the materials.

Uh, is my direct, um, uh, approach to solving this problem a humanoid, uh, I don't believe it is. So we thought, okay, what do I really need to do? And I, again, I don't wanna be attached to, oh, humanoid is really great.

It might be in the future, but what am I trying to solve today? For me right now, I want to solve material flow, uh, an efficient way that, uh, increases safety and productivity. So you build a form factor without having any attachments to some preconceived notion of what a solution is.

We thought, Hey, uh, we're trying to move 3000 pounds backwards, forwards, up and up and down, uh, left and right. Uh, is it a humanoid? Forget about that.

Now, let's, let's focus on what the form factor needs to look like. Form factor looks like a thing that carries a pallet that moves 3000 pounds. And, uh, today, forklifts that do this are 9,000 pounds at least, and they cost 10, 20, $70,000 each.

And we're trying to say, what am I trying to do? I'm just trying to move this material in these directions. And so to do that, I could come up with a device that is 500, 600 pounds only, and it'll do exactly the same work or a fraction of the, the energy, and it'll increase my productivity in the end.

I solved the problem. I did not, you know, get distracted by, I need to build a humanoid. Uh, maybe a humanoid would've solved s problem.

But really, the way that your approach from first principles is, here's the thing that needs to be solved. What is the best form factor for it? It's almost like biology, uh, the best evolved thing that extremely is that is extremely effective.

Like solving this s problem is the thing that wins. And this is the form factor that, that work for us. I believe that companies that want to innovate and improve in, um, uh, industrial automation need to look at the problem and, uh, work backwards.

Not say, I know the solution, I'm just gonna go build it. Now. Go, go and invest and see what are you trying to solve?

What are the first principles, uh, that lead you to arrive, uh, to the best solution? And you come down to the numbers. I'm trying to move 3000 pounds and three, uh, 3D the best form factor for that looks like what you see here.

Um, it has the elements of the humanoid, it balances it, it has a lot of technology that we invested there to, to accomplish that. The climbing up and down, um, uh, from any cell to any cell, it's a big Minecraft grid. It is innovative, it is very cool.

It just didn't have, um, uh, the preconceived notion of, oh, it's gonna look like a humanoid. If I were to build a humanoid today, um, to solve this problem, I would ask the human I to get on a forklift and stop moving. It's like, okay, so I didn't solve the really the problem.

Um, I'm trying to move the material efficiently and, uh, work from there. And you arrive at something different and innovative and perfect, uh, for the problem that you're trying to solve. How long will it be before we achieve this great future?

I mean, I know that we're seeing robots today in factories and warehouses, but this notion of a robot that is more, uh, cognizant, shall we say. Um, where are we on this journey? Um, it is hard to tell because we've been promised, uh, if you remember back in the eighties, maybe late seventies when automation started, uh, appearing in, uh, uh, auto manufacturing at that time, people were like, oh, it's over.

That's it. Robots are taking over. Everybody's gonna be, you know, uh, out of a job nobody's gonna build anymore.

Uh, yeah. And that was good. And I was gonna get a flying car that I didn't have a job to pay for.

Right, exactly. Uh, so, and you see, until today, I mean, uh, very little automation is practically deployed. It's, uh, it's still a manual, uh, world.

And so there is a thing that just, um, prevented that from happening. Um, it was too optimistic to say the robots are just gonna do it. There's, um, a lot of variants, uh, that robots are not good at.

The humans are really good at, if a, um, a box falls while I'm in the warehouse, if the box falls on the floor and I'm supposed to go pick it up, if it was a robot, like it'll just sit there unless you go as a programmer and program all these exceptions in there and say, Hey, watch out for Falling Box. Here's how you solve it. Watch out for this.

Here's how you have to pre-program it to do that. You throw a human at this problem. You don't even have to tell them they see a box on the floor that shouldn't be there.

They'll go pick it up and continue the job. That gap between handling exceptions without needing any training whatsoever. And, uh, you know, having a prescriptive directed, uh, you know, programming for the bots is, is the thing that's lacking the promise of ai, especially at, um, the inference level, at the edge on the bot itself is very interesting.

Now, if you have these inference models that runs on these, uh, robots, uh, on the floor without needing any cloud or anything, it's interesting. They start to, uh, handle, handle the exceptions that humans used to do. Um, what's happening right now is the reliability of it.

And we've all seen examples of people asking Chad, GT or any one of the LLMs asking a question, getting a ridiculous answer back, or, that's insane, unfortunately, until that has been solved, where you're getting really reliable, really good, um, uh, answers, uh, it's still gonna be, we're gonna need some humans in there. The robots are not gonna take over. You can't really rely on them yet.

It's not impossible. Uh, and there is progress being made. It's very hard to tell.

Like maybe two years ago when, uh, Chad, I believe three came out, um, you go like, how long until it like answers everything for us. Here we are, two years later, we're still going like, right? It's still impressive, but it's still like, there's still something missing.

Uh, I just recently heard about, uh, uh, apple turning off their, their news summarizing, uh, ai. This is a big company that invested a lot, and they had to kind of just say, Hey, this is not the, the headlines, it was summarizing as an AI sounded like really off. Like, it, it wasn't useful.

I just turned it off, not, I mean, it's an attempt and we should always encourage people like, yeah, it didn't work. Keep going. And so we're at the phase of promising, let's just keep going.

How long will it take? Uh, it's everybody's guys, everybody's working really hard to get to an answer. It could be this year, could be next year.

Um, but it's really hard to say when innovation, when a breakthrough is gonna happen. Uh, a lot of people have been trying to figure out, you know, uh, flight maybe for hundreds of years. Everybody's like, yeah, possible.

Yeah. I mean, see attempts after attempts until the Wright Brothers breakthrough was made, and now we have aviation. We just couldn't predict exactly when it would happen.

Will this evolve into, um, robots that essentially we're trying to orchestrate rather than us performing the tasks, we are becoming the managers of the robots? Uh, that is, uh, hopefully the, uh, the short term, uh, outcome that is, that is possible. Uh, and it's not binary.

It's not like a, Hey, everything is automated and we're just sitting there pushing buttons, or we're doing all the work ourselves there. There's a, a gradual, um, um, progression where some tasks start to be reliably automated to the point where we go, yeah, this is, this is good. Uh, so here's an example.

One of our deployments, um, um, the, the, uh, set of, um, pallet that need to be pulled out, uh, it used to be manually done where somebody goes driving around with a forklift, pulling out a pallet manually, like spend a whole day, you know, driving to belt and taking it out and putting it in a trailer. Um, a system like ours made it. Uh, so it's simple.

You come to the, to the, uh, the edge of the, uh, uh, the installation and you say, here are all the pallets that are watched to pull out of the warehouse a order and just enter it on a little iPad. It's just a very simple, very pleasant experience. And then these things, the, the bots go automatically.

They pull the material out in order, and you just pick 'em up one by one from the same location. You don't have to go search for it everywhere. Um, that is a, you know, it, it's, it's a very pleasant, it made it, so you're doing the same work.

We're still, you know, picking up pallets and putting in the trailer, but it just made your life way easier. It took off a lot of the mundane, uh, monotonous, you know, uh, work that you have to do, and it made your life easier. Uh, at some point it is gonna make it even more easier.

There are other areas where you just make your life simpler. You're still doing the work, but the, the hard work is, is out of the way. And you don't have to spend your time in a harsh environment.

Sometimes these warehouses are like minus it's 20 degrees, you know, you don't wanna spend the whole day and minus 20 degrees. It's just, it's punishing. And so the hard work, okay, let the bots go get the material out for me.

I'll just take it. I now prefer this than, uh, you know, the alternative, which is physically going, doing. So there are other areas where, where automation is just gonna make our lives slowly easier and easier.

And, uh, the smarter they get, the better for us because it'll be closer to the push button warehouses taking to, you know, it's the dream. And, and, and hopefully everybody will be in a better mood when they get home. But let me ask you this last question.

What's the economic impact of all of this? Because are we gonna see more manufacturing move closer to the point where whatever's being made is consumed? And we might not need to, you know, ship goods halfway around the world just because there was cheaper human labor someplace.

We could just have a lot of smaller factories and warehouses closer to the point where things are gonna be used. Um, the, the impact of, at least in my view, the impact of that is, uh, dwarfed by, uh, the possibility of, uh, enabling manufacturing where it wasn't feasible before. If, if you were to say, um, you know, I, I would like to create a, a new, um, auto establish a new auto manufacturer in California or Kansas or somewhere else, uh, you have to take into account a lot of inputs, a lot of things that you have to build out first.

Automation is a big part of that. Your warehouse operations is a big part of that, and it comes with a lot of costs. And, uh, if, uh, automation makes it so things that were not possible before are now possible, uh, now you decide, yes, I will decide to go and build that auto factory, and you create more jobs, more economic growth, more opportunities, and no more innovation.

Just because automation added that extra component of feasibility. Where before it was hard and there you had to ship things from everywhere, but now automation made it so, or it's easier. I can actually, uh, um, uh, decide to actually go ahead and build out these, uh, um, warehouses and, and manufacture facilities.

So a lot of the opportunities that were not feasible will become feasible now that automation is introduced. So I think the impact, um, is gonna be extremely positive. Um, there's gonna be these, um, uh, e uh, ecosystems that say, Hey, there's a new manufacturing facility now, it's highly automated.

And now because of it's there, all the restaurants in that area are now prosper. So all the real estate gets improved. All the, uh, logistics, uh, that's happening.

It's bringing more business. There's huge impact, uh, that is extremely positive that's gonna happen because of automation. All right, Nan here, the robots are coming.

The question now is how are we all gonna work alongside them? They come up with something that is a better outcome for all concern. Hey, Ahman, thanks for being on the show.

Thank you very much. Appreciate it. And thank you all for watching the latest episode of the Textron AI video series.

You can find this episode and others on our website. By all means, check them out. Until then, we'll see you next time.

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Hi everyone. Welcome back to Marketing, art and Science, the podcast where we sit down with B2B B2C chief marketing officers, and really get to understand their profile as well as how their and their teams are pulling the levers of art and science and marketing to drive business outcomes. We're talking pipeline revenue and a seamless customer journey.

I'm your host, Lisa Martin, and I'm so thrilled to welcome Denise Pearson, the CMO of Snowflake to this latest episode. Denise, it's so great to see you. Thank you so much for joining us today.

So Great to be here with you, Lisa. Thanks for having me. So let's, oh, it's My pleasure.

Let's go ahead and start with your CMO profile. You have a great background. You've been in marketing for a long time, but tell the audience a little bit about you and how you got to the level where you are today.

I started in marketing in tech in 1996, so it's almost 30 years here, uh, soon. And I think it was a little bit by accident to begin with. I had this passion for marketing, and especially, you know, advertising.

You know, I grew up going, uh, to watch, uh, the commercials when I went to the movies. I grew up in Sweden. We didn't have commercials and tv, so I went to the movies to watch the commercials, uh, watching the, you know, the Yu of Fruit and the Levi's commercial you from the us and kind of dreaming myself away to, uh, to California.

Um, but again, I ended up, uh, in, in tech. Yeah. So I had a, one of my friends' sister was working for a tech startup in Sweden.

It was a French company. The company was called Genesis. Uh, and the product they developed is very similar to what Zoom is today.

So they actually develop a first automated conferencing service. So I ended up joining there, and the company took off and did really, really well. And, um, so I grew with the companies.

I started as a marketing coordinator in Sweden, and I was promoted to marketing manager. And then a few years later, I got the call from the headquarters in Paris asking me, do you want to come down? We have this product manager position open.

And the big goal I've set for myself was that I wanna become a product manager before I was 28 years old. I mean, I thought it was gonna be more, again, in the B2C world right? Than a company like, you know, Proctor and Gamble or something.

That was my, my big dream and, and vision I had for myself. And, um, so when I got that question, I said, yes, you know, I'm, I'm coming. I'm to Paris, you know, next week.

And, uh, moved to Paris. And then a year later, the, uh, global head of marketing took on another role, and the CEO said to me, Denise, I'm appointing you head of global marketing next week. And by the way, all the executives around the world are coming in to Paris, and you have to present the marketing plan.

So, um, oh my gosh, definitely scared to death. Yeah. But it was also that a pivotal moment by the, probably the biggest pivotal moment for me in my career where I had to say yes.

You know, I had to, yeah, I mean, uh, and I cannot lived by that. You have to say yes when the opportunities come your way. And I've had that philosophy that the worst thing that can happen is that I have to go home and sleep on my mom's couch.

That that's the worst thing that can happen. So I said yes, and then I stayed, uh, with a company for another, you know, six years. You know, we opened up country markets in 30 countries around the world, and, uh, I spent 12 years there and, um, also moved to the us you know, with, um, the US was our largest, you know, market.

And, um, I moved to California, uh, the Bay Area in 2000, um, two, but then they sent me to other places. They sent me to Down River for a year and DC for four years until the company was eventually acquired. But, uh, it was definitely kind of the place where I, where I grew my career, and where I learned a lot in marketing where I've made the greatest mistakes, you know, of my life, you know, as well.

And then since then, I've been the c of three other, um, tech startups and, uh, including on 24 for five years, and then Apogee for almost three years, and now Snowflake for, for nine years. And I actually had opportunity to take all those four companies, you know, public, so they done, um, Uh, quite well, been incredible journeys to, to be on. Amazing.

Your Pathway is so fantastic. It reminds me of mine a little bit when I got plucked from being an, a payload scientist at NASA working on the space program to being in sales and tech from a family friend who saw probably like your family friend of talents that you might not, not have known that you had. Yeah.

But what I also really admire about your background is you have this kind of like, I do this, get comfortably uncomfortable. It's, it's hard, but like, when you had the opportunity in Paris to lead global marketing, and you knew the only answer is yes, and I'm gonna make mistakes along the way, but I'm gonna learn from them. I think that is just one of the best attitudes that you can possibly have.

Tell us a little bit about Snowflake. If anyone listening doesn't know, I'd be shocked, but I've been watching this Snowflake trajectory for years now. I've had the chance to interview you before.

Talk to us a little bit about that and where marketing fits in. Mm-hmm. And the overall corporate strategy.

Yeah, I mean, overall, snowflake is a data platform built, you know, for the cloud from the beginning. The company was founded in 2012, and then, um, the, um, product was ready in 2015. So that was a big launch, you know, for, for Snowflake.

And I joined about 10 months, you know, later. Essentially, if you look at, okay, what does Snowflake, you know, do that is different from, from what we had in the past? And Snowflake essentially handle all types of data, and you, and, and you can have that data all in one place, and you can give access to that data to everyone in your organization with the highest level of security, you know, governance, access control, et cetera.

And not just people, but also machines, right? Yeah. We know today that the biggest consumers of data today are, are, you know, AI models, for instance, right?

That use this type of data. But again, the revolutionary thing was that back in the, in the, in the days, um, still today, right? A lot of data sits in, in basements, you know, locked in.

And the, the only people that can access the data is, you know, the IT folks, right? And that makes of course, very difficult for the people on the business side who wants to use that data to create business value or, or insights or develop, you know, data products for their company. Makes it really, really hard.

And that's kind of what, what Snowflake has made, you know, possible is, again, to make that data accessible, you know, for, for, um, for anyone. And that's essential for customers across every industry that, that have to become data companies. If they haven't already, they're behind the curve there.

Talk a little bit about the corporate structure. You roll up to the CEO, but you have really tight alignment with the CRO, which you and I both share, is something that is so incredibly important for marketing. Give us kind of a little high level view of that relationship.

Yeah, I mean, I report into the CEO from an org chart, you know, standpoint, but my mentality essentially that I work for the CRO, and I think I've always had that, and maybe it's because I started in marketing in, in the nineties and back then, I mean, sales was truly king, right? I mean, in, in B2B it was every deal was run by the sales team. The sales team did education, you know, of the, of the customer from the very early on.

But that of course changed right? When, when the internet came, right? And everyone could start, you know, self-educate and read up upon products and, and learn by themselves, right?

Marketing became more and more influential on the entire, you know, buyer'ss, you know, journey, but still have that sort of mentality that that sells is, is, is king, and that sells is my, my, my, my, my customer. And in B2B marketing, I mean, the most important objective we have in marketing is really to make the selling process as easy as possible. So it's more just that mentality.

Again, what can we do to make the, uh, sales process more efficient? What is it that sales need at any given time? And just really being in lockstep with the sales strategy all the time.

I'm not saying that sales is more important than, than marketing, right? But it's still the mentality of being a service in or of, of sales and making sure that we're always in a fully, fully aligned as an organization. Absolutely.

That alignment is key. It's not a trite thing, but yeah. One of the things I admire about you too, Denise, is you said, you know, it's it critical to our success of looking at projects from the outside in.

Talk about what that means and how is that critical to your success? Yeah. And, and we'll talk about who we ultimately work for.

I mean, we all ultimately work, you know, for the customer. I mean, if you look at them, the most successful companies around the world, I mean, those are the companies that are really, really focused on, on the, uh, customer right? At any given time.

Yeah. I think all, I think all companies, of course, have that ambition to put customer at the center on being in a customer centric, but it's hard to execute on that because as you grow as an organization, you really start thinking more about all the processes, you know, internally, you start thinking more that kind of, you know, inside, you know out. And I think we need to remind ourselves every day, right?

To think outside in. Yeah. And I can say I find myself guilty all the time for that too.

And then I just have to kind of remind ourself, you know what, we need to look at this from the customer perspective whenever this decision we make is that the right decision for the customer? And also at Snowflake, I think what's been unique is that many times we made decisions that have had short term impact on our business, you know, our revenue, whether it's, uh, making our product much, much more efficient. So cost will go down for a customer, right?

I mean, short term, that of course impacts snowflake negatively, if you will. Yeah. But long term is the right thing to do for a, for a company.

'cause customers, you know, will trust us even more, right? I mean, uh, we become a much more important and, and valued in a business partner to our customers. So there's so many aspects of that thinking outside in.

And every year we do a very comprehensive survey, uh, where we are really tracking every part of the customer journey, right? Everything from how they start learning about Snowflake to, to the sales process, contract process, to to building process, to product experience, everything. So we can really look at, okay, is anything falling in the red, essentially?

Is there anything we need to address from a customer perspective? Customer value, it's everything, right? For every company and every industry.

Let's segue Denise into our second subject. And that's really kind of looking at snowflake's, MarTech stack in action. I know that you really focus on a team of artists and scientists.

Talk to us a little bit about that, where you as the CMO are really the conductor of the orchestra, But I think this is why we have so much fun in marketing. I mean, it's all about bringing these two skill sets together, right? Bringing the creativity together with, you know, analytics, right?

I mean, it's a creative part that makes in our marketing engaging it will helps, you know, our, you know, capture attention. But the analytical side, right? It's all about making sure, right?

Is it all working? And then are we targeting, you know, the right, you know, people and is the right, are we putting the right message in front of the right people as well? So I think this is why I'm having so much fun in marketing.

I think this is why I'm in marketing, is to, to kinda bring this to sites together and say, at Snowflake, we actually do personality testing. So we, we, we, you know, we're using the insights discovering program, looking at the kid who is blue, you know, who is yellow, who is red, who's green, right? And that's what you find all those analytical folks, you know, on the blue side, right?

All the spectrum. And you have the more creative people, you know, they're more on the yellow and on the, on the, on the green side, right? And as the head of marketing, your job is really to have both of those skill sets.

You cannot just have analytical people. I mean, your market is gonna be really, really boring. You just cannot just have those people with, you know, tons of creativity, you know, either.

So, uh, it's that balance of bringing them both, uh, together. And this is of course where, you know, technology comes in. I mean, uh, and on the analytic side, there's so much we can do now with them, with technology as well.

I love how marketing has become so scientific. I think that's because I was a scientist in a former life. Talk a little bit about the marketing technology stack that you've put together and where that science piece comes in to really help course correct in as near real time as possible to really deliver that customer journey that's seamless.

So the value to the customer is really there. Yeah. And we use, of course, several different, you know, platforms on the, on the sales side, of course, we use, you know, Salesforce.

So that's one of our big, you know, platforms across marketing as well. We use, you know, Marketo, you know, and, and Adobe, you know, for, for our website. And those are the platforms we use in terms of engaging and activating our programs.

We use, uh, a company called BOMB to get intent data, right? That's intent data we use from the analytical side, making sure that, um, again, right, you know, how how is the messaging, you know, working in front of different audiences. We use, you know, work more cado a platform to really automate kind of the workflow of all our different campaigns across our organization.

And then we use, um, you know, role works and, you know, Google and, you know, LinkedIn advertising as in our digital ad platforms, obviously, but then also we use Snowflake is a big component of, of our stack as well, with all this data, right? Goes into one place. And that's really how we can get this 360 view, you know, of our, of our customer.

That's a great point. The 360 view, we hear that a lot from, I mean, pretty much everyone I talked to, but from, from your point earlier, the execution piece is challenging to do. How do you achieve that?

And where does the science come ins to really build a foundation to give you that 360 view of the customer? Yeah, and again, uh, as I said, we, we run a lot of that in on, on Snowflake, uh, today, and we're using all the different, you know, signals that are coming in, you know, from our, from our customers. And what's unique in the B2B space is that you're not targeting one individual, right?

You're looking at the whole company with different personas and different roles. And that, that's a challenge we have in, in, in, uh, B2B marketing. It's much more complex right?

Than B2C, you know, marketing. So it's really about utilizing all these signals coming in from, uh, from the, the business side of your customers. And also, of course, on the, on the technical side, you know, we are also targeting developers, you know, when in customer base really bringing all these signals together to get that 360 view on really where is this prospect or customer on the buyer's journey, you know, with Snowflake, and what is the next, you know, program that we need to put in place to drive this company forward.

And where does the artistry come into place to you? You've got all the signals there, you've got that customer at 360, the data is there. It sounds like a great snowflake on Snowflake story.

If you guys don't already have one, you should. But where does the artistry come in to go, okay, we're understanding all of these technical signals that, you know, B2B customer A is giving us, how do you pull the levers of art to really engage them, to make them captivated and want to learn more and, and progress through that buyer's journey? Yeah, and the biggest part is really around the customization of content and personalization.

I think a big theme here during the past couple years has been about hyper personalization. And that's of course huge in the BGC space. We are all as consumers seeing that every day, how we are being, you know, hyper-personalized, right?

With the, with content, you we're just out searching for something, and then of course, a second later, right? You're getting an ad, you know, served up, you know, for you. And this is of course, you know, happening now in the B2B world as well.

And for us then it's really about, you know, customizing content from an industry perspective. And marketing in the end of the day, it becomes, it's all about, you know, relevance and the timing of, of that. So it's about timing and being relevant and really, you know, again, making sure that you put yourselves in the customer's world, then trying to put, pull them into your world.

And this is really where all these signals help us to do, is really to identify, okay, what content should we put in front of this customer at the time they're in now on their buyer's, you know, journey. That timing and that relevance that you bring up are absolutely critical. They're, they're no longer nice to have.

So we have this expectation, I think, in our B2C lives as consumers, that we're gonna get ads served up to us that are highly contextual, highly relevant at the right time, not in a creepy way. And that has come over into the B2B side where that expectation from a buyer is, is now there. But how are you using tools like generative ai, for example, to really drive that personalization, that customization of the experience per buyer to push them through that journey and get them to sales?

Yeah, and I just say that, I mean, there's no better time to be both a marketer and consumer, I think, at this time. Yeah. There's also the consumer, what AI, et cetera, helps with is also relevant content for you.

I think so for us, right? We're being bombarded with messages that are irrelevant to us. Yes.

The fact is, if I was actually just out searching for something and then I get a highly relevant, you know, offer, it kind helps me save time as well. So I think it goes both ways. It's actually really to the advantage, you know, of the consumer, you know, as well.

And in terms of our definitely, you know, generative ai, you know, and, and, and ai, I mean, of course there's so much we can do now to be even more personalized and customized at scale. We of course, see a lot on the content side. I mean, we're doing a lot of experimenting.

We have our, uh, our account based, you know, marketing program, right? We wanna be a bit done, customized content and messages, you know, at scale. And we've been able to do a lot with that.

With, with degenerative, you know, um, ai also on our SDR R side, we have 300 SDRs that are following up on all the different incoming, you know, leads, you know, coming in. And many of them are, are young, right? They're in their early twenties.

It's super hard for them to come up, you know, with the relevant and, and everything. So that generative AI has also helped them to really be much more, you know, relevant, you know, to the, uh, prospects that they're talking to as well. But I think also the, the big thing with AI that, that has brought us is the ability to forecast, you know, the future.

And for us in B2B, uh, the biggest part of B2B marketing is really driving our sales pipeline, right? Right. That's what we're really responsible for.

It's not just about, you know, bringing in the leads. It's about how these leads, you know, converting into pipeline out in the field. And what we've been able to do now with more predictive and AI is really forecast what is our pipeline looking at in, at a spec in a specific tour territory, you know, six months from now.

'cause often in B2B, you're in a quarter you realize like, wow, okay, the West coast doesn't have enough pipeline this quarter. How are we gonna make it? And from a market standpoint, it is really hard to go in and try to fix something and, and build pipeline, you know, in quarter.

'cause the work we do is really about building pipeline, right? You know, six months from now in a territory. So that ability to really forecast based on all the actions you're taking right now, what is that gonna result to in six months from now?

So you can then go in and fix issues before it's too late. And marketing is also very much about, you don't wanna overinvest, there's no reason to overinvest in demand gen in a territory. If you have 20 reps in a, in a territory, you wanna make sure they all have enough, you know, leads and the ability to build pipeline.

You don't need to generate demand for four people if you only have 20. Right? But you also wanna make sure you don't underinvest, right?

You don't want tell 10 sellers sitting somewhere and there's not enough demand coming in for them. So that ability to do predictive, you know, forecasting, I think that has been a huge, you know, game changer for us, um, in B2B. Well, that visibility is absolutely critical.

To your point, you've got three achieve of 300 SDRs who are overwhelmed. They're early in their careers. They, they need assistance from marketing and from tools like generative AI to be able to craft those messages that are relevant, contextually relevant, send them at the right time, and be able to have the data that can inform when they should pull different levers and marketing as well.

So it sounds like what you have is, this, is is not just that 360 view, but the visibility into, from a death perspective, customers, what they're doing, what should we serve them, how should we do it? What's the right channel? How do we give them that omnichannel experience and let them know we understand them and we want to help them understand us.

No, exactly. I think marketing, sales, you know, the SDRs is very much like an assembly, assembly production in a line, right? Yeah.

Yeah. And you need to make sure that everyone has, are doing, you know, the right work at, at the right in a time, and that you're again, you know, generating enough, you know, demand and not, you know, too much and, uh, that you're investing, you know, the right amount in each territory. So I think data is really what enables us to adapt to at scale to this thing.

So, uh, I just looking back, you know, 30 years ago in marketing, I mean, I started in marketing when leads came in from the fax machine, right? Where I had to, oh my gosh, yes. Had to enter them manually into an Excel spreadsheet, download to a flock per disc, and go over right on the sell side with that flock per disc.

And after that, you handed a dis over, you had no idea what was going on with those leads anymore. No. So, uh, where we are today, it's incredible.

And just kind of thinking about now, next, what's gonna happen next with AI is that is super exciting. And also from a marketing standpoint, I hear a lot of people that are there is exciting, you know, about this as well, right? I mean, what is the future gonna look like?

What's gonna happen to our jobs, et cetera. Yeah. What we have done there that I think, uh, is quite impactful.

We have formed an AI council across the marketing team with individuals from each different function, and these are the individuals who really are super curious, right? They like to lean, they like to kind of really test new things. And so they're really leading kind of these efforts, testing new use cases, and also looking at, okay, what do we need to educate the rest of the team on?

'cause we cannot just have everyone learning and do testing every day, right? Right. But have that sort of black belt group on your team who goes out, tests different things and wanna share those findings with the rest of the team, I think seems to be working really, really well.

Are you finding that the hand raisers on your AI council are a mix of both the artistry folks and the science folks as well? Or is it more the scientist folks? I mean, imagine that I think if curiosity is a, is a great creative outlet.

No, no, definitely. And I mean, on the creative side, they understand that they need to do more with less as well. Yes.

Yeah. We have a lot, often the people sitting on the creative side in a company, they're often at the last, the last, right. Uh, at the end of the assembly line, right.

There's a, there's planning happening and sometimes they, they're informed very late, right? They often will have visibility into everything that is happening. And, you know, someone develops a programs and then they give the creative team, okay, I needed this in a week.

Right? So they're often quite, you know, overwhelmed, right? As a scale to a company of the size we're at right now.

They're at the, they're often the last and they feel like they, they, they don't have full control over that situation. Yeah. So for them, they're really looking at ways, okay, how can we get more out of everything we do?

They're, they're, they're looking at really, again, how can generative AI help us just do more with less as well? But again, you know, creativity, right, from, from a human mind standpoint will of course never go away. And we also need, of course, to have that, um, uh, check checks and balances of things too.

So I don't think anyone is worried about their jobs here. It's more about, um, again, how can we amplify everything we're doing? And that, that curiosity in, in terms of how can technology help And help us scale?

To your point, I think one of the things too that's always interesting to me is from a generative AI perspective, from an AI perspective, there's so much coming at us so quickly, we saw this catalyst about, what, 20, was it 2022, end of 2022? Uh, or was it 23 when chat GPT was born? I forget.

It's, I feel like I haven't worked without it in so long. It's hard to remember a time without it. But from, from that, um, ROI perspective, it's hard to measure ROI from, from tools like generative ai.

Are I, I hear a lot of productivity improvements, but are there ways where you're really seeing ROI at Snowflake in marketing, because you're leaning into emerging technologies? I think the ROI we're seeing is, again, I talked about before about the ability to forecast, you know, outcomes. And marketing has always been around, again, of course, finding out what is working and what is not.

And often we don't find out until it's too late. Right? Back in the days when we had very limited data, it could take, we might have the data three months after a program was over, right?

And that, that then is too late. Right? It's too late.

Yeah. But yeah, today the ability to make real time, you know, changes is huge, right? You need, you need to make, on the digital side, for instance, digital advertising is a huge spend, right?

For, for most marketing departments, both in B2B and B2C. And that has become so incredibly sophisticated in terms of how you can now change programs on the fly, right? Yeah.

In the midst campaign. And also, it's becoming also fully automated, you know, as well, right? You don't even need to do something.

There's really, there, there tools kind of, uh, making the changes, you know, for you. So, uh, that optimization is really where the ROI come from and that we can easily cannot calculate. And that we, we have able to calculate every year.

Now when we got sort of realtime data, how are those digital programs performing from an ROI standpoint down building pipeline close business to compared to what did they do six months ago? So I think that's where you can immediate see incredible return on, um, on investment. That's outstanding.

That's, that's a message. And for all of the CMOs and the perspective CMOs that are watching, 'cause we hear often it's hard to measure, but to your point, from a forecasting perspective, you can see those trends. You can see the data, it's right there telling you what it needs to tell you, and you can react proactively to that.

To your point, in terms of course, correcting campaigns and things like that, that before you wouldn't have data on performance for a while, and it was almost campaigns done. Well, how do we create the next one? Because they're all continuous.

What are some of the emerging technology that you're eyeing next, um, with the AI council and within marketing and within your executive leadership team that's really gonna help improve that customer journey and deliver that value just beautifully even more? Yeah, no, it, well, it comes back to again, you know, hyper, you know, customization, I would say. I think that's a key thing.

And again, the timing, uh, content creation is huge, uh, in terms of how do we, you know, personalize content down to, you know, one persona with an enterprise, and not just from an industry standpoint, which we've done before. So that, that's a huge thing. And, and again, we could have on the a BM side, we could have 5,000 campaigns running simultaneously, right?

And the ability to go in and again, customize even further at the much more granular level, that's where we're gonna get to our eye from. So that's really where we are putting most of our, our, um, efforts on, on testing. Um, that, and, uh, uh, again, also comes back to that SDR side.

We've done a lockdown of testing in terms of helping them customize messages. How do we roll that this out now to the entire, you know, team, you know, globally Is sales kind of chopping at the bit to get the, the rest of the sales organization since you have that great alignment, I imagine they are. And probably there's a lot of curiosity on the sales side as well.

Yeah, no, on their side, what they're seeing is, of course, much, much more high quality demand, you know, coming in. Right? Perfect.

Again, the, when they're, when they're at that meeting, they're, they're in front of a much, much more qualified audience right. Than they were before. Yeah.

Yeah. They have much more information on the person that they're talking to than they had to in, in the past. And that really enables them to have a much more effective conversation, you know, as well from the beginning.

Well, it sounds like It's beyond alignment, Denise, that you've built with your CRO at Snowflake. It's really a symbiotic partnership, and that's really when you can execute on that, it's brilliant. And when the customers feel that they're understood as well as the sales folks understanding the customer, it's a recipe for success.

But I want to kind of wrap things up here. You talked a great deal about your history time in marketing, going from floppy disk fax days to now having AI to help you really forecast territory by territory. But I imagine there's been some kind of ebbs and flows along the way.

And we love to end this podcast with what we call the Fail to fab segment. And that is, could be a business initiative, marketing initiative that wasn't going according to plan where you came in and made changes and saw results after that. Share with us a story, if you will, that's you think is inspiring to the next generation of CMOs.

And I think, again, failing is clearly a part of, of learning. Sounds super, you know, cliche, but again, uh, every time you fail again, right? There's sort of failures, of course you can't, you can't do.

But, uh, you have to look at them as, as, uh, learning moments. And I think one particular one comes back to more like creative, you know, development and also marketing, right? Is about not being tone deaf, right?

And I think so often we see, you know, uh, a video or something fun coming from a company, and you realize it wasn't really fun, right? It were, it was more fun for those people creating it than, than it was for the people watching it. And we have had several moments, you know, like that.

But I remember specifically in the early days, we made this big investment in, in a video, and we had actually been quite successful with the series of really fun, engaging, you know, comic videos. And we thought, okay, let's take this to the whole net new level now. And we did this product, big production, and it has this West side story, um, uh, theme to it where basically, and it was with the cloud and the on-prem kind of, you know, competing in with each other.

Oh, we got it. Yeah. And we, yes, and we've hired this actors in, in LA and it was this big production.

And for us, back in the days, the investment, it was such a fairly big investment for us in the, in the early days. And when we saw the results, it was just, it was, it was pretty, it was pretty terrible. Only two people within our company have ever seen that, that video.

And, uh, oh, wow. But it's one of those moments where like, okay, we have, we have to pull the p plug on this. Maybe we can show this at an internal meeting one day.

We've still not, haven't showed it yet. It's maybe time to do that soon. But again, it was at moments where like, this is not, this is not fun.

This is not, this is not, this is not customer centric. This is not because there's no entertainment value. It, it didn't turn out the way, uh, we expected.

And therefore, again, you have to, to, okay, you have to pull the plug on things as well, right? When they're, when they're not, um, working, Right. And then, and move forward and take the lessons learned.

To your point, I always say failure is not a bad F word because there's so much that we can learn from it. And, and if we all are honest and look in the mirror, we, we've all failed a lot. But you can course correct.

You can understand and learn from these experiences, what didn't go well, what did we not do that we should have done? And then make the next one even better. Denise, thank you so much for sharing that West side story analogy.

I, I love that movie. So I, I thought that's gotta be a huge success. But you realized it wasn't, you made the decision at the executive level moved forward.

And maybe more than two people will get to see it one day and understand, hey, marketing did a great job of using the data that they have, the artistry to understand when it was time to move forward. I can't thank you enough for being on the program today, sharing your history of almost 30 years in marketing, going from fax machines to marketing data clouds. It's, it's an amazing progression.

And we appreciate you also sharing what you're doing from a MarTech stack perspective, how you're leaning into AI and generative AI and other emerging technologies to deliver that customer value through the sales organization and marketing. We just really appreciate all of your insights. Thank you so much for having me, Lisa.

My pleasure. We wanna thank you for watching and remind you to tune in next time for the next episode of Marketing, art, and Science. For my guest, Denise Pearson.

I'm Lisa Martin, and we'll see you next time. In response to the Soviet Union's launch of Sputnik, the United States has started a new space program called Mercury. You're watching Textron Gang.

Hey everyone, happy Wednesday. Welcome to another edition of, uh, Textron Gang. You know, it's, it's no kidding around.

We've had our little Sputnik moment here, perhaps in ai. We're gonna discuss that. We've got some other AI stuff in as well.

It's a little quantum, it's AI and quantum. I feel like I'm living in the future. Um, who would've thought?

But we've got a lot of good stuff to go over to, and we've got good people to go over it with. Let me introduce you to our gang lineup for today. First of all, sporting her Valentine's colors in red and pink and white.

It's our, uh, resident. Well, weren't you, you were just on somewhere in Asia, was it Bloomberg, Asia, or Middle East? I was Released, yes, last week.

Very cool. It's our own. Lisa Martin.

Hi, Lisa, how are you? Hey, Alan. I am so good.

I love the Sputnik reference. I'm a space geek. I used to work for nasa, so, and I'm big into ai, and this is gonna be a really interesting show.

I, I agree with you. I'm, I'm into, I'm a space geek too. Um, going from California, we'll stop over in Colorado for just a quick check in with our very own Mitch Ashley.

Hey, Mitch, how are you? Hey, very good. You know, one of the things I remember, you remember Edward Teller, who was, you know, part of the Manhattan Project.

Sure. I know Things he said about Sputnik was, it's a greater defeat for our country than Pearl Harbor. That's how impactful Sputnik was when it happened.

Yeah. Interesting. Interesting.

Mike, do you wanna remind people out there what Sputnik was? Sputnik was an effort where the Russians put a satellite up in the space just before, uh, we got our act together around the moon program, and everybody flipped out, and suddenly we were needed to be on the moon before anything else during the Kennedy administration. And, um, I don't know, you know, I'm sure it was a big moment at the time, but then they, you know, looking back in retrospective, I'm not sure anybody remembers it as bigger moment, but you Guys figure, well, some would, some would say we wouldn't have had a moon program had it not been for Sputnik.

It, it spurred the, the US to take a hard look and maybe win it all cost. Some people say it might have cost the lives of a couple Gus Gris and a couple of the Apollo one astronauts, right? Because they were under such pressure to keep ahead of the Russians and the Soviets.

The real, the real fear was it was a ballistic missile that put Nik up there, and now we can reach anywhere via space. That's what really freed people. Yeah, I mean about it, Sputnik itself was nothing more than a little ball that sent out a beep beep beep signal, right?

Uh, but we'll, we'll talk more about that. I was afraid to bring it up too early, and I did. And anyway, Mike Ard, welcome to our show today, thanks to the historical reference.

Let's move. Well, They, Hey. Hey.

I would, I would just say that I don't, if we're talking space, I'm going with Steve Miller and Space Cowboy. That's where I think it's at. Yeah, Well, well, that, okay.

Nothing but a space. Some people call him Maurice, but that's okay. Let's, let's move on over to, uh, Amanda, our editor at Lodge, Amanda Ani down in San Angelo, Texas, which is not quite Houston, but, uh, we don't have a problem there, do we, Amanda?

Nope. Doing well, happy to be here today. Okay.

Just full of space references today. Right. Um, anyway, so look, we're going to talk deep, deep seek because everyone's talking about it, but we'll, we're gonna save that for our second block.

Let's go to our first block. Mike, you wanna kick it off? All right.

Well, there's been some noise in the social system, and Mark Andreessen is out once again, banging the drum, but he put out a tweet saying that ultimately the goal of AI was to destroy wages to the point where we could then rebuild the economy. That seems like a rather broad statement. I'm sure there's a lot of hyperbole involved.

But, um, I'm gonna start with, uh, Lisa, is that kind of stuff helpful at the end of the day? Because it seems like it just sends everybody running for cover and it's, it's really kind, counterproductive, and maybe we do need a national AI strategy, because otherwise it's left in the hands of people who don't seem to care what happens to anybody else. Yeah, I thought it was a very bold statement, but that is Mark Andreessen.

So I wasn't surprised by that. But, you know, we, we just came off of talking about the world economic format's. Well, actually, what I was talking Bloomberg Middle East talking about where, you know, the big fear is AI will take jobs, and I talk about that on the radio to help, um, lay people kind of get their concerns as squashed, like guess.

There, there we're already seeing jobs replaced by automation. We haven't talked about wage impact yet. We're seeing that, but we're also gonna see 170 million jobs created in the next five years according to the World Economic Forums feature of jobs report, which shows positive gain of 78 million as 92 million jobs are replaced.

So there's some positive messages coming out there, and I think what Andreessen puts out in terms of wages, I haven't heard that before. I thought it was a bit of a pessimistic statement. And I don't know if that means we need AI regulations.

I think that's, that's a topic that we could unpack on its own. We probably will. But it was, um, something that I think will send shockwaves through Silicon Valley investors, wall Street and Washington.

Well, I, I had trouble interpreting, well, trouble, I interpreted two ways that could mean that we, we, our future is Star Trek, where we don't have wages and, you know, people don't have to make a, make a living. Um, or it could be, uh, medieval England, you know, back, you know, and the, the serfs and the, you know, the, the, the Lords and et cetera. I mean, it could, it could be very draconian.

It didn't make sense to me. Who's gonna buy anything if they don't have any wages, Resource based economy, Right? Am Am, am I gonna afford that Tesla from Elon if I don't have anything?

Yeah, that's, that's kind of like a, let's figure that part out. No, I just seemed, uh, it seemed like a very arrogant statement in my opinion. Uh, So the piece I, and I don't think we're calling for more regulations as much as I just feel like maybe we need an actual strategy, because it seems like, you know, right now there isn't anybody who's got their hands on the steering wheel, You know, given the deep seek stuff.

Forget a national strategy. This is an all out race to the finish line or whatever the perceived finish line is. And rules and strategies and processes and safeguards be damped, right?

That's, that's what you're going to get out of, out of our government right now. But look, there was a time where I really respected Mark and Jason, right? The work he did in building out Mosaic and then Netscape and everything when he was still in school.

I mean, he in many ways gave us a web that we u the web that we see today and use today as a, as a venture capital, as a venture capitalist. He's be, he's very successful, but he's come, he's become a b******e, right? He cares about nothing other than the return on his funds.

And maybe that's what vulture capitalists are about. And he's, he's the head vulture, right? And of course, he'd like to see a crush wages to be more efficient, to get greater returns on his investments.

And that's all the man cares about and curing favor with our president. And if that's what, that's what he's become, honestly, I pity him. It Makes me think of that movie.

Show me the money. Yeah, I mean, that's the first f*****g head. All the money in the world don't buy happiness and doesn't make you a good person.

And, and so, but let's, let's get to the, to the crux of the matter, now that I've unloaded that off my chest, um, is AI going to crush wages? Yeah, when it takes away jobs, it makes menial jobs. I'm reminded, and I probably have told this story before, my, my oldest son got into the Kelly School at Indiana University when he was applying to college.

And I went up there with him on a Friday. And, uh, first thing they did is they brought us into a class with a professor. And this was an economics professor.

And he started telling, telling the students about why minimum wage laws were a bad thing, because it prevents people from giving, it prevents employers from providing jobs to people because the jobs would be too expensive. And so instead, they're going to use machines or some automation or robots to replace people with those jobs. And I raised my hand and I said, I don't know what PhD you program you went to or what, but this is not a school for my son.

Because as an employer, I will tell you, if I have a technology, whether it's robots or AI or anything else that can take the place of a human more economically, I'm a fool not to do it. Right? They never get sick.

They never take a day off. They never steal, they never do things that, you know, some employees could do. And so, you know, creating keep, keep getting rid of minimum wages just to create busy work jobs maybe that worked for FDR and the Depression and the TVA, the Tennessee Valley Authority at Mike being as your historical commentary.

You could tell people what that is, but it doesn't work in the, in the, in the real world. So if AI is gonna take jobs away, let it take the jobs away. 'cause as Lisa says, it's gonna create other jobs, and those other jobs are gonna be high paying good jobs.

Go. Henry Ford, when he took, when he made the assembly line, everyone said, oh my God, it's taken away the jobs of these craftsmen who were building the horseless carriages. And you know what, it gave rise to the middle class, right?

So let the chips forward where they may, this is economics 1 0 1. Yeah, some, some, some jobs are going to be d you know, taken away. Some jobs are not gonna be worth paying the kind of wages they pay now.

But this is how the, the economy, it's like first growth forest, right? The old forest burns down, new trees come in. This is how the economy and, and the job market renews itself.

Yeah. And as you mentioned in the last show, um, people going to school should maybe think more about AI related degrees and certifications, because you're gonna be working with a lot more AI that's taking those jobs. So there's gonna be people needing to manage the ai.

I think there's a real concern that you're gonna have political unrest driven by people who don't understand what you just described, and are gonna wake up one morning and go, let me get this straight. I can't make a living. So I think I'll go vote for this other fellow over here who's gonna tell me he's gonna restore order to the natural way it was before all this chaos ensued.

And, you know, we could have a serious problem. So, so I think we have that already. We have that all.

I was just gonna say that, Mitch, we're going to have, look, but here's the reality of the way our politics works. Two months ago, people were ready to, had their pitchforks and torches out because the price of eggs were so high. Well, this week, the price of eggs went back up.

And all of a sudden those people with the pitchforks and torches are saying, my God, do you know what goes into pricing eggs here between global climate change, the bird flu and everything else? We've gotta give this thing a chance. Our country is so g*****n tribalized that they will move heaven and earth and high water to make an excuse for their tribes shortcomings or successes, right?

If the, if AI crushes job wages, the red are gonna say it's the blue fault. The blue is gonna say it's the red fault. And that's just the way it is in this country.

I'm sorry. Well, mob rule might be coming this way because you know what happens? Isn't that what January 6th was?

And now they're pardoned. Let's not even go here. I was gonna, I was gonna say also, Alan, thank you.

You, you and I were in sync on that one is, you know, we, we've had this multiple times. Just one example is manufacturing, leaving the United States, right? Business pushed manufacturing overseas, because it's more economical.

It, you know, saves money. It helps you be more competitive. Uh, it brings a few challenges with it, but it disrupts jobs, right?

Yeah. And can talk about this steel industry or, or manufacturing community. And we as a country don't have a history of putting together programs to help people, like seriously help people that are displaced when big changes happen in, in our economy, except for the depression.

That's probably really the only exception I can think of. Um, so it's a, it is a natural order. And I, my, just my personal opinion, I think a lot of the unrest has been multiple sequence of that between manufacturing and steel, and then, uh, robotics and then the cloud.

And now AI is the next thing. And, you know, is there, is there a, uh, tipping point or a flash point where, you know, all of a sudden everybody, there's a big uprise? Hasn't happened yet, but possible.

I I just think this is another cycle that we're going through. I, I, I, you know, and I was gonna bring it up, when we talk about deep seek later, unfortunately, the knee jerk reaction of too many of the quasi intellectuals or not, they're not intellectuals. The people who are in power right now is, let's put up a wall to keep the barbarians out, whether it's immigrants, technology, jobs, factories, whatever.

Let's put up a wall to keep the barbarians out. They, they only did, they were only able to accomplish this. 'cause they used our technology, they used our chips, they used our universities.

They came in here with as criminals and Dr. And brought drugs. Let's put up a wall and keep the barbarians out.

It didn't work well for China 2000 or a thousand years ago. It won't work well for us. This is you, you've gotta embrace change.

You've gotta embrace new realities, adapt and thrive. You can't, you can't react by trying to move ti move time back. You can't turn back time like Cher says.

And that's, that's Shimmy's uh, shimmy's advice for you all today from the great philosopher. Yeah. Or worst.

God, if I could turn back time. So anyway, We got a, we got a lot of music on this show today. Yeah, we've got a couple of music references there.

Mitch may wanna break out the guitar, but, um, in a minute, I'm, I'm holding myself back. All right, let's take a break then. We're gonna come back.

And this is we the main event coming on up. Uh, we're gonna talk about deep seek target for cyber attack poppycock. We'll be right back.

com is the leading resource for news analysis and education on challenges facing the cybersecurity industry. com covers all aspects of cybersecurity, including data security, DevSecOps, cloud security, application security, network security, security threats, and more. com has the largest selection of security content featuring breaking news, blog posts, podcasts, and more.

com to learn more. com. Home of security bloggers network.

All right, folks, we're back. And yes, well, everybody's talking about this new AI model from China that's got everybody kind of bent outta shape. And the latest is allegedly that, uh, that suffered a cyber attack and they were limiting registrations for the most popular downloaded piece of software in recent memory.

But some people argue that that may or may not be the case. But Amanda, I know you've been following this on the socials. What are you hearing?

What are you seeing? Oh, so many things. So, um, I actually have a few comments fold up that I'm gonna share.

And it kind of goes back to what we're talking in the first segment. Um, but, uh, so one of 'em says yes, and the irony is that Nvidia gave them the lesser hardware and leftovers. But one thing I'm reminded is that the Chinese government has invested heavily in their infrastructure.

And I think the United States will be doing that over the next four years. It will be a big race. Uh, then somebody else said, um, they're now under the microscope, whether political or legitimate concerns, China is the new Russian spy.

So in my opinion, we should be aware of that implication in our conversations. Um, and then, uh, I was reading that article that was, um, talking about, oh, here we have another popular app from China. Um, and you know, if you listen to the conspiracy theories out there, which sometimes turn out to be true, I'm just saying, um, if everybody was paying attention to TikTok, when it got, um, pulled down, I was on TikTok when it got pulled down.

Um, and then it came back up the next day. And now everybody's saying that, um, the content's completely different. The algorithm changed.

There's nothing about fires, there's nothing about the drones, there's nothing about the border. So was China trying to spread descent there through that app? So in that regard, my thought, like the wheels are spinning in my brain, yes, it could be an attack.

So deep seek suffered this attack. It could be an attack from somebody that, um, is trying to break 'em down because their competition. But could it be it's their own attack because people are already on it and now their own attack got all this information.

I don't know. I'm just saying. So let me weigh in here.

Mitch. I did you wanna go first? Mitch, you go ahead.

You go ahead. I'll jump in. All right, so first of all, I think this is as simple as we live in a crazy world where all of a sudden you're the top app on the app store, and it's like that IBM commercial, 10 orders, 12 orders, 200 orders, 5,000 orders, 5 million orders.

Oh my God. Oh my God, we succeeded beyond our dreams. I think that's what happened here.

I think that they just didn't have the infrastructure to set up account to set up account registrations, and it crashed their servers. It's a very, it happens all the time now, but the world we live in and the stakes, you know, at, at play here right away we say, oh, it's a cyber attack. It was brought out who, who's looking to bring these people down?

The NSA what you, do you really think open AI launched a cyber attack on deep sea? Come on. You know, it's just conspiracy theorists.

I'm quoting, I'm just saying. Yeah, no, I did. And there's a, you know, what they say about conspiracy theory?

It's every toilet has one sitting on top of it. Okay? Now who, who is going to attack them?

Right? It now you want to get really conspiracy theories. It was the Chinese attacking themselves to make it look like the Americans attacked the Chinese.

Back to my article that I wrote Sunday, I know that, you know, that I know that, you know, let's stop. There was no financial gain here necessarily in, in, in this attack nation state. I don't think it's, it's not the u it's, it's a short term silliness to, to block that right now.

Right? That's, I I think, you know, again, Sputnik is launching ballistic missiles on us, and oh my God, I better go dig my shelter out and make sure I got a lot of water saved up. It, it's all part of this fearmongering, keep the barbarians out.

Stop. It was, it's as simple. They crashed their servers.

It wasn't a cyber attack, and they're claiming it's a cyber attack to curry sympathy favor and patience from the audience. Because you know how we all are here? My internet doesn't work at 35,000 feet on the plane.

G*******t. I'm not flying that American airline anymore. So they don't want people to say that.

It's easier for them to say, well, we can't get you registered 'cause someone cyber attacked us must be those ugly Americans, right? Boris? Yes, Natasha.

Hey, Did you hear about this new afternoon show? It's called As the AI World Turns. Exactly.

Exactly. Well, hey, to quote a, a famous philosopher, Madonna, don't cry for me. Argentina, who gives a crap, you know, you know, another attack on another website, who gives a Crap?

I'll, I'll, that's not Really story. I don't think I will point out something. I don't know if you caught Pat Gelsinger having a last laugh, but he was pointing at the fact that, uh, gee, isn't it a, an amazing achievement that somebody actually created all these AI models using cheap, inexpensive hardware?

And he was like, Hmm. Well, so that brings up the real mean of the issue here. Yes.

Whether it was a cyber attack or just a, the stuff, you know, uh, over their, their machines couldn't handle the load. The real issue here is this Sputnik kind of reaction that we're seeing, right? It, it created such, I I I'm too young for Sputnik, right?

I wasn't born in, I was born in 1960, so that was already, I think after Sputnik. I do remember the early astronauts. I certainly remember the moon landing.

Um, that being said, from what I learned about it in school and being a history buff, is it created tremendous angst in the West and in the US specifically. And had it not been for that moment, and it really was just a moment, right? The US never would have moved the way it moved in, in, in, you know, with NASA and the space program and it, and it was Sputnik.

And then if you remember, the Russians were the first one to put Gregorian the first man in space mm-hmm. A couple months before the us John Glenn, Alan Shepherd. So I think that's what we're looking at here, right?

The fact of the matter is, stop trying to say, I, you know, I read people, even some of our fu and brethren on LinkedIn today saying, until I see this replicated on, on, on our reliable hardware, I won't believe a word that said stop. Maybe it's not a hundred percent, but 90% of what these people have done is true. They might have found a a, a better mouse chap.

That doesn't mean the game is over. It just means now we got a new mousetrap to play with and can we take that build on it, innovate out, innovate, outcompete, outperform. That's America.

That's what we need to do. We chose to go to the moon, not because it's easy, because it's hard, right? Here's what I like.

Doesn't that call like about deep fake, is that it, it it actually has a unintended consequence. Wait, Mitchell, what did you call it? Oh, deepfake.

Sorry. That was Ian. That was Ian.

Oh, was a Talk about Woo. Okay moment. So deep fake is, you know, I don't believe what the Chinese say about how much time it took and how much money, and who knows could be true, could not be true.

But the, I think the fact of it is it gets us off of this hardware infrastructure train of that's, you know, a bigger, better hammer solves all problems, and it's putting it back on the board to say, all right, if this is possible, whether they did it that way or not, or whether they had Nvidia chips or not, and let's go back the drawing board and let's use a constrained model of trying to do this much faster, much cheaper, and how can we do innovation down that path instead of just complaining we can't get enough chips from Nvidia. So I think there's actually a, a, it's sort of the, uh, the Star Wars effect. You remember that when Reagan announced that against the Russians about we're gonna be able to do all these things in space and, you know, attack Russia and defend ourselves.

And 90% of it was fake, but it was, you know, misinformation. In some ways, this is kind of that moment with deep seek for us, like to get us, it's, it's gotten us to react. We'll see if we forget about it by tomorrow, but it could have a really, A lot of reaction.

Isn't it? Call into question though. Sorry, Mike.

Um, you know, something that we've been hearing about for such a long time is just that hundreds of billions being invested in AI infrastructure and investors wanting to see where's the ROI, where's the proof in the pudding? And we just saw a big commitment with Project Stargate, what, a week ago-ish committing 500 billion for AI infrastructure In the US that didn't age well. Did at least It didn't age well, not at all.

But does it call into question the metas uh, the Microsofts who are saying, we're gonna be investing $60 billion each in AI infrastructure. Does it call that into question? Can it be done?

Is it, it's, you know, they're saying we can do this way faster, way cheaper. Now there's rumors that they have access to 50,000 Nvidia chips that they can't disclose because of export rules. But does it call into question the validity of the, of the, some of those in the magnificent seven who are investing bill tens of billions of dollars in AI infrastructure?

I I, I've got a few thoughts on that, if you don't mind. 2, 2, 2 streams of thought there. One is, look what they've done here with, and I, I've been reading some of the technical stuff and admittedly it's over my head, right?

I don't claim to be an AI engineer or know the final workings of neural network and, and training and models and so forth. However, what I have read is that what they've done here is really optimized for math, mathematics, and code. Now, will that allow it to branch out into more general reasoning, perhaps, but it's really focused around mathematics and code.

And it may not do the kinds of things, but you know, for instance, they say it's more comparable to the four oh, uh, chat GPT model, not the oh one or even the oh three, which is newer, that it wouldn't necessarily, they don't think it would necessarily scale, for lack of a better word, or compete with oh one or oh three, so that this methodology of re reinforced training and so forth on, on cheaper hardware and less resources may work for this narrow type of use, which is a very useful use, right? But it may, use, may still need to, to scale the heights, if you will. So of, of you know of a GI or whatever, Mike one another.

I got you. I'll come back to my next point. Go ahead.

You jump in. To me, that kind of sounds very similar to what we see in the car industry, right? There are very expensive Maseratis that might be built by OpenAI.

We're using their model, and then there's gonna be generic models that are next to free because we built them using open source software and that's gonna be what the bulk of most people use could Be, Is that gonna limit the number API calls to OpenAI. Absolutely. But, And so be it.

If that's the way it is, that's the, that's the market at work. But you mentioned the key point there, the open source. That's the real winner here.

The real winner here is open source. This thing was supposedly built using open source models. And it, and in and itself is, was released under an open source license.

Right. Which allows other people to, you know, Mitch and I have been through this with open source. Uh, it allows other people to use it to innovate off of it, to continue developing and, and seeing it in their own labs and seeing what happens there.

Can they replicate it? Can they improve upon it? For everyone who wants to put up the wall to keep the barbarians out, it it's sand.

The harder you squeeze it, the more it slips outta your fingers. And when you release this kind of thing as open source, I mean, I'm trying to think of a movie that, that once it got out there, it was like you, in everybody's hand. Covid You mean like Covid?

Well, COVID, well, that's not a movie, but No, you know, ano another words, once it's out there, it's wildfire. Right? And, and you are not gonna be able to build a wall around it and keep it out or, or what have you.

It's open source. And that's, that's the real beauty of, of open source. And so we're gonna have to see how this plays out.

It, it may be a world, Mike, as you, where, you know, OpenAI is a Cadillac or a Maserati, but a lot of people are happy driving a Chevy. Well, I think it's, it's myopic to only have a view of the goal is general ai. Right?

And every, and that's the only thing that's valuable. I think it's this, this shows that there are multiple paths. And sometimes good enough is good enough.

Not everyone has to have the latest iPhone 17 Pro Max, like Alan and I do. Some folks are happy to have the starter entry level. Right?

And, and, you know, is, is everybody gonna be able to use the highest end model that does the greatest latest thing? No. A lot of people will be able to leverage a service that is much cheaper and is good enough for what they need that job to do.

So I think it's myopic to think that there's one path we're all headed towards one destination. Yeah. That's part of the race.

Well, but Mitchell, they call it the singularity lot come outta that. Right? We we're gonna get Velcro, we're gonna get Tang, we're gonna get all these other things that come out as part of the space race.

But Mitch, they call it the singularity. Mike, I'm sorry, go ahead. I know, I'm, I'm looking forward to when they try to quote unquote ban this.

And then, you know, when we're chasing immigrants, we'll be chasing smugglers of the Deep sea AI model. Yeah. Let me check your chips in plane.

Lemme check, lemme check your chips. I mean, that, that, but, but unfortunately, you know, I look, you know, I'm not a huge fan necessarily of Donald Trump, but did you hear what he said about this? Right?

He said, wow, this, this is, this is, this has gotta give us reason to, to try harder to do more. But the good news is, is ultimately it's gonna be better for everyone. 'cause it's gonna bring the price down.

It's gonna make it more available. That's the right attitude here. Where other people in our government are saying, this is the Chinese Communist Party attacking us.

They built it using the technology they stole from us. They, you know, those are the keep build the wall, keep the barbarians out people. And no, we, I think you, you take this and say, okay, thank you.

Thank you for waking us up. Thank you for showing us. And that maybe there is another way.

Let's, let's out innovate them. Let's, let's build on this. Let's go do it better.

Let's do what we do. Build it better. This can be a great catalyst.

Yeah, exactly. That's, that's what I, that's the added, this isn't the time to build your bomb shelter. We don't need any ducking covered drills.

Let's go for it. Absolutely. All right, Mitch, take us over the hill.

Alright, Um, take us over the hill. Thank you. We're gonna take a break here on Text and Gang.

Let's, let's, let's get outta this cutting edge stuff of AI and we'll talk about some quantum. Um, you're watching Text and Gang Discover Textron Group, the epicenter of tech innovation. We are your go-to for reaching IT leaders and practitioners worldwide.

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Let's revolutionize your tech journey. Contact us today and tell your story to the world in the most powerful way with Textron Group. All right, folks, the next conversation is about Q Day.

It's coming. We don't know when, but the theory is, is that we're seeing rapid advances in quantum computing, and that's gonna break all our cryptographic algorithms. So we need to replace those algorithms, which is much easier to say than apparently do, because apparently some folks are saying it's gonna take years.

And if we don't start now, we won't have it in time for when Q Day arrives. Uh, I don't know, Alan, you've been following this too, but first there was Google talking about a new quantum willow chip, and then there was, uh, some other startup talking about an even faster chip. So it seems like the faster these systems get, the more realistic they are.

The sooner QA arrives. What do you think? Well, let me first deal with the story at hand and then I'll, I'll go, I'm gonna do it backwards.

I'm gonna go from that specific story to the more general quantum stuff. I usually like to go general to specific Socratic method, but That's the martini glass part, right? We'll, we'll go the other way.

Um, this Palo Alto network announcement about post quantum cryptography, APIs available, congratulations, of course. NIST put out post quantum algorithms for encryption. Now, about a year and a half ago, DigiCert and some of the other cert providers have been using post quantum or have available post quantum algorithms for their certificates now for some time.

So this sounded very much to me, like a me too. And hey, they're talking about all this AI stuff. We don't wanna be just another AI story.

Let's put out a quantum story. Congratulations Palo Alto. Um, but to quantum in general, you're right, we have been seeing a steady drumbeat here.

Uh, the Willow chip you mentioned, uh, I know some of the folks who are at the, uh, not CES, the Supercomputing show in Atlanta. There were two different quantum computers. One from A IBM, which is still the perceived leader in Quantum, um, one from a startup.

Google does have the, the, the Willow chip as you said, but also importantly, Google's, um, not deep seek and not deep fake deep Mind. They bought Deep Mind a while ago. Not you thought either, right?

Oh, no. But Deep Mind, uh, announced, I don't know, maybe a week or two ago, uh, that they were actually using AI on quantum computing, uh, issues and programs that made, made using quantum existing quantum chips, existing quantum technology, much more feasible and maybe pushing up than the the day for Q day, right? When quantum becomes more.

Um, but it's funny, I, this isn't, I I, I'm fascinated with quantum computing 'cause I'm fascinated with quantum mechanics in general. I, I'm just not smart enough to grasp it. Uh, but I read it and my head goes, um, I don't know if I can make that noise again.

But anyway, um, it's coming. It's coming. But when the more people I speak to, the more I realize it's probably not coming till 2030, maybe even later.

'cause there are significant hurdles, right? For most tests that you're going to use the computer for, you're gonna have to bifurcate what needs to be done at a quantum, on a quantum processor versus what needs to be done on traditional binary and then bring it back together, put it back, segregate, bifurcate it back out, bring it the, the, you know, just the, the, the, the blueprint, the, the, the process for what you're going to need to really run real world things. Where Quantum's gonna be able to help you like that, I think is still a while away.

Probably beyond my horizon. I is how I put it. I heard something kind of about that.

Alan, um, Dr. Bob Suiter, who is a mm-hmm. Uh, analyst at Futurum.

He covers Quantum. Yep. And he comes from like, longtime IBM and he worked on quantum and other things there.

So he, he is a real authority on this. He, he said something, and again, you know, I'm learning too, right? I'm no quantum expert by far.

He said that, um, the challenge with Quantum is it, it's not good at things that require a lot of data. So if you have problems that don't, that don't require a lot of data, I'm not sure why that's an issue. Um, it's really good for it.

So to your point about offloading, how do you, what kinds of things do you offload or handle other ways maybe in traditional, I mean, you need microservices to the umpteenth degree, right? And Wait a minute, wait minute. What, what about this Chinese DC quantum startup that found cheaper hardware in thesis?

No. Did they got taken down by a cyber attack, Right? RIP.

So, uh, my thoughts when I, when I was reading up on Quantum is, um, there's a lot of preparation on the cybersecurity front when we do hit the, you know, QA because all those people holding the keys that are gonna be easily encrypted at that point. So what are your thoughts on the cyber aspect? So I, I think this is a case where we actually are out in front of things and that, you know, because NIST and I think Mitre was involved and private industry have been putting out kind of quantum proof algorithms now for well over a year.

By the time you have Q Day, all the locks on all the doors and the safes and the banks and everything aren't gonna break, right? There's a Y 2K, I don't, I, well, maybe the banks will have the resources to prioritize it, but I think there's large numbers of companies that are dependent upon encryption that are gonna wake up one more and discover, discover that the stuff that they had today that they thought was encrypted was harvested five years ago and is now being decrypted and all kinds of embarrassing things. Well, that, that could happen, right?

Stuff that was mm-hmm. Take like salted or hash salted, uh, information that is now ri but, but that information, Mike has a fungible life. It was five years ago.

It may very well be useless by now. It's kinda like email addresses. Unless Q day comes a little sooner maybe, or, you know, there are things that companies do that they still don't want anybody to know about.

Well, this is why there's such a big push to make the lives of, of encryption certificates shorter, right? They wanna go from never ending to two years to a year, to 90 days, to 30 days to two weeks, because they wanna force the system to adopt the latest and greatest, uh, encryption for your, for your encrypted, you know, certificates and stuff. That'll be quantum proof.

And they're doing applications of, of certificate life of minutes and seconds, because that's how quickly they want to turn over. I mean, I think that risk of the, the data being, you know, unencrypted later, there's all kinds of rumors about government storing and maybe bad guys storing encrypted information to be able to use it. The problem is, uh, quantum computing has to be at a price point that you can get to use it besides being the, I mean, government of the US or, I mean, this room is the NSA might have a quantum computer already.

Yeah. Just rumor. But most, most people are not gonna have access to it for some time.

I'm sure the dream is the Chinese Communist Party does too. That's how they got into this deep seek thing. I'm kidding.

I'm kidding. There is. I mean, the, the bottom isn't gonna fall out of the quantum market the day they announce they've got a quantum computer you can buy.

Yeah. It's gonna be a while before it's accessible to a lot more people. I'm not sure nation states care about the cost that much.

So China is just gonna build one. We're gonna build one. Oh, that's who can afford to do it.

I'm saying I and everybody else, so be used for that. But that being said, you know, I, I'm gonna call this my Zein Pike theory, uh, um, right. I'd like the thon, we no, no, no.

Zein pi, right? Is don't underestimate the ability of some lone wolf genius who figures out a way to, to make quantum computing or figures out a cheaper, faster AI thing and launches himself into low earth orbit just as a Vulcan ship is going by. And, and ha you know, boom.

The next thing you know, we're part of the federation, um, this, that could very well be, I mean this, this right outta Star Trek law for all you zeis out there, right? The earth, you know, there's a, there's a, some sort of caustic war between the powers that be and, and, you know, people are dispersed, kinda living in small groups. But, you know, some guy out in the, was it in Minnesota or somewhere he was, or Wyoming niche, I don't remember.

He, he manages to, to break this, the speed of light barrier and, and achieve warp speed. And, and, uh, and that's how he was in the set. Yeah.

In this, uh, we might have similar kind of breakthroughs. Yeah. Deep seek is sort of like that.

We might have a sim there might be someone right now in, in, uh, Nebraska working on some quantum Siri, right? It's, it's right next to their coal fusion reactor on the other side of the garage. Well, could be, could be.

Hey, this is the year for nuclear fusion. That's what I'm waiting for. I've been waiting for that my whole life too.

Zephyr Cochrane. Zein Cochrane, not Zil, not Pike. Yep.

Captain Pi, Captain Pike. Right. But in Zephyr Cochrane, that was the myth.

Mm-hmm. Right. See, we just rendered the Warp driver.

We just saved you 4,672 emails. So there you go. There you go.

Thank you. I think you deep seek for that answer. I just got Exactly.

No, they filtered that out. But, um, anyway, it's been an interesting show here today. I hope you guys have had as much fun as I've had talking about some of this stuff.

These, these, these are great tasting. I'm glad we have this forum to talk about it. I'm gonna just do a quick plug for my own thing this Thursday, 2:30 PM Eastern Time.

I'm gonna be on LinkedIn live for my second episode of Shimmy. Says it's only about 10, 12 minutes, but I'm gonna be talking about Deep Seek. If you want to talk to me about it, come on to LinkedIn live.

com page where and LinkedIn Live will be there. And I'm welcome your questions and comments. I'll try to hit 'em if they're there.

If not, you could just listen to me. Um, Mitch, you are heading out to, uh, tech Field Day ai, right? Yeah.

AI Field Day in San Jose. Some great companies we're talking to about the latest steps that they're doing. You can watch it on Techstrong TV tomorrow, and I guess today and tomorrow.

Um, here are some great conversations. We have delegates that are asking all those good questions you'd love to ask, right? Um, we have several vendors that are coming in, so join us there.

It's also on technical day website. YouTube, YouTube, YouTube channel, all the papers. It's YouTube channel, Textron.

It'll also be the Techron gang for, uh, um, Thursday and Friday This week will be live from San Jose at, well not live, but recorded from San Jose Tech Field Day, AI Mitchell will be there, along with John Willis, Steven FoST, some of our other friends. So stay tuned for that. Lisa, what do you've got going on?

I've got going on just more research on ai. One of the things I'm gonna be talking about on the radio is AI powered serendipity. Ever wonder when you're scrolling through like Netflix and it pops up something that you would never have, have thought was the right thing.

What is AI powered serendipity? How does it work? And why is it a good thing?

And something to also be a little bit concerned about. So that's something I'm gonna be digging into for this week. Very cool.

You'll have to come back and tell us. Yeah, that will be on, That will be on the next episode of Textron Gang that you're on. So we'll be coming back To that.

We're coming back to that. Perfect. Um, I would just say, you know, um, are you gonna translate all that LinkedIn, shimmy says stuff into Chinese for our new AI overloads.

How's that going? I don't have to. It gets done.

It gets done automatically. All right. On, on Pentium chips, by the way.

Oh, Old School. Amanda, what are you? I know you've been up to your ears here.

We should announce Amandas recently, uh, assumed a new role here at Techstrong. Amanda is now our senior managing editor for All Techstrong sites. So Congratulations to Amanda.

Thank you. Go Amanda. You go.

Alright. It is, It is, it comes with great responsibility because, well, if anything goes wrong, the first call goes to Amanda. So There you go.

That's good. That's good. Maybe you can get one of those Chi Chinese AI to help you.

Um, just kidding. All right, look, thank you. Wherever you are.

Was it Ephraim Cochran. Raim Cochran. Yep.

You know, I actually saw a thing at somewhere in Iowa. There's a, it's the future birthplace of James T. Kirk, because maybe there's somewhere there.

Anyway, we've had enough, we've gone into what, What, what episode does he make his first appearance on? Star Trep Cochrane on the original series, they crash land on a planet, and he has a companion who's, who's cured him and keeps him alive. Im Mortally and she becomes a, a human person 'cause they're in love and they live happily ever after, but not forever, because now they're human.

Do you remember that one, Mitch? Oh, I do. I do.

And I think it was like the movie Star Trek seven or something. When was the new Generation Next Generation took over. When, when he, when he breaks the wart barrier?

Yes. Yes. I think it was the Sierra Mountains is where he was.

Or somewhere like that. All good. For all your Trekkies out there, don't say we don't cover it here on Textron Gang.

We'll be back tomorrow. Trans aluminum. That's another invention.

That's a great one. So, yep. We'll be, we'll be back tomorrow, actually.

Excuse me. We'll be back tomorrow, uh, from Silicon Valley and, and Tech Field Day. So stay tuned for that.

Stay tuned for, uh, we actually have Tech Field Day AI live on our show today, as well as some more great news and content on Tech Drunk tv. Until then, though, this is Alan Shimmel on behalf of all our gang members here today. Good luck.

We're out. This is Textron tv. Hey guys, it's Alan Shimmel here for Techron tv.

I've got a gentleman I want to introduce you to. His name is Art Gilliland. Good morning.

Yes, that right. Art Gilliland. You did it.

Hey, good morning. Thank you very much. Excellent.

Art is the CEO of a company called Deline. We're going to find out all about Deline and about a new report they've recently released. But before we do that, let's find out a little bit about art.

Art. Give us kind of the, uh, the art story, if you will. Uh, the quick two, uh, two second version of it.

Uh, so Alan, thank you for having me on the show. I really appreciate it. Uh, so hello folks.

My name is Ard Gilland. I'm the CEO of Delineate. As Alan said, I've been in the security industry now for about 26 years or so.

I, I like to say I tripped over it on accident at a startup back in the late nineties. Uh, and then have been a part of the ride. Uh, did Most of us.

Yes, exactly. We a we accidentally tri. Same thing here.

I've been in it since then and no one wants Oh, for security then that unheard of. It was, it was not so cool then. But now that, of course people are making movies about it and it's in the news all the time.

So it's been, uh, it's been quite a ride for sure through, for me, through small companies, startups, as well as, uh, very large companies running, uh, the businesses at Symantec and hp. And then, uh, a a time as a CEO of a startup that got acquired by Cisco and now, uh, here at Deno. I love it.

com. And again, by Axis Slam or Worm and all that excitement. Well, we were, we were operating data centers, and the next thing you know, we, we had to do manage firewalls and, and people started caring about what was kept in those data centers.

And That's right. And then my next company that I, I also did a lot of startups. It was a full on InfoSec company, as we called it.

Um, how long are you with Deline Art? I've been here now, uh, almost four years. It'll be four years in March.

Uh, so I was part of TPG who owns us, uh, when we brought these companies together to, to create delineate in the identity security space. Ah, t pg we deal with them also on, uh, oh, ai, a DevOps company also. Kinda a little bit of a rollup within it.

That's right. Um, given, if you wouldn't mind, you know, let's talk about De Linea. So it sounds like it, it's the, the product of a, a roll up of a couple of companies put together.

Give us kind of the genesis there, if you'd Yeah, the, Yeah, so I mean, I think, uh, I'll, I'll give you a, a quick history if that's all right. A little, uh, it's a little bit of a stroke, but I'll I'll do that. So I think one of the, it de Linea was really formed at the, uh, at, at a conversation we were having around sort of what areas of security was I most interested in.

Um, and I think the, the two areas in security that I think are the most going to be the most impactful and the most interesting right now are identity, security and data security. And the reason I think that is because if you look at what's happening for most companies today, they're in some part of the process of moving a lot of their infrastructure into cloud or SaaS computing. Um, you know, back, you know, when we started, it was all walled gardens and you had firewalls and stopped everything, uh, kind of at the edge.

But if you look at it today, most companies are basically renting infrastructure from someone else, whether that's the application you use for SaaS or the, the infrastructure from like a hoster or something. And the reality is, is you don't control the policy in those. They, they attest to you what they're doing and you, and you try to set, uh, sort of boundaries for what they do.

But the reality is the only place that you really are gonna be controlling the policy is on your users and what your users are allowed to do. Uh, and then hopefully at some point on the actual information itself, no matter where that information is living. And so when I look out over the, you know, the horizon of where do I think the most interesting innovation and challenges, where are they gonna, where is it gonna occur?

Um, that's kind of how this came about. And so, uh, we started looking at the identity security space and saying where, uh, where are there opportunities? And, uh, the opportunity was really to build a platform for authorization.

Um, and so that means like being able to set the rules, centralize and set the rules on what are users allowed to do in any environment, whether it's your own environment, whether it's your laptop, whether it's in the DevOps space, whether it's in cloud. Um, and so Delania is building that platform for intelligent authorization, essentially to define what privileges, what entitlements are available to the users after they, uh, after they log in. So there's, there's authorization saying, okay, you, you can go here, but you can't go there.

That's right. But then, you know, a another piece of of this of course, though, is identification, right? Before I can let you hear there, I gotta make sure you really aren't, Yeah.

So I, I break identity into sort of three basic spaces, just, I mean, look simple. Mm-hmm. But it helps me 'cause I, I, I think simply about stuff.

Um, so step one is authentication. That is I am art and improve it, right? And so in that world, you have obviously the Microsofts and the Pings and the Okta's and those folks, Okta and Yeah.

And so my my view of that is that go, that world is gonna continue to commoditize, right? You're gonna look at Microsoft and Google kind of owning the I Am art and I can prove it. There may be open source.

If you look at what happens right now in consumer, it's kind of a bring your own. I can authenticate to websites using my Google ID or my Facebook, and people still are using that. Yeah.

That throws zero off or whatever. Yeah. Yep.

And so this, this ability to authenticate. And so that's step one. Step two is then deciding after I've proven I'm art, what should art be allowed to do in my environment?

And that's the area where we are focused. Um, and then the next part is governance. So tell me everything Art has access to give me a workflow for making sure that I refresh those things.

And so if you look at companies like SailPoint and Savvy, and yeah, to a certain extent, uh, Deline as well now too. 'cause uh, because of an acquisition, we made those, that whole workflow of defining and, and reporting on and governing sort of the privileges that people have, that's step three. And so if you look at those sort of three steps, we are very focused on the area that I think is most interesting, which is tell me what ARC should be allowed to do.

Yep. Yep. And, and, and, and you're right that that is how the identity space is sort of broken down.

You know, I've always felt that identity was kind of the, the killer app for cloud security, right? 'cause when we moved away from the, you know, moat and castle model to a cloud model, having big boxes on a perimeter that doesn't exist anymore, was trying to, you've Been talking about that for, uh, for a, like a couple decades, Alan, with the Jericho Forum and the sort of perimeter, Yo, you, but that's Still do really good. So Perimeters, But exactly now micro ones.

But companies like Palo Alto and others have been continuing to just, uh, to accelerate. And I think, you know, their view is there are some, there are digital boundaries. And I think that's still gonna be a really important part of the security landscape.

But I do think identity is gonna become more and more critical in partnership with these kinds of technologies to really secure the environment. You know, those technologies are, are they're traffic cops. Yep.

Right. That's right. They, they could lower the gate, raise the gate, yep.

What information they use on when to lower the gate or how to lower the gate, the kind of stuff that Right. And that, that's the kind of stuff I imagine they could get from a delineate. But that's a great way of looking at it.

Yeah. Um, and it it more true than ever. Uh, you know, for me, of course the big thing in identity and then authorization is non-human identity and authorization.

The explosion of that is humongous. It's uh, it's really, and then AI obviously just adds another layer of that kind of connectivity. Well, I just had this discussion with another vendor.

What about agents, right? Everybody's talking about this is gonna be the year of AG Agenta ai. Yep.

You and I are gonna have dozens of agents doing our bidding and we'll have an agent orchestrator hopefully that manages orders. But quite frankly, how do I know arts agent is arts agent? And that Arts Agent one is okay for this, but Arts Agent two isn't.

And you know, just a another level of complexity that I'm sure you guys are already thinking about and how do we go there? Yeah. I think what, when you're looking at the, when you look at sort of the, the explosion of what I'll call service accounts, uh, and you know, these were API connecting to another API and those things talking, and you see it in DevOps, you see it in the way we build applications.

You know, when outside talks to inside through an API connection, there was already a lot of investment in trying to lock those down and find them all and secure them. And that, that, if you look at sort of the environments that we serve today, there's the human privileged users, but then there's also these connect connections and service accounts and non-human connections. I think what is happening with AI is now you're giving that, uh, that intelligent quote unquote, uh, connection.

You're connecting it to a bunch of stuff because that robot, we'll just call it a robot. 'cause I think it's easier that robot is actually needing a bunch of different information from different places. Typically, those API connections have very, very broad rights and they can get access and take actions and do things.

And so, uh, there's going to be a lot of focus on how do you manage those robots? How do you let, how do you make sure that those connections should happen, number one, and, and verify it. And then how do you control what they're allowed to do once they make that connection.

So certain eight robots will connect to the same API, but they may only need to use a small little piece of it. And I think that's mm-hmm. Me, that's the, that's the place where customers are really gonna focus on privilege management solutions like deline, which is, yeah, you can authenticate it, you can make sure that password is safe, but then how are you managing that rotation of the password?

How do you make sure those connections are, uh, sort of rebooted that the passwords are not hardcoded in the robot's mind, that it actually is a token that gets changed a lot so that if the robot gets compromised, you can turn it off, uh, and you can stop its actions. And then also how do you limit just give it enough, just enough or just in time access. Um, and I think that's gonna be, that the pace of that and the speed of that is going to require system centralized management systems like ours to be able to help companies take advantage of it.

Um, personally, I'm excited about it. Zero trust. Zero trust, just right isn't enough.

There's, there's gonna be Exactly Layers and layers, But I'm excited about it 'cause it, for the first time I'm seeing sort of a pathway to having a security product be a business enabler with an ROI behind it versus just insurance. I mean, I think our euro, Euro life and my life, we've been sort of glorified insurance salesman. And I think now you can have a conversation that basically says we have ROI calculators.

Exactly. I'm sure you did too. Right?

We have have, Now we can tell a company, I'm gonna help you go faster. I'm gonna help you adopt this cool stuff. 'cause I'm gonna help you manage it a little better.

Kind of like the brakes on a car, you can drive faster for Breaks. I think we can do that And you're gonna need it because it's just, as you said, the, it's not just people. When you start adding these agents and API I and a APIs API traffic already accounts for majority of the traffic out there.

Right. Yeah. It's crazy.

Anyway, art, I wanted to move along. You guys recently released a report on, uh, identity and, and one of the topics was, you know, what do budgets look like? Yeah.

Yeah. I think, look, if you look at the, the overall sort of output of that, uh, report, there's not a lot of surprises, at least in, except for in one area. And I think, uh, the things that are not surprising, but, uh, but just reinforce what we see is this growth in budgets.

Um, for identity in particular, uh, companies are spending sort of 20 to 30% of their entire budget on identity solutions, whether that's the authentication piece or the authorization or the governance. They're spending a massive amount of their budget. And that's only growing.

Um, and so we see a lot of, uh, data like that. I think the other thing that's, uh, not surprising but interesting, um, is just how much, uh, security risk identity has posed. And sort of part of the reason the budgets are continuing to go up is just, uh, you know, still explosions of, uh, attacks on and sort of focus on, um, using identity as a, a way to get access.

And so, you know, h huge percentages like 80, you know, in the 80 percentage range of sort of identity based attacks. Uh, and I think those, uh, areas are sort of reinforced again by this research. And you see a lot of research out there that is showing that.

And so, you know, it's, it's one of those sort of, again, like a little bit like the insurance side. You're like, great budgets are growing in security, uh, attacks are becoming, uh, more, uh, sort of more, uh, more, more relevant. Um, yeah.

But again, I think the tho those things I think are, uh, are clear, they're reinforced, um, by, um, by the research for sure. Here. I, I, I couldn't agree more with you.

Um, if you don't mind me asking, when was the data for this report assembled? Like get how, how? Yeah, so this was, this was, this was research done over sort of the November through December, uh, and early January term.

So it's, it's there, It's fresh chat. It's an interview at somewhere 350 or so, uh, different IT security folks. And so this is, you know, this is 2025 data for sure.

Uh, and how we and how we go there. Um, and so obviously there's that. Yep.

Go ahead. Mm-hmm. No, no.

I'm sorry. I'm waiting to stop you. Go art.

Yeah, no, I, I, I was gonna say, like there's, there's that interesting information that we, that we see from, from the, the data. I think the part that is the most interesting in the research and the, the place that I spent, uh, a lot of time sort of trying to interpret and think about sort of what is it actually telling us is the, the, the massive numbers of companies that are thinking about using and being attacked by ai. And so, uh, that part of the report, I think, uh, is kind of an eyeopener, right?

And so the other ones I think people read and go, yep, yep, that seems reasonable. I think the part that is the most impactful is, is the AI one. I I as seems that's the story everywhere, right?

The AI being most impactful. Yeah, but I mean, but, but here's the thing. You know what, quite frankly we've been hearing when it comes to security budgets that a lot of organizations are saying, Hey, we've been on a binge for 10 years buying a shiny new trinkets, and, and our security is not demonstrably better than it was necessarily.

Yep, That's right. And, and so the fact that they are still increasing their identity security spending this year says that may be true, but we know how important identity is and because of the rise of, let's call it machine identity, whether it's robots or APIs or what have or agents Yep. Um, people are recognizing that we're gonna need to spend dollars on that.

Yeah, no, I mean, look, I think what they're, what they're seeing is that they believe, uh, you know, in the high eighties, uh, that they have, uh, seen identity, identity-based attacks. And so I think what's happened is they've done a good job of securing their endpoints. They've done a good job of securing the perimeters.

And so they have a lot of technology that's in place that actually is doing its job. And so because the adversary is a learning human, uh, environment or a marketplace or whatever you want to think about as the adversary, they've moved to the place that is, uh, that probably is the most vulnerable. And so, you know, people really aren't breaking in as much as they used to.

They're basically just logging in. And so they're stealing credentials, they're tricking the humans to giveaway credentials. They're looking at applications that, how did you proceed?

And so now they're just logging in. And so I think companies are starting to think about what do I do about that? Um, and then of course, you add AI on top of that, and now you have sort of a, a, an autonomous system to a certain extent attacking the most vulnerable place in the environment, which is the identity elements.

And so companies are, are putting a lot of resources there. Um, Yeah, we, we just, you know, we were, the day you and I recorded this happens to be National Data Privacy Day. I don't know if you're aware of that.

Um, Right. You could actually go, you know, Get A t-shirt down to the Hallmark store and get a card, something, right? But, but the fact, and, and so we were having a discussion and a few people on our text on gang we're like, well, we need to be encrypting our daner and, you know, and protecting our daner.

And that was the point I made art, is that people are brute forcing their way in and they're going to steal all this salted dash, you know, hash data and no, they get in right through the front door with your credentials, and you do have access to that data. And until we, until we hit that hair square on, you know, everything else is kind of secondary. So that, that should be job one, as we absolutely ate data privacy type.

Anyway. Yeah, Go ahead. Go Ahead.

I was gonna say to your point, yeah, so I think to your point, um, the reality is, is there, there's a couple of levels of that security that need to happen. One is obviously you wanna manage those passwords, you wanna change them frequently. So if they do get stolen, it's hard to use.

I think the other thing that I think companies are starting to realize is it's not just your admins and your super users, your privilege users, that you need to be thinking about sort of managing privilege in this way. And it's, it's all your users. And so it, it's not to the same level of, of stringency that you would control your privilege, your admins, but you know, our, our giland has access to our customer data.

Our giland has access to our financial systems. You want to be managing exactly that access in a more sort of privileged minded way. Uh, and so this expansion of authorization, this expansion of privilege, um, and then you wanna watch what art does with threat detection and other kinds of identity based monitoring.

And so a lot of the work that we've been doing is not only building the foundation to do the authorization, but also expanding capabilities into threat detection and into governance and managing the sort of whole ecosystem. 'cause right now, a lot of those systems are separate in companies, and that makes it difficult. 'cause you're managing in different UIs, you're managing different data sets, you're sort of, sort of swivel chair trying to do it.

And in the world of ai, you kind of need the system to also be smart about it and respond fast. Um, and so a lot of our investment has been there. Fair enough.

Hey, art, we're about outta time. For people who want to get more, maybe download the report or get more information on the report, or maybe even just find out more about deline, where can they go? com.

Uh, you can get access to this report and a bunch of it, and then also a lot more info. Yep. Sorting, well, it, it's on your background, so they should be able to read it from there.

Right. com. Yep.

com. And, uh, there's lots of information there, a bunch of reports, this report, and many others. Fantastic.

Hey, art, thanks for coming on here and, and telling us, talking a little bit about identity security and, and all. It's, it's a brave new world, right? Exactly.

But, but here's the good news. We're making progress. We really are.

We're here, we're here to find bad guys with you. So thank you very much, Alan. I Appreciate Exactly.

We're here, we're here, we're here to spread the good news, art diad, CEO Delania, here on text Trump tv. We're gonna take a break. We'll be back in a moment.

It's funny 'cause a lot of people when they hear about Davos, this is actually what they imagine. They imagine the mountains, they imagine the fog. They imagine, you know, we got skiers in the background.

They don't actually, they haven't seen what we experienced, which was sunshine. I thought it was busier. And, you know, you being a first timer novice, you know, me being a wily experienced second year veteran, um, it was different.

The streets were busier, the energy was higher. Maybe it was all the excitement about, you know, what we think is gonna be several years of growth ahead. Maybe people just like seeing the sun.

Hey everyone, welcome to Davos. Daniel Newman here, back again and joined, by the way, first time brought my bestie with me, Patrick Moorhead. I know I have given this event, uh, world Economic Forum, a lot of grief, uh, prior, but I have to tell you, uh, when it comes to combination of meeting with senior leaders in tech, with governments and finance, this is the best event for them.

We're all in one place. We are literally on one street. Chuck, uh, thanks for coming on the show.

It's great to be here. No, it it is. This Weather's terrible too, Huh?

Oh no. They tell me. It's always like This's always like this.

It's an incredible place. You can actually spend a lot of time talking to companies about next generation technologies, what's going on, the geopolitical dynamics that are happening around the world. It's just a great convening.

Lot of great people to spend time with. It's highly Efficient. It creates a level of energy when you have so many people across the world coming together to discuss both business as well as the social impact item.

You and I get used to coming to tech event and the focus is products. That's right. What are we launching here?

That's not why these CEOs are here. You're seeing the CEOs engaging with these countries, right? With these leaders, with these sort of policy makers, pundits.

It was all about like, how does the world absorb this AI movement? The six five is on the road with a view from Davos. Thank You for having me, and welcome to, that was an amazing place and an interesting time.

Happy to See Both of you, my friend here, Talk in tech. You're empowering women. I don't know if I'm gonna do your whole speech for you, but welcome To The show.

Very happy to be here, is my favorite thing to do. I Appreciate that. Thank you.

You probably say that to all The folks. Ransomware attacks on the rise, 90% increase. You can probably imagine what the cause of that is.

Sure. It's startling. Wait, wait.

Were we supposed to say ai? You're, You are. Okay, hold on.

You missed your cue. AI is top of agenda for all governments. They have to control the destiny with this amazing technology.

And we had, for example, the Prime Minister of Belgium, the finance minister of Germany, the AI Minister of France. It was a good having conversation as you saw about importance of Europe to understand the need for innovation and how they're gonna reshape. I think their policies, I Felt like it was more affirmation.

Of course, they want less regulation, uh, particularly across doing commerce cross border. Yeah, I, I think that was really, the tone was set with our rooftop conversation with, uh, chief Commercial officer at IBM, Rob Thomas. The thing I'm the most hopeful on as you look out the next four years is less regulation.

We want to be able to create growth, create jobs, do m and a, and I think the incoming administration gives us a lot of opportunity to do that. So really excited about that. Growth solves every problem.

Well, clearly there are gonna be changes, uh, you know, different view of, uh, climate and business, and, uh, hopefully it'll be positive. One thing we're gonna see is greater government efficiency, but a big change obviously coming. There's not a single government or business who doesn't agree that that doing more with less energy is a good thing.

And To hear the CEO finally sort of acknowledge it, and it was like a safe space. What I like to say, and this might sound a little bit controversial, but it's a little bit like feeding vegetables. It's fine to the camera, feeding vegetables to your children.

You know, we need to sort of cover up the decarbonization technologies with some returns and future proofing these legacy industries. I think those industries are ready now. And what I hope is the younger generations are gonna embed those new technologies.

AI is gonna demand more and more energy. And I think a lot of the forecast that we're reading of how much energy we will need over the next 10, 12 years are too low. Uh, I could see the demand and doubling over the next 12 years.

Most of it's gonna come from the traditional sources, oil and gas, and even coal. The only answer ultimately for humanity is fusion, because fusion produces no greenhouse gases and, uh, will be a real breakthrough. But how far off it is and is it gonna be available on a huge scale that remains to be seen?

But AI may help us solve that problem. All of the customers are saying, you know, especially in the IT departments, they're like, I have got to deliver all this innovation, but I also have to think about the, the long-term sustainability of both cost and right energy at compute. So how do I think about managing that?

Interesting. And so we're able to kind of bring all that metadata together as well and give those recommendations around. How do you think about where those workloads go?

How do you think about fulfilling this demand? So What we did, that's very different than GPUs. GPUs have a lot of external memory and they will do part of the problem, bring stuff in from memory, and then do part of the problem and bring stuff in from memory.

It's very slow, very energy intensive. So we use about a third of the energy versus A GPU. 'cause what we do is we'll take hundreds or thousands of our chips, lay the problem out completely in those chips, so we don't touch any external memory.

And it's like an assembly line. We'll just go through that very quickly. So we take advantage of that double exponential, and we're the first to do this.

There's only two types of companies in the world, ones that are gonna be AI forward. What I, who I, what I like to call is people that know how to take advantage of AI really well. They have high dexterity with AI and others that are gonna struggle for relevance.

And, and so the first, the great ones, I think they, they wanna move fast, but they're being held back because of safety and security in ai. So that's an area that we have really doubled down on at Cisco, Our report said that 67% of organizations said, yes, AI will have the biggest impact to my security posture, but only about 30% are doing anything about it. And that's where it's really important to consult with and partner with the cybersecurity professionals around you.

2025, really the, the March, and I don't think it was unrealistic March, that they wanted to start seeing return on investments from what the, the investments they're making into AI infrastructure, AI, SaaS, and everything in between. On an aggregate basis. Our customers saying that they're gonna spend three times more around ai.

So there's a lot of experimental thing that we see here and there happened in the past few years, how to do that so that we can maximize the return on investment, right? So those are the, are the feedback that we have got from our customer. And as a result, we announced together with, uh, Nvidia a new framework, which is called the Lenovo AI Hybrid Advantage, right?

As a framework of solution with an aim to help our customer to land AI use cases fast. Uh, and with ease, Like I can be more efficient, I can get stuff done. There's things that used to take me hours or I couldn't even do at all, that are, are very, very easy to get done.

That actually helps companies retain 20 to 30% higher retention rates. That's money directly back into customer's hands. Yeah.

We came into Davos 2025, the World Economic Forum, really in a moment of change. It was really interesting because what I was wondering is were people gonna be sort of a little bit nervous and uncomfortable with what ha is to come that that's right? Or are people gonna be feeling super optimistic?

There Is a optimism right now in the industry and especially about the US economy. I think there's expectation. There's gonna be growth.

You know, the combination of availability of energy, the regulation and technology innovation usually is a good combination. I Think we kind of hit a pain point at the right time on resiliency, which is every company's trying to say, how do I have an infrastructure that can stand The test of time is not gonna be exposed to threats. I think we hit something with concerts.

You'll see more on that in May. Looking forward to it. Nice little, uh, tip there.

We Made an announcement last week on a product called AI Defense that really helps take out the unpredictability from ai. Christina, you had a pretty big few months. Um, first of all, you, you, you raised a little money just a little bit.

Just a little bit. Several hundred million at a 800 million. Yeah, I said several hundred that, that was effectively, but I didn't have it memorized.

I think the correct word is, uh, almost a billion dollars. Rumors are, we may have taped out a four nanometer chip that may be coming soon. I'm not gonna confirm or deny 10 20 customers large CapEx deployments in the tens of billion dollars.

They soon will be consuming million GPUs. They already are in the tens of thousands, in some cases, hundreds of thousands. We came, we saw, we conquered.

I mean, we had so many great conversations, many memories, right? I'll leave you with this question 12 months from now. Will you be back here in Davos?

Yeah, I Think my answer would be a conditional yes, probably match it, uh, next year closer to the experience that, that I would like to have and to be able to live better value, uh, to our audience and also, uh, our clients. Well, I think that's a great way to wrap it up. I will plan to be back.

We'll see what happens. The next year is gonna be fast paced and furious, and of course you can keep up with most of what's going on. We only pick the good stuff to talk about here on the six five.

Thanks so much for joining us. Thanks for coming to Davos with us. See you in 2025 on the next show.

Hello and welcome to the latest edition of the Techstrong AI video series. I'm your host, Mike Za. Today we're talking with Amad Al, who's CTO for Mitra, and we're talking about how things like warehouses are being automated using various robotic platforms and software and all kinds of fun stuff.

But maybe we need to actually physically understand what it is we're trying to automate first, because, well, a lot of people are putting the cart before the horse. Ahman, welcome to the show. Thank you.

Great to be here. What is happening here with these types of projects? Because on the one hand I wonder, do we really wanna replicate what we already have and is that what's working?

And or people trying to jump ahead and re-engineer the entire thing without understanding what it is they're re-engineering. And so maybe we do need some more hands-on experience with the process. I, I, I think there's a, um, an approach that we've seen before where people see a new emerging market that is, uh, interesting and, um, uh, they see an opportunity to create value.

And so they jump in. Um, when, uh, social media started, uh, taking, taking off, uh, we had a lot of entrance to say, Hey, it'd be really nice to have, you know, a social media platform. And a lot of apps being built trying to build that.

Um, many of them failed. Uh, some of them, uh, one maybe by mistake. But a lot of the, um, uh, the right approaches is to get customer insight.

Meaning try to understand what you know, what your potential customers are trying to solve first. And that is hard to do by guessing, uh, especially from the outside. If you haven't experienced, uh, when yourself, uh, if you take, uh, Instagram as an example, you know, people might have an idea about, I just wanna share pictures.

Great. See if, if it's a workflow that, that you actually enjoy yourself first and go, I wanna share pictures with friends and, you know, become a customer of that service first and see what works. And then go ahead and say, okay, now I know what to do.

I'm gonna go build an app that does something special. This is the same thing now applied in industrial robotics. It's really a hot market right now.

Uh, a lot of entries are coming in and, uh, it's easy to go and say, I'm gonna build this nice looking, you know, uh, using my robot, that kind of baby dances. And, uh, it looks great. Wonderful.

It's fun. I want to do that as an engineer. Wonderful.

But then you have to become a customer first. The reason, uh, we believe, you know, our approach is, um, uh, more applicable is we've been customers of material flow at industrial, um, uh, manufacturing, you know, Ian Rivian. And it wasn't that we wanted something that looked nice or really cool, we had a real problem to solve.

And we thought, oh, what really is important right now as a customer, what it would solve? My problem is a, an efficient flow of material that was, uh, easy to deploy, that had, uh, minimum number of integrations and complications. After that, after that insight, I know what I, what would make my life great.

I go and invest and build a new solution that does exactly that. And so the step of you actually doing, uh, becoming a customer is generally skipped. People go, I know what to do.

Yeah, this would be great. You're making assumptions. And it's not a matter of, um, serving people.

Say, Hey, tell me what's important, it's kind of a, a, a, a known wisdom that the customers will never tell you exactly what they want. They, they'll tell you like, I, I need a faster horse. Like, maybe it's a car.

Mm-hmm. And so you will not get that insight by just asking, you have to physically become the customer. Uh, in the case of warehouse or industrial, um, or manufacturing.

Go to the floor. I mean, it's not really hard. Go to floor, say, okay, show me how it works.

Become that person to work, uh, in, in the factory, work in the warehouse, and see what are the friction points that with your skills as an engineer or whatever field that you're in, how can I solve this, uh, from my, from my perspective, um, that, that is the approach that, that we're pushing. And as I understand it, it's not all that simple because a lot of the AI technologies that we're trying to embed into these industrial robots don't really understand physics just yet. And so they don't understand the movement and how much distance there is between different things and how much hitting something might hurt.

And a lot of times, a lot of these robotic projects get scrapped, and that costs millions of dollars simply because they didn't think through the physics of the thing. So, um, what is your sense of, uh, what do we have to overcome here to kind of make the industrial robot smart enough to see and understand what it's looking at? Uh, it needs to, um, there are a few data points that need to be collected more than just saying, uh, I have a great, uh, generative ai.

It can answer a few questions. Now I'm gonna take that and apply it to, uh, physical world, and it's just gonna work. Uh, maybe you really have to try it out and be ready to, uh, admit to the gap.

Say, Hey, there is a gap here. I invested all this time. There's a gap between the generative part or the thing that the AI is giving me, and what really needs to happen.

Um, it could be that you have some wishful thinking, like, it, it really must work. It's really cool. I, it'll, it'll actually work.

But unless it is, um, actually effective at, uh, giving you more productivity, you need to pivot. Uh, this is, you know, pivoting is a very, uh, well understood, uh, business process where people go, Hey, this, this didn't work. You shouldn't be emotionally attached to it.

This did not work. Let's move on to the next thing. Or say, Hey, it needs more tweaking.

Uh, get it done better. Uh, I think there's a little bit of wishful thinking, um, that, that comes with the deployment of AI that says, this will solve everything. It, it's smart enough that that's it.

We're done. We don't have to invent anything new. Um, it that, that has yet to be proven.

Um, it is not impossible, but I think the homework hasn't been done yet. Deploy the ai, see what the gap is, try to fill it. Uh, try to, to, you know, bridge this gap.

Uh, that doesn't happen by just, um, building an algorithm, handing it over as like, Hey, this will solve everything. You have to actually apply it. So where would I find the people who understand the warehouse or wherever I'm gonna put this industrial robot and have the AI skills?

I mean, am I looking for a unicorn? Or is there, is this more of a team sport? How does that come together?

We're lucky in the, in the Bay area, we have, um, multiple disciplines, um, that come together that, uh, you know, feed each other and, um, uh, tech and software is, is prevalent in this area. And so when manufacturing started happening, California with Tesla, uh, it was a fresh look, uh, at, uh, you know, established practices. Uh, I think the best, uh, uh, people, uh, and, um, uh, at least culture approach to solving these problems right now is to look at other disciplines, uh, how they solve things and apply it, let's say, uh, industrial automation.

The best engineers have a good solid background in the problem, say warehousing, uh, manufacturing, uh, uh, uh, industrial automation. And they start to branch out. It's like, how was this problem solved in, uh, computer science?

So engineering or, uh, DevOps where you take that, uh, uh, innovation and apply it to the physical group. A very good example is, um, if you notice, uh, maybe 10, 15 years ago, we started not worrying about the search engine, not not showing up or your mail not arriving, because, you know, companies like Google and, and other providers, they kind of figured out how to get a service to reliably, uh, be available, extreme high availability. That's their business.

Um, they had to invent new processes, you know, things like containerization and Kubernetes and, you know, fault tolerate design. Logically, those things were solved. Today, we don't really worry too much about opening your browser and, and getting a search result.

It's, it's extremely reliable. And that was a, a, uh, an innovation in distributed computing and reliability that happened in that discipline, um, that can easily migrate into the physical world. How do you ensure that a production line runs exactly like a stream of, you know, data, uh, is always available, or you build in this fault tolerance?

When one machine dies, the next machine takes over automatically and smoothly. Uh, the data is shared between machines. It's a stupid extraction.

These disciplines, um, uh, or innovations haven't migrated to the physical world, uh, fully. And there's a huge opportunity to apply these, uh, at these techniques to the physical world where you can look at your material flow, just like data running in a, in a data center. Um, if you apply the same principles, uh, to the physical world, the people who understand how this physical world operates today, um, uh, indoctrinated in, in a new way to do, you know, quick sorting, uh, uh, fault tolerance, uh, high availability, take that and apply it to the physical world, and they're the best people to, uh, implement that as we go down this path, am I gonna see kind of these humanoid robots capable of performing multiple tasks, or is it more likely we're gonna see, you know, a network of highly specialized robotic systems, devices, whatever it may be, that are working in concert with each other to accomplish a task?

'cause it seems to me it's difficult to teach something that's humanoid multiple job functions. And so maybe we're better off just having a little, a lot of specialist things. There is a, um, hypothesis that a generalized human humanoid, uh, is exactly what we need forever.

Uh, maybe it's a hypothesis, I believe, uh, at this point. Um, a good example is the, the bot behind me. Uh, the problem we're trying to solve is, um, uh, material flow in manufacturing.

And we, how most of what you're doing is moving the materials, uh, is my direct, um, uh, approach to solving this problem a humanoid, uh, I don't believe it is. So we thought, okay, what do I really need to do? And I, again, I don't wanna be attached to, oh, humanoid is really great.

It might be in the future, but what am I trying to solve today? For me right now, I want to solve material flow, uh, an efficient way that, uh, increases safety and productivity. So you build a form factor without having any attachments to some preconceived notion of what a solution is.

We thought, Hey, uh, we're trying to move 3000 pounds backwards, forwards, up and up and down, uh, left and right. Uh, is it a humanoid? Forget about that.

Now, let's, let's focus on what the form factor needs to look like. The form factor looks like a thing that carries a pallet that moves 3000 pounds. And, uh, today, forklifts that do this are 9,000 pounds at least, and they cost 10, 20, $70,000 each.

And we're trying to say, what am I trying to do? I'm just trying to move this material in these directions. And so to do that, I can come up with a device that is 500, 600 pounds only, and it'll do exactly the same work or a fraction of the, the energy.

And you increase my productivity in the end, I solved the problem. I did not, you know, get distracted by, I need to build a humanoid. Uh, maybe a humanoid would've solved the problem.

But really, the way that your approach from first principles is, here's the thing that needs to be solved. What is the best form factor for it? It's almost like biology.

Uh, the best evolved thing that extremely is, that is extremely effective by solving this problem, is the thing that wins. And this is the form factor that, that work for us. I believe that companies that want to innovate and improve in, um, uh, industrial automation need to look at the problem and, uh, work backwards.

Not say, I know the solution, I'm just gonna go build it. No, go, go and invest and see what are you trying to solve? What are the first principles, uh, that lead you to arrive, uh, to the best solution?

And you come down to the numbers. I'm trying to move 3000 pounds and three, uh, 3D the best form factor for that looks like what you see here. Um, it has the elements of the humanoid.

It balances it, it has a lot of technology that we invested there to, to accomplish that. The climbing up and down, um, uh, from any cell to any cell, it's a big Minecraft grid. It is innovative, it is very cool.

It just didn't have, um, uh, the preconceived notion of, oh, it's gonna look like a humanoid. If I were to build a humanoid today, um, to solve this problem, I would ask the humanoid to get on a forklift and start moving. It's like, okay, so I didn't solve the really the problem.

Um, I'm trying to move the material efficiently and, uh, work from there. And you arrive at something different and innovative and perfect, uh, for the problem that you're trying to solve. How long will it be before we achieve this great future?

I mean, I know that we're seeing robots today in factories and warehouses, but this notion of a robot that is more, uh, cognizant, shall we say. Um, where are we on this journey? Um, it is hard to tell because we've been promised, uh, if you remember back in the eighties, maybe late seventies when automation started, uh, appearing in, uh, uh, auto manufacturing at that time, people were like, oh, it's over.

That's it. Robots are taking over. Everybody's gonna be, you know, uh, out of a job.

Nobody's gonna build anymore. Uh, yeah. And I was get, and I was gonna get a flying car that I didn't have a job to pay for.

Exactly. Uh, so, and you see, until today, I mean, uh, very little automation is practically deployed. It's, uh, it's still a manual, uh, world.

And so there is a thing that just, um, prevented that from happening. Um, it was too optimistic to say, the robots are just gonna do it. There's, um, a lot of variants, uh, that robots are not good at, that humans are really good at.

If a, um, a box falls while I'm in the warehouse, if the box falls on the floor and I'm supposed to go pick it up, if it was a robot, like it'll just sit there unless you go as a programmer and program all these exceptions in there and say, Hey, watch out for a falling box. Here's how you solve it. Watch out for this.

Here's how you have to pre-program it to do that. You throw a human at this problem. You don't even have to tell them they see a box on the floor that shouldn't be there.

They'll go pick it up and continue the job. That gap between handling exceptions without needing any training whatsoever. And, uh, you know, having a prescriptive directed, uh, you know, programming for the bots is, is the thing that's lacking the promise of ai, especially at, um, the influence level at the edge on the bot itself is very interesting.

Now, if you have these inference models that runs on these, uh, robots, uh, on the floor without needing any cloud or anything, it's interesting. They start to, uh, handle, handle the exceptance that humans used to do. Um, what's happening right now is the reliability of it.

We've all seen examples of people asking Chad gt or any one of the LLMs asking a question, getting a ridiculous answer back. But that's insane, unfortunately, until that has been solved, where you're getting really reliable, really good, um, uh, answers, uh, it's still gonna be, we're gonna need some humans in there. The robots are not gonna take over.

You can't really rely on them yet. It's not impossible. Uh, and, and there is progress being made.

It's very hard to tell. Like maybe two years ago when, uh, I believe three came up, um, you go like, how long until it like answers everything for us. Here we are, two years later, we're still going like, right.

It's still impressive, but it's still like, there's still something missing. Uh, I just recently heard about, uh, uh, apple turning off their, their news summarizing, uh, ai. This is a big company that invested a lot, and they had to kind of just say, Hey, this is not the, the headlines, it was summarizing as an AI sounded like really off.

Like, it, it wasn't useful. I just turned it off, not, I mean, it's an attempt and we should always encourage people like, yeah, it didn't work. Keep going.

And so we're at the phase of promising, let's just keep going. How long will it take? Uh, it's everybody's desk.

Everybody's working really hard to get to an next, so it could be this year, could be next year. Um, but it's really hard to say when innovation, when a breakthrough is gonna happen. Uh, a lot of people have been trying to figure out, you know, uh, flight maybe for hundreds of years.

Everybody's like, yeah, possible. Yeah. And you see attempts after attempts until the Wright Brothers breakthrough was made.

And now we have aviation. We just couldn't predict exactly when it would happen. Will this evolve into, um, robots that essentially were trying to orchestrate, rather than us performing the tasks, we are becoming the managers of the robots.

Uh, that is, uh, hopefully the, uh, the short term, uh, outcome that is, that is possible. Uh, and it's not binary. It's not like a, Hey, everything's automated and we're just sitting there pushing buttons, or we're doing all the work ourselves there.

There's a, a gradual, um, uh, progression where some tasks start to be reliably automated to the point where we go, yeah, this is, this is good. Um, so here's an example. One of our deployments, um, um, the, uh, set of, um, pallets that need to be pulled out, uh, used to be manually done, where somebody goes driving around with a forklift, pulling out a pallet manually, like spend the whole day, you know, driving to a pallet and taking it out and putting it in a trailer.

Um, a system like ours made it. Uh, so it's simple. You come to the, to the, uh, the edge of the, uh, uh, the installation, and you say, here are all the pallet that are like to pull out of the warehouse a order.

Just enter it on a little iPad. It's system w simple, very pleasant experience. And then these things, the, the bots go automatically.

They pull the material out in order, and you just pick 'em up one by one from the same location. You don't have to go search for it everywhere. Um, that is a, you know, it, it's, it's a very pleasant, it made it, so you're doing the same work.

We're still, you know, picking up pallets and putting in the trailer, but it just made your life way easier. It took off a lot of the mundane, uh, monotonous, you know, uh, work that you have to do. And it made your life easier.

Uh, at some point, it is gonna make it even more easier. There are other areas where you just make your life simpler. You're still doing the work, but the, the hard work is, is out of the way.

And you don't have to spend your time in a harsh environment. Sometimes these warehouses are like minus it's 20 degrees. You don't wanna spend the whole day and minus 20 degrees.

It's just, it's punishing. And so the hard work, okay, let the bots go get the material out for me. I'll just take it.

I now prefer this than, uh, you know, the alternative, which is physically going, doing. So, there are other areas where, where automation is just gonna make our lives slowly easier and easier. And, uh, the smarter they get, the better for us, because it'll be closer to the push button.

Warehouse is taking care, you know, it's the dream. And, and, and hopefully everybody will be in a better mood when they get home. But let me ask you this last question.

Yes. What's the economic impact of all of this? Because are we gonna see more manufacturing move closer to the point where whatever's being made is consumed?

And we might not need to, you know, ship goods halfway around the world just because there was cheaper human labor someplace. We could just have a lot of smaller factories and warehouses closer to the point where things are gonna be used. Um, the, the impact of, at least in my view, the impact of that is, uh, dwarfed by, uh, the possibility of, uh, enabling manufacturing where it wasn't feasible before.

If, if you were to say, um, you know, I, I would like to create a, a new, um, auto establish a new auto manufacturer in California or Kansas or somewhere else, uh, you have to take into account a lot of inputs, a lot of things that you have to build out first. Automation is a big part of that. Your warehouse operations is a big part of that, and it comes with a lot of costs.

And, uh, if, uh, automation makes it so things that were not possible before are now possible, uh, now you decide, yes, I will decide to go and build that auto factory, and you create more jobs, more economic growth, more opportunities, and no more innovation. Just because automation added that extra component of feasibility. Where before it was hard, and there you had to ship things from everywhere.

But now automation made it so, or it's easier. I can actually, uh, um, uh, decide to actually go ahead and build out these, uh, um, warehouses and, and manufacturing facilities. So a lot of the opportunities that were not feasible will become feasible now that automation is introduced.

So I think the impact, um, is gonna be extremely positive. Um, there's gonna be these, um, uh, e uh, ecosystems or say, Hey, there's a new manufacturing facility now. It's highly automated.

And now because of it's dairy, all the restaurants in that area are now prosperous. So all the real estate gets improved. All the, uh, logistics, uh, that's happening.

It's bringing more business. There's huge impact, uh, that is extremely positive that's gonna happen because of automation. All right, here, the robots are coming.

The question now is, how are we all gonna work alongside them? They come up with something that is a better outcome for all concern. Hey, Ahmed, thanks for being on the show.

Thank you very much. Appreciate it. And thank you all for watching the latest episode of the Textron AI video series.

You can find this episode and others on our website. By all means, check them out. Until then, we'll see you next time.

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Hi, everyone. Welcome back to Marketing, art and Science, the podcast where we sit down with B2B B2C chief marketing officers, and really get to understand their profile as well as how their and their teams are pulling the levers of art and science and marketing to drive business outcomes. We're talking pipeline revenue and a seamless customer journey.

I'm your host, Lisa Martin, and I'm so thrilled to welcome Denise Pearson, the CMO of Snowflake, to this latest episode. Denise, it's so great to see you. Thank you so much for joining us today.

So Great to be here with you, Lisa. Thanks for having me. So let's, Oh, it's my pleasure.

Let's go ahead and start with your CMO profile. You have a great background. You've been in marketing for a long time, but tell the audience a little bit about you and how you got to the level where you are today.

I started in marketing in tech in 1996. It's almost 30 years here, uh, soon. And I think it was a little bit by accident to begin with.

I had this passion for marketing, and especially, you know, advertising. You know, I grew up going, uh, to watch, uh, the commercials when I went to the movies. I grew up in Sweden.

We didn't have commercials and tv, so I went to the movies to watch the commercials, uh, watching the, you know, the Yu of Fruit and the Levi's commercial, you know, from, from the us and kind of dreaming myself away to, uh, to California. Um, but again, I ended up, uh, in, in tech. Yeah.

So I had a, one of my friend's sister was working for a tech startup in Sweden. It was a French company. The company was called Genesis.

Uh, and the product they developed is very similar to what Zoom is today. So they actually develop a first automated conferencing service. So I ended up joining there, and the company took off and did really, really well.

And, um, so I grew with the company. So I started as a marketing coordinator in Sweden, and I was promoted to marketing manager. And then a few years later, I got the call from the headquarters in Paris asking me, do you want to come down?

We had this product manager position open, and the big goal I've set for myself was that I wanna become a product manager before I was 28 years old. I mean, I thought it was gonna be more, again, in the B2C world right? Than a company like, you know, Proctor and Gamble or something.

That was my, my big dream and, and vision I had for myself. And, um, so when I got that question, I said, yes, you know, I'm, I'm coming, um, to Paris, you know, next week. And, uh, moved to Paris.

And then a year later, the, uh, global head of marketing took on another role. And the CEO said to me, Denise, I'm appointing you head of global marketing next week. And by the way, all the executives from around the world are coming in to Paris, and you have to present the marketing plan.

So, um, oh my gosh, definitely scared to death. But it was also that a pivotal moment by the, probably the biggest pivotal moment for me in my career where I had to say yes. You know, I had to, yeah, I mean, uh, and I cannot lived by that.

You have to say yes when the opportunities come your way. And I've had that philosophy that the worst thing that can happen is that I have to go home and sleep on my mom's couch. That that's the worst thing that can happen.

So I said yes, and then I stayed, uh, with a company for another, you know, six years. You know, we opened up country markets in 30 countries around the world. And, uh, I spent 12 years there.

And, um, also moved to the us you know, with, um, the US was our largest, you know, market. And, um, I moved to California, uh, the Bay Area in 2000, um, two, but then they sent me to other places. They sent me to Down River for a year and DC for four years until the company was eventually acquired.

But, uh, it was definitely kind of the place where I, where I grew my career, and where I learned a lot in marketing where I've made the greatest mistakes, you know, all my life, you know, as well. And then since then, I've been the c of three other, um, tech startups, and, uh, including on 24 for five years, and then Apogee for almost three years, and now Snowflake for, for nine years. And I actually had opportunity to take all those four companies, you know, public.

So they done, um, uh, quite well, been incredible journeys to, to be on. Amazing. Your Pathway is so fantastic.

It reminds me of mine a little bit when I got plucked from being an, a payload scientist at NASA working on the space program to being in sales and tech from a family friend who saw probably like your family friend of talents that you might not not have known that you had. Yeah. But what I also really admire about your background is you have this kind of like, I do this, get comfortably uncomfortable.

It's, it's hard, but like, when you had the opportunity in Paris to lead global marketing, and you knew the only answer is yes, and I'm gonna make mistakes along the way, but I'm gonna learn from them. I think that is just one of the best attitudes that you can possibly have. Tell us a little bit about Snowflake.

If anyone listening doesn't know, I'd be shocked, but I've been watching this Snowflake trajectory for years now. I've had the chance to interview you before. Talk to us a little bit about that and where marketing fits in.

Mm-hmm. And the overall corporate strategy. Yeah, I mean, overall, snowflake is a data platform built, you know, for the cloud from the beginning.

The company was founded in 2012, and then, um, the, um, product was ready in 2015. So that was a big launch, you know, for, for Snowflake. And I joined about 10 months, you know, later, essentially if look at, okay, what does Snowflake, you know, do that is different from, from what we had in the past?

And Snowflake essentially handle all types of data, and you, and, and you can have that data all in one place, and you can give access to that data to everyone in your organization with the highest level of security, you know, governance, access control, et cetera. And not just people, but also machines, right? We know today that the biggest consumers of data today are, are, you know, AI models, for instance, right?

That use this type of data. But again, the revolutionary thing was that back in the, in the, in the days and still today, right? A lot of data sits in, in basements, you know, locked in.

And the only people that could access the data is, you know, the IT folks, right? And that makes of course, very difficult for the people on the business side who wants to use that data to create business value or, or insights or develop, you know, data products for their company. Makes it really, really hard.

And that's kind of what, what Snowflake has made, you know, possible is, again, to make that data accessible, you know, for, for, um, for NOL. And that's essential for customers across every industry that, that have to become data companies. If they haven't already, they're behind the curve there.

Talk a little bit about the corporate structure. You roll up to the CEO, but you have really tight alignment with the CRO, which you and I both share, is something that is so incredibly important for marketing. Give us kind of a little high level view of that relationship.

Yeah, I mean, I report into the CEO from an org chart, you know, standpoint, but my mentality essentially that I worked for the CRO, and I think I've always had that, and maybe it's 'cause I started in marketing in, in the nineties, and back then, I mean, sales was truly king, right? I mean, in, in B2B, because every deal was run by the sales team. The sales team did education, you know, of the, of the customer from the very early on.

But that of course, changed, right? When, when the internet came, right? And everyone could start, you know, self-educate and read up upon products and, and learn by themselves, right?

Marketing became more and more influential on the entire, you know, buyer's, you know, journey, but still have that sort of mentality that, that sells is, is, is king, and that sells is my, my, my, my, my customer. And in B2B marketing, I mean, the most important objective we have in marketing is really to make the selling processes easy as possible. So it's more just that mentality.

Again, what can we do to make the, uh, sales process more efficient? What is it that sales need at any given time? And just really being in lockstep with the sales strategy all the time.

I'm not saying that sales is more important than, than marketing, right? But it's still the mentality of being of service in or of, of sales and making sure that we're always in a fully, fully aligned as an organization. Absolutely.

That alignment is key. It's not a trite thing, but yeah. One of the things I admire about you too, Denise, is you said, you know, it's it critical to our success of looking at projects from the outside in.

Talk about what that means and how is that critical to your success? Yeah. And, and we'll talk about who we ultimately work for.

I mean, we all ultimately work, you know, for the customer. I mean, if you look at them, the most successful companies around the world, I mean, those are the companies that are really, really focused on, on the, uh, customer right? At any given time.

Yeah. I think all, I think all companies, of course, have that ambition to put customer at the center on being in a customer centric, but it's hard to execute on that because, uh, as you grow as an organization, you really start thinking more about all the processes, you know, internally, you start thinking more that kind of, you know, inside, you know, out. And I think we need to remind ourselves every day, right?

To think outside in, yeah. And I can say I find myself guilty all the time for that too. And then I just have to kind of remind ourself, you know what, we need to look at this from the customer perspective.

Whatever this issue we make is that the right decision for the customer? And also at Snowflake, I think what's been unique is that many times we made decisions that have had short term impact on our business, you know, our revenue, whether it's, uh, making our product much, much more efficient, so cost will go down for our customer, right? I mean, short term, that of course impacts snowflake negatively, if you will.

Yeah. But long term is the right thing to do for a, for a company. 'cause customers, you know, will trust us even more, right?

I mean, uh, we become a much more important and, and valued in a business partner to our customers. So there's so many aspects of that thinking outside in. And every year we do a very comprehensive survey, uh, where we are really tracking every part of the customer journey, right?

Everything from how they start learning about Snowflake to, to the sales process, contract process, to to building process, to product experience, everything. So we can really look at, okay, is anything falling in the red, essentially? Is there anything we need to address from a customer perspective?

Customer value, it's everything, right? For every company and every industry. Let's segue Denise into our second subject.

And that's really kind of looking at snowflake's, MarTech stack in action. I know that you really focus on a team of artists and scientists. Talk to us a little bit about that, where you as the CMO are really the conductor of the orchestra, But I think this is why we have so much fun in marketing.

I mean, it's all about bringing these two skill sets together, right? Bringing the creativity together with, you know, analytics, right? I mean, it's a creative part that makes in our marketing engaging it.

What helps in, you know, you know, capture attention. But the analytical side, right, is all about making sure, right? Is it all working and you know, are we targeting, you know, the right, you know, people and is the right, are we putting the right message in front of the right people as well?

So I think this is why I'm having so much fun in marketing. I think this is why I'm in marketing, is to, to kinda bring these two sites together. And it was there at Snowflake, we actually do personality testing.

So we, we, we, you know, we're using the insights discovering program, looking at the, who is blue, you know, who is yellow, who is red, who's green, right? And daf that you find all those analytical folks, you know, on the blue side out of the spectrum, and you have the more creative people, you know, they're more on the yellow and on the, on the, on the green side, right? And as the head of marketing, your job is really to have both of those skill sets.

You cannot just have analytical people. I mean, your market is gonna be really, really boring. You just cannot just have those people with, you know, tons of creativity, you know, either.

So, uh, it's that balance of bringing them both, uh, together. And this is of course where, you know, technology comes in. I mean, uh, and on the analytics side, there's so much we can do now with, um, with technology as well.

I love how marketing has become so scientific. I think that's because I was a scientist in a former life. Talk a little bit about the marketing technology stack that you've put together and where that science piece comes in to really help course correct in as near real time as possible to really deliver that customer journey that's seamless.

So the value to the customer is really there. Yeah. And we use, of course, several different, you know, platforms on the, on the sales side, of course, we use, you know, Salesforce, so that's one of our big, you know, platforms across marketing as well.

We use, you know, Marketo, you know, and, and Adobe, you know, for, for a website. And those are the platforms we use in terms of engaging and activating our programs. We use, uh, a company called bombo to get intent data, right?

That's intent data we use from the analytical side, making sure that, um, again, right, you know, how how is the messaging, you know, working in front of different audiences. We use, you know, work Workato a platform to really automate kind of the workflow of all our different campaigns across our organization. And then we use, um, you know, role works and, you know, Google and, you know, LinkedIn advertising as, you know, our digital ad platforms, obviously.

But then also use Snowflake is a big component of, of our stack as well, with all this data, right? Goes into one place. And that's really how we can get this 360 view, you know, of our, of our customer.

That's a great point. That 360 view, we hear that a lot from, I mean, pretty much everyone I talked to, but from, from your point earlier, the execution piece is challenging to do. How do you achieve that?

And where does the science come ins to really build a foundation to give you that 360 view of the customer? Yeah, and again, uh, as I said, we, we run a lot of that in on, on Snowflake, uh, today, and we're using all the different, you know, signals that are coming in, you know, from our, from our customers. And what's unique in the B2B space is that you're not targeting one individual, right?

You're looking at the whole company with different personas and different roles, and that, that's a challenge we have in, in, in, uh, B2B marketing. It's much more complex right? Than B2C, you know, marketing.

So it's really about utilizing all these signals coming in from, uh, from the, the business side of your customers. Also, of course, on the, on the technical side, you know, we are also targeting developers, you know, when in customer bases really bringing all these signals together to get that 360 view on really where is this prospect or customer on the buyer's journey, you know, with Snowflake, and what is the next, you know, program that we need to put in place to drive this company forward. And where does the artistry come into place to you?

You've got all the signals there, you've got that customer at 360, the data is there. It sounds like a great snowflake on Snowflake story. If you guys don't already have one, you should.

But where does the artistry come in to go, okay, we're understanding all of these technical signals that, you know, B2B customer A is giving us, how do you pull the levers of art to really engage them, to make them captivated and want to learn more and, and progress through that buyer's journey? Yeah, and the biggest part is really around the customization of content and personalization. I think a big theme here during the past couple years has been about hyper personalization.

And that's of course huge in the BGC space. We are all as consumers seeing that every day, how we are being, you know, hyper-personalized, right? With, uh, with content, you we're just out searching for something, and then of course, a second later, right?

You're getting an ad, you know, served up, you know, for you. And this is of course, you know, happening now in the B2B world as well. And for us then it's really about, you know, customizing content from an industry perspective.

And marketing in the end of the day becomes, it's all about, you know, relevance and the timing of, of that. So it's about timing and being relevant and really, you know, again, making sure that you put yourselves in the customer's world than trying to put, pull them into your world. And this is really where all these signals help us to do is really to identify, okay, what content should we put in front of this customer at the time they're in now on their buyer's, you know, journey.

That timing and that relevance that you bring up are absolutely critical. They're, they're no longer nice to have. So we have this expectation, I think, in our B2C lives as consumers that we're gonna get ads served up to us that are highly contextual, highly relevant at the right time, not in a creepy way.

And that has come over into the B2B side where that expectation from a buyer is, is now there. But how are you using tools like generative ai, for example, to really drive that hyper-personalization, that customization of the experience per buyer to push them through that journey and get them to sales? Yeah.

And I have to say that, I mean, there's no better time to be both a marketer and consumer, I think at this time. Yeah. It's also the consumer.

What AI, et cetera, helps with is also relevant content for you, I think is so for us, right? We're being bombarded with messages that are irrelevant to us. Yes.

The fact is, if I was actually just out searching for something and then I get a highly relevant, you know, offer, it kind of helps me save time as well. So I think it goes both ways. It's actually really to the advantage, you know, of the consumer, you know, as well.

And in terms of, you know, generative ai, you know, and, and, and ai, I mean, of course there's so much we can do now to be even more personalized and customized at scale. We of course, see a lot on the content side. I mean, we're doing a lot of experimenting.

We have our, our account based, you know, marketing program, right? We wanna be able done customize content and messages, you know, at scale. And we've been able to do a lot with that, with, with degenerative, you know, um, ai also on our SDR side, we have 300 SDRs that are following up on all the different incoming, you know, leads, you know, coming in.

And many of them are, are young, right? They're in their early twenties. It's super hard for them to come up, you know, with irrelevant, you know, and everything.

So that generative AI has also helped them to really be much more, you know, relevant, you know, to the, uh, prospects that they're talking to as well. But I think also the, the big thing with AI that, that has brought us is the ability to forecast, you know, the future. And for us in B2B, uh, the biggest part of B2B marketing is really driving our sales pipeline, right?

Right. That's what we're really responsible for. It's not just about, you know, bringing in the leads.

It's about how these leads, you know, converting into pipeline out in the field. And what we've been able to do now with more predictive and AI is really forecast what is our pipeline looking at in, at a spec in a specific tour territory, you know, six months from now. 'cause often in B2B, you're in the quarter you realize like, wow, okay, the West coast doesn't have enough pipeline this quarter.

How are we gonna make it? And from a market standpoint, it's really hard to go in and try to fix something and, and build pipeline, you know, in quarter. 'cause the work we do is really about building pipeline, right?

You know, six months from now in a territory. So that ability to really forecast based on all the actions you're taking right now, what is that gonna result to in six months from now? So you can then go in and fix issues before it's too late.

And marketing is also very much about, you don't wanna overinvest, there's no reason to overinvest in demand and in a territory, if you have 20 reps in a, in a territory, you wanna make sure they all have enough, you know, leads and the ability to build pipeline. You don't need to generate demand for four people if you only 20, right? But you also wanna make sure you don't underinvest, right?

You don't want tell 10 sellers sitting somewhere and there's not enough demand coming in for them. So that ability to do predictive, you know, forecasting, I think that has been a huge, you know, game changer for us, um, in b2b. Well, that visibility is absolutely critical.

To your point, you've got three achievement, 300 SDRs who are overwhelmed. They're early in their careers. They, they need assistance from marketing and from tools like generative AI to be able to craft those messages that are relevant, contextually relevant, send them at the right time, and be able to have the data that can inform when they should pull different levers and marketing as well.

So it sounds like what you have is, this, is is not just that 360 view, but the visibility into, from a depth perspective, customers, what they're doing, what should we serve them, how should we do it? What's the right channel? How do we give them that omnichannel experience and let them know we understand them and we want to help them understand us.

No, exactly. I think marketing, sales, you know, the SDRs is very much like an assembly, assembly production in a line, right? Yeah, yeah.

And you need to make sure that everyone has, are doing, you know, the right work at, at the right in a time, and that you're again, you know, generating enough, you know, demand and not, you know, too much and, uh, that you're investing, you know, the right amount and it territory. So I think data is really what enables us to adapt to at scale to this thing. So, uh, I just looking back, you know, 30 years ago in marketing, I mean, I started in marketing when leads came in from the fax machine, right?

Where I had to, oh my gosh, yes. Had to enter them manually into an Excel spreadsheet, download to a flock per disc, and go over right on the cell side with that flock per disc. And after that you handed a dis over.

You had no idea what was going on with those lids anymore. No. So, uh, where we are today, it's, it's incredible and just cannot thinking about now next, what's gonna happen next with AI is that is super exciting.

And also from a marketing standpoint, I hear a lot of people that are, there is anxiety, you know about this as well, right? I mean, what is the future gonna look like? What's gonna happen to our jobs, et cetera.

Yeah. What we have done there that I think, uh, is quite impactful. We have formed an AI council across the marketing team with individuals from each different function, and these are the individuals who really are super curious, right?

They like to lean in, they like to kind of really test new things. And so they're really leading kind of these efforts, testing new use cases, and also looking at, okay, what do we need to educate the rest of the team on? 'cause we cannot just have everyone leaning and do testing every day, right?

Right. They have that sort of black belt group on your team who goes out to tests different things and wanna share those findings with the rest of the team, I think seems to be working really, really well. Are you finding that the hand raisers on your AI council are a mix of both the artistry folks and the science folks as well?

Or is it more the scientist folks? I mean, imagine that I think of curiosity is a, is a great creative outlet. No, no, definitely.

And I mean, on the creative side, they understand that they need to do more with less as well. Yes. We have a lot, often the people sitting on the creative side in a company, they're often at the last, the last, right, uh, at the end of the assembly line, right.

There's a, there's planning happening and sometimes they, they're informed very late, right? They often will have visibility into everything that is happening. And, you know, someone develops a programs and then they give the creative team, okay, I need this in a week, right?

So they're often quite, you know, overwhelmed, right? As a scale to a company of the size we're at right now. They're at the, they're often the last and they feel like they, they, they don't have full control over that situation.

Yeah. So for them, they're really looking at ways, okay, how can we get more out of everything we do? They're, they're, they're looking at really, again, how can generative AI help us just do more with less as well?

But again, you know, creativity, right from, from a human mind standpoint will of course never go away. And we also need, of course, to have that, um, um, check checks and balances of things too. So I don't think anyone is worried about their jobs here.

It's more about, um, again, how can we amplify everything we're doing? And that, that curiosity in terms of how can technology help And help us scale. To your point, I think one of the things too that's always interesting to me is from a generative AI perspective, from an AI perspective, there's so much coming at us so quickly, we saw this catalyst about what, 20, was it 2022, end of 2022, uh, or was it 23 when chat GPT was born?

I forget, it's, I feel like I haven't worked without it in so long. It's hard to remember a time without it. But from, from that, um, ROI perspective, it's hard to measure ROI from, from tools like generative AI or I, I hear a lot of productivity improvements, but are there ways where you're really seeing ROI at Snowflake in marketing, because you're leaning into emerging technologies.

I think the ROI we're seeing is, again, I talked about before about the ability to forecast, you know, outcomes. And marketing has always been around, again, of course, finding out what is working and what is not. And often we don't find out until it's too late.

Right? Back in the days when we had very limited data, it could take, we might have the data three months after a program was over, right? And that, that then is too late.

Right? Too late. Yeah.

But yeah, today the ability to make real time, you know, changes is huge, right? You need, you need to make, on the digital side, for instance, digital advertising is a huge spend, right? Yeah.

Uh, for, for most marketing departments, both in B2B and B2C. And that has become so incredibly sophisticated in terms of how you can now change programs on the fly, right? Yeah.

In the mid campaign. And also it's becoming also fully automated, you know, as well, right? You don't even need to do something.

There's really, there, there are tools kind of, uh, making the changes, you know, for you. So, uh, that optimization is really where the ROI come from and that we can easily cannot calculate. And that we, we have able to calculate every year now where we've got sort of real time data, how are those digital programs performing from an ROI standpoint now building pipeline close business to compared to what did they do six months ago?

So I think that's where you can immediately incredible return on, um, on investment. That's outstanding. That's, that's a message.

And for all of the CMOs and the prospective CMOs that are watching, 'cause we hear often it's hard to measure, but to your point, from a forecasting perspective, you can see those trends, you can see the data, it's right there telling you what it needs to tell you, and you can react proactively to that. To your point, in terms of course correcting campaigns and things like that, that before you wouldn't have data on performance for a while and it was almost campaigns done. Well, how do we create the next one?

Because they're all continuous. What are some of the emerging technology that you're eyeing next, um, with the AI council and within marketing and within your executive leadership team that's really gonna help improve that customer journey and deliver that value just beautifully even more? Yeah, No, it, well, it comes back to again, you know, hyper, you know, customization, I would say.

I think that's a key thing. And again, the timing, uh, content creation is huge, uh, in terms of how do we, you know, personalize content down to, you know, one persona with an enterprise, and not just from an industry standpoint, which we've done before. So that, that's a huge thing.

And, and again, we could have on the a BM side, we could have 5,000 campaigns running simultaneously, right? And the ability to go in and again, customize even further at the much more granular level, that's where we're gonna get our eye from. So that's really where we are putting most of our, our, um, efforts on, on testing.

Um, that, and, uh, uh, again, also comes back to that SDR side. We've done a lot down of testing in terms of helping them customize messages. How do we roll that this out now to the entire, you know, team, you know, globally Is sales kind of chopping at the bit to get the, the rest of the sales organization since you have that great alignment, I imagine they are.

And probably there's a lot of curiosity on the sales side as well. Yeah, no, on their side, what they're seeing is of course, much, much more high quality demand, you know, coming in, right? Perfect.

Again, when they're, when they're at that meeting, they're, they're in front of a much, much more qualified audience, right? Than they were before. They have much more information on the person that they're talking to than they had to in, in the past.

And that really enables them to have a much more effective conversation, you know, as well from the beginning. Well, it sounds like It's beyond alignment, Denise, that you've built with your CRO at Snowflake. It's really a symbiotic partnership, and that's really when you can execute on that, it's brilliant.

And when the customers feel that they're understood as well as the sales folks understanding the customer, it's a recipe for success. But I want to kinda wrap things up here. You talked a great deal about your history, time and marketing going from floppy disc facts days to now having AI to help you really forecast territory by territory.

But I imagine there's been some kind of ebbs and flows along the way, and we love to end this podcast with what we call the fail to fab segment. And that is, could be a business initiative, marketing initiative that wasn't going according to plan where you came in and made changes and saw results after that. Share with us a story, if you will, that you think is inspiring to the next generation of CMOs.

And I think, again, failing is clearly a part of, of learning. Sounds super, you know, cliche, but again, uh, every time you fail again, right? There's certain failures, of course you can't, you can't do.

But, uh, you have to look at them as, as, uh, learning moments. And I think one particular one comes back to more like creative, you know, development and also marketing, right? Is about not being tone deaf, right?

And I think so often we see, you know, uh, a video or something fun coming from a company and you realize it wasn't really fun, right? It would, it was more fun for those people creating it than, than it was for they've been watching it. And we have had several moments, you know, like that.

But I remember specifically in the early days, we made this big investment in, in a video, and we had actually been quite successful with the series of really fun, engaging, you know, comic videos. And we thought, okay, let's take this to the whole net new level now. And we did this product, big production, and it has this west side story, um, uh, theme to it where basically, and it was with the cloud and the on-prem kind of, you know, competing in with each other.

Oh, we love it. Yeah. And we, yes, and we've hired these actors in, in LA and it was this big production.

And for us back in the days, the investment, it was such a fairly big investment for us in the, in the early days. And when we saw the results, it was just, it was, it was pretty, it was pretty terrible. Only two people within our company have ever seen that, that video.

And, uh Oh wow. But it's one of those moments where like, okay, we have, we have to pull the plug on this. Maybe we can show this at an internal meeting one day.

We've still not haven showed it yet. It's maybe time to do that soon. But again, it was at moments where like, this is not, this is not fun.

Yeah. This is not, this is not, this is not customer centric. This is not because there's no entertainment value.

It, it just didn't turn out the way, uh, We Expected. And therefore, again, you have to, to, okay, you have to pull the plug on things as well, right? When they're, when they're not, um, working.

Right. And then, and move forward and take the lessons learned. To your point, I always say failure is not a bad F word because there's so much that we can learn from it.

And, and if we all are honest and look in the mirror, we, we've all failed a lot. But you can course correct. You can understand and learn from these experiences, what didn't go well, what did we not do that we should have done?

And then make the next one even better. Denise, thank you so much for sharing that West side story analogy. I, I love that movie.

So I, I thought that's gotta be a huge success. But you realized it wasn't, you made the decision at the executive level moved forward and maybe more than two people will get to see it one day and understand, hey, marketing did a great job of musing that data that they have the artistry to understand when it was time to move forward. I can't thank you enough for being on the program today, sharing your history of almost 30 years in marketing, going from fax machines to marketing data clouds.

It's, it's an amazing progression and we appreciate you also sharing what you're doing from a MarTech Stack perspective, how you're leaning into AI and generative AI and other emerging technologies to deliver that customer value through the sales organization and marketing. We just really appreciate all of your insights. Thank you so much for having me, Lisa.

My pleasure. We wanna thank you for watching and remind you to tune in next time for the next episode of Marketing, art, and Science. For my guest, Denise Pearson, I'm Lisa Martin, and we'll see you next time.