Techstrong Gang – January 31, 2025

Hey everyone. We're all trying to figure out how this AI thing is gonna work in the enterprise. At the same time, we're faced with all sorts of new applications, new technologies, AI code generation, and yet another model coming to, uh, the, the world from China.

You're watching Textron Gang. Hey, everyone. We're coming to you live from San Jose.

Once again here for the Textron Gang. I'm Steven FoST, organizer of AI Field Day and the Tech Field Day events for the Futurum Group. And also, you might recognize me from Tuesday episodes of Textron Gang.

I am not Alan Shimmel, however, but I'm pretending to be be him For these, uh, couple of episodes that we're recording live here. We are joined in San Jose by a great panel of independent technical folks who are, uh, participating, asking questions, discussing, uh, with our AI companies during AI Field Day. You'll catch all of those things live streaming on Techstrong tv.

But let's meet who we've invited to be on the panel with us today. So first up, uh, we've got, uh, a special guest. Uh, once again, we've invited her back.

Uh, Gina Rosenthal is one of my favorite, uh, tech thought leaders. I'm sorry to say that to you, Gina, but oh Lord, she is absolutely a tech thought leader. Uh, joining us here on The Gang for the second day in a row.

Whoop, Good morning. How are you doing this morning? Well, it's okay.

Uh, bad news outta dc Boy that, oh my gosh, that was some crazy, crazy stuff. That's, Yeah. That's just hard to even think about.

So, uh, also we've got another great, uh, gang member here, uh, John Willis. Great. We'll take it.

We'll take it right. Take 'em when they come. Yeah.

Right. Hey, yeah. Great to be here with, um, you know, uh, the Tech Field Day has been great to be involved and doing, uh, tech Gang.

Yep. Here live. It's great, great stuff.

And of course, uh, familiar face. Mitch Ashley. Always good to be here.

Good to be bumping elbows and talking to AI. And, you know, out here in the real world, I dunno if, if Silicon Valley's the real world, world, real world, yeah, yeah, yeah. Sort of maybe kind of thing.

Good, good to see everybody here on the gang and welcome Gina. Good to have you and time. Thank you.

Yeah. Yeah. You could, to be back with you too, Steve.

So, we, uh, yesterday, uh, had, uh, tech field day presentations by a number of different companies. Um, the, the, the big ones, uh, that we saw that were sort of, uh, independent third party product presentations were, uh, from VMware and from member. And it was interesting during those sessions that we had, uh, both, both companies, at some point, some of the delegates internally were saying, Hey, these guys are trying to be the VMware of ai.

Now, it's no surprise that VMware or Broadcom more specifically, it's no surprise that Broadcom's VMware team would want to be the VMware of AI because they wanna be the v VMware of everything. I mean, remember, they wanted to be the VMware of storage. They wanted to be the VMware of networking.

They want to be the VMware of cloud. And so that makes sense. MERG was an interesting one because, you know, previously I had seen, uh, their, their technology, uh, we had witnessed what they're doing.

Uh, they have some very cool tech, but I didn't understand the scope of their ambitions until they presented yesterday. And it became very clear that they also are trying to become the VMware of ai. In other words, they're trying to build a system that is scalable, that allows sharing of resources seamlessly, that maximizes your hardware investment.

And the big thing that brought that home to me was in the first few minutes of their presentation, when they showed that chart showing that only, uh, you know, that, that, that the majority, uh, a third of enterprise AI installations are only used using about 15% of the compute resources they have available. That was pretty mind bending. And it shows the opportunity for somebody to be the VMware of ai.

'cause you know, Gina, we were there at the beginning when VMware first started. Mm-hmm. The reason VMware took off was because essentially the same stat.

Most enterprises were using very little time of their resources, and there was an opportunity to do convergence. So, uh, talk to us a little bit about what you see as an analogy. Well, first of all, I don't think VMware's trying to be the VMware of ai.

I think they are the VMware of ai. So they were, they've been working on, and this particularly when we're talking about merge, is, um, the GPUs, you know, you spend so much money on A GPU, you need it to be able to have the compute go fast enough to run all of these, um, jobs that, uh, building an AI platform will require. Um, so VMware's been working on that.

You can, you can virtualize a VM or get to a, I mean A GPU or get to a GPU three different ways. And that's been true for, since I was at VMware, so seven or eight years ago. So they have done it, but that's what they're looking at is how do we virtualize have a virtualized machine?

How do I get a slice of A GPU? And they also talked about wanting to make sure that, uh, the GPUs were being utilized across an organization and not hoarded, but shared by the vSphere Management system, which all of that does make sense. Um, and I, I think member is doing something very similar.

They're looking at how do you provide, it was really cool. How do you provide fractional access to A GPU and how can I put that in a management plane so that if I have three and you have six and you have 10, we have put the whole lot of those that the company owns into a, a platform that we can share the GPUs across. And then if it gets too busy on one, we can move 'em off pretty transparently, which is a awful lot like vMotion of GPUs.

So, um, I, I think that it comes back to we we're looking at how do I make, instead of, uh, the compute on a CPU on a server shareable, how do I make those GPU shareables, those GPU shareable because they're so expensive to get so critical to ai, uh, workloads. I think there's a spectrum of, of what they work on. And to me, VMware is how do you extend VMware into the world of ai, AI applications and AI hardware?

Whereas Verge was, you know, in lieu of creating the next a MC show called GPU Hoarders, let's figure out a way to, to do GPU sharing and, and their approach. They started with talking about the developer access through the IDE and being able to schedule that as part of your dev workflow. Um, and they were, and then they were adding all the things of snapshotting and, and restarting across different hardware, uh, which kinda led it to look like a bit of, of VMware.

But my thought was, I don't know how member becomes the VMware of, of VMware of Gen ai. 'cause they're already gonna be it. That's, that's my, so they've gotta bifurcate some, by The way.

Well, my point is, it's an apples and oranges conversation because GPU as a service and interest a service is a piece, and it might be the most interesting piece, giving all this constraints and things we've learned and we know about. But the real problem is how do you supply AI in all of its spectrum, from containers, from virtualization mm-hmm. From, you know, things like Harbor and let's not forget networking you NSX, you know, uh, or you know, I'll go down the list, right?

Go governance monitoring. So when you talk about the, the Apple, which is Verge, which is a really interesting solution for a very interesting and hard problem. But there, when you start comparison Broadcom versus emh, you, you know, I I I pointed out that like the, I think you could use the analogy of do you run OpenShift or do you just piecemeal Kafka?

You are an OpenShift. You can get Kafka, you get policy, you get monitoring, you get all this stuff. And I'm, I'm, I, you know, I go back and forth depending on the client.

Or do I do Kafka upstream? Do I do, you know, do I do, uh, you know, Kubernetes upstream? Do I put in all the monitoring myself?

You know, and I think Broadcom's gonna have a more compelling argument because the technical debt of doing all that yourself becomes a problem. Well, and, but I don't think Broadcom is gonna attract new customers become, because they become the AI of VMware. I think it's the path for VMware customers.

Exactly. Yeah. Yeah.

And it's an easier sell to walk in and say, Hey, do you really want to have a, a Kubernetes for this, a Kubernetes for that, a Kubernetes for that? Or do you want to have a control plane? Mm-hmm.

We kind of invented, We already got it. Right. And The thing, the thing about that is though, that's what you have to buy.

So if I'm a, if I'm a broad current Broadcom customer, I know that I can only buy the whole kit and caboodle. Right? And it's expensive, right?

So yeah, it, it might be the best in class and have everything all there, but you're still gonna have to pay for that if, even if all I want is to have the ability to, uh, democratize the GPUs in an organization. For me, what I thought was interesting, I kept thinking it sounds, when they were talking about, um, when Member Ver was talking about sharing the GPUs, I kept thinking about, this just sounds like SRDF to me back from the EMC days. And then having a conversation with the CAOI find out, oh, you're a storage guy, so you're approaching this as, how do you know we've got this data sitting over here, we gotta get it in really fast over here.

I've got this equipment that's slowing me down because I can't get to all the equipment. How do we solve that problem? So I think that's an interesting problem.

And I think if, you know, you're talking about they've got that they're sitting on top of a layer of Kubernetes. So if I've infrastructure as code infrastructure is coded, all the things that I have, I should be able to build those pipelines and get it in there. I mean, that's another thing, right?

When you talk about, like, the things they don't, didn't show, uh, Broadcom yesterday is when I asked like, what script? Well, there's years of Terraform in there. Yeah.

Like, that's not gonna show. I mean, you know, they're, they're based on the questions Verge, again, I'm rooting for 'em. They were very immature about what it was gonna take to manage Kubernetes.

And, and that's gonna include a lot of stuff that historically has run at scale. The only thing I, I mentioned to, I, I talked to the, the CEO last night too, and I, you know, I'm gonna give you unsighted, you know, three margaritas. So I'm like, yeah.

Like, I think you need to go open source. You know, you need to become the Lang chain. If this is gonna something you're gonna solve, you need to get in the middle of everything.

'cause you're gonna have such an uphill bo battle dealing with the Broadcom messaging and whoever else is gonna come out with this, this sort of like, full solution. You know, red Hat obviously is going to have some, they do it in Instruct Lab and stuff like that. So, you know, in order for them to get, like, become the lang chain of, of this new stack, you know, so I, I don't know if you, you Know, well, it's like sitting between, you don't need to be VMware.

You already have that. Mm-hmm. You, you're running on Kubernetes.

I'm not sure they know what they've got there yet. Right. Right.

All the capabilities that, that, uh, has as well as what they can do. But they want to be the stack on top of it. Right.

There's a lot of companies pushing for the, how do I get AI into production? There's the neural magics that, uh, right. Red Hat bought.

Right. Very much down that lane. And these guys could be, uh, on that same path.

And I think there are other, I mean, it's, I think it's gonna be a very competitive, because Kubernetes doesn't step in and solve all of it, right? No. Manage.

It's just a workload Control plane, and it is just a control plane. Exactly. And, and it may not be a great control plane for ai.

You know, that was one of the things that came up in the discussion was that, uh, you know, many of the aspects of Kubernetes that makes it compelling for, uh, webscale modern applications makes it not all that compelling to run, uh, machine learning training and inferencing and so on. Especially The training. Yeah.

Yeah. And so it's gonna be interesting to see where that goes. But I think that the reason for me, I, I think I'm basically just exposing my n tech nerd, hardware nerd background, because, you know, when I look at what they're doing with the fractional GPUs and GPU sharing and splitting and optimizing infrastructure utilization, it does, it reminds me so much of the messages that we got from classic VMware way back in the day, really, you've got this incredible investment in hardware.

It's being underutilized, you've gotta utilize it. But I think you're right. I, I'm gonna say that, you know, number one, we have the VMware of ai.

It's called VMware. Yeah. Uh, number two, uh, MERG is a very different solution.

And, you know, more of a, more of a, a technical solution that may end up finding its way in a completely different way than, you know, becoming some sort of, uh, you know, hypervisor for training to Your, to your point, I mean, they're starting at, obviously at different places, but they're talking in different audiences, right? You're VMware's talking to the running very large infrastructure. Mm-hmm.

So they're solving the AI of VMware from that perspective. Members is gonna be talking about a AI development and model training, AI engineering teams trying to do work to get things into production. They're gonna solve different problems.

I, than I think VMware will ever get to. I Don't know if I fully agree with you. I know we talked about it earlier, because if you, they did have, they did focus on the assists admin, the DevOps, right?

But, but that was to create the developer experience, right? So I think there is a path of like, development experience, and they did talk about, you know, sort of GPU management and there was a difference between how they did that. So, and, and the other thing, I'm, again, I'm, I'm not like well-versed in, you know, CUDA or Nvidia APIs all, but like, it sounded like Verge is really just using most of what, um, NVIDIA provides.

Mm-hmm. Right? There's no, like se you have to work with their management tools or APIs.

And, and so, like, to me, that just says broad count will be able to catch them as quick, you know, if it's all exposed and they're just using, what's the IP that comes out in nvidia? I'm not sure. I think there might be a race problem.

That's why I think they're gonna have to find what it is they're gonna pivot to. That was the, one of the que they really have to be clear about what exactly are they doing. Uh, and, and it's A very interesting product marketing problem from a messaging perspective, because both companies, the tools are, uh, are used by operations people.

And operations people don't really, you know, necessarily, we, none of us really have the vocabulary and the understanding for how an AI workload works. It's, it's just a workload. So you should be able to manage it and make every, all of the hardware performant and do everything that developers want to do.

And that's the, the goal of now a platform engineer, which is assist admin, in my view. But, you know, tell me I'm wrong, but to get the platform engineers to a place where they have a self-service platform for the developers. The developers and that team, the product owners are running that show for how we want to share our GPUs or how we want to do things.

So you may have this IT person that's connected to the IT department that's specifically trying to figure out how do I get them to stop fighting over the GPUs? How do I make this so I can tell when the server, the GPU is attached to is gonna go down and I can provide all the security and data protection and fault tolerance and everything that I need to provide to keep a business running. But they've got a really weird messaging problem where the people that are gonna be using it and need to understand how the, the developers work, so they, they can serve that team, have no say in the buying at all.

And that's, that's a really hard problem. So you have to have, that's why they both led with this. We know you got a bunch of GPUs sitting out there underutilized.

That's a lot of money you're wasting. Let's fix it. Because that's a problem everybody can Understand.

And, and I'll tell you this. So I'm glad you brought that up because one, I, I worked, I was early in the docker, right? And Pivotal killed us because you know who they sold to, the infrastructure people.

Mm-hmm. Right? And the infrastructure people who are by, that's gonna be Broadcom's, they're walking in.

We were saying, well, should they be more demo? The, the most dangerous thing you can do is take the Docker out where you tried to solve you. You believed that if you could get billions of developers using your tool, you'd win.

And it, that, that theory did not play out. I mean, Docker won, but Docker as a corporation lost. And, and, and it really was because the competitors of Docker were focusing, you know, pivotal.

Um, you know, then, um, you know, Mesosphere, like they, they were coming in at the infrastructure level. And Broadcom's walking into your point, they already have the infrastructure and you have to win the infrastructure. They have it.

Yeah. And they already own it. And Broadcom, that's the thing.

I think that's important here. Broadcom, when bought VMware, uh, Broadcom already Yeah. Was a leader in infrastructure.

Now they're even more a leader. Yeah. Yeah.

I mean, Broadcom with VMware is even stronger than VMware ever was in that market. And you're right. I think that that's where a lot of the money is.

Yeah. That's where a lot of the buyers are. And, um, you know, and, and so I, again, I think that shows that these are not like competitive products.

These are two completely different products, even though the messaging is similar. Well, we do have to move on, uh, to our next segment. And in our next segment, we're gonna talk more about developers, uh, keep watching Discover Techron Group, the epicenter of tech innovation.

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Welcome back to the Textron Gang. So we are going to turn the page here from, uh, infrastructure to developers. And we're gonna talk about one of the primary applications of ai, which is coding.

Now, I think that there's this thought that somehow a chatbot can just spit out a functional application with no real input. And we don't need all those developers anymore. But I'm not sure that's really the case.

So John, uh, talk to us a little bit about the landscape of AI coding tools. Yeah, no, I mean, you know, there was the chat GPT moment, then there was the co-pilot moment, which sort of were really close to each other. And they, they had similar effect on different groups, like the developers.

Immediately, a lot of people like just is nonsense. This is terrible. You can't do it.

It'll generate terrible code. And, and that was all baloney because I, you know, I talked to banks that have 18,000 Java developers, and I'm like, what percentage of them actually create great code? You know?

And like, so let, let's get off of this, that the AI agents create bad code, right? Like, it actually creates better code than most of the coders in your organization. So then, like, co-pilot was interesting, but then what came out, if you've heard Cursor, cursor sort of blew everybody's mind because they, somebody got their 9-year-old kid to write an application with it, which again, was more marketing and nonsense, but it sort of changed everything about Copilot is just like, gimme Code Cursor was like intelligence in the IDE.

And right now there's like three or four products that are really interesting, cursor ada, but it, you know, and now it's getting confusion. Like, what do we, you know, what do I use John? Right?

And I, I've been working on this like blog article that I'm trying to write. Like, the truth is you're gonna use more than one, and it's gonna be based on the task that you do. Like, if I'm just wanted to prototype an idea, I might just do that in code.

If I'm gonna refactor code, I'm going to use probably Cursor or something called ADA or client, right? So, and you're probably gonna use, you're gonna come up with your sort of best two or three. And then you start understanding like, cost.

And then I read something interesting that like 80% of whatever coding tool you use is really comes down to the model you use. 20% is the interface, right? So now you gotta really think about the coding tool and the model where 80% of what you're going to accomplish is based on the model you use and some model combination.

So there's some really interesting, I think as cut as executives and team leads try to decide, should we just pick one product and everybody has to use it? Do we allow them to have freedom? I think, you know, and then there's the sort of scale of the coder.

There's junior coders. Like for me, I'm not an industrial coder that codes every day, eight hours. I like Eighter, friends of mine who are industrial coders, like cursor it, it appears, it appeals to them better.

So the IDE itself, the tool, um, so any, I, I think there's just like token costs, use cases, multimodal. And one last thing I'll say is context is interesting. Some of the tools actually allow you to pull the full context.

You know, what happens when you go in copilot, the context is just code. And that's not really how a software engineer builds an application. No.

They have documentation, they have Terraform scripts, they have all these non functionals. And if the AI tool is not aware of those things, you're sort of, uh, collaboration with the tool is limited. So I think what we're finding is some of the, these tools that really allow you to expand the context reads in a repo and understands not just, you know, runs into how it was built.

What are the dependencies, you know, what, you know, what's the documentation? Even Jira tickets, right? That kind of stuff.

So some of these ones, and I'll just, you know, ADA, that ADA seems to do a really good job. And there's a new one called Windsurfer. And then as you pointed out earlier, in all that craziness Monday of, uh, deeps seek, um, bike dance, put out something called Trey.

And I haven't, I'm for obvious reasons, I haven't tried it yet. The same reason I didn't use the API over at Deep Se, but, well, I think you did a really good job of kinda laying out the, sort of the emergence of all the different tools, right? That are built on what kinds of models.

Um, and a couple things that you eliminated. One is it's context as in what's the whole code bank? What are the other things that developers do, right?

What, what is that whole thought all mine process of, of managing and delivering software, not writing a piece of code. It's like the equivalent of what model generates the best code. It's like, which model generates the best TV script versus a blog post, right?

But you need really the full context of it. And a lot of what drive drives it is graph technology that interconnects, here's your, here's your Jira tickets, here's your open items that you're supposed to, here's the, the technical debt backlog. Here's five ways that we do have implemented this particular coding pattern in our software.

What's the best application of it for this situation? That's what a developer looks at, right? Yeah.

What do I know about how we do? So, so we're, we're still at the experimentation stage, which is a lot of the choose the best tool for whatever, and still focused on the point of generating code. I think the next step beyond that is really taking it to what's a real workflow Look, right?

And it isn't black or white. It isn't like cursive versus this versus code. It's like, is it a quick fix?

Is it a refactoring, which is a big job. Like the different tools will have better workflow for the type of thing you do. 'cause the developer might be somebody who's just fixing to do commits and development might be refactoring like, you know, 40,000 lines of code.

Those are different type of things that require the tools react differently for those. So to your point, my best advice is do the bakeoffs. There's some great video bakeoffs out there.

Literally try to just take an application, take a source code base that you know really well, and try it in different scenarios. Quick fix, refactoring, uh, from scratch, you know, new componentry, new features. And then you'll get a feel for which of these tools actually are gonna work Kind of beat, take an engineering approach to it, right?

Yeah. Like who Go figure. Yeah.

Very much so engineering. Yeah. Well, and there's others as tab nines, there's also companies who are doing a lot of work around analyzing code bases, like for app modernization, whether it be mainframes Yeah.

What job or whatever it is, just telling you what this code does. What are the, like what, what it's, what is it solving? What Functions?

Well, the good is, is a lot of those have all that built in app. Yeah. And that's combining those things.

Exactly. Right. So it's the, the point of the article that we kinda surfaced that started this conversation is develop, well, how much time do they spend writing code?

But also, how much time are you gonna spend understanding and fixing code that's generated for you? Right. Or just, you know, you know, the developer days of I'll just do it myself.

It's faster. Right? Well, when that goes over past that productivity hub, no, I'll just take what they gave me and I'll fix it when I need to fix it.

Right. I'll use the tool to help me understand what it's doing. You know, that sounds terrifying to an op person because work worked in dev is a thing.

Right? Hey, it would've worked in worked In production. Yeah.

But I mean, But no. And, and, but That doesn't change, right? No, it doesn't sound like it's gonna change at all.

We, we, I Think It just happens. Stack overflow Cut, cut and copy and paste to now, you know? So, I mean, 'cause look.

Yeah. Give me, yeah. Like, I could get wrong.

Yeah. And, and I think that one more thing that I'll throw in here, and I think probably Gina's thinking about this too, because I know, I know what your, uh, your concerns are about a lot of these things is the privacy aspect. So when you're talking about, you know, oh yeah, you know, maybe you put your code base in this one and try it out.

Put your code base in this other one and try it out. I'm like, wait, whoa, you are gonna do what with your source code, you're gonna put it into, you know, your proprietary enterprise source code and all of your commits and all of your bug reports and, and, and everything. You're gonna put that into an AI engine that is, uh, you know, and so Cursor, for example, uh, claims, you know that they're soc two compliant and that they keep everything local.

I like that. Um, not endorsing it by any means, but I'm just saying, you know, I'm glad that they surfaced that right away by the, but, but that's my concern is, you know, you put your code into these things. How do you know where your code is going?

You Don't. Well, but when you enable, uh, Google Gemini on your Google Workspace account is access to everything. A lot of that's happening with it's, it's happening all over the place, but it doesn't, it's a legitimate concern.

But, but the thing about it is people don't know. So this is going back to like, the ops people don't understand the architecture. So we're having a hard time figuring out how do you, how do you manage everything?

But I think for all of us, like what if you, if you put something into Google or Microsoft and you don't have a paid account, at least with a paid account, you've got at least the, I dunno if it's a facade, but you got the, they tell you that they're not gonna do anything with your, your personal data. If you go to some of these other new ones that are popping up, all bets are off. And I think people need to educate themselves about that.

It's so important. Gene, I don't know if this will, this will help, but I can imagine a day not very far down the road that because we understand your code base, AI does, maybe it generates some of the codes. Some of the not and ops person can say, why is Kubernetes doing this?

Absolutely. And it isn't gonna say, well, Kubernetes has these three features and here's how they work. It's gonna say, here's what your code, our code is doing, and here's three things you might do.

Right. That's, and I think that the, that's, can you imagine that it, it worked in dev, it's not working in ops because of all the, all the other things we have to do in ops to lock everything down. And I can just ask a question and it tells me.

Yeah, that's amazing thing. And, and, and again, the models itself, you know, like, I mean, and I, I wouldn't trust like NSA code to this, but like OpenAI, they don't where they state that we not trained on your data. Right.

Like, I still think there's dragons out there of putting anything in context, but at least there's model providers that clearly state we do not train. They know it's an issue. Yeah.

But you have to pay for it. And in my experience working with devs, they, I mean, I, I used to work one place that I worked, um, we had, it was astrophysicists, postdocs, and they had to, um, get on our network and we'd get a ticket if they tried to plug in. And we'd go and help 'em out, help them, you know, fill out all the protective forms they needed to have.

One time I went to look, and the dude, I swear to goodness, had like an all cardboard box with a towel in it that like, you put lost kittens in, and he had breadboarded his Linux machine together. And I was like, this was way before like cell phones. So I don't have a picture, but I just turned around and went to get my boss.

Like, I'm not even like An Academic Research environment To Open things Up. The good news is, a lot of the large corporations that I've been working with are really setting standards on things like I, they have to, what, here's the only copilot you can use. And it's already, you know, so, and you know, again, do you, I'm Sorry, I'm laughing at that.

Just because any, I mean, setting corporate standards for anything is not always that effective, especially when it's happy as easy as going to a website. I, I agree. But they have to do that.

Otherwise it's just a free for all. You know what show? Oh, I, I'm not saying they shouldn't, I'm just saying that it's not gonna work.

Well, It works in the sense that as we get, you know, there's another whole subject, but, but the whole sort of auditing and, and I think what's going on in, you know, what we wrote about Investments Unlimited, that what's going on there about making sort of DevOps auditing, if you will paraphrase. Um, it, it's putting more light on the things that you're not doing as organization. We're informing internal auditors way better now than we were pre five years ago.

And that puts a flashlight on use. 'cause you have to document the chain, the supply chain of what you did. You know, how did you get from your code to deployment to all that stuff.

If you're sort of creating attestations around that Fantastic book, by the way Yeah. Download that, that allows investment for the organization to start finding those shadowy. We have to talk to the individual, everybody, because right now there's so much hype around ais and models and do this and do that.

And it's exciting and it's, oh my God, I wanna go try it. And I think there needs to be a little bit of education of like, I know you wanna try it, but really, really think about do what happen if you connect to this api. Mm-hmm.

What will happen if you do anything with data you really care about, or that your boss might really care about. Yep. Yeah.

I, I, The hype Is it somebody said, uh, I think it was John, uh, that this was the greatest data exfiltration Oh, Monday, um, in, in history. Because by dance or, um, not by dance, because deep Seat got so much attention that, um, you know, probably literally millions of people register, dumped all sorts of sensitive information into it immediately. And that was that.

Yeah. TikTok went, why didn't we think of That? That's a good one.

So we gotta move on. Thank you very much. Uh, this is a conversation.

I know that's gonna continue on the Textron Gang. Uh, up next, uh, we're gonna look at a new model, uh, coming in from China called Quinn. Stay tuned.

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Welcome back to the Textron Gang. We are at AI Field Day, and, uh, if you've noticed, every one of our conversations has been about ai, but that's not all that unusual. I think everything on every episode of Textron Gang for the last couple of months has been ai.

Yeah. Uh, let's talk a little bit about another new, uh, model coming outta China that claims to be even better than deep seek. 5.

Max says that it outperforms deep seeks R one, and that it uses, um, low amounts of energy and all this kind of stuff. Essentially, it's the same story again from, uh, Alibaba, which is a huge, uh, Chinese cloud company. Uh, who wants to take this to start?

I, I'll go just quickly. I mean, Quinn has been around for a little bit. Um, so, uh, and there's been versions of it.

Um, you know, Alibaba's real, I mean, you know, Alibaba Cloud is real. It has been real. It's been, you know, there, there have been a number of retail companies that used Alibaba Cloud because you couldn't use Amazon and China.

So if you were Starbucks, you literally had to bifurcate your cloud infrastructure between sort of Amazon and Alibaba. So Alibaba has been, you know, like sort of vetted to the quote that any company based out China can be. Um, the Quinn models are, are interesting.

If you follow Ruben Cohen, I'll try to get some show notes for y'all. Uh, he's done investigations going back a couple of months now on the, the, all the China models, including Deep Sea, are very restrictive. Like, so they create inference guardrails on things like, you can't ask about Tandem Square, you, and the, that, you know, that's just the obvious.

The unobvious is when you put those constraints in an inference model, the algorithm type models that you do, it actually creates more B for Jailbreaks. And Ruben was able to show some really good examples of how you can use inference jailbreaks to get it to ask questions that the model itself is designed not to answer. You know?

So, um, so again, I think this is a, this will be a problem that will bubble up on a lot of the, the models that come outta China. The fact that they create restrictions in these sort of algorithms themselves, or the inference actually makes them more vulnerable to Jailbreaks. I wonder if it, if it sort of sets the race, the space race to get to general AI a bit to the side, right?

We're all focused on kind of what the next model is. That's who's gonna get to the singularity first. And I was making this point the other day.

I think it was on Monday or Tuesdays show, talking about, yeah. But there's also good enough ai, not everybody needs, you know, the singularity to solve every problem. NGI and I are false flags.

Yeah. I, again, you know, the, the, the, it it takes us, it's a distraction. It's a distraction from absolutely what do you need for ai?

Let's stop worrying whether it's gonna take over humankind or Terminate. And it's, and it's not a single Point computer scientist. Let's be engineers Yeah.

And work with what we got. Exactly. It's not a single point.

Isn't, isn't general AI gonna be Well, like what? Q like Q General, you steal that. My general AI is smarter than your, anyway.

I think that's sort of a Yes. That's part of the race. The money's made.

'cause nobody's gonna be able to afford to run it. Right? I mean, that's gonna take the, the highest compute of anything, everybody.

What what's interesting is the application of AI and how accessible it's to people. And I think that's why TikTok being jealous of, of, uh, a deep seek. I called it Deepfake the other day, that by accident, oops.

Uh, I guess Freudian in some ways, but Tiktoks got its own stuff going on. It's the cul They're after the culture. They're not after the They're they are, but they wasn't quite, they're also by dance though, by the way.

It's all coming from Byan. So, yeah. So, So for me, I think if I look at it from a product marketing, marketing perspective, it's not an accident that these two models were, um, released one right after the other.

I don't believe, especially a few days after the us um, government had a huge announcement about, uh, investing Stargate tons of money into Stargate. And this is what we're gonna do as an, as our country to, to, you know, get up on AI and, and get all of this done in, in this side of the world. Um, it's also, they announced yesterday was Chinese New Year.

So that's a great big celebration day. I think that's a thing too. Um, and I, I kind of find it, it, it's all around the hype, right?

Which is what marketing does. We drive the hype to get everything excited, everybody excited about it. But the, the market is not the same in China as it is in the West.

It is controlled by the government. Everything that happens is orchestrated by the government. So, and, and allowed or not allowed by the government.

Mm-hmm. Mm-hmm. So, very interesting that, you know, we have to start, we have to look at it.

China is a, a foreign adversary to America. And we have to look at it from that perspective, because we are in America. We're not in China, and we are a global universe.

And we do want to cooperate and, and be colleagues with all of our people, all of our friends, um, from other countries at the same time. They're not playing by, they're not playing the same playbook that our companies play by. And the playbook has changed too, right?

Just as an author and, and like, you know, um, five years ago, if I was offered to speak in China, I'd go like that. 'cause you sell crazy amount of books. When you, if you're an author in China, I won't go near that place right now if you read about people being detained.

So the, the climate of even the global economy within the last three or four years has changed these as a person who speaks, I know we all speak around the world, right? Right. Like, I wouldn't go, I would, there's no amount of money I would go into China, right?

Well, And I'm not, and I'm, and I'm not saying that as like a down thing. I'm just saying, you know, let's look at this from, if I'm looking from a marketing perspective, what are they trying to do? What are hype are they trying to achieve?

Because we know that there are definitely some constraints as far as privacy goes with those models and sharing information. But, so they wanna get it hyped up, get everybody excited about the next model. And they have done, let's be honest, some really cool engineering work.

That part's amazing, but why is it so hyped? Why is it so why did they pick the timing? And what does the, is what does the Chinese government have to, to win from that?

And, and also, I think we should point out too, as as was said at the beginning here, uh, deep seek didn't come out of the blue. Uh, this was, you know, just the latest iteration from a company that it, you, you know, you would've seen this coming if you had been watching closely. Uh, the same with Quinn, uh, you know, as we just heard, you know, there's been other Quinn models.

Uh, this is just the newest one. Okay. Um, it seems pretty good.

And, and as we talked about yesterday, uh, when talking about deep seek as well, uh, in many cases, the us uh, restrictions and controls on, uh, on AI components, forced them to build these models in a more efficient manner. So just like deep seeq, you know, the Quinn is a mixture of experts model with, uh, that uses a lot of small models kind of teaming up together. Um, it makes a lot of sense.

You know, this is the, this is the microcomputer versus the mainframe in the, in AI sense. And, and so it's not really a surprise that this is, that this thing came. And it won't be a surprise next tomorrow or next week when another model comes out claiming to beat, uh, deep seek or chat GPT or, or whichever the benchmark is.

But ultimately what we're talking about here is basically who's got the biggest, baddest, fastest, strongest, when what we really should be talking about, I think, is, uh, the application side of things, which is what we were talking about. Yeah. Earlier on this discussion.

Well, Elevated a level. You mentioned the marketing of it. It, it's, it's geopolitical Yes.

Marketing of it, right? Because, because China's the way it is, then it creates all kinds of conspiracy theory or reality, whichever you choose to believe is, is this the Star Wars moment for China of setting this big thing that now we have to go, go chase them after, kinda like Reagan did with Star Wars back in the eighties, right? Or is this something legitimate?

And I think, by the way, self self shameless plug, either today or yesterday, um, Futurum research came out with a paper, our kind of position thinking about it, different parts of Oh, cool. The deep seek announcement Daniel Newman contributed than I did as a bunch of other analysts. So go check that out.

com. Well, we do have to wrap up now. Uh, thank you for Joining us.

Wait, got Had One point. Okay, hit hit me. There's no doubt China is an adversarial nation.

But, but yet Monday and Tuesday, we heard a lot of, um, well, Chi, you know, oh, there's America and China. Well, you know, the stuff that came outta France Mystro. Yeah.

So there wasn't so in on the geo, geo computer science spectrum, this is just d different countries that are just showing that they have ip, they have knowledge. But I can't exclude the fact that like, I'd be more trustworthy of Mytral from France than I would from Quentin. When, so when did Minstrel come out?

That Was a year or two years Ago. Okay. That's what I'm saying.

I think it's very interesting. No, No timing. Oh, I totally timing.

I totally agree with you. Your timing's everything. It's Interesting.

Yeah. I totally agree with the timing point. Anyway, Anyway, I was saying awe, because I was sad.

We were, it was over. Well then we will, we will have to, I'll, I'll, uh, put the screws on Alan to invite you back And, uh, I get to meet Alan. That'll be awesome.

That would be amazing. Um, so thank you very much for watching. Uh, tune in, uh, uh, Textron TV for all of the AI Field Day sessions.

Uh, we've got some great presentations, discussions. We're gonna do a round table discussion. Uh, we've got the, uh, weekly Tech Field day podcast streaming on Textron tv every Tuesday.

Uh, and of course, uh, check out the Textron Gang every day, every weekday. Uh, you'll find, um, at least three of us, and I'm gonna guess four of us, uh, back on the Textron Gang in the, pretty soon in the coming weeks. And, uh, we'll see you there.

So thanks for watching. And Alan, um, uh, come on back. You're our only hope to rescue us from, uh, from my hosting.

Uh, take care.