Booz Allen CTO: AI in Government, Autonomous Driving, Quantum's Promise

Channel: Alex Kantrowitz

Published at: 2025-09-10

YouTube video id: 1zNkQGchFXE

Source: https://www.youtube.com/watch?v=1zNkQGchFXE

How can governments use AI to become
more efficient? We'll dive into it in a
fascinating conversation with the CTO of
Booze Allen and a former Amazon
executive right after this. Welcome to
Big Technology Podcast, a show for
coolheaded nuance 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 today. Because
today we're joined by the CTO of Booze
Allen, Bill Vas. 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 the show.
>> Yeah. 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 Booze Allen, uh 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 Booze Allen's work or
even more is um connected to government
work.
>> Yeah. Yeah. So Boo 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 genai
experts and about 8,600 cyber experts
and primarily we do hardware and
software you primarily for the
government. We have some commercial
business as well and that's starting to
grow also. Uh 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 the intelligent
satellites to 3D printing organs uh
experimenting with that for organ
transplants with 3D printing. So it's
it's a pretty broad range of tech. Um
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
uh New York City government or a New
York City's economic development
corporation, which is a quasi
governmental uh agency. I don't want to
bore you with the details. Uh but I'm
stunned and and sort of upset as a 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 uh in the '9s
when I was at
>> that. So who knows what it is today?
>> No, it could be less. There's been also
a lot of consolidation that occurs, you
know, that across system.
>> I somehow don't believe that it's less
given the sprawl 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 was 255 then. And I
I some of it is that you have just all
these parallel stovep or organizations,
right, that are operating independently,
right? The the government's broken into
a lot of different agencies that operate
independently from each other. Um and I
think um 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
he we would have redundant systems at
Amazon, but then work to consolidate
them over time. Some of it is politics.
Um, you've got, you know, different, you
know, agency heads and other things like
that over time and 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. Um,
when I used to work in the IC, one
agency would would sometimes do the
opposite of the other agency just to
avoid uh overlapping. And that used to
piss me off as a taxpayer, but uh there
was not not too much I could do about
that back then. But I I think uh I think
there's just a lot of places like that
where that kind of stuff shouldn't be
tolerated and I 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. Uh but is this
actually happening now? Like is this a
agency? I suppose it's not really an
agency. It's kind of like a a side
agency I think is the best way to put
it. Um because it was the US digital
service now it's Doge. Is it working to
actually centralize technology today and
is booze 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 outcomebased firm
fixed price contracts from cost plus and
time and material. Um I always hated
cost plus and time. Can you define what
this what this is?
>> Um so um outcome based firm fixed price
is is you know to put like uh you know
you're going to have a house built you
you have a price that you pay for the
house up front. Uh uh time and material
is you know you you you have a house
built and you just pay as you go based
on the changes and all those other
things right. Um and so um both have
advantages and both have disadvantages.
I think uh early days in the government
mo many things were from fixed price um
and outcome based. In other words, you
want an outcome at the end. I want to I
want to land a you know a person on the
moon or I want you whatever happens to
be could be an outcome based type
contract. Um sometimes time and material
makes a lot of sense when you're asking
uh the government to do something
extraordinary they've never done before
uh 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 outcomebased
contract like that. But uh migrating uh
from on-prem to the cloud should be an
outcomebased contract that we know how
to do that. So so when you know how to
do something um uh outcomebased makes a
lot of sense. Uh and from fixed 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 contract in
place. And I think what's happened is
there's just too many time and material
contracts over time. And the shift back
to outcomebased that that Doge is
pushing is a really good thing in my
opinion. It's good for the taxpayers. Uh
it's good for delivery. Um alternately
though, uh a lot of people in the
government may not like it as much
because they don't have as much
flexibility, right? They they define it
and they get what they've asked for. Um
and they like to make lots of, you know,
changes in pivot execution. Again, back
to this analogy of building a house. Oh,
I don't like we painted the the the
dining room green. I I didn't realize
the green would look that bad. I want to
paint it white. You know, then the, you
know, time and material you're paying
for that. With a firm fixed price
contract, you couldn't change that. It
would be like you got to live with a
green living room,
>> right? That's good. It raises the stakes
for the people that are making the
decisions in government. And frankly,
they should be raised. Uh I've heard the
term good enough for government work. I
don't know if you've heard that as well.
I I have heard that. I don't I don't
agree that makes me so upset because
you're like you put it this is something
that does uh land on the taxpayers's
doorstep at the end of the day.
>> I think though having been in the
government I I think that 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 war fighters. I mean you
know the I mean look at all the things
that came out of the government
integrated circuits the internet GPS
u you know I mean it goes on and on and
on those came from government programs
all of Silicon Valley is built on top of
it right
>> definitely
>> um and and so I think that kind of core
research is still important I think it's
still a place where the the government
can innovate and and continue to deliver
there
>> okay so let's talk now about technology
centralization uh 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 uh within the US
government. By the way, this is a model
I think like we're going to talk about
US today because that's where Bill is
working or working as a partner of uh
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 uh Booz Allen
or whether your from your vantage point
we're seeing uh the government actually
work to consolidate those uh you know
let's say many multiple systems that
seem to do the same thing for for you
know different agencies.
>> Yeah I I I do I do see that direction
and I think that's the big push. So so
for example um there's a bunch of
different organizations that manage
satellites. There's a bunch of different
organizations
um that you know manage u financial
data. There's a bunch of different
organizations
um that manage healthcare data. Um 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. Uh for
example, um there's a lot of healthcare
data in VA, there's a lot of healthcare
data in uh HHS and other things like
that. Um, 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 they need a certain
amount of uniqueness there, for example.
So, it it it varies. I I think you just
have to use your judgment on that,
right? Um, 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, uh,
easier to get payments from the
government, uh, 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. Uh, how does that
happen? Um
so so so that's not really accurate
exactly right there there are there
there is the need uh for long-term
storage at NAR and other places like
that of of data uh that is underground
um and that data is also stored
purposefully
um uh on non electronic formats and the
reason for that is um we have legal
requirements but the government to keep
that data forever. Um, 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, 1600
dpi tapes, then you would had to migrate
that to 2,800, and then you would have
had to migrate that to 6250, then you
migrate that to 37K, then you would have
migrate that to discs, and then you and
on and on and on and on, right? Um so
there there's a certain amount of logic
to that. Um it is stored in OCR
characters. So it can be uh automated at
any time. So there there are you know
there 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 Booze Allen
technology and deemed it to be good
>> so far. And I want to tell you or talk
to you a little bit about the perception
of government technology
>> and I mean and then sort of uh get your
perspective on what the truth is. Yeah.
>> Uh and again this will lead into AI and
I do keep teasing it but I know we're
going to get to it.
>> Okay.
>> 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 Boo Allen
uh but there is a there is a perception
that there's a certain amount of
companies that figure out what to do to
get through government procurement
processes
>> and they uh are the ones that end up
serving a lot of these government
agencies and while everybody else is on
the current technology and using chat we
get the sense that the government is
running on Windows 95 and like the
nuclear uh processes are like in
basically running on MS DOS. Now, I'm
exaggerating a little bit, but I'll just
give you one example. Uh I did this uh
internship on Capitol Hill and anyone
who did it in the time that I was doing
it had to use the system called IQ,
which was basically their CRM.
>> Uh this was gener like maybe a decade
behind the state-of-the-art technology.
Now, of course, it's a lot of work to
modernize government tech. Um but how
close is this perception of the
government working on outdated
technology to a reality and what can be
done to change it if it's true?
>> So I I think um the government's a big
organization and what you're what 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 an important
distinction though that is a government
developed technology
>> 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 it's you have
things like GPS and the intelligent
satellites and um 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 you know so so I think there I I
think categorizing it as all government
technology is 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
Um there's also um and and this happens
in private industry as well. Gez, I I 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.
Scar.
>> Isn't it amazing how many people are
still using Windows 95? I mean,
>> the system really had legs.
>> Is it? Well, because it just they didn't
change it, right? They didn't have time
to change it. And it's not it's was a
decision in that corporation not to fund
that, right? Um and you 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.
Um I would not say that the government
is necessarily behind in a lot of other
areas. I mean the we do a tremendous
amount of genai 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 there there, you know, we
were using it in quite a number of
places. We started using AI 7, eight
years ago, 10 years ago. We started like
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
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 um
that do do, you know, age over time and
are not the best technology, right? Um
and there are areas where we've we've
really focused in the military and in
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 and the latest
things that you see. So I 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 um as well. And I
also see things where the government's
partnered up with Silicon Valley to
deliver things. I don't I don't think
there's any any you know you see
Palunteer 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,
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 uh entry badge
swiping system that's still running,
you know, Windows XP, right, or
whatever, you know, like and and that is
true in private industry, too, right?
I've seen it in private industry also.
So, this isn't I don't think that's
unique to the government. I think that's
just, you know, a 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 healthcare 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, it goes wild.
>> Yeah. I mean it's it's you know it's
it's a um 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 a common
healthcare 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 um you congressional folks
impact the technology significantly in
positive and negative ways. I mean, um,
when I first got to the Pentagon, I'll
never forget this, in 19 this was this
was about 1994ish
95ish,
>> um,
>> right in the best edition of Windows.
>> Yeah. Yeah. my my boss uh who is the the
CIO for the all of DoD
um was complaining that our security
facilities still had those old-fashioned
pio electric buttons
uh to to put your combination in as
opposed to a biometric and and other
things like that. So unfortunately for
him, he mentioned that during his
confirmation hearing and Senator Bird,
the company that made those was in
Senator Bird's district and they held up
his confirmation because he threatened
to upgrade the PZ electric buttons.
>> Yeah. Right.
>> 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 you run into these
areas where you've got people protecting
their their techn I mean, look at it
this way. The way I I 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, so how does AI then uh
fix this? you mentioned that you've been
using so so again booze Allen is a is a
government contractor 95% plus business
uh that booze Allen does is basically
building things for the government so uh
how do how has generative AI come into
play here and what I mean yeah what sort
of things have we have you found with
chatbots in particular or any large
language model um how does that end up
make make how does that end up uh
enabling the government to work more
efficiently and more effectively Yeah.
So let's start with something we just
did. So we just put llama on the
international space station on the edge
on satellites, right? So that that
enables the astronauts who are working
on the international space station to
have llama 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 u or you
know augmented into into llama running
on the international space station to
allow them to more quickly diagnose
problems and help them diagnose
problems. Um we have uh uh large
language models going on to satellites
to allow them to identify and tip and
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 uh large language models being
engaged for autonomous systems. There's
a a big fight going on right now between
what I'll call traditional AI uh and
procedural based autonomy and large
language model based autonomy. So, Scout
AI, for example, a company we just
invested in, uh, is very focused on
these large language modelbased
autonomy, right? Um, and and based based
on procedural input from humans, you
know, learning most autonomy systems
convert uh from a perceived environment
into a 3D environment and then navigate
through the 3D environment in the
machine's brain, if you like. um 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 um there's
large language models involved in um uh
how we're doing you know autonomy in
general or coordinating across ISR
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 um I mean
certainly we use large language models
for code development we use uh uh
co-pilot and claude um and um uh Q and
Curser and um Klene uh for doing code
development here at Boo Allen. Um I see
the government using it more and more
for code development for their internal
development as well. So so I think those
those kinds of tools are happening also
to accelerate development. I I think
it's you know I wouldn't say there's
other areas where it's not being used at
all and it should though. I mean, so
this isn't going to happen everywhere
overnight.
>> And I would be
>> Where else do you
>> think it should be used?
>> Um, it should be used a lot more uh 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 too. Um, you know, and
and large language models aren't a
panacea. They're not perfect and
everything. You need to have the proper
guard rails in place. You need to have
um one model checking another model to
make sure there isn't hallucination
going on. Um you need to often have uh
uh for example, you know, it it being
the first round of things and then a
human checking it in the second round.
So, so, so for example, if you're doing
uh with just regular AI, we at Amazon,
we did a lot of um uh like uh cancer
identification from MRIs and and and uh
CAT scans. And you know, the the the ML
was about 98% accurate, which is
tremendously good. It's not 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 um I think there's
and and then as the um uh 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 this all all ML is just math
it's you know uh vectors and it's uh
tensors and it's you know it's all just
math uh it's not magic it's just math
and so the more dense data you provide
that's accurate the more accurate the
model's going to be over time, the more
you control the tuning parameters, the
more direct it's going to occur, right?
Uh 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're at this book called the AI
con. They don't really trust that AI
should be used for uh information
retrieval. I suppose uh hallucinations
are an issue. I suppose they think that
the the this may I don't know. I don't
want to speak for them, but um it's top
of mind. I guess they would suppose that
instead of going out and doing the
research yourself, having the AI uh go
and do it for you will atrophy your
brain. Uh so uh I'm curious a couple let
me ask you so given that let me ask you
a couple questions.
>> Okay.
>> The a having astronauts use generative
AI to decide what to do on a spaceship
is pretty high stakes. So, how can we be
confident that
they're not going to, you know, kill
themselves in the process of using these
chat bots? And then secondarily,
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, use a calculator.
>> So, no, Bill, I I've heard this before.
This there this is I'm just again
channeling the critics.
>> I know. I know. Uh but let me let me I
just I want to address this. This is the
big question. Sam Alman 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 overreiance of on GPS also limits
the ability to to think critically. So,
I do think that there's there is 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 and
a large language model are two very
different technologies when it comes to
this question.
>> Um, yeah, I I don't think so. I think,
you know, the the the astronauts are 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 so 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. Um so so does it um
atrophy us? Gez, that's an interesting
question that that's hard 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 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. I can
always do those kinds of things, right?
Um I think the just like any tool, um
you know, you can cut yourself with a
knife in the kitchen, you know, you
don't you don't have to tear things
apart with your hands, right? It's just,
you know, oh gez, we've lost the talent
of tearing things apart with our hands
because we've invented knives, right? I
I I think that that's uh um uh
overblown. Uh but I also think that
people just need to remember that um it
just like any other tool it's not
perfect. It's going to have limitations
and they need to understand those
limitations, right? Um and uh I mean
I'll just give you a perfect example.
myself personally when I was first I was
using chat GDP and I'm putting you know
a home 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 they're getting to be you know
affordable and so uh 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
Okay.
>> It gave me exactly what I asked for.
>> Maybe that's a business idea.
>> Yeah. Yeah. But exactly. But that's
really though the important thing to
understand on how these tools work.
They're just doing statistics,
right? And and understanding the two
parameters and all these other things.
They they really are just doing a lot of
math um on what's the most likely answer
that you're asked for, right? But the
same thing could be true for a Google
search. is saying, you know, I I think
people will say that, you know, Google
has has made people lazy, too, because
you all the world's uh uh um information
at your fingertips, but that's a
wonderful thing, too. But just like on
Google, you can you can go down a rat
hole of all sorts of things that don't
really exist.
>> Same thing 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. uh
>> if we dream about what the be and by the
way I love this conversation because we
never talk about public sector here and
we really should uh so again appreciate
you being here uh if we think about the
best case scenario like we've outlined a
number of problems uh with 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 uh what does
the public sector what does it look like
what are the benefits that we see within
the government and 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 that's exactly where it
would be is is better citizen services
um 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 that we really
question sometimes the ROI of some of
these things because of the cost of all
of it. Um so that's another balancing
factor. I think we don't have good data
yet um on the ROI and so that that'll be
you know the cost of operating the model
and training the model um and running
the inference on the model versus the
feedback. Um, 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 uh and
do validations faster. But you know,
there's other sides to this too where
you you shouldn't go overboard. At some
point in time, a citizen should expect
to talk to a person.
>> Yes.
>> All right.
>> That's going to be the case for the all
companies that go to this. But I guess I
would take a really smart uh large
language model over a phone tree uh
where you hit the number and it says
goodbye. Uh but anyway, uh 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 uh this week
we're talking at a week where uh
President Trump is out in Saudi Arabia.
This episode will air a couple weeks
after, but the investments I don't think
they're time bound. And that is that uh
we see that Nvidia is going to do uh
multiund thousand hundreds of thousands
of GPU uh data center uh with the
Saudis. Amazon your former employees
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 uh sovereign AI experiment
uh 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 um
what governments can do with this
technology and will you at booze 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
uh at the LEAP conference in so in you
just outside of Riyad there. Um 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 um uh
you know uh
diversify their oil investments into
other areas. You know both both clean
energy um 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. Um, but certainly we'll be
watching it. We'll watching it h how it
evolves. Um, and you know, hopefully,
you know, at some point I'll we'll be
involved in it. I'll be involved in it
again. I I really enjoyed the work that
I did um getting the region uh up and
running in Saudi Arabia and the work I I
did in the UAE and others when I was out
uh working at Amazon. Um and um uh you
know, I think you know that's an area to
watch. I think that's that's a good
investment and the right thing to do to
to transform uh 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
uh after this. We're going to talk about
robotics, autonomous, uh quantum, and uh
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 Vas. is 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 got to ask I kept pushing
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. Um, 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 US
government and do they shift time to
time because of the fact that the CEO
quote unquote uh shifts every every
couple years?
>> That's interesting. Um I I think that um
you know I you you caught me off guard.
It take me a while to come up with
leadership principles for the
government. So um but they certainly do
shift um and it it depends on the focus
of the government at different times um
in different areas, right?
>> Um
>> how about today then?
>> H how about today? I think there there
is a a focus on efficiency. The other
thing that I like um 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 uh
was bias for action right that was 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 um uh the the other thing
that was a great Amazon principle I
liked was think big uh because a lot of
working a lot of 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 Um, customer obsession is one of
my favorites at Amazon. Uh, 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
that that could be improved. Uh, another
area that I'm seeing is dive deep. Uh,
and that's another thing I like at
Amazon as well because I like to dive
deep, you know, into the technology. Uh,
and I do a lot of whiteboard sessions,
things like that, you know, like of of
diving into how the architectures are
going to work and how all the different
components going to work together. Um, I
was just actually diving deep into a big
AI project we're working on to do
actually transform contracts from uh
time and material uh and and cost cost
plus to firm fixed price which we talked
about a little bit earlier using AI to
do that. But um you know I think um
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 I 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.
>> Yeah.
>> So, you know, speaking of think big, um
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 uh 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 uh training
in synthetic environments and that
leading to results in the real world and
adding synthetic data. Um but there's
also if if you think about the reality
of where self-driving is today um
there's Whimo which I think is obviously
it's expanding fast and it it
generalizes a bunch of tech of its
technology but also you know takes some
shortcuts. Uh I think there are a lot of
human operators out there that will sort
of get those whimos out of tricky
situations if I'm not mistaken. And then
there's Tesla which is um which is I 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
>> autonomous driving conversations. How
far away are we from seeing this stuff
beamed?
>> That That's That's So, I have two Teslas
and I 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 the vehicle running across hundreds
of thousands of synthetic simulations.
And so um we work really closely for
example with Nvidia on Omniverse. So
Omniverse is a synthetic simulator or
environmental simulator that has uh full
physics and and full um 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. Um, and uh uh
Omniverse is sort of the first to 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 a episode with Rev.
Liberadian uh in the library. So folks,
you can go search for excellent
conversation.
>> He's great. Yeah. So um and then you
know that you're 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. Uh 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
uh article that I wrote for Boo Allen is
is how this you know flywheel is
accelerating autonomous driving and all
these other things. Uh, I know this is a
very long answer getting back to your
question of when we will have it. Um, I
think, um, you'll start to see, um, real
autonomous driving over the next five
years. Um, you know, maybe I'll I'll
I'll be burned by that prediction. Um,
there's still a lot of complexity in
doing it. Um, I I worry sometimes. 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 than 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 sometimes I get
scared with some of the things it does
also. So, um, I, you know, and and the
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 Tesla has this advantage very
much like the Echo devices at 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. 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? But eventually it'll have
to learn to do that. The resolution will
have to be good enough on the sensors to
see that. Um 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. Uh we
have a lot of traffic circles here in in
uh Washington DC. Um, 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.
>> That's a bad thing.
>> You don't want that.
>> Yeah. I So, 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, you
know, I'm optimistic that there will be
a day when I can, uh, go to sleep in my
back seat and the car can drive itself,
but right,
>> 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 I imagine the same system is being
used. They have their own foundational
model now uh 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. Uh
there was this half but also
interesting. There was this half
marathon in China, 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° turn
and you see its trainer with a rope
attached to it like flying out of the
out of the scene and the 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.
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
US is behind China on this and
>> well I yeah so I but yeah 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 US 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 um I don't think China's ahead
but I don't think they're behind and I
think that's an important important
thing. Yeah.
>> 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 um a little bit about
government adoption of AI and uh
investment in technology to keep up with
the Chinese. And so Boo Allen because we
are so involved in the highest
technology in the government um uh was a
great way I felt to more directly
influence and improve that technology
and that's why I joined uh Boo Allen is
to pivot to really focusing on that uh
because I was worried about I worried
about us falling behind the Chinese and
the it's a combination of government and
private industry that's going to do it.
Um, you're right. The the the uh
government in China very much invests in
technology. They're very smart and
long-term thinking about how they
invest. Um, and there's a blurred line
between government and private industry
in China. Um, and I think some of the
stuff we're doing now, um, in pivoting
to a a big focus on AI and a big focus
on what we call the pacing threat, which
is, you know, you know, making sure our
technologies ahead of China um, in the
event that there was some type of
conflict. Um, we want to avoid the
conflict by making sure our technology
is superior. And so that's that's what
we want to do and that's where the focus
uh in the DoD on lethal lethalology
lethality
system that's the focus on advanced
technology and pushing DARPA harder. Um
that's the push the focus on uh this
public and private investments in in in
in AI and public and private investments
in space and public and private
investments in in silicon development
and quantum computing are going to be
very very important as they've been in
the past right um so so I think um 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 joined was to make sure the
government is moving faster to take
everything I'd 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
let's close here with quantum. We rarely
talk about quantum on this show. Uh 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.
>> Um, but you're you're touching quantum
stuff as well.
>> Uh, what is the realistic picture of
this where the state of quantum is
today? Yeah. So, um, we've been I
started the quantum initiative at at AWS
when I was there and we've got a lot of
great people working on that. Uh, I was,
you know, involved in in getting DoD to
invest more in quantum in the in the
early 90s. Um, and some of the core
research in there um, especially around
uh, ion traps and electromagnetic
cryogenic machines at the time. Um, so
the good news about quantum is that the
machines actually work and you can get
outputs from them. The bad news is that
the uh 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 amount of the
compute because you have alpha particles
flipping the memory on the machines
we're working on right now and they're
correcting that in the error correction
code. So maybe one or two% 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 the atomic
particles are so affected by in the
environment. Um and so um 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 uh 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
in perspective that's going to be around
7 million physical cubits to do that.
Um, that's a big number. And so the
first machines that you're going to see
coming, uh, I don't think people will
realize this yet, are going to be about
the size of a football field.
>> Wow.
>> That'll 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
correcting each other. Um uh quantum
computers differentiate from digital
computers in or classical computers as
we call them now um uh uh in that they
have this two unique things that are
unique to quantum physics that are hard
for people to understand. One is superp
position and the other one is
entanglement.
Um and um if anyone tells you they
actually understand how those things um
happen um they're lying to you. Um uh we
don't
>> because 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 I'm a car guy and and uh 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 are an electric
car, I understand exactly how the motor
and, you know, the the the inverter and
and all those things work and the
batteries are working together to do
that. Um, when my wife drives a car, um,
she doesn't understand any of those
things, but she can drive as well as I
can, right? She doesn't care to
understand any of the skinny pedal, the
fat pedal, and turning the wheel, right?
You can drive a car without
understanding other things. We can drive
uh entanglement and superp position
extremely well without actually
understanding how they work, what causes
them, right? Um and the way you program
a quantum computer is by using superp
position to control the cubits and uh
microwaves for electromagnetic machines
or lasers for the other machines which
are neutral atoms uh charged or ions
atoms and photons primarily. Um and um
uh 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 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 uh is going to
be material sciences and uh chemistry
first. So in fact one of the targets for
Amazon's working backwards document for
a quantum computers a thousand errored
cubits could do a Hamiltonian on
ammonia. Ammonia is the most produced
uh we've been producing ammonia since 19
for almost over over 100 years. Um and
it's probably the most produced
chemical. It's in fertilizer. It's in
prochemicals. It's in plastics. It's in
just about everything. um and it's very
expensive and energy intense to produce.
We know by watching bacterial
interactions that that it can be
produced at low energy state. We just
don't know how. So in the past
like a high temperature superconductor,
superconductors in general have been
discovered accidentally in the labs and
then leveraged. Uh in the future with a
Hamiltonian simulation, you can say
here's the outcome I want. Give me the
chemical formula that will give it. So
you can reverse engineer an outcome in
in chemistry. Um 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 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. Uh
with a with a thousand error rect
cubits, it would take about three
minutes on a quantum computer. Right. So
it's tremendously
lifechanging 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.
>> How far away are we from that Bill?
>> Um I think um 2032.
>> So less than a decade.
>> Yeah. Not that far. Not that far for the
first ones. So I think you'll see in
2027 2028 the first 100 air corrected
cubits um on fast machines. Um I think
you'll see that before on slower
machines um uh and on on the neutral
atom machines we'll pro probably see
that that they'll be be too slow to
solve some of these problem but they
they'll beginning 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 Booze 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 cubits to start
to break encryption and secrets last
longer than that.
>> So 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 um you really
should have urgency on deploying and
turning on quantum state cryptography.
That's something big can help you with
and others can help you with as well if
you're worried about that because people
can record the transport of your
information and then break it later. And
so that's 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 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. not just uh to be
ahead for the cloud, it's also for their
internal use. And so um uh I'm very
bullish on where this will go. I think
the um we're at a point now where it's
more engineering than science, which is
a good point to be. You know, when I
started working with these machines, it
was more science than engineering. Um
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 at Boo Allen is a
company called Seek, which I'm very
bullish on. So, uh, the nice thing about
them is they build like the equivalent
on on classical computers like the AS6
and all of the BIOS and that would sit
around the CPU. That's what they build.
They don't focus on the CPU or the
cubits. They focus on everything around
it. So, they 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.
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, um,
yeah, it's it's an exciting area. Um,
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 fieldsiz machines for these
first machines,
>> right?
>> 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 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. So,
>> yeah, I could spend I spent a whole show
going 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 Micro Systems Federal.
>> Yep.
>> From 2006 to 2011.
>> Yeah.
>> So,
>> let me just put it that's the that is
the federal version of Sun. Yeah. 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
>> as a um 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. Yeah, having worked in the tech
industry for quite some time, Bill, um
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 who's whose motto is always day
one. Um
>> yeah, I know.
>> So, so talk a little bit about the 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 of the better. You know, you can
always be working that. Another one
would be um you know, you must be your
own best cannibal. That's an Andy Grove
statement, right? So, so whatever you do
uh that's great technology, celebrate
it, get it working and then replace it.
If you don't replace it, your
competitions will. Um I think Sun's
challenge and I loved working for Scott
McNeely. He's an amazing leader. Um and
and uh it was fantastic working with him
and Andy Bealshine and Bill Joy and
James Gosling. I mean uh Sun invented a
tremendous amount of technology. I was
always impressed. You know they invented
uh routing and IP. They invented um
symmetric multipprocessors. They
invented you know network storage. They
invented a lot of these things. I think
the the challenge that Sun had is a
couple of things. one is they built
things for engineers.
Um, and I think that's a lesson that we
all have to watch. Um, if if our our our
end customer needs to be people, not
engineers. Not that engineers aren't
people, but you know what I mean. That
and I think that's
>> uh I think the other thing that um 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 uh the invention to the sales cycle
in a lot of cases. And they did a couple
of transitions. They transitioned uh
successfully from being a desktop
company to a server company. They became
the dotten.com if you like. You know,
they that was a good transition. But
they did attempt they had an early day
of cloud called Sunrid. Um I was
involved in it. A bunch of people were
wronged. It was like EC2 on on AWS. But
you they ran into this uh innovator's
dilemma where they didn't they couldn't
sell it well because of the transition
uh from uh selling capital to selling
service.
>> The street loves recurring revenue. The
Wall Street, right? Um uh but they hate
a transition. they don't give you any
any any break uh in a transition of a
business model, right? So, so they they
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 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, right? which in the 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 trans transition. I think
there were other mistakes we made. Um 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 uh uh
transformation because there was a lot
of amazing things in Solaris. still an
amazing operating system, just not
heavily used anymore. Um, 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 uh linear scalability u I mean
a huge number of things you know
advanced threading systems um that are
you know still struggled in some other
operating systems today to get um 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 uh and the advantages that they
felt spark had um and to understand the
value of open source at the time they
eventually did But it it 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 uh and a lot
of people have benefited from those
things being open source still today but
um 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.