Booz Allen CTO: Can AI Fix The Government — With Bill Vass

28:13Right. And it brings it references the manuals directly so they can see what the manuals say. Right. And they can still search the manuals directly. right? So I think there's, you know, we use large language models at Amazon to help debug things in our fulfillment centers, and it was very successful in those areas. But you still have, it references the manuals directly, so you can avoid hallucination. You can see what it actually, you know, found and how it found it. So does it atrophy us? Jeez, that's an interesting question. That's hard for me to answer in some ways i mean we uh um certainly like myself personally i use gps all the time and i use navigation systems all the time uh and if you asked me to drive somewhere that i drive a few times with gps without the gps i i'd have to really go look at a map and figure it out right i mean like it isn't like you know people ask me well did you take this street or that street i'm like i didn't pay attention to the names of the streets but but is that important i can always go look at a map.

29:13I can always do those kinds of things, right? I think that just like any tool, you know, you can cut yourself with a knife in the kitchen. You know, you don't have to tear things apart with your hands, right? It's, you know, oh, geez, we've lost the talent of tearing things apart with our hands because we've invented knives, right? I think that that's overblown. But I also think that people just need to remember that it just like any other tool, it's not perfect. It's going to have limitations and they need to understand those limitations. Right. And I mean, I'll just give you a perfect example.

29:55Myself personally, when I was first, I was using chat GDP and I'm putting, you know, a whole home theater and I asked it, I want the best laser projector and laser projectors used to be like 30 grand and now they're like two grand. And so they're getting to be, you know, affordable. And so I asked it for, I gave it all these parameters. And it came back with five laser projectors, several I'd already heard of. And then there were two that were perfect. And I must have spent 20 minutes on Google looking for them and realized that it had made them up. Okay. It came to me exactly what I asked for.

30:28Maybe that's a business idea. Yeah, exactly. That's really, though, the important thing to understand on how these tools work. They're just doing statistics, right? And understanding the tumor parameters and all these other things. They really are just doing a lot of math on what's the most likely answer that you're asked for, right? But the same thing could be true from a Google search. I think people will say that Google has made people lazy too because all the world's information at your fingertips. But that's a wonderful thing too. But just like on Google, you can go down a rat hole of all sorts of things that don't really exist.

31:06Same thing is true with these models. So let me ask you just one last question about this. Then we're going to move on to robotics and quantum and some other cool experimental technology in the second half. If we dream about what the best, and by the way, I love this conversation because we never talk about public sector here and we really should. So again, appreciate you being here. If we think about the best case scenario, like we've outlined a number of problems with, and some good things, but a number of problems with the way that the government operates this. If we get to a place where AI lives out its promise, what does the public sector, what does it look like?

31:46Like, what are the benefits that we see within the government? Does it enable the government to provide services better? Does it enable us to interact with citizens in a smarter way? Like, if we dream about a best-case scenario, what does that look like? I think that's exactly where it would be, is better citizen services, a faster, more efficient delivery of citizen services, a reduced overall cost, ideally. But remember, on the reduced overall cost piece, these models use a lot of GPUs. they are really expensive to train and they are really expensive to run inference on today. So that's another area that we really question sometimes the ROI of some of these things because of the cost of all of it.

32:28So that's another balancing factor. I think we don't have good data yet on the ROI. And so that'll be the cost of operating the model and training the model and running the inference on the model versus the feedback. And I think some of that is we don't have good metrics to be able to track those things. And so we're working on those as well. That is something we're working on. But I would imagine a world that's got better citizen services that can deliver things faster and get things done faster and do validations faster. But, you know, there's other sides to this, too, where you shouldn't go overboard.

33:01At some point in time, a citizen should expect to talk to a person. Yes. That's going to be the case for the old companies that go to this. But I guess I would take a really smart large language model over a phone tree where you hit the number and it says goodbye. But anyway, these are personal gripes. OK, you just made me think of one more thing. I'm going to ask this before we go to the break here, which is this week we're talking at a week where President Trump is out in Saudi Arabia. This episode will air a couple of weeks after. But the investments, I don't think they're time bound. And that is that we see that NVIDIA is going to do hundreds of thousands of GPU data center with the Saudis.

33:47Amazon, your former employer, is committing to invest$5 billion in Saudi Arabia. What they're going to do is, I think, it seems like it might be the largest scale sovereign AI experiment we've ever seen. So I'm kind of curious if you think that that is going to be a good testing ground for what governments can do with this technology. And will you at Booz and do you think the world will be watching closely what Saudi does there? Yeah, we'll definitely be watching. I mean, I was actually in at AWS. I was a big advocate for the Saudi region. And I was actually at the Saudi region launch at the LEAP conference in just outside of Riyadh there.

34:29I think there's a tremendous amount of brain trust happening in Saudi Arabia and investment there in their movement to technology and their movement to diversify their oil investments into other areas. Both clean energy and tourism and technology is really the areas that SMB is focused on. So I was excited to see all that. I thought it was moving in a positive direction. But certainly we'll be watching it and we'll be watching it, how it evolves. And, you know, hopefully, you know, at some point we'll be involved in it. I'll be involved in it again. I really enjoyed the work that I did getting the region up and running in Saudi Arabia.

35:16The work I did in the UAE and others when I was out working at Amazon. And, you know, I think that's an area to watch. I think that's a good investment and the right things to do to transform that region in a lot of ways. Okay. Well, look, we're going to go to break now and then talk about some of the more sexy tech topics after this. We're going to talk about robotics, autonomous, quantum, and maybe a little Amazon with Bill when we come back right after this. Shape the future of enterprise AI with Agency. A-G-N-T-C-Y. Now an open source Linux Foundation project, Agency is leading the way in establishing trusted identity and access management for the Internet of Agents, a collaboration layer that ensures AI agents can securely discover, connect, and work across any framework.

36:13With Agency, your organization gains open, standardized tools and seamless integration, including robust identity management to be able to identify, authenticate, and interact across any platform. Empowering you to deploy multi-agent systems with confidence, join industry leaders like Cisco, Dell Technologies, Google Cloud, Oracle, Red Hat, and 75-plus supporting companies to set the standard for secure, scalable AI infrastructure. Is your enterprise ready for the future of agentic AI? visit agency.org to explore use cases now that's agntcy.org and we're back here on big technology podcast with bill vast he's the chief technology officer of booze allen and it's been a fascinating conversation so far all right look during the break i i said i gotta ask i kept pushing the break off so we're back from break but i have one more question that i want to ask you sort of related to our last segment, and then we move on to autonomous and robotics.

37:16Amazon had very clearly or has very clearly defined leadership principles set by really one leader, Jeff Bezos, and that's been the way that the company operates. What would you say the leadership principles are for the US government? And do they shift time to time because of the fact that the CEO, quote unquote, shifts every couple of years. That's interesting. I think that, you know, you caught me off guard. It'd take me a while to come up with leadership principles for the government. But they certainly do shift. And it depends on the focus of the government at different times in different areas, right?

37:58How about today, then? How about today? I think there is a focus on efficiency. The other thing that I like is there is a focus that we had at Amazon. We had a leadership principle, and one of my favorites, there's a number of them, was bias for action. That was one of my favorites. So I think the government's got a lot more bias for action right now, and I think that's a positive thing. The other thing that was a great Amazon principle I liked was think big, because a lot of working on innovative things, And I think that people are willing to think big about what could be accomplished and throw off some of the shackles that have been there before and accomplish big things.

38:39Customer obsession is one of my favorites at Amazon. I don't think the government is as customer obsessed as it should be, and they need to be thinking about that in citizen services. And I think that's an area that could be improved. Another area that I'm seeing is dive deep. And that's another thing I like at Amazon as well because I like to dive deep into the technology. and I do a lot of whiteboard sessions, things like that, you know, like diving into how the architecture is going to work and how all the different components are going to work together. I was just actually diving deep into a big AI project we're working on to do actually transform contracts from time material and cost plus to firm fixed price, which we talked about a little bit earlier, using AI to do that.

39:23But, you know, I think those are things I'm seeing. And those are positive things. And those are things that I liked at Amazon and continue to like. Okay, so you're, it seems like what you're saying is that some of the Amazon thinking is starting to make its way into the US government, which is interesting. Okay. So, you know, speaking of Think Big, that is a good one and leads us to some of like the bigger projects that you're involved with. And one of those is autonomous driving. And I think if I'm right about this, those are some of the projects that are both related to the government and not.

40:02And some of the clients you might have that are outside of the government. And so can you give us a sense? I mean, you're you're very big into training in synthetic environments and that leading to results in the real world and adding synthetic data. but there's also if you think about the reality of where self-driving is today there's Waymo which I think is obviously it's expanding fast and it generalizes a bunch of tech of its technology but also you know takes some shortcuts I think there are a lot of human operators out there that will sort of get those Waymos at a tricky situations if I'm not mistaken and then there's Tesla which is which is I would say advancing but not quite there yet we don't have autopilot now.

40:47So how far away are we? I mean, this is sort of the essential question for autonomous driving conversations. How far away are we from seeing this stuff be mainstreamed? That that's that's I'm so I have two Teslas and I play with full self driving all the time. It's it's entertaining, but I wouldn't trust it entirely, right? If you trust it, you're going to be in trouble. So it's not it's not 100 % there yet. It's a hard problem. It's interesting that you mentioned that the picture on the whiteboard behind me is for a software-defined vehicle and all the different components of vehicle running across hundreds of thousands of synthetic simulations.

41:25And so we work really closely, for example, with NVIDIA on Omniverse. So Omniverse is a synthetic simulator or environmental simulator that has full physics and full fidelity. And that's really amazing. A lot of the autonomous driving training that has been done and robotics training has been done using Unity and Unreal over time. And those are great environments as well. They look very much like video games when you run them, but people don't watch them. They're all running in the machine memory. And Omniverse is sort of the first to go that next level of not being constrained on something that might have to run on a console.

42:08So it's pretty amazing. Rev out there, I've been working with him for years on this. Yeah, we have an episode with Revla Beretti in the library. Yeah, he's great. He's great. He's definitely a good conversation. He's great, yeah. And then you're working in that environment. He would have talked about the three-computer problem where you've got the computer that is the training computer, that's their H200s and things like that, that is looking at or learning from the synthetic environment where you're feeding in real and synthetic data into it. and then there's this after you create your inference model that runs in car and that's the smaller computer that's the third computer and I talked about this a lot in the velocity article that I wrote for Booz Allen is is how this you know flywheel is accelerating autonomous driving and all these other things I know this is a very long answer getting back to your question of when we will have it I think you'll start to see real autonomous driving over the next five years.

43:11You know, maybe I'll be burned by that prediction. There's still a lot of complexity in doing it. I worry sometimes, I love having my Tesla drive itself. My wife hates it, but I love it. It's entertaining, but I do have to take over and I do have to pay attention. I'm probably paying attention more when my car is driving itself than when I'm driving my car myself, because I'm watching everything it does. And I'm very proud of it when it does things well, you know and and sometimes i get scared with some of the things it does also so um i you know and and the thing that's interesting tesla gives me the option when i correct it and take over you can hit the steering wheel button and and explain to the person who's going to look at what your correction what you did and why and i do that all the time because i want feedback i want it to get better right and that that kind of feedback remember the tesla has this advantage very much like the Echo devices at Amazon where they're able to crowdsource training from the users.

44:14So basically they're learning and training their model based on all the millions of people driving Teslas every day. That's given them a big upfront lead in autonomy in a lot of ways because they have that training set and they have the ability to generate synthetic data for the edge cases in that training set as well. And the more data you have with these models, the more parameters you have the more accurate the model becomes which we discussed earlier right if you don't have enough density in your parameters you're not going to have a good model um there's areas where i think um the models still have a long way to go like you probably look at someone at a stop sign which way their wheel is faced in their car like to know where they're going to go even if they're not signaling right i i think that's a nuance that's going to be very hard to train a model to do at this time, right?

45:07But eventually it'll have to learn to do that. The resolution will have to be good enough on the sensors to see that. When you stop at a stop sign and you've all stopped at the same time, one person waves the other one on, the models couldn't understand those kinds of things today, but they're going to have to be trained to do that. We have a lot of traffic circles here in Washington, D.C., and not many people can drive in them well and neither can autonomous vehicles. Um, there's, um, right now an oblique angle, um, with my Tesla, um, the stoplight going the other direction on an oblique angle, if it can see it, it thinks it's green on my stoplight.

45:48That's a bad thing. You don't want that. Yeah. So I think those are, uh, those will, all of those edge cases will get solved over time. Um, and the models will continue to get better. So I'm optimistic that there will be a day when I can go to sleep in my backseat and the car can drive itself. But it's not tomorrow. And it's a similar system that actually is being used to train robots, just like the Omniverse system with NVIDIA trains cars in simulated environments. I imagine the same system is being used. They have their own foundational model now to help robots, humanoid robots, navigate the real world.

46:30and it's interesting i mean i'm sure you saw there was this half we've talked about on the show it's kind of hilarious uh there was this half but also interesting there was this half marathon in china oh yeah humanoid robots and like you know most of them ended up falling on their face or one of them with some fans on its arms i believe propellers uh took a hard uh 90 degree turn and you see its trainer with a rope attached to it, like flying out of the scene and that robot crashes into the boards and falls apart. But one of them did finish and had to change batteries three times, but finished the half marathon in a respectable time.

47:09And so I think there's a, again, speaking, throwing the conventional wisdom out there for you to comment on, there's a conventional wisdom that the U.S. is behind China on this. And yeah, I'm curious, like, I'd love to hear you. I'll just say this and you can decide to bat it down or whatever. Is the U.S. paying attention to what's going on there? And does the government then take a role in saying we need to help accelerate this or is it completely left to private industry? Because in China, we know the government is pushing it. Yeah. So I don't think China's ahead, but I don't think they're behind.

47:50And I think that's an important thing. One of the reasons I left at AWS, and I loved being at AWS, I worked on 63 of the services there and built a lot of them myself, worked on quantum computing and robotics and a whole bunch of things, is I was worried a little bit about government adoption of AI and investment in technology to keep up with the Chinese. And so Booz Allen, because we are so involved in the highest technology in the government, was a great way I felt to more directly influence and improve that technology. And that's why I joined Booz Allen, is to pivot to really focusing on that.

48:33because I was worried about it. I'm worried about it falling behind the Chinese. And it's a combination of government and private industry that's going to do it. You're right, the government in China very much invests in technology. They're very smart and long-term thinking about how they invest. And there's a blurred line between government and private industry in China. And I think some of the stuff we're doing now in pivoting to a big focus on AI and a big focus on what we call the pacing threat, which is making sure our technology is ahead of China in the event that there was some type of conflict.

49:15We want to avoid the conflict by making sure our technology is superior. And so that's what we want to do. And that's where the focus in the DoD on lethality system, That's the focus on advanced technology and pushing DARPA harder. That's the focus on this public and private investments in AI, public and private investments in space, and public and private investments in silicon development and quantum computing are going to be very, very important, as they've been in the past, right? So I think the government needs to move faster. and it's good to see a lot of these things happening. And that's part of why I joined was to make sure the government is moving faster, to take everything I've learned at Amazon and at Sun and at Liquid Robotics where I did the autonomous systems and bring all the best of private industry to bear in the government.

50:18Well, appreciate you doing it. Let's close here with quantum. We rarely talk about quantum on this show. Not because it's not interesting, just because it seems so far off. In fact, there was this moment where obviously the stocks don't tell the entire story, but quantum stocks were riding up. And then Jensen Wang was like, don't expect quantum to show up any time in the next decade and just sort of sought off half the value of almost all these stocks. But you're touching quantum stuff as well. What is the realistic picture of where the state of quantum is today? Yeah. So I started the quantum initiative at AWS when I was there, and we've got a lot of great people working on that.

51:08I was involved in getting DoD to invest more in quantum in the early 90s. and some of the core research in there, especially around ion traps and electromagnet-cryogenic machines at the time. So the good news about quantum is that the machines actually work and you can get outputs from them. The bad news is that they're way too noisy to get valuable outputs yet. And so it's really the error correction that we're focused on right now. And so with your iPhone or your laptop, you've got error correction code on it. A very small round of the compute because you have alpha particles flipping the memory on your machines we're working on right now.

51:56And they're correcting that in the error correction code. So maybe one or two percent of your CPU usage or your compute usage is for error correction. On a quantum computer, it's the opposite. that you have a massive amount of work you have to do to do error correction because the atomic particles are so affected by the environment. And so the big challenge is getting that error correction to work. Now, again, the positive news, we're at a point where we understand the engineering necessary to make the error correction get fixed, right? And what it will take to get to hundreds of error corrected qubits.

52:33The goal would be to get to 1 ,000 error-corrected qubits, right? But just from that perspective, that's going to be around 7 million physical qubits to do that. That's a big number. And so the first machines that you're going to see coming, I don't think people will realize this yet, are going to be about the size of the football field. Wow. That'll be the size of the machine. And that's because you have to have millions and millions of qubits to get just a few fully functional error-corrected qubits. You have to have them constantly error-correcting each other. Quantum computers differentiate from digital computers, or classical computers as we call them now, in that they have these two unique things that are unique to quantum physics that are hard for people to understand.

53:23And one is superposition and the other one is entanglement. And if anyone tells you they actually understand how those things happen, they're lying to you. I was about to say, I cannot tell you how that works. Yeah, but an analogy I'll use is I'm a car guy and when I hit the accelerator in the car, I know if I'm in a gas car exactly how the cam works and the crank shaft. and the spark plugs and the valves are an electric car. I understand exactly how the motor and the inverter and all those things work and the batteries are working together to do that. When my wife drives a car, she doesn't understand any of those things, but she can drive as well as I can.

54:12She doesn't care to understand anything. The skinny pedal, the fat pedal, and turning the wheel. You can drive a car without understanding all those things. We can drive entanglement and superposition extremely well without actually understanding how they work, what causes them. And the way you program a quantum computer is by using superposition to control the qubits and microwaves for electromagnetic machines or lasers for the other machines, which are neutral atoms, charged or ions, atoms and photons primarily.

54:50And we can set it, we can operate it, we can measure it, and we can entangle it, and we can run formulas on it and get outputs today. And so what does this enable? Like when this is, let's say you have that football field size quantum computer, what does that enable? So the biggest thing that it will enable first, because effectively you can think of it as building molecules in memory and using those molecules, is going to be material sciences and chemistry first. So, in fact, one of the targets for Amazon's working backwards document for a quantum computer is a thousand ericryptocubits could do a Hamiltonian on ammonia.

55:28Ammonia is the most produced. We've been producing ammonia since 19, for almost over 100 years. And it's probably the most produced chemical. It's in fertilizer. It's in petrochemicals. It's in plastics. It's in just about everything. and it's very expensive and energy intense to produce. We know by watching bacterial interactions that it can be produced at low energy state. We just don't know how. So in the past, like a high temperature superinductor, superinductors in general have been discovered accidentally in the labs and then leveraged. In the future with a Hamiltonian simulation, you can say, here's the outcome I want.

56:11Give me the chemical formula that will give it. so you can reverse engineer an outcome in chemistry. On today's classical computers for ammonia, if you took all the iPhones and all the laptops and all the Android phones and all the cloud computers on Earth and put that simulation into it, it would run for longer than the history of the universe. Wow. So in other words, you can't do it. With 1 ,000 error-recorded cubits, it would take about three minutes on a quantum computer, right? So it's tremendously life changing, if you like. It will change our lives in a big way as as these material sciences come into fruition and we start using them.

56:52How far away are we from that, Bill? I think 2032. So less than a decade. Yeah, not that far. Not that far for the first ones. I think you'll see in 2027, 2028, the first hundred air created qubits on fast machines. I think you'll see that before on slower machines. And on the neutral atom machines, you'll probably see that. They'll be too slow to solve some of these problems, but they'll begin to solve some of these problems. So material sciences will be the first thing you'll see. There's certainly worry in the government and banks and others about having quantum computers break cryptography.

57:32So we are deploying today, both at Amazon and at Booz Allen and others, a quantum-safe cryptography. because quantum computers don't do everything well. You're not going to run a website on a quantum computer, right? It's not going to replace your computer. It's going to be like a math coprocessor, if you like. That's the way they'll be used. And so there are algorithms, quantum computers, as far as we understand, will not be able to solve well. And so we do classical encryption plus another layer of quantum safe encryption today. And the reason to start doing it now is in around 2040, we think there'll be enough qubits to start to break encryption and secrets last longer than that.

58:12So we need to start. Oh my goodness. You need to start encrypting. So most of the banks are already using quantum safe cryptography. A lot of retail starting to use it. The government's starting to deploy it. But I think you really should have urgency on deploying and turning on quantum safe cryptography. That's something Buzal can help you with and others can help you of as well. You're worried about that because people can record the transport of your information and then break it later. And so that's a big deal. And I think this is important for our country too. The country that has this first will have a tremendous lead over all the other countries in material sciences at first, but later in cryptographic sciences.

58:55And then ultimately, a quantum computer will be able to solve the traveling salesman problem and things like that, which is very interesting to people like Amazon who ship packages around. So optimizing the shipping of packages would save billions of dollars for Amazon. And so that's one of the reasons they're investing in quantum computers as well. It's not just to be ahead for the cloud. It's also for their internal use. And so I'm very bullish on where this will go. I think we're at a point now where it's more engineering than science, which is a good point to be. When I started working with these machines, it was more science than engineering.

59:31and there's still a lot of hard problems to solve. There's scalability problems. How are you going to scale all this? One of our big investments at Booz Allen is a company called Seek, which I'm very bullish on. So the nice thing about them is they build like the equivalent on classical computers like the ASICs and all of the BIOS that would sit around the CPU. That's what they build. They don't focus on the CPU or the qubits. They focus on everything around it. So they're kind of will win no matter which of the four different types of quantum computers win, they'll be able to provide the control systems and other things like that very efficiently.

1:00:06In fact, I'll be heading to New York in a few days to go do a deeper dive on their lab and things like that. So, yeah, it's an exciting area. It's not for the faint of heart. It is complicated. There are many still challenges to overcome, especially scaling machines to be data center size or football field size machines for these first machines. having them be stable enough to run long enough to complete a calculation once you get them working, and error correction, error correction, and error correction. I mean, that's really the name of the game right now. Okay, you've convinced me that we have to cover this more on the show.

1:00:40Yeah, I could spend a whole show going over. Doing this. Maybe we should. Maybe we should. I'm sure we're going to get some feedback on this part. Okay, last question for you, then we're going to wrap. You were the president and COO of Sun Microsystems Federal. Yep. From 2006 to 2011. So let me just put it, that is the federal version of Sun. Yeah, it's state, local, federal, all of that, yeah. All right, so at Meta's headquarters, I'm sure you know this, they kept the old Sun sign sort of as an indication to themselves that you could be at the top of the world one day in tech and things move so fast that next thing you know, somebody else is using your building and your name is going to be painted over.

1:01:28Having worked in the tech industry for quite some time, Bill, what is your sort of lesson about how fast this technology moves? I mean, it's interesting that you went from Sun to a company whose motto is always day one. Yeah, I know. So talk a little bit about what it takes to survive and sort of the lesson that we can learn from Sun? So the only constant is change in this industry. That's one motto that I have. Another one is don't let the best be the enemy, the better. You know, you can always be working that. Another one would be, you know, you must be your own best cannibal. That's an Andy Grove statement, right?

1:02:11So whatever you do that's great technology, celebrate it, get it working, and then replace it. If you don't replace it, you're going to competition as well. I think Sun's challenge, and I loved working for Scott McNeely. He's an amazing leader, and it was fantastic working with him and Andy Bechtelshein and Bill Joy and James Gosling. I mean, Sun invented a tremendous amount of technology. I was always impressed. They invented routing and IP. They invented symmetric multiprocessors. They invented, you know, a network attached storage. They invented a lot of these things. I think the challenge that Sun had is a couple of things.

1:02:53One is they built things for engineers. And I think that's a lesson that we all have to watch. If our end customer needs to be people, not engineers. Not that engineers aren't people, but you know what I mean. And I think that's the core piece. I think the other thing that they did, they didn't do well, is they didn't know how to sell a lot of their technology. They didn't do a good job of transforming from the invention to the sales cycle in a lot of cases. And they did a couple of transitions. They transitioned successfully from being a desktop company to a server company. They became the dot and dot com, if you like.

1:03:37You know, that was a good transition. But they did attempt, they had an early day of cloud called SunGrid. I was involved in it. A bunch of people were on it. It was like EC2 on AWS. But they ran into this innovator's dilemma where they couldn't sell it well because of the transition from selling capital to selling service. The street loves recurring revenue, Wall Street, right? But they hate a transition. They don't give you any break in a transition of a business model, right? So they just, what have you done for me this quarter? And so Sun had a lot of challenge moving from, I could sell a capital asset and recognize revenue immediately, large revenue.

1:04:31So sell a million dollar server, recognize a million dollars of revenue to sell a server as a service for 15 cents an hour. Right. Which in the end ends up making more revenue, but starts off making a lot less revenue. And so I think it was a combination of not being able to manage that financial transition. I think there were other mistakes we made. I was an advocate for open sourcing Solaris X86 early and we didn't. and I think Linux wouldn't exist if we'd open-sourced Solaris x86 early and that would have been a tremendous transformation because there was a lot of amazing things in Solaris.

1:05:07It's still an amazing operating system just not heavily used anymore. You know, Linux is reinventing a lot of the things that, you know, Solaris had containers back in the early 2000s, right? Now containers are all, you know, it had virtual machines, it had, you know, a trusted environment, it had, you know, all of these linear scalability. I mean, a huge number of things, advanced threading systems that are still struggled in some of the operating systems today to get. But it should have been open source and it should have been on x86, right? But it was very hard, I think, for Sun to give up Spark and the advantages that they felt Spark had and to understand the value of open source at the time.

1:05:52They eventually did, But, you know, they open source Java, they open source their identity systems, they open source Solaris, they open source all those things. And it was great. But and a lot of people have benefited from those things being open source still today. But they didn't do it soon enough. Well, Bill, this has been such a fascinating conversation. We covered so much public sector, AI and government, Doge, robotics, autonomous, quantum and sun. So I would say we've done our work today. Great having you on. Please come back. Yeah, if you want me to come back and spend a day about quantum computing, happy to do that.

1:06:26And thanks again. It's been a great discussion. Thank you so much. All right, everybody. Thank you for listening. And we'll see you next time on Big Technology Podcast.