Big Technology Podcast - Booz Allen CTO: Can AI Fix The Government — With Bill Vass

Episode Date: September 10, 2025

Bill Vass is the Chief Technology Officer of Booz Allen. Vass joins Big Technology Podcast to discuss how governments can harness AI to cut redundancy and deliver better citizen services. Tune in to hear his inside view on LLM deployments from the VA to the International Space Station and the difficulty of modernizing mass bureaucracies. We also cover autonomous driving, humanoid robots, and quantum computing’s first real use-cases. Hit play for fascinating look into public sector AI, along with deep perspective on technology’s state of the art. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Questions? Feedback? Write to: [e-mail]

How can governments use AI to become more efficient? We'll dive into it in a fascinating conversation with the CTO of Booz Allen and a former Amazon executive right after this. Welcome to Big Technology Podcast, a show for cool-headed, nuanced conversation of the tech world and beyond. Today, we have a conversation that I've been looking forward to for quite some time. We're going to talk a lot about how AI can be used to make the government more efficient and effective. And not only that, not only the how it can be, but how it is being used to. today because today we're joined by the CTO of Booz Allen, Bill Vass. He is the man that is on the ground working on this, and he's going to tell us what's going

on inside the United States government, what the state of Doge is, and then everything else from robotics to quantum. It's going to be great. Bill, so great to see you again. Welcome to this year. Yeah, thanks for having me on. I'm excited to talk a little bit about what we're doing. Me too.

So we're going to cover AI. We're going to cover Doge at the very beginning here. But first, for those who don't know Booz Allen, I'd love for you to tell us exactly what it does in about 60 seconds. My understanding is it's a government technology contractor and about 95% of Booz Allen's work or even more is connected to government work. Yeah, so Buzz Allen used to be a business consulting company and they sold that off in 2008. And now they have 22,000 engineers, about 3,000 AI, Gen AI experts and about 8,600 cyber experts. and primarily we do hardware and software, primarily for the government. We have some commercial business as well, and that's starting to grow also.

But basically just a bunch of software developers that do everything from building hardware cubits for the government to running the GPS satellites and a lot of intelligence satellites to 3D printing, organs experimenting with that for organ transplants with 3D printing. So it's a pretty broad range of tech, pretty exciting, actually. And talk a little bit about how we have so much redundancy in government. I mean, to me, you know, I'm not in government. I spent a little bit of time working at New York City government or a New York City's Economic Development Corporation, which is a quasi-governmental agency.

I don't want to bore you with the details. But I'm stunned and sort of upset as a taxpayer that there could be this many. I mean, what did you say, 255 different systems in the Pentagon? That was back when I was in the taxpayer. the 90s when I was right that that was so who knows what it is today yeah and it could be less there's been also a lot of consolidation that occurs you know that okay across this i somehow don't believe that it's less given this for all of this but i don't know i you know that that's one of those things i'd have to go look at to give you an accurate number on what it is today but it

so let's there's two fifty five then and and i some of it is that you have just all these parallel stovepite organizations, right, that are operating independently, right? The government's broken into a lot of different agencies that operate independently from each other. And I think it's very hard for them to coordinate. You know, it's interesting. Jeff Bezos at Amazon used to have this saying that two is better than zero. So we would have redundant systems at Amazon, but then work to consolidate them over time. Some of it is politics. You've got, you know, different, you know, agency heads and other things like that over time, different divisions that want to do it themselves and want to do it their way and they think it's better than the other agency's way.

When I used to work in the IC, one agency would sometimes do the opposite of the other agency just to avoid overlapping. And that used to piss me off as a taxpayer, but there was not too much I could do about that back then. But I think there's just a lot of places like that where that kind of stuff shouldn't be tolerated. And I think that the push to consolidate is a good thing. Okay. And so I just want to get your on the ground knowledge here. So again, speaking of Doge, a lot of people have talked about the layoffs.

But is this actually happening now? Like, is this agency, I suppose it's not really an agency. It's kind of like a side agency. I think it's the best way to put it because it was the U.S. Digital Service. now it's Doge. Is it working to actually centralize technology today and is Booz Allen working to help that division on doing this? Absolutely. Absolutely. I think the other thing they're pushing on which we like is moving to outcome-based firm fixed price contracts from cost plus and time of material. I always hated cost plus and time. Can you define what this, what this is?

So, outcome-based firm fixed price is, you know, to put like, you know, you're going to have a house built, you have a price that you pay for the house up front. Time and material is, you know, you have a house built and you just pay as you go based on the changes and all those other things, right? And so both have advantages and both have disadvantages. I think early days in the government, many things were firm fixed price. and outcome-based. In other words, you want an outcome at the end. I want to land a person on the moon, or I want to do whatever happens to be.

You could be an outcome-based type contract. Sometimes time and material makes a lot of sense when you're asking the government to do something extraordinary they've never done before, or no one's ever done, like 3D printing an organ transplant, right? We don't know that we can do that. No one's going to sign up for an outcome-based contract like that. But migrating from on-prem to the cloud should be an outcome-based contract that we know how to do that.

So when you know how to do something outcome-based makes a lot of sense, and firm-fix price makes a lot of sense. When it's something that the government's really pushing the edge of technology on, that's when you sort of have more of a time and material kind of contracting place. And I think what's happened is there's just too many time in material contracts over time. and the shift back to Outcome Based, that Doge is a really good thing, in my opinion. It's good for the taxpayers. It's good for delivery. Alternately, though, a lot of people in the government may not like it as much because they don't have much flexibility, right? They define it and they get what they've asked for, and they like to make lots of, you know, changes in pivots and cues and again, back to this analogy of building the house. Oh, I don't like, we painted the dining room green.

I mean, I didn't realize the green would look that bad. I want to paint it white. You know, and the, you know, time of military you're paying for that. With a firm fixed price contract, you couldn't change that. It would be like you've got to live with a green living room. Right. That's good. It raises the stakes for people that are making decisions in government.

And frankly, they should be raised. I've heard the term good enough for government work. I don't know if you've heard that as well. I have heard that. I don't agree with it. It makes me so upset because you're like you put it. This is something that does land on the taxpayers' doorstep at the end of the day. I think, though, having been in the government, I think that a lot of people don't understand.

There are a lot of people who are incredibly technical, incredibly good, and incredibly committed working within the system, and are delivering amazing things for our country and our warfighters. I mean, look at all the things that came out of the government, integrated circuits, the Internet, GPS. as, you know, it goes on and on and on. Those came from government programs. All of Silicon Valley is built on top of it, right? Definitely. And so I think that kind of core research is still important.

I think it's still a place where the government can innovate and continue to deliver there. Okay, so let's talk now about technology centralization. The contract thing, I think, is important. Thank you for bringing that up. But I think that we should talk a little bit about the technology centralization efforts that are going on within the U.S. government, by the way, this is a model, I think, like, we're going to talk about U.S. today because that's where Bill is working or working as a partner of, but I think a lot of this is going to be applicable to all governments and especially the AI components. So this is going to build right into that. But talk a little bit about technology centralization and whether Booz Allen or whether from your vantage point, we're seeing the government actually work to consolidate those, you know, let's say many multiple systems. that seem to be the same thing for, you know, different agencies. Yeah, I do see that direction, and I think that's the big push.

So, for example, there's a bunch of different organizations that manage satellites. There's a bunch of different organizations that, you know, manage financial data. There's a bunch of different organizations that manage health care data. And in some cases you can consolidate them, in some cases you can't. And so I think it's just a matter of judgment where you can and can't consolidate them. For example, there's a lot of health care data in VA. There's a lot of health care data in HHS and other things like that. And there's some consolidation and overlap that can be done.

But there's also a very bunch of unique things in taking care of our veterans. There's things that veterans are exposed to and have to go through that you and I don't. And so they need a certain amount of uniqueness there, for example. So it varies. I think you just have to use your judgment on that, right? You know, I think there's a lot of places where citizen services could be much better through consolidation, making it easier to do your taxes, easier to make payments, easier to get payments from the government, those kinds of things.

And I think you're going to see a lot of that. Okay. I mean, did you see there was that story of, I think it was veterans records being held in a cave? How does that happen? So that's not really accurate exactly, right? There is the need for long-term storage at NARA and other places like that of data that is underground. And that data is also stored purposefully

on non-electronic formats. And the reason for that is we have legal requirements but the government to keep that data forever. Now, you could change those legal requirements. There's reasons for those legal requirements. We have legal requirements to keep that data forever. And if you stored it on some type of technology, you'd be constantly having to upgrade that technology.

You know, you would have started storing it on, you know, 1,600 DPI tapes, then you would have had to migrate that to 2,800, and then you would have had to migrate that to $37K, then you would have migrate that to disks and on and on and on, right? So there's a certain amount of logic to that. It is stored in OCR character, so it can be automated at any time. So there are, you know, there are certain things that are true. Other things are, in my opinion, are misrepresented.

Okay. And at the beginning of our conversation, you mentioned that the Doge team took a look at the Booz Allen technology and deemed it to be good. So far. Yeah. So far. And I want to tell you, we'll talk to you a little bit about the perception of government technology and, I mean, and then sort of get your perspective on what the truth is. And again, this will lead into AI and I do keep teasing it, but I know we're going to get to it. But I think this is an important foundational question before we start that part of the conversation.

All right. So I think the perception of government technology is that it's terrible, that there's a certain amount. And this is not a comment on Booz Allen, but there is a, there is a, there is a, a perception that there's a certain amount of companies that figure out what to do to get through government procurement processes, and they are the ones that end up serving a lot of these government agencies. And while everybody else is on the current technology and using CHAPT, we get the sense that the government is running on Windows 95 and like the nuclear processes are like in basically running on MS DOS. Now I'm exaggerating a little bit, but I'll just

give you one example. I did this internship on Capitol Hill and anyone who did it in the time that I was doing it, you had to use the system called IQ, which was basically their CRM. This was generally like maybe a decade behind the state of the art technology. Now, of course, it's a lot of work to modernize government tech. But how close is this perception of the government working on outdated technology to reality and what can be done to change it if it's true? So I think, think the government's a big organization and what you're you said is going to be true in certain areas depending upon how it's funded and how it's planned right i mean um i assume you drive a car and you go places and you use gps every day do you think your gps is out of date no gps is working

great yeah that's a government technology right that is a that is but that's a government this is important distinction though that is a government developed technology yeah that companies like Google have with a I guess a profit motive developed and put into Google Maps and that is the technology I use but I'm talking about because we're again talking about how a government operates and the operating systems for the government those logistic systems this is what the perception so like I said I think you have things like GPS and the intelligence satellites and you know the Mars rover that you would say are working incredibly well right the Mars rover has done amazing things on. We like the Mars rover over here for sure. Yeah. I think there, I think categorizing

it is all government technologies bad is absolutely wrong. A lot of it is quite good. A lot of it is quite impressive. There are times when the government, the taxpayers decide and the administration decides to underfund things. And when they underfund things, then you have stale technology over time. There's also, and this happens in private industry as well. I can't tell you having migrated so many companies to the cloud, how many ancient Windows 95 systems I've seen in private industry in the OT and IOT environments. Isn't it amazing how many people are still using Windows 95? I mean, that system really had legs.

Well, because they just didn't change it, right? They didn't have time to change it. And it was a decision in that corporation not to fund that, right? And you hear about it all the time. So I don't think that this is unique to the government. I think it's a normal thing that you see. I would not say that the government is necessarily behind in a lot of other areas. I mean, we do a tremendous amount of gen AI with the government.

We've been doing it for two years. We started doing it before it was vogue in Silicon Valley, right? Before chat GDP was so popular, we were using it in quite a number of places. We started using AI seven, eight years ago, 10 years ago. We started, when I was in 1978, when I was doing government contracting for autonomous vehicles in the ocean, for an ocean engineering company, we were writing a neural network using AI in 1978, right? So I don't, I think it's a mischaracterization. I think what you see is there's areas where we haven't invested intentionally. There's been decisions made there that they'd rather

spend money on other things that do, you know, age over time and are not the best technology, right? And there are areas where we've really focused in the military and the intelligence agencies, things that are life critical, where we have spent the necessary money and spent the necessary investment in technology and the, you know, the latest architectures coming out of DARPA about the latest things that you see. So I think, you know, I see more cutting-edge technology in the government often than I see in Silicon Valley and having been in Silicon Valley a lot as well.

And I also see things where the government's partnered up with Silicon Valley to deliver things. I don't think there's any, you know, you see Palantir all over the government. You see, you know, things happening with Andrew. You see Shield AI and Scout and, you know, it goes on and on and on. So I don't think there's,

any lack of the government's interest in adopting the latest technology and being the most competitive. But at the same token, you know, you could get to, I don't know, a building entry badge swiping system that's still running, you know, Windows XP, right, or whatever. You know, like, and that is true in private industry too, right? I've seen it in private industry also. So I don't think that's unique to the government. I think that's just, you know, how things are. matter of priorities. Yeah, it's definitely a tough to see this issue really resonate on the campaign trail. It's sort of like, we're going to fix health care and everybody cheers and we're going to

help small businesses operate without the red tape and everyone cheers. And it's like, we're going to make sure the Department of Energy has a badge swiping system that doesn't run on Windows XP anymore. And yeah, and nobody goes wild. Yeah. I mean, it's, you know, it's, it's a, you know, you see these kinds of things like a simple thing like let's have a common health care record that all the insurance companies can use would save so much money right a common format common way to store data common health care exchange we've been trying to do that for years but there's what you end up with is all these different companies and all these different software providers and all these different you know congressional folks impact the technology significantly in positive

in negative ways. I mean, when I first got to the Pentagon, I'll never forget this. In 19, this was about 1994-ish, 95-ish. Right in time for the best edition of Windows. Yeah, yeah. My boss, who is the CIO for the all of DOD, was complaining that our security facilities still had those old-fashioned piezoelectric buttons to put your combination in as opposed to a biometric and other things like that. So unfortunately for him, he mentioned that during his confirmation hearing. And Senator Byrd, the company that made those was in Senator Byrd's district. And they held up his confirmation because he threatens to

upgrade the piezoelectric buttons. That's infuriating. Yeah, but that's how these things happen. It's not that that Art Money didn't want to have a full biometric. system, which we eventually did. It isn't that he didn't want to have all these other things. It's, you know, you run into these areas where you've got people protecting their technology. I mean, look at it this way. The way I view this in corporate, it's very true.

And in government is very true. Whenever you see a bad technology decision, it's always politics. Yes. Okay. So how does AI then fix this? You mentioned that you've been using. So, again, Booz Allen is a government contractor, 95%. plus business that Booz Allen does is basically building things for the government.

So how does generative AI come into play here? And what, I mean, yeah, what sort of things have you found with chatbots in particular or any large language model? How does that end up making, how does that end up enabling the government to work more efficiently and more effectively? Yeah, so let's start with something we just did. So we just put Lama on the International Space Station, on the edge on satellite. So that enables the astronauts who are working on the International Space Station to have Lama to chat with in space with no latency to determine when things go wrong how to fix them better. So all of the manuals are ragged into that for the International Space Station, or augmented into Lama running on the International Space Station to allow them to more quickly diagnose problems and help them diagnose problems. We have large language models going on to satellites

to allow them to identify and tip in queue faster. We have large language models helping the VA do claims processing. What used to take many hours for a person researching on the claim process happens in a few seconds through the use of a large language model. We have large language models being engaged for autonomous systems. There's a big fight going on right now between what I'll call traditional AI and procedural-based autonomy

and large-language model-based autonomy. So Scout AI, for example, a company we just invested in, is very focused on these large language model-based autonomy, right, and based on procedural input from humans. Learning most autonomy systems convert from a perceived environment into a 3D environment and then navigate through the 3D environment in the machine's brain, if you like. What they're doing is saying, well, we don't need to do that. We can go straight from the 2D image that comes from the cameras directly into navigation by learning from humans.

That's a transformation in how autonomy will happen. There's large language models involved in how we're doing autonomy in general or coordinating across ISR environment, intelligence surveillance and reconnaissance environment i mean it's it's everywhere and it's in everything already right so so it's i would say that um the government has been an early adopter of machine learning and an early adopter of a lot of these large language models in specific areas where it makes sense right so we just had a go ahead it's not everywhere right it's not everywhere they're just another thing that i'm seeing more and more i mean certainly we use large language models for code development. We use co-pilot and Claude and Q and Cursor and Klein for doing code

development here at Boz Allen. I see the government using it more and more for code development for their internal development as well. So I think those kinds of tools are happening also to accelerate development. I think it's, you know, I wouldn't say there's other areas where it's not being used at all when it should, though. I mean, it says this isn't going to happen everywhere overnight. And I also would be true. Where else do you provide it should be used? It should be used a lot more for doing fraud management and financial systems. It could be used a lot more in the IRS. It could be used. I mean, I could go, I mean, there's a lot of other places it can be used to. you know and large language models aren't a panacea they're not perfect in everything you need to

have the proper guardrails in place you need to have one model checking another model to make sure there is a hallucination going on you need to often have for example you know it being the first round of things and then a human checking it in a second round so so for example if you're doing with just regular AI. At Amazon, we did a lot of, like, cancer identification from MRIs and CAT scans. And, you know, the ML was about 98% accurate, which is tremendously good. So, 100% accurate, though. So you do still want a doctor to look at it, right?

So you have the ML filter ahead of time, and then it goes to the doctor with recommendations. So I think there's, and then as the doctor provides feedback, the model just gets better and better and better, you know. I mean, the reality is this is all just math, right? All ML is just math. It's, you know, vectors and it's tensors, and it's, you know, it's all just math. It's not magic, it's just math. And so the more dense data you provided that's accurate, the more accurate the model is going to be over time. the more you control the tuning parameters, the more direct it's going to occur, right,

into what you're trying to get an outcome of. Right. So, Bill, we just had a couple of AI critics on the show a couple weeks ago. They wrote this book called The AI Con. They don't really trust that AI should be used for information retrieval. I suppose hallucinations are an issue. I suppose they think that this may, I don't know, I don't want to speak for them, But it's top of mind.

I guess they would suppose that instead of going out and doing the research yourself, having the AI go and do it for you will atrophy your brain. So I'm curious, a couple, let me ask you that. So given that, let me ask you a couple of questions. Okay. The, having astronauts use generative AI to decide what to do on a spaceship is pretty high stakes. So how could we be confident that, um, that they're not going to, you know, kill themselves? in the process of using these chat bots.

And then secondarily, uh, do you worry that we're going to get, um, you know, government workers relying on these, uh, AI bots and then not able to think critically about the work they're doing. Do you use a calculator? Well, that's okay. So no, Bill, I've heard this before. This is there, this is, I'm just again, channeling the critics. Yeah, I know, I know, uh, but let me, let me, I just, I want to address this.

This is the big question. Sam Altman would say that large language models. are just like the calculator, but there is, there has been research, including some research from Microsoft that shows that the, the reliance on LLMs can, uh, decrease the ability to think critically. And in fact, you've brought up GPS a couple of times. Yeah. And there have been some studies that say over reliance on GPS also limits the ability to think critically. So I do think that there's, there's an argument to be made. And I'm still not sure where I fall on this argument, which is why I love speaking with experts like you, that a calculator in a large language

model are two very different technologies when it comes to this question? Yeah, I don't think so. I think, you know, the astronauts are using the large language models to augment where they'd have to go through tons of manuals, right? 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 atrophias? Geez, that's an interesting question. That's hard for me to answer in some ways. I mean, we, certainly like myself personally, I use GPS all the time,

and I use navigation systems all the time. And if you ask me to drive somewhere that I drive a few times with GPS. Without the GPS, I'd have to really go look at a map and figure it out, right? I mean, like, is it 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 is that important? I can always go look at a map. I 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. myself personally, I was using chat GDP, and I'm putting 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 2 grand, and so they're getting to be, you know, affordable. And so I asked it for, it 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 became exactly what I asked for. Maybe that's a business idea. Yeah, exactly. But that's really, though, the important thing to understand on how these tools work. They're just doing statistics, right?

And understanding the two 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 for a Google search. I think people will say that, you know, 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.

Same thing through 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. they're 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? Like 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, 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 during inference on today. So that's in their area that we really question sometimes. the ROI of some of these things because of the cost of all of it. So 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 of this too that we you shouldn't go overboard at some point in time a citizen should expect to talk to a person yes that's going to be the case for the all companies that go to this but i guess i would take a really smart large language model over a phone tree uh where you hit the number and it says goodbye uh but anyway these are personal gripes okay 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 timebound. And that is that we see that Nvidia is going to do multi-hundred thousands of GPU data center with the Saudis. Amazon, your former employee is committing, 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, you know, just outside of Riyadh there. I 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, you know, diversify their oil investments into other areas. You know, 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 again. I really enjoyed the work that I did getting the region up and running in Saudi Arabia. Yeah, the work I did in the UAE and others when I was out working at Amazon. And, you know, I think, you know, 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 now and then talk about some of like 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. And we're back here on Big Technology Podcast with Bill Vass. He's the chief technology officer of Booz Allen. And it's been a fascinating conversation so far. All right. Look, during the break, I said, I got to 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. Amazon 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. Yeah. Are there, what would you say the leadership principles are for the U.S. 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. So, but they certainly do shift. And it depends on the focus of the government at different times in different areas, right? How about today then? How about today? I think there, 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. One of my favorites, there's a number of them, was bias for action, right? That was one of my favorites. And 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 had been there before and accomplish big things. Customer 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, you know, into the technology. And I do a lot of whiteboard sessions, things like that, you know, like of 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 of material. and cost plus to firm fixed price, which we talked about a little bit earlier,

using AI to do that. But, you know, I think those are things I'm seen, and those are positive things, and those are things that I liked at Amazon and continue to like. Okay. So it seems like what you're saying is that some of the Amazon thinking

is starting to make its way into the U.S. government, which is interesting. Okay. Yeah. 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.

And 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, take some shortcuts. I think there are a lot of human operators out there that will sort of get those Waymos

out of 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 so how far away are we I mean this is sort of the essential question for yeah autonomous driving conversations how far away are we from seeing this stuff be mainstreamed that that's 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 I wouldn't trust it entirely right that if you trust it you're going to be in trouble so it's 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 and so we work really closely for example with invidia on omniverse so omniverse is a synthetic simulator or environmental simulator that has full physics and and full of fidelity and that's really amazing a lot of the autonomous driving training that has been done in 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 um omniverse is sort of the first to to go that next level of not being constrained on

something that might have to run on a console so it's it's pretty amazing rev out there i've been working with him for years on this yeah we have uh episode with Rev. Laboretian in the library. So folks, you can go search for an excellent conversation. He's great, yeah. So, and then, you know, 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 H-200s 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 the, after you create your inference, model that runs in car, and that's the smaller computer, that's a third computer. And I talked about this a lot in the velocity article that I wrote for Booz Allen is how this 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. Maybe I'll be burned by that prediction.

Um, there's still a lot of complexity in doing it. Um, I, I worry sometimes, uh, I love having my Tesla drive itself. My wife hates it, but I love it. Uh, it's, 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 and when I'm driving my car myself because I'm, I'm watching everything it does. And, um, I'm very proud of it when it does things well, you know, and, and, and sometimes I get scared with some of the things it does also. So, I, you know, 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 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 kind of feedback.

Remember, Tesla has this advantage very much like the echo devices of Amazon where they're able to crowdsource training from the users. So 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. There's areas where I think the models still have a long way to go.

You probably look at somewhat at a stop sign which way their wheel is faced in their car to know where they're going to go, even if they're not signaling. I think that's a nuance that's going to be very hard to train a model to do at this time. But 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. There's right now an oblique angle with my Tesla.

the stoplight going the other direction on an oblique angle. If it can see it, it thinks it's green on my stoplight. That's a bad thing. You don't want that. Yeah. So I think all of those edge cases will get solved over time, 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, and 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. And 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. There was this half, but also interesting. There was this half marathon in China of 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 took a hard 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, it had to change batteries three times, but finished the half marathon in a respectable time. Yeah. And so the, I think, there is 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 yeah so i but i'm curious like i'd love to hear you let me i'll just say this and you can decide to bat it down or whatever um is the u.s paying attention to what's going on there and is 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 ad, but I don't think they're behind. And I think that's an important thing. One of the reasons I left at AWS, and I loved being in 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. Because I was worried about it. I worried about us falling behind the Chinese. And it's a combination of government and private industry that it'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. We 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 push, the focus is 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 um 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 and at liquid robotics where i did the autonomous systems and um bring all the best of private industry to bear in the government well appreciate you doing it um 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 Wong was like, don't expect quantum to show up anytime 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. law. What is the realistic picture of this, where the state of quantum is today? Yeah. So we've been, I started the quantum initiative at AWS when I was there and we've got a lot of great people

working on that. I was, you know, involved in getting DOD to invest more in quantum in the, in the early 90s and some of the core research in there, especially around ion trends. and electromagneticrogenic 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 around of the compute because you have alpha particles flipping the memory on your machines we're working on right now,

and 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. You have a massive amount of work you have to do to do error correction because the atomic particles are so affected by in 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 cubits.

The goal would be to get to a thousand error corrected cubits, right? But just put that perspective, that's going to be around 7 million physical cubits 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 will be the size of the machine and that's because you have to have millions and millions of cubits to get just a few fully functional error-corrected cubits you have to have them constantly air-correcting each other quantum computers differentiate from

digital computers in our classical computers that we call them now in that they have this two unique things that are unique to quantum physics that are hard for people to understand 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 we don't I was about to say I cannot tell you how that works yeah but but you know An analogy I'll use is, you know, 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 crankshaft and the spark plugs and the valves

or 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 the car, she doesn't understand any of those things, but she can drive as well as I can. She doesn't care to understand anything. The skinny pedal, the fat pedal, and turning the wheel, right? You can drive a car without understanding of this. We can drive entanglement and superposition extremely well without actually understanding how they work, what causes them.

Right. 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 erions, atoms, and photons primarily. And 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 computers, a thousand error cryptocubes could do a Hamiltonian on ammonia. Ammonia is the most produced, we've been producing ammonia since 19, for almost over, 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 intents 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, superconductors 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, give me the chemical formula that we'll 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 a thousand error rectum cubists, 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 these material sciences come into fruition and we start using them. How 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 100 air-created qubits on fast machines. I think you'll see that before on slower machines, and on the neutral atom machines, they'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. So we do, 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 co-processor, 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 Q is to start to break encryption. And secrets last longer than that. So we need to start.

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 state cryptography. That's something Bezal can help you with and others can help you with as well. Yes. If 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 scientists. And 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. And 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 is 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 class computers like the A6 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 QBITs. They focus on everything around it. So they're kind of will win, no matter which of the four different types of quantum computers, when they'll be able to provide the control systems and other things like that very efficiently.

In 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 thing 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. Yeah, 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 CEO of Sun Microsystems Federal. Yep. From 2006 to 2011. Yeah. So let me just put it. That is the federal version of Sun. Yeah, it's a 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 a 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. Yeah. Having 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 the, like, like 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? So 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 McNally. He's an amazing leader, and it was fantastic working with him. Andy Bechtelchein and Bill Joy and James Gosling. I mean, Sun invented a tremendous amount of technology. I was always impressed. You know, 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. One is they built things for engineers. And I think that's the 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. That 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 Dotten.com, if you like. You 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 wrong 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, so sell a million dollar server, recognize a million dollars of revenue, to sell a server as a service for 15 cents an hour, 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 Act 86 early, and we didn't. And I think Linux wouldn't exist if we'd open source SolarisX 86 early, and that would have been a tremendous transformation.

Because there was a lot of amazing things in Solaris. It'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, you know, advanced threading systems that are, you know, still struggled in some other 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

the advantages that they felt Spark had and to understand the value of open source at the time. They eventually did, but it, you know, they open source Java, they open source their identity systems, they open source Salaris, 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 and 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 Thank you.