Techstrong TV February 5, 2026

Good morning everyone. Welcome back here to Techstrong tv. My next guest is Roy Catmore.

It's joining us from New York where it's pretty cold today, almost as cold, well colder than it is here. But, um, Roy is the co-founder and CEO of Orchid Security, and let's welcome him. Hey Roy, how are you man?

I'm good. I'm good as the, as cold as you can think. Yeah, yeah.

Well, you know, I, it stays like that, that I remember why I moved from New York to Florida, but it's, it ain't warm, it's not warmed down here either, so I get it. Roy, I mentioned you're the co-founder and CEO for orchid security, but, you know, give us, give us kind of your own personal arc. You know, how'd you come here today?

So, um, Ellen, it's, uh, it's not my first rodeo. So I've been, uh, doing startups for a few years. I had a startup before, which we sold to Fortinet in, uh, 2019.

I ran the business unit there for endpoints, um, security for more than three years. So I'm, I'm definitely not a stranger to, uh, ramping up, uh, uh, startups and also not starting products from scratch. But actually my story is a little different than, uh, just, uh, yet another one with a good idea.

That figure that the market actually meets is great advice, but it's actually came from the market. Um, in my journey, actually, I would say, uh, more than 10 years ago, I was part of a founding of, um, of a VC that had a different state of mind named Team eight. And Team eight had a different point of view of market, basically to say, don't come to us with solutions.

Let us offer you problems, and let's see you figuring out the right pain and the right solution for the right pain. And the way they do this is through a village. And that means, you know, if I'm working security, and that's, yes, uh, by all means I did security my entire life.

But, um, that means that you actually going to, um, a villagers of professionals in security space, that's the area that I'm, I was, uh, interested in and asking them what bothered them the most. Where's the biggest budget, you know, how much timing the date actually takes them to do something. Where is the biggest surprise on budget?

Where's the mis misalignments between where they thought they're gonna be and where they are? You know, where did they miss? And, and there's a lot of questionnaires and they're very structured as opposed to what I just did.

But the sense is very well known, right? The idea is where does it hurt? Feels like a doctor, right?

You're punching, you're pinching, you're punching, you're pinching. And then when you hear the ouch, like, okay, we got a pain, now let's see if that pain is repetitive. And we actually ran through a process of 250 round, kind of give or take, uh, global 2000 CISOs.

And when we had a solution, we actually pitched it back to the same 250 to say, would you buy? 'cause it's not just finding a pain, which is, I would say 80% of the problem. 'cause then you can understand the ecosystem competitors, partners, um, um, you know, FS versus allies, everything is sitting right there how to go to market.

It's also about, is the market ready to accept, like the solution? So yeah, we hit the problem, but can you take that pill and does your body actually gonna react to it the way we thought? And so that's how I came to it.

Validation, pain, figuring solution, uh, alignment, and of course people that actually customer that actually converted fairly quickly. And you got yourself orchid in, uh, in funding. Excellent, excellent.

Um, so as a result of the, the prodding, the pinching, what was the problem that orchid security is, is designed to, you know, what, what, what is the itch that orchid scratches? I like the itch. And I'll give you the itch from a, um, from a somebody who did a few things before.

Very simple. If you get a solution in endpoint solution, right? I want to protect your iPad, then I'm gonna put something on your iPad and it's gonna protect your iPad.

Simple. Same thing with the network. You're gonna say, Hey Roy, I got a problem at home with my network.

I'll put something on your network. It's gonna protect your endpoint. I'm not asking you to do anything other than putting me there, right?

And I'll do the work identity is, is does fundamentally doesn't work like that. When you deploy identity, let's talk, what is identity? Identity is authenticating, which we run into every day because we are getting these a little annoying messages into our phones and into our computers.

And that's the authentication. And there is an authorization, right? What do I, what type of privilege and entitlement do I need to provide to Alan to do his work, right?

That, that's what I need anywhere within those solutions. And, and there's a lot of point solutions out there. And they're great solutions require you.

Now, when you finish the deployment of those, you're gonna ask, okay, so where, where's my message? Right? And you're gonna say, well, wait a second, Ellen, you need to onboard that app that you wish to have that message to our solution.

We just broke the model. We ask the user to do something for us in order to extract the added value. I'm sorry.

And that is where the biggest problem in identity is. That creates fragmentation because who are you gonna onboard? The one that you are aware of, not necessarily the one that are most disposable, you're gonna do the ones that are easiest because you wanna show progress, right?

We all wanna show progress. Hey, look how many we did, but are these the right ones? Are these the, um, high priority from security, from, from usability, from disposability, from everything that we need?

And that created the, what we had coined as the identity dark matter. You did not onboard Alan to a certain app. And so you allow him to do things local, but now you're not governing him.

So when Ellen decide, okay, I'm done here. I I'm, I'm leaving the company, I'm removing you from the yellow pages. Oh, but I forgot to remove you.

And then I got myself a user that is actually can use it now, or an orphan account or a dormant account. There's a lot of terminologies in this space, but the risk lay down in the fragmentation between those connectivity. And if you're trying to quantify that 46% of an enterprise identities are ungoverned.

So now think about it. Let's digest it. When we are making an informed decision in identity, we are making it on half of our data.

How good are you feel about that Now that is the problem that, that is the itch that we are as orchid coming to itch for you. Yep. So Roy, I, I don't know, I've been in security a little bit myself.

Um, you know, and I've done a few venture backed startups I've co-founded. I really believe that. Well, first of all, I always believed that IAM identity and access management was the killer cloud security app, right?

When we moved a lot to the cloud, identity became the, the focal point. 'cause you no longer had the Moton castle, you no longer had the big firewall at the edge there. You no longer had your access.

You know, data was everywhere. Apps were everywhere. And the only way you had any control was the identity.

But then we never, and we never quite solved that, to tell you the truth, right? We never got that straight, frankly. But then machine identity, let's call them non-human identities, came into the picture, right?

Whether we're talking IOT devices or, or containers, internal infrastructure that had to have its, you know, their own identities. Now of course you've got APIs and, and you've got AI agents. And, and when you look at these non-human identities to human identity, human identities, probably 20% of the identities that we need to manage if it's that much 20% of the identities we need to manage out there.

Is this something, when you talk about how Orchid is giving you a complete picture, is that non-human identity as well? That's a good question. One, I didn't refer to what we do.

I just referred to the itch. I didn't refer to how we scratch it. But you are absolutely right.

You, you know, if I refer to the dark matter, which is again, the fundamental deployment of, of identity creates a dark matter, a fragmentation, because we're not gonna do it all. The world is not perfect. And it's so much not perfect that the traditional, like human identities are 46% dark, meaning I can't see them.

Now you can ask me, Roy, that's great, but you know, what's new here? And, and, and my point of view would be you are right. It didn't change a lot since the cloud exploded and, and our amount of applications have grown, but it's growing now faster.

The 46, like the dark matter is now poised to take over. And if you would've asked why, because everything became a hybrid, you may think that your app is to completely governed, but guess what? It has a machine to machine that does some things on the backend to do disaster recovery.

And that does not govern. You cannot, you cannot challenge the machine. And of course if you add to that service accounts, because we need third parties because maybe API and on top of everything, the, the beast in the room that nobody knows how it's gonna grow up, right?

We just see a little, a little, uh, puppy right now. But that puppy could become in a huge tiger and take over. But the agent's ai, why is that even bigger?

Because think about it, the problem is now ex it's exponentially bigger because the, what you're seeing is one, an agent working on behalf of who, who is typing that check. Do you know what we are used to? Kind of the check GPT, who is the operator?

What is the operator authorization of doing it? So it's great. And what is the context on what intent is this working?

Things that we knew on human, we don't know on machines. Yeah. And that is, this Weekend was a great exam.

I'm sure you are following what went on this weekend. Uh, a great example of it, a great exam. And then to find out these things, there's security like everything else that gets built, they thought about security.

We'll get to it later. And, and now, I mean, this is a disaster waiting to happen. You're, and you said it, right?

You know, even when, you know, I always, when I walk in this, I walk exactly in the same steps that you did. I said, listen, we started very easy. Everything was on premise.

We were in the late nineties, beginning of two thousands. Even then the dark matter was three out of 10, 30%. So even in a easiest world, we were not perfect then we had, We never figured it out.

Yeah. Then we had a cloud explosion, 46 invisible. Now with agent ai, the idea is that we're gonna go over 60%.

And the why now from a business perspective is because we are really looking at the, you know, at the EB, like the tiniest portion of it. And we are making decisions on a portion of a very little portion of the data. And that is not the way you can make decisions.

And you know what the best thing is? You got good point solutions out there, great vendors that can do the work, but you are not onboarding. 'cause you don't know who, when and why.

And then it's becoming very dynamic and yes, this is when orchid is actually coming into play. So right. Let's, let's shift to how does Orchid actually do it?

So kind of think about it orchid as the solutions of solution. I know that everybody likes to think about orchid, the nice flower, but also think about it as an orchestration of identity. Basically what we're saying is, as opposed to the network and endpoint where I started to say, where there is a very known progress of deployment because the infrastructure is there, we need to have infrastructure for identity, which means I need to know what do I have, what assets are actually I have within my environment when it comes to applications.

I need to understand how much exposed are they, how much risk are they providing me? So I'm not gonna just gonna do the one that I know or the one that are the easiest for me to do. But I'm gonna do the right pieces, the right applications.

And of course, if I can do all of this and I can do it from a compliance, I can do it from a security, I can do it from NIG, I can do it from a context. You can already imagine why LLMs are becoming a critical pass of this. And I can do it by intent.

So what Alan is trying to do to a finance system that has orphan accounts, and it is not by compliance by a certain level. And so I can highly prioritize it from the business point of view, from disposability point of view, from compliance point of view, and connect it with no code, no engineers straight to the tools that you already made, your investment in an identity. And that is exactly what we're bringing in to the, basically it's an infrastructure for the modern identities world, including agents, ai, but also the legacies that we didn't do 20 years ago.

And so we need to do mo to the adherence of security and compliance. I love it. You guys recently did a little research.

I understand that you, uh, I don't know if that was what you were referencing with the 46% to 60%, but give us a little more on that if you don't mind, Ray. So one of the things that we are announcing here today is an audit. And all customers were asking us, wow, you know, so the dark matter is really big and, but we do, how do you give us the evidence that these things that you claiming and we're not debating, but show me the money.

You know, show me that, because for me it's a, it's a blind spot. And so what we started to do is to generate audit of users, activity of identities for applications that are ungoverned for the application that are governed or hybrid. So just like you said, the nonhuman is not gonna be audited, but the human is audited.

So we mesh those together. We generate for the ones who are ungoverned for the identity, we mesh the data that is already managed together to give you business insights. And the research was once, now that we got that to add the dimension of context, intent into our prioritization, we started to look at the data.

You know, we are a little upside down company, usually company starts with the small vendors. And growing up, we actually started with the Fortune 20 vendors and started to go to the Fortune five hundreds going to the global. So we started at the top with vendors like, uh, like Costco, like Hub, uh, hub International, uh, um, uh, repsol, a lot of fortunes, very high.

Uh, and, and, and, and we started to look at, okay, then let's see what this new audit, let's see how bad the hygienists or how good the HY is. And what we find is that 85% of the applications estate, think about it, these are Ellen business applications have accounts from legacy or external domains. That means you have leftovers inside that are encapsulated within those 20% of those of these are consumer email domains.

So you have a lot of mix of identities within them. 70% of the applications have excessive, six have excessive privileges in them. 60% have admin or, or already you mentioned API access to external third parties to them.

And 40% of all of them were found as orphan. 40% thinking, I'm, if I'm a hacker, I'm not gonna go to the one that you're using the most. As opposed to your strategy to say, well I'm using this app the most, let's start with this app and onboard it.

Right? No, they know you're governing this, you know, they're monitoring it. They're gonna go to the ones that have 40% of all the accounts have orphan accounts.

I'm gonna go to the orphan account because it's not supposed to be there. You are not tracking those. Definitely not governed.

That's my base. I'm working from there. I'm not going to the known very high profile applications of yours.

Got it. Very, very, very cool. Roy.

I don't know, did we, I didn't remember us mentioning the website. I don't think we did. You're gonna find a lot of our use cases, um, you know, um, uh, technology wise, patents, management team, of course.

Um, and it's all in the orchid doc security portal. So always that's What I was looking for. Orchard security.

Very cool. Um, Roy, we're about outta time, but listen, I wanna wish you well here with Orchard. This, this is, look, this is a problem, as I said earlier, right?

It's been a problem that we never like a festering soar that we never really solved. But then what happens is stuff gets layered and laid on top and top problem gets bigger, right? And you, you know, dark matter, like real dark matter, we can't see it, but the force it exerts on, on us, on the universe is, is substantial.

So I love it. Come back to us, keep us posted on Orchid and best of luck. Stay warm up there.

Same Here. Let's illuminate the dark matter with some light, right? We need some light in this world.

Bring The light. Thank you. That's a good way of doing it.

Roy Catmore, co-founder, CEO Orchid Security. That's orchid security. Check it out.

We're gonna take a break. We'll be right back. Hey everyone, it's Alan Sch Techstrong again.

Hey, for this next session, you know, we call it agents plus Apps, plus chat. It's features Richard Riley, who's GM of the Power platform marketing at Microsoft, as well as our own FU analyst. Dion, iff, Dion, and Richard will build upon the foundational vision of agents plus apps, plus chat, creating a solution orientated view into the heart of a agentic business transformation.

In this session, you're gonna learn how agents are enhancing workflows and redefining the architecture of business value. Today you're gonna hear about practical applications for agents in terms of UI logic layer and decision engines across business functions. Everything from finance to HR to sales, and even customer service.

I think you're gonna enjoy it. Here's Richard and Diane. Thanks, Alan.

This is Diane Hinchcliffe, VP and, uh, practice Lead for our Chief Information Officer, uh, research group. And I'm with the Futurum Group. I'm here with Richard.

Richard, can you give us the introduction? My name's Richard Riley. I, uh, lead the low-code go to market team at Microsoft.

So that covers all of our low code portfolio power apps, power Automate, and Includes co-pilot studio, our, our agent building platform. Well, it's great to have you, Richard. And what's interesting, uh, the CIOs I talk with, they're undergoing all these changes with, uh, with ai and now Ag Agentic has arrived and they, they're under this realization that they are going to have to transform their organizations using AI agents.

And so I was wondering, you know, to help them understand this, how do you define agentic business transformation that, uh, goes, uh, beyond just mere automation and is really explains kind of why it's a new paradigm? Well, I mean, I think agents can do lots of things. We, we tend to think of 'em existing on this kind of, uh, spectrum.

So you can have very simple agents on one side of the spectrum and very complex agents on the other. So to give you a bit of an example there, I would say on, on the left side, on the simple side, we kind of say agents that just do, you know, regular q and a, they grounded on a few documents. You ask 'em a few questions and they can give you answers.

They're super useful in certain situations. And then on the right hand side, uh, you've kind of got things like autonomous agents that they can work off triggers, they can run business processes. They can, they can literally work autonomously and have very complex logic, complex instructions, um, and run very complex business business processes.

So, and then you've got everything in between. So we kind of think it, it, it really depends, um, on what the, who's asking the question and what they're asking in the context they're asking the question in. But it's helpful to think about these as a, as a spectrum of things versus, um, an agent is just a, is just the same agent.

We appear to be on the cusp of a massive wave of agentic solutions. Uh, some are narrow, some are broad, and, uh, some are open source. What makes Microsoft's approach defensible and distinctive in the flood of agent frameworks and offerings that we're seeing show up?

Yeah, that's a good question. So I'd probably start with the breadth of tooling that we've got. We kinda span from the very easy to use experiences that are built into M 365 copilot.

With copilot light, you can literally describe an agent that you want. You can give it some grounding content, you can tell it to go talk to, um, to SharePoint and a bunch of other kind of content sources. Super easy to do, super easy to share, very valuable.

We then have copilot studio, which kind of takes that a step further. You get access to a lot more capability, including things like triggers. And then we take that an even step further.

When you think of, uh, Azure, AI Foundry, all of these things are kind of tied together in a, in a very integrated way. Um, so it's very easy, very easy to move from one to the other and incorporate one to the other in, in your solution. So I'd say our, our breadth of tool set is probably number one.

Uh, number two, I would probably talk about access to data. Any customer that's an M 365 customer of Microsoft, um, has an enormous amount of kind of that tacit knowledge. That data in Microsoft Graph, we make it very easy for you to get at that, to make your agents, uh, more intelligent.

You know, uh, it's one thing to be able to run off a, you know, s and columns in a CRM, um, database or in our ERP or f and o kind of, uh, service. It's something else when you can marry that with, uh, that tacit knowledge that you've got in your enterprise to bring intelligence, much more intelligence to what the agent can do. And then thirdly, I'd say, um, our kind of core strength in security governance and compliance.

You know, we, um, we've, we've invested heavily for a long time to help customers, uh, secure and manage their, their enterprise data. All of that investment flows through to, to your investment on the Microsoft stack for agents, agents, respect, all of the policy, the labeling, all that kind of stuff that you've already deployed to manage your data. If you're building your agents on the Microsoft stack, that stuff just flows through.

So I say those, these three things are probably the three primary. Yeah, that makes a lot of sense. And so, so let's zoom in for just a moment.

You know, help people understand how do agent powered interfaces change the way that workers interact with business systems? You know, compared to traditional user interfaces, There is a lot going on in the indu industry with how kind of apps and agents are coming together in, in super interesting ways. Um, I think over time we'll see agents starting to adopt some of the app modalities that we expect from apps today.

So think of a time when, um, when an agent can actually start to build an app, or even just a ui, like a, a form on the fly for it to solve a business process or to, to, to help you, you know, move forward with whatever the thing is that you're working with the agent on. I think we're gonna start to see that show up. It's not today, but, um, over, over the, over the, over the course of time, I do think that app modality and the agent modality will start to mix.

And ultimately, if you think about it, there's a, there's a, there's a very good reason for bringing those two things together. The, the example that I like to use is around, uh, you know, a, a procurement process. Like every company has to buy stuff today at Microsoft, you know, you have to know three people.

There's three different tools, a bunch of approval stuff happens that could be, uh, a couple of apps you have to go use that could be built into a series of agents and co-pilot kind of marshaling the whole process. You could just literally ask co-pilot, like, I need to open a PO for fu Co-pilot could know the agents that it needs to go talk to, to be able to do that. They could be a, like a, a legal agent to check the, the, the statement of work that you're trying to, trying to kind of, uh, work through.

Um, they could be a procurement agent that could go make sure you've got access to the right, um, and you're hiring the right vendors like every step of the way. Co-pilot could run that whole process. You don't necessarily need to go through three different tools.

If copilot needs some data from you. It could create a form if needed, if it's not a conversational type of, uh, thing that it's, it's asking for. So you can see how these things start to really blend together when you start to think about agents working with other agents, and then having something like copilot kind of manage that interaction for you.

So I'm glad you brought up talking about different agents, uh, working across the business and, and that's something we call orchestration. And one, uh, challenge Enterprises face is orchestrating multiple agents across different domains. Finance, hr, customer service, without chaos, uh, integration or security issues.

How does Microsoft envision enabling agent orchestration across the enterprise? And what governance mechanisms will make it all work and save? Uh, that's a good question.

So there are a couple of things that, um, I don't think we're particularly unique in the industry with, um, you know, like supporting MCP for agents to be able to use tooling and, uh, supporting a to a for agents to talk to other agents, those kind of table stakes these days. The things, the thing where it starts to get interesting with Microsoft is, uh, ability to use capabilities like Agent 365 to provide kind of end-to-end observability across all of the agents that you've got running, um, in your, in your organization. Um, to the extent that you can even give an agent, you know, uh, an agent ID and treat it like a, like a user.

Things like conditional access, all that kind of stuff. Um, just work. And then I also think like once you give, uh, organizations the ability to kind of observe and manage and govern agents, it starts to open up new kind of frontiers of where they can use agents.

It allows them to think about business problems that they've had in the past that have historically been kind of automateable. Maybe they're too determined, uh, non-deterministic or, um, too complicated to, um, to do with traditional automation tools and apply agents to those problems. And we've seen a ton of customers kind of look at existing business pro processes and decompose them and figure out where they can replace certain parts of them with an agent.

They don't replace the whole process, but they start to replace parts of that process. And over time, as the technology matures, as their understanding and confidence with the technology, um, matures, they start to build out more, but also look at brand new problems that they've faced for a long time that have just been very difficult to address with technology. And that's when, if you kind of bring all the, all of the, all of the assets together, the things like HOA and MCP, things like Agent 365, things like the security and governance and compliance capabilities that the Microsoft stack offers you, it really does let you start to look at these problems in a very different light and start to apply technology to, to things that you just previously wouldn't have tried to 'cause it was too hard.

So let's talk about really getting started with agents. You've talked about agents being on a spectrum. How should organizations assess where they are and what kind of agents do they need?

Uh, you talked to a lot of different customers and you have a good sense of that. Can you share that with us? Yeah, totally.

And there's no single answer to this 'cause everybody's different, but I think it's important for people to start in a place they feel comfortable with. It's very easy to run into this too fast, not be super successful, and then, and then feel, you know, a little bit terrified for, for trying to scale it. So, um, it's actually very similar as well to the way that people in the past, over the past 6, 7, 8 years have deployed low-code.

The deployment patterns that we see with things like power apps are very similar to the customers that we see deploying, you know, agents in any meaningful way. I mean, actually the comp customers that have already deployed low-code at scale seem to have a very familiar pattern to follow with, with how they deploy agents. So it really does fall into that picker business process.

Maybe it's the, maybe just pick one, find the one that's the most inefficient, the most expensive, the most painful, you know, it's, it's creating the most work and then break it down to it into its com, decompose it into its component parts, and start to figure out where you could apply agent technology to those component parts. Don't try and replace the whole thing. And, uh, I've had many conversations with customers where they, you know, they think I'm just gonna replace my whole procurement process or my whole, you know, hiring recruitment process with an agent.

Now that may be possible, but it's really difficult. So basically figure out what that recruitment process looks like and break it down and then attack it in chunks, and then you'll learn what the product, the, the technology is capable of. You'll learn what your, you know, your teams are capable of.

And as both mature, you'll be able to expand and expand it within that process and expand it outside of those two other processes as well. So it really is a, it really is a journey and, and most people will find their own way through it. We have a ton of best practices, guidance patterns and practices, that kind of thing, uh, thing and templates that we, that we can share.

But, but ultimately every customer's different and everyone's gonna look at this differently and figure out which, which is the best place to start. So when we talk about ag agentic business transformation, um, we see a lot of organizations are trying to figure out, well, how does this fit into my legacy, uh, systems, the architecture I have today? Uh, what thoughts do you have about how do we, how do we bridge that gap between, you know, the modern agent stack and, uh, legacy it?

Yeah, there's a couple of things there. The first one would be find ways to integrate, like MCP is a perfect way. The, the, the rate of adoption, uh, for MCP has been phenomenal.

So go learn about that. Go figure out like how you could stick an MCP server in front of the services that you wanna expose to, to your agents. That's step one.

And then step two is, um, if you already have, you know, deterministic workflows, which I'm certain everybody does, there are ways you can start to pull those into things like co-pilot studio, keep them deterministic, but build them as agents. And that's one of the, kind of the great value props of Copilot Studio is you can blend this kind of non-deterministic AI agent driven approach to workflow with very deterministic, with things like agent flows. So you can actually replicate what you've got in an agent and then over time you can start to create, you know, replace parts of it.

So it's more agent like AI agent driven than deterministic. So there's lots of ways you can start to bring these two worlds together without kind of diving in or, or not. Agents will become the core logic and decision making layer in businesses, uh, business processes soon enough.

How does agents, as the new logic layer, uh, change the way that, uh, enterprises think about their core systems? What are the implications there? Yeah, I think, um, that there's gonna be a, a meaningful change with how people interact with core line of business systems like ERP, like, like CRM, um, traditionally today there's, this is kind of a forms over data type web experience.

You have to go somewhere, fill a bunch of forms in and put a bunch of data and, you know, press submit. I think fairly soon that, that there'll be kind of an inversion of where the IP and the logic sits. It'll be less on the database and forms layer.

It'll be much more on the agent layer. And what that means for users is they're gonna get faster access to data, they're gonna get smarter answers, they're gonna be able to use multiple data sources to, to assess a situation. You know, if you're a seller, why am I sending you to the, to our ser cus you know, our customer service, uh, solution plus your CRM to go figure out what the customer you're about to meet, whether they've got any open support tickets and they're angry at with you like that, that it should just be able to ask an agent to be able to do that.

Um, if you're a sales person and you've just got off the phone with a customer, maybe the call's transcribed, maybe there's a bunch of email traffic between you, why am I making you go repeat that, cut and paste it, make it up again and submit it into a, into a CRM system Somewhere an agent should just be able to do that. So I think, I think there's gonna be that switch from people labor in a way, in, you know, web forms and web services to, to an agent proactively in many cases, uh, working with these systems, um, and helping the, the sales person or the service person or whoever else it is to get more done, um, and be way more productive. So how are agents enabling organizations to leapfrog, uh, traditional low code development and go straight to intelligent orchestration?

So I'll give you a few examples. The first one is, I wouldn't exactly say leapfrogging. I would say we're using agents to make that kinda low code paradigm even stronger.

Uh, so things like the ability to use plans in power apps where gone are the days where you're trying to describe an app and build an app, what you do is describe a business process and then power apps plans can use, uh, several agents. There's an architecture agent, there's a data agent, and you can actually go build a business solution for you. So, and that business solution could be two or three apps, some workflows, a couple of agents, they'll actually do the end-to-end thinking, like, if you were a business architect, what would you build versus just building an app.

So that's number one. We're we're leveraging agents to help you build kind of battle, low code, uh, solutions. And then number two, uh, you know, we have new capabilities in M 365 copilot, like workflow builder and an app builder where someone with absolutely zero coding experience can come and literally describe an agent or a workflow.

An app builder and workflow builder will go build it for you and run it. Uh, and then you can, it, it, it can store persistent data and SharePoint it, you can share that with other people. Uh, you can even start to modify it.

So if it's not exactly what you want the first time you kind of describe the app, you can modify and change it as much as you want conversationally. And that really does open the door to many more people being able to kind of use this technology. These aren't gonna be apps that you end up running your business on.

They can help a team, an individual achieve more, be more productive, uh, using ai, uh, to build apps. Things like apps and apps and workflows. So what we're seeing as companies, uh, plan for the role of the maker evolving, when that maker has really been that low code developer has been using things like power apps, uh, to build point solutions in their corner of the organization, what we're seeing is that role is growing up and it's becoming, uh, one that that's gonna take advantage of this, uh, of these all these intelligent agents that are, uh, being built inside the organization, and now they can build new solutions that span departments and, and cross, uh, silos, uh, in terms of the business processes that they can integrate.

And so creating higher order more strategic solutions out of that agent landscape that's, that's forming. And these are things that, that, you know, now will automate and run on their own, but is that, that with you see that that maker still has a role and in fact an even more important one? I was wondering what you thought about that.

Yeah, it's a really important point. I also think it are gonna play a very important role in this as well. You know, they're, they'll be the ones building the MCP servers, um, so they can provide access to, you know, data and, and, and logic to, to business users, units that, that want to go build their own agents.

They, they'll be the ones that are building the kinda high order bit agents so that, uh, business users can take advantage of those. So I think when you bring those two things together, you know, you create a, definitely create a, uh, total is great in some of the parts type math for, for customers. So in this new world of agents where it, it's, you know, the code, uh, the, the AI is touching your business and your data, the, the concern is around compliance, uh, and security.

And I was wondering if you could speak a little bit to, uh, how does Microsoft ensure that these very powerful agents that are working with your business, uh, are following the rules and are secure? Yeah, it comes up a lot, uh, in conversations that I have. The thing to remember though is, um, agents only have access to the data that you give them access to.

They also respect all of the policy, all of the governance controls that you've got over the data. They only get access to it if they are granted access to it. And I think that's fairly, uh, unique to the Microsoft stack in the sense that all of the investments that you've made on things like purview and, and managing SharePoint, that kinda stuff, that all just flows through to agents.

Uh, when you, when you go outside of the Microsoft stack, you essentially have to kind of copy paste that data somewhere, and at that point you lose all of that governance control that you once had when it was in the kind of your M 365 security boundary. So it's a super important point and one where I think, um, we have a, we have a fairly meaningful differentiator for, for customers. Everything just works.

I think it's, it's fairly just pretty much as simple as that. And so thanks so much for, for being here, Richard. It was, uh, uh, great to talk with you.

Thanks the, yeah, it was a relief for conversation. I appreciate your time. So what was really interesting to me is, is seeing how far Microsoft has come so quickly with agents is, uh, we see this as, uh, you know, a whole new stack that's forming inside the organization.

It was really fascinating to hear that, that, that, uh, you're spending so much time, uh, looking at how do I make agents work across the business. Uh, I think we've looked at a lot of, uh, individual automation, uh, in, in the past and low code. Uh, but I think it's that, it's understanding that Microsoft has that big picture view, uh, and it's the breadth of the tooling.

Um, it's the, the scope of, uh, of understanding. It's, you know, not just departmental, of course it's departmental, but it's also how do we make this enterprise wide and how do we elevate the thinking so that, uh, we're preparing those inside of our organization, uh, to, to take advantage of the big shift that's happening, going, you know, from building maybe those point low code applications to orchestrating across the business. Um, and a lot of the pieces around making, you know, agents work together, providing that security and compliance that ensures that you can do all of this safely and not constantly sweating, am I going to go outside the security boundaries?

Am I going to go against corporate policy? And knowing that all your agents can do that, it really is gratifying to see how far Microsoft has come and, and assembling this vision, uh, and then offering it. And, you know, it's not none.

No one can say this is mature, uh, yet, but, you know, hearing about, um, how the, the stories we see at your events about how far the customers are coming and already getting a lot of these things out there. So, uh, that was the takeaway I had, uh, is just, you know, uh, uh, getting a sense that Microsoft is really trying to make sure that they're leading this, uh, this agent transformation. Hi, I am Mitch Ashley with the Futurum Group.

Today we're unpacking the Nokia Bell Labs recently developed model for data center fabric reliability. We'll look at how the fabric design plus operations, especially automation and AIOps, can move enterprises from a legacy PMO baseline to an FMO with Nokia's Sr. 1 nines availability and shrink downtime significantly.

And I'm Scott Roan with solution. It's the age of operations. You know, hardware still matters, but operations dominate outcomes, you know, day two and beyond.

This Bell Labs model includes significant detail, and today we'll zoom in on some key areas and operations. We'll also talk on talk, touch on the significant financial impact as well. Um, you know, the model shows that reliability gains can result in real cost reduction.

And, uh, that should be no surprise to those of us who've been, you know, in the ops world for some time. Very good. Well, the model treats configuration, NetOps is one of the main areas for improvement, including common cause failures.

So what does it mean in practice, Scott? The, the model shows the most significant reduction in downtime measured in absolute terms, you know, minutes, um, eliminated per year is achieved during the config and provisioning phase, driven by decrease in confi related errors. There are multiple key components and mechanisms in the Nokia solution at the heart of the model.

First you have SR Linux, the operating system developed for modern data center operations. It facilitates efficient structured and language agnostic comms between system components, minimizing operational complexity and reducing the likelihood of user error. Next, you've got features like ZTP zero touch provisioning, which are integral to EDA, that further eliminate manual intervention takes the human out of the loop for opportunities to inject errors, um, and contributing to that enhanced reliability for operational efficiency.

And then there's ida's digital twin construct. It provides like, for like environment, um, to design and validate network configs with correct intent inputs. This minimizes config and provisioning errors as in the configs generated in the digital twin directly mirror production.

That's not special configs in the twin, um, and then modified to push in production. It's actually the same configurations reliability is further enforced and improved by it's built-in dry run validation routines prior to every config being pushed into the network, helping ensure accuracy and consistency before changes are put into the live network. Now you mentioned the term age of operations.

So talk to us about why is ops a primary lever in this model? At the simplest level, you know, once you stand up a data center fabric, most of the day-to-day work in net ops is around operations and monitoring. And with AI ops entering the room here, we have a day one killer app that drives the use for NetOps natural language processing.

I can talk to the fabric to see what's going on. We also benefit from increased programmatic access to the fabric. For example, SR Linux provides a single API for set get and streaming telemetry resulting in less operational complexity and minimizing, minimizing this configs that programmatic telemetry from SR Linux feeds into EA to detect and act quickly.

1 nines. When all these features are combined from a network operating system architecture perspective, the event handling system and Sr. Linux and EDA is capable of proactive predefined actions based on things like port saturation packet drops, looking at other network conditions to automatically trigger corrective actions and prevent network degradation and mitigate issues before they occur.

So Putting that all together, it sounds like what you're saying is tools operationalize the design. That's a really good summary of a very long description, Mitch. Um, exactly.

You know, design gives you redundancy, operations makes it reliable every day. The details matter for sure. So planned work can still hurt, uh, availability.

How does, how does the model address that and, and Sr. Linux in particular, and how do they handle ongoing maintenance such as upgrades, patches, the things that we commonly do? We need to point out the fact that SR.

Linux uses an unmodified Linux kernel. This is what the rest of the world is using for Linux. That's gives you, uh, the ability to tap into a worldwide community of developers hammering away at it every day, identifying potential performance and security issues across many, many different application areas, not just networking.

In addition to that, e DAP provides built-in capabilities to ensure network reliability during maintenance operations. You can use the platform to gracefully gain traffic, drain traffic from uh, a node and put nodes into maintenance mode. Um, preventing service disruptions, minimizing traffic, G loss, EA also centralized and stream centralizes and streamlines the upgrade process.

When a route router's locked and is ready to reboot, it significantly reduces the actual maintenance window. It also gives you group-based upgrades and stage promotion, um, to shrink maintenance windows. Reduce the time used for maintenance windows and limit that blast radius.

You can pick targeted nodes, you can automate pre and post checks, you can roll forward only when you're a process gates get passed altogether. These features, um, result in enhanced reliability and minimize downtime. All the upgrade steps can be tested, um, with, um, the EA digital twin functionality in a like, to like environment.

This is what gives you impact, um, and reduces, uh, maintenance issues for far less annual downtime as you move from your present mode of operation to your future mode of operation. Excellent. So who should people talk to at Nokia to see how this model and can apply to their environment, their data center network?

Well, to learn more about the model Sr. Linux and Ed, a contact your Nokia account team or your regional business center contacts, and they'll work with you to work through the details. Thank you, Scott, appreciate, uh, you sharing us some interesting facts from the study.

Thanks, Mitch. Hey guys, thanks for the throw. We are here with Arne Bellini, who's CEO of Bellini Capital, and we're having a little chat about cybersecurity and how the scams are getting a little more sophisticated, starting with these cold weather scams that we're seeing.

I mean, there's nothing sacred. People are freezing and now people are trying to steal their money and compromise their data and do all kinds of nasty things. Hey, Arnie, how you doing?

Welcome to the show. Good, great. Thanks for having me.

It seems like every time that there's some sort of emergency, the scammers come out from the woodwork and start making pitches for various things that are related to that. And the latest one seems to be these cold weather snaps that are becoming more common. Um, is there something we should be doing about this?

Is it just noise in the system or is there something we can do to maybe thwart this stuff, but 'cause it sure is annoying. Yeah. Uh, well, they're gonna come outta the woodwork because they don't really ex they're exploiting the opportunity for us to be distracted.

I mean, it, every scam works better when we are distracted, right? So when cold weather hits and utilities shut down and you know, there's mayhem going on, that's the perfect time for a hacker, uh, to come in and create some several sense of urgency to maybe spoof you and think that make we're the utility company and you know, click here if you want your power restored, right? I mean, you get to pull off a lot of clever reuses, if you will, that can, that can catch people off guard and then catch them when they're really focusing on something else.

Another, you know, a distraction is, you know, a disaster that's happened. You know, you see it in Florida, I mean, I'm from Florida. You see it happen right after hurricanes like crazy, right?

You'll see it up north when, when they, you know, when they have a, a white out, you know, day where the power goes down. You know, you'll see it for that. So, so urgency is one of the things that hackers look for.

They look for you being dis uh, you know, distracted and they look for you needing to do something urgently to restore maybe your current situation. So essentially that's a tell though, right? I mean, the fact that it's urgent is suggests to me immediately that there might be some sort of fraud at work here because well, um, you know, folks who are offering to help you are not necessarily gonna say, you know, and you gotta do it by Thursday, right?

They're generally gonna be, Hey, we're here to help on any kind of time and effort. But, um, is, are there other tells that we should be looking for? Yeah, urgency is one of them.

Uh, and that's the obvious one because there's no reason to do something urgently. Now in a, in a cold weather disaster, for instance, you might feel urgent to, to try and get your power restored. So they've gotta play on that.

So you gotta look for, I mean, you actually have to just outsmart them, you know, it really comes down to that. You can't, you can't cave into the need for urgency. You just can't click anything.

I mean, at the end of the day, if you have even a second thought about a text or an email that you get with a link, do not click it unless you know it's from a known source. That is how they're gonna get in almost every single time is once you click, they're gonna deploy malware that you're basically clicking and saying, it's okay for you to come in and put something in my system that you can come back and get. So that's one, one way that they're gonna create urgency.

Don't click on things and then don't respond. Right? It's like, and if an offer's too good to be true, then it is too good to be true and it's not true, right?

It's probably a hacker. So it's all of those things. They're gonna play on your fear, they're gonna play on your urgency, they're gonna play on your greed, right?

It also seems like the attacks are getting a little more sophisticated in the age of ai we see in these deep fakes. And I think, you know, an email is one thing. You, you can kind of discount that, but I think people, if they get an audio message or a video of something, does that make it more believable?

And will this become a bigger problem? It is a bigger problem already. So, yes, and you hit the nail on the head.

AI makes these, you know, I don't even say it anymore, but you said, how can we detect it? Well, you used to be able to detect it because things were misspelled. You used to be able to detect it because it was grammatically incorrect.

You used to be able to detect it because it didn't sound like it came from your culture, right? All of that is washed away with ai. So it doesn't matter who I am, it doesn't matter how I operate culturally, it doesn't matter how well I can sell, uh, AI can clean all of that up.

And so they are getting much more sophisticated and it goes beyond that. So it's not just that the messaging is more sophisticated, everything is way more sophisticated, or I should say can be. So it's trending to be highly sophisticated, and we have to realize that because with AI it can be a really good spoof, just like you see, you know, AI versions of, uh, say a politician, right?

It looks pretty similar, right? It's close enough. We would have to do a second look, right?

AI's gonna let the, the, the hackers get to that point where you're really gonna have to, you know, get, gonna have to be discerning, right? Uh, because they're there to fool you. Everything is there to fool you.

I can create a website and make a website look way more legitimate with artificial intelligence, you know, and sper the finger. I can do a denial of service attack on your company, you know, where I just barrage your internet connection with requests to come in, request to come in, right? And you, you can't do anything but deny it.

So you can't, can't even use your internet line denial of service attack. I can spin up servers all around the world with AI much quicker than I could before. So it makes this, this new technology will be used against us.

And that's really why Lauren, my wife and I have invested, you know, $50 million at University of South Florida to actually create a, a, a college where you combine both artificial intelligence and cybersecurity. Because if they're using it, we have to use it to defend ourselves. They're being incredibly more effective offensively with artificial intelligence and their sophistication.

We have to be that much more dedicated to defending ourselves with artificial intelligence and cybersecurity. And so it's, and I wanna also point this out, okay? It's, it's a big deal, okay?

It's a $10 trillion problem a year right? Now, let's just put that in perspective. United States gross domestic product, 30 trillion a year.

China's gross domestic product, 17 trillion, 13 trillion. If they're being honest, they're not. Okay.

Uh, and then, and then who comes next? Next comes if you rank it as an economy, next comes cyber theft at $10 trillion. Oh, and then Japan, and then Germany.

And then, you know, you see my point, my point is, and it's growing at 15%, right? So the cyber theft is growing at 15%. Hey, we're really excited in the United States that our economy's finally growing at like close to 5%.

That's an amazing thing. We haven't seen that since I was in high school in the seventies. Okay?

So that's an amazing thing. We have to protect our economy. We have to defend ourselves, uh, because even growing at 15 5%, it's growing at 15%.

So by 2030, cyber crime will be bigger than the United States economy, the number one economy in the world, and they're not doing anything good with the money that they steal, right? So it's one of our biggest problems that is going undetected, right? If we had a crime wave in a city where, you know, everybody was being pickpocketed in Times Square or um, you know, there was some sort of massive rash of robberies.

The police departments locally and federally would respond, and, you know, we do something about this, but in the, in this case, it's so ephemeral, right? It doesn't have a physical kind of connection. So what is it that maybe we should expect law enforcement and governments to be doing about this?

Because to your point, it is sapping our economy. Well, you hit the problem perfectly. That is the problem.

You frame the problem, we're not responding. It's hard to respond, right? It's, there's several problems with this.

One of the biggest problems is if this was crime, to your example, if this was crime and we saw things happening in the streets, we would demand something be done about that, right? Because we can see it, we can work, probably we could be involved in it, right? We could be the victim of it.

But with cyber crime, it's digital. It's, it's invisible, it's silent. Uh, and it doesn't, doesn't, doesn't seem to sting our emotions as much, right?

So we don't really do that much about it, right? But it is, that's why I'm raising the red flag here, saying 10 trillion going at 15% gonna be bigger than any of us, you know, in, in less, in five years. In five years, okay?

Five to six years. Okay? If it continues to grow at this pace, we have closed our physical borders here in the United States.

Thank God, we now have to close our digital borders, right? And there's a big project to be had there that I don't really think, you know, well, lemme just say that that's where I'm putting all of my time and effort is like tracking the code on that. How do we defend our digital borders?

That's the reason for the new cop, Bellini College of Artificial Intelligence and Cybersecurity at University of South Florida. It's also why I came outta retirement, because this is my civic duty. I think I can help crack the code and figure these things out.

But it comes down to a lot of things. But one of 'em is, we've gotta have more people entering the cybersecurity profession in the United States of America. And it is a very lucrative profession.

And so that's one of the things that we're doing, is we're really getting the word out to young, young, young folks that this is a great career path, right? Uh, we need more people. We need more cyber soldiers, right?

To defend the walls, the digital walls. Right? That's one thing.

Um, we have to have some government policies that are guiding Lights North stars that start to tell all of us business people and others, uh, how we should be defending ourselves and what they would consider a decent standard. It doesn't have to be regulation, it just has to be a North Star standard that, that we could follow, because it's a very confusing cybersecurity. And cybersecurity hygiene is very expensive and very confusing to most people.

It does not need to be. So, some leadership in this area is gonna be very important in the government. Uh, some initiative from folks like myself and the business community and the tech community, very important.

And, and at some point, we're gonna, we're gonna solve this at some point. We just need to solve it sooner rather than later. How far can we go?

Because you'll hear security folks will say, well, I can clean up the mess, but I can't prevent those people from launching an attack in the first place. So, and they'll say the defenses are too porous. And, um, to quote, I think it was Frederick, they're great.

If you defend everything, you defend nothing. So, um, what is the role of cybersecurity people here and what can we do? Well, I would say if I were to declare a North Star, it would be pretty simple, right?

And, and I'll actually lay it out here. You know, I would say every business in the United States, every organization, in fact, in the United States must use, and I'm not gonna regulate it, but North Star should have, if you wanna be way more secure, if you wanna lock down 85% of your exposure to being hacked, here's five things that you can do. Multi-factor authentication.

Make sure everybody in the company's using it, okay? Have an antivirus, uh, solution that is deployed to every single device in the company, okay? Uh, make sure that you've got proper backups.

Make sure that you can go back a, a week, a month, maybe even a year. 'cause for data gets stolen, we're gonna rely on that backup, okay? Or if it gets locked down, we gotta rely on that backup.

Gotta make sure you're doing that well. Okay? Fourth thing you've gotta do, you got to have user awareness training.

What is that? It's teaching every one of your employees what they need to do and what they should not do. How to be safe, how to not click on those links that are suspicious.

How not to, uh, respond in an emergency. You know, users, basic training, all the things that we've talked about. There's basic training that every user needs to know as well, because it doesn't matter how good I defend the digital wall, if they can get someone inside to click, they're in.

Right? So that's really important. And then the fifth thing, and the last thing that's really super important is constantly assessing your risk.

Constantly walking your digital, your organization's digital wall to see where there are vulnerabilities, making sure you're marching down that wall on a regular basis, evaluating whether you have any openings in the wall, whether you have any t chinches in the arm, whether they can get in, right? And so, you know, if you did those five things, I'd say that there's, you've taken it from massive exposure to being hacked down to maybe a 10% chance. So, you know, that's pretty darn good, right?

If we could defend the digital wall, like you said, you, you can't defend at all. If you defend at all, you defend nothing. Okay?

Here's five things you could do to defend 90% of your digital wall. How aggressive should we be? Because it seems like governments especially have been a little hesitant to be on the offensive with this stuff.

And yet we know that some of these attacks come from specific regions and specific areas and involve specific, uh, cyber criminal syndicates that are being protected by various things. I mean, do we need to engage in some quote unquote gunboat diplomacy here? Or is there some way we can think about this differently?

I, I love the idea of gunboat diplomacy here. Okay. We could pull that off.

Okay. But I don't think that would do any good. Right?

What we've gotta do is we've really gotta look, okay. It's pretty simple, right? $30 trillion gross domestic product, right?

That number is why America's number one, okay? Number one, economy, okay? Number one country, okay?

You see how much power we can yield if we use our power around the world, okay? We can do just about anything. We have to stay number one, you don't want China being number one.

You don't want Russia being number one. You certainly don't want rocket man being number one, right? So, you know, the game that we're playing now is autocracy versus democracy.

That's the new game in town, okay? And it's probably never gonna end. It's always gonna be spy versus spy autocracy versus democracy.

They take advantage of the fact that we are an open society, and they, we invite everyone, you know, into our nation, right? And they take advantage of that. So there's a few things that we really should be doing.

I can tell you this. We should not be allowing any citizens from a foreign, from the United States of America, foreign adversary list. We should not allow any citizens from people who are on that list, or I'm sorry, nations that are on that list.

And that list includes China, Russia, North Korea, Iran, uh, the Venezuelan regime, and I forgot the seventh one. But none of those na, if you're a citizen of any of those nations, those are our foreign adversaries. We should not allow them to attend colleges in the United States.

We should not allow them to work in our colleges in the United States, because this is where we create a lot of our innovative thinking. Okay? That's the, that's, I mean, you're gonna have to do something like that because every one of those nations has a law that says that every citizen and every company inside those nations must perform spying or intel.

They call it intelligence gathering for the government. They must, and it doesn't matter where they are in the world, they must do it. And that's why you've seen so many of these Chinese nationals that say Michigan University, six of them got, uh, arrested for spying on an Air Force base.

Three of 'em got arrested for smuggling and, uh, biologically hazardous material into the United States. They could bled are called corn crop. Okay?

Not, not a small thing, not at all, a small thing. Okay? So, you know, there's some really basic steps that are so obvious that are right in front of us that we can take to really solve this problem.

Um, and the North Star is another one that I discussed, but I mean, there's, it's not that hard, but it is, it's something we've never done before. So it's all new, right? It just has to require, you know, kind of innovative thinking on how we solve this problem.

If we wanna be an open society, how can we be an open society and have people still as freely coming back and forth? I think the answer is, you gotta put a filter on it. You gotta put an appropriate filter on who can come and who can't come into the United States and what they can do when they're in the United States.

That's really important right there. Um, if you, if we were able to, you know, do the, the, the, the, the, the, the North Star and everybody in every business followed that we'd be in great shape. But if we lose our position with our economy, we lose our position geopolitically as well.

And that's what's really at stake here, because they are destroying our economy and siphoning off our economy with these $10 trillion worth of, of hacking that's going on. By the way, 30 billion was just confirmed, I believe, stolen from, from the state of California, $30 billion. Some would say that, you know, we, the United States give as good as we get.

So do we need some sort of, I don't know, cybersecurity, non-proliferation treaty the way we have with some other things that are dangerous to everybody in the world? Yeah. And no one would follow that.

Like everyone would sign it and no one would follow it. Okay. So let me just tell you example.

Okay? Like, I, like China could sign it now or not be in violation of it. Because what they do is they just go to private, they go to private, uh, tech companies and they give them contracts to hack into the United States so they can say, you know, once removed, it's like, we didn't do it.

We paid someone to do it, but we didn't do it right? So it it, there's all kinds of ways of getting around that and China's already doing that. So, uh, and they're, they're getting really good, by the way, at hacking.

They weren't good before. The Russians have been the original gangster, right? So Russia, during the Cold War, they're the og right?

The original gangster of hacking, right? They were really good. They are really good at, they're kind of distracted right now.

China was always there and they were just, you could tell when they were trying to you, we could easily fort their hacks. They've gotten really, really good at it. Really good at it.

Alright. You mentioned your work with the University of South Florida. Is there other things that universities should be doing?

Can we maybe pull our collective university resources better and kinda work on this problem? It seems like it's bigger than one school. Yes, sir.

We can. In fact, I just met with the chairman of the Board of Governors at the state of Florida. That's the person that's in charge of the entire university system.

And I've been proposing that we take, you know, a hub and spoke, uh, approach where we pull all of our resources on both cybersecurity and artificial intelligence and, and, and see how we can make a difference in our, in our national posture, in cybersecurity. Because someone's gotta lead here. No one's really leading here, you know?

And it, it requires leadership. It just requires some leadership here. And it will, we can solve the problem well enough.

We, it won't affect our economy. All right, folks, while you heard it here, hey, the fight is on. That's the good news.

The bad news is we're not quite as organized as we should be. Hey Ernie, thanks for being on the show. Alright, thank you so much.

All right. And back to you guys and Steve, Control, This is agent dev. I'm in position.

Copy that. Dev. Stand by for go standing by.

Hey everybody. Welcome to Agents of Dev, the podcast where we'll talk about anything related to development and have fun doing it. My name is Mitch Ashley and I lead the analyst practice around software development, software lifecycle engineering with Futurum Group.

And my co-host, Brad Shiman, also an analyst with the company. Hey, Brad. Coincidence?

I think not, uh, Hey, Mitch, good to see you. Yeah, Good to see you. And you're, you lead data intelligence, infrastructure and analysis and some combination of those words.

It's, It's, yeah. It, it can be any combination. I I, I'm not picky on that.

It's not a dag It's a word jumble. That's kind of, it's a word. Salad.

That's the world world anyway. Isn't true. Well, you know, it's, it's great doing this podcast 'cause there is a, a plethora of topics that we can talk about every week.

I mean, we have to like pare it down to like, can we fit all that in? Can we talk about all that? Yeah.

And then what do we say we're gonna do? Because there's lot to choose from. And it's definitely true.

You, you brought up the, uh, well, it a lot of interesting things about Malt Bot, which was Quad Bott with a wd um, yes. Over the, that really kind of blew up over the weekend for several reasons. One of, just because what it does, talk about how it was created, the, it has some kind of biblical reference or something.

I'm not quite sure I'm making that up, but, Well, I, I mean, any software developer worth their salt will look for any number of puns, obscure references as they can, and build that into the, the working title, which eventually becomes the actual title of their software. Thateveryone knows that Living in infamy the title of Well, yeah. Right.

And is that a bad thing? I don't know, because sometimes it, it really gets the point across. And when I, when I looked at, um, Claude Bot, which is the, the current craze right now, um, as of 9 0 7 on Tuesday the 27th, um, it is, uh, you know, built by a, a fellow named Peter, who is what I would call a, a serial vibe coating enthusiast, uh, who's made a tremendous number of, of tools and is just, you know, a font of creativity.

Uh, and he has been working on for a little while, but has created this, uh, basically what I would call it is, is, is like an extension, or not even a, not a maturation, but an extension of what we saw with Claude Cowork. Um, but the difference is that it, it, you know, well, it works the same way in terms of it's designed around an autonomous agent that can, uh, do take action on your behalf, on your machine, uh, whether that machine is your local laptop or A-A-V-P-S, um, uh, yeah, in the cloud. And, uh, the difference though is that this, this has a bunch of, uh, built in sort of connectors and a running listener, uh, which, which I think they call a gateway that will, um, basically just run 24 7 looking for things to do, waiting for instructions, or, oh, I don't know, checking Aron job, uh, or, or responding, I should say, to Aron job to kick off some sort of action on your behalf, whether that is, uh, go get me the news for today so I have a nice message in my inbox, or I mm-hmm.

Get that message with news sent to my Slack channel. Um, or, uh, it's to send out a, a, an email in, in mass to your LinkedIn, uh, family of, of, uh, friends. It can do all of it because it's just built to be a general purpose agentic, uh, interface to your personal life.

Whether that's calendar, uh, whether that's files, whether that's your line of business apps, whether it's your code base or your desktop. Doesn't matter. Anything local to your computer.

Um, browser included, right? Yeah. Because it has chromium support.

So there you go. That, you know, you can, uh, give a, give a man a browser tab or you can teach him how to the full chromium API, sorry, it's, it's a weird, it's too early too many months here. It's, um, but, but, but that's, it's okay to be early and that's what this is, right?

Because I, I guarantee that you and I will be on this podcast, you know, um, tech of God's willing in less than a month going, okay, we're gonna talk about Google's, you know, Gemini bought and anthropics Claude Bots, uh, which will be an extension to cowork. Uh, and they're all gonna do this. Everyone is gonna do this.

Yep. It's, it's kind of what, uh, copilot should be in, in Microsoft land, right? If you will.

Yes. It's of cut and paste mentality. And you, you and I have talked about this too, that, that, that, uh, the way that people inter interface with software is, is changing very quickly right now.

And, uh, we, we went through the phase of a separate chat window, uh, that you took, you know, cut and paste from, and then we went through the sidebar phase, which was a little more contextual, but still cut and paste. And now we're in the M-C-P-A-P-I, uh, era of just letting, letting the, the machine take action directly. So, and we'll talk about Itcp another day 'cause there were some security vulnerabilities that came out about it.

Oh, that, that won't be the last, uh, it is won't in incredibly dangerous can, can we just give a psa? Is that okay? Yeah, Sure.

Go right ahead. If you do this, everybody, um, please use it on a dedicated machine. Put it in a, a container that's, that's like cordoned off from everything.

So meaning if you're gonna run it in docker, lock down all the ports, uh, that you don't wanna use or others to use. Um, and, you know, don't, don't just put it on your, your existing work laptop. That, that it is, is a very bad idea.

And the, the main reason for that is the same reason that generative AI in particular is quite dangerous and will always be so, um, is that it's, it's subject to injection prompt injection attacks. Mm-hmm. It's very susceptible to that because it's a probabilistic system that responds to what it reads and what it's told.

So you're Careful out coding if you're a citizen developer using AI tools, right? Mm-hmm. And even a developer, not every developer is a security expert, you know, by far.

Oh. But, um, it's really easy to generate yourself into a, well, we not only took the, opened the, the locks, we took them off the doors, and then we just took the doors off the, the, the house and just come right in. The whole thing, you know, the, the attack surface just went, you know, so suddenly, like everything in your life is now open up because it's all on your computer.

Yeah. So you have to be careful about what you're, what you're doing when you're going over the network and accessing services or using open source code, any of that kind of stuff. And that's what I think is particularly dangerous about, uh, cloud bot here is, is that it, it, um, has an attack surface that is, you know, would, would really demand somebody who's, you know, steeped in system security to, to really set it up and manage it correctly, um, and safely.

Yeah. And interestingly doing, did you know, it actually comes with its own auditing, um, workflow? Um, so it'll, I did not, you can, you know, once you set it up, you can actually just run like a, you know, an extensions command forward slash audit, and it will tell you, you know, what the, what sort of vulnerabilities it might have and suggest to fix them and obviously fix them because hey, it's autonomous.

Yeah. Yeah. Interesting.

Okay. So self-correcting, self-healing, if you'll, well, you know, there was a whole big controversy about it too. Now, cloud bot was renamed to Malt Bot MLT because some, uh, trademark.

Oh, yes. Yeah. Well, you would be bad too, wouldn't you?

Yeah. Yeah. I'd be a little, little upset.

I can't imagine why somebody had an issue with It's pro pronunciation. It's not the spelling. It's not the spelling.

Yeah. I, I dunno who could have objected to that. But during the renaming, uh, during kind of that, that window, uh, scammers hijacked the old GitHub repo and X accounts, so they onto dollar Right.

With an token. And then they briefly hit about, I think it was $16 million market cap before crashing. Yeah.

Yeah. They pushed a fake dollar, Claude. Um, so it was, it, it hit a big suddenly, like, I mean, innocent thing to do, I need to rename my project.

Right? Boom. Okay, well, what do you do with the old thing?

Oh, since that was, had its own following, let's take that over, right? As a, as, uh, you know, the, the bad guys are wanting to do. So it, it, um, it sure brought to light a lot of, uh, attention to Claude Bot and Malt Bot, whichever, Whichever one you wanna call it.

I, I still call it Claw bot. Yeah. Just for, just for, uh, nostalgia.

That's right. Thinking back to last week. Yes.

Yes. Fonder times. Yeah.

That's all it takes. Um, well, anything else to say about this? I, I, you know, I haven't checked it out yet.

I'm gonna give it a try and, you know, see if I can open up my computer to the world. Yeah. I'm gonna, I'm gonna do it too.

Uh, I've got a Mac Mini that, that's not doing as much as it should. So I'm, I'm gonna wipe it and give it a, give it a go see, see how it likes that. Um, but yeah, I do actually wanted to, to mention briefly that, um, one of the things that I, that I found very interesting about Mt.

Claude Bot is that, um, it, the way it is, the way that it manages memory and, uh, you know, you, you and I are fans of, of e old markdown, uh, and plain text files as, as the lingua franca of all file formats that we Should everything, eventually you should use it For markdown spreadsheets, you know. Um, but, uh, it's, uh, it's interesting the way that it, it's trying to, you know, Claude Mtba is trying to smartly manage memory using, uh, key value pairs and, and time to live as well as, um, you know, automatic trash compaction and or trash removal and compaction as well as manual. Um, you know, remember this kind of things that we we're all used to mm-hmm.

Uh, in ag agentic tooling. Um, so it's this kind of multi-level, uh, memory management package that they've built into the, the tool. And I think that's not how it's gonna be.

Um, but I do think that it points toward what we should start to see, which is, uh, something that is less what you, the developer or you the user has to manage and, and instead exists as, as sort of a, um, you know, uh, I hate to say, say the words, but, you know, like a, a a message bus for, for, for memory and context for, uh, for models that just manages itself and make sure everything is running. And it makes sure that the models have, uh, the right context at the right time. It is a, it is a challenge.

You and I have run into this, and Im sure everyone has in their way, different AI tools, LLM systems manage memory differently, right? It's all contained within a project. It's contained within, across sessions of the or not, or, um, you put it into artifacts, whatever the, whatever the method is, and they're all a little different.

Or you use skills and use other ways of mm-hmm. Persisting some capabilities, but then you switch tools and now you're all that, all that, I dunno if I call it training, but all that curating of memories and how you want things done and how you work and what you do, and, you know, the stuff already done are kind of trapped in that. And, you know, you mentioned Mark, mark done, I'd recently switched from doing a whole bunch of things in, in, uh, chat PT and moving over to Claude to do some other work.

And I'm like, well, I've got this other stuff set up. So essentially in, in chat, PT created this, it's a 17, uh, file markdown, uh, liturgy of all the stuff from my writing style to how I use AI, to how I play games, to all these different things into this, you know, here's the index and how it works, and then put different files into different projects. And Claude, because they're relevant, they don't need to load the whole thing every time into the context window, um, as a transition.

Now, it's not perfect, but it sure like, saved me about three months worth of work to kind of get back to where I was. How did you make that, Mitch? How did you create that, you know, corpus of knowledge?

Well, it was interesting. I was, um, I was in Claude and I said, all right, here's my problem. How do I do this?

How can I, how can I port this over? And so it said, well, here's a series of questions to ask, um, to ask Chachi, bt. And I tried that out.

And after I got to that, I said, well, I need a structured way of doing this 'cause I'm not gonna play copy and paste and, you know, that kind of thing. And said, well, here's, here's a set of topics and here's a set of, uh, draft markdown files structurally, um, ask GT to fill those things out. So it build those things out.

And then I went in and modify it where it wasn't quite exactly whatever. And then I, I, I remember, oh, yeah, I have a system of work the way I kind of classify what work I'm doing and what its priorities. And so let's add that to it.

TE pt, describe, I already had those things, documents. So I took those things, put it into this format, and I moved that over. So it went from about 11 files to 17 over a process of, eh, probably a weekend plus.

And now I've, then that's, and then the, uh, canonical reference is obsidian. That's where it lives. Nice.

And so when I make changes to it, it always goes back there. And then I pull it from there into, now I use it in both environments. I have it in, in gt and I have it in CLO and I use it in Gemini.

So I ha it's transportable i'll, albeit I'm the transport, but at least it's transport. Well, you could, you could sync it with GitHub or actions or, or just use their builtin sync, right? And then Yep.

Every machine you're on would would have access to that obsidian vaults and, and be able to iMedia by Theia. Yes. If there, if you ever wanted such a thing here, it's at least itself, it's useful to me anyway.

Yeah. But it was, it was an, a very interesting exercise in how much memory I had in invested in Chet pt and how important it was to have that. Um, 'cause it just saved me a lot of times.

And along the way I kind of proved some other things. Like, here's some things I wanna templatize that I do frequently, and here those will be here and let's, let's do things this way. So, you know, and of course, like every project it turned into seven other projects.

Let's reorganize my vault while I'm at it, and, you know. Right. Well, I, I, I think you, you probably got a ways to go before you, you beat Peter Steinberg.

Uh, is it Steinberger? Yeah. The the quad bot guy.

So get going Mitch. Get, get, get on that. But it's an interesting point that you bring up to me with, with the way memory works with, with agentic tooling, and that is, um, locality.

Uh, and, you know, I do appreciate how it has evolved over the last six or so months wherein if you're in cloud code or you're in Gemini, CLI, for instance, and you, uh, you, you would have, as you have the edia, that is everything you like about development in general. You know, I like Python, I wanna stay at three 13, you know, you know, on this more, you know, specific side. More generally, I, you know, hate PEP eight.

Uh, I made my own, I use five and a half spaces for, for this and, um, you know, whatever other, each Three quarters line preparation, right? Uh, you know, all of those, I don't wanna say and tricities, but, but the, the things that are specific to you, and the way that these tools have evolved over the last six months is, um, you know, such that I can have that knowledge generalized for Gemini CLI, for instance, but I can also have knowledge that takes precedence, uh, within a specific project. And I can have that for every project.

And it's not just, you know, two conflicting competing, um, bits of memory that the agent is working with, but instead a hierarchy, um, to, to say, you know, if I see an instruction for, you know, do use Python with four spaces for this project and go for this other project was, you know, some other, you know, strange thing, you might, I can't think of what you might wanna do. That would be weird and go, but at any rate, um, you don't have to try to, to reconcile across projects so long as you, you know, create that edia for the project, you know, it can live with the project and it can have the broader context that lives with you, the developer that can, can move from project to project from tool to tool. I like that.

Well, you, you have described what I'm working on, which I call it the memory portability system. N ps I have a already, by the way, the Wikipedia is called the Context operating system. Yes.

Yeah, yeah, yeah. That's nice. I like yours better though.

Well, EDIA is a good, you know, maybe infamy, living in infamy, um, but it, it solves a problem. I don't know if anybody's working on solving yet is, and that's the transportability because it's in the interest of, maybe it's a good open source project, but it's in the interest of all the vendors to like, I wanna create the environment that you wanna be in. Right?

And so are you trapped there? No. But you have so much invested in it, you have preferences to stay there because there's advantages, right?

Well, that is their goal. Is it not? That is the argument for proprietary software, uh, or the argument for open source software with proprietary extensions or proprietary services on top as the monetizing force behind it.

But, um, you know, it, it is very much the case. I think that across the industry, especially for software development, that, you know, developers are lazy because, you know, developers are always looking for the best. Developers are the lazy, I mean, seriously, that is, it's, It's optimize everything.

What can I do to optimize so I don't have to do this stupid task again and again, that's the laziness I'm talking about. Yeah. And, and so it, it very quickly is a crucible of, of openness to, to it, you know, pushes you toward openness such that we, you know, with, um, MCP, even if philanthropic hadn't have donated it, uh, to, to an open source organization mm-hmm.

Would have become a standard simply because everyone just like, you know, Claude slash Malt bot was like, wow, that's great. Let's do this. Let's, let's make use of it.

And it's the same with skills. Claude Code, you know, came up with skills relatively first. Mm-hmm.

Um, and that was a very simple markdown or yaml, uh, sorry, is it tomal or yaml? Which one did they use? I can't remember now.

I don't recall. One of the two, which I always have problem with, Everything's YAML and Tom is with, with some like constraints. Um, but at any rate, um, you know, it's just a text file with basic header information and instructions.

Mm-hmm. And that's very portable. And as we have seen it already is portable.

So if I go to Claude, um, you, um, hub, what do they, what do they call it? The skills hub, I believe Home. Yeah.

I dunno the name of it. Is It Skills Home? Well, we'll, we'll, we'll, we'll put it in the links.

Uh, but, but at any rate you can download those and run them in Gemini, CLI for instance, without doing anything to them. I could just install them the same way and run them Just like prompts or code, text code. Right.

Same way. Hey, if I can just kind of staying on the theme of now that I have, uh, AI driven tools, I can create anything I want theme. There was a gentleman, uh, I think it was over, it was I think late last week or so.

It came out that he had ported, and I don't know if his name is Peter too, but he had Ported, chances are, chances are, Um, he had taken Cuda code, uh, and ported it to run an AMD rock stack. Uh, so it wasn't Nvidia that ported it, it was someone that talked about it on Reddit that what he'd done, um, and he used cloud code to do this, to translate, translate the entire cuda back in, um, into an AI engine for a MD rock And did it 30 minutes to do it. Right?

Oh my gosh. That is crazy. No Manual edits.

Now I'll take that to mean the first time it worked, it was, it took 30 minutes. It probably didn't take 30 minutes to, and I'm done and walk away, you know, drop the keyboard, drop the mouse. Um, but it really showed the, like, you know, what these tools can do in the hands of someone who is, I have, I, I wanna be able to do this.

How can I get this done? There's not a product, I'm not gonna wait for somebody to create it. I'm just gonna do myself, do it myself.

Now this created the whole thing about, oh, is this the thing that's gonna bring down the Nvidia Cuda moat? No. Somehow I think not The ecosystem is, is the key.

Yes. You know, he was not, you know, David and the Goliath. Right.

He had knocked the giant out quite yet. I don't think that's gonna happen, but it, it does bring up, for me, it brings up a lot of things. Now he, he put it under GP four for the license.

So it has some, you know, it has some strings attached. Right. So I'm not a big fan of that license.

I'm not sure why he did it that way. Yeah. Either had reasons or that's the one you knew.

Well, Those gpls typically if you don't want your competitors making money with your software, but everyone else is okay. Yep. Gotta contribute it back if you, if you're gonna distribute it and you have to contribute your changes back.

Yep. Um, and g and four is even more restrictive. So yeah, there wasn't any mention of any kind of profit motive or where, where he might take this, but, you know, maybe he wants to be the, the loan shark that you hire to go port whatever to whatever.

And that's his, you know, mantra. I dunno. But it, it, it, it certainly brings up the licensing.

Like are you gonna see Nvidia and everybody else tweak their licensing to say, well, you know, they had already thing, I checked this out, they already had some restrictions in the Nvidia license, but talking about specific things like the SDK that you can't do with it. And since he ported the back end, it didn't really apply. Right.

Um, so is translating code, you know, is is kind of the term that of art that folks are using about this is translating code from A to BA violation of copyright of, of, or a violation of a, of a, uh, user agreement. I gotta believe there's gonna be reaction. Like we're gonna lock that down.

We don't want our stuff to be reverse engineered because it really is reverse. How do you lock it down? Is that is the question?

I, I get them wanting to do that. But if you can reverse engineer and create something without just piling transcoding, you're refactoring, um, is that a copyright violation if you're basically figuring out how something works and what it does and creating it or recreating it? Well, So here, here's, here's my thought about this is, you know, the way to get around those things is your clean room develops something or reverse engineering something, right?

Mm-hmm. You take the team that's gonna go take what we want, we know what it is 'cause it's a product or an, uh, something that's been protected copyrighted and say, go do that. We're not gonna let you be influenced by anything else other than you developing this.

Isn't that what AI is doing? Now you can say it's being influenced by other things, but I don't have control over what it's influencing. It, it's development.

I dunno what Claude resources, cloud codes gonna use, um, you know, from so, or whatever model, but it's using to develop from. But, so I'm kind of outsourcing that transfer. So me, you know, it's not me.

I didn't do anything. It's somebody else's model that did all of that work. And if go sue the LLM, you know, that kind of thing.

Is that, is that responsibility Because you instructed it to do so? Well, yes. I I was the, you know, that's a conspiracy.

Was I Conspiracy to commit fraud? I guess. So I, I we're gonna see a lot of this happen.

I think where this is gonna explode into everything's being rebuilt. I even saw, um, a gentleman named Patrick Dub Dubo, I dunno if you, Patrick, he is one of the originators of the DevOps movement. I think he actually coined the term and he's very much into AI native development.

And part of this group that's focused on that, he posted about this, that I'm kind of scaring myself. 'cause I'm running into situations where instead of going, getting an open source library and finding the right one and using it or whatever, I just, I just write it again. I just have the ai, write it again, don't worry about it.

So suddenly reuse is not a big deal. Right. Why do I need to reuse code if I can just create what I want?

And if it does the same thing, why do I care? 'cause it's gonna maintain it. Well, right.

I'm being flippant about it, but that's essentially where we're headed. I, I could basically vibe code Pandas in a weekend, I'm sure. Yeah, yeah.

And have the basic functionality to, to handle data frames. Uh, but should I do that? Um, you know, I I think that, you know, and this is something you see with open source in general, is that, um, somebody gives of their time and expertise and their energy to create something, whether that is a re-envisioning of something or something completely new.

And if people appreciate it and like it, or they want to support it, or him, they get involved in one of many ways and, and therefore contribute to it, and it grows as a project and you end up with Pandas, um, which is, you know, a beloved, you know, tool that, that widely used ports everywhere, everyone's using it. Um mm-hmm. But it, it is, it is very much the case that, um, in, you know, without that, without somebody who first said, wow, you know, I need a way to manage tabular data in Python.

Um, does it exist? It didn't at one point. Right.

It didn't. And so they made it. Not everything is like that though.

Um, you know, if you think about like Linux and the core utilities, the, the new core utilities and how, you know, people will swap out LS for EZA for instance to, you know, because it's better. You know, somebody came along and said, you know what, I, I think I can, I can do one up on that. Let's rewrite it in go.

'cause go is a great CLI language and, uh, right, Right. Or, you know, little larger projects, let's like Zed, you know, I hate Electron vs code, and it's not just me talking, you know, how much I Hate the universe. You're speaking for the universe.

I'm, But, but it's me right now. Uh, and, and said, let's, let's do it in rust and, and make something super fast. Um, you know, is we can manage on our own.

And I see that more and more everywhere I go on, on the interwebs. You know, you go down, uh, any, you know, forum that's technical, uh, on Reddit, and you will see post after post of people saying, well, I, I got tired of not being able to do X, so I built something, or I wrote this. And they'll toss out their GitHub, URL that has their, their code base with, you know, an MIT or whatever license that they, they put on.

Or most, a lot of 'em do creative comments, believe it or not. Uh, but at any rate mm-hmm. Um, you know, it's, it's a very different world and I kind of like it.

Well, you know, and this, this is, we should do it an episode just on this. Um, you know, what, what hap you know, I, I talk about systems change when constraints change. Mm.

Okay. Now, the constraint of who can build code and what it takes to build and how much you can build, obviously is shifting. Um, you, you, yeah.

Especially when you lift a constraint out a system. So the, the, the question is, where are we headed with this? Because I think we're going from a world where developers are valued because there are the few who can create code, but when code can be created easily, and, and in large volumes of it, it's less about Right.

The writing of it, and it's more about the architecting and designing and instructing how it gets built. Right. Whether you, it's sort of like, um, Edison, Edison didn't do all that work himself.

Right? He had a team of engineers and whatever folks that are doing all this, and he claimed all the patents for it. It's kind of what we're headed to.

What a developer is, is, hey, yeah, I don't like that go library. And I've never liked it. Just, just do what you need created a create what I want.

Right. My version of it. Yep.

And so the amount of code we're gonna have is just gonna explode. And what we value, well, maybe I don't want a licensure. I'm just gonna clean room it and do my own thing.

And, uh, yeah. You, if you wanna start a lawsuit, go ahead. But, you know, lawyer lawyers will be happy.

But it, it does create a world where the code isn't what's valuable. It's the people who can construct, which opens it up. Oh, I very much agree.

More people. Yep. Yeah.

It's, it's, it's an interesting proposition. Yeah. When DHH makes something, you know, people are like, oh, I like that guy.

You know, if, if the fellow behind HashiCorp makes something, people like, yeah, I like that guy. It's the energy and, and the, the thoughts and the ideas that, that, you know, revolve around these open source projects that are the real, the real value to me, I think it's the renaissance of software. You know, it's the entrepreneurial renaissance of software where entrepreneurs are really thrive in this environment, right.

Because now the world, your oysters, they say of what you can do. And you know, I'm, I, they're not, that's a big company and that company's not gonna license to me 'cause I'm whoever, I'm puny whatever right now, but I will be great someday. And here's how I'm gonna be great by doing it this way.

So it, it, I think, you know, everybody talks about developers, well, they did talk about developers not having any jobs anymore, and suddenly we build, we're building tools for developers. Um, but I think, you know, my, my theme for 2026 is that developers become the engineers of agents creating software. That's what, that's the world we're, we're creating right now.

And we're, we can argue with if we moved into that, certainly not fully, but that's, that's where we're heading towards, and that shifts the dynamic and the value. So yeah, I guess we just had the podcast episode right there. We don't even need the podcast.

No, we do. We need the podcast and, and, you know, we need the creativity and, and, uh, I, I like that analogy. Mine is, um, the architect, you know, that builds a skyscraper.

It isn't just one guy on a table drawing everything out all the way down to every nut and bolt. Mm-hmm. It's a sort of idea to fruition, you know?

And, you know, to create something requires many different skills at many different levels, uh, of coordination and responsibility. And so, like you say, if, if you can have that funnel, you know, shortened where it's, it's like almost zero, but still have, you know, the idea person at the head of it, starting with the, I've got an idea. I want something that does structured data in Python.

You know, what if pandas had been made in a weekend, It could be, it can now, Right. Probably already has actually it Can, you can feed, feed it to and do an LM say, create the requirements for this for me to create my own version now tweak it how I want it, and I'll go build it. Right.

Essentially reverse engineer that way. Um, well, well, one things I'd love to, um, I think you have, uh, the mantra of the, is it the data shepherd the shepherd? Yes.

The, The analogy you're using the engineer to Data Shepherd. Yes. The AI Shepherd, I should say.

Yeah. Shepherd. Say, say a little bit about that and then maybe we'll do an episode on both of those themes.

Yeah. Yeah. So, you know, we, we've seen over the last year that, uh, you know, through Gen AI in particular, that, um, the tasks that the data professionals do on a typical basis, you know, on a daily basis, you know, modeling data, importing data, managing, you know, knowles, uh, imputing, you know, missing bits and all of those ta tedious tasks mm-hmm.

Are increasingly getting automated, uh mm-hmm. And not just to automate them in terms of, you know, here's something we already know how to do now, just do it. That that's, that's just automation programmatically, like you could just write a script to do it.

What I'm talking about is understanding the data professional's intent and then executing on it, you know, a la Mm-hmm. You know, to to, to just say, I, I want to incorporate a new data source and to create a data product on the back of that that serves this customer with this use case and need, help me spec out how to do that and construct it. And so it is the exact same trajectory that you're talking about with software engineering in general that we've just been talking about.

Mm-hmm. It's just playing out in a, in a specific domain where mm-hmm. Your data engineers are no longer doing con you know, do doing syntax instead they're doing intent, you know, trying to, whoever can, uh, articulate and describe, and then shepherd, uh, that description from idea to fruition is going to succeed, uh, in 2026 and going forward, you know, and, and so it's, that's a cool model Metaphor.

Cool. Model Y. Yeah.

I, I I, I, I feel like it's, you know, I want to have sheep dogs that, that keep the sheep, you know, going where I want them. And I, I don't want to run after every sheep. I don't wanna, all my sub agents need to have somebody watching over them, in other words.

Yeah. The, the, the colleague Border Collie that's rounding all those up doesn't know. Well, it might know what you want.

'cause it's learned what from what you instructed to do, and you have to tell every, not even Know why you're Analogy of, We're gonna the shear, we're gonna go get sheared. He doesn't know that. Yep, exactly.

Well, we've, we've had, uh, a pretty wide ranging, but a thematical Right. Podcast episode here about, you know, you can do anything, put your mind to it now with these tools, and that's just gonna increase the level of complexity and breadth of what you can do. And the Is like the attack surface.

It's like the attack surface. Yes. Yeah.

Be careful what you do to yourself along the way. You might engineer a bunch of wolves to go after your sheep at the same time, Hired wolves accidentally open Season to the analogy. Um, well, like we, we've got a long time, so maybe just a quick version of the, of our next last segment, which is the drop.

Okay. It's time for the drop. All right.

So, so real quick for mine for the drop is I just dropped the new, um, addition of the dataset for software lifecycle engineering, which encompasses observability, encompass software testing, software engineering, um, DevOps, platform engineering is a wide, wide ranging, um, dataset with some really good information. And the number of people that are investing in using AI tools and development has exploded. It's over 90%, uh, between, I'm already using it, I'm using it for everything too.

I'm evaluating it. And, uh, that's how rapidly we're changing. So that's just one of the data points that's, that's in it through the future of intelligence platform.

So how about you, Brad? Yeah. Similarly, uh, just dropped.

Um, it's like, you know, the, uh, mass drop, if you remember it, like they're still going actually to, to have projects that are self-funded. I'm just, I'm getting ready to drop the one H 2026 data intelligence, analytics and infrastructure market sizing and five year forecast. Boy, here's a mouthful.

So we just, just finished it and, um, it's, it's, you know, uh, something you can get a lot of that data on our public site, but of course we, we keep the majority of it for our, for our paying customers because we do, uh, need to make money to, to keep doing what we do. And, uh, just, you know, give, give you guys one, one stat for the overall market, which I look at pretty much, you know, the entire spectrum of data acquisition to, uh, AI outcome. 9%, which is up from where we were last time we did this.

2 trillion by 2031 for this, this little market that I, I like to call home. Wow. Yeah.

CAGR is over 10. You're talking, that gets people's attention, right? I got 16 and above.

Wow, that's phenomenal. So it's, it's amazing. You can claim a, there's an AI bubble, but I think the AI bubble is creating new bubbles, new not, and not the bubbles that pop, but new growth areas.

But even if they do pop, they, they create value. I mean, you know, we, we would be kidding ourselves if we didn't say that, you know? Um, the, the idea of capitalism is, is predicated upon disruption.

Um, and Oh, absolutely. You know, a bubble is nothing, if not disruptive. Yeah.

My, my, my entrepreneurial job is to pop your bubble while I create mine when somebody their mind For sure. Without getting any Stuff. com slash mitch Ashley, Brad, uh, Shiman.

Think it's all Brad Shiman. I'm a, I'm a one Worder. Oh, is it?

Yeah. Oh, wow. You, you've progressed.

I haven't evolved that far, so I'm still a dash. You're hyphenated. Yes.

And there's all kinds of great stuff we're producing, you know, some really, I think awesome content across the board with all the analysts and your stuff's phenomenal. So thank you Brad, for the work that you do. And I back at, yeah, thank you.

I'm not hyphenated actually, I'm sorry I am hyphenated. I just really, You are hyphenated. Well, okay.

We both have some work to evolve to the next stage Of evolution. That's, I guess, future. Alright, everybody, thanks for tuning in.

Thanks for liking, thanks for following. Please share the podcast with your friends. You have ideas for topics.

I, uh, uh, guests that we might have. We're kind of getting some, getting some things laid down here about what the podcast is about. And then we'll be adding some guests, uh, to our, and it can be practitioners, can be, uh, thought leaders in the industry, vendors, et cetera.

We'll try to bring a mix of really interesting, uh, conversations and topics. So thanks for tuning in on half of myself, Brad, and our illustrious podcast engineer. Corey, thanks for joining us today.

We'll see you on the next episode. Control. This is agent Dev.

I'm in position that stand by for Go standing by Funding for AI has been in all of the news on the tech field Day rundown, billions of dollars, none of which are coming to me or any of my panelists on this edition of the Tech Field Day podcast. Instead, we are gonna dive deeply into what you can really do with ai. Join me for the Tech Field Day podcast Right now.

Welcome to the Tech Field Day podcast, where every week we gather together a panel of experts drawn from the Tech Field Day delegate family, and we discuss a single topic around some key issue in the IT industry. Uh, this particular episode is recorded on site, possibly even on premises at the AI infrastructure field Day four event here in Santa Clara. You'll find this and many of the Tech Field Day podcasts, also across on the Techstrong media sites and on the Techstrong app, possibly on your smart tv.

In this episode, we're gonna take a look at the thought that all of the big headlines that we are seeing around AI and the funding of AI are really a distraction from the reality that AI is being built and needs hard work to build. Before we dive into the discussion, let's meet who's on the panel today. Well, thanks, glad to be here.

My name is Frederick Van Herrin. I'm the CTO of Hyphens, is a company providing consulting and services for AI market. Uh, I'm Chris Grundman.

Uh, I'm the executive advisor at Kaja Consulting and we look at transforming teams and technology to make networks more efficient. And I'm Brian Martin Signal 65 VP AI data Center performance. And of course, I'm Alistair Cook.

I'm the event lead here at Tick Field Day for AI Infrastructure Field Day four. And I'm also one of the hosts of the Tech Field Day rundown. And, uh, it's probably because of me that it's been full of news of Core Weave gets this much funding and Nvidia invests so many millions or billions, and there's contracts for five years of power supply for building bigger data centers that have ever existed in the world before.

But we're increasingly learning that that's not necessarily connected. Maybe that noise is getting in the way of real business value from ai, particularly from generative ai, rather than the predictive AI that we've been using for years and years. And that's something I've been looking for.

I've been saying as, as we've been through these AI infrastructure field events, I've been really looking for where's the business value we, when we can get to the point where AI does something for us. And that's really inference, isn't it, Frederick? Yeah, definitely.

I think, uh, a lot of the news you're talking about is, is really the, the people that developing the large language models, and they do that to provide functionality to the people doing inference. The problem is, is with the shortage of infrastructure, it's very difficult for people now to apply this, the inference, right? There's a lot of innovation around agenda and so on, but still, you still have to implement any applications and buy infrastructure for this.

Yeah. And the splashy, like you said, the splashy headlines, I think right, are, are definitely not helping. I, I see like conversations around AI seem to be very, very polarizing.

I think this is part of it, right? Where you've got people who are, you know, really jumping on the bandwagon of, of AI's gonna change the world and changes everything, and everything's different now. Um, and then other people who kind of ppo a lot of this is being, you know, smoke and mirrors.

Um, and, and I think these, this news cycle around the investments that are happening, a lot of times circular investments, right? These companies are investing in each other. So like what cash is actually moving, um, is part of that distraction and that noise where we can actually talk about, you know, what is actually different about AI workloads, uh, to Gina's point, um, in the recent, uh, AI infrastructure field, day four, she brought that up a couple times, and I think that's really interesting to dive into the nuts and bolts of like, what is actually changing here.

Mm-hmm. Versus, you know, who's spending what money. Mm-hmm.

Yep. And, and looking at inferencing as we head out to the edge, head out to inferencing, I think there's a big trade off happening around what's good enough. I've been doing a lot of work recently with AI assisted coding, and that has come leaps and bounds from just six months ago, 12 months ago.

Uh, I am a favorite. I love the best models out there, but they're expensive. I don't always need them.

So how do I find out and how do users find out what's the right model, what's the right inferencing for the job? And I think there's a lot of tuning to be done there. We're still very early on in the enlightenment phase, and I think as always, the future is here, but it's not evenly distributed.

There are people who are getting a lot of value out of some mature use of ai. And I do think we are also guilty of just saying AI as if it's a single thing. And that really is another element of, of getting in the way of understanding what's really going on when somebody talks about this product is ai, AI ready and AI enabled, uh, doesn't mean that it's not doing anything different than it used to do, and AI just consumes it the way in whatever the last application is.

Um, you know, one of our standard raises is to say, if, if I could take the buzzword out and substitute the previous buzzword, and all of the presentations still made sense, it's not about the buzzword. Right. And, and I, and I think you're, you're, you're right on with that, right?

So ai, when you, when you look back at the 1950s and the 1960s, the definition of AI was, was more significant around mimicking what humans are doing in some kind of a, a automated fashion. Um, in AI nowadays, I feel like certainly since the early two thousands, it's more a focus on a data driven aspect where a anybody who uses data to do something would consider it ai. I think, uh, what Brian was saying earlier about the, um, the large language model being built, the, the, the, the challenge is, is that those models are so generic that there's a lot of work that needs to happen to make those useful on an inference side.

And I think from a a from a news perspective, you know, we, we always hear, uh, companies saying, we need more GPUs and we need more power and more data center space. I think what the market really wants or needs are smaller, uh, language models. And so today we have a tendency to start with a large language model and then kind of slice it to a point where it's usable, and then we, we add some fine tuning and some rack to make it very useful for us.

I think where it could be very useful, uh, from an inference perspective is more focused on smaller, large language models, which would then mean, we don't need that many GPUs. You would fit more language models on the same GPUs and so on. So I think there's a whole whole, uh, evolutional cycle there, And we're seeing that, right?

I mean, I've already started to see some benchmark tests that have come out that are showing, you know, purpose built small models outperforming the large models. Now obviously, they are more purpose built, right? Like you said, if you're training on a smaller set of data to get more specific context around a more specific problem.

But I think that is something that's very interesting. And I, and I think that goes back to kind of, you know, yes, AI is definitely happening, things are happening. To your point, I think language is very important around this.

Um, and again, where most people, when they say AI today, it feels like they, they mean LLM, um, specifically with chat interface, natural language processing, these kind of things. Um, which, which to your point isn't just ai, right? AI is something that's been going on since at least the forties and fifties, uh, and it's been used in a lot of, a lot of technology.

So, so getting specific about this, and again, back to what I said earlier, right? What's actually different here I think is, is super helpful to look at, you know, more than just slapping that AI label on it. What's, what's, what's actually different about this use case and, and, and how are we treating that right?

To your point where with smaller models, this may end up looking a lot different than we think it will, um, in, in the long run. And, and mixing those components, like I, I've, I've picked, I've picked up a, a way of talking about AI recently where I'll call AI level zero. Like you don't need it.

Rules-based works fine. Uh, AI level one machine learning, we've had that for years. Classification, um, very important component of a system.

AI level two is the generative part. That's when we get to the LLM, and that adds the creative aspect onto the other two layers. Uh, and I think assembling systems with all three of those together gives us some very powerful solutions.

And we see that in some of the agents that are being built. There's a part of the agent that's very rules-based. There's a part of the agent that's depending on classifiers to understand what it's seeing, and then there's a part of the agent that's asking creative questions and looking for answers.

Uh, I think having an understanding of that lets us bring the different models Frederick was talking about together, um, and letting them work together Yeah. For a solution. I think that's great.

I think then, you know, the next step below that, right? Again, we're sitting here at, at AI infrastructure field days. I'm kind of thinking through that lens still, and, and again, looking at what's needed to support that, right?

And, and, and I think a lot of this just looks like web traffic, um, at the edge, right? And so, like, if, like, like, is there, you know, again, like I guess I'm repeating myself here, was like, what's actually different, right? What do we actually need to address to make AI work?

And, and if we can solve that or at least talk about that, then we can get a lot further, a lot faster, I think. Yeah. And I think, you know, that that's really what's, what needs to happen are standards, right?

I think agents is, is one step in the right direction. 'cause it gives you the ability to kind of include multiple large language models, but then you need some kind of an open standard to move data around because that's, that's one of the biggest problems. That's where MCP comes into play.

So I think if you, if you look at inference and the ability to build things, it's smaller models that are more on target. You know, don't drag all the noise with you every time, uh, you deploy something. Um, and, and that, that all combines with, with agents and agent ai, uh, will make, as far as I'm concerned, not only inference a lot easier to deploy, but also for more people to be able to deploy certain, certain things, right?

We always say, you know, my grandmother or my mother is kind of the reference kind of a thing. Mm-hmm. It's the same thing.

And they understand the concept of agents. They don't necessarily understand what AI means. Right.

We saw some of that earlier this week. Looking at, uh, context, um, context engineering is so important to filter out the noise and bring in what you really want the LLM to work on. Right?

One of the other aspects I wanna cycle back to, um, Chris's comment that it just looks like web traffic. Um, and link back to a presentation, the previous cloud field day where access to a chat bot was used as an attack vector and the chatbot, because it was a full large language model that had all the capabilities of a large language model, was gleefully helping an attacker find credentials and find resources to further be attacked. And recognizing that, that the potential scope and the speed at which we're deploying these things without necessarily having the security safeguards controls that we should, is leading us to some fairly significant, um, security issues that we wouldn't have necessarily had with older web technologies.

You know, ProSite scripting attacks are relatively easy to mitigate, um, but they weren't initially they were, when it was a new thing, it was a new thing. And LLMs as an attack vector are all far more, uh, powerful tool for attacking your environment. I was thinking about this Alistair during the, uh, round table earlier today.

You know, we've seen AI driving, you know, extreme innovation, the G-P-U-T-P-U market, we've seen it driving innovation and networking, intelligent networking. We've seen it driving innovation in storage. What I'm waiting to see is that next drive of innovation and security, what I like to consider the, the fourth pillar of it, Right?

We we're waiting for the, the PayPal of this generation, right? I think, I think because right? 'cause remember back, like, it was very scary to input your credit card number on online, right?

I mean, online purchases were, were really freaky at first. Mm-hmm. Um, and we've, we've collectively gotten over that in the last whatever it is, 20, 30 years now, 35 years maybe, something like that.

Um, and part of that was like, like PayPal was one of, of the payment processors that kind of came in and made that. So like, oh wait, like I'm working with this known entity. I'm putting my my credit card information here and I, I can trust it.

Um, I don't know if that's the kind of thing we need for, for data for AI to understand that. Like, I'm actually, you know, and again, I don't know what this looks like, I'm not gonna invent it on the spot, but, but containerizing this information in a, in a package where I can actually use AI without being afraid that it's going to rob me, right? What Does trustability look like?

Yeah. How do I build a, a chain of, um, evidence equivalent in an AI conversation, right? And it's, it's, it's difficult because you don't know what they put in a large language model as far as data and its capabilities, right?

Mm-hmm. It's like asking, you know, what kind of meat is in the hotdog, right? Typically they will say, you know, you don't want to know.

Right. It came from Animals. That's right.

And so with large language models and security, it's almost like you need kind of a post post element where, where all the security is dealt with, no matter which large language model you are plugging into it And give some certainty own governance that the information you are providing to the large language model is going to be used in the way and for the purposes that you've permitted it to be used for. Yeah. And, and the other question here though is, is like again, right.

How does this actually end up looking? I I think we, you know, one, one of the pieces of news that's distracting us right? Is these big investments and things as we started the conversation with, I think another one is this churn of kind of new AI tools and techniques and technologies.

Some of them are actually really useful, right? Like MCP seems to be a step forward potentially, right? And, and just ag agentic in general.

Um, but, but I do think we're, we're, we tend to, those of us who are paying attention tend to be watching this kind of roiling, you know, wave crashing here and, and need to zoom out a little bit and look at the fact that, you know, the current models are probably not even the models we're going to end up using, right? We're working on determinism. Like if you go to like some of like research coming out of MIT and other places, they're looking at like actually deterministic models.

They're looking at, you know, um, uh, visibility into the model's decisions and things like that. So I think, you know, it's, it's really interesting. We are kind of shooting for a moving target where, you know, the security we're talking about, right?

Securing the current model methodology may be completely useless because we're gonna be using a completely different methodology with different models in the future. Yeah. And, and we need more regulation to, um, just as an example, I mean, I, I feel like a, as a data scientist, I feel that some of the large language models are kind of escaped out of the lab.

Hmm. Um, meaning with that, that there is much more pressure from competition between the large language model providers to deliver something. And you could see it, like one example, um, was when, uh, the first large language models had these coding capabilities, uh, so people were using that to generate, um, all kind of tools for, uh, public clouds.

And so their security keys were in there. Mm-hmm. Which is not necessarily a problem, but it was then uploaded because you had to, it was uploaded as a prompt, but then was part of the engine.

And then other people who had nothing to do with the original user had access to that key because suddenly it became part of the source Code. Yeah. And then some prompt engineering to go harvest.

Yeah. Uh, all of those pieces of credentials comes out. And, and then we learned to put some guard rails around that and attackers can move on.

I, I wanted to circle back to one of the things that, that Brian said and, and some consequences. He said that the, uh, AI coding tools had grown so much mature over the last six months. And it leaves us with a, a challenge of having old fashioned thinking, being old gray people, even though we're only thinking about what was new six months ago.

And that, that the hangovers of the ways we thought and approach things as recently as six months ago is no longer valid. Hangovers a great word for that. Alistair, uh, I'm advising a startup and the CEO has a marketing background, has been doing vibe coding, uh, for demos and prototypes, uh, and I'm helping, um, consult on some of the backend coding on that.

And what we've done is it's this interesting toggle back and forth as he'll discover something new that suddenly works 'cause he doesn't know it doesn't work. Um, and then I'll still be in my old way of thinking because I know too much, and then I'll try something and it'll be a breakthrough. So we have this ping pong effect of sort of laddering up the layer of capability, uh, and keeping each other at the cutting edge as a result of that.

Mm-hmm. Uh, it's, it's been very helpful, uh, for staying on top of the change. 'cause the rate of change is almost impossible for mere mortals to keep up with.

So do we need an AI to inform us of how AI has changed? Right. I mean, there are different use cases, right?

Yeah. There're different use cases. I, for a programmer, you know, did a lot of programming in the, in the past.

I use kind of those tools as a, as a, as a assistant if you want. Mm-hmm. I don't necessarily let it create all the code I need, it's just if I need an area where I need to look up something or need some guidance.

But I agree. I mean, they, the last six months, seven months, it's, it's incredible to a point of scary, right? Because you just push out an ID with zero coding understanding and it will generate something that most likely will work.

Mm-hmm. And then will generate the test cases to validate that it will do the things that it thinks you asked it to do. Yeah.

It reminds me of this, uh, there's a short story by Isaac Asimov, um, and he, there's this, you know, far future civilization. They've been in this war with another civilization, and basically it's this like space drone battle, right? Where there's these like, you know, unmanned ships that are basically fighting each other and it's, they're in a log jam because, you know, our AI and your AI is fighting and it's just, we're just destroying bots, and it just keeps going.

And, and this person comes into like the chairman's office and he's, he's got this great discovery and he works out that like, we can actually like, get in these machines and fly them ourselves. And he's, he's basically like rediscovered math. Um, and so he actually, he's like, oh, look, we can, like, we can actually plot the trajectory ourselves of these planes and Right.

And then, and then all of a sudden they win the war because they've now taken over. And, and I think this is something that's, is an interesting layer of abstraction, right? Where if you can like, remove semantics from configuring devices, from writing code, from all these things, then in one way, this is really, really liberating that anyone can now create the app that they want.

Um, which could be great. But I also see this future of us drifting away from like, anyone knowing how to create the app. And so, like, I, I don't know what that means exactly, but like, there's a balance there that needs to be struck, I think.

How does, right. How does that translate? Where, where does our creative spark go?

Do we become experts at, you know, management? Does everyone become a manager now? Um, because we have a pile of workers to help us do something?

Do we become the creative force? Another interesting aspect of that that I've seen start to evolve is the trade off between learning how to use a tool to do a job. Mm-hmm.

Versus just writing a tool to do a job. Mm. Uh, I've seen the CEI was talking about earlier, you know, stopped learning how to use tools and just wrote his own tools.

Like, I needed to do this specific thing. I can write that it's done, I use it, and then when I'm done, I let it go. It's A really, really interesting point, right?

And especially if you look at, you know, other areas of like business software, right? Things like, uh, CRMs or, or ERPs and these kind of things where literally organizations have force fit their entire organization, their process, their structure in to work with SAP or Salesforce or you know, name your name your dragon there, the tool defines The business process. Exactly.

Business process. Exactly. Change your business model.

And so what if you could actually build a CRM that works the way that you work? And now maybe we can, so that, that's a very interesting idea, right? Of like, just like everyone using custom software that actually works the way that their business works, which potentially leads to a huge bloom in like, just business model innovation and business process innovation.

That is A fascinating and terrifying leap forward. I, I was thinking of bespoke tools that you throw away when you're done. You're talking about running your entire business.

I like it. Yeah. Right?

This is how fast things are moving, right? This, you're probably Not off. It's on the horizon.

Six, seven months. Yeah. So we've strayed away from, from my original headline around, uh, having big news on investments being very different from the reality of building things.

And I think we've gotten to mostly looking at the reality of building things because mm-hmm. We are hands-on practical people who want to solve solutions rather than just build the biggest interface with the most money. And, uh, whoever, uh, shuffles off the mortal coil with the biggest data center wins, um, at the moment, Brian's winning that one for, for the four of us.

True. Um, we could, of course, we like to keep talking about these things forever, uh, but we do need to call an end to this podcast and, uh, let Corey wave his magic on it. But before we do, where can people carry on the conversations with you all?

com. Yeah, Chrisman, find me on LinkedIn is probably the best way to get in touch with me. Or also Kaja Consulting.

We'll take you to that page as well. Uh, Brian Martin, you can find me at Signal 65 or on LinkedIn. And I'm Alistair Cook.

You can find me on LinkedIn. Also, you can find myself and all of my delegates from AI Infrastructure Field, day four on the Tech Field Day, uh, website you can find right now. It's the current event.

Uh, thank you so much for joining us for this episode of the Tech Field Day podcast. If you enjoyed this conversation, find us in your favorite podcast application and subscribe to the Tech Field Day, uh, podcast. Or find us on YouTube or wherever else across the tech strong gang, uh, tech strong community that we are published.

We'll be bringing you another episode looking at some other aspect of the depths of the IT industry from our delegate panels in the coming week. In fact, next week, uh, thank you for coming, uh, to join us. Follow us all on, uh, YouTube, follow us on your social media is, uh, tick Field day.

com/podcast to find all of our podcast. Cory has been making an awesome collection of podcasts. Thank you so much for joining me and for myself and my, for my panel.

I hope you're having a great week. Good morning, everyone. Welcome back here to Techstrong tv.

My next guest is Roy Catmore. He's joining us from New York where it's pretty cold today, almost as cold, well colder than it is here. But, um, Roy is the co-founder and CEO of Orchid security, and let's welcome him.

Hey Roy, how are you man? I'm good. I'm good as the, as cold as you can think.

Yeah. Yeah. Well, you know, I, it's days like that that I remember why I moved from New York to Florida, but it's, it ain't warm, it's not warmed down here either, so I get it.

Roy, I mentioned you're the co-founder and CEO for orchid security, but, you know, give us, give us kind of your own personal arc. You know, how'd you come here today? So, um, Ellen, it's, uh, it's not my first rodeo.

So I've been, uh, doing startups for a few years. I had a startup before, which we sold to Fortinet in, uh, 2019. I ran the business unit there for endpoints, um, security for more than three years.

So I'm, I'm definitely not a stranger to, uh, ramping up, uh, uh, startups and also not starting products from scratch. But actually my story is a little different than, uh, just, uh, yet another one with a good idea. That figure that the market actually meets is great advice, but it's actually came from the market.

Um, in my journey, actually, I would say, uh, more than 10 years ago, I was part of a founding of, um, of a VC that had a different state of mind named Team eight. And Team eight had a different point of view of market, basically to say, don't come to us with solutions. Let us offer you problems, and let's see you figuring out the right pain and the right solution for the right pain.

And the way they do this is through a village. And then that means, you know, if I'm working security, and that's, yes, uh, by all means I did security my entire life. But, um, that means that you're actually going to, um, a villagers of professionals in security space, that that's the area that I'm, I was, uh, interested in.

And asking them what bother them the most, where's the biggest budget, you know, how much timing the date actually takes them to do something. Where is the biggest surprise on budget? Where's the mis misalignment between where they thought they're gonna be and where they are?

You know, where did they miss? And, and there's a lot of questionnaires and they're very structured as opposed to what I just did. But the sense is very well known, right?

The idea is where does it hurt? Feels like a doctor, right? You're punching, you're pinching, you're punching, you're pinching.

And then when you hear the ouch, like, okay, we got a pain, now let's see if that pain is repetitive. And we actually ran through a process of 250 round, kind of give or take, uh, global 2000 CISOs. And when we had a solution, we actually pitched it back to the same 250 to say, would you buy?

'cause it's not just finding a pain, which is, I would say 80% of the problem. 'cause then you can understand the ecosystem, competitors, partners, um, um, you know, foes versus allies. Everything is sitting right there how to go to market.

It's also about, is the market ready to accept, like the solution? So yeah, we hit the problem, but can you take that pill and does your body actually gonna react to it the way we thought? And so that's how I came to it.

Validation, pain, figuring solution, uh, alignment, and of course people that actually customer that actually converted fairly quickly. And you got yourself orchid in, uh, in funding. Excellent, excellent.

Um, so as a result of the, the prodding, the pinching, what was the problem that orchid security is, is designed to, you know, what, what, what is the itch that orchid scratches? I like the itch. And I'll give you the itch from a, um, from a somebody who did a few things before.

Very simple. If you get a solution in endpoint solution, right? I want to protect your iPad and I'm gonna put something on your iPad and it's gonna protect your iPad.

Simple. Same thing with the network. You're gonna say, Hey Roy, I got a problem at home with my network.

I'll put something on your network. It's gonna protect your endpoint. I'm not asking you to do anything other than putting me there, right?

And I'll do the work identities is, doesn't, fundamentally doesn't work like that. When you deploy identity, let's talk, what is identity? Identity is authenticating, which we run into every day because we're getting these a little annoying messages into our phones and into our computers.

And that's the authentication. And there is an authorization, right? What do I, what type of privilege and entitlement do I need to provide to Alan to do his work, right?

That's what I need anywhere within those solutions. And, and there's a lot of point solutions out there. And they're great solutions require you.

Now, when you finish the deployment of those, you're gonna ask, okay, so where, where's my message? Right? And they're gonna say, well, wait a second, Alan, you need to onboard that app that you wish to have that message to our solution.

We just broke the model. We asked the user to do something for us in order to extract the added value. I'm sorry.

And that is where the biggest problem in identity is. That creates fragmentation because who are you gonna onboard? The one that you are aware of, not necessarily the ones that are most disposable.

You're gonna do the ones that are easiest because you wanna show progress, right? We all wanna show progress. Hey, look how many we did, but are these the right ones?

Are these the, um, high priority from security, from, from usability, from accessibility, from everything that we need? And that created the, what we had coined as the identity dark matter. You did not onboard Alan to a certain app.

And so you allow him to do things local, but now you're not governing him. So when Alan decide, okay, I'm done here. I I'm, I'm leaving the company, I'm removing you from the yellow pages.

Oh, but I forgot to remove you. And then I got myself a user that is actually can use it now, or an orphan account or a dormant account. There's a lot of terminologies in this space, but the risk lays down in the fragmentation between those connectivity.

And if you're trying to quantify that, 46% of an enterprise identities are ungoverned. So now think about it. Let's digest it.

When we are making an informed decision in identity, we are making it on half of our data. How good are you feel about that? Now that is the problem that, that is the itch that we are as orchid coming to itch for you.

Yep. So Ro I, I don't know, I've been in security a little bit myself. Um, you know, and I've done a few venture backed startups I've co-founded.

I really believe that. Well, first of all, I always believed that IAM identity and access management was the killer cloud security app, right? When we moved a lot to the cloud, identity became the, the focal point.

'cause you no longer had the Moton castle, you no longer had the big firewall at the edge there. You no longer had your access. You know, data was everywhere.

Apps were everywhere. And the only way you had any control was the identity. But then we never, and we never quite solved that, to tell you the truth, right?

We never got that straight, frankly. But then machine identity, let's call them non-human identities, came into the picture, right? Whether we're talking IOT devices or, or containers, internal infrastructure that had to have its, you know, their own identities.

Now of course you got APIs. APIs and, and you've got AI agents. And, and when you look at these non-human identities to human identity, human identities, probably 20% of the identities that we need to manage if it's that much 20% of the identities we need to manage out there.

Is this something, when you talk about how Orchid is giving you a complete picture, is that non-human identity as well? A good question. One, I didn't refer to what we do, I just refer to the itch.

I didn't refer to how we scratch it, but you are absolutely right. You, you know, if I refer to the dark matter, which is again, the fundamental deployment of, of identity creates a dark matter, a fragmentation, because we're not gonna do it all. The world is not perfect.

And it's so much not perfect that the traditional, like human identities are 46% dark, meaning I can't see them. Now you can ask me, Roy, that's great, but you know, what's new here? And, and, and my point of view would be you are right.

It didn't change a lot since the cloud exploded and, and our amount of applications have grown, but it's growing now faster. The 46, like the dark matter is now poised to take over. And if you would've asked why, because everything became a hybrid, you may think that your app is to completely govern, but guess what?

It has a machine to machine that does some things on the backend to do disaster recovery. And that does not govern. You cannot, you cannot challenge the machine.

And of course if you add to that service accounts, because we need third parties because maybe API and on top of everything, the, the beast in the room that nobody knows how it's gonna grow up, right? We just see a little, a little puppy right now. But that puppy could become a huge tiger and take over.

But the agent ai, why is that even bigger? Because think about it, the problem is now it's exponentially bigger because the, what you're seeing is one, an agent working on behalf of who, who is typing that check. Do you know what we are used to?

Kind of the check GPT, who is the operator? What is the operator authorization of doing it? So it's great.

And what is the context on what intent is this working? Things that we knew on human, we don't know on machines. Yeah.

And that is, this Weekend was a great exam. I'm sure you are following what went on this weekend. Uh, a great example of it, a great exam.

And then to find out these things there, security, like everything else that gets built, they thought about security will get to it later. And, and now, I mean, this is a disaster waiting to happen. You're, and you said it, right?

You know, even when, you know, I always, when I walk on this, I walk exactly in the same steps that you did. I said, listen, we started very easy. Everything was on premise.

We were in the late nineties, beginning of two thousands. Even then the dark matter was three out of 10, 30%. So even in a easiest world, we were not perfect.

Then we had the Clear, we never figured it out. Yeah. Then we had a cloud explosion, 46 invisible.

Now with agent ai, the idea is that we're gonna go over 60%. And the why now from a business perspective is because we are really looking at the, you know, at the EB, like the tiniest portion of it. And we are making decisions on a portion of a very little portion of the data.

And that is not the way you can make decisions. And you know what the best thing is? You got good point solutions out there, great vendors that can do the work, but you're not onboarding.

'cause you don't know who, when and why. And that is becoming very dynamic. And yes, this is when orchid is actually coming into play.

So, all right, let's, let's shift to how does Orchid actually do it? So kind of think about an orchid as the solutions of solution. I know that everybody likes to think about orchid, the nice flower, but also think about it as an orchestration of identity.

Basically what we're saying is, as opposed to the network and endpoint where I started to say, well, there is a very known progress of deployment because the infrastructure is there, we need to have infrastructure for identity, which means I need to know what do I have, what assets are actually I have within my environment when it comes to applications. I need to understand how much exposed are they, how much risk are they providing me? So I'm not gonna just gonna do the one that I know or the one that are the easiest for me to do, but I'm gonna do the right pieces, the right applications.

And of course, if I can do all of this and I can do it from a compliance, I can do it from a security, I can do it from an IGI can do it from a context. You can already imagine why LLMs are becoming a critical pass of this. And I can do it by intent.

So what Alan is trying to do to a finance system that has orphan accounts, and it is not by compliance by a certain level. And so I can highly prioritize it from the business point of view, from disposability point of view, from compliance point of view, and connect it with no code, no engineers straight to the tools that you already made, your investment in an identity. And that is exactly what we're bringing into the, basically it's an infrastructure for the modern identities world, including agents, ai, but also the legacies that we didn't do 20 years ago.

And so we need to do all to the adherence of security and compliance. Love it. You guys recently did a little research.

I understand that you were, I don't know if that was what you were referencing with the 46% to 60%, but give us a little more on that if you don't mind, Ray. So one of the things that we are announcing here today is an audit. And all customers were asking us, wow, you know, so the dark matter is really big and, but we do, how do you give us the evidence that these things that you're claiming and we're not debating, but show me the money.

You know, show me that, because for me it's a, it's a blind spot. And so what we started to do is to generate audit of users, activity of identities for applications that are ungoverned for the application that are governed or hybrid. So just like you said, the non-human is not gonna be audited, but the human is audited.

So we mesh those together. We generate for the ones who are ungoverned for the identity, we mesh the data that is already managed together to give you business insights. And the research was once, now that we got that to add the dimension of context, intent into our prioritization, we started to look at the data.

You know, we are a little upside down company, usually companies starts with the small vendors. And growing up, we actually started with the Fortune 20 vendors and started to go to the Fortune five hundreds going to the global. So we started at the top with vendors like, uh, like Costco, like Hub, uh, hub International, uh, um, uh, repsol, a lot of fortunes, very high.

Uh, and, and, and, and we started to look at, okay, then let's see what this new audit, let's see how bad the hygienists or how good the HY is. And what we find is that 85% of the applications estate, think about it, these are Ellen business applications have accounts from legacy or external domains. That means you have leftovers inside that are encapsulated within those 20% of those of these are consumer email domains.

So you have a lot of mix of identities within them. 70% of the applications have excessive, six have excessive privileges in them. 60% have admin or, or already you mentioned API access to external third parties to them.

And 40% of all of them were found as orphan. 40% thinking, I'm, if I'm a hacker, I'm not gonna go to the one that you're using the most. As opposed to your strategy to say, well I'm using this app the most, let's start with this app and onboard it.

Right? No, they know you're governing this, you know, they're monitoring it. They're gonna go to the ones that have 40% of all the accounts have orphan accounts.

I'm gonna go to the orphan account because it's not supposed to be there. You are not tracking those. Definitely not governed.

That's my base. I'm working from there. I'm not going to the known very high profile applications of yours.

Got it. Very, very, very cool. Roy, I don't know, did we, I didn't remember us mentioning the website.

I Don't think we did. You're gonna find a lot of our use cases, um, you know, um, uh, technology wise, patents, management team, of course. Um, and it's all in the orchid doc security portal.

So that's There, that's what I was looking for. Orchard security. Very cool.

Um, Roy, we're about outta time, but listen, I wanna wish you well here with Orchid. This, this is, look, this is a problem, as I said earlier, right? It's been a problem that we never like a festering sore that we never really solved.

But then what happens is stuff gets layered and layered on top and top problem gets bigger, right? And you, you know, dark matter or like real dark matter, we can't see it, but the force it exerts on, on us, on the universe is, is substantial. So I love it.

Come back to us, keep us posted on Orchid and best of luck. Stay warm up there. Same here.

Let's illuminate the dark matter with some light, right? We need some light in this worldl, bring the Light. Thank you.

That's a good way of doing it. Roy Catmore, co-founder, CEO Orchid Security. That's orchid security.

Check it out. We're gonna take a break. We'll be right back.

Hey everyone, it's Alan Shim, tech Strong again. Hey, for this next session, you know, we call it agents plus Apps plus chat. It's features Richard Riley, who's GM of the Power platform marketing at Microsoft, as well as our own FU analyst, Dion Hench, cliff, Dion, and Richard will build upon the foundational vision of agents plus apps, plus chat, creating a solution orientated view into the heart of agentic business transformation.

In this session, you're gonna learn how agents are enhancing workflows and redefining the architecture of business value. Today you're gonna hear about practical applications for agents in terms of UI logic layer and decision engines across business functions. Everything from finance to HR to sales, and even customer service.

I think you're gonna enjoy it. Here's Richard and Diane. Thanks, Alan.

This is Diane Hinchcliffe, VP and, uh, practice Lead for our Chief Information Officer, uh, research group. And I'm with the Futurum Group. I'm here with Richard.

Richard, can you give us the introduction? My name's Richard Riley. I, uh, lead the low-code GoTo market team at Microsoft.

So that covers all of our low code portfolio power apps, power Automate, and includes copilot studio, our, uh, agent building platform. Well, it's great to have you, Richard. And what's interesting, uh, to the CIOs I talk with, they're undergoing all these changes with, uh, with ai and now AgTech has arrived and they, they're under this realization that they are going to have to transform their organizations using AI agents.

And so I was wondering, you know, to help them understand this, how do you define agentic business transformation that, uh, goes, uh, beyond just mere automation and is really ex explains kind of why it's a new paradigm? Well, I mean, I think agents can do lots of things. We, we tend to think of, um, existing on this kind of, uh, spectrum.

So you can have very simple agents on one side of the spectrum and very complex agents on the other. So to give you a bit of an example there, I would say on, on the left side, on the simple side, we kind of say agents that just do, you know, regular q and a, they grounded it on a few documents. You ask 'em a few questions and they can give you answers.

They're super useful in certain situations. And then on the right hand side, uh, you've kind of got things like autonomous agents that they can work off triggers, they can run business processes, they can, they can literally work autonomously and have very complex logic, complex instructions, um, and run very complex business business processes. So, and then you've got everything in between.

So we kind of think it, it, it really depends, um, on what the, who's asking the question and what they're asking, the context they're asking the question in. But it's helpful to think about these as a, as a spectrum of things versus, um, an agent. It's just a, it's just the same agent.

We appear to be on the cusp of a massive wave of Agent X solutions. Uh, some are narrow, some are broad, and, uh, some are open source. What makes Microsoft's approach defensible and distinctive in the flood of agent frameworks and offerings that we're seeing show up?

Yeah, that's a good question. So I'll probably start with the breadth of tooling that we've got. We kinda span from the very easy to use experiences that are built into M 365 copilot.

With copilot light, you can literally describe an agent that you want. You can give it some grounding content, you can tell it to go talk to, um, to SharePoint and a bunch of other kind of content sources. Super easy to do, super easy to share, very valuable.

We then have copilot studio, which kind of takes that a step further. You get access to a lot more capability, including things like triggers. And then we take that an even step further.

When you think of, uh, Azure, AI Foundry, all of these things are kind of tied together in a, in a very integrated way. Um, so it's very easy, a very easy to move from one to the other and incorporate one of the other in, in your solution. So I'd say our, our breadth of tool set is probably number one.

Uh, number two, I would probably talk about access to data. Any customer that's an M 365 customer of Microsoft, um, has an enormous amount of kind of that tacit knowledge. That data in Microsoft Graph, we make it very easy for you to get at that, to make your agents, uh, more intelligent.

You know, uh, it's one thing to be able to run off a, you know, rows and columns in a CRM, um, database or, you know, ELP or f and o kind of, uh, service. It's something else. When you can marry that with, uh, that tacit knowledge that you've got in your enterprise to bring intelligence, much more intelligence to what the agent can do.

And then thirdly, I'd say, um, our kind of core strength in security governance and compliance. You know, we, um, we've, we've invested heavily for a long time to help customers, uh, secure and manage their, their enterprise data. All of that investment flows through to, to your investment on the Microsoft stack for agents, agents, respect, all of the policy, the labeling, all that kind of stuff that you've already deployed to manage your data.

If you're building your agents on the Microsoft stack, that stuff just flows through. So I say those, these three things are probably the three primary. Yeah, that makes a lot of sense.

And so, so let's zoom in for just a moment. You know, help people understand how do agent powered interfaces change the way that workers interact with business systems? You know, compared to traditional user interfaces, There is a lot going on in the indu industry with how kind of apps and agents are coming together in, in super interesting ways.

Um, I think over time we'll see agents starting to adopt some of the app modalities that we expect from apps today. So think of a time when, um, when an agent can actually start to build an app, or even just a ui, like a, a form on the fly for it to solve a business process or to, to, to help you, you know, move forward with whatever the thing is that you're working with the agent on. I think we're gonna start to see that show up.

It's not today, but, um, over, over the, over the, over the course of time, I do think the app modality and the agent modality will start to mix. And ultimately, if you think about it, there's a, there's a, there's a very good reason for bringing those two things together. The, the example that I like to use is around, uh, you know, a, a procurement process.

Like every company has to buy stuff today at Microsoft, you know, you have to know three people. There's three different tools, a bunch of approval stuff happens that could be, uh, the couple of apps you have to go use that could be built into a series of agents and copilot kind of marshaling the whole process. You could just literally ask copilot, like, I need to open a PO for fu copilot could know the agents that it needs to go talk to, to be able to do that.

They could be a, like, um, a legal agent to check the, the, the statement of work that you're trying to, trying to kind of, uh, work through. Um, that could be a procurement agent that could come make sure you've got access to the right, um, and you're hiring the right vendors like every step of the way. Copilot could run that whole process.

You don't necessarily need to go through three different tools if copilot needs some data from you. It could create a form if needed, if it's not a conversational type of, uh, thing that it's, it's asking for. So you can see how these things start to really blend together when you start to think about agents working with other agents and then having something like copilot kind of manage that interaction for you.

So I'm glad you brought up talking about different agents, uh, working across the business and, and that's something we call orchestration. And one, uh, challenge Enterprises face is orchestrating multiple agents across different domains. Finance, hr, customer service, without chaos, uh, integration or security issues.

How does Microsoft envision enabling agent orchestration across the enterprise? And what governance mechanisms will make it all work and safe? Uh, that's a good question.

So there are a couple of things that, um, I don't think we're particularly unique in the industry with, um, you know, like supporting MCP for agents to be able to use tooling and, uh, supporting a to a for agents to talk to other agents. That's kind of table stakes these days. The things, the thing where it starts to get interesting with Microsoft is our ability to use capabilities like Agent 365 to provide kind of end-to-end observability across all of the agents that you've got running, um, in your, in your organization.

Um, to the extent that you can even give an agent, you know, uh, an agent ID and treat it like a, like a user. Things like conditional access, all that kind of stuff. Um, just work.

And then I also think like once you give, uh, organizations the ability to kind of observe and manage and govern agents, it starts to open up new kind of frontiers of where they can use agents. It allows them to think about business problems that they've had in the past that have historically been kind of un automateable, maybe they're too determined, uh, non-deterministic or, um, too complicated to, um, to do with traditional automation tools and apply agents to those problems. And we've seen a ton of customers kind of look at existing business pro processes and decompose them and figure out where they can replace certain parts of them with an agent.

They don't replace the whole process, but they start to replace parts of that process. And over time, as the technology matures, as their understanding and confidence with the technology, um, matures, they start to build out more, but also look at brand new problems that they've faced for a long time that have just been very difficult to address with technology. And that's when, if you've kind of bring all the, all of the, all of the assets together, the things like HOA and MCP, things like Agent 365, things like the security and governance and compliance capabilities that the Microsoft stack offers you, it really does let you start to look at these problems in a very different light and start to apply technology to, to things that you just previously wouldn't have tried to 'cause it was too hard.

So let's talk about really getting started with agents. You've talked about agents being on a spectrum. How should organizations assess where they are and what kind of agents do they need?

Uh, you talk to a lot of different customers and you have a good sense of that. Can you share that with us? Yeah, totally.

And there's no single answer to this 'cause everybody's different, but I think it's important for people to start in a place they feel comfortable with. It's very easy to run into this too fast, not be super successful, and then, and then feel, you know, a little bit terrified for, for trying to scale it. So, um, it's actually very similar as well to the way that people in the past, over the past 6, 7, 8 years have deployed low-code.

The deployment patterns that we see with things like power apps are very similar to the customers that we see deploy in, you know, agents in any meaningful way. Um, and actually the comp customers that have already deployed low-code at scale seem to have a very familiar pattern to follow with, with how they deploy agents. So it really does fall into that pick a business process.

Maybe it's the, maybe just pick one, find the one that's the most inefficient, the most expensive, the most painful, you know, it's, it's creating the most work and then break it down to, into its com, decompose it into its component parts, and start to figure out where you could apply agent technology to those component parts. Don't try and replace the whole thing. And the, I've had many conversations with customers where they, you know, they think I'm just gonna replace my whole procurement process or my whole, you know, hiring recruitment process with an agent.

Now that may be possible, but it's really difficult. So basically figure out what that recruitment process looks like and break it down and then attack it in chunks, and then you'll learn what the product, the, the technology is capable of. You'll learn what your, you know, your teams are capable of.

And as both mature, you'll be able to expand and expand it within that process and expand it outside of those two other processes as well. So it really is a, it really is a journey and, and most people will find their own way through it. We have a ton of best practices, guidance patterns and practices, that kind of thing, uh, thing and templates that we, that we can share.

But, but ultimately every customer's different and everyone's gonna look at this differently and figure out which, which is the best place to start. So when we talk about a agentic business transformation, um, we see a lot of organizations are trying to figure out, well, how does this fit into my legacy, uh, systems, the architecture I have today? Uh, what thoughts do you have about how do we, how do we bridge that gap between, you know, the modern agent stack and legacy it?

Yeah, there's a couple of things there. The first one would be find ways to integrate, like MCP is a perfect way. The, the, the rate of adoption, uh, for MCP has been phenomenal.

So go learn about that. Go figure out like how you could stick an MCP server in front of the services that you are exposed to, to your agents. That's step one.

And then step two is, um, if you already have, you know, deterministic workflows, which I'm certain everybody does, there are ways you can start to pull those into things like copilot studio, keep them deterministic, but build them as agents. And that's one of the, kind of the great value props of Copilot Studio is you can blend this kind of non-deterministic AI agent driven approach to workflow with very deterministic, with things like agent flows. So you can actually replicate what you've got in an agent and then over time you can start to create, you know, replace per parts of it.

So it's more agent driven, like AI agent driven than deterministic. So there's lots of ways you can start to bring these two worlds together without kind of diving in or, or not. Agents will become the core logic and decision making layer in businesses, uh, business processes soon enough.

How does agents, as the new logic layer, uh, change the way that, uh, enterprises think about their core systems? What are the implications there? Yeah, I think, um, there, there's gonna be a, a meaningful change with how people interact with core line of business systems like ERP, like, like CRM, um, traditionally today there's, this is kind of a forms over data type web experience.

You have to go somewhere, fill a bunch of forms in, input a bunch of data, and, you know, press submit. I think fairly soon that, that there'll be kind of an inversion of where the IP and the logic sits. It'll be less on the database and forms layer.

It'll be much more on the agent layer. And what that means for users is they're gonna get faster access to data, they're gonna get smarter answers, they're gonna be able to use multiple data sources to, to assess a situation. You know, if you're a seller, why am I sending you to the, to our s cus you know, our customer service, a solution plus you CRM to go figure out with the customer you're about to meet, whether they've got any open support tickets and they're angry at with you like that, that it should just be able to ask an agent to be able to do that.

Um, if you're a sales person and you've just got off the phone with a customer, maybe the call's transcribed, maybe there's a bunch of email traffic between you. Why am I making you go repeat that, cut and paste it, make it up again, and submit it into a, into a CRM system Somewhere an agent should just be able to do that. So I think, I think there's gonna be that switch from people laboring away in, you know, web forms and web services to, to an agent proactively in many cases, uh, working with these systems, um, and helping the, the sales person or the service person or whoever else it is to get more done, um, and be way more productive.

So how are agents enabling organizations to leapfrog, uh, traditional low-code development and go straight to intelligent orchestration? So I'll give you a few examples. The first one is, I wouldn't exactly say leapfrogging.

I would say we're using agents to make that kinda low code paradigm even stronger. Uh, so things like the ability to use plans in power apps where gone are the days where you try to describe an app and build an app, what you do is describe a business process, and then power apps plans can use, uh, several agents. There's an architecture agent, there's a data agent, and you can actually go build a business solution for you.

So, and that business solution could be two or three apps, some workflows, a couple of agents, they'll actually do the end-to-end thinking, like, if you were a business architect, what would you build? Versus just building an app. So that's number one.

We're we're leveraging agents to help you build kind of battle, low code, uh, solutions. And then number two, uh, you know, we have new capabilities in M 365 copilot, like workflow builder and an app builder where someone with absolutely zero coding experience can come and literally describe an agent or a workflow. An app builder and workflow builder will go build it for you and run it.

Uh, and then you can, it, it, it can store persistent data and SharePoint, you can share that with other people. Uh, you can even start to modify it. So if it's not exactly what you want the first time you kind of describe the app, you can modify and change it as much as you want conversationally.

And that really does open the door to many more people being able to kind of use this technology. These aren't gonna be apps that you end up running your business on. They can help a team, an individual achieve more, be more productive, uh, using ai, uh, to build apps.

Things like apps and apps and workflows. So what we're seeing as companies, uh, plan for the role of the maker evolving when that maker has really been that low-code developer who's been using things like power apps, uh, to build point solutions in their corner of the organization, what we're seeing is that role is growing up and it's becoming, uh, one that that's gonna take advantage of this, of, of these all these intelligent agents that are, uh, being built inside the organization, and now they can build new solutions that span departments and, and cross, uh, silos, uh, in terms of the business processes that they can integrate. And so creating higher order more strategic solutions out of that agent landscape that's, that's forming.

And these are things that, that, you know, now will automate and run on their own. But is that, that with you see that that maker still has a role and in fact an even more important one? I was wondering what you thought about that.

Yeah, It's a really important point. I also think it, you're gonna play a very important role in this as well. You know, they're, they'll be the ones building the MCP servers, um, so they can provide access to, you know, data and, and, and logic to, to business users, units that, that want to go build their own agents.

So they, they'll be the ones that are building the kind of high order bit agents so that, uh, business users could take advantage of those. So I think when you bring those two things together, you know, you create a, definitely create a, uh, total is great in some of the parts type math for, for customers. So in this new world of agents where it, it's, you know, the code, uh, the, the AI is touching your business and your data, the, the concern is around compliance and security.

I was wondering if you could speak a little bit to, uh, how does Microsoft ensure that these very powerful agents that are working with your business, uh, are following the rules and are secure? Yeah, it comes up a lot, uh, in conversations that I have. The thing to remember though is, um, agents only have access to the data that you give them access to.

They also respect all of the policy, all of the governance controls that you've got over the data. They only get access to it if they are granted access to it. And I think that's fairly, uh, unique to the Microsoft stack in the sense that all of the investments that you've made on things like purview and, and managing SharePoint, that kind of stuff, that all just flows through to agents.

Uh, when you, when you go outside of the Microsoft stack, you essentially have to kind of copy paste that data somewhere, and at that point you lose all of that governance control that you once had when it was in the kind of, um, 365 security boundary. So it's a super important point and one where I think, um, we have a, we have a fairly meaningful differentiator for, for customers. Everything just works.

I think it's, it's fairly just pretty much as simple as that. And so thanks so much for, for being here, Richard. It was, uh, uh, great to talk with you.

Thanks Thea. Yeah, it was a really fun conversation. I appreciate your time.

So what was really interesting to me is, is seeing how far Microsoft has come so quickly with agents as, uh, we see this as, uh, you know, a whole new stack that's forming inside the organization. It was really fascinating to hear that, that, that, uh, you're spending so much time, uh, looking at how do I make agents work across the business. Uh, I think we've looked a lot of, uh, individual automation, uh, in, in the past and low code.

Uh, but I think it's that, it's understanding that Microsoft has that big picture view, uh, and it's the breadth of the tooling. Um, it's the, the scope of, uh, of understanding. It's, you know, not just departmental, of course it's departmental, but it's also how do we make this enterprise wide and how do we elevate the thinking so that, uh, we're preparing those inside of our organization, uh, to, to take advantage of the big shift that's happening going, you know, from building maybe those point low code applications to orchestrating across the business.

Um, and a lot of the pieces around m making, you know, agents work together, providing that security and compliance that ensures that you can do all of this safely and not constantly sweating, am I going to go outside the security boundaries? Am I going to go against corporate policy? And knowing that all your agents can do that, it really is gratifying to see how far Microsoft has come in, in assembling this vision.

Uh, and then offering it in, you know, it's not none. No one can say this is mature, uh, yet, but, you know, hearing about, um, uh, the, the stories we see at your events about how far the customers are coming and already getting a lot of these things out there. So, uh, that was the takeaway I had, uh, is just, you know, uh, uh, getting a sense of Microsoft is really trying to make sure that they're leading this, uh, this agentic transformation.

Hi, I am Mitch Ashley with the Futurum Group. Today we're unpacking the Nokia Bell Labs recently developed model for data center fabric reliability. We'll look at how the fabric design plus operations, especially automation and AIOps, to move enterprises from a legacy PMO baseline to an FMO with Nokia's Sr.

1 nine's availability and shrink downtime significantly. And I'm Scott Roon with, so it's the age of operations, you know, hardware still matters, but operations dominate outcomes, you know, day two and beyond. This Bell Lab's model includes significant detail, and today we'll zoom in on some key areas in operations.

We'll also talk on talk, touch on the significant financial impact as well. Um, you know, the model shows that reliability gains can result in real cost reduction. And, uh, that should be no surprise to those of us who've been, you know, in the ops world for some time.

Very good. Well, the model treats configuration, NetOps is one of the main areas for improvement, including common cause failures. So what does it mean in practice, Scott?

The, the model shows the most significant reduction in downtime measured in absolute terms, you know, minutes, um, eliminated per year is achieved during the config and provisioning phase, driven by decrease in config related errors. There are multiple key components and mechanisms in the Nokia solution at the heart of the model. First you have SR Linux, the operating system developed for modern data center operations.

It facilitates efficient structured and language agnostic comms between system components, minimizing operational complexity and reducing the likelihood of user error. Next, you've got features like ZTP zero touch provisioning, which are integral to EDA, that further eliminate manual intervention takes the human out of the loop for opportunities to inject errors, um, and contributing to that enhanced reliability for operational efficien efficiency. And then there's ED a's digital twin construct that provides like, for like environment, um, to design and validate network configs with correct intent inputs.

This minimizes config and provisioning errors and the configs generated in the digital twin directly mirror production. It's not special configs in the twin, um, and then modified to push and production. It's actually the same configurations reliability is further enforced and improved by it's built-in dry run validation routines prior to every config being pushed into the network, helping ensure accuracy and consistency before changes are put into the live network.

Now you mentioned the term age of operations. So talk to us about why is ops a primary lever in this model? What the simplest level, you know, once you stand up a data center fabric, most of the day-to-day work in NetOps is around operations and monitoring.

And with AIOps entering the room here, we have a day one killer app that drives the use for NetOps natural language processing. I can talk to the fabric to see what's going on. We also benefit from increased programmatic access to the fabric.

For example, SR Linux provides a single API for set get and streaming telemetry resulting in less operational complexity and minimizing, minimizing mis configs. 1 nines. When all these features are combined from a network operating system architecture perspective, the event handling system in Sr.

Linux and EA is capable of proactive predefined actions based on things like port saturation packet drops, looking at other network conditions to automatically trigger corrective actions and prevent network degradation and mitigate issues before they occur. So putting that all together, it sounds like what you're saying is tools operationalize the design. That's a really good summary of a very long description, Mitch.

Um, exactly. You know, design gives you redundancy, operations makes it reliable every day. The details matter for sure.

So planned work can still hurt, uh, availability. How does, how does the model address that and, and Sr. Linux in particular, and EA, how do they handle ongoing maintenance such as upgrades, patches, the things that we commonly do?

We need to point out the fact that SR. Linux uses an unmodified Linux kernel. This is what the rest of the world is using for Linux.

That's gives you, uh, the ability to tap into a worldwide community of developers hammering away at it every day, identifying potential performance and security issues across many, many different application areas, not just networking. In addition to that, EAP provides built-in capabilities to ensure network reliability during maintenance operations. You can use the platform to gracefully gain traffic, drain traffic from, uh, a node and put nodes into maintenance mode.

Um, preventing service disruptions, minimizing traffic gloss, EA also centralized and stream centralizes and streamlines the upgrade process. When a route router's locked and is ready to reboot, it significantly reduces the actual maintenance window. EA also gives you group-based upgrades and stage promotion, um, to shrink maintenance windows.

Reduce the time used for maintenance windows and limit that blast radius. You can pick targeted nodes, you can automate pre and post checks, and you can roll forward only when your, uh, process gates get passed altogether. These features, um, result in enhanced reliability and minimize downtime.

All the upgrade steps can be tested, um, with, um, the EA digital twin functionality in a like, to like environment. This is what gives you impact, um, and reduces, uh, maintenance issues for far less annual downtime as you move from your present mode of operation to your future mode of operation. Excellent.

So who should people talk to at Nokia to see how this model and can apply to their environment, their data center network? Well, to learn more about the model Sr. Linux and EA contact your Nokia account team or your regional business center contacts, and they'll work with you to work through the details.

Thank you, Scott. Appreciate, uh, you sharing us some interesting facts from the study. Thanks, Mitch.

Hey guys, thanks for the throw. We're here with Arnie Bellini, who's CEO of Bellini Capital, and we're having a little chat about cybersecurity and how the scams are getting a little more sophisticated, starting with these cold weather scams that we're seeing. I mean, it's nothing sacred.

People are freezing and now people are trying to steal their money and compromise their data and do all kinds of nasty things. Hey, Arnie, how you doing? Welcome to the show.

Good, great. Thanks for having me. It seems like every time that there's some sort of emergency, the scammers come out from the woodwork and start making pitches for various things that are related to that.

And the latest one seems to be these cold weather snaps that are becoming more common. Um, is there something we should be doing about this? Is it just noise in the system or is there something we can do to maybe thwart this stuff, but 'cause it sure is annoying.

Yeah. Uh, well, they're gonna come out of the woodwork because they don't really ex exploiting the opportunity for us to be distracted. I mean, it, every scam works better when we are distracted, right?

So when cold weather hits and utilities shut down and you know, there's mayhem going on, that's the perfect time for a hacker, uh, to come in and create some several sense of urgency to maybe spoof you and think that make we're the utility company and, you know, click here if you want your power restored, right? I mean, you get to pull off a lot of clever reuses, if you will, that can, that can catch people off guard and then catch them when they're really focusing on something else. Another, you know, a distraction is, you know, a disaster that's happened.

You know, you see it in Florida, I mean, I'm from Florida. You see it happen right after hurricanes like crazy, right? You'll see it up north when, when they, you know, when they have a, a white out, you know, day where the power goes down.

You know, you'll see it for that. So, so urgency is one of the things that hackers look for. They look for you being dis uh, you know, distracted and they look for you needing to do something urgently to restore maybe your current situation.

So, so essentially that's a tell though, right? I mean, the fact that it's urgent is suggests to me immediately that there might be some sort of fraud at work here because well, um, you know, folks who are offering to help you or not necessarily gonna say, you know, and you gotta do it by Thursday, right? They're generally gonna be, Hey, we're here to help on any kind of time and effort.

But, um, is, are there other tells that we should be looking for? Yeah, urgency is one of them. Uh, and that's the obvious one, because there's no reason to do something urgently.

Now in a, in a cold weather disaster, for instance, you might feel urgent to, to try and get your power restored. So they've gotta play on that. So you gotta look for, I mean, you actually have to just outsmart them, you know, it really comes down to that.

You can't, you can't cave into the need for urgency. You just can't click anything. I mean, at the end of the day, if you have even a second thought about a text or an email that you get with a link, do not click it unless you know it's from a known source.

That is how they're gonna get in almost every single time is once you click, they're gonna deploy malware that you're basically clicking and saying, it's okay for you to come in and put something in my system that you can come back and get. So that's one, one way that they're gonna create urgency. Don't click on things and then don't respond.

Right? It's like, and if an offer's too good to be true, then it is too good to be true and it's not true, right? It's probably a hacker.

So it's all of those things. They're gonna play on your fear, they're gonna play on your urgency, they're gonna play on your greed, right? It also seems like the attacks are getting a little more sophisticated in the age of ai.

We're seeing these deep fakes, and I think, you know, an email is one thing. You, you can kind of discount that, but I think people, if they get an audio message or a video of something, does that make it more believable? And will this become a bigger problem?

It is a bigger problem already. So, yes, and you hit the nail on the head. AI makes these, you know, you, I don't even say it anymore, but you said, how can we detect it?

Well, you used to be able to detect it because things were misspelled. You used to be able to detect, detect it because it was grammatically incorrect. You used to be able to detect it because it didn't sound like it came from your culture, right?

All of that is washed away with ai. So it doesn't matter who I am, it doesn't matter how I operate culturally, it doesn't matter how well I can spell, uh, AI can clean all of that up. And so they are getting much more sophisticated, and it goes beyond that.

So it's not just that the messaging is more sophisticated, everything is way more sophisticated, or I should say can be. So it's trending to be highly sophisticated. And we have to realize that because with ai it can be a really good spoof, just like you see, you know, AI versions of, uh, say a politician, right?

It looks pretty similar, right? It's close enough. We would have to do a second look, right?

AI's gonna let the, the, the hackers get to that point where you're really gonna have to, you know, get, gonna have to be discerning, right? Uh, because they're there to fool you. Everything is there to fool you.

I can create a website and make a website look way more legitimate with artificial intelligence, you know, and sper a finger. I can do a denial of service attack on your company, you know, where I just barrage your internet connection with requests to come in, requests to come in, right? And you, you can't do anything but deny it.

So you can't, can't even use your internet lag denial of service attack. I can spin up service all around the world with AI much quicker than I could before. So it makes this, this new technology will be used against us.

And that's really why Lauren, my wife and I have invested, you know, $50 million at University of South Florida to actually create, uh, uh, a college where you combine both artificial intelligence and cybersecurity. Because if they're using it, we have to use it to defend ourselves. They're being incredibly more effective offensively with artificial intelligence and their sophistication.

We have to be that much more dedicated to defending ourselves with artificial intelligence and cybersecurity. And so it's, and I wanna also point this out, okay? It's, it's a big deal, okay?

It's a $10 trillion problem a year, right? Now, let's just put that in perspective. United States gross domestic product, 30 trillion a year.

China's gross domestic product, 17 trillion, 13 trillion. If they're being honest, they're not. Okay.

Uh, and then, and then who comes next? Next comes if you rank it as an economy, next comes cyber theft at $10 trillion. Oh, and then Japan, and then Germany.

And then, you know, you see my point, my point is, and it's growing at 15%, right? So the cyber theft is growing at 15%. Hey, we're really excited in the United States that our economy's finally growing at like close to 5%.

That's an amazing thing. We haven't seen that since I was in high school in the seventies. Okay?

So that's an amazing thing. We have to protect our economy. We have to defend ourselves, uh, because even growing at 15 5%, it's growing at 15%.

So by 2030, cyber crime will be bigger than the United States economy, the number one economy in the world, and they're not doing anything good with the money that they steal, right? So it's one of our biggest problems that is going undetected, right? If we had a crime wave in a city where, you know, everybody was being pickpocketed in Times Square, or, um, you know, there was some sort of massive rash of robberies, the police departments locally and federally would respond, and, you know, we do something about this, but in the, in this case, it's so ephemeral, right?

It doesn't have a physical kind of connection. So what is it that maybe we should expect law enforcement and governments to be doing about this? Because to your point, it is sapping our economy.

Well, you hit the problem perfectly. That is the problem. You frame the problem, we're not responding.

It's hard to respond, right? It's, there's several problems with this. One of the biggest problems is if this was crime, to your example, if this was crime and we saw things happening in the streets, we would demand something be done about that, right?

Because we can see it, we can work, probably we could be involved in it, right? We could be the victim of it. But with cyber crime, it's digital.

It's, it's invisible, it's silent. Uh, and it doesn't, doesn't, doesn't seem to sting our emotions as much, right? So we don't really do that much about it, right?

But it is, that's why I'm raising the red flag here, saying 10 trillion going at 15% gonna be bigger than any of us, you know, in, in less, in five years. In five years, okay? Five to six years, okay?

If it continues to grow at this pace, we have closed our physical borders here in the United States. Thank God, we now have to close our digital borders, right? And there's a big project to be had there that I don't really think, you know, well, lemme just say that that's where I'm putting all of my time and effort is like tracking the code on that.

How do we defend our digital borders? That's the reason for the new cop, Bellini College of Artificial Intelligence in cybersecurity at University of South Florida. It's also why I came outta retirement, because this is my civic duty.

I think I can help crack the code and figure these things out. But it comes down to a lot of things. But one of them is we've gotta have more people entering the cybersecurity profession in the United States of America.

And it is a very lucrative profession. And so that's one of the things that we're doing, is we're really getting the word out to young, young, young folks that this is a great career path, right? Uh, we need more people, we need more cyber soldiers, right?

To defend the walls, the digital walls, right? That's one thing. Um, we have to have some government policies that are guiding Lights North stars that start to tell all of us business people and others, uh, how we should be defending ourselves and what they would consider a decent standard.

It doesn't have to be regulation, it just has to be a North Star standard that, that we could follow, because it's a very confusing cybersecurity. And cybersecurity hygiene is very expensive and very confusing to most people. It does not need to be.

So some leadership in this area is gonna be very important in the government. Uh, some initiative from folks like myself and the business community and the tech community, very important. And, and at some point, we're gonna, we're gonna solve this at some point.

We just need to solve it sooner rather than later. How far can we go? Because you'll hear security folks will say, well, I can clean up the mess, but I can't prevent those people from launching an attack in the first place.

So, and they'll say the defenses are too porous. And, um, to quote, I think it was Frederick, they're great. If you defend everything, you defend nothing.

So, um, what is the role of cybersecurity people here and what can we do? Well, I would say if I were to declare a North Star, it would be pretty simple, right? And, and I'll actually lay it out here.

You know, I would say every business in the United States, every organization, in fact in the United States must use, and I'm not gonna regulate it, but North Star should have, if you wanna be way more secure, if you wanna lock down 85% of your exposure to being hacked, here's five things that you can do. Multi-factor authentication. Make sure everybody in the company's using it, okay?

Have an antivirus, uh, solution that is deployed to every single device in the company, okay? Uh, make sure that you've got proper backups. Make sure that you can go back a, a week, a month, maybe even a year.

'cause for data gets stolen, we're gonna rely on that backup, okay? Or if it gets locked down, we gotta rely on that backup. Gotta make sure you're doing that well.

Okay? Fourth thing you've gotta do, you got to have user awareness training. What is that?

It's teaching every one of your employees what they need to do and what they should not do. How to be safe, how to not click on those links that are suspicious. How not to, uh, respond in an emergency.

You know, there's basic training, all the things that we've talked about. There's basic training that every user needs to know as well, because it doesn't matter how good I defend the digital wall, if they can get someone inside to click, they're in, right? So that's really important.

And then the fifth thing, and the last thing that's really super important is constantly assessing your risk. Constantly walking your digital, your organization's digital wall to see where there are vulnerabilities, making sure you're marching down that wall on a regular basis, evaluating whether you have any openings in the wall, whether you have any t chinches in the armor, whether they can get in, right? And so, you know, if you did those five things, I'd say that there is, you've taken it from massive exposure to being hacked down to maybe a 10% chance.

So, you know, that's pretty darn good, right? If we could defend the digital wall, like you said, you, you can't defend at all. If you defend at all, you defend nothing.

Okay? Here's five things you could do to defend 90% of your digital wall. How aggressive should we be?

'cause a, it seems like governments especially have been a little hesitant to be on the offensive with this stuff. And yet we know that some of these attacks come from specific regions and specific areas and involve specific, uh, cyber criminal syndicates that are being protected by various things. I mean, do we need to engage in some quote unquote gunboat diplomacy here?

Or is there some way we can think about this differently? I, I love the idea of gun bloat diplomacy here. Okay?

We could pull that off. Okay? But I don't think that would do any good, right?

What we've gotta do is we've really gotta look, okay? It's pretty simple, right? $30 trillion gross domestic product, right?

That number is why America's number one, okay? Number one, economy, okay? Number one country, okay?

You see how much power we can yield if we use our power around the world, okay? We can do just about anything, all right? We have to stay number one, you don't want China being number one.

You don't want Russia being number one. You certainly don't want rocket man being number one, right? So, you know, the game that we're playing now is autocracy versus democracy.

That's the new game in town, okay? And it's probably never gonna end. It's always gonna be spy versus spy autocracy versus democracy.

They take advantage of the fact that we are an open society and they, we invite everyone, you know, into our nation, right? And they take advantage of that. So there's a few things that we really should be doing.

I can tell you this. We should not be allowing any citizens from a foreign, from the United States of America, foreign adversary list. We should not allow any citizens from people who are on that list, or I'm sorry, nations that are on that list.

And that list includes China, Russia, North Korea, Iran, uh, the Venezuelan regime, and I forgot the seventh one, but none of those na, if you're a citizen of any of those nations, those are our foreign adversaries. We should not allow them to attend colleges in the United States. We should not allow them to work in our colleges in the United States, because this is where we create a lot of our innovative thinking.

Okay? That's the, that's, I mean, you're gonna have to do something like that because every one of those nations has a law that says that every citizen and every company inside those nations must perform spying or intel. They call it intelligence gathering for the government.

They must, and it doesn't matter where they are in the world, they must do it. And that's why you've seen so many of these Chinese nationals that say Michigan University, six of them got, uh, arrested for spying on an Air Force base. Three of 'em got arrested for smuggling and, uh, biologically hazardous material into the United States that could bled are called corn crop.

Okay? Not, not a small thing, not at all, a small thing, okay? So, you know, there's some really basic steps that are so obvious that are right in front of us that we can take to really solve this problem.

Um, and the North Star is another one that I discussed, but I mean, there's, it's not that hard, but it is, it's something we've never done before. So it's all new, right? It just has to require, you know, kind of innovative thinking on how we solve this problem.

If we wanna be an open society, how can we be an open society and have people still as freely coming back and forth? I think the answer is, you gotta put a filter on it. You gotta put an appropriate filter on who can come and who can't come into the United States and what they can do when they're in the United States.

That's really important right there. Um, if you, if we were able to, you know, do the, the, the, the, the, the, the North Star and everybody in every business followed that we'd be in great shape, but if we lose our position with our economy, we lose our position geopolitically as well. And that's what's really at stake here, because they are destroying our economy and siphoning off our economy with these $10 trillion worth of, of hacking that's going on.

By the way, 30 billion was just confirmed, I believe, stolen from, from the state of California, $30 billion. Some would say that, you know, we, the United States give as good as we get. So do we need some sort of, I don't know, cybersecurity, non-proliferation treaty the way we have with some other things that are dangerous to everybody in the world?

Yeah. And no one would follow that. Like everyone would sign it and no one would follow it.

Okay? So let me just tell you example. Okay?

Like, I, like China could sign it now and not be in violation of it. Because what they do is they just go to private, they go to private, uh, tech companies, and they give them contracts to hack into the United States so they can say, you know, once removed, it's like, we didn't do it. We paid someone to do it, but we didn't do it right?

So it it, there's all kinds of ways of getting around that and China's already doing that, so, uh, and they're, they're getting really good, by the way, at hacking. They weren't good before. The Russians have been the original gangster, right?

So Russia, during the Cold War, they're the og, right? The original gangster of hacking. All right?

They were really good. They are really good at it. They're kind of distracted right now.

China was always there and they were just, you could tell when they were trying to you, we could easily thwart their hacks. They've gotten really, really good at it, really good at it. Hmm.

You mentioned your work with the University of South Florida. Is there other things that universities should be doing? Can we maybe pull our collective university resources better and kinda work on this problem?

It seems like it's bigger than one school. Yes, sir. We can.

In fact, I just met with the chairman of the Board of Governors at the state of Florida. That's the person that's in charge of the entire university system. And I've been proposing that we take, you know, a hub and spoke, uh, approach where we pull all of our resources on both cybersecurity and artificial intelligence and, and, and see how we can make a difference in our, in our national posture, in cybersecurity.

Because someone's got a lead here, no one's really leading here, you know, and it requires leadership. It just requires some leadership here. And it will, we can solve the problem well enough where it won't affect our economy.

All right, folks, while you heard it here, hey, the fight is on. That's the good news. The bad news is we're not quite as organized as we should be.

Hey Ernie, thanks for being on the show. Alright, thank you so much. All right.

And back to you guys. And Ste.