Techstrong TV October 29, 2025

Hey, everyone, good and bad at the same time. It's quantum economics. You're watching Textron Gang.

Hi everyone. Happy Wednesday. Welcome back to Textron Gang.

It's right, it's hump day. We are, i, it, it sometimes it amazes me once you get into that kind of wind tunnel or that, you know, down the stretch towards the holidays, it just seems like time accelerates, right? So here we are.

It's, it's the, the 29th. We have Halloween this weekend. Always a fun time.

I don't know what you guys are planning to dress up on or do for Halloween, but it's great. Halloween was always big to me as a kid, but I must say it's become a, a much bigger holiday than the last 10, 15 years maybe, right? I think it's not just for kids in candy anymore, right?

It's, it's, uh, it's really become a celebration. Um, anyway, but we've got a lot more to do before Halloween, including a great text on gang show today. Let me introduce you to our Wednesday panel members, and I feel like this is our normal Wednesday panel.

This has become rather locked in stability in the gang. Uh, of course we've got Chris Blas, Kate Scarsella, Dan O'Brien, Mike Ard, welcome gang members. It's good to see you.

I know some of you have toothpicks holding your eyes open, having stayed up late for a Dodgers game the night before, or Dodgers Blue Jays game. Excuse me. Um, appreciate you all getting on here.

So, I wanted to first talk today, I kind of hinted at about quantum economics, right? And, and you could, we could really see the drum beat, you know, the drum, the drums beating here towards Q deck towards quantum becoming more real. We're seeing it a a steady flow, not only in kinda of tech, uh, media, but even in the mainstream media.

And, and usually, you know, where there's smoke this fire you can smoke and fire, smoke, no fire, fire, no smoke. All of those at the same time in a quantum future. But, um, you know, a recent announcement came out.

Now, the US government in talks to take stakes in quantum computing startups. Mike, you could probably guess how I where I lay on this, but I'm gonna throw it out at you to kick us off. So, there's a report in the Wall Street Journal talking about how the US government is at least talking about $10 million stakes.

And I think that there's four or five of these startups. And what's interesting about all this from my perspective is, you know, we see that Google and IBM and, uh, all kinds of folks are investing heavily in quantum computing. So I'm not quite clear why the US government feels the need to invest in a couple of startups.

And Alan, I'm gonna toss that back to you. But is this, you know, maybe just putting the thumb on the scale in a way that's not good, or are we setting the stage for something bigger? Like, I don't know, the nationalization of quantum computing.

So let me try to be fair, fair and balanced here. Okay? There's nothing of manner with investing in quantum technology.

We, if we deem it a strategic imperative here in the us, we should invest in it. We should. However, the way that it's been, if we're gonna do it the way we took stakes in Intel and, and have invested in some of the AI stuff, it's totally wrong because we can have a, a tech bro oligarchy.

And that's my fear, is that the people who the, this, this particular administration are gonna invest in are gonna have to take an oath of loyalty. They're gonna have to be part of the, of the old gang, of the tech bro oligarchy. And that is wrong.

You wanna do this right? Do it the way the rest of the world does it set up a sovereign fund with real principles of who we invest in, why we invest in them, what our goals are, right? It, this is not, this is not like a brand new playbook.

And you go out and you pick, you pick the companies that are going to, that you think are gonna be winners, and you think you're gonna help your national interest. There are plenty of countries that do this well with a sovereign fund. Instead, my fear, this is gonna be run like a slush fund by MAGA to reward their, their followers and punish their perceived adversaries.

And that I don't want my tax dollars going for that. You're right. I'd rather let Google and IBM and A MD and, and the rest do their thing.

It's a shame that we can't act like adults and reasonable people and do this the right way, because it is that important. It is that important where the, the, the, the progress is coming faster and faster. You can feel the momentum building here.

Let's not fritter it away over at like everything else lately that we do. We'll talk more about it, I think in the third, uh, session today. The third, uh, uh, section today politicizing our strategic interest.

And that's a problem. I'll throw it out to the rest of the game. Dan, what's your take here?

Because, um, you know, under the heading of fair and balance, let's see if there's another point of view Was that I think it's a complicated topic. Alan said earlier, you know, where there's smoke, there's fire, you know, I'll throw another saying out there, right? You know, things happen slowly then all at once.

Um, you know, putting the politics aside for a second, clearly there's a lot of progress happening watching computing, right? Um, you know, it's the age old argument, exactly how far we are away from, you know, real useful quantum computing. But it seems to be getting closer and closer.

Quantum is very much one of those problems, one of those challenges where it's really a convergence of technologies problem. And when you, you know, when everything kind of times itself right across hardware, software, you know, cooling, you know, the cryogenics, all the things that need to, you know, intense engineering that needs to come together to make this thing real. It does feel like we're getting closer and closer.

Um, I, you know, I I think, uh, you know, back to maybe more a little bit of the politics side of this. This is a game changing technology. You know, to the degree we think AI is a game changer, technology quantum is even more so a game changing techno technology, right?

You know, the ability to discover things that were previously undiscoverable, you know, massive opportunity to disrupt a number of different industries. I think this really gets into a question of, you know, and this is a big question that we gotta figure out whether we actually wanna tackle or not, but where does the defense industry begin and the technology industry end and vice versa, right? Defense and tech are increasingly getting closer and closer, blurry and blurrier.

And listen, the amount of money we're talking about being invested in this quantum compute computing companies, it's kind of a joke, right? If you consider it as part of, you know, what we spend on defense overall, I mean, it's truly a drop in the bucket, right? Um, I think it's a little bit interesting the way they're doing it because listen, the US government's a massive investor in the technology industry.

And when I say that, I mean as a customer, right? They, they decide winners and losers in industries based on where they put federal contracts and, you know, the entire tech industry benefits, you know, from a lot of that. Typically that's the way they would do it.

You know, they're being a little bit more direct with the investment and taking more of an ownership stake versus really helping to, you know, propel certain companies and, you know, help that progress along on what could be, you know, um, a majorly important, you know, strategic, uh, you know, technology advantage for the United States versus its, you know, versus its enemies, you know, at a, at a global level. Um, I think the reason they're doing it this way, Competitors stand competitors, were signing treaties this week. Supposedly they're not enemies fair, Maybe not enemies too strong.

Uh, but, you know, I I think this administration to some degree measures itself daily in the performance of the stock market, right? And I think that's where, you know, the, the politics are blending a little bit on how they're investing and what they're really after. I think a lot of this is really trying to make sure that we shore up a really important industry in a potentially massively disruptive competitive technology out there.

Uh, but, you know, the way they're doing it also feels like it's a little bit of, you know, getting into the, you know, stock, stock market side of things and really trying to drive a, a, a few companies forward. Chris, to Dan's point, are we in danger of creating something that starts to feel and smell like the technology industrial complex? And do we need to kind of bring Eisenhower back?

Yeah, I mean this, this, you know, tickles my, uh, my libertarian bones right around the turn of the century, you know, I was, I was deep in it and ran, bill Redpath was the, was the chair of the American libertarian party. And we had a long conversation right when I was moving outta that phase of my political life, where, where at the end of it, I just love it, right? 'cause he said, I, I think as a former libertarian, you know, he said, look, I know we need social programs.

I just hate freeloaders, right? And, and I get that. So I get that entirely.

And I think this is one of these times where, you know, I'll just agree with, with Alan in, in your basic arc as, as an American, as a capitalist, free speech, democracy, all these wonderful things, libertarian ideals, um, I'm happy to let you know those ideas compete and I think they compete better for nation states. When you know, you, you take more of the approach that you're framing Alan, right? You know, just like let the market actually work itself out, right?

That stuff actually does work. You start putting politics in it and somebody else won't, and it'll be better, faster, cheaper, and they'll get the lead and that will determine itself, right? So it's less worth, I think, arguing about it on moment by moment basis.

You know, that's sort of when it's hold my coffee, it's like, really? You wanna go down that path? Alright.

You look around, it's like, who isn't? I'll be talking with these folks over here. And when you find the error of your ways and, and, and look, you know, it, it is a complicated world.

And to be fair, I could be wrong at any one of these things. Sometimes it's the right time to do the wrong thing. Um, but, you know, it is, it is hard to avoid the nested jokes in a quantum topic because you guys have already used several of them.

But, you know, this may be several things at once, but to me, you know, this, this makes, you know, uh, on my teenage days reading Anne Rand, you know, sound like a, like an object lesson. It is, it is, it is. And and Dan, to your point, you're right, it's 10 million bucks.

That's, that's chump change in today's, in today's marketplace. So they're not doing it because that money by in and of, by itself, is gonna hoist. It's for the optics space.

It's the, yeah, they're trying to create, what they're doing is they're, they're, they're anointing their chosen ones. And if their chosen ones happen to be people who swear fey to the administration or to a particular political movement, well, that smacks that smacks of, of, of oligarchy and fascism and, and all of the bad things. We don't want to be, right.

If you have, if they set up a fund and said, Hey, we're gonna run a, a contest and we're gonna have a, a, a panel of blue ribbon VCs, a panel of blue ribbon scientists, a panel of experts who we're gonna pick the next generation of quantum leadership and the prize is a $10 million investment from the US government, man, I'd, I'd sign on for that today. I think that's the kind of things that we used to do. We we could do that Good.

Not not handing it out to, you know, my, my, my loyal supporters as a thank you. But let me, let me get away from the politics for a second. I, I just did an interview this morning with the CTO of Cloudera, come over in Europe.

He's had some serious positions in quantum, actually, for the eu. And he said something to me that I think we need to remember when you look, let's say the previous three or four stages, if you will, of the industrial revolution, there was always sort of one key technology that, you know, was, was the catalyst for an industrial revolutionary age. The steam engine, for instance, right?

Brought about a, a huge change, an industrial revolution. The internet brought about another stage of the industrial revolution. And one could argue that shortly thereafter, not long thereafter, the cell phone right, has, has ushered in another era.

He said, how lucky are we? How lucky are we that we have ai, quantum and robotics all converging and, and playing off each other potentially here at the same time? At the same time.

Then you throw in some of the other nanotechnology space, some of the other things that we've got going on right now. And man, what, what an exciting time to be alive. What an ex what a, I mean, you know, you're talking about the new front.

So I'm, I I, I was born in 1960 and I grew up in the seventies, but we kind of idolized the sixties. You know, the, and the whole Kennedy thing was the new frontier. What a, what an exciting new frontier to, to live at this convergence point where we have three, at least three technologies coming together.

Each one of them can kick off its own industrial revolution, right? This, this is, this is an amazing time for mankind, for humanity. I don't want to use the word mankind 'cause it doesn't take, it sounds too male.

Do I wanna pull Kate in here for a second? Um, Kate, let's imagine if you would, that you're working for a company in the quantum computing space, and you wake up one morning to discover that the United States is investing $10 million each in five different companies that are competitors of yours. What's kinda your reaction?

You know, what's your kind of, you know, thought process as you kind of wake up one morning and go, Hey, United States government is betting against me. 0, I think it's, it's extremely important. I don't know why we are rewriting the way that we have succeeded in the United States.

And from your point of view, if the, if the United States government was putting money into my competitors, I would be really worried. Meaning that, um, I think the, the playground changed on me and I wouldn't be able to get the ball to play basketball with the other folks, you know? And so if you can't play, you know, even though I have really good technology, I would be really concerned.

And, and I hate that, that we don't let, um, companies fail if they should fail and let the good ones succeed. And we are playing with things that we, the government doesn't understand. We are barely getting our hands around this, and we're in the IT industry.

So we're expecting government to jump in and play in an area that, you know, they, they really don't understand. Hmm. I wouldn't be so happy.

Agreed. Agreed. You know, I, and, and then I, well, I I do wanna just close out positively here, right?

Google, uh, their willow chip, they made some announcements this past week. Uh, you know, progress is happening, right? Yeah.

That I-B-M-A-M-D thing is pretty impressive. Yes, it is. Yes it is.

So it, it, it is exciting. Quantum Future is a hybrid future, right? I mean, I think that that's the, the thing you're seeing stack up here, right?

You've got new quantum algorithms happening, you know, with Google, you've got, you know, quantum algorithms being able to run on kind of classical hardware being demonstrated by IBM and, and A and D. So, you know, all the progress is adding up. I think it just means we're getting closer and closer.

And if I could just add one thing real quick. I was watching, um, Mary Erdos from JP Morgan Chase, uh, this morning on, on, on Bloomberg News. And what was fascinating to me was she's, she said that, um, she wants somebody with curiosity.

That's what she wants today. Like when she hires a person, she doesn't care so much about talent. It's, she wants somebody coming who's curious and with questions.

And that's where I think we are today. I, I agree with that, Kate. I, I think that is, I, I want people, if I've gotta tell you to use AI or to see what quantum can do for you, or what physical and robot, you know, robotics can help us with.

I don't want you working for me. I want people who can't wait to sink their teeth into new stuff like that. And, and, um, we'll see, we'll see what happens.

Anyway, we're over time on this one. We need to take a break. Let's come back and talk about Qualcomm and Qualcomm ai.

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It's convincing you that your personal life isn't at risk. Black clerk, digital executive protection, defending the new attack surface your personal life. Hey folks, we're back and maybe talking about something that might happen sooner than Quantum, but Qualcomm is out there saying that they're gonna build an AI chip, and it might take a little while for that to find its way in the marketplace, but Dan, wall Street went crazy and everybody decided that this was the greatest thing of the weekend.

I don't know, what's your take on it? 'cause I'm of two mixed minds. One is, well, I'm happy to see another supplier.

And the second part of it though, is I feel like this was a pre-announcement that goes under the heading of, as soon as I get off this couch, I'm gonna kick some. Well said Mike. Uh, yeah, no, I think the market reaction was positive.

You know, it's certainly, you know, enabling kind of a narrative here from Qualcomm in terms of their exposure, you know, to this massive AI infrastructure build out. Um, you know, listen, I I think it's gonna be a few years before we see, you know, just how impactful it is to them. They've got a, you know, a nice interesting roadmap that they've laid out there.

There's some interesting differentiated bits, you know, particularly around, you know, low power use of low power memory versus side bandwidth memory. Um, you know, clearly I, I think a sign of kind of the maturing AI landscape in a lot of ways, and that, you know, they do seem to be focused on, you know, kind of specific use cases. Um, you know, as, as things really kind of get optimized as we scale out, you know, they seem to be really coming at it that way.

Um, and now it's two different kind of generations of systems. Interestingly, this was not just a chip announcement, this was a full rack scale system, right? So, you know, seeing that, you know, that trend is really kind of stuck in the market, and that's the way that people are coming at it.

Um, you know, I, I think this is a, this is a long-term game for Qualcom, right? I mean, do I think this is gonna be a massive needle mover on their revenue in the very short term? No.

Uh, but I think, you know, they're announcing their intent. They're building an ecosystem. Uh, they're targeting a specific part of kind of the inference, you know, use case.

Um, Qualcomm's always done really well on, you know, the edge on mobile. Um, you know, they're really the dominant player in that space. And as you know, as AI gets closer out to the edge, and, you know, we see more and more of these use cases scale where, you know, large models, consumer ai, you know, out there, you know, on device.

Um, you know, I, I think it's a really, you know, interesting play for them. Um, you, you gotta remember too, you know, if this is a long-term play for Qualcomm, you know, six G is what, five years away? Um, you know, think about wireless data centers.

You know, they, they've got an interesting angle to play from a long-term perspective that, you know, nobody else has. You think about the amount of cabling and, uh, needed in a data center. There's an enormous amount of expense there.

Um, and I think, you know, Qualcomm has some disruptive technologies in their technologies arsenal that, you know, could really give them an interesting long-term play as the market matures here. Um, but, you know, clearly the stock market loves the narrative, loves the angle. Um, but, you know, we'll, we'll see kind of how impactful it is from a revenue perspective over the years to come.

So, look, I, I commend Qualcomm for doing this, right? Because I think, you know, there are two kinds of people in the world, two kind two kinds of companies in the world. There's one set that looks at it and says, wow, Nvidia is an 8,000 pound gorilla.

They have years of a headstart, it seems, in, in this AI chip race. And why even bother? Right?

Why even bother with let's find something complimentary to them, or, you know, work with them. And then there's the other kind of people who say, you know what? I'm gonna chip away at that stone, and I realize they have a, a dominant position today, but it, it isn't always gonna be that way.

And if I don't, if I don't start now, it'll, oh, I'll never get there. You gotta start somewhere. And, and you know what?

You look at a MD versus Intel in the PC wars of the, of the nineties and two thousands. It, it was the same kind of thing. It was, you know, kind of shoveling sand against the tide a little bit at some times it seemed.

But their day came too. Their days come too, and Qualcomm and everyone else getting into this AI chip, including a MD, right? Look, AMD's had some great success recently in this AI chip business.

So, you know, we need to foster competition. Competition is good. It'll keep Nvidia on their toes, right?

And, and keep them trying to maintain their lead. Um, like Dan, like you said, six G, you know, when and if it becomes real and how well it's implemented could be something that tips the scales into, into the Qualcomm favor because of their expertise there. Competition, you know, going back to what we spoke about in the last segment, competition is good.

It, it, that is kind of the American way. We don't want monopolies or, or government, you know, mandated winners and losers. Competition's a great thing.

I I applaud Qualcomm for doing it, and I, I'm not surprised the street has as well, Dan, when do people kind of design products around chips? And I'm asking the question because if I ever look over at Nvidia, you know, they all tell me about like, Blackwell is coming, and they were, gave me like a year, year and a half lead time on it, and then we all sat around and waited to see if they could actually do it. Qualcomm will be probably the same thing, but am I designing something three years out on the premise that Qualcomm will have a chip that I'm gonna use?

Or do I design something that kind of assumes that there is some chip from somebody that I'll use when it's available, but I don't really know for sure who's got what when? Yeah, I think the relationship between the leading chip makers, and let's be honest, there's a handful of meaningful chip makers in the world. There's a handful of meaningful OEMs and ODMs in the world, and the engineering relationships between those companies are incredibly tight, right?

I mean, they're sharing three to five year roadmaps with some level of specificity, um, and five to 10 year roadmaps that have, you know, probably a little bit of a higher level of abstraction, right? So, you know, this, this is not a world in which, you know, you don't know what's going to be available for you. Um, you're, you're looking at these roadmaps, you're talking to all the suppliers, and you know, you're reeling all using all that information to design what you're making, you know, as, as somebody who uses chips as an input to, you know, some other larger good or service that they produce.

So, you know, I, I think there's a, you know, pretty strong telegraph signals being sent by everybody within the industry on kind of what's to come. Um, you know, like I said, I, I think Qualcomm's got an interesting play here from a long-term perspective near term. I think it's more of a narrative shift, unless you'll less something you'll see in the actual results.

But, you know, this is a massive market with an incredibly large tam. You know, if they can find a few use cases where, you know, they can pick off 10%, 20% of the market, I mean, that's billions and billions, hundreds of billions of dollars, right? Um, you know, we've talked a lot about, you know, how Qualcomm excels really in mobile on the edge.

Uh, you know, I can't help but think of the world of physical ai, you know, they're a big player in that space as well. Um, and as we launch all of these, you know, autonomous physical AI driven robotic devices out into the world, they're gonna need connectivity that will likely, you know, come through a cellular connection where Qualcomm's dominant and that could easily go back to a server farm where, you know, they're running AI inference on these massive Qualcomm racks, right? So, you know, I, I think we, we can, we could easy to be skeptical, uh, given, you know, they're entering the market a little bit late relative to some of their com established competitors.

Uh, but, you know, this is a, a company that has really led the mobile and internet revolution in a big way from a network perspective. Um, and I would not call 'em out. Mm-hmm.

Great. Chris, you're building out AI apps. How much of this factors into your thinking as you start to build out a project?

And are you, you know, are you planning a year from now from a certain amount of capability being available and then we'll be severely disappointed if it's not, or are you just going with what, you know? I think it's, I, I, I've mentioned on this show, the show a number of times, you know, that, that this show itself is one of my little metrics, you know, because every week we come back and talk about these things, and I can go back to March and March, April, may, particularly, we Yeah. And Dan, you know, you and Dan, and, and, uh, and, and I, so it's on that same path.

I, I think that on this side, yeah, the hardware compute base thing will continue to go as we discussed, right. You know? Right.

You know, earlier this year we were still thinking, I'm gonna have this not just one GPU, I'll have a million GPUs and I'll take every single thing I do and go all the way to the top and come back down. And that doesn't make any sense. You know, and through the year, we've gotten more into the, at the chip level and, and all these different deals that's, you know, that workload is spreading out.

We've talked about how an LLM, what we horribly call artificial intelligence, an LLM as a semantic tool in a workflow embedded in things is different than a chat, you know, LLM that you're talking to. And, you know, and we're just working through what this even means. So all of this, you know, you know, almost everybody on the screen here is more expert than I am to say at this particular point.

These individual moves are right or wrong, but they're all going in that direction, right? That this is you. If Alan, you said earlier, this is, this is one of these times, it's fascinating, all of quantum, the last segment, you know, that's happening.

And we've all been seeing that company and coming in, that's happening now. Oh, AI's coming. And we saw that coming, and that's happening now.

Oh, and by the way, it's semantics systems hold mine, beer. That's the next segment. We'll talk about that.

And, and, and, you know, and turns out we built trillions of chips and put 'em everywhere, and most of 'em really aren't doing anything. But now we can be smart enough to actually make them all work, the ones that are already there, much less the ones we're building next. So I don't expect to be disappointed at all.

I think our ability to tap the hardware is limitless. I don't know. Mm-hmm.

Alan, do you think that the CEO of Qualcomm will be making a trip to Mar-a-Lago to bend the knee looking for a little extra cash to fund this? What do you say? You know, it's not the cash, it's, it's, you know, this is kind of like, uh, Don Vito going to visit Don Cheche to get permission for genco olive oil.

Right? It's not the cash, it's the blessing, if you will, of, of, you know, excuse me, maybe being part of this AI factory building buzz or, or what have you, right? Being one of the anointed one, it goes back to what I said before about the investments in, in quantum.

We shouldn't have the US government anointing our winners and losers, especially based on who bends the knee not right? One of the principles of America, of America and the American way of life is the guy who comes up with the better mousetrap wins that, right? We the best and the brightest, not, not the weakest and the most kiss butts.

And so, I hope not to tell you the truth. I I don't think they need to. I, I think, look, Qualcomm is a, is a qua, no pun intended.

Qualcomm's a quality company, right? They've built an amazing business. We haven't even mentioned, no one's mentioned, look, the snap, the success they've had with snapdragons on PCs, right?

And when it's done to the price points for PCs go, I don't know how many of you go shop for laptops anymore, but man, I think there's some inherent go government support though, Alan, right? Like, I don't see a future in which, you know, we have Huawei towers going up as the six G build Outs. Great.

No, Qualcomm will have, These are markets where there's one to two players, you know, kind of on each side of the geo pli geopolitical divide. Um, and they're inherently gonna get the backing of, you know, not only the us but lot of large western governments. I mean, you, you don't want a monopoly either, though, right there, you want competition, Right?

But to be fair, and to Dan's point, do we have a choice but to not fund these companies because we are up against nation states like China that are funding all kinds of stuff on their side. Oh, come on. That sounds like red scare b******t, honestly.

You know what I mean? Well, We've got one fab, right? We've got one memory company as a nation, right?

You know, across all the critical layers of the stack, there's one to two winners, right? Well, if you talk to classic VCs there, there's always three winners. You need three, right?

For the market. You need three. And, and if we are in, in company, you know, this was my thing with Intel.

I'm not against funding intel and keeping Intel viable. If, if it proves that it can be viable, if you will, not that it's propped. I mean, you go back to what Ronald Reagan said about tariffs and stuff like that, you know, and I hope if no offense to my Canadian friend there, but if you go back to what Ronald Reagan said about tariffs, he said one of the, the weaknesses of it is it are, it creates an artificial market where the companies are being propped up because they're not truly competing, right?

We need a strong Qualcomm to compete on the world stage. I mean, I, I look at the two vectors, right? We can compete on performance and we can compete on economics, right?

Right. I think on some of these critical technologies, they'll kind of be okay losing on the economics as long as you win on the performance. I, by the way, I, I don't believe in the three company rule.

There's actually just two winners in a third company that hasn't figured out they lost already. Well, that, well, most markets there's more than three companies, and that, that's the key, right? And the, there's, you know, that's why in the Olympics you got your gold, silver, bronze, and then everyone else, For sure.

I just think on the, the markets where, you know, the barrier to entry is so high and the amount of investment needed is so high, that that tends to gravitate more towards winner take all, um, you know, that kind of a healthy competitive environment, right? You've seen that at Hyperscale Cloud. You've seen that in semiconductor fab.

You've seen that Now, you know, in AI models, you know, anything where, you know, you're measuring the investment in hundreds of billions of dollars, no defense sector, no different, um, seems to, seems to gravitate more towards that oligopoly model. So a couple of things there. Let's take the hyperscale cloud.

That's exactly what I go A s Microsoft Google three, right? And I-B-M-I-B-M had a coffee. Well, well, but you know what, to be fair, there's the top three in everyone else, Or Oracle's name a player, I would say.

And then if I look at Oracles a player too, top three in everyone else, take, take another, uh, one that you mentioned there, Dan and I, my brain's blanking on me. Uh, ai, you've got open ai, you've got anthropic, maybe perplexity, right? Or or another, uh, uh, Elon, uh, grok, right?

You have at least three players there. Well, and meta, you've got, you got at least three players And the entire open source community. But, well, That, that's a whole nother story.

Yeah. We always used to say we would, we would be competing against other, yeah. Other had a Huge block or competing against a spreadsheet.

But I, I think it, it's, it's good because I think having competition hastens and Fosters bringing out the best in people, healthy competitive, right? Not, not weighted or anybody putting thumbs on scales. Intel was driven by A-M-D-A-M-D may have always come in second, but they drove Intel to stay one step ahead.

Absolutely. It's the same thing of play here. It's what makes, it's, it's the market.

That's what makes the market the market, right? You, if you, if you are not pushing as hard as you can, someone else is, man, and they're right on your heels. And that's what keeps us moving.

That's what keeps us moving, right? No matter if you are the leader in your space, you're, you know, you could, you're always kinda looking over your shoulder a little bit saying, Hey, who's coming up fast? And if there's no one coming up fast, I think it's just human nature to kind of take your foot off the pedal a little bit.

Anyway, hey, we're over time on this one, and we've got a, it's still another segment to go. So let's take a break here on the gang. We're gonna come back and talk about the politics of ai.

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Contact us today and tell your story to the world in the most powerful way with Textron Group. Hey, folks, we're back. And we're talking about this, uh, position that, uh, super PAC has taken, that's led by Mark Andreesen and his folks in the Silicon Valley area.

And basically, they came out and said that they were gonna support political candidates from Republicans or Democrats as long as they were pro ai and not all for regulating ai, which probably might eliminate a lot of Democrats anyway, but it was interesting to see a, are they trying to kind of pull back and establish some mutual ground and immediately got a response from the White House, where basically White House said, how dare you slap us in the face after we did all these wonderful things for you. Chris, what's your take? What's going on here?

I haven't heard better news in a long, long time, right? So I've been predicting this sort of response from this administration at this, at about this time. And in general, as we discussed in previous segments, you know, organizations with this sort of sort of view would probably not like this sort of thing, which is a positive indicator that we're getting to the point where it's a real threat to narrative, uh, malicious narrative influence operators, right?

So as, as, as Alan, you know, I, you know, uh, uh, back in 2008, I spend a whole year, you know, thinking about this and with the Obama campaign in leadership of the Rapid Response team. And it was interesting. I was con I was the conservative in that group, right?

It was a funny time in politics. And, uh, and Steve Bannon started from what I know, two weeks, two months before I got involved with that. And it stayed on that path.

And Valerie Groff and Putin in, in Russia and, and these other, you know, uh, full, full-time malicious narrative threat actors who made livings out of that have been dominating the landscape since. And the entire posit and premise of civic AI and attestation systems and, you know, everything I've been working on, not, not just in, in this space, in cybersecurity, technically, everything the quiet wire is about now in the civic ai, uh, open source project is on the premise that at a certain point to our, our conversations in the, in the show about the economics of competition and so forth, it's, it works in the short term to just flood the zone. You know?

Uh, we'll, we'll keep it Russian for the moment. Valerie Grass, ma the Grass ma of doctrine, since 2012, Putin has built his entire national strategy on that. And it's about the ability to use bullhorns to dominate all public narrative forever.

What's happening with a number of things, a, you know, being driven by AI in multiple ways is that that's not going to be viable for much longer. So I take this administration making exactly this pushback exactly right now as the most definitive proof that those of us in the narrative sovereignty nor narrative integrity, um, uh, move, you know, world are correct. That these systems of mass narrative domination are like dinosaurs at the end of their era.

They're, they're, they're the large corporation with 30 years in the space when the startup comes in, that turns a lot faster and, and, and brings 'em down. Yeah. This, this is, I think, the strongest indication.

We're at the end of this mind numbingly tiring era of narrative bullhorn all over the world, right? It just doesn't work that well anymore. Chris, to quote Michael Corleone godfather, part one, now who's being naive?

Kate? Oh, Why, why, why, why are they so offensive? Why is a, is a voice like this, and I'm trying to keep it out of, you know, this, out of, you know, personal politics.

But if I'm correct, if this, if this narrative voice, this, this US administration is not a sincere narrative partner, why else would they be pushing back against this right now than the fact that there's concern? Well, I, I know that the world power are At prep 'cause fascists are gonna, gonna fas or whatever the word is. But, but here's the real deal.

When you combine that stance with deciding who I'm going to invest in and anoint as my chosen ones while I'm in power, that's where stuff goes south. I don't know if south's the right word. That's where stuff goes bad.

Well, You mean the grain capital, you know, in this economics is money, or, or, or money is, is speech in a way. I don't wanna get into that whole thing, but in the same sort of way, you know, again, we, as we said in the last segment, I like libertarianism. I may seem like a socialist in state environment, but I'm a capitalist libertarian, freedom of speech, narrative integrity, fanatic.

And I'm that way for a reason. I think there's competitive advantages, and I think certain actors on the global stage that I'm not in favor of, are making moves defensively that show they're weak, that their approach so far. Oh, absolutely.

Absolutely. But, And others, if they don't do it right, others will. I, I hope so, Mike, to your point, I don't think it's necessarily just Democrats who are looking to regulate ai.

I, I think, I think that shows that, to Chris's point, you've been sucked into the narrative, right? Because I remember someone who runs X starting a lawsuit that wanted to regulate ai, and I remember the people who signed the a hundred people who signed the pledges about let's stopping ai. I don't think that was necessarily a Republican or Democrat thing either.

So let, let's not put people into camps based upon the, the propaganda that comes out of, you know, our out, out of our political machines, right? Well, and we're, you know, we've actually called this se segment politics and we're talking, so we'll, we'll, let's run the gamut on it. You know, I don't, I, to be clear, I'm not picking, I'm not picking on any one particular political party or structure.

I'm kind of fed up with all of them, you know, kind of commonly, I think perhaps for, uh, uh, more nuanced reasons. I think the liberal, uh, international order has spawned liberal in, you know, literally called liberal, sometimes, sometimes the opposite. Australia, what are you thinking?

Um, but anyways, all these, what we would see as Western liberal, uh, political parties are inefficient at best, terrible, at worst. Well, democracy, a lot criticism Are wrong. Any of regulating this, I, you know, this is, again, I, I should be arguing with the Republicans.

I should be a Republican again right now because this is where my libertarian Republican free market, you know, uh, uh, economics, bones, just tingles. It's like, you can't regulate this. This is like regulating speech.

Forget it. And if you see this as a threat, then you're not in, in favor of free markets, free speech economics, and, and you'll get run over by those Who are, but here's the thing, we're missing the irony here, guys. This is an ironic moment here.

You've got Mark Andreessen, who during the last election cycle really came out hard, right? In supporting this administration, in supporting the candidacy of Trump, right? They, he put big money and put his money where his mouth is, and put his mouth where maybe, where it shouldn't have gone in, in supporting Trump versus Kamala, or, you know, in supporting the hard, right?

He's now recognizing that it's not a question of what political label you have on whether you are wearing a red tie or a blue tie. He wants to back people who are going to further his own business ambitions, which is the way it's supposed to be. That's America.

That's America. I support people who are gonna help me. Am I selfish?

Yeah, but that's America, that's what it's supposed to Be. That's the problem of it, Alan. Right?

I mean, I, I think part of what they're trying to do here is kind of ask that famous question of, you know, just because you can doesn't mean you should, right? You know, you, you're right. We Actually societal impacts of achieving a GI, right?

You know, we probably have it, you know, I'm not sure we could, um, crispy has, yeah, No, no, Dan, I agree with you. I agree with you there, but, you know, we don't have the will to regulate that today, I don't think is is is the issue. No.

And, uh, you can't judge the intention good. And honestly, because of what you were saying, Alan, which is, you know, because there's an inherent kind of economic interest alignment behind it. I think we're kind of missing the point of what is a pretty good question of, you know, do, do we actually wanna do this?

Right? We're kind of driving very, very hard to it without having a lot of the hard conversations about what it really means. But, but look, again, look at the history of mankind.

When have we ever, and this, I'm sorry, Chris, let me just finish this, This, this is where I, I specifically say libertarian because you, I always, you know, look to be clear the libertarian party, libertarian ideals, and man, it's all very simplistic and naive. However, right? There are principles in there, and I fundamentally believe them, and this is a perfect example, where we're running down a path that's, that's testing that belief.

They we're like, well, maybe not this time. Maybe we'll, actually, and I'm just looking at this and I have my same concerns. I think I, I think, well, we will be, okay, let me deliver that message.

If I'm right, we'll be fine. But we're finding ourselves saying, well, we fundamentally believe in, but not in this case. It's like, well, I think we should, but When have we, I don't think Ever, at your point, Dan, I don't think we have a choice.

We're going down the path anyways. Exactly. Dan, that's my point.

I don't disagree. Just because we, I think that's, you know, just because we can, doesn't mean we should. We're not, that's what supposedly separates us from animals, right?

But when have we not? Because at the end of the day, humans are animals. And that's my fear.

Or I think this goes to what we talked about last week with the Valley. It's getting a little out of touch and kind of pursuing its own interest apart from the rest of the country, and the rest of the country's gonna take notice. And do not be surprised when you start spending billions of dollars on making sure that there's no regulation for ai, that you wake up one morning and there's gonna be regulations for ai.

'cause everybody's gonna be paying attention to the conversation and conclude the Opposite. But to Dan's point, maybe it's a good thing. I don't know.

Yeah, Just think a lot of the paths where AI could take us ultimately start to get into the territory of kinda redefining what it means to be human right. And, you know, I don't think the everyday person has really put much thought into that. No, Well, we haven't.

And, and not, not to this counter based concerns, but yo, because these, this is existential in the extreme. Um, it is just that I, and, and the people I've been spending a lot of time with this year have been thinking about this an awful lot. And I think, I think, again, I think, Alan, you right, this is happening anyway.

There's no stopping it, right? And it, if we don't, they, you know, and my, my, my kids can pick on me for even saying that I'm old. I get to, but it's, it, it will work out because the, the downsides are so extreme that we don't want to go there.

We won't go there, but we have to go through it like we do with everything else. And again, this is the American Libertarian, just seriously individualistic approach. Open your eyes, pay attention.

This is where we're going. You know, the storm is coming, you know, this is, you know, the entire world is Jamaica this morning, right? So the storm's here, deal with it.

Heart's out to the people in Jamaica. Let's end it on that. Guys, I, I gotta run Kate, Chris, Dan, Mike, thank you for joining.

Thank you for watching us. I hope you've enjoyed the discussion. We'd love to hear your thoughts on this, right?

People vote with their voices and their, their keyboards and everything else. So don't be afraid to, to, you know, log in and, and speak out on this. I think it's important to all of us.

I hope you enjoy Text Drunk TV immediately following this. Of course, we'll have another fresh gang tomorrow for Thursday. But until then, this is Alan Shimmel on behalf of the Textron Gang, have a great day, everyone.

Hey everyone, welcome back here to Text Drunk tv. I'm really happy to have this next guest on. It's a new, a new company we haven't featured before, and he's, it's his first time here.

So let me introduce you all to Pavlo Barron. Pavlo is the co-founder and CEO of a company called Platform Engineering Labs. Pavlo, it's great to have you on.

Nice to meet you. Welcome. Thank you.

Thanks. Thank you so much for having me. It's our pleasure, Pablo.

Before we jump into platform engineering labs and something called Fori fori, I, I want to talk a little bit about you and give our audience a sense, as I mentioned, you're the co-founder and CEO of the company. Tell us a little bit about you. Alright, I'm looking back at a, uh, very long career in, um, well broader IT space, let's call it like that.

I'm, uh, pretty much a veteran and, um, well I basically was in every single role you can imagine in different kinds of companies, different domains, always staying curious, going after domains. I didn't know so far was architecting, creating platforms, leading engineering teams, uh, building teams, building startups. So the most recent, uh, achievement is, uh, co-founding and, uh, co-founding a company called Insana, uh, that exited to IBM 2020.

And that stayed with IBM for a few years, um, to, to have an internal role as a, um, in the, in innovation space. And, um, yeah, I represent the company, so I need to tell a few words about my co-founder. My co-founder Zach Schneider.

He is a good friend of mine. And, uh, we worked together at Inana already, and we always, always kept talking about, um, well, how to change the things in the space we're going into now as a company. So Zach lives in, uh, sunny Traverse City, Michigan.

And, uh, his, uh, most recent activity was, uh, he was one of the core engineers of iCloud at Apple. Really? Right.

Very cool. Very cool. And he was at stanner as well?

Yes, Sir. So of course, well, I'm personally familiar with Stanner. We worked a ton with Instana, you know, prior to the IBM acquisition.

com, I think. But, um, interesting. So were you a co-founder in Stan, or is that the story?

Yes, I'm, I'm co-founder and CTO and I invented big chunks of it. Very cool. And Zach also was a co-founder there, or he just worked in Incenter?

Yeah, He was my first hire actually in America. Really? Yeah, because, uh, it was 24 7 with humongous ingress, a lot of data flying by and, uh, in the early days I took over also the responsibility to actually run this thing, not only to build this thing with the team, and then at some point I had to make a decision to actually cover the US hours because we had us customers and sure, I was myself in Europe like I am right now.

And, uh, this is how I met Zach through another friend and, uh, it turned, uh, into a really good relationship and friendship. That's fantastic. What a great story.

You know, my, my very first company I co-founded was back in 95, 96. Uh, I didn't know what it was at the time, but it became known as web hosting. I thought I was a virtual landlord.

I was just buying hard drives to store more websites. Right. And, and we, I had the same thing in reverse all of a sudden we had a lot of European customers and that meant we had to do tech support for us.

It was the middle of the night. Right. So, so after a few, a few months of staying up all night, you know, running, we, we ran Solaris, that's how long ago this was?

Oh yeah. Right. Being trying to be a Solaris admin and then go to work in the morning and run a business.

I said, this is crazy. I need, and I did the reverse thing. We actually picked up a partner in Germany, right.

Uh, who, who was a, he was a great customer. We gave, basically gave him hosting for free and made him, made him our, you know, our tech support overnight until we went to 24 7. Right.

So familiar with that. Familiar with that. Very much so.

So Pablo, let's talk about platform engineering labs. No one, you, you've started enough companies, you know this, no one undertakes starting a company lightly. It's a commitment, right?

Right. It's, you put your life and your soul into it. Yes.

Tell us, what was the passion that drove, what's the mission of platform engineering labs? Well, the broader vision, we actually want to eliminate the toil, the unnecessary labor, the human error from, let's start with the infrastructure space, but generally everything that is about delivering software, um, the problem we are addressing is old and it's not solved. So Zach and I, as I said, going through very complicated, um, environment and, uh, you know, massive data ingress and a lot of responsibility for 24 7 platform running for a lot of customers, OnPrem as well in SaaS.

We actually learned a lot of things that we wanted to improve. And, uh, we met a lot of people because I mean, you can imagine that when you are an a PM solution that gets, uh, a lot of customers and a lot of attention, you basically meet everybody in the ops space, right? And let's call it the broader ops space because there is just a little difference between the disciplines, but it's the same people with the, the same skillset.

It is about people who are running software and, uh, we met a lot of them. And you know, there is so much pain out there with just a few things that are not properly addressed in the space that we just thought that we need to change that at some point, once we learned that, uh, uh, there's a, a lot of movement in the markets. For example, back then when HashiCorp acquisitions for IBM was announced, we just, hey, it's about time to start it now because we know that, uh, this space is something we want to change, even if this is the last thing I do in my life.

So that is, let's hope not, let's hope not. No but's you and me both my friend, but this is the commitment. I mean, we definitely, we are on a mission to give platform engineers, and I'm talking about platform engineers is a species.

This is finally a discipline that allows us to work the way best teams actually work, which is a few real good people and the few people who are just willing to go full stack as deep as possible, and who willing to go on call all night. This is a special species, as I said. Uh, these people work for a hundred other people.

So basically one team is serving a hundred other teams and uh, that's the cool thing about the platform engineering, because after all this cultural dancing around DevOps and whatsoever I am, I'm just seeing this as a chance to really set it up the way it was supposed to be. But, and here's where a company comes into play. These people have to deal with tools that are completely outdated and, and, and have never been designed for this, uh, mode of operation.

We're talking about develop experience or user experience from the, uh, from the late sixties. I mean, everything is low level. Everything is completely detailed.

You are the guy that is part of the tool, not a single tool they have to work with, can allow you to lean back and, and, and, and let it go. Um, while everything in the dev space is being constantly improved and simplified and abstracted, none of this is happening in the ops space. And that is exactly what we want to change.

And, uh, we will definitely go one by one with a current focus on the infrastructure management because with no matter what tool we tried in the best, and you can imagine, Zach and I, we really went through all of them and we had a pretty big responsibility using them. We tired. And we, when we are tired, we talk about everybody else was tired.

I know enough people in the space who are really good, but they're close to checking it all in and just going, gardening. That is how bad it is. And I wanna change that.

That's, that's my mission. Good for you, man. I love it.

com. I, I understand. I feel your pain.

I understand the mission. I, I, I get it. Um, I also look at platform engineering, not as a replacement for DevOps.

You, you're still going to do DevOps once you have your platform laid out, but it's sort of laying the groundwork that we can build on top of. I, I think one of the things we learned, and, and you know, Pavlo, you've been around, I've been around, right? We, we, we see what's, how this works.

One of the things we, we've learned in the DevOps movement is onesies and twosies and small teams. It's, you can really control things and, you know, fine tune and dial in when you go to scale. Right?

Scale's a different animal, a different beast. Yes. And, and what works in a small team well, doesn't necessarily work at scale.

Right? Well, and and to me, that's really the mission. Build a platform that scales, right?

That allows your developers, your DevOps engineers, your SREs, your, your qa and your security people to go as fast as they can go. Exactly. Sir.

Yes. I love that. This is exactly the point.

We have more kind of professions coming up in this industry. I'm talking about all this AI engineers and everything they get. Sure.

All of the infrastructure management built in, all of the security tools actually manage the infrastructure themselves. You are in the situation that you cannot manage everything with one tool. And I'm hearing people using within the same setting, two to three different infrastructure management tools, that is absolutely impossible to, um, well, to call it being under control because everything is happening in parallel.

And if you slow them down, actually that, that's, that's bad, right? I mean, you, you want, yeah, you want everybody, as you said, to move at at the speed they want and have to move it. And what is the job of the platform team or operations team or SRE team, however we call them?

Of course it is to support them. But since this is exploding and it's gonna explode more and more and more, the more AI is gonna be used because everything is landing on your desk. Your only job is to get rid of routine work and put yourself in a driver's seat and always have a feeling of control.

And this is, this is exactly what our goal is with this first tool we are building regarding the infrastructure management. We want to put you exactly in dust in that driver's seat. Let everybody go at the speed they want.

But you are the one who control and manage things. I love it. You know, and, and you mentioned ai.

Look, AI doesn't do well in, in chaos. It'll do best in a, in a very defined environment, right. The more you can define it, the, you know, if it knows what the rules are, it operates within those usually.

Um, so I, I think it, it, it behooves us as well, if we're gonna go with an ai, everyone wants to go ai, ai, ai. I get it. If you have, again, you have a platform with guardrails, with clearly defined, you know, routes, then AI can go faster than any humans are, are gonna be able to go as well.

Um, so it, it all makes sense. I gotta do a little business housekeeping, pavlo platform engineering labs. What's the website?

It's platform engineering. We have a great number. Love it.

Great. Um, and company. It, it's, it's out of, it's, it's in it, it's out of stealth, I should say.

It's public. It's, is it funded? I, I don't know.

Yeah. Did you raise, raise money? We have, we have investors we precede.

Okay. We have an announcement that is a few months old already of our prese round. Uh, it's out there in the public, so it's not a secret.

Mm-hmm. Very good. Um, alright, let's turn to a and I, I, you know, as we were talking off camera, Latin's not my forte, though.

A little pun there. Forte's Latin, but, uh, but for, is it for me or for my, it's forme. Forme.

Yeah. Lucky me. My daughter studies medicine, so, uh, she can teach me that.

Excellent. So forme is, is this new, uh, open source infrastructure is cloud platform that Platform Engineering Labs has, has pioneered or has developed. And it, it's basically, you know, infrastructure is code built for the future.

Tell, you know, that's at a high level. Tell us more. Right?

Infrastructure's code is a very interesting space. There's a few players, it's not really crowded. This, uh, the space is dominated by, uh, Terraform.

Terraform, uh, is developed by HashiCorp. HashiCorp is is acquired by IBM. Uh, there's a few other players, uh, in the market with their own, uh, uh, advantages and disadvantages.

But our goal is a little bit different than what everybody else is doing. We just looked at what is, what is slowing down those people who work for the other teams in regards to infrastructure management. There's a, there's a humongous divide between what those platform teams are doing and how developers are working.

For example, I'm yet to see a developer, a regular developer who wants to go on call and wake up at 2:00 AM to fix their stuff. They're not doing that. This is the responsibility of somebody else.

We call them the operators. We can put them in whatever discipline you want. Uh, it doesn't matter how we call it.

Even platform engineering. That is a role. It's a responsibility.

We want to support this role with a, with the adequate tool. And honestly, we learned, uh, for many years of experience, the best tool people work with and everybody agrees on is code. So what is, what is for me, for me is a hundred percent entire coding code out infrastructures code tool where tools like Terraform rely on a separate state file that is then your code.

So your code in your Git is not the real code anymore. And you really need to, you really need to play that catch up game with your, between your state and reality. Our approach is totally different.

What we say is, first of all, we embrace embrace the reality. The reality looks like everybody will be using their own tool. You cannot enforce in a, a reasonably well in any reasonable setup.

You cannot enforce one single tool. So let's forget about that. Now, what do you want to do?

You want to give them their own tools, let them move of their, at their own speed, as we already discussed, but then catch them with a single source of truth. And that is the most important thing. That is what Forme does.

Forme can perfectly coexist with any other ISC tool, click ops, any other security tool that makes changes in your cloud, including your own cloud providers that can constantly make changes. It is an agent based tool that is important. So it's not what you run exclusively on your local machine and babysit it?

No, you don't have to. It has an active backend component, and this active backend component takes over the responsibility of actually converging the work to the proper result. And, uh, as I said, it's coding code out.

This means that you can make changes in this platform at any granularity for any resource in your infrastructure. Um, with as minimum, um, blast radios as you want, you can do whole rollouts. You can reconcile a hundred percent from your code.

You can consider us as your source of truth regarding the code because you are extracting resources from their current real state. You are extracting them as code, you're changing them, you're putting them back, and we apply the change. So you always work through code, uh, with this tool.

And we're catching up by automated, uh, fully automated discovery and synchronization. We're catching up on what's happening in your cloud state. So we basically are absorbing any change that happens through whatever tool you use, whatever people are using, and version them all of the changes and put them into our internal database, which allows us also to completely eliminate, um, the necessity for tools like Git well, let's say in the future, because when we early was still developing that.

But what you already get is a fully versioned a hundred percent code oriented tool. Got it. Couple questions.

Sure. First of all, you mentioned of course, Terraform, hap, HashiCorp, you know, our audience is sophisticated and this, they know Linux Foundation, our CNCF, you know, uh, open Tofu Yes. Is a fork.

Sure. But it, you know, it's in that same vein as as as Terraform, though They are, they're diverging. Right.

As time goes on, I think you'll see more of a divergence between the two of them. Um, the other big thing with platform engineering is it's become so much about internal developer platforms, right? Backstage, things like this.

Um, where almost that has become, if you will, the mission, right? Maintaining the idea. I, I think there's a bigger mission for platform engineering than just the id.

I don't want to minimize Right. The id, you know, they're having an an ID is very important. Yeah.

But there's, there's more to the platform than the ID is I guess what I'm saying. Right. Um, now your tool for my for is also open source.

Yes, sir. Correct. Yep.

And you guys are maintaining that. What about the community? Yeah, we're gonna build a community.

I mean, everything in the system is a plugin, basically. So it's so pluggable that people can develop their own plugins, they can maintain their own licenses for the plugins, of course, uh, as a, uh, company that is funded by, uh, investors and has a commercial meaning we are going to go after well monetary aspects of the business. Um, but right now it is important to us to open this to the world and, uh, allow everybody to enjoy the infrastructures code it as it was supposed to be.

That is the most important thing for us right now. But of course, we're welcoming contributions. I, we want to build a healthy ecosystem.

We want to build a marketplace, we want to build all of these things. The coming, definitely the important, uh, aspect, uh, regarding this, uh, um, developer, uh, portals that you mentioned. Well, the problem they all have is that in the space of infrastructure, they have to opt out to what is there.

So this means that some of them are ending up generating Terraform code. And the problem with generation of Terraform code like HCL code, somebody needs to check it still. I mean, you cannot rely on, on, uh, on machines, uh, spitting out proper code.

Um, no, not even ai. And, uh, this is number one. The number two is that, you know, even if you have that developer portal, your infrastructure management is still not solved.

You want to have it on proper feed. And, uh, the abstractions through the portal only means that you need to provide services that are reusable and, uh, um, can be claimed by, if you wish, by those developers. But in order to create an infrastructure service, you need to think, when you think about a database, you think about a t-shirt size of the database.

You don't talk about gigabytes whatsoever. Not a single developer knows how many gigabytes this database will ever need. Who knows that?

Nobody knows that this is not my job as a developer. So the platform team needs to create abstractions. And guess what?

You can't create abstractions with the current tools. It is literally impossible. That's what we give them as an add-on.

You can actually create layers of abstraction involving every single engineer in your team at the level of their experience and in the level of their responsibility. This is another important aspect. Excellent.

Agreed. Um, I, besides the platform engineering, I'd imagine the for is also on like GitHub and so forth. Yes, absolutely.

You wouldn't happen to know the GitHub, uh, url, would You? Yeah, it's, uh, so the company is called Platform Engineering Lab. So on GitHub, it's Platform Engineering Lab slash for me.

Excellent. I just wanna make sure we give 'em everything. Yes, Absolutely.

Um, what about Discord or Slack or other ways for the community to communicate? Yes, there's a Discord. The Discord is an, the read on GitHub, the Discord is in the Read Me on the organization.

That's all set up. So people are welcome to join and there are GitHub discussions. We're completely open.

And, uh, actually very excited about talking to as many users as possible. I love it. Pavo, we're about outta time, but I wanna wish you all the success.

Thank you so Much. On, on this new venture platform, engineering labs. com.

So keep us posted and you know, we've got a good place to put some news up there. You got it. So we thank You.

I hope to hear plenty from you. Alright, thank you. Palo Barron, co-founder and CEO for Platform Engineering Labs.

That's platform engineering, uh, makers of forme, uh, open source infrastructure is CLO code platform built for the future. You're watching Tech Drunk tv. We'll be right back.

Hey guys, thanks for the throw. We're here with Shiva Palle, who's senior Vice President for the Americas for Veeam. And we're talking about their acquisition of security AI and what that all means because, well, as I understand it, at least they're adding a data security posture management platform to their portfolio.

Shiba, welcome to show. Hey, Mike, absolute pleasure to be here. So thank you.

Very, very exciting news. We haven't acquired them yet, but, uh, we announced our intent and we're in progress to do so, so very, very exciting time. Where does A-D-S-P-M platform fit in the portfolio?

'cause it seems like a natural extension, but, um, what are we maybe not appreciating about this whole thing? 'cause it seems like we live in the age of AI and maybe the whole way we think about securing data needs to change. Yeah, it's a great question.

Um, firstly, I think security AI is much broader than just A-D-S-E-M provider, but I'll, I'll set a little bit of context. I think Veeam, we've done a great job of being, you know, the folks that protect and recover your data. Um, but in the AI era, I think things are changing.

Um, people don't know the surface area of what data they have out there, uh, how to protect it or having to classify it. Uh, and then when you add ai, LLMs AI pipelines and a bunch of other complexity, this explodes to a problem that I think most customers or most, you know, CIOs, CEOs even haven't ever faced. Um, so bringing those two parties together, the mend, uh, security ai, I think we just help solve for a multitude of problems.

We make sure your data's fully secure, backup immutable on the, on the, uh, data protection side. Um, but we also look at classifying data across the cloud SaaS, even your apps backups. And so we, the intent here is bringing these two parties together gives a very unified and trusted view.

And the outcome for A CEO and CIO is to be able to accelerate into the world of infinite possibilities that AI brings, but doing that in a very safe, um, safe manner. And I think that's a big problem for everybody at the moment. Mm-hmm.

Now, as I understand it, one of the core technologies that the acquisition will bring is that there's a data graph built into their platform. And it seems like there's an opportunity to extend that and the, and the reach of that to provide more visibility into all the various types of data we have, because, well, not all data is created equal and therefore needs to be secured differently. Yeah, a hundred percent.

The data command graph is probably one of the more exciting pieces of this. Uh, maybe I'll give you a quick, simple explanation of how I see it, and I'm still getting, you know, up to speed on a lot of this. But, uh, I, I view it as a nervous system for your organization's data.

So the data command graph will connect data across all sorts of, uh, you know, endpoints, SaaS, cloud, um, even your backups. And then it continually maps relationships like ownership, sensitivity and access. And those three things make compelling context when you look at which data is important, why is it important, and what governance and compliance you need to apply to it.

So if you made that the foundation for everything else, your security, compliance, recovery and how you pursue AI becomes inherently easier because you're looking at it from a common frame of reference. And to me, that also extends to, hey, it's not metadata, it's it's actionable intelligence, um, where your data is who can see it, and whether it's safe to use is like the common questions that I think, um, it industry leaders are now challenged to face and execute upon. Are we gonna see some level of convergence here along this line?

Um, I've been watching data science teams kind of hunt around or pull together the right data and figure out whether or not they can use it and how to apply it. And it's a whole effort. And a lot of them spend more time and effort on that than they actually do on the models.

And yet, if I look over at the data protection platforms, a lot of that data's already been aggregated and classified. And so is this a way to kinda enable two groups of people to use the same data for different purposes, but in a way that maybe will make things more accessible for all? I think you're spot on.

I think, uh, when you think about it, um, those two departments, I guess one of them kind of never existed. If you really think about it. The AI organizations and the ai, um, uh, org charts have only just started becoming a part of existence.

The backup recovery data resilience posture has existed for a long time. I think the problem for customers is they wanna move very fast with ai. They really, they realize it's super important, but you can't do that without trusted data.

And so I think we've seen lots of AI initiatives failing as a model. It's, it's not because the models are bad, but the data feeding them is incomplete. So bringing the these two together takes, you know, um, you've got Veeam's Protection recovery.

You also got the discovery and governance capabilities from security ai. So really gives a huge foundation for our customers to be able to innovate and also feel safe at the same time. So you are, you're spot on in terms of what this means by bringing these two sort of concepts together into one unified, uh, uh, outcome for our customers.

Alright. Do you think in the age of AI that our mindset about security is finally changing? And if I look back in time, we spent so much of our efforts on first obsessed about the perimeter, and then we said the endpoint was the perimeter, and maybe it was all about the data in the first place, or should have been?

Um, I mean, you, you, yeah. So I have a long history in security. I, I worked at RSA and a few other companies.

There was the Jericho principle, which was reduce the perimeter. I think you might've remembered that one. There was the, and Cipher, I, I sold the Cipher HSMs, and it was all about cryptography.

I think you're spot on. The one thing that is growing at an exponential rate is data. And the one thing that, uh, is the most critical asset is data.

So if you can get your hands around it, classify it, be able to explain it, um, and also protect it while using it for innovation, uh, you're right. I think, you know, I think your analogy is like, yeah, the waves of, uh, the different things we focuses on. This is kind of getting narrowed down to the ultimate truth, which is, I need the data.

The data is my currency, and what I, the more I can do safely with that data, uh, the more competitive advantage I have as a company versus, uh, you know, the, the market that I compete in. Mm-hmm. Um, as we kind of go down that path, not all data is of equal value.

So do we need to not just classify data by its type, but also maybe come up with some metric that says, here's its relative value to the business, and then that determines the level of protection we should apply? Yeah, a hundred percent. It's a really good way of looking at it.

Um, different types of data have different types of value. Some are required for compliance, some are actually more customer driven and help with Intel. So the ability to one, classify what it is, know, classify the type of data, know where it sits and know who uses it, um, is leads you down a path of being able to put a value on that data set.

And I think once we can do that, I think you can put different controls for different types of data, and you could also react to different problems. Like, if I had a file that, uh, broke GDPR in a certain geography and I could identify very quickly, um, I can remediate that really quickly. So the future is very exciting in terms of how we go about and solving and quantifying, not all data is equal, um, but being able to know which data is what and what price tag you put on it.

And I, when I say price tag, I mean the controls and the level of, um, protection, um, is, is gonna become super important in the, in the future that we have ahead of us. Hmm. Our, the whole notion of data management and data protection and security now also gonna converge.

I mean, for so long they were kind of different disciplines, but if you think about security sometimes, well, we boil it all down. It's a data management problem or challenge. So, um, are we also starting to see some sort of convergence along that line?

Yeah, you know, I think it's early for me to tell you what it would look like. I think you have two separate category leaders coming together. Um, it may define a new category, uh, in that category is centered around data.

What I would say is like, what is the customer's impact and value? My thoughts are pretty simple. A customer gains a single command center for their entire, uh, entire data state, one pane of glass.

They understand where it lives, how it's being used, where it's being governed and recoverable. And like from a CIO's perspective, that means great visibility across all cloud apps endpoints. For CISOs, it means pro proactive posture management.

So knowing where sensitive data resides before it even becomes a risk. And then you have CDOs and they can safely use. And, and I think Chief Digital Officers or those folks that are out on the, the front end of using data as a tool, uh, they can safely unleash AI initiatives with data that's accurate, compliant, and trusted.

So we'll be solving for many types of folks and a bunch of new personas as we go and bring these two assets to market. Okay. Yeah.

Bringing this home a little bit for security people, um, one of the challenges that we routinely incur is that it takes too long to recover the data when there's an attack. So even when we can recover the data, we pay the ransom because it's gonna take us three days to do it, and the business will be down for three days, et cetera. However, are we getting to a point soon where, um, I will know the relationships between various data sets, I will be able to recover them faster, and I will be able to respond to those attacks in a way that I can say no to ransomware without having to incur, you know, in three days worth of downtime?

Yeah, I think this is the vision. You know, I was looking at the ransomware recovery report that we recently published for this year. 69% of customers pay a ransom and almost that same amount get, have to pay again on top of that, even though they've paid the ransom.

So, in truth have, and, and that's because they don't know what data is out there that don't know the implication. And something you said, Mike, around the linkage between certain data sets and certain business outcomes. Um, with security, AI and Veeam coming together, I think you'll have super clear clarity on where the data is, what's the exposure, what's the governance requirement, what, what is the governance requirements and how do I attack for compliance?

All of that. If you add on top of that with Ware, uh, the company that we also acquired, um, around incident response, puts you in a really good position to be able to negotiate aside, um, and act very, very quickly, um, when something like this happens. And, you know, it's not, it's not the win, it's the, if I have this running joke that I'd never hire a CISO that didn't face ransomware, maybe 10 years ago, you would, you, you wouldn't hire one that that went through ransomware right now.

If you haven't been through it, um, you're of no value to the organization 'cause you dunno how to respond to it. So it's a, it used to be a Scarlet letter, and I think it's now a badge of honor if you've been able to handle, uh, something like this gracefully. And our job is to provide the tools to help you be safe.

And then on the other side, unleash, uh, your ability to harness ai. What is your best advice to folks then about how to kind of organize this and put everything in the right position to, uh, get lucky? Sometimes they say, you know, luck is the residue of good design.

So how do we kinda get the security people, the data management people, and the AI people aligned in a way that'll allow something really good to happen? It's a great question. And, uh, it's commonly, you know, it's been a traditional problem for us.

If you take ransomware as a concept before we even get it to ai, ransomware I think brought natural convergence. You took the, the infrastructure folks and the security folks long for a long time. I've been part of the industry.

They're two separate departments that don't talk to each other. Ransomware was the catalyst for those two groups to come together. AI would accelerate that even further.

Uh, so to me, I think you're gonna start seeing CEOs pushing their teams to say, how am I adopting AI at the same token and the same breath saying, how am I doing that safely? And, um, what we bring to the, to the table will allow that. We have a wonderful, um, tool called the DRMM or your Data resilience maturity model that we love to take customers through a journey on.

And it shows, hey, on a scale of one to four, we've worked with, uh, McKenzie, MIT, uh, Splunk, Palo Alto, and Microsoft, uh, to help build this framework. And we, the goal is we're gonna plug in the security AI components and give customers like a, a genuine roadmap of how safe, secure the current posture is and what would be the direction to move to, you know, closer to Nirvana. Uh, that's obviously a moving target for everybody, but I think with these two assets, our ability to accelerate and get to that moving target becomes much quicker and much more relevant for our customers.

All right. Hey folks. Sha heard in here, a primordial soup of technologies are coming together.

All it needs is a little bit of a catalyst from here to change the way we think about security to begin with. Shiva, thanks for being on the show, Mike. Absolute pleasure.

Thank you. All right, and back to you guys in the studio. Hey everyone, welcome back here to Techstrong TV for another interview.

I am really happy to have this gentleman on. You know, I, I first met Giddy Cohen, I'm going to guess it was 15 years ago. Giddy.

Yeah. Maybe more, at least. Yeah, at least.

Uh, giddy had, I, I think at the time, giddy had just recently come out of IDF or had, he was starting a company, right? Called Skybox Security. And he showed me something that at the time rocked my world, right?

It was, it was called an like an attack map, a 3D attack map where, where it actually you could, it looked at your network infrastructure, your vulnerability profile, and could draw a map of how a hacker would attack you and what you can do to prevent it at various places throughout your network. Sounds matter of fact, now I still see Giddy, I still see companies showing me that today, right? Yeah.

Like, like came just out Ofir. We, I, them I Seen we read 20 Years ago. Yes, I seen about a guy named Giddy.

They're like, no, no. I said, I saw this 20 years ago. Finally.

It's funny, just a, a couple days ago, I, I was talking to a founder of a company, an Israeli company, and, and he was talking, I said, you know, this sounds a lot like a company I knew called Sky. Sky. He said, Skybox, I said, yes.

He said, yeah, I know. We, we, I, I remember much younger I was using that. And anyway, it was good stuff.

Giddy Hass been a, a pioneer in, in what today we call Cyber for a long time. He has a new company. He's, he's co-founded called Bonfire.

We're gonna get into, but let's welcome Giddy, welcome to Techstrong tv. It's a pleasure to have you on. Yeah, Thanks.

I didn't to my you and give you the whole like, background, but I, I didn't give you the whole story. Why don't you share with the people a little bit about your story? Sure.

So let's start with actually now, and let's go back a bit. So I live in the Silicon Valley in Palo Alto for quite many years. That's where we founded Pon Fire ai.

We're going to talk about a soon as well. But background actually started, uh, with IDF, as you mentioned, uh, back in Israel in 8,200, right? The, like the Israeli NSA, but I mean, kind of thing.

The, all of my life was in the cyber data analytics technology since then. And they seen, and they, and basically now bonfire, right? My third company, my third startup starting, all of them are dealing with different facets.

Let's call that way cyber data analytics and all that they place together. Uh, so that's very absolutely my very, very short back on This. You know, Ginny, I, I've started a few companies.

You have, it's not something you do, you know, lightly. It's something you make a commitment to. You have to be passionate.

Yeah. You gotta believe in what you're doing. You gotta think that in some, at least even a small way, what you're doing makes the world better.

It makes, there's a reason people want, need to have what you're, you're developing. Talk to me about your passion for bonfire. Why, why bonfire?

What was the passion that drove you for this? Yes. So, um, Danny Bel, my co-founder and CTO and myself, uh, got together almost three years ago.

And, uh, I, it was just after I left Skybox, uh, about six months before, nine months before then he left cyber rock. He mentioned the entire RD of Cyber Rock. And he said, okay, we, we see what's going on with ai.

That's just the beginning, not of ai. Of course that started, yes, between us 30 years ago, but the massive adoption with a of gen AI started the end of 2022, beginning of 2023. And we said, that's going to be great opportunity for innovation on one end, right?

Adopting smart beast called for a lot of different uses, but we believe that can create a huge amount of risk for people, society, organizations. So both then is in a, a nice, uh, mining background, right? Are in enterprise cyber security.

So we said, okay, we want to tackle a all the challenges and help organizations adopt AI to do it much more safely, trustworthy, without the all of the exposures that everyone could just imagine. Then I think there are a lot of proof proofs since then that those exposure really happened. So that's, that's, we say, okay, we must be part of that, right?

We all technologies would love cyber and AI and data now machine learning and the, all of those new technologies with ai, and we want to put it together to work for the benefit of, uh, enterprise around the globe. And that's how we started when we dug in. More than that, it was good to, that are probably two, and maybe there would be more along the way, but two main areas from cybersecurity.

One, you can either think about, let's deal with the model, let's protect the model, prompt response, supply chain vulnerabilities, a lot of other stuff. We've seen a lot of startups do that. We decide not to go on this route, not because it's not viable, but because it's, we thought one, it's crowded and I think it was proven correct.

And second, the we're not sure. That's where the majority of the focus of enterprises as they adopt gen AI will happen. Therefore, we, we decide to go a different route, which focusing on the data side of that, right?

At the end of the day, the entire reason cybersecurity exists, the, the things you feed models with is information or data. They feed with data. You get data out of that.

It's true regardless of the LLM is, uh, used by individual or embedded in a system like Microsoft 365 copilot, and for the benefit for productivity of users, whatever form factor, it's all about the data, data that gets into AI or AI enabled machines, data that comes out of that with humans by agents or whatever it might be. So we said to ourself, that's a huge problem to solve and that's what we want to tackle. Maybe last piece of the, uh, origination of the bonafide is that, uh, which by the way, bonfire stands for ified ai, if I didn't say that before.

So AI in good faith. So the other fest of that, it was clear to us that why AI creates a lot of new challenges, new exposures to organizations, which is, which are dangerous, risky, and is a vendor exciting to solve and help organizations deal with it. Actually, there's also a huge amount of problems that print data, gene ai, data security, and for unstructured data, uh, one of the spaces that I would say was the least serve with quality technology over the last 15, 20 years.

Everyone knows about DLPs and false positives and false negatives and total cost of ownership, which is all true. And all of that is now getting much tougher when you are putting this, uh, ai, right, ML gen, ai, AI agent on top of that, that we say, okay, that's exciting problem as a startup, right? To address both addressing the, the rising issues, uh, and the expansion of let's call the data surface, but also with the same soup, basically handle a lot of those historical challenges that were never solved properly.

And we thought that's a great opportunity to help end the prices. That's, and that's why we started the company. Absolutely.

You know, it, it's a, it's a common thing in security. Giddy, if we could just stop the world for a day or two and let us catch up, then we'd be okay. But unfortunately, in security, it seems like we're always, we're always trying to catch up.

We're always trying to catch up and, and new stuff keeps piling in. Yes. So you, you look at something like legacy DLP, right?

You and I have seen DLP solutions come and go and we, we never quite got it right. And now, you know, you start adding AI and, and the, and the, the, the sheer volume of data, let alone the velocity and the automation. There's no human in the loop sometimes with this stuff.

Exactly. If we didn't get it right before, how, you know, how are you going to get it right now? But every once in a while technology affords you a a little bit of a miracle, right?

The technology allows you to almost make up for past mistakes. Because with this newer technology, not only am I able to work with things like ai, but it allows me to maybe hit the legacy DLP issue square in the head too. And I, I, you know, I think exactly, exactly.

That's the opportunity here for bonfire. Yeah. Uh, absolutely.

Right. So I would say that from innovation perspective, which is that's what get me excited, excited, right? As an entrepreneur, is that, uh, we, we actually take advantage of two things and bring it together in technology we developed, which is now, by the way, available in production.

And, uh, anyone wants to see it, we'd love to people to get, get to us, send me an email, get to our website, et cetera. But there are two concepts we actually brought together, or two abilities. One, obviously, right?

With all of the innovation of Gene ai, not only create risk, but also create, create set of tools and technology we could use. And of course we're using in order to understand content in much better ways for the benefit of classification, content analysis, detection, prevention, and lot of other use cases that are related to data security. So that's one, but that's not enough.

And you mentioned Skybox Security, right? By previous company. They ran for a, a was one of the founders ran for many years.

One of the innovations we had there in day one, as you mentioned, is how to utilize network context on the infrastructure in order to manage vulnerabilities better and manage compliance better, et cetera. So that's, that's how we actually start the company using, using context for the benefit of, uh, various use cases and provide a lot more intelligence, uh, and accuracy. Therefore, uh, for a, for those type use cases.

So think about what we did in what we're doing. Bonfire is actually the analogy, nothing to do with the technology of Skyworks, right? We're not dealing with infrastructure there, we're dealing with data and content information, but actually the innovation we have here is actually how to utilize business context that we serve, learn from the organization perspective, the own data for their benefits solely.

Of course, we never take the data, other customer never see that. So it's all securely done for them and how to utilize this business context for the benefit of analyzing their data in motion address generated by machines, generated by humans for all of those type of flows. And that's basically what we bring together, the kind of modern AI technologies we are developing and using, plus the innovation of how to utilize smartly business context, put them together to significantly smarter a datasecurity solution, which is significantly more accurate.

Covers a poly five times is more scenarios in terms of, uh, uh, the things you want to detect, which are critical for this modern world of, uh, of, uh, data security or data compliance type of risks. And to do it in a much more cost effective way in terms of the amount of human load on the security team set up, dealing with the operation in matters significantly more streamlined than ever before. Excellent.

I love it. Um, you know what, let me do some housekeeping. Giddy bon fee.

Bon Bon Bon Fi is B. Yes. BO bon, yes.

ai is the website tote Ai. And then the, the, the product service, if you will, is bonfire's adaptive content security. Yeah.

Or bonfire a CS. Exactly. This our platform.

Exactly. How could people check it out, test it out? Is there a free version or trial version, something they could get their hands on?

Yeah. Then no, that's, that's great. So first of all, the best and the easiest way is contact us via the website.

There are multiple forms for different type of interest. Uh, the, that, uh, customer's, prospects, uh, might want to have. We'll be happy to provide demos, eh, schedule some qvs right?

Paper value so we can actually demonstrate for organizations that can walk in their environment, it's super simple. We design part of our lessons, right? All of us, right?

Lesson serving and enterprise cybersecurity users and buyers along the years that, uh, on one end, right, the industry grew bigger and bigger, but there are more solutions and the load and security is actually some is un verbal, I would say, in terms of complexity of solutions and the amount of knowledge. So bonfire will design on one end, super sophisticated backend business context, learning other stuff for the benefit of the use case, but very easy to deploy, very to use. So that's true for production, but also very easy to perform a proof value with our solutions.

So just contact us and we'll be happy to show it and, and show ly talks in your environment. Giddy, I I, I speak to a lot of security folks, a lot of entrepreneurs, ai, DevOps, platform engineers, and, and I think the consensus is, look, you going to need AI to secure ai, you're going to need AI security solutions to defend Yeah, for sure. AI malware or AI assisted malware and and so forth.

If you wouldn't mind talk a little bit about how Bon Fee is using AI to make the solution better. Yeah, so we are, we are using, uh, multiple, actually both models and AI technologies as you can imagine, uh, at bonfire, right? To provide the answer at scale and accurately requires a lot of technologies.

Just it's not one thing, right? And if someone think that, yeah, it's just going to get, let's say the email I want to analyze and send to the DLP that, to the LLM as a DLP function to tell me if it's good or not, that wouldn't work, right? So we're, we're mixing together multiple technology software.

We developed something that we are taking off the shelf and tuning for our needs. So for example, knowledge graph creation, right? The business context, entity linking technology, so can identify what the entities in the content.

Uh, we have different AI engine engines that are looking for features in the content that allows us to assemble it together to understand what's there. So we can, in a very abstract way, understand the, you know, whether this content is, uh, good or not, right? To pass through an email or file sharing or web traffic or whatever it might be.

So we, we have that capability. We have a deep explainability capability. So someone gets an alert part of the issue once they get alert, of course, you want it to be accurate, right?

But you want it to be understood, right? Think about the, a security analyst is get an alert from Bon Bonfire or any other system, something might have happened. They have no context what it is, right?

How do you know that? How do you help that? So we have AI used for explainability of a, of a, let's say, complex alert.

So we are using it in, in, in quite a few different ways because it's necessary, right? And that's part of our technology software developer ourselves, as I said, some that we're adapting existing models for, for the need. Small models, mid-size models, large models, and plus our knowledge graph and learning technology and all this put together in a solution.

Gi, we're about out time. I just want to emphasize this is available now. People go to the website right now and go check it out.

Yeah. We're in production already serving customers, looking for more customers as we build up and penetrate the market. Uh, yeah.

So next gen, please call to us next gen here. Giddy, you're out in front again. Mazel tough as they say, right?

Good for you. Keep us posted, come back. Yes.

Keep us posted on progress here and what you're seeing. Absolutely. I'm, I'll be happy to be there anytime you invite me.

Giddy Cone. Uh, uh, I co-founder CEO of bony, that's B-O-N-F-Y, do AI bony, excuse me, Bon Bon Fi. You know, I got Bon this funny French accent.

Bon Bon Fi. Yeah. All of us our own accents.

Yes. There you go. Yeah.

For, for Latin speakers, Latin speakers can say bon. So it's, As they come from the Website, fine for Latin speakers or Latin languages, Bonfire ai. Exactly.

It, it, it's something that's needed in today's ai. We'll check it out. We're gonna take a break here on Text Trunk tv.

We'll be back in a minute. Hey everyone, we're back here. We're live at Qualys Rock on our day two coverage.

I'm really happy to have my friend Jim Riva sitting here with me. For those of you who don't know Jim, and if you've been in the security world, you, you should know Jim, but Jim was the founder, co-founder of the Cloud Security Alliance, 2005 or 2006. That was 2008.

There you go. I was thinking about it before that. Yeah.

So, you know, I'm kind of slow, slow on the draw. 17 years I was at RSA when we had the meeting at RSA was 2008. Yeah, yeah, yeah.

I always thought it was, I, you know, I guess in the midst of time it's gotten pushed back further. 2008 for Jim still. It's been 17 years.

Yes. Yeah. And my goodness, what a, what a 17 year trip this has been, right?

Yeah. The CSA has really expanded its wings, really kind of fulfilled the mission. But you know, just when you think, what do they say?

God laughs at men's uh, plans. Yep. Just when you think you've got your hands around things, something new comes out.

I was thinking of more Al Pacino and Godfather three. Okay. Yeah.

When you're out, they pulled you back in. Yeah. Oh, that too.

That too Godfather. God. Yeah.

Well, I guess it depends who you are. That's right. Um, but anyway, first of all, let's get a, just an update on what's going on with CSA.

Yeah, Absolutely. So, you know, there is this aspect of like cloud, like where are we at with cloud? It's getting pretty mature and honestly it's a full two thirds of our research and work and, and demands from the community is around what are we doing about ai, which is really, it's really merged with cloud now.

It's just, well, It's merged, merged with everything. Yep. It's, yep.

You know, everything has to be looked at, I think through the lens. Yep. Yep.

How does AI affect us? Yep. And, and so like, we're getting good indicators of what we think is gonna happen next.

And kind of how I would, if you wanted to characterize where we're at, it's like version two of our AI journey where now we're getting into, and it's can be a buzzword, but age agentic, meaning we're, we're not just using the chat bots and going back and forth, but now we're trying to actually build autonomous systems. We gotta figure out where the human in the loop needs to be. And so it's like all the building blocks, all the security best practices we need to do around that.

That's, that's probably the biggest single area we're focused on right now. Got it. Um, I, I just feel compelled to say that you, you've added a new analyst in residence over there.

Mm-hmm. Yeah. Our friend Rich Vogel.

Yep. Rich, rich is a guy that, it's like he's, uh, I, I hope he is not watching it 'cause he is a little bit of a Michael Jordan to me and like really being able to execute and think about things. He's helped so many different companies.

He's, he's on, you can't defend against him like, right, like Michael. And so he's our chief analyst. He meets with our, um, corporate members.

Here's what their strategy is, what their like real pain points are and kind of helps guide him. And you know, we, we've got like thousands of documents we've worked on over the years that a curation of like those best practices can help organizations maybe a lot more than they think, but he's their navigator now, so we're like pleased to have him and Sure. I like making, you know, I've Just looked thinking of the visual though.

Rich Mogul Mike Jordan redhead. Well, they both don't have much hair these days that that's true. But I don't know if anyone's ever compared him to Michael Jordan.

Yeah. Who's that? Rich, rich, if you're watching this, you heard it from Jim's mouth, not mine.

Yeah. Um, anyway, let, let's get back to Class Security Alliance and you know, we're here at this QUAS event, which is no longer the QUAS Security conference, but is now rock con. Yeah, right.

Risk Operations Center. Jim, how does this whole look? Managing risk was always at the heart of security anyway.

Yeah. But how does the emphasis on risk management do you think affect cloud security and cloud security practitioners? Yeah, so, you know, we've had some dominoes that have happened in the world and people like getting in trouble and, um, corporations not like treating this right?

And so they've gotten the message CEOs, CFOs, they've gotten this message that cyber is not just like some it risk, it's overall corporate risk, financial risk, like operational risk. And so we got the seat at the table and like the issue is translating like how we operate our cloud securely to the right language and the right metrics that those groups understand. So we're getting more and more of the budget, we're getting more and more of that, Hey, you've gotta be in the quarterly like audit committee.

We need a cyber report, everyone. And they're asking better and better questions 'cause they have some personal liability around this if they don't get it. And so, um, but we're, we're still, I think there's some struggles between like people's ab I talk about the CVEs that we've sort of mitigated and patched or you know, we, we, um, these certain incidents, we stopped this certain phishing attack and like, okay, what, what was the overall financial impacts on the organization?

Did it create any sort of delay in our ability to release new products and service the customers? Things like that. So we're, we're getting there and like the better, like CISOs, I think they understand how to translate that, but the, the cloud itself produces, the answers are in the data somewhere.

Right? It's like finding it the Needle in the haystack or the needle In the needle. Right.

And That's, and that's actually, and it kind of goes back to like some of what I, um, saw summed here talk about in some of the new solutions and what we're seeing the industries, well you can actually use some of the AI 'cause it's a big data management problem to actually sort of surface like the, the information at the level that the board wants as, you know, finding specific incidents, putting the right context and doing compliance. Like, hey, I can take like how we are compliant to one standard, put it into AI and it'll at least gimme a good 80%. This is how you comply to something else.

Sure. And so, um, but yeah. So we're getting there, Jim, to how, how does, so you come here, look, Qualys has been a partner of the security lines right?

From day one. Yeah. Just like day one.

Um, how do you take what you hear here and what you're seeing here and how does that filter down to the membership of, of the alliance and I mean, look, the Alliance has, there's a ton of Yep. Of the vendors in this space, but there's a lot of end user practitioners. Yep.

How does what, you know, how do you filter this in? How do you, how does it become, you know, part of the bedrock if you'll Yeah. Well we, we've always sort of had this philosophy of like, let's, let's have like a healthy, positive, symbiotic relationship between practitioners and the technology providers.

There's a lot of like areas where it's pretty antagonistic. Yeah. And it's like, okay, we're, we're, we're gonna put 'em through the paces and we're gonna like, you know, test 'em and be like very tough on 'em and or we're not gonna allow the vendors into like meetings, which there's can be like some reasons where you do that, but this is where you get the information.

Like on, you know, when a company like Qualys and there's definitely like several others, they aggregate, aggregate so much data that they tend to see things before. A lot of people are gonna see it. So like we, we try to sort of create those, those, uh, communication lines.

So, hey, you gotta listen to what doesn't mean you have to buy everybody's product, but you gotta listen to the things that they're seeing and how they're tweaking their technology to address these issues. Uh, 'cause we gotta be a lot more flexible and a lot more agile. So we try to like make it, Hey, you know, positive fun, let's communicate.

We're all first responders here, whether you're the practitioners or you're, you know, at a soc that's managing a lot of different customers as well. So that's the philosophy. I get it.

I get it. Jim, we, we briefly talked to you and I beforehand RA coming up. Of course I saw you last.

The black hat, you know, the, the, the CSA is ingrained into the industry, but as you sit here, it's, it's, you know, we're coming into the end of 2025 and we are ears deep in, in this whole AI agent, AI and generative AI and disruption and data sovereignty, cloud sovereignty. There's so many issues you didn't think about in 2008, for sure. Yeah.

Yeah. If I had to ask you to look in your crystal ball about the cloud security alliance and where it's going the next not too far out, let's just say 18 months to three years, what do you think? I think we are gonna have to level up our game and be much more smart to, uh, to handle just how much more rapid we're going to see bad and good things do.

And like, I'll give you an example. We've been struggling to like create course for ai and we had this sort of breakthrough, let's have the course just have these evergreen principles and let's create prompts that you could run two years from now to say, Hey, based on these evergreen principles you articulated, give me the latest knowledge that I need with the versions of, you know, chat GPT eight or nine or whatever it is. So like, I think we're, we're CCSA is gonna be a lot of this AI with the human in the loop to kind of guide like our white papers are gonna get generated.

Like I think next year they're gonna be generated to degree with ai, but then like tuned. But then we'll give, like, people, like Rich Mogul we talked about, he'll be able to use that to say, I'm gonna create guidance reports specifically for one company. I think we're gonna see security, just like people are talking about, you're gonna create, with ai, you're gonna create applications that are unique to a person.

I think you're gonna create like security solutions that are hyper customized. Hyper localized. So I think we got some opportunities on like the data sovereignty issues and all of these things.

It's just, I wish we were more proactive. 'cause we're always like, reacts. I, I wish we were too, Jim.

But, you know, you can't escape the laws of nature. And I, I, I think it's almost reactive. Yep.

You know, until quantum comes in, then we could be proactive and reactive at the same time. Schrodinger's cybersecurity security. There You go.

Exactly. Jim, it's great seeing you. Always keep up the great work.

Good luck with CSA. We'll, um, well, if we're some source, I'll see you at ours. Yes, for sure.

But maybe before. Yeah. Love that.

All right. All right. Can we, I think, uh, the folks, have you scheduled me every month or two now?

Oh, okay. Extra tv. Okay.

Not in person, but yeah, yeah, yeah. Almost it's You. I'll do those.

Okay. Although you smell fine in person, so. Alright.

Thank you. Alright. Jim Re is here on, uh, our Qualys Rock Con coverage.

I think we're taking a break for lunch. We'll be back in about a half hour, but we've got a full afternoon, so don't miss it. Or for now, we're out text on tv.

Hello and welcome to the DevOps experience. DevOps goes native. I'm Nathan Harvey and I'm really excited to be here today to bring you insights into navigating this generative AI revolution.

These insights are going to come from Dora, a research program that is part of Google Cloud, Google Cloud's Dora research program has in investigating the conditions, capabilities, practices, and measures of high performing technology driven teams and organizations. For more than a decade, Dora helps those teams apply those capabilities leading to better organizational performance. This research is all about how do we help teams and individuals like yourself get better at getting better.

Now, if we look back to last year, we published the 2024 State of DevOps report. This was the 10th report in our series. This groundbreaking report gave us some first real insights into the impact of generative ai and generative AI in software development showed some real promises, but our report also revealed some significant challenges setting the stage for us to dig even deeper this year.

So what's changed and what have we learned? Well, I encourage you to go download the 2025 door report right now. The state of AI assisted software development.

Go ahead, download the report. It's right there at that QR code or at that URL. I'll wait.

Okay. I'm not really going to wait. You didn't come here to wait on me.

You can download that report later, but be sure to grab the QR code there. Alright. So what did we find this year?

Well, the first major change is that AI adoption is no longer a question. It's nearly universal, up 14% from last year. AI is now a standard part of a technology professionals toolkit, and a significant majority, about 65% of those surveyed are relying heavily on AI for software development with 30% reporting a moderate amount of reliance, 20% a lot, and 8% a great deal.

So we're relying on AI and we're using it. These professionals, from developers to product managers now integrate AI into their core workflows, typically dedicating a median of two hours a day working with it. Beyond that, we also see that over 80% of respondents indicate that AI has enhanced their individual productivity.

And even further, a majority 59% report a positive influence of AI on the code quality of the code bases that they're working with. And only 10% are saying that they've observed negative negative effects on code quality. Despite this widespread adoption and perceived benefits.

Some developers remain cautious about using AI in their work this year. We see the trust paradox return while 24% of respondents report, uh, trusting AI 30% only trust it a little bit or not at all. This indicates that AI outputs are perceived as useful and valuable by users despite a lack of complete trust in them.

You know, I think that this is really good. I don't think we should trust anything, any tool that we're using a hundred percent, nor should we trust it 0%. So I really believe that this implies AI is being incorporated into workflows as supportive, as a supportive tool to really enhance productivity and efficiency rather than serving as a full substitute for human judgment.

When we look across a bunch of different outcomes, this is how we see AI interacting with those outcomes. So let me just show you how to read this chart really quickly. I'll describe it to you on the left hand side, the vertical axis.

We have a bunch of outcomes that we think are particularly important for technology professionals like yourself. Outcomes like individual effectiveness, software delivery, instability, organizational performance, and so on. And we wanna really understand, as you adopt more ai, how do those outcomes get impacted?

And so the dash line that kind of goes up the middle, that's the zero line, that's basically average ai. But if you increase your AI adoption, what happens to those outcomes? Well, you can see something like individual effectiveness is really improving.

So that's a good, a good sign for us. Unfortunately, the next outcome that we care about is software delivery instability. Now, instability has gone up as well.

And just to be very clear, we'd rather not have instability increase. So there's not an a, a wonderful picture here. This actually reflects something that we saw in 2024.

But we see other really good outcomes of increasing your AI adoption. Things like organizational performance, the time spent doing valuable work, software delivery, uh, performance, product performance, and so forth. Now, the, there are a couple of things at the bottom that also are basically at zero, and those are burnout and friction.

Now, to be very clear, our preference is to reduce both burnout and friction. But given how close they are to that zero line, we really think that AI is not really impacting them much at all. Which kind of makes sense.

You know, if you're experiencing burnout within your organization, that's probably a sign of something systemic going on. And I don't think you can alleviate burnout simply by adopting a new tool. There are bigger changes that you might need to address.

But when we compare this data to what happened or what we saw last year, we see some consistent beneficial effects. Things like individual effectiveness, code quality, these are improving, improving last year and continue to improve this year. We see some stubborn effects, some things that were not really, uh, impacted at all, like friction and burnout last year, and software delivery instability also increasing last year.

That seems to be the case this year as well. But we also see some changes for the better. In fact, we see software delivery throughput go from negative to positive.

We see product performance go from neutral to positive. So this is a really good sign. I think this is a signal that as teams we're all adapting to these new ways of working.

And of course it's not just us. The door adapting, the tools, the models, the workflows, all of these things are changing so quickly. So as an organization, it really is important that we focus on learning and adaptation.

AI at the end of the day, really turns out to be kind of a mirror reflecting and amplifying the existing organizational capabilities. But given that this is a mirror and it reflects and amplifies what we have today within our organizations, when it comes to understanding how we're doing, we probably need to look beyond just software delivery performance to really understand this amplification effect. So this year we took a very much a, a, a much wider view of how do we evaluate team performance.

We looked at human-centric areas, we looked at product and organizational areas and so forth. So we actually evaluated teams across these different characteristics. Team performance, product performance, software delivery, performance, individual effectiveness, the time that you spend doing valuable work, friction and burnout.

And as we looked at various teams across all of these different dimensions, what we saw is a number of different archetypes of team profiles sort of emerged from the data, if you will. And what this shows us is that while AI has become sort of standard, everyone is using it, there is no one single AI experience that everyone has and that everyone experiences. So our survey, our analysis, sorry, of these results actually revealed seven distinct team profiles from the harmonious high achievers to those, uh, caught in the legacy bottleneck.

This view really offers a much richer understanding of how teams are working. And with this understanding, you might identify where your team is today and more importantly, how you might improve. We don't have time to look at all seven of these team profiles, but I do wanna look at two.

And I kind of wanna contrast to side by side. We'll start with what we call cluster two, the legacy bottleneck. Now, I should note that, you know, we named these clusters.

For example, this cluster is called the legacy bottleneck. Your team might feel like this, but legacy bottleneck might not feel like the right label for your team. That's okay, that's all right.

We understand that we can't get it exactly correct all the time, but maybe you are experiencing some of these constraints and some of these ways of working. You see, when we look at this cluster with this team profile, we see teams that are in a constant state of reaction where unstable systems are dictating their work and undermining their morale. Now, 11% of our survey respondents fell into this cluster.

Key metrics like product performance are low. While the team delivers regular updates. The value realized from those updates is diminished by ongoing quality issues.

And there are significant and frequent challenges with the reliability of the software and its operational environment. This leads to a high volume of unplanned work, and that work is oftentimes reactive. Now, let's contrast this legacy bottleneck team to those harmonious high performers.

We believe that this is what excellence looks like. It's a virtuous cycle where a stable, low friction environment empowers teams to deliver high quality work sustainably and without burnout. Now, some of the good news here is 20% of our survey respondents fell into this archetype or this team profile.

The team is showing positive metrics across multiple areas, including wellbeing, product outcomes, software delivery, and the team operates on a stable technical foundation that supports both the speed and quality of their work. Right? Let's take a look at these two teams side by side.

On the left, you have the harmonious high achievers. For them, AI amplifies their already stable low friction environment to create a virtuous cycle. On the right, you see the performance characteristics of the legacy bottleneck teams.

For them AI might increase code volume, but that only amplifies the chaos. They might be hitting walls of their unstable systems, and that might be undermining morale, same tooling, very different outcomes. So it really sees, you can really see here how the way that you're working with these tools and the way that your organization works, plays a very large role.

So if AI is an amplifier, how can we ensure that it's amplifying the good? Well, I'm really excited to announce that this year we will, uh, uh, we will are releasing our inaugural DORA AI capabilities model. This capabilities model, it has identified seven essential capabilities that are a blend of technical and cultural factors that are proven to amplifies a amplify AI's pro, uh, positive impact.

This can be a blueprint for your success. These different capabilities are really the levers that you and your team and your leaders can pull on, can adjust so that you can improve the power that AI is bringing to your organization. Improve and amplify those positive impacts.

Now, there are seven different capabilities here. It's not dissimilar to the seven clusters that we saw earlier. And again, we don't have time to to go through all seven, but I do wanna touch on a couple of them.

The first is the idea of a clear and communicated AI stance. You know, when we're thinking about how to get the most out of technology, it almost always starts with culture. And a new technology like AI can bring with it some ambiguity and ambiguity within our organizations creates fear, and it has a tendency to kill experimentation.

So one of the things that we've found is that a clear and communicated policy on AI use provides psychological safety, reduces friction, and really unlocks innovation. In fact, we see that teams that have a high level, uh, or a very clear, very well communicated AI stance, they see increases across a lot of outcomes. Individual effectiveness, team performance, organizational performance, software delivery, throughput, all while reducing friction within their organization.

So if you don't have a clear and communicated AI stance, this might be the first place for you to start as an organization. But there are other things that go along with this. One of the things that Dora has found, has had that has always contributed to better software delivery performance and other outcomes that we care about is working in small batches.

This really is an indicator of how well you're able to take the work that you're doing and break it down into smaller pieces of work. And when it comes to software delivery, you know that smaller pieces of work might mean smaller deployments, smaller changes that you're shipping out to the production environment. These smaller changes help reduce our instability and help improve our software delivery throughput.

So we see that when you're using ai, working in smaller batches becomes even more important. And working in small batches can with, together with AI, can really increase product performance while also again, reducing friction. The third capability from our model that I wanna look at is the user-centric focus.

This is the degree to which teams think about the experience of their end users of the pri of their primary application or service. And you know, I love technology just like you do, and I love playing with new tools and new toys, and AI is one of those. It is so fun to play with.

I I wanna sprinkle AI everywhere, but I always remind myself that we have to think about the users of our application. The users don't actually care if we've built this application with or without ai, what they care about or are things like, does the application do what they want it to do? Are they able to successfully accomplish their goals with the application?

Keeping these users in mind and incorporating their feedback into the things that we're doing helps ensure that we're building the right thing. This is so, so important. And with user-centric focus and the adoption of ai, we see these two coming together to provide some really powerful insights here.

In fact, there's even a cautionary tale built into this. You see, if you have low user centricity, but you increase your AI adoption, you're kind of just using AI more and more without really thinking about this, these users, this can actually lead to decreases in your team performance. But when you are really focused on the users and how we as a team come together to serve our users and build the right applications and services for them, this can lead to an increase in team performance.

So it's really important to think about and, and show up in a way that serves your users. So here's an, a visualization of the entire AI capabilities model. This shows that as you increase AI adoption with these capabilities in the center, you're going to lead to positive outcomes on the right hand side.

So one of the ways that you can use this particular capability model is think about those outcomes on the right hand side. What goals are you setting? What outcome are you trying to improve?

Maybe you're trying to improve something like product performance. Well follow the arrows back to which capabilities are really driving, driving AI's impact on product performance. And this can give you some insights into the capabilities that you might want to start enhancing within your organization.

And again, as you follow that arrow back, you'll see that one of the arrows leads right back to that clear and communicated stance on how to use ai. So taking all of this together, what advice do I have for leaders? Well, there's a lot of leaders here today that are really interested in leveraging AI to drive the best outcomes for their organizations.

My advice to you first, treat AI adoption as an organizational transformation. Remember, AI is going to reflect and amplify the ways that your organization is working today. Those organizations that are streamlined and free of friction and where change and experimentation are encouraged, these organizations see the best results.

On the other hand, if, if your organization is kind of disjointed and there's a lot of handoffs and friction in getting work done and a lot of ambiguity about how things get done, this is going to be amplified by ai. And you're gonna see some detrimental results as you improve AI adoption or increase AI adoption. So I encourage you to use this new tooling, this new way of working to help drive organizational change.

Break down those barriers, streamline the way that information flows throughout your organization and the way that change flows. Next, I want you to shift the conversation from adoption over to effective use. I see so many organizations trying to measure the impact of ai, and they start by measuring simple adoption metrics.

Adoption is important, don't get me wrong. In fact, you can't have real impact from AI if no one is using the tool. Simply buying the license doesn't get you any real impact.

Adoption is a prerequisite for real impact, but adoption does not guarantee real impact. So think about those capabilities in the Dora AI capabilities model and make sure that you are building up those capabilities at the same time that you're driving for better adoption. Next, look at the co take, a comprehensive view of team performance.

There are so many different facets that make up team performance within an organization. We are working in very complex ways. We're dealing with human factors, technology factors, process factors.

All of these different things are coming together to create this rich, complex, beautiful environment that is able to really deliver results not only for the business, but of course for your customers as well. It is important to diagnose team health with more than just software delivery metrics. And this is really what we've leaned into this year in this report.

Next up, prioritize and fund your platform engineering initiatives. We see platforms as a great enabler when it comes to AI adoption. You will see high quality platforms on our Dora AI capabilities model.

This really unlocks the power of AI across the entire organization. And finally, be sure that you're turning localized productivity gains into significant organizational advantages. This is kind of the story of some of the things that we've seen, for example, with software delivery performance.

Remember, we see software delivery throughput improving this year, but software delivery instability is also increasing. We believe that this may be a sign that we're hyper-focused on using AI to generate code. We're generating more and more code, but maybe don't have the feedback mechanisms and the validation mechanisms in place to handle that amount of code.

When we're unable to validate code changes, we should expect them to be unstable when we ship them into our production environment. So this is a great opportunity for you also to take a systemic view of how work flows through your organization. In fact, in this year's report, we have a whole chapter on the concept and the practice of value stream mapping.

This is a really powerful tool that can help organizations identify where is value getting stuck, where is there friction that we can eliminate or automate? This is a really great thing for you and your team to look at. So as I'm wrapping up today, I wanna leave you with a few big key takeaways.

First and foremost, AI adoption is here and it's here to stay. The challenge is not whether or not we should use ai, but the new challenge is the effective and valuable use of ai. And this, again, requires us to think more broadly about our entire organization and how we're using AI throughout everything that we do.

AI truly is an amplifier. It reflects and multiplies your existing systems strengths and weaknesses. So again, we want to look to using AI as that transformational agent.

Now is the time to improve those processes. Now is the time to improve those systems and maybe use AI to help along that journey. Success requires more than just tools.

I encourage you to take a look at that Dora AI capabilities model. Think about all of the different components of that model that you could invest in as a team to really help improve the way that you're working, and importantly, the value that you're getting out of ai. So I want to leave you with this.

The state of AI assisted software development. Dora's latest report is available now. I encourage you to go download that report.

I'm going to leave this QR code up on the screen a little bit longer. The this research is been so enlightening. We've learned so much from all of you this year, everyone around the world.

The next step is for you, take this research, read it, use our findings as the hypotheses for the next experiment and the next improvement work that you are going to drive within your organization. You see, the most important thing that you can do is take this research, put it into context of your organization, your team, your applications, and then put that research into practice so that as an industry, as a profession, as a, as a community, we can all improve the value that we're getting out of our investments in technology and technologists. In short, we can all get better at getting better.

Thank you so much for having me today. I am really, really excited to be here. I appreciate you coming along and I look forward to hearing about how you've put Dora's research into practice.

Enjoy the rest of the DevOps experience. Thank you. Hey everyone, good and bad at the same time.

It's quantum economics. You're watching Textron Gang. Hi everyone.

Happy Wednesday. Welcome back to Text on Gang. It's right, it's hump day.

We are, i, it, it sometimes it amazes me once you get into that kind of wind tunnel or that, you know, down the stretch towards the holidays, it just seems like time accelerates, right? So here we are. It's, it's the, the 29th.

We have Halloween this weekend. Always a fun time. I don't know what you guys are planning to dress up on or do for Halloween, but it's great.

Halloween was always big to me as a kid, but I must say it's become a, a much bigger holiday than the last 10, 15 years maybe, right? I think it's not just for kids in candy anymore, right? It's, it's, uh, it's really become a celebration.

Um, anyway, but we've got a lot more to do before Halloween, including a great text on gang show today. Let me introduce you to our Wednesday panel members. And I feel like this is our normal Wednesday panel.

This has become rather locked in stability in the gang. Uh, of course we've got Chris Blas, Kate Scarsella, Dan O'Brien, Mike Ard, welcome gang members. It's good to see you.

I know some of you have toothpicks holding your eyes open, having stayed up late for a Dodgers game the night before. A Dodgers Blue Jays game. Excuse me.

Um, appreciate you all getting on here. So I wanted to first talk today, I I kind of hinted at about, about quantum economics, right? And, and you could, we could really see the drum beat, you know, the drum, the drums beating here towards Q deck towards quantum becoming more real.

We're seeing it a a steady flow, not only in kind of tech, uh, media, but even in the mainstream media. And, and usually, you know, where there's smoke is fire. You can smoke and fire, smoke, no fire, fire, no smoke.

All of those at the same time in a quantum future. But, um, you know, a recent announcement came out. Now, the US government in talks to take stakes in quantum computing startups.

Mike, you could probably guess how I where I lay on this, but I'm gonna throw it out at you to kick us off. So there's a report in the Wall Street Journal talking about how the US government is at least talking about $10 million stakes. And I think that there's four or five of these startups.

And what's interesting about all this from my perspective is, you know, we see that Google and IBM and, uh, all kinds of folks are investing heavily in quantum computing. So I'm not quite clear why the US government feels the need to invest in a couple of startups. And Alan, I'm gonna toss that back to you.

But is this, you know, maybe just putting a thumb on the scale in a way that's not good, or are we setting the stage for something bigger? Like, I don't know the nationalization of quantum computing. So let me try to be fair, fair and balanced here.

Okay? There's nothing of manner with investing in quantum technology. We, if we deem it a strategic imperative here in the us, we should invest in it.

We should. However, the way that it's been, if we're gonna do it the way we took stakes in Intel and, and have invested in some of the AI stuff, it's totally wrong because we can have a, a tech bro oligarchy. And that's my fear, is that the people who the this, this particular administration are gonna invest in are gonna have to take an oath of loyalty.

They're gonna have to be part of the, of the old gang, of the tech bro oligarchy. And that is wrong. You wanna do this right?

Do it the way the rest of the world does it set up a sovereign fund with real principles of who we invest in, why we invest in them, what our goals are, right? This is not, this is not like a brand new playbook. And you go out and you pick, you pick the companies that are going to, that you think are gonna be winners and you think are gonna help your national interest.

There are plenty of countries that do this well with a sovereign fund. Instead, my fear, this is gonna be run like a slush fund by MAGA to reward their, their followers and punish their perceived adversaries. And that I don't want my tax dollars going for that.

You're right. I'd rather let Google and IBM and A MD and, and the rest do their thing. It's a shame that we can't act like adults and reasonable people and do this the right way, because it is that important.

It is that important with the, the, the, the progress is coming faster and faster. You can feel the momentum building here. Let's not fritter it away over at like everything else lately that we do.

We'll talk more about it, I think in the third, uh, session today. The third, uh, uh, section today politicizing our strategic interest. And that's a problem.

I'll throw it out to the rest of the game. Dan, what's your take here? Because, um, you know, under the heading of fair and balance, let's see if there's another point of view.

Listen, that I think it's a complicated topic. Alan said earlier, you know, where there's smoke, there's fire, you know, I'll throw another saying out there, right? You know, things happen slowly then all at once.

Um, you know, putting the politics aside for a second, clearly there's a lot of progress happening. Quantum computing, right? Um, you know, it's the age old argument, exactly how far we are away from, you know, real useful quantum computing.

But it seems to be getting closer and closer. Quantum is very much one of those problems, one of those challenges where it's really a convergence of technologies problem. And when you, you know, when everything kind of times itself right across hardware, software, you know, cooling, you know, the cryogenics, all the things that need to, you know, intense engineering that needs to come together to make this thing real.

It does feel like we're getting closer and closer. Um, I, you know, I I think, uh, you know, back to maybe more a little bit of the politics side of this. This is a game changing technology.

You know, to the degree we think AI is a game changing technology, quantum is even more so a game changing techno technology, right? You know, the ability to discover things that were previously undiscoverable, you know, massive opportunity to disrupt a number of different industries. I think this really gets into a question of, you know, and this is a big question that we gotta figure out whether we actually wanna tackle or not, but where does the defense industry begin and the technology industry end and vice versa, right?

Defense and tech are increasingly getting closer and closer, blurrier and blurrier. And listen, the amount of money we're talking about being invested in those quantum computing companies, it's kind of a joke, right? If you considered it as part of, you know, what we spend on defense overall.

I mean, it's truly a drop in the bucket, right? Um, I think it's a little bit interesting the way they're doing it because listen, the US government's a massive investor in the technology industry. And when I say that, I mean as a customer, right?

They, they decide winners and losers in industries based on where they put federal contracts and, you know, the entire tech industry benefits, you know, from a lot of that. Typically that's the way they would do it. You know, they're being a little bit more direct with the investment and taking more of an ownership stake versus really helping to, you know, propel certain companies and, you know, help that progress along on what could be, you know, a a majorly important, you know, strategic, uh, you know, technology advantage for the United States versus its, you know, versus its enemies, you know, at a, at a global level.

Um, I think the reason they're doing it this way, competitors, Way competitors, Dan competitors were signing a treaties this week. Supposedly they're not enemies Fair, maybe not enemies too strong, uh, uh, but you know, I I think this administration to some degree measures itself daily in the performance of the stock market, right? And I think that's where, you know, the, the politics are blending a little bit on how they're investing and what they're really after.

I think a lot of this is really trying to make sure that we shore up a really important industry in a potentially massively disruptive competitive technology out there. Uh, but, you know, the way they're doing it also feels like it's a little bit of, you know, getting into the, you know, stock, stock market side of things and really trying to drive a, a, a few companies forward. Chris, to Dan's point, are we in danger of creating something that starts to feel and smell like the technology industrial complex?

And do we need to kind of bring Eisenhower back? Yeah, I mean this, this, you know, tickles my, uh, my libertarian bones right around the turn of the century. You know, I was, I was deep in a, and Rand Bill Redpath was the, was the chair of the American libertarian party.

And we had a long conversation right when I was moving outta that phase of my political life. Where, where at the end of it, I just love it, right? 'cause he said, I, I think as a former libertarian, you know, he said, look, I know we need social programs.

I just hate freeloaders, right? And, and I get that. So I get that entirely.

And I think this is one of these times where, you know, I'll just agree with, with Alan in, in your basic arc as, as an American, as a capitalist, free speech, democracy, all these wonderful things, libertarian ideals, um, I'm happy to let you know those ideas compete and I think they compete better for nation states. When you know, you, you take more of the approach that you're framing Allen, right? You know, it's like, let the market actually work itself out, right?

That stuff actually does work. You start putting politics in it and somebody else won't, and it'll be better, faster, cheaper, and they'll get the lead and that will determine itself, right? So it's less worth, I think, arguing about it on a moment by moment basis.

You know, that's sort of when it's hold my coffee, it's like, really? You wanna go down that path? Alright.

You look around, it's like, who isn't? I'll be talking with these folks over here. And when you find the error of your ways and, and, and look, you know, it, it is a complicated world.

And to be fair, I could be wrong at any one of these things. Sometimes it's the right time to do the wrong thing. Um, but, you know, it is, it is hard to avoid the nested jokes in a quantum topic because you guys have already used several of them.

But you, this may be several things at once, but to me, you know, this, this makes, you know, uh, on my teenage days reading Anne Rand, you know, sound like a, like an object lesson. It is, it is, it is. And and Dan, to your point, you're right, it's 10 million bucks.

That's, that's chump change in today's, in today's marketplace. So they're not doing it because that money by in and of, by itself, is gonna hoist for the optics. It's the, yeah, they're trying to create, what they're doing is they're, they're, they're anointing their chosen ones.

And if their chosen ones happen to be people who swear fey to the administration or to a particular political movement, well, that smacks that smacks of, of, of oligarchy and fascism and, and all of the bad things. We don't want to be, right. If you have, if they set up a fund and said, Hey, we're gonna run a, a contest and we're gonna have a, a, a panel of blue ribbon VCs, a panel of blue ribbon scientists, a panel of experts who were gonna pick the next generation of quantum leadership and the prize is a $10 million investment from the US government, man, I'd, I'd sign on for that today.

I think that's the kind of things that we used to do. We we could do that Good. Not not handing it out to, you know, my, my, my loyal supporters as a thank you.

But let me, let me get away from the politics for a second. I, I just did an interview this morning with the CTO of Cloudera, come over in Europe. He's had some serious positions in quantum, actually for the eu.

And he said something to me that I think we need to remember when you look, let's say the previous three or four stages, if you will, of the industrial revolution, there was always sort of one key technology that, you know, was, was the catalyst for an industrial revolutionary age. The steam engine, for instance, right? Brought about a, a huge change, an industrial revolution.

The internet brought about another stage of the industrial revolution. And one could argue that shortly thereafter, not long thereafter, the cell phone right, has, has ushered in another era. He said, how lucky are we?

How lucky are we that we have ai, quantum and robotics all converging and, and playing off each other potentially here at the same time? At the same time. Then you throw in some of the other nanotechnology space, some of the other things that we've got going on right now.

And man, what, what an exciting time to be alive. What an ex what a, I mean, you know, you talk about the new friends, so I'm, I I, I was born in 1960 and I grew up in the seventies, but we kind of idolized the sixties, you know, and the, and the whole Kennedy thing was the new frontier. What a, what an exciting new frontier to, to live at this convergence point where we have three, at least three technologists coming together.

Each one of them can kick off its own industrial revolution, right? This, this is, this is an amazing time for mankind, for humanity. I don't want to use the word mankind 'cause it doesn't take, it sounds too male deep.

I I wanna pull Kate in here for a second. Um, Kate, let's imagine if you would, that you're working for a company in the quantum computing space and you wake up one morning to discover that the United States is investing $10 million each in five different companies that are competitors of your, that's kind of your reaction. You know, what's your kind of, you know, thought process as you kind of wake up one morning and go, Hey, United States government is betting against me.

0, I think it's, it's extremely important. I don't know why we are rewriting the way that we have succeeded in the United States. And from your point of view, if the, if the United States government was putting money into my competitors, I would be really worried.

Meaning that, um, I think the, the playground changed on me and I wouldn't be able to get the ball to play basketball with the other folks, you know? And so if you can't play, you know, even though I have really good technology, I would be really concerned. And, and I hate that, that we don't let, um, companies fail if they should fail and let the good ones succeed.

And we are playing with things that we, the government doesn't understand. We are barely getting our hands around this, and we're in the IT industry. So we're expecting government to jump in and play in an area that, you know, they, they really don't understand.

Hmm. I wouldn't be so happy. Agreed.

Agreed. You know, I, and, and then well, I, I do wanna just close out positively here, right? Google, uh, their willow chip, they made some announcements this past week, you know, progress is happening, right?

Yeah. That I-B-M-A-M-D thing is pretty Impressive. Yes, it is.

Yes it is. So it, it, it is exciting Futures hybrid future, right? I mean, I think that that's the, the thing you're seeing stack up here, right?

You've got new quantum algorithms happening, you know, with Google, you've got, you know, quantum algorithms being able to run on kind of classical hardware being demonstrated by IBM and, and A and D. So, you know, all the progress is adding up. I think it just means we're getting closer and closer.

And if I could just add one thing real quick. I was watching, um, Mary Erdos from JP Morgan Chase, uh, this morning on, on, on Bloomberg News. And what was fascinating to me was she's, she said that, um, she wants somebody with curiosity.

That's what she wants today. Like when she hires a person, she doesn't care so much about talent, it's, she wants somebody coming who's curious in with questions. And that's where I think we are today.

I, I agree with that, Kate. I, I think that is, I, I want people, if I've gotta tell you to use AI or to see what quantum can do for you, or what physical and robot, you know, robotics can help us with. I don't want you working for me.

I want people who can't wait to sink their teeth into new stuff like that. And, and, um, we'll see, we'll see what happens. Anyway, we're over time on this one.

We need to take a break. Let's come back and talk about Qualcomm and Qualcomm ai. You're watching Text on Gang.

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Hey folks, we're back and maybe talking about something that might happen sooner than Quantum, but Qualcomm is out there saying that they're gonna build an AI chip, and it might take a little while for that to find its way in the marketplace, but Dan Wall Street went crazy and everybody decided that this was the greatest thing of the weekend. I don't know, what's your take on it? 'cause I'm of two mixed minds.

One is, well, I'm happy to see another supplier. And the second part of it though, is I feel like this was a pre-announcement that goes under the heading of, as soon as I get off this couch, I'm gonna kick some ass. Well said Mike.

Uh, yeah, no, I think the market reaction was positive. You know, it's certainly, you know, enabling kind of a narrative here from Qualcomm in terms of their exposure, you know, to this massive AI infrastructure build out. Um, you know, listen, I I think it's gonna be a few years before we see, you know, just how impactful it is to them.

They've got a, you know, a nice interesting roadmap that they've laid out there. There's some interesting differentiated bits, you know, particularly around, you know, low power use of low power memory versus side bandwidth memory. Um, you know, clearly I, I think a sign of kind of the maturing AI landscape in a lot of ways in that, you know, they do seem to be focused on, you know, kind of specific use cases.

Um, you know, as, as things really kind of get optimized as we scale out, you know, they seem to be really coming at it that way. Um, and now it's two different kind of generations of systems. Interestingly, this was not just a chip announcement, this was a full rack scale system, right?

So, you know, seeing that, you know, that trend is really kind of stuck in the market, and that's the way that people are coming at it. Um, you know, I I think this is a, this is a long-term game for Qualcomm, right? I mean, do I think this is gonna be a massive needle mover on their revenue in the very short term?

No. Uh, but I think, you know, they're announcing their intent. They're building an ecosystem.

Uh, they're targeting a specific part of kind of the inference, you know, use case. Um, Qualcomm's always done really well on, you know, the edge on mobile. Um, you know, they're really the dominant player in that space.

And as you know, as AI gets closer out to the edge, and, you know, we see more and more of these use cases scale where, you know, large models, consumer ai, you know, out there, you know, on device. Um, you know, I, I think it's a really, you know, interesting play for that. Um, you gotta remember too, you know, if this is a long-term play for Qualcomm, you know, six G is what, five years away?

Um, you know, think about wireless data centers. You know, they, they've got an interesting angle to play from a long-term perspective that, you know, nobody else has. You think about the amount of cabling and, uh, needed in a data center.

There's an enormous amount of expense there. Um, and I think, you know, Qualcomm has some disruptive technologies in their technology arsenal that, you know, could really give them an interesting long-term play as the market matures here. Um, but, you know, clearly the stock market loves the narrative, loves the angle.

Um, but, you know, we'll, we'll see kind of how impactful it is from a revenue perspective over the years to come. So, look, I, I commend Qualcomm for doing this, right? Because I think, you know, there are two kinds of people in the world, two kind two kinds of companies in the world.

There's one set that looks at it and says, wow, Nvidia is an 8,000 pound gorilla. They have years of a headstart, it seems, in, in this AI chip race. And why even bother, right?

Why even bother with let's find something complimentary to them or, you know, work with them. And then there's the other kind of people who say, you know what? I'm gonna chip away at that stone and I realize they have a, a dominant position today, but it, it isn't always going to be that way.

And if I don't, if I don't start now, it'll, oh, I'll never get there. You gotta start somewhere. And, and you know what?

You look at a MD versus Intel in the PC wars of the, of the nineties and two thousands. It was the same kind of thing. It was, you know, kind of shoveling sand against the tide a little bit at sometimes it seemed, but their day came too.

Their days come too, and Qualcomm and everyone else getting into this AI chip, including a MD, right? Look, a MD's had some great success recently in this AI chip business. So, you know, we need to foster competition.

Competition is good. It'll keep Nvidia on their toes, right? And, and keep them trying to maintain their lead.

Um, like Dan, like you said, six G, you know, when and if it becomes real and how well it's implemented could be something that tips the scales into, into the Qualcomm's favor because of their expertise there. Competition, you know, going back to what we spoke about in the last segment, competition is good. It, it, that is kind of the American way.

We don't want monopolies or, or government, you know, mandated winners and losers. Competition's a great thing. I I applaud Qualcomm for doing it, and I, I'm not surprised the street has as well, Dan, when do people kind of design products around chips?

And I'm asking the question because if I ever look over at Nvidia, you know, they'll tell me about like, Blackwell is coming and they were, gave me like a year, year and a half lead time on it, and then we all sat around and waited to see if they could actually do it. Qualcomm will be probably the same thing, but am I designing something three years out on the premise that Qualcomm will have a chip that I'm gonna use? Or do I design something that kind of assumes that there is some chip from somebody that I'll use when it's available, but I don't really know for sure who's got what when?

Yeah, I think the relationship between the leading chip makers, and let's be honest, there's a handful of meaningful chip makers in the world. There's a handful of meaningful OEMs and ODMs in the world, and the engineering relationships between those companies are incredibly tight, right? I mean, they're sharing three to five year roadmaps with some level of specificity, um, and five to 10 year roadmaps that have, you know, probably a little bit of a higher level of abstraction, right?

So, you know, this, this is not a world in which, you know, you don't know what's going to be available for you. Um, you're, you're looking at these roadmaps, you're talking to all the suppliers, and you know, you're reeling all using all that information to design what you're making, you know, as, as somebody who uses chips as an input to, you know, some other larger good or service that they produce. So, you know, I, I think there's a, you know, pretty strong telegraph signals being sent by everybody within the industry on kind of what's to come.

Um, you know, like I said, I, I think Qualcomm's got an interesting play here from a long term perspective near term. I think it's more of a narrative shift and less you'll less something you'll see in the actual results. But, you know, this is a massive market within incredibly large tam.

You know, if they can find a few use cases where, you know, they can pick off 10%, 20% of the market, I mean, that's billions and billions, hundreds of billions of dollars, right? Um, you know, we've talked a lot about, you know, how Qualcomm excels really in mobile on the edge. Uh, you know, I can't help but think of the world of physical ai, you know, they're a big player in that space as well.

Um, and as we launch all of these, you know, autonomous physical AI driven robotic devices out into the world, they're gonna need connectivity that will likely, you know, come through a cellular connection where Qualcomm's dominant and that could easily go back to a server farm where, you know, they're running AI inference on these massive Qualcomm racks, right? So, you know, I I think we, we ca we could, it's easy to be skeptical, uh, given, you know, they're entering the market a little bit late relative to some of their com established competitors. Uh, but, you know, this is a, a company that has really led the mobile and internet revolution in a big way from a network perspective.

Um, and I would not call 'em out. Mm-hmm. Great.

Chris, you're building out AI apps. How much of this factors into your thinking as you start to build out a project? And are you, you know, are you planning a year from now from a certain amount of capability being available and then we'll be severely disappointed if it's not, or are you just going with what, you know, I think it's, I, I, I've mentioned on this show, this show a number of times, you know, that, that this show itself is one of my little metrics, you know, because I, every week we come back and talk about these things, and I can go back to March and March, April, may, particularly we, and Dan, you know, you and Dan, and, and, uh, and I, so it's on that same path.

I, I think that on this side, yeah, the hardware compute based thing will continue to go as we discussed, right. You know? Right.

You know, earlier this year we were still thinking, I'm gonna have this not just one GPU, I'll have a million GPUs and I'll take every single thing I do and go all the way to the top and come back down. And that doesn't make any sense. You know, and through the year, we've gotten more into the, at the chip level and, and all these different deals that's, you know, that workload is spreading out.

We've talked about how an LLM, what we horribly call artificial intelligence, an LLM as a semantic tool in a workflow embedded in things is different than a chat, you know, LLM that you're talking to. And you know, and we're just working through what this even means. So all of this, you know, you know, almost everybody on the screen here is more expert than I am to say at this particular point.

These individual moves are right or wrong, but they're all going in that direction, right? That this is, you know, it is, Alan, you said earlier, this is, this is one of these times, it's fascinating. All of quantum, the last segment, you know, that's happening.

And we've all been seeing that company and coming, and that's happening now. Oh, AI is coming, and we saw that coming, and that's happening now. Oh, and by the way, it's semantic systems Hold my beer.

That's the next segment. We'll talk about that. And, and, and, you know, and turns out we built trillions of chips and put 'em everywhere, and most of 'em really aren't doing anything.

But now we can be smart enough to actually make them all work, the ones that are already there, much less the ones we're building next. So I don't expect to be disappointed at all. I think our ability to tap the hardware is limitless.

I don't know, Alan, do you think that the CEO of Qualcomm will be making a trip to Mar-a-Lago to bend the knee looking for a little extra cash to fund this? What do you say? You know, it's not the cash, it's, it's, you know, this is kind of like, uh, Don Vito going to visit Don Cheche to get permission for genco olive oil, right?

It's not the cash, it's the blessing, if you will, of, of, excuse me, maybe being part of this AI factory building buzz or, or what have you, right? Being one of the anointed one. It goes back to what I said before about the investments in, in quantum.

We shouldn't have the US government anointing our winners and losers, especially based on who bends the knee not right? One of the principles of America, of America and the American way of life is the guy who comes up with the better mousetrap wins that, right? We the best and the brightest, not, not the weakest and the most kiss butts.

And so, I hope not to tell you the truth. I I don't think they need to. I, I think, look, Qualcomm is a, is a quality, no pun intended.

Qualcomm's a quality company, right? They've built an amazing business. We haven't even mentioned, no one's mentioned, look, the snap, the success they've had with snapdragons on PCs, right?

And what it's done to the price points for PCs go, I don't know how many of you go shop for laptops anymore, but man, I think there's some inherent go government support though, Alan, right? Like, I don't see a future in which, you know, we have Huawei Towers going up as the six G Buildout Act. No's great Qualcomm will have, These are markets where there's one to two players, you know, kind of on each side of the geo pli geopolitical divide.

Um, and they're inherently gonna get the backing of, you know, not only the US but a lot of large western governments. I mean, you, you don't want a monopoly either, though, right there, you want competition, Right? But to be fair, and to Dan's point, do we have a choice but to not fund these companies because we are up against nation states like China that are funding all kinds of stuff on their side.

Oh, come on. That sounds like red scare b******t, honestly. You know what I mean?

Well, We've got one fab, right? We've got one memory company as a nation, right? You know, uh, across all the critical layers of the stack, there's one to two winners, right?

Well, if you talk to classic VCs there, there's always three winners. You need three, right? For the market.

You need three. And, and if we are in, in company, you know, this was my thing with Intel. I'm not against funding intel and keeping Intel viable.

If, if it proves that it can be viable, if you will, not that it's propped. I mean, you go back to what Ronald Reagan said about tariffs and stuff like that, you know, and I hope, no offense to my Canadian friend there, but if you go back to what Ronald Reagan said about tariffs, he said one of the, the weaknesses of it is it are, it creates an artificial market where the companies are being propped up because they're not truly competing, right? We need a strong Qualcomm to compete on the world stage.

I mean, I, I look at the two vectors, right? We can compete on performance and we can compete on economics, right? I think on some of these critical technologies, they'll kind of be okay losing on the economics as long as you win on the performance.

I, by the way, I, I don't believe in the three company rule. There's actually just two winners in a third company that hasn't figured out, they lost already. Well, that, well, most markets there's more than three companies, and that that's the key, right?

And the, there's the, you know, that's running in the Olympics. You got your gold, silver, bronze, and then everyone else. For sure.

I just think on the, the markets where, you know, the barrier to entry is so high and the amount of investment needed is so high, that that tends to gravitate more towards winner take all, um, you know, that kind of a healthy competitive environment, right? You've seen that at Hyperscale Cloud. You've seen that in semiconductor fab.

You've seen that. Now, you know, in AI models, you know, anything where, you know, you're measuring the investment in hundreds of billions of dollars, you know, defense sector no different. Um, seems to, seems to gravitate more towards that oligopoly Model.

So a couple of things there. Let's take the hyperscale cloud. That's exactly what I'm saying.

The WS Microsoft Google three, right? And I-B-M-I-B-M had a coffee. Well, well, but you know what, to be fair, there's the top three in everyone else.

Or Oracle's never a player, I would say. And then if I look at Oracles a player too, top three in everyone else. Take, take another, uh, one that you mentioned there, Dan and I, my brain's blanking on me.

Uh, ai, you've got open ai, you've got anthropic, maybe perplexity, right? Or or another, uh, uh, Elon, uh, grok, right? You have at least three players there.

Well, and meta, you've got, you got at least three players And the entire open source community. But, Well, that, that's a whole nother story. Yeah.

We always used to say we would, we would be competing against other, yeah. Other had a huge Block. Well, competing against a spreadsheet.

But I, I think it, it's, it's good because I think having competition hastens and Fosters bringing out the best in people, healthy, competitive, right? Not, not weighted or anybody putting thumbs on scales. Intel was driven by A-M-D-A-M-D may have always come in second, but they drove Intel to stay one step ahead.

Absolutely. It's the same thing at play here. It's what makes, it's, it's the market.

That's what makes the market the market, right? You, if you, if you are not pushing as hard as you can, someone else is, man, and they're right on your heels. And that's what keeps us moving.

That's what keeps us moving, right? No matter if you are the leader in your space, you're, you know, you could, you're always kinda looking over your shoulder a little bit saying, Hey, who's coming up fast? And if there's no one coming up fast, I think it's just human nature to kind of take your foot off the pedal a little bit.

Anyway, hey, we're over time on this one, and we've got a, it's still another segment to go. So let's take a break here on the gang. We're gonna come back and talk about the politics of ai.

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Contact us today and tell your story to the world in the most powerful way with Textron Group. Hey folks, we're back. And we're talking about this, uh, position that, uh, superPAC has taken, that's led by Mark Andreessen and his folks in the Silicon Valley area.

And basically, they came out and said that they were gonna support political candidates from Republicans or Democrats as long as they were pro ai and not all for regulating ai, which probably might eliminate a lot of Democrats anyway, but it was interesting to see a, or they trying to kind of pull back and establish some neutral ground. And that immediately got a response from the White House, where basically White House said, how dare you slap us in the face after we did all these wonderful things for you. Chris, what's your take?

What's going on here? I haven't heard better news in a long, long time, right? So I've been predicting this sort of response from this administration at this, at about this time.

And in general, as we discussed in previous segments, you know, organizations with this sort of sort of view would probably not like this sort of thing, which is a positive indicator that we're getting to the point where it's a real threat to narrative, uh, malicious narrative influence operators, right? So as, as, as Alan, you know, I, you know, uh, uh, back in 2008, I spent a whole year, you know, thinking about this and with the Obama campaign in leadership of the Rapid response team. And it was interesting.

I was, I was the conservative in that group, right? It was a funny time in politics. And, uh, and Steve Bannon started from what I know, two week, two months before I got involved with that and has stayed on that path.

And Valerie Moff and Putin in, in Russia and, and these other, you know, uh, full, full-time malicious narrative threat actors who made livings out of that have been dominating the landscape since. And the entire poit and premise of civic AI and attestation systems. And, you know, everything I've been working on in not, not just in, in this space, in cybersecurity, technically, everything the quiet wire is about now in the civic ai, uh, open source project is on the premise that at a certain point to our, our conversations in the, in the show about the economics of competition and so forth, it's, it works in the short term to just flood the zone, you know?

We'll, we'll keep it Russian for the moment. Valerie Grass Mob, the Grass Mob of Doctrine since 2012, Putin has built his entire national strategy on that. And it's about the ability to use bullhorns to dominate all public narrative forever.

What's happening with a number of things, a, you know, being driven by AI in multiple ways is that that's not going to be viable for much longer. So I take this administration making exactly this pushback exactly right now as the most definitive proof that those of us in the narrative sovereignty nor narrative integrity, um, uh, move, you know, world are correct. That these systems of mass narrative domination are like dinosaurs at the end of their era.

They're, they're, they're the large corporation with 30 years in the space when the startup comes in, that turns a lot faster and, and, and brings 'em down. Yeah. This, this is, I think, the strongest indication.

We're at the end of this mind-numbingly tiring era of narrative bullhorn all over the world, right? It just doesn't work that well anymore. Chris, to quote Michael Corleone godfather, part one, now who's being naive?

Kate? Um, Why, why, why, why are they so offensive? Why is a, is a voice like this, and I'm trying to keep it out of, you know, this, out of, you know, personal politics.

But if I'm correct, if this, if this narrative voice, this, this US administration is not a sincere narrative partner, why else would they be pushing back against this right now? Then? The fact that there's concern?

Well, I, I, that the methods world power are at Prep. 'cause fascists are gonna fasc or whatever the word is. But, but here's the real deal.

When you combine that stance with deciding who I'm going to invest in and anoint as my chosen ones while I'm in power, that's where stuff goes south. I don't know if south's the right word. That's where stuff goes bad.

Well, I mean, the grain capital, you know, in this economics is money, or, or, or money is, is speech in a way. I don't wanna get into that whole thing, but in the same sort of way, you know, again, as we said in the last segment, I like libertarianism. I may seem like a socialist in today environment, but I'm a capitalist libertarian freedom of speech, narrative integrity, fanatic.

And I'm that way for a reason. I think there's competitive advantages, and I think certain actors on the global stage, and I'm not in favor of, are making moves defensively that show they're weak, that their approach so far. Oh, absolutely.

Absolutely. But, And others, if they don't do it right, others will. I, I hope so, Mike, to your point, I don't think it's necessarily just Democrats who are looking a regulate ai.

I, I think, I think that shows that, to Chris's point, you've been sucked into the narrative, right? Because I remember someone who runs X starting a lawsuit that wanted to regulate ai, and I remember the people who signed the a hundred people who signed the pledges about let's stopping ai. I don't think that was necessarily a Republican or Democrat thing either.

So let, let's not put people into camps based upon the, the propaganda that comes out of, you know, our out, out of our political machines, right? Well, and we're, you know, we've actually called this se segment politics and we're talking, so we'll, we'll, let's run the gamut on it. You know, I don't, I, to be clear, I'm not picking, I'm not picking on any one particular political party or structure.

I'm kind of fed up with all of them, you know, kind of commonly, I think perhaps for, uh, uh, more nuanced reasons. I think the liberal, uh, international order has spawned liberal, you know, literally called liberal, sometimes, sometimes the opposite. Australia, what are you thinking?

Um, but anyways, all these, the, what we would see as western lib liberal, uh, political parties are inefficient at best, terrible, at worst. Well, democracy of criticism Are not wrong in Many of regulating this. I, you know, this is, again, I I should be arguing with the Republicans.

I should be a Republican again right now, because this is where my libertarian Republican free market, you know, uh, uh, economics, bones just tingle. It's like, you can't regulate this. This is like regulating speech.

Forget it. And if you see this as a threat, then you're not in, in favor of free markets, free speech economics, and, and you'll get run over by those Who are, well, here's the thing, we're missing the irony here, guys, there. This is an ironic moment here.

You've got Mark Andreessen, who during the last election cycle really came out hard, right? In supporting this administration and supporting the candidacy of Trump, right? They, he put big money and put his money where his mouth is, and put his mouth where maybe, where it shouldn't have gone in, in supporting Trump versus Kamala, or, you know, in supporting the hard, right?

He's now recognizing that it's not a question of what political label you have on whether you are wearing a red tie or a blue tie. He wants to back people who are going to further his own business ambitions, which is the way it's supposed to be. That's America.

That's America. I support people who are gonna help me. Am I selfish?

Yeah, but that's America, that's what it's supposed to Be. I think the problem of now, right? I mean, I, I think part of what they're trying to do here is kind of ask that famous question of, you know, just because you can, doesn't mean you should right?

Know you. You're right, Dan, We actually societal impacts of achieving a GI, right? You know, we probably have it.

I'm not sure we could, um, Chris Beauty has. Yeah, No, no, Dan, I agree with you. I agree with you there, but, you know, we don't have the will to regulate that today, I don't think is is is the issue.

No. And, uh, you can't judge the intention good. And honestly, because of what you were saying on, which is, you know, because there's an inherent kind of economic interest alignment behind it.

I think we're kind of missing the point of what is a pretty good question of, you know, do, do we actually wanna do this? Right? We're kind of driving very, very hard to it without having a lot of the hard conversations about what it really means.

But, but look, again, look at the history of mankin. When have we ever, and this, I'm sorry, Chris, let me just finish this, This, this is where I, I, I specifically say libertarian because, you know, I always, you know, to be clear, the libertarian party, libertarian ideals, and man, it's all very simplistic and naive. However, right?

There are principles in there, and I fundamentally believe them, and this is a perfect example, where we're running down a path that's, that's testing that belief. They are, we're like, well, maybe not this time, maybe we'll, actually, and I'm just looking at this and I have my same concerns. I think I, I think, we'll, we will be okay, lemme deliver that message.

If I'm right, we'll be fine. But we're finding ourselves saying, well, we fundamentally believe in, but not in this case. It's like, well, I think we should, But when have we, I don't think ever Your point, Dan, I don't think we have a choice.

We're going down this path anyways. Exact, Dan, thats my point. I don't disagree.

Just because we, I think that's, you know, just because we can, doesn't mean we should. We're not, that's what supposedly separates us from animals, right? But when have we not?

Because at the end of the day, humans are animals. And that's my fear. Or I think this goes to what we talked about last week with the Valley.

It's getting a little out of touch and kind of pursuing its own interest apart from the rest of the country. And the rest of the country's gonna take notice. And do not be surprised when you start spending billions of dollars on making sure that there's no regulation for ai, that you wake up one morning and there's gonna be regulations for ai.

'cause everybody's gonna be paying attention to the conversation and conclude the opposite. But to Dan's point, maybe it's a good thing. I don't know.

Yeah, I just think a lot of the paths where AI could take us ultimately start to get into the territory of kind of redefining what it means to be human right. And, you know, I don't think the everyday person has really put much thought into that. No.

Well, we haven't. And, and not, not to discount based concerns, but, you know, but because these, this is existential in the extreme. Um, I, it is just that I, and, and the people I've been spending a lot of time with this year have been thinking about this awful lot.

And I think, I think, again, I think, Alan, you're right, this is happening anyway. There's no stopping it, right? And if we don't, they, you know, and my, my, my kids can pick on me for even saying that I'm old.

I get to, but it's, it, it will work out because the, the downsides are so extreme that we don't want to go there. We won't go there, but we have to go through it like we do with everything else. And again, this is the American Libertarian, just seriously individualistic approach.

Open your eyes, pay attention. This is where we're going. You know, the storm is coming, you know, this is, you know, the entire world is Jamaica this morning, right?

So the storm's here, deal with it. Heart's out to the people in Jamaica. Let's end it on that.

Guys, I, I gotta run Kate, Chris, Dan, Mike, thank you for joining. Thank you for watching us. I hope you've enjoyed the discussion.

We'd love to hear your thoughts on this, right? People vote with their voices and their, their keyboards and everything else. So don't be afraid to, to, you know, log in and, and speak out on this.

I think it's important to all of us. Uh, hope you enjoy Text Drunk TV immediately following this. Of course, we'll have another fresh gang tomorrow for Thursday.

But until then, this is Alan Shimmel, on behalf of the Textron Gang, have a great day, everyone. Hey everyone, welcome back here to Tech Drunk tv. I'm really happy to have this next guest on.

It's a new, a new company we haven't featured before, and he's, it's his first time here. So let me introduce you all to Pavlo Barron. Pavlo is the co-founder and CEO of a company called Platform Engineering Labs.

Pavlo, it's great to have you on. Nice to meet you. Welcome.

Thank you. Thanks. Thank you so much for having me.

It's our pleasure. Pavlo, before we jump into Platform Engineering labs and something called Fori fori, I, I want to talk a little bit about you and give our audience a sense, as I mentioned, you're the co-founder and CEO of the company. Tell us a little bit about you.

Alright, I'm looking back at a, uh, very long career in, um, well, broader IT space, let's call it like that. I'm, uh, pretty much a veteran and, um, well, I basically was in every single role you can imagine in different kinds of companies, different domains, always staying curious, going after the means. I didn't know so far was architecting, creating platforms, leading engineering teams, uh, building teams, building startups.

So the most recent, uh, achievement is, uh, co-founding and, uh, a co-founding a company called Instana, uh, that exited to IBM 2020. And that stayed with IBM for a few years, um, to, to have an internal role as a, um, in the, in innovation space. And, um, yeah, I represent the company.

So I need to tell a few words about my co-founder. My co-founder is Zach Schneider. He is a good friend of mine.

And, uh, we worked together at Inana already, and we always, always kept talking about, um, well, how to change the things in the space we're going into now as a company. So Zach lives in, uh, sunny Traverse City, Michigan. And, uh, his, uh, most recent activity was, uh, he was one of the core engineers of iCloud at Apple.

Really? Right. Very cool.

Very cool. And he was at stanner as well? Yes, Sir.

So of course, well, I'm personally familiar with Stanner. We worked a ton with Instana, you know, prior to the IBM acquisition. com, I think.

But, um, interesting. So were you a co-founder in Stanner? Is that the story?

Yes, I'm, um, co-founder and CTO and I invented big chunks of it. Very cool. And Zach also was a co-founder there.

He just worked in Stanner. Yeah, he Was my first hire actually in America. Really?

Yeah, because, uh, it was 24 7 with humongous ingress, a lot of data flying by. And, uh, in the early days I took over also the responsibility to actually run this thing, not only to build this thing with the team, and then at some point I had to make a decision to actually cover the US hours because we had us customers. Yeah, sure.

I was myself in Europe like I am right now. And, uh, this is how I met Zach through another friend. And, uh, it turned, uh, into a really good relationship and friendship.

That's fantastic. What a great story. You know, my, my very first company I co-founded, it was back in 95, 96.

Uh, I didn't know what it was at the time, but it became known as web hosting. I thought I was a virtual landlord. I was just buying hard drives to store more websites.

Right. And, and we, I had the same thing in reverse all of a sudden we had a lot of European customers, and that meant we had to do tech support for us. It was the middle of the night.

Right. So, so after a few, a few months of staying up all night, you know, running, we, we ran Solaris, that's how long ago this was. Oh, yeah.

Right. Being trying to be a Solaris admin and then go to work in the morning and run a business. I said, this is crazy.

I need, and I did the reverse thing. We actually picked up a partner in Germany, right. Uh, who, who was a, he was a great customer.

We gave, basically gave him hosting for free and made him, made him our, you know, our tech support overnight until we went to 24 7. Right. So familiar with that.

Familiar with that. Very much so. So Pablo, let's talk about platform engineering labs.

No one, you, you've started enough companies, you know this, no one undertakes starting a company lightly. It's a commitment, right? Right.

It's, you put your life and your soul into it. Yes. Tell us, what was the passion that drove, what's the mission of platform engineering labs?

Well, the broader vision, we actually want to eliminate the toil, the unnecessary labor, the human error from, let's start with the infrastructure space, but generally everything that is about delivering software, um, the problem we are addressing is old and it's not solved. So Zach and I, as I said, going through very complicated, um, environment and, uh, you know, massive data ingress and a lot of responsibility for 24 7 platform running for a lot of customers on premise as well in SaaS, we actually learned a lot of things that we want to improve. And, uh, we met a lot of people because I mean, you can imagine that when you are an a PM solution that gets, uh, a lot of customers and a lot of attention, you basically meet everybody in the ops space, right?

And let's call it the broader ops space, because there is just a little difference between the disciplines, but it's the same people with the, the same skillset. It is about people who are running software and, uh, we met a lot of them. And, you know, there is so much pain out there with just a few things that are not properly addressed in this space that we just thought that we need to change that at some point, once we learned that, uh, uh, there's a, a lot of movement in the markets.

For example, back then when HashiCorp acquisitions for IBM was announced, we just, Hey, it's about time to start it now because we know that, uh, this space is something we want to change, even if this is the last thing I do in my life. So that is, Let's hope not, let's hope not. No, you and me both My friend, but this is the commitment.

I mean, we definitely, we are, are on a mission to give platform engineers, and I'm talking about platform engineers as a species. This is finally a discipline that allows us to work the way best teams actually work, which is a few real good people, and the few people who are just willing to go full stack as deep as possible, and who willing to go on call all night. This is a special species, as I said, uh, this people work for a hundred other people.

So basically one team is serving a hundred other teams. And, uh, that's the cool thing about the platform engineering, because after all this cultural dancing around DevOps and whatsoever I am, I'm just seeing this as a chance to really set it up the way it was supposed to be. But, and here's where a company comes into play.

These people have to deal with tools that are completely outdated and, and, and have never been designed for this, uh, mode of operation. We're talking about develop experience or user experience from the, uh, from the late sixties. I mean, everything is low level.

Everything is completely detailed. You are the guy that is part of the tool, not a single tool they have to work with can allow you to lean back and, and, and, and let it go. Um, while everything in the dev space is being constantly improved and simplified and abstracted, none of this is happening in the ops space.

And that is exactly what we want to change. And, uh, we will definitely go one by one with a current focus on the infrastructure management because we, no matter what tool we tried in the best, and you can imagine, Zach and I, we really went through all of them and we had, uh, pretty big responsibility using them. We tired.

And we, when we are tired, we talk about everybody else was tired. I know enough people in the space who are really good, but they're close to checking it all in and just going gardening. That is how bad it is.

And I wanna change that. That's, that's my mission. Good for you, man.

I love it. com, I, I understand. I feel your pain.

I understand the mission. I, I, I get it. Um, I also look at platform engineering, not as a replacement for DevOps.

You, you're still gonna do DevOps once you have your platform laid out, but it's sort of laying the groundwork that we can build on top of. I, I think one of the things we learned, and, and you know, Pavlo, you've been around, I've been around, right? We, we, we see what, how this works.

One of the things we, we've learned in the DevOps movement is oh, onesies and twosies and small teams, it's, you can really control things and, you know, fine tune and dial in when you go to scale. Right? Scale's a different animal, a different beast.

Yes. And, and what works in a small team well, doesn't necessarily work at scale. Right?

Well, and and to me, that's really the mission. Build a platform that scales, right? That allows your developers, your DevOps engineers, your SREs, your, your qa, your security people to go as fast as they can go.

Exactly. Sir. Yes.

I love that. This is exactly the point. We have more kind of professions coming up in this industry.

I'm talking about all this AI engineers and everything they get. Sure. All of the infrastructure management built in, all of the security tools actually manage the infrastructure themselves.

You are in the situation that you cannot manage everything with one tool. And I'm hearing people using within the same setting, two to three different infrastructure management tools, that is absolutely impossible to, um, well, to call it being under control because everything is happening in parallel. And if you slow them down, actually that, that's, that's bad, right?

I mean, you, you want, yeah, you want everybody, as you said, to move at at the speed they want and have to move it. And what is the job of the platform team or operations team or SRE team, however we call them? Of course it is to support them.

But since this is exploding and it's gonna explode more and more and more, the more AI is gonna be used because everything is landing on your desk. Your only job is to get rid of routine work and put yourself in the driver's seat and always have a feeling of control. And this is, this is exactly what our goal is with this first tool we are building regarding the infrastructure management.

We want to put you exactly in dust in that driver's seat. Let everybody go at the speed they want. But you are the one who control and manage things.

I love it. You know, and, and you mentioned ai. Look, AI doesn't do well in, in chaos.

It'll do best in a, in a very defined environment, right? The more you can define it, the, you know, if it knows what the rules are, it operates within those usually. Um, so I, I think it, it, it behooves us as well, if we're gonna go with an ai, everyone wants to go ai, ai, ai.

I get it. If you have, again, you have a platform with guardrails, with clearly defined, you know, routes, then AI can go faster than any humans are, are gonna be able to go as well. Um, so it, it all makes sense.

I gotta do a little business housekeeping, pavlo platform engineering labs. What's the website? It's platform engineering.

We have a great, love It. Great. Um, and company is, it's, it's out of, it's, it's in it, it's out of stealth, I should say.

It's public. It's, is it funded? I, I don't know.

Did you raise money? We have, we have investors. We're prese.

Okay. Uh, we have an announcement that is a few months old already of our pre-seed round. Uh, it's out there in the public, so it's not secret.

Mm-hmm. Very good. Um, all right.

Let's turn to, and I, I, you know, as we were talking off camera, Latin's, not my forte, they'll get a little pun there, Forte's Latin, but, uh, but for, is it for me or for my, it's foray. Foray. Yeah.

La lucky me. My daughter studies medicine, so, uh, she can teach me that. Excellent.

So for me is, is this new, uh, open source infrastructure is cloud platform that Platform Engineering Labs has, has pioneered, or has developed. And it, it's basically, you know, infrastructure is code built for the future. Tell, you know, that's at a high level.

Tell us more. Right? Infrastructure is, code is a very interesting space.

There's a few players. It's not really crowded. This, uh, the space is dominated by, uh, Terraform.

Terraform, uh, is developed by HashiCorp. HashiCorp is is acquired by IBM. Uh, there's a few other players, uh, in the market with their own, uh, uh, advantages and disadvantages.

But our goal is a little bit different than what everybody else is doing. We just looked at what is, what is slowing down those people who work for the other teams in regards to infrastructure management. There's a, there's a humongous divide between what those platform teams are doing and how developers are working.

For example, I'm yet to see a developer, a regular developer who wants to go on call and wake up at 2:00 AM to fix their stuff. They're not doing that. This is the responsibility of somebody else.

We call them the operators. We can put them in whatever discipline you want. Uh, it doesn't matter how we call it.

Even platform engineering. That is a role. It's a responsibility.

We want to support this role with the, with the adequate tool. And honestly, we learned, uh, for many years of experience, the best tool people work with and everybody agrees on is code. So what I, what is for me, for me is a hundred percent entire coding code out infrastructure's code tool, where tools like Terraform rely on a separate state file that is then your code.

So your code in your Git is not the real code anymore. And you really need to, you really need to play that catch up game with your, between your state and reality. Our approach is totally different.

What we say is, first of all, we embrace, embrace the reality. The reality looks like everybody will be using their own tool. You cannot enforce in a, a reasonably well in any reasonable setup.

You cannot enforce one single tool. So let's forget about that. Now, what do you want to do?

You want to give them their own tools, let them move of their, at their own speed, as we already discussed, but then catch them with a single source of truth. And that is the most important thing. That is what Forme does.

Forme can perfectly coexist with any other IAC tool, click ops, any other security tool that makes changes in your cloud, including your own cloud providers that can constantly make changes. It is an agent-based tool that is important. So it's not what you run exclusively on your local machine and babysit it?

No, you don't have to. It has an active backend component, and this active backend component takes over the responsibility of actually converging the work to the proper result. And, uh, as I said, it's coding code out.

This means that you can make changes in this platform at any granularity for any resource in your infrastructure. Um, with as minimum, um, blast radios as you want, you can do whole rollouts. You can reconcile a hundred percent from your code.

You can consider us as your source of truth regarding the code because you are extracting resources from their current real state. You are extracting them as code. You're changing them, you're putting them back, and we apply the change.

So you always work through code, uh, with this tool and we catching up by automated, uh, fully automated discovery and synchronization, we're catching up on what's happening in your cloud estate. So we basically are absorbing any change that happens through whatever tool you use, whatever people are using, and version them all of the changes and put them into our internal database, which allows us also to completely eliminate, um, the necessity for tools like Git. Well, let's say in the future, because when we early, we're still developing that.

But what you already get is a fully versioned a hundred percent code oriented tool. Charlie, couple questions. Sure.

First of all, you mentioned of course, Terraform ha, HashiCorp, you know, our audience is sophisticated on this. They know Linux Foundation or CNCF, you know, uh, open Tofu Yes. Is a fork.

Sure. But it, you know, it's in that same vein as as as Terraform, though They are, they're diverging. Right?

As time goes on, I think you'll see more of a divergence between the two of 'em. Um, the other big thing with platform engineering is it's become so much about internal developer platforms, backstage, things like this. Um, we're almost, that has become, if you will, the mission, right?

Maintaining the IDEI, I think there's a bigger mission for platform engineering than just the IDE. I don't want to minimize, right. The id, you know, they, having an an ID is very important.

Yeah. But there's, there's more to the platform than the ID is I guess what I'm saying. Right.

Um, now your tool for a Formula A is also open source. Yes, sir. Correct.

Yep. And you guys are maintaining that. What about the community?

Yeah, we've gotta build a community. I mean, everything in the system is a plugin, basically. So it's so pluggable that people can develop their own plugins, they can maintain their own licenses for the plugins, of course.

Uh, as, uh, a, uh, company that is funded by, uh, investors and has a commercial meaning we are going to go after well monetary aspects of the business. Um, but right now it is important to us to open this to the world and, uh, allow everybody to enjoy the infrastructures code it as it was supposed to be. That is the most important thing for us right now.

But of course, we're welcoming contributions. Uh, we want to build a healthy ecosystem. We want to build a marketplace.

We want to build all of these things. The coming, definitely the important, uh, aspect, uh, regarding this, uh, um, developer, uh, portals that you mentioned. Well, the problem they all have is that in the space of infrastructure, they have to opt out to what is there.

So this means that some of them are ending up generating Terraform code. And the problem with generation of Terraform code like HCL code, somebody needs to check it still. I mean, you cannot rely on, on, uh, uh, machines, uh, no spitting out proper code.

Um, not even ai. And, uh, this is number one. The number two is that, you know, even if you have that developer portal, your infrastructure management is still not solved.

You want to have it on proper feet. And, uh, the abstractions for the portal only means that you need to provide services that are reusable and, uh, um, can be claimed by, if you wish, by those developers. But in order to create an infrastructure service, you need to think, when you think about a database, you think about a t-shirt size of a database.

You don't talk about gigabytes whatsoever. Not the single developer knows how many gigabytes this database will ever need. Who knows that?

Nobody knows that this is not my job as a developer. So the platform team needs to create abstractions. And guess what?

You can't create abstractions with the current tools. It is literally impossible. That's what we give them as an add-on.

You can actually create layers of abstraction involving every single engineer in your team at the level of their experience and in the level of their responsibility. This is another important aspect. Excellent.

Agreed. Um, I, besides the platform engineering, I'd imagine the formula is also on like GitHub and so forth. Yes, absolutely.

You wouldn't happen to know the GitHub, uh, URL, would you? Yeah, it's, uh, so the company is called Platform Engineering Lab. So on GitHub, it's Platform Engineering Lab slash for me.

Excellent. I just wanna make sure we give 'em everything. Yes, Absolutely.

Um, what about Discord or Slack or other ways for the community to communicate? Yes, there's a Discord. The Discord is in the, read me on GitHub, the Discord is in the Read Me on the organization.

That's all set up. So people are welcome to join and there are GitHub discussions. We're completely open.

And, uh, actually very excited about talking to as many users as possible. I love it. Pavo, we're about outta time, but I wanna wish you all the success.

Thank You so much. On, on this new venture platform, engineering Labs. com.

So keep us posted and you know, we've got a good place to put some news up there. You got it. So we, I hope to hear plenty from you.

Alright, thank you. Pavlo Barron, co-founder and CEO for Platform Engineering Labs. That's platform engineering, uh, makers of, for, uh, open source infrastructure's, CLO code platform built for the future.

You're watching Text Drunk tv. We'll be right back. Hey guys, thanks for the throw.

We're here with Shiva Palle, who's senior Vice President for the Americas for Veeam. ai and what that all means because, well, as I understand it, at least they're adding a data security posture management platform to the portfolio. Shiva, welcome to show.

Hey, Mike, absolute pleasure to be here. So thank you. Very, very exciting news.

We haven't acquired them yet, but, uh, we announced our intent and we're in progress to do so, so very, very exciting time. Where does A-D-S-P-M platform fit in the portfolio? 'cause it seems like a natural extension, but, um, what do we maybe not appreciating about this whole thing?

'cause it seems like we live in the age of AI and maybe the whole way we think about securing data needs to change. Yeah, it's a great question. Um, firstly, I think security AI is much broader than just, uh, DSPM provider, but I'll, I'll set a little bit of context.

I think Veeam, we've done a great job of being, you know, the folks that protect and recover your data. Um, but in the AI era, I think things are changing. Um, people don't know the surface area of what data they have out there, uh, how to protect it or having to classify it.

Uh, and then when you add ai, LLMs, AI pipelines and a bunch of other complexity, this explodes to a problem that I think most customers or most, you know, CIOs, CEOs even haven't ever faced. Um, so bringing those two parties together, Veeam and, uh, security ai, I think we just help solve for a multitude of problems. We make sure your data's fully secure, backup, immutable on the, on the, uh, data protection side.

Um, but we also look at classifying data across the cloud SaaS, even your apps backups. And so we, the intent here is bringing these two parties together gives a very unified and trusted view. And the outcome for A CEO and CIO is to be able to accelerate into the world of infinite possibilities that AI brings, but doing that in a very safe, um, safe manner.

And I think that's a big problem for everybody at the moment. Mm-hmm. Now, as I understand it, one of the core technologies that the acquisition will bring is that there's a data graph built into their platform.

And it seems like there's an opportunity to extend that and the, and the reach of that to provide more visibility into all the various types of data we have, because, well, not all data is created equal and therefore needs to be secured differently. Yeah, a hundred percent. The data command graph is probably one of the more exciting pieces of this.

Uh, maybe I'll give you a quick, simple explanation of how I see it, and I'm still getting, you know, up to speed on a lot of this. But, uh, I, I view it as a nervous system for your organization's data. So the data command graph will connect data across all sorts of, uh, you know, endpoints, SaaS, cloud, um, even your backups.

And then it continually maps relationships like ownership, sensitivity and access. And those three things make compelling context when you look at which data is important, why is it important, and what governance and compliance you need to apply to it. So if you made that the foundation for everything else, your security, compliance, recovery and how you pursue AI becomes inherently easier because you're looking at it from a common frame of reference.

And to me, that also extends to, hey, it's not metadata, it's it's actionable intelligence, um, where your data is who can see it, and whether it's safe to use is like the common questions that I think, um, it industry leaders are now challenged to face and execute upon. Are we gonna see some level of convergence here along this line? Um, I've been watching data science teams kind of hunt around to pull it together the right data and figure out whether or not they can use it and how to apply it.

And it's a whole effort. And a lot of them spend more time and effort on that than they actually do on the models. And yet, if I look over at the data protection platforms, a lot of that data's already been aggregated and classified.

And so is this a way to kinda enable two groups of people to use the same data for different purposes, but in a way that maybe will make things more accessible for all? I think you're spot on. I think, uh, when you think about it, um, those two departments, I guess one of them kind of never existed.

If you really think about it. The AI organizations and the ai, um, uh, org charts have only just started becoming a part of existence. The backup recovery data resilience posture has existed for a long time.

I think the problem for customers is they wanna move very fast with ai. They really, they realize it's super important, but you can't do that without trusted data. And so I think we've seen lots of AI initiatives failing as a model.

It's, it's not because the models are bad, but the data feeding them is incomplete. So bringing the these two together takes, you know, um, you've got Veeam's Protection recovery. You also got the discovery and governance capabilities from security ai.

So really gives a huge foundation for our customers to be able to innovate and also feel safe at the same time. So you are, you're spot on in terms of what this means by bringing these two sort of concepts together into one unified, uh, uh, outcome for our customers. Right.

Do you think in the age of AI that our mindset about security is finally changing? And if I look back in time, we spent so much of our efforts on first obsessed about the perimeter, and then we said the endpoint was the perimeter, and maybe it was all about the data in the first place, or should have been. Um, I mean, you, you, yeah.

So I have a long history in security. I, I worked at RSA and a few other companies. There was the Jericho principle, which was reduced the perimeter, I think you might've remembered that one.

There was the, and Cipher I I sold in Cipher HSMs, and it was all about cryptography. I think you're spot on. The one thing that is growing at an exponential rate is data.

And the one thing that, uh, is the most critical asset is data. So if you can get your hands around it, classify it, be able to explain it, um, and also protect it while using it for innovation, uh, you're right. I think, you know, I think your analogy is like, yeah, the waves of, uh, the different things we focuses on.

This is kind of getting narrowed down to the ultimate truth, which is, I need the data. The data is my currency, and what I, the more I can do safely with that data, uh, the more competitive advantage I have as a company versus, uh, you know, the, the market that I compete in. Mm-hmm.

Um, as we kind of go down that path, not all data is of equal value. So do we need to not just classify data by its type, but also maybe come up with some metric that says, here's its relative value to the business, and then that determines the level of protection we should apply? Yeah, a hundred percent.

It's a really good way of looking at it. Um, different types of data have different types of value. Some are required for compliance, some are actually more customer driven and help with Intel.

So the ability to one, classify what it is, know, classify the type of data, know where it sits and know who uses it, um, is leads you down a path of being able to put a value on that data set. And I think once we can do that, I think you can put different controls for different types of data, and you could also react to different problems. Like, if I had a file that, uh, broke GDPR in a certain geography and I could identify very quickly, um, I can remediate that really quickly.

So the future is very exciting in terms of how we go about in solving and quantifying, not all data is equal, uh, but being able to know which data is what and what price tag you put on it. And I, when I say price tag, I mean the controls and the level of, um, protection, um, is, is gonna become super important in the, in the future that we have ahead of us. Hmm.

Our, the whole notion of data management and data protection and security now also gonna converge. I mean, for so long they were kind of different disciplines, but if you think about security sometimes, well, we boil it all down. It's a data management problem or challenge.

So, um, are we also starting to see some sort of convergence along that line? Yeah, you know, I think it's early for me to tell you what it would look like. I think you have two separate category leaders coming together.

Um, it may define a new category, uh, and that category is centered around data. What I would say is like, what is the customer's impact of value? My thoughts are pretty simple.

The customer gains a single command center for their entire, uh, entire data state, one pane of glass. They understand where it lives, how it's being used, where it's being governed and recoverable. And like from a CIO's perspective, that means great visibility across all cloud apps endpoints.

For CISOs, it means pro proactive posture management. So knowing where sensitive data resides before it even becomes a risk. And then you have CDOs and they can safely use.

And, and I think Chief Digital Officers or those folks that are out on the, the front end of using data as a tool, uh, they can safely unleash AI initiatives with data that's accurate, compliant, and trusted. So we'll be solving for many types of folks and a bunch of new personas as we go and bring these two assets to market. Yeah.

Bringing this home a little bit for security people, um, one of the challenges that we routinely incur is that it takes too long to recover the data when there's an attack. So even when we can recover the data, we pay the ransom because it's gonna take us three days to do it, and the business will be down for three days, et cetera. However, are we getting to a point soon where, um, I will know the relationships between various data sets, I will be able to recover them faster, and I will be able to respond to those attacks in a way that I can say no to ransomware without having a curve, you know, in three days worth of downtime?

Yeah, I think this is the vision. You know, I was looking at the ransomware recovery report that we recently published for this year. 69% of customers pay a ransom and almost that same amount get, have to pay again on top of that, even though they've paid the ransom.

So in truth have, and that's because they don't know what data is out there that don't know the implication. And something you said, Mike, around the linkage between certain data sets and certain business outcomes. Um, with security, AI and Veeam coming together, I think you'll have super clear clarity on where the data is, what's the exposure, what's the governance requirement, what, what is the governance requirements and how do I attack for compliance?

All of that. If you add on top of that with Ware, uh, the company that we also acquired, um, around incident response puts you in a really good position to be able to negotiate aside, um, and act very, very quickly, um, when something like this happens. And, you know, it's not, it's not the win, it's the, if I have this running joke that I'd never hire a CSO that didn't face ransomware, maybe 10 years ago, you would, you, you wouldn't hire one that that went through ransomware right now.

If you haven't been through it, um, you have no value to the organization 'cause you don't know how to respond to it. So it's, uh, it used to be a Scarlet letter and I think it's now a badge of honor if you've been able to handle, uh, something like this gracefully. And our job is to provide the tools to help you be safe.

And then on the other side, unleash, uh, your ability to harness ai. What is your best advice to folks then about how to kinda organize this and put everything in the right position to, uh, get lucky? Sometimes they say, you know, luck is the residue of good design.

So how do we kind of get the security people, the data management people, and the AI people aligned in a way that'll allow something really good to happen? It's a great question. And, uh, it's commonly, you know, it's been a traditional problem for us.

If you take ransomware as a concept before we even get it to ai, ransomware I think brought natural convergence. You took the, the infrastructure folks and the security folks long for a long time. I've been part of the industry.

They're two separate departments that don't talk to each other. Ransomware was the catalyst for those two groups to come together. AI would accelerate that even further.

Uh, so to me, I think you're gonna start seeing CEOs pushing their teams to say, how am I adopting AI at the same token and the same breadth, saying, how am I doing that safely? And, um, what we bring to the, to the table will allow that. We have a wonderful, um, tool called the DRMM or your Data Resilience maturity model that we love to take customers through a journey on.

And it shows, hey, on a scale of one to four, we've worked with, uh, McKenzie, MIT, uh, Splunk, Palo Alto, and Microsoft, uh, to help build this framework. And we, the goal is we're gonna plug in the security AI components and give customers like a, a genuine roadmap of how safe, secure their car posture is and what would be the direction to move to, you know, closer to Nirvana. Uh, that's obviously a moving target for everybody, but I think with these two assets, our ability to accelerate and get to that moving target becomes much quicker and much more relevant for our customers.

All right. Hey folks, you heard, and here, our primordial soup of technologies are coming together. All it needs is a little bit of a catalyst from here to change the way we think about security to begin with.

Shiva, thanks for being on the show, Mike. Absolute pleasure. Thank you.

All right, and back to you guys in the studio. Hey everyone, welcome back here to Techstrong TV for another interview. I am really happy to have this gentleman on.

You know, I, I first met Giddy Cohen, I'm going to guess it was 15 years ago. Giddy. Yeah.

Maybe more, At least. Yeah, at least. Uh, Giddy had, I, I think at the time, giddy had just recently come out of IDF or had, he was starting a company, right, called Skybox Security.

And he showed me something that at the time rocked my world, right? It was, it was called an like an attack map, a 3D attack map where, where it actually you could, it looked at your network infrastructure, your vulnerability profile, and could draw a map of how a hacker would attack you and what you can do to prevent it at various places throughout your network. Sounds matter of fact, now I still see Giddy, I still see companies showing me that today, right?

Yeah. Like came, they came just out Ofir. We, I We read 20 years ago.

Yes, I seen about a guy named Giddy. They're like, no, no. I said, I saw this 20 years ago.

Finally. It's funny, just a, a couple days ago, I, I was talking to a founder of a company, an Israeli company, and, and he was talking, I said, you know, this sounds a lot like a company I knew called Sky. Sky.

He said, Skybox, I said, yes. He said, yeah, I know. We, we, I, I remember much younger I was using that.

And anyway, it was good stuff. Giddy has been a, a pioneer in, in what today we call Cyber for a long time. He has a new company.

He's, he's co-founded called Bonfire. We're gonna get into, but let's welcome Giddy, welcome to Techstrong tv. It's a pleasure to have you on.

Yeah, thanks Me. I didn't mean to my you and give you the whole like, background, but I, I didn't give you the whole story. Why don't you share with the people a little bit about your story?

Sure. So let's start with actually now, and let's go back a bit. So I live in the Silicon Valley in Palo Alto for quite many years.

That's where we founded Pon Fire ai. We're going to talk about a soon as well. But background actually started, uh, with IDF as you mentioned, uh, back in Israel in 8,200, right?

The, like the Israeli NSA, but I mean, kind of in the, all of my life was in the cyber data analytics technology since then. And they seen and um, and basically now bonfire, right? My third company, my third startup, by starting all of them are dealing with different facets.

Let's call that way of cyber data analytics and now all that they place together. Uh, so that's very absolutely my very, very short deck on This, you know, Guinea, I, I've started a few companies. You have have, it's not something you do, you know, lightly.

It's something you make a commitment to. You have to be passionate. Yeah.

You gotta believe in what you're doing. You gotta think that in some, at least even a small way, what you're doing makes the world better. It makes, there's a reason people want, need to have what you're, you're developing.

Talk to me about your passion for bonfire. Why, why bonfire? What was the passion that drove you for this?

Yes, so, um, Danny Ki, my co-founder and CTO and myself, uh, got together almost a years ago. And, uh, I, it was just after I left Skybox, uh, about six months before, nine months before Danny left Cyber rock, he mentioned the entire RD of Cyber Rock. And he said, okay, we, we see what's going on with ai.

That's just the beginning, not of ai. Of course that started between us 30 years ago, but the massive adoption with, of Gen AI started the end of 2022, beginning of 2023. And we said, that's going to be great opportunity for innovation on one end, right?

Adopting Smart Beast, uh, called land for a lot of different uses, but we believe that can create a huge amount of risk for people, society, organizations. So both thens in a, a nice, a mind background, right? Are in enterprise cybersecurity.

So we said, okay, we want to tackle, uh, all of the challenges and help organizations adopt AI to do it much more safely, trustworthy, without, uh, all of the exposures to everyone could just imagine. Then I think there are a lot of proof proofs since then that those exposure really happened. So that's, that's, we say, okay, we must be part of that, right?

We all technologies, we love cyber and AI and data now, machine learning and the, all of those new technologies with ai, and we want to put it together to work for the benefit of, uh, enterprise around the globe. And that's how we started. When we dug in.

More than that, it was good to us that there are probably two, and maybe there would be more along the way, but two main areas from cybersecurity. One, you can either think about, let's deal with the model, let's protect the model, prompt response, supply chain vulnerabilities, a lot of other stuff. We've seen a lot of startups do that.

We decide not to go on this route, not because it's not viable, but because it's, we thought one, it's crowded and I think it was proven correct. And second, the we're not sure. That's where the majority of the focus of enterprises as they adopt gen AI will happen.

Therefore, we, we decide to go a different route, which focusing on the data side of that, right? At the end of the day, the entire reason cybersecurity exists, the, the things you feed models with is information or data. They feed out data, you get data out of that.

It's sure, regardless of the l lm is, uh, used by individual or embedded in a system like Microsoft 365 copilot. And for the benefit, uh, for productivity of users, whatever form factor, it's all about the data, data that gets into AI or AI enabled machines, data that comes out of that with humans by agents or whatever it might be. So we said to ourself, that's a huge problem to solve, and that's what we want to tackle.

Maybe last piece of the, uh, origination of, uh, Bon Ffi is that, which, by the way, bonfire stands for ified ai, if I didn't say that before. So AI in good faith. So the other first of that, it was clear to us that why AI creates a lot of new challenges and new exposures to organizations, which is, which are dangerous, risky, and is a vendor exciting to solve and help organizations deal with it.

Actually, there's also a huge amount of problems that print data, gene ai, data security, and for unstructured data, uh, one of the spaces that I would say was the least serve with quality technology over the last 15, 20 years. Everyone knows about DLPs and false positives and false negatives and total cost of ownership, which is all true. And all of that is now getting much tougher when you are putting this, uh, AI, right, uh, ML gen ai, AI agent on top of that, that we say, okay, that's exciting problem as a startup, right?

To address both addressing the, the rising issues, uh, and the expansion of let's go the data surface, but also with the same soup, basically handle a lot of those historical challenges that were never sold properly. And we thought that's a great opportunity to help enterprise. That's, and that's why we started the company.

Absolutely. You know, it, it's a, it's a common thing in security. Giddy.

If we could just stop the world for a day or two and let us catch up, then we'd be okay. But unfortunately, in security, it seems like we're always, we're always trying to catch up. We're always trying to catch up and, and new stuff keeps piling in.

Yes. So you, you look at something like legacy DLP, right? You and I have seen DLP solutions come and go, and yes, we never quite got it right.

And now, you know, you start adding AI and, and the, the, and the, the, the sheer volume of data, let alone the velocity and the automation. There's no human in the loop sometimes with this stuff. Exactly.

If we didn't get it right before, how, you know, how are you going to get it right now? But every once in a while, technology affords you a a little bit of a miracle, right? The technology allows you to almost make up for past mistakes.

Because with this newer technology, not only am I able to work with things like ai, but it allows me to maybe hit the legacy DLP issue square in the head too. And I, I, you know, I think exactly, exactly. That's the opportunity here for bonfire.

Yeah. Uh, absolutely. Right.

So I would say that from innovation perspective, which is that's what get me excited, uh, excited, right? As an entrepreneur, right? Is that, uh, we, we actually take advantage of two things and bring it together in technology we developed, okay, which is now, by the way, available in production and, uh, anyone wants to see it, we'd love to people to get to us, send me an email, get to our website, et cetera.

But there are two concept we actually brought together, or two abilities. One, obviously, right? With all of the innovation of Gene ai, not only create risk, but also create, create set of tools, technology we could use, and of course we're using in order to understand content in much better ways for the benefit of classification, content analysis, detection, prevention, and lot of other use cases that are related to data security.

So that's one, but that's not enough. And you mentioned Sky Book Security, right? By previous company.

They ran for a, a was one of the founders ran for many years. One of the innovations we had there in day one, as you mentioned, is how to utilize Netto context on the infrastructure in order to manage vulnerabilities better and manage compliance better, et right? So that's, that's how we actually start the company using, using context for the benefit of, uh, various use cases and provide a lot more intelligence, uh, and accuracy, therefore, uh, for, uh, for those type use cases.

So think about what we did in what we're doing. Bon is actually the analogy, nothing to do with the technology of Skyworks, right? We're not dealing with infrastructure there.

We're dealing with data and content information, but actually the innovation we have here is actually how to utilize business context that we serve, learn from the organization perspective, their own data for their benefits, solely. Of course, we never take the data. Other customer never see that.

So it's all securely done for them. And how to utilize this business context for the benefit of analyzing their data in motion address generated by machines, generated by humans, all of those type of flows. And that's basically what bring together you the kind of modern AI technologies we are developing and using, plus the innovation of how to utilize smartly business context, put them together to significantly smarter, uh, datasecurity solution, which is significantly more accurate.

Covers, uh, poly five times more scenarios in terms of, uh, uh, the things you want to detect, which are critical for this modern world of, uh, of, uh, data security or data compliance type of risks. And to do it in a much more cost effective way in terms of the amount of human load on the security team set up dealing with the operation, it matters significantly more streamlined than ever Before. Excellent.

I love it. Um, you know what, let me do some housekeeping. Giddy Bon fee.

Bon is B. Yes. BON.

Yes. ai is the website. Go Ai.

And then the, the, the product service, if you will, is bonfire's Adaptive content security. Yeah. Or bonfire a CS Exactly.

The our platform. Exactly. How could people check it out, test it out?

Is there a free version, a tough trial version, something they could get their hands on? Yeah. Then no, that's, that's great.

So first of all, the best and the easiest way is contact us via the website. There are multiple forms for different type of interest, uh, the, that, uh, customers, prospects, uh, might want to have. We'll be happy to provide demos, uh, schedule some ovs right values, so we can actually demonstrate for organizations that can walk in their environment.

It's super simple. We design part of our lessons, right? All of us, right?

Lesson serving and the price cybersecurity users and buyers along the years that, uh, on one end, right, the industry grew bigger and bigger, but there are more solutions and the load in securities is actually, so it is un verbal, I would say, in terms of complexity of solutions and the amount of knowledge to have. So bonfire will design on one end, super sophisticated backend business context, learning other stuff for the benefit of the use case, but very easy to deploy, very to, to use. So that's true for production, but also very to perform a value with our solution.

So just contact us and we, we'll be happy to show it and, and show talks in your environment. Ly Giddy, I I speak to a lot of security folks, a lot of entrepreneurs, ai, DevOps, platform engineers, and, and I think the consensus is, look, you are going to need AI to secure ai. You're going to need AI security solutions to defend Yeah, for sure.

These AI malware or AI assisted malware and and so forth. If you wouldn't mind, talk a little bit about how Bon Fee is using AI to make this solution better. Yeah, so we are, we are using, uh, multiple, actually both models and AI technologies, as you can imagine, uh, at bonfire, right?

To provide the answer at scale and accurately requires a lot of technology. It's just, it's not one thing, right? And if someone think that, yeah, it's just going to get, let's say the email I want to analyze and send to the DLP, the, to the LLM as a DLP function to tell me if it's good or not, that wouldn't work, right?

So we, um, we're mixing together multiple technologies of that. We develop something that we are taking off the shelf and tuning for our needs. Our, so for example, knowledge graph creation, right?

The business context, entity linking technology. So can identify what the entities in the content. Uh, we have different AI edge and engines that are looking for features in the content that allows us to assemble together to understand what's there.

So we can, in the very abstract way, understand the, we, you know, we, whether discounted is, uh, good or not, right? To pass through an email or file sharing or web traffic or whatever it might be. So we, we have that capability.

We have a deep explainability capability. So someone gets an alert, part of the issue once they get alert, of course, you want it to be accurate, right? But you want to be understood, right?

Think about the security is getting an alert from Bon Fi or any other system, something might have happened. They have no context what it is, right? How do you know that?

How do you help that? So we have AI used for explainability of a, of, uh, let's say complex alerts. So we are using it in, in, in the quite a few different ways because it's necessary, right?

And that's part of our technology, software developed by ourselves, as I said, some that we're adapting existing models for, for the need. Small models, midsize models, large models, and plus our knowledge graph and learning technology and all the put together in the solution. Gi, We're about out of time.

I just want to emphasize this is available now. People go to the website right now and go check it out. Yeah.

We're in production already serving customers, looking for more customers as we build up and penetrate the market. Uh, yes. Next gen, please call Gen Gi.

You're out in front again, mazel tough as they say, right? Good for you. Keep us posted.

Come back again. Yes. Keep us posted on progress here and what you're seeing.

Absolutely. I, I'd be happy To be There anytime you Invite me. Kitty Coh, uh, I minutes co-founder CEO of Bon fee.

That's B-O-N-F-Y. Do AI Bon Fi, excuse me. Bon Bon Phi.

You know, I got this funny French accent. Bon Bon bonfire. Yeah.

All of us are on accents. Yes. There you go.

Yeah. For, for letting speakers, letting speakers can say bon. So It's fine.

Come from the website For Latin speakers or Latin languages, Bonfire ai. Exactly. It, it, it's something that's needed in today's ai.

We'll check it out. We're gonna take a break here on Text Drunk tv. We'll be back in a minute.

Hey, everyone, we're back here. We're live at Qualys Rock on our day two coverage. I'm really happy to have my friend Jim Riva sitting here with me.

For those of you who don't know Jim, and if you've been in the security world, you, you should know Jim, but Jim was the founder, co-founder of the Cloud Security Alliance, 2005 or 2006. That was 2008. There you go.

I was thinking about it before that. Yeah. So, you know, I'm kind of slow, slow on the draw.

17 years. I was it RSA when we had the meeting at RSA was 2008. Yeah, yeah, yeah.

I always thought it was, you know, I guess in the midst of time it's gotten pushed back further. 2008 for Jim still. It's been 17 years.

Yes. Yeah. And my goodness, what a, what a 17 year trip this has been, right?

The CSA has really expanded its wings, really kind of fulfilled the mission, but you know, just when you think, what do they say? God laughs at men's uh, plants. Yep.

Just when you think you've got your hands around things, something new comes out. I was thinking Of more Al Pacino and Godfather three. Okay.

Yeah. Just when you're out, they pulled you back in. Yeah, well that too.

That too godfather. God. Yeah.

Well, I guess it depends who you are. That's right. Um, but anyway, first of all, let's get a, just an update on what's going on with CSA.

Yeah, Absolutely. So, you know, there is this aspect of like cloud, like where are we at with cloud? It's getting pretty mature and honestly it's a full two thirds of our research and work and, and demands from the community is around what are we doing about ai, which is really, it's really merged with cloud now.

It's just, it's Merged with everything. Yep. It's, yep.

You know, everything has to be looked at, I think, through the lens. Yep. Yep.

How does AI affect us? Yep. And, and so like, we're getting good indicators of what we think is gonna happen next.

And kind of how I would, if you wanted to characterize where we're at, it's like version two of our AI journey, where now we're getting into, and it's can be a buzzword, but age agentic, meaning we're, we're not just using the chat bots and going back and forth, but now we're trying to actually build autonomous systems. We gotta figure out where the human in the loop needs to be. And so it's like all the building blocks, all the security best practices we need to do around that.

That's, that's probably the biggest single area we're focused on right now. Got it. Um, I, I just feel compelled to say that you, you've added a new analyst in residence over there.

Mm-hmm. Our Rich Vogel. Yep.

Rich, rich is a guy that it's like he's, uh, I hope he is not watching it 'cause he is a little bit of a Michael Jordan to me and like really being able to execute and think about things. He's helped so many different companies. He's, he's on, you can't defend against him like Right.

Like Michael. And so he's our chief analyst. He meets with our, um, corporate members.

Here's what their strategy is, what their like real pain points are and kind of helps guide 'em. And you know, we, we've got like thousands of documents we've worked on over the years that a curation of like those best practices can help organizations maybe a lot more than they think, but he's their navigator now, so we're like pleased to have him and Sure. I like making, you know, I've just looked thinking of the visual though.

Rich Mogul Mike Jordan redhead. Well, they both don't have much hair these days that that's true. But I don't know if anyone's ever compared him to Michael Jordan.

Yeah. Who's that? Rich, rich, if you're watching this, you heard it from Jim's mouth, not mine.

Yeah. Um, anyway, let's get back to Cloud Security Alliance and you know, we're here at this QUAS event, which is no longer the QUAS Security conference, but is now rock con. Yeah, right.

Risk Operation Center. Jim, how does this whole look? Managing risk was always at the heart of security anyway.

Yeah. But how does the emphasis on risk management do you think affect cloud security and cloud security practitioners? Yeah, so, you know, we've had some dominoes that have happened in the world and people like getting in trouble and, um, corporations not like treating this right?

And so they've gotten the message CEOs, CFOs, they've gotten this message that cyber is not just like some it risk, it's overall corporate risk, financial risk, like operational risk. And so we got the seat at the table and like the issue is translating, like how we operate our cloud securely to the right language and the right metrics that those groups understand. So we're getting more and more of the budget, we're getting more and more of that, Hey, you've gotta be in the quarterly like audit committee.

We need a cyber report, everyone. And they're asking better and better questions 'cause they have some personal liability around this if they don't get it. And so, um, but we're, we're still, I think there's some struggles between like people's, a I talk about the CBEs that we've sort of mitigated and patched or you know, we, we, um, these certain incidents we stop this certain phishing attack and like, okay, what, what was the overall financial impacts on the organization?

Did it create any sort of delay in our ability to release new products and service the customers? Things like that. So we're, we're getting there and like the better, like CISOs, I think they understand how to translate that, but the, the cloud itself produces, the answers are in the data somewhere.

Right. It's like finding it the Needle in the haystack or the needle In the needle. Right.

And that's, and that's actually, and it kind of goes back to like some of what I, um, saw summed here talk about in some of the new solutions and what we're seeing in the industries. Well, you can actually use some of the ai 'cause it's a big data management problem to actually sort of surface like the, the information at the level that the board wants, as, you know, finding specific incidents, putting the right context and doing compliance. Like, hey, I can take like how we are compliant to one standard, put it into AI and at least gimme a good 80%.

This is how you comply to something else. Sure. And so, um, but yeah.

So we're getting there, Jim, to how, how does, so you come here, look, Qualys has been a partner of the security line straight from day one. Yeah. Just like day one.

Um, how do you take what you hear here and what you're seeing here and how does that filter down to the membership of, of the Alliance and I mean, look, the Alliance has, there's a ton of Yep. Of the vendors in this space, but there's a lot of end user practitioners. Yep.

How does what, you know, how do you filter this in? How do you, how does it become, you know, part of the bedrock if you'll Yeah. Well we, we've always sort of had this philosophy of like, let's, let's have like a healthy, positive, symbiotic relationship between practitioners and the technology providers.

There's a lot of like areas where it's pretty antagonistic. Yeah. And it's like, okay, we're, we're, we're gonna put 'em through the paces and we're gonna, we like, you know, test 'em and be like very tough on 'em and or we're not gonna allow the vendors into like meetings, which there's can be like some reasons where you do that, but this is where you get the information.

Like on, you know, when a company like Qualys and there's definitely like several others, they aggregate, aggregate so much data that they tend to see things before. A lot of people are gonna see it. So like we, we try to sort of create those, those, uh, communication lines.

So, hey, you gotta listen to what doesn't mean you have to buy everybody's product, but you gotta listen to the things that they're seeing and how they're tweaking their technology to address these issues. Uh, 'cause we gotta be a lot more flexible and a lot more agile. So we try to like make it, Hey, you know, positive fun, let's communicate.

We're all first responders here, whether you're the practitioners or you're, you know, and a soc that's managing a lot of different customers as well. So that's the philosophy. I get it.

I get it. Jim, we, we briefly talked to you and I beforehand about RSA coming up. Of course I saw you last of the black hat.

You know, the, the, the CSA is ingrained into the industry. Mm-hmm. But as you sit here, it's, it's, you know, we're coming into the end of 2025 and we are ears deep in, in this whole AI agent, AI and generative AI and disruption and data sovereignty, cloud sovereignty.

There's so many. Any issues you didn't think about in 2008? For sure.

Yeah. Yeah. If I had to ask you to look in your crystal ball about the cloud security Alliance and where it's going the next not too far out, let's just say 18 months to three years, what do you think?

I think we are gonna have to level up our game and be much more smart to, uh, to handle just how much more rapid we're going to see bad and good things do. And like, I'll give you an example. We've been struggling to like, create course for ai and we had this sort of breakthrough, let's have the course just have these evergreen principles and let's create prompts that you could run two years from now to say, Hey, based on these evergreen principles you articulated, give me the latest knowledge that I need with the versions of, you know, chat GPT eight or nine or whatever it is.

So like, I think we're, we're CCSA is gonna be a lot of this AI with the human in the loop to kind of guide, like our white papers are gonna get generated. Like I think next year they're gonna be generated to a degree with ai, but then like tuned. But then we'll give, like, people like Rich Mogul we talked about, he'll be able to use that to say, I'm gonna create guidance reports specifically for one company.

I think we're gonna see security, just like people are talking about, you're gonna create, with ai, you're gonna create applications that are unique to a person. I think you're gonna create like security solutions that are hyper customized, hyper localized. So I think we've got some opportunities on like the data sovereignty issues and all of these things.

It's just, I wish we were more proactive. 'cause we're always like, react. Well, I, I wish we were too, Jim, but, you know, you can't escape the laws of nature.

And I, I I think it's almost reactive. Yep. You know, until quantum comes in, then we could be proactive and reactive at the same time.

Schrodinger's cybersecurity security. There you go. Exactly.

Jim, it's great seeing you. Always keep up the great work. Good luck with CSA.

We'll, um, well, if we're some sort, I'll see you at rs. Yes, for sure. But maybe before.

Yeah. Love that. All right.

All right. Okay. We, I think, uh, the folks, have you scheduled me every month or two now?

Okay. Strong tv. Okay.

Not in person, but yeah, yeah, yeah. Almost it's you. I'll do those.

Okay. Although you smell fine in person. Alright, thank you.

Alright. Jim Re is here on, uh, our Qualys Rock Con coverage. I think we're taking a break for lunch.

We'll be back in about a half hour, but we've got a full afternoon so don't miss it. But for now, we're out. Text drunk tv.

Hello and welcome to the DevOps experience. DevOps goes native. I'm Nathan Harvey and I'm really excited to be here today to bring you insights into navigating this generative AI revolution.

These insights are going to come from Dora, a research program that is part of Google Cloud, Google Cloud's Dora research program has been investigating the conditions, capabilities, practices, and measures of high performing technology driven teams and organizations. For more than a decade, Dora helps those teams apply those capabilities leading to better organizational performance. This research is all about how do we help teams and individuals like yourself get better at getting better.

Now if we look back to last year, we published the 2024 state of DevOps report. This was the 10th report in our series. This groundbreaking report gave us some first real insights into the impact of generative ai and generative AI in software development showed some real promises, but our report also revealed some significant challenges setting the stage for us to dig even deeper this year.

So what's changed and what have we learned? Well, I encourage you to go download the 2025 door report right now. The state of AI assisted software development.

Go ahead, download the report. It's right there at that QR code or at that URL. I'll wait.

Okay. I'm not really going to wait. You didn't come here to wait on me.

You can download that report later, but be sure to grab the QR code there. Alright, so what did we find this year? Well, the first major change is that AI adoption is no longer a question.

It's nearly universal up 14% from last year. AI is now a standard part of a technology professionals toolkit, and a significant majority, about 65% of those surveyed are relying heavily on AI for software development with 30% reporting a moderate amount of reliance, 20% a lot, and 8% a great deal. So we're relying on AI and we're using it.

These professionals from developers to product managers now integrate AI into their core workflows, typically dedicating a median of two hours a day working with it. Beyond that, we also see that over 80% of respondents indicate that AI has enhanced their individual productivity. And even further, a majority 59% report a positive influence of AI on the code quality of the code basis that they're working with.

And only 10% are saying that they've observed negative negative effects on code quality. Despite this widespread adoption and perceived benefits. Some developers remain cautious about using AI in their work this year.

We see the trust paradox return while 24% of respondents report, uh, trusting AI 30% only trust it a little bit or not at all. This indicates that AI outputs are perceived as useful and valuable by users despite a lack of complete trust in them. You know, I think that this is really good.

I don't think we should trust anything, any tool that we're using a hundred percent, nor should we trust it 0%. So I really believe that this implies AI is being incorporated into workflows as supportive, as a supportive tool to really enhance productivity and efficiency rather than serving as a full substitute for human judgment. When we look across a bunch of different outcomes, this is how we see AI interacting with those outcomes.

So let me just show you how to read this chart really quickly. I'll describe it to you on the left hand side, the vertical axis. We have a bunch of outcomes that we think are particularly important for technology professionals like yourself.

Outcomes like individual effectiveness, software delivery, instability, organizational performance, and so on. And we wanna really understand, as you adopt more ai, how do those outcomes get impacted? And so the dash line that kind of goes up the middle, that's the zero line, that's basically average ai.

But if you increase your AI adoption, what happens to those outcomes? Well, you can see something like individual effectiveness is really improving. So that's a good, a good sign for us.

Unfortunately, the next outcome that we care about is software delivery instability. Now, instability has gone up as well. And just to be very clear, we'd rather not have instability increase.

So there's not an a, a wonderful picture here. This actually reflects something that we saw in 2024. But we see other really good outcomes of increasing your AI adoption.

Things like organizational performance, the time spent doing valuable work, software delivery, uh, performance, product performance and so forth. Now the, there are a couple of things at the bottom that also are basically at zero, and those are burnout and friction. Now, to be very clear, our preference is to reduce both burnout and friction.

But given how close they are to that zero line, we really think that AI is not really impacting them much at all. Which kind of makes sense. You know, if you're experiencing burnout within your organization, that's probably a sign of something systemic going on.

And I don't think you can alleviate burnout simply by adopting a new tool. There are bigger changes that you might need to address. But when we compare this data to what happened or what we saw last year, we see some consistent beneficial effects.

Things like individual effectiveness, code quality, these are improving, improving last year and continue to improve this year. We see some stubborn effects, some things that were not really, uh, impacted at all, like friction and burnout last year and software delivery instability also increasing last year. That seems to be the case this year as well.

But we also see some changes for the better. In fact, we see software delivery throughput go from negative to positive. We see product performance go from neutral to positive.

So this is a really good sign. I think this is a signal that as teams we're all adapting to these new ways of working. And of course it's not just us, the door adapting, the tools, the models, the workflows, all of these things are changing so quickly.

So as an organization, it really is important that we focus on learning and adaptation. AI at the end of the day, really turns out to be kind of a mirror reflecting and amplifying the existing organizational capabilities. But given that this is a mirror and it reflects and amplifies what we have today within our organizations, when it comes to understanding how we're doing, we probably need to look beyond just software delivery performance to really understand this amplification effect.

So this year we took a very much a a, a much wider view of how do we evaluate team performance. We looked at human-centric areas, we looked at products and organizational areas and so forth. So we actually evaluated teams across these different characteristics.

Team performance, product performance, software delivery, performance, individual effectiveness, the time that you spend doing valuable work, friction and burnout. And as we looked at various teams across all of these different dimensions, what we saw is a number of different archetypes of team profiles sort of emerged from the data, if you will. And what this shows us is that while AI has become sort of standard, everyone is using it, there is no one single AI experience that everyone has and that everyone experiences.

So our survey, our analysis, sorry, of these results actually revealed seven distinct team profiles from the harmonious high achievers to those, uh, caught in the legacy bottleneck. This view really offers a much richer understanding of how teams are working. And with this understanding, you might identify where your team is today and more importantly, how you might improve.

We don't have time to look at all seven of these team profiles, but I do wanna look at two. And I kind of wanna contrast to side by side. We'll start with what we call cluster two, the legacy bottleneck.

Now, I should note that you know, we named these clusters. For example, this cluster is called the legacy bottleneck. Your team might feel like this, but legacy bottleneck might not feel like the right label for your team.

That's okay, that's all right. We understand that we can't get it exactly correct all the time, but maybe you are experiencing some of these constraints and some of these ways of working. You see, when we look at this cluster with this team profile, we see teams that are in a constant state of reaction where unstable systems are dictating their work and undermining their morale.

Now, 11% of our survey respondents fell into this cluster. Key metrics like product performance are low. While the team delivers regular updates.

The value realized from those updates is diminished by ongoing quality issues. And there are significant and frequent challenges with the reliability of the software and its operational environment. This leads to a high volume of unplanned work and that work is oftentimes reactive.

Now let's contrast this legacy bottleneck team to those harmonious high performers. We believe that this is what excellence looks like. It's a virtuous cycle where a stable, low friction environment empowers teams to deliver high quality work sustainably and without burnout.

Now, some of the good news here is 20% of our survey respondents fell into this archetype or this team profile. The team is showing positive metrics across multiple areas, including wellbeing, product outcomes, software delivery, and the team operates on a stable technical foundation that supports both the speed and quality of their work. Alright, let's take a look at these two teams side by side.

On the left, you have the harmonious high achievers. For them, AI amplifies their already stable low friction environment to create a virtuous cycle. On the right, you see the performance characteristics of the legacy bottleneck teams.

For them AI might increase code volume, but that only amplifies the chaos. They might be hitting walls of their unstable systems, and that might be undermining morale, same tooling, very different outcomes. So it really sees, you can really see here how the way that you're working with these tools and the way that your organization works, plays a very large role.

So if AI is an amplifier, how can we ensure that it's amplifying the good? Well, I'm really excited to announce that this year we will, uh, uh, we were are releasing our inaugural DORA AI capabilities model. This capabilities model.

IT has identified seven essential capabilities that are a blend of technical and cultural factors that are proven to amplifies a amplify AI's pro, uh, positive impact. This can be a blueprint for your success. These different capabilities are really the levers that you and your team and your leaders can pull on, can adjust so that you can improve the power that AI is bringing to your organization.

Improve and amplify those positive impacts. Now, there are seven different capabilities here. It's not dissimilar to the seven clusters that we saw earlier.

And again, we don't have time to go through all seven, but I do wanna touch on a couple of them. The first is the idea of a clear and communicated AI stance. You know, when we're thinking about how to get the most out of technology, it almost always starts with culture and a new technology like AI can bring with it some ambiguity and ambiguity within our organizations creates fear and it has a tendency to kill experimentation.

So one of the things that we've found is that a clear and communicated policy on AI use provides psychological safety, reduces friction, and really unlocks innovation. In fact, we see that teams that have a high level, uh, or a very clear, very well communicated AI stance, they see increases across a lot of outcomes. Individual effectiveness, team performance, organizational performance, software delivery, throughput, all while reducing friction within their organization.

So if you don't have a clear and communicated AI stance, this might be the first place for you to start as an organization. But there are other things that go along with this. One of the things that Dora has found has had that has always contributed to better software delivery performance and other outcomes that we care about is working in small batches.

This really is an indicator of how well you're able to take the work that you're doing and break it down into smaller pieces of work. And when it comes to software delivery, you know that smaller pieces of work might mean smaller deployments, smaller changes that you're shipping out to the production environment. These smaller changes help reduce our instability and help improve our software delivery throughput.

So we see that when you're using ai, working in smaller batches becomes even more important. And working in small batches can with, together with AI, can really increase product performance while also again, reducing friction. The third capability from our model that I wanna look at is the user-centric focus.

This is the degree to which teams think about the experience of their end users of the pri of their primary application or service. And you know, I love technology just like you do, and I love playing with new tools and new toys, and AI is one of those. It is so fun to play with.

I I wanna sprinkle AI everywhere, but I always remind myself that we have to think about the users of our application. The users don't actually care if we've built this application with or without ai, what they care about or are things like, does the application do what they want it to do? Are they able to successfully accomplish their goals with the application?

Keeping these users in mind and incorporating their feedback into the things that we're doing helps ensure that we're building the right thing. This is so, so important. And with user-centric focus and the adoption of ai, we see these two coming together to provide some really powerful insights here.

In fact, there's even a cautionary tale built into this. You see, if you have low user centricity, but you increase your AI adoption, you're kind of just using AI more and more without really thinking about this, these users, this can actually lead to decreases in your team performance. But when you are really focused on the users and how we as a team come together to serve our users and build the right applications and services for them, this can lead to an increase in team performance.

So it's really important to think about and, and show up in a way that serves your users. So here's an a visualization of the entire AI capabilities model. This shows that as you increase AI adoption with these capabilities in the center, you're going to lead to positive outcomes on the right hand side.

So one of the ways that you can use this particular capability model is think about those outcomes on the right hand side. What goals are you setting? What outcome are you trying to improve?

Maybe you're trying to improve something like product performance. Well follow the arrows back to which capabilities are really driving, driving AI's impact on product performance. And this can give you some insights into the capabilities that you might want to start enhancing with your organization.

And again, as you follow that arrow back, you'll see that one of the arrows leads right back to that clear and communicated stance on how to use ai. So taking all of this together, what advice do I have for leaders? Well, there's a lot of leaders here today that are really interested in leveraging AI to drive the best outcomes for their organizations.

My advice to you first, treat AI adoption as an organizational transformation. Remember, AI is going to reflect and amplify the ways that your organization is working today. Those organizations that are streamlined and free of friction and where change and experimentation are encouraged, these organizations see the best results.

On the other hand, if, if your organization is kind of disjointed and there's a lot of handoffs and friction in getting work done and a lot of ambiguity about how things get done, this is going to be amplified by ai and you're gonna see some detrimental results as you improve AI adoption or increase AI adoption. So I encourage you to use this new tooling, this new way of working to help drive organizational change. Break down those barriers, streamline the way that information flows throughout your organization and the way that change flows.

Next, I want you to shift the conversation from adoption over to effective use. I see so many organizations trying to measure the impact of ai, and they start by measuring simple adoption metrics. Adoption is important, don't get me wrong.

In fact, you can't have real impact from AI if no one is using the tool. Simply buying the license doesn't get you any real impact. Adoption is a prerequisite for real impact, but adoption does not guarantee real impact.

So think about those capabilities in the Dora AI capabilities model and make sure that you are building up those capabilities at the same time that you're driving for better adoption. Next, look at the co take. A comprehensive view of team performance.

There are so many different facets that make up team performance within an organization. We are working in very complex ways. We're dealing with human factors, technology factors, process factors.

All of these different things are coming together to create this rich, complex, beautiful environment that is able to really deliver results not only for the business, but of course for your customers as well. It is important to diagnose team health with more than just software delivery metrics. And this is really what we've leaned into this year in this report.

Next up, prioritize and fund your platform engineering initiatives. We see platforms as a great enabler when it comes to AI adoption. You will see high quality platforms on our Dora AI capabilities model.

This really unlocks the power of AI across the entire organization. And finally, be sure that you're turning localized productivity gains into significant organizational advantages. This is kind of the story of some of the things that we've seen.

For example, with software delivery performance. Remember, we see software delivery throughput improving this year, but software delivery instability is also increasing. We believe that this may be a sign that we're hyper-focused on using AI to generate code.

We're generating more and more code, but maybe don't have the feedback mechanisms and the validation mechanisms in place to handle that amount of code. When we're unable to validate code changes, we should expect them to be unstable when we ship them into our production environment. So this is a great opportunity for you also to take a systemic view of how work flows through your organization.

In fact, in this year's report, we have a whole chapter on the concept and the practice of value stream mapping. This is a really powerful tool that can help organizations identify where is value getting stuck, where is there friction that we can eliminate or automate? This is a really great thing for you and your team to look at.

So as I'm wrapping up today, I wanna leave you with a few big key takeaways. First and foremost, AI adoption is here and it's here to stay. The challenge is not whether or not we should use ai, but the new challenge is the effective and valuable use of ai.

And this, again, requires us to think more broadly about our entire organization and how we're using AI throughout everything that we do. AI truly is an amplifier. It reflects and multiplies your existing systems strengths and weaknesses.

So again, we want to look to using AI as that transformational agent. Now is the time to improve those processes. Now is the time to improve those systems and maybe use AI to help along that journey.

Success requires more than just tools. I encourage you to take a look at that Dora AI capabilities model. Think about all of the different components of that model that you could invest in as a team to really help improve the way that you're working and importantly, the value that you're getting out of ai.

So I want to leave you with this. The state of AI assisted software development. Dora's latest report is available now.

I encourage you to go download that report. I'm going to leave this QR code up on the screen a little bit longer. The this research has been so enlightening.

We've learned so much from all of you this year, everyone around the world. The next step is for you, take this research, read it, use our findings as the hypotheses for the next experiment and the next improvement work that you are going to drive within your organization. You see, the most important thing that you can do is take this research, put it into context of your organization, your team, your applications, and then put that research into practice so that as an industry, as a profession, as a, as a community, we can all improve the value that we're getting out of our investments in technology and technologists.

In short, we can all get better at getting better. Thank you so much for having me today. I am really, really excited to be here.

I appreciate you coming along and I look forward to hearing about how you've put Dora's research into practice. Enjoy the rest of the DevOps experience. Thank you.