Intelligence in the Age of AI with new CTO of the CIA
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We're a human intelligence Operation.

We can now make anybody, be anybody and sounds like anybody, and look like anybody.

Stop thinking about automating, getting a ten percent, twenty percent, thirty percent on your job. Tell me, in five to ten years, how are you gonna imagine your job.

This is no like asiatic c superpower, which, by the way, is very.

very different in the internet. It's because policymakers now have a nob where we now have to decide explicit this or that.

These are the largest computer projects that mankind has ever done anymore, like we ve never done anything close to .

this life finds away, which is there is demand for IT. People will supply IT.

Artificial intelligence has taken this world bystrom. I mean, just think about IT here in twenty twenty four, anyone with an internet connection and a few minutes despair can literally spin up a disney avatar of themselves, translate a foreign podcast into their native language and even get help writing their vows. But artificial intelligence is not just impacting the creative spheres.

In fact, you'll be hard pressed to identify an industry that's not touch by this technology, and the defense of our country is no exception. In today's episode, originally recorded in the heart of washington, D C. Back in january during asic sense's american dynastic summit, A E general partner to and a exe eeda darras are joined by first of C T O of the CIA.

Yes, that is the central intelligence agency rejoined by C T O, none multitask to discuss the future of defense intel gent, in this wide ranging conversation, they discussed the involving relationship between A, I and kane's, how governments can keep up with this exponential technology and finally, how it's impacting not just offence, but also defense. I hope you enjoy this conversation. As a reminder, the content here for informational purposes only should not be taken as legal, business, tax or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any asic sense fund.

Please note that a six sense year and a symphorien z may also maintain investments in the companies discussed in this park cast. For more details, including a link to our investments, please see a six inc. 点 com splash disclosures。

Martial, so most people watching, listen to this are fairly familiar with you in your old eight sixteen z, but not because the C A C T O roles relatively new. Can you give us a quick background on that role? And kind of way your objective is.

yeah so there really two stories here.

What is the agency needing a CTO and kind of what created that? And my own journey to IT in all starts with director burns checking over the agency in the administration, and he just like getting great business leader SAT down in a business review, which is what business we are, what are the big threats in the other pieces? And the decision was actually fairly interesting and not a surprise, was that we had to to pave IT from CT, which had been the big sort of focus for the agency for couple of decades, to great power competition.

And then the interesting, unusual sort of thing was this big and more is called technology. There was huge interest, obviously, from policymakers in technology that we needed to start looking into and build policymaking around. And I think that I might be helpful to the listeners to understand kind of what C I actually does, because I had to actually learn a lot about the agency when I came on board, and sort of what image gr called spinal inter to like what we actually do.

And so this pivot, in terms of rethinking the agencies, focus on technology. There were three things that happened, when is we created a china mission center, which is how we actually focus on threats and opportunities. T, two, M, C, which is transnational technology mission center.

And this weird thing called A C T. O. And where are seventy six year old? So we've been doing technology for a long, long, long, long time, but technology has been somewhat latent.

What happened is with this focus on tech, we basically needed to focus on three different things that I think we're different from what we were doing for. Number one is we are, as an agency, fairly vertically focused. So we have five directorates that focus on five things that sort of come together in the mission centers.

So the idea of the CTO is really to go harza, versace, herms, al. The second thing is we is an agency are very focused on the hero now, which is there's a crisis and we jump on IT. And the co function really gives us the luxury a little bit of looking a little bit out rather than focus sort of inner. And the third thing is a little more extern versus into so engaging with the outside world, those are kind of the big dimension.

And I guess more people, people know us, investor and onder. But maybe it's worth noting, you also worked in the intelligence q me for a little bit. I say you come to this with a little .

bit backup yeah.

twenty years ago. So before you back out to may be policy and I let's start a talking about A I, which is kind of disrupting, I think, everything at the moment. I like to get worthy your perspective. Maybe my team can start with you unlike how you think of what ais are, relates to the intelligence community like where we're adding and .

probably where we're head A I has been around for a very long time, and I will say that even when I was part of the delicious community twenty years ago, we talk a lot about, if you have all of this information, how do you kind of detect signal? A lot of this was like very significant big data processing. And the of the more kind of advanced notions actually came out of the intellectual.

And a lot of what the intellect community deals with is things like intrigue. Hand covert comes at such. A lot of these ideas are just fundamentally tied with AI. And so I just think this a long standing history.

What's interesting to ask is, how does this kind of new generation ai world impact intelligence y agency intelligence in general? And one idea is like, well case, you can now make anybody be anybody and sound like anybody and look like anybody. And oh, that could be a huge problem, because now deep face could be a problem, and they not tell you.

My conclusion on a lot of this is IT actually turns out that if a computer generates something, the ability to kind of fingering print that isn't that difficult, it's actually not that hard, right? I actually think IT becomes much, much easier to detective. People are using A I and tooling actually is the reality.

But that also means that we can use them as tools. And then we've got to go much more to the fundamental. So in this kind of weird irony, like we got this set of new tools allows anybody to something like anybody, or be anybody, and that can be heavily, heavily used around the world. But like, for those are sufficiently sophisticated, it's can be quite possible to detect them. And I think that some ways this is of this nice cover in chaos for what our agencies and many agencies are very good at, which is kind of much more human focus, less than core technical approach to these intelligence problems.

I guess this is probably fair to say yes, like a has been for a long time, machine learning, even deep learning, back a decade to go about general ms. And then that sort of thing you're definitely do you think about IT like Martine might reference kind like the defensive about the offensive?

yes. And i'll just compliment what Martin said really, when you look at the two big functions that we have as an agency, right, we ve got the Operation side and we ve got the animal excite. And of course, this are composed in the different things, but doesn't really broadly the two bic function.

So what Martin absolutely nail is the Operational side of IT. It's spiers despite its captain mouse. It's all the usual stuff, right? The democratization ation of this stuff in the availability we know is gona drive each one of our competitors to be driving this up.

We're going to be aware of that. We're going to drive our own step hop. So there is the aspect of we don't know where this is going to go, but it's definitely not pulling back.

This is where the stops are off. However, the thing we always got to keep, at least we keep in mind we are a human intelligence Operation where a foreign intelligence Operation, where all source. So we have a particular focus within the eighteen intelligence agencies to focus on a particular thing.

So everything he said in terms of using or wielding or scaling this A I and Operations all is within the context of taking our case officers and Operations teams and making them successful, right? Because any good sort of team sports, we play a particular role on the field. And applying this tactics, scaling and making our teams basic, our folks more effective and win is basically the game.

Now on the analytics side, it's a complementary but a different problem. And this is where the big data and the other pieces come in is. To me, what's revolutionary about this is underlying AI is the promise and excitement always is the pattern, the ability to discover patterns in large amounts of data that typically humans can't see.

What's different now in the older days of when we were doing big data, Alyssa, and things I call this to pull model versus to push model, which is the analysts had to come up with creative stuff to think about first. And i'm going to put a clear in to go find the information. The problem is, is the conceptual boundary of an analyst to hit the query that hits the data.

IT was like best fishing, right? You're like going in and trying to get this one analytic idea out of this massive data. What's beautiful about this tech is now this stuff can actually push stuff, right? You can almost invert the process where a lot of the stuff gets pushed to you because IT starts to understand what you're looking for in some sense and starts taiLoring the stuff and gets deeper and deeper and deeper over time, right? This, the stuff you've driving to.

Now that has a evil twin problem to IT that i've been spending time on thinking about, just as IT really to our work, is what I call the sort of rabbit holding problem, which is the very thing that makes these products and technology so effective, which is learning about you and knowing these algorithms are built to please you, they want to make you happy. Well, IT can then also unfortunately, take the things that you, as an analyst, are weak st at, or it's your weak knee, or get into your head and start rabbit holding you down, this thing which amplifies your biases. So we've got to be very careful, very smart about where we apply IT, when and how module, all these particularly things that are out there.

how close are we actually mean? Maybe going back to the early days of talking with big data, like how personal like something will be or how easy it's gonna beat a sorts if I mean, are we still away away from like very much automating like maybe a CIA analysts.

So I think we're now understanding the kind of power limits of this technology, I think is a very important topic for us maybe prediction to make, which is will this change the existing equilibrium that is now an intelligence between kind of bike offence and defense. That a my deep belief is is exactly when he says, which is like IT doesn't change equilibrium in the sense that there is can be more link for reference, be more thing for defense.

This is no like academic superpower to, by the way, is very, very different in the internet. And this is why this is such a bad. And lock the internet was a metric, which is like the more capabilities you had with the internet, the more vulnerable you were.

Which is why, like when we were working on the terrorist threat, we're like, we're the most voltige nation in the world. And like many the people that we are focused on, I didn't even have laptops. It's nothing like that.

A I is something that anybody can use, doesn't change some fundamental equilibrium in this metric way. So that's the first entity. Your question is doesn't allow for personalization or doesn't allow for a kind of anything specific.

I think that IT doesn't create any imbaLance between two opposing side. So the second action that is, I think we're pretty clear about like even the individual limits of the C L S right now, which is they're very, very good for a few use cases. They're very, very good for like you ask one question to give you one response and if that question is in the corpus of training data, it'll give you a good response.

The problem is that that the question is out of the corpus of the training data. Like you don't really know if the responses good or not, which is fine if you're just asking one question because most humans will be able to check that, right? So it's very good of you like have like a copilot for analyst.

And you help them with their job. The thing that we've not seen any evidence of his agent's behavior by agent's behavior IT means like you ask one question that you step away, you go get coffee and IT does a own thing. And the reason is, is if IT generates anything out of distribution, like any way out of distribution means, is not commonly represented in the training set, then that errors going to crew and intends were crew exponentially provably right. And so I think right now we duties these new tools that you use side by side, but they don't become their own separate kind of entity.

Yeah and that absolutely brilliant point is this idea of the accrual of the probabilities, time probability probabilities, which keeps the illuminator when IT comes to my eleven year old drawing unicorns.

that's a future.

Not about no way. That's awesome. That's great. IT playing games, doing all kinds of crazy like that. amazing. When IT comes to analytic capabilities, when IT comes to Operations, we cannot have this level of uncertainty and not knowing explain ability.

You mean the piece that sort of interesting is I think that we're in such the sort of early stages of this game, right? So everything we're talking about here is like we were in thousand and ninety five years, two years. exactly.

We just happened and we were just arguing for that's like all of us, all four, three old foxes here sitting around the porch. This very well could be a porch with rocking chair and sitting around talking about the early days of the international. We could not have possibly imagine this stuff that happened past years.

So at the agency we we were doing is saying, great, there's a whole bunch of these basic use cases that are just there's no question this stuff can get applied. And by the way, we're all in on IT, right? So I didn't never wanted to give you the impression that this is something that we're slow rolling ahead or thinking IT.

But to Martin's point, the applications on a per use basis, we have to think them through. This is not peut Better, that you can just spread everywhere and you get goodness everywhere with no thinking. So for this is where public with the fact that we actually have allies in production at the agency, we have an in production in the open source team.

So those easy use cases, business automation, other pieces were experimenting. We're trying. We're doing stuff. now. The copilot piece, the way I view IT, is typically people jump immediately to the hardest of hard problems and say, where's going to go replaces were going to go to do this that but we're chAllenging everyone to do inside the agency though, is it's one thing to like look at the low hanging fruit that that stuff automated, get the value.

The thing that's most interesting though is we're chAllenging folks to say, stop thinking about automating, getting a ten percent, twenty percent, thirty percent on your job. Tell me in five to ten years, how are you gonna imagine your job. Now here's where things get tRicky because many of the people who are talented to reimagine their job are looking at the technology and learning IT right for the first time and understand the power of IT.

I'm still I mean, all of us still very far away from knowing where it's going to go. So it's really hard for them to imagine this prototype thing that's like still playing around how's you gonna impact? Rethink my job. So this is where we're pushing and experimenting and encouraging everyone try stuff, learn stuff, get up the experience curve. But we're not going to settle on an answer because that's not going to just appear magically off of that.

So i'd read something recently. I was based CIA analyst, imagining their job. I don't know what the time frame was like five or ten years out and and I was very much what you describe, right like into a probe to go a cough up. And nobody I did the day we've arrested someone in france for something.

That's how people it's like. We took the kids to disney world and go on the epcot ride, which was the right of the future. IT was from the nineteen and fifties or sixties imagining what the world would be like in twenty twenty four. And it's like a hand held phone with a video mother like, okay, so it's one of these things which is is really hard to see where the stuffs go again.

I get get noting that it's early. I mean, realistically, is this a thing where analysts just did have different skill sets going forward? They have different .

tools that there are disposed SE. Well, I think we can say something really specific and there a Better stuff. But here's the thing is that the large the work is, they got a whole bunch of data and then they basically have a distribution.

You know, how come the data was represented? That's what they do, right? They do basically with called kernel l smoothing over a positional embedding, which is just like averaging a bunch of words.

So average is a bunch of words, and then for any time that you ask you a question and kind of gives you like the most common outcome. So what does this mean? This means for mean things you want to do for the everything you want to would be very good to getting your answers.

So for any kind of standard rote thing you want to do is going to give you an answer. The problem is, is if you wants to do something in the tail or in something that's new or an exception, IT doesn't know how to do that, that there's no mechanism within IT that will do that. And so much of intelligence work, I would argue, is actually in the tail, right? I mean, it's like these are the problems the intel is agcy is particularly good at.

And so I think we can believe that there's a world with every will have strapped on an another, and that will help them with the routine stuff. But like so much of the job is tail reasoning, reasoning in the tail that this is not going to remove the humans. And by the way, the intelligence community, I think this is most work, but I think is particularly in cuti OS.

yeah. And I bring that up into the first species. Does the technology replace the enlist? The second piece that marching talked about is the copilot model, which is I do my work and I have a little wing person that helps me with all the routine staff for scaling.

But it's exactly this point, which is it's not the creation of new information from old information, like human beings uniquely create new things, new information. It's unclear whether these systems actually produced new information or new thought. It's just finding IT or or routine.

Zing IT. The third one is what I call this sort crazy drunk friend problem, which is the illustrating, which has a role in some disciplines, right, making new art, poetry. And in the analyst function.

Is this point of finding that point on the distribution if the average policymaker could think through the average use case? And what the role of the analyst the role of the analyst is to have this holistic piece of thinking through probabilities? I mean, it's kind of what an a program would do to some extent of you start modulating where on the sort of distribution you want to go.

That was a great explanation, I think, is exactly. So you imagine you're an analyst and imagine if you get paired up with some person has been in the agency for like forty years. So this person doesn't know new technology IT just knows when everybody's done a whole bunch.

Is that the entire perspective? Of course, not like you have to evolve, but it's a very important perspective. So alan, ams are very good at being that old person.

They're very good at being like, well, this is how we ve done in in the past. Here's a recognition ation. That's why we have people to make a decision.

I do I want to do something new, something that's in the tail or go on to listen to this person has spent around for a very long time. So it's a very concrete kind of mental book in for doing things. But the majority of the value, which is the new stuff, will still remain with the person.

One interesting thing for me has been having spent twenty five years and valley done about to startups. And now being on the government side is a lot of the tech discussion around this is about the possibilities and all the great stuff in creation site, which is awesome innovation, invention, getting great people to do awesome stuff. But you have to flip over to the bright side of technology.

This is my second run. Pentagon was two and half years, and now CIA of being on the byline of technology and seeing all the stuff happening. I just actually took a red.

And for california, and they like over there at all of our possibilities and hear it's about how do we take the job, the function that we have to Operate in with all the constrains and things which are, by the way, not constraints that are artificially imposed. I mean, the co office said the agency, right next doors where the P. D, B gets made for the president.

And so by the time something hits that rush hold of getting into the presidential daily brief, if you can imagine the level of scrutiny, analysis and focus on our analysts put into this work. So it's funny because like the excitement and hype about the technology versus us, absorb IT and making a battle ready is a long, long distance. And so hopefully portraying or representing that side of the equation, which IT doesn't happen rapidly, right? That takes a long time for folks in their own disciplines and life and careers to understand what's the actual impact. The absorption of the technology does take a long time, and for IT to disrupt a particular individual .

or an individual discipline. Very similar, but modestly different, which is the following. And I was an ops, and we would have missions and stuff to do, and they would have very specific requirements.

And the piece of technology that came from the private sector just were not suited, and in fact, little, no story. So my P. H.

D. Work was in software to find that working that was before I was done in harder was too tough to program. Like honestly, the insight for that work all came from my time working with the intelligence agencies.

We had to build a network that like deal with certain things. And I actually came from the computing side of the world. I like these things aren't programmable.

And to do what we need to do here, I have to program them because cisco just doesn't know what we need to do. So there is also a foot tight everything. You, I I total agree. IT takes a long time to be adopted.

but also your requirements and need are a i've been a huge soft find networking fan for a very long time, but I stole IT for a paper that I code with the shinhan. Called and warfare.

I love IT, but yeah basically this idea like how do you actually take something that's on a hardware carbon and pushed onto a software curve? How do you do this programme sector? And so the question we've been asking inside is what to suffer to find the intelligence look like, right? So what's the next level of like stuff where to point? It's maybe more push versus pull. It's the idea of going across that distribution curve and starting to understand one other piece that's been sort of right in the ddd of this whole thing is a whole policy making debate inside.

One key point I wanted to make us, even in this analytic function, the work that each of us do has encoded in IT the policies and outlines of what we have to do as part of a job function, and call this code is law, which is that when you look at the applications that you probably use at the agency and that we use their, we encode all those rules, regulations inside of IT. And I called this a thresholding problem to some extent, which is inside the line of code in our application. There is something that says, if probability of x happens beyond this and do this first of that, right? We have lines and lines, and millions of lines of code has those.

If statements in there now is interesting, because what that means is we've implicated taking human decisions that a programmer or a policy maker made in code. Now with this new sort of A I B systems about the previous sort of supervised learning and unsupervised, and now with these new algorithms, these are still probably stic algorithms, except now the probabilities actually scare you in the face in a way that previous systems didn't push, right? So previous application systems never came up with said, do you want to forty nine percent? The sixty nine percent answer.

And now you decide whether sixty nine is high enough or not, right? IT would basically would encoded and say, great, there's an arbitrary fifty and anything can converse below. Now why this is becoming such a debate is because the probabilities are now surfaced to the user s face.

And if they aren't, we have to train people to start thinking about when the system punches out a number, how do you make a decision on a probability? So I think that that's the big different stream before. And now that we're having to retrain everybody and why is to become a policy issue, all of the son, it's because policymakers now have a nob where we now have to decide explicit this or that. So to me, it's actually we're in a kind of the same world, but just more an explicit world before .

we know that policy. Specifically, is there a sensor like, I mean, the advent of open source and that just a general acceptance of open source now is machine in A I and also other emerging tech? Is that is the adoption of tech.

right? Because in an intel we have the open source intelligence in terms of like you .

anna dpp something, right? So that's commerce. Technology not have to power what you needed to be able to to remake in your own image and and get something that actually functional for you.

I would love to hear that, that I actually have.

I've got a historic perspective on this, but I like to hear your current perspective.

So the thing would chAllenged with just this whole landscape right now is each of the companies is offering really particular LLM into me. It's turning into sort of like different types of line or different variables. And i'm not a wine snob or know why that well, but i'm imagining IT has something, the lineage and the data excluded.

This one was grown in the hills of more indeed. And this one comes from this of you think it's all nonsense. 你好， 对， right? So you're getting these out of apps with these different ages and different vintages and different data set a and each of these systems are going to behave very differently over time right now.

The question is the going big problem of like, well, everybody's going to train on the whole internet, so everything is going to look the same, right? This worth of that. But I think there's a second question that we're having to do is and this where the open source question comes in is the ability to start with your own data.

And the question becomes, do I take up base algorithm or system that somebody has built? That, by the way, has injured ted, the entire internet, which both good and garbage that has been injured ted in. And now I use that as a base platform, and I may have a certain set of biases that have brought along with the garbage and sessions internet is.

And all of a sudden now i'm using that is my base, or do I want my pristine, handcrafted, sort of made in the spoke fashion thing? That's where the availability of these allegorist ms becomes really interesting. However, IT has the opposite problem of IT doesn't have the improved or in the stamp of a large company that has the experience building large office systems and training and verification other pieces. So we then have to sort of no one understand the stuff ourselves and do all that work. So I think that's the trade off that we're dealing with.

Yeah, yeah, yeah. That makes a toner sense. Here's a bit of a historical prospect on this, which is somewhere to what I touch time before, which is in my experience, the government, the intelligence agencies have to solve problems that market forces don't really solve for.

In order to do that, there has been some sort of the flexibility program. Mably and open sources always been a key complaints of this, right? I mean, quite famously, esc linux came out of the na, and they use linux to do this because they are required.

And like there's been a number of training to algorithms like crypto, right? I don't remember the details, but remember that the na was like portrait tic linear programing will break this to go ahead, do this kind of change in the algorithms. M, then it's Better.

I me. So these are types of things that have been coming out of intel gent for quite a while now. I remember twenty years ago when I was in the depth working on one of these problems.

And there's no, no time are there and he goes, music man, he's a tackle time. He's, I get so great, you have the open source because we can work with that. He says. I remember in the time when all we get with supercomputers and then come out of IBM or come out of the I come out of case or whatever IT is, we buy one of everything, but we only got what the vendors created and something that I do think about IT perspective and which is it's one thing to open source weight and basis, that's one thing. But these are the largest compute projects that mankind has ever done before that we ve never done anything close to this. And so even if the wait and biases are open source, I don't know how much you can modify IT, right? That almost feels like we're going back to this old main frame day where it's great to have IT and you can Operationalize that, which you're not going to have the same level of flexibility is you have with our traditional software.

At least this would be my absolutely anybody is ability to do something with that information is limited because it's not just the numbers you have to have, the expertise need to compute. You need all the other dc hundred. great.

I have that information. I just don't have the computer cycles to be able to do anything with or modified or change. You do the right IT will help you with verification and will help a training, testing and help with all that.

That's fine. But you're told the right to going back into this. Most of the government is the hundred. The current using them in is a long history of abbott. A very different.

The ball game is completely different. The other piece of with the open source that we have to deal with a lot, by the way, is the supply change issues, right? And this is a whole new level of paranoia that to have working in a spy agency is it's one thing for your program to not run or some e commerce customer having a bug or something. These issues are still, again, also really, really important for us.

So what does that look like that if you get back, if you make the supercomputer and logy right, like what is the new that, say, public private dcc on valley partnership look like in terms of actually implementing and Operation alizon A I S. And a?

I'll take a show that because it's something that were, well, I think the U. S. Government is thinking about. This is a large is when you go talk to universities right now, one point they make us, they don't have the computer power to be able to rival totally microsoft and opening I in these companies, which is mind boggling, right? Because to your point, the supercomputer systems and everything.

I when I was at coral, we remember we had a supercomputing site on campus with money that the government had put in place to have these supercomputing centers, right? Illini had one cornet seta, and we don't have that equal. So now the national science foundation, I think some of the money the government allocated, i'm assuming, is going towards building these large scale or not. So we're not a posting your organizations .

trying to keep people .

building them. Well, we shot. I think there needs to be this model of IT has to come out from away from just corporate organizations doing this to you know, and we're part of government. So it's a different thing. But the question is, is whether this needs to get sort open up and bigger way.

So the thing that brought into the intelligence community, as I was doing computation physics on supercomputers and a national lab in the weapons program, when nine eleven happened, right? And they're like, you have all the clearances, you need to move to this kind of new area and they move me to the and committed.

I learned a bunch of stuff that way, I will say the work that we did on the superb computers, the government was the best IT created entire new disciplines of scientists and career professionals in universities. IT leaned totally into this. And that's why we maintained the literary position in the world to that in computer.

And we still do. I do think that it's a risk that we don't take the same attitude. Listen, i'm a VC here and i'm saying I don't think the private market to solve everything.

I do believe in public private partnerships. I do believe in institutions. I do believe that the government has a big role to play here, but I think that role to play is investing heavily in people and tech and careers and reaching out.

And my fear is that they're kind of doing the opposite. Where back in the night, this is exactly what you said. I mean, I want to like northern is on the university small mountain school.

And we had asking programs where they would come out invest in us. And I just feel that now, though, we have this new technologies is very powerful and we are eternal from the united states. Instead, we're kind of pulling back from that. And so I do think that this is a moment of I think the use has to kind of take pause and understand, are we undergoing a doctor change where when new technologies can run away from instead towards and you know, I think it's a real .

conry yeah does seem like that the maybe there was a shift at some point to maybe is the interview and not sure where like the supercomputer days. Yes, you bought a system .

from IBM or yes.

but was a hardware make right running some very special affery today? Yes, everything comes out of these huge companies that have access to all the data and all the computing power. And like I don't know that how that affected like the the powerful ft, but you sent a way to bridge yeah I mean.

so here's the other side of the argument, which is the very dynamics that have let us to this point of creating these algorithms and these systems, these breakthroughs. There's also hundreds of companies that you and other VS are funding hacking away the problem to make these things available, right?

There's huge amounts we're calling on in AI specific chip sets on the training side and the inference inside a whole bunch of algorithm changes that are going to happen. That reactor, these algorithms, ms, to do Better job in terms of scaling and being able to shrink them without a dramatic loss of performance. So again, we're such in the early stages in innings of this game that we don't know what the next five years gna bring.

But for sure, you've got thousands of really, really smart people hacking away of the problem that I think we'll come to some medium where, yes, hopefully the government or maybe the government funds or academia a ends up with these large compute places to be able to rival. And at the same time, the availability of hard work, modernization and other pieces will get to a point where we'll be able to run all kinds of interesting algorithms at scale with really cheap, readily available hardware, right? So that's a sort technical optimist aspect of IT, which is, as they say, life finds away, which there's demand for IT people will supply IT.

yeah. I just the question is, will the private market to solve the problems needed further things like global defense or like national security like that? Just historical. The government has played a role in innovation, in training. I mean, think about like nuclear engineers like a of this actually came up from government programs.

So this is a slightly different topic. Is this idea of what are we doing in government to do a Better job of working with industry, right? A large portion of this job that I have is this idea, this new idea of, well as american dynamism.

It's this idea of silk value leaning in and the government having to lean in together for us to meet in the middle to be a Better supplier and a great customer. So in my role at the agency, one of the big areas of focus for us is how do we become a dramatically different customer. I spent two hundred and half years of the pentagon, which was its own gigantic problem in terms of that's where offered find warfare ideas came.

And in all, want to see we don't do a great job in certain things where we could be world class or Better. And we are working really, really hard to change that. So for instance, as we point out inside many cases, there is no ready APP store for spice software.

So there are absolutely certain things that we need to build and right inside the agency, that's very specific. It's also our competitive advantage, right, which is we're not going to be buying this stuff that's readily available for everyone. We have a secret sauce.

We build IT. It's our competitive advance. However, what we don't do sometimes is analyzed that we take that too far, which is their stuff that's readily available outside from commercial land that we don't think about buying, deploying and implementing at scale.

And in the past year and a half, we have actually spent a lot of i've personally spent a lot of time focusing on what I call commercial first strike. Is this idea that we need to be rethinking our strategy that if something is available on the outside, how to bring in in, however, we have procured proceed. We have E, T, O, proceed.

We have security proceed that don't lend themselves well for rapid acquisition in pieces. So we're trying to hack away at those on top of that. The other issue is that the security needs and requirements to run the stuff on the high side is very expensive and for commercial vendors to provide and go through that process is expensive and an investment.

And so we have to create incentive structures to be able bringing them. And so it's not as simply we can win IT into existence, but it's a systemic problem that we're trying to attack and hack away at. There are certain things that have been big breakthrough inside the agency over the past year, and I can say probably can tell you what those are, but we've made huge, huge progress in rethinking in other ways there.

And as an agency, there is a number of cultural shifts that we're going through internally, right? So first is the human tech. right? Is this idea that as a human intelligence organization is very interesting because of the changes in tech outside that are case officers and we Operate.

And right, we're publicly talk about what we call ubiquity, technical surveilLance, uts video cameras, biometric and set a. So we, as a spy agency, hate tech went, has applied against us. But we also real IT, right? So it's that aspect.

The other interesting cultural thing in the agency that's been fascinating is the power of the individual, which is we train individuals to go do heroic efforts and things, which that's our job, right, that's the agencies job, is go into foreign countries. And five, however, the tech aspects of is putting in the however, the other aspect of of the thing is, is that applying tech, what is the big change in tech that we've seen over the past couple of decades? scale.

And so this idea of the individual versus scale is a cultural thing that we're trying to rationalized, right IT. Applying enterprise large scale tech to an organization that teaches individuals to basically have agency to go do things. So that's a very interesting one, the short term versus long term, which is the idea of us being in the agency that's ready to go at a moments notice, which the agency does incredibly well.

But again, to do large scale enterprise wide technology transformation and things takes time. It's the open versus sort of clandestine, which is as an agency are folks aren't not trained to be out there in public. And director burnses mean the suspect priority in terms of engagement with the outside world, engagement with the technology industry, which happens out in the open right, the idea of Carrying a business card and being present and being on podcast.

These are all culturally new things for the agency to so ourselves are going through this huge, huge transform, dealing with attack. How do we actually change the thinking in this new world? And then the AI stuff on top of IT, which then is another lare of complexity in terms of changing how we Operate, what we do in the discipline. So it's a very interesting, exciting but also somewhat confusing and transformational time for the agency.

Do you think that given american diagonals m is having a moment now, dc and silk balis going to be talking more than ever that certainly not into early days, but like people are talking like you guys both have both .

intel and software experience.

you think that starts to help maybe ease some of the friction in terms of the enough, whether it's making procurement process is fast or making adoption little easier, making these .

hacking and stuff? So maybe I would be to be took us home, which is the issues you just talked about have been around for a very long time. And I don't think that's the hired a bit right.

Like, of course, we can make Better. Of course, make communication Better. Of course, we can have Better public of partnership.

I remember talking about this two thousands and nine sea. Here's what I think is the most important. And you eluted IT to IT before.

And I think it's so important, which is the internet caught the U. S. Flat footed a bit. There was this no sense of a symmetry. IT ended up having exponential effects because there are so much connectivity. And so when I came out, IT took us a while to come to grips s with IT.

And what I fear, my biggest fear is with A I people are fighting the last war, and it's to our detriment, which is a lot of things people are concerned about with A I or really internet things, and we've kind of got on top of. So you can take that money. You can take this kind of investing in this stuff is bad because the assets tric.

You can take the mindset of like this is inherently exponential. You can take the mindset of this is core critical infrastructure because it's just very, very different. This is a new type of technology.

It's as useful for us for doing good as IT is for changing the third environment, which I actually don't think you change the threat environment lot. And so I think that both the government and as an industry need to come together and ignored is of a new technology that's beneficial. And then we're Better learning about IT than running away from me.

And we can take these old lessons from the internet and somehow kind of route apply them because then we're gna miss the train. And so for me, this is kind of like that, have been the most important thing. And then a lot of things you talk about are important, but people kind of follow. Of course.

i'm not sure i'm to top, and I do actually have to be careful in the sense that again, sort of the big disclaimer patch on this is CIA means you we're not a policy making shop. Our job is to support policymakers with very objective by the book analytics support on the questions that they're asking. What a Martine, I think, just outlined is exactly the policy making debate.

Discussion going on is this idea of how do you regulate versus incentivize, right? Because I think the thing is, is that what happened with things like privacy and security and other things has impacted consumers and therefore impact lawmakers. And so we got pulled into lean and and now whatever is happening in that area, right? The issue around five g was a big national security concern all of a sudden.

Then all of us now the chips act in terms same conductors. Look at what happened in crypto and now AI. So i'm listening them because at C, I, we track, you can list five, ten, fifteen.

There's all these emerging tech areas that we follow now and we've got we're leading to main experts that follow each merging test area because again, there's demand and support from downtown on those questions. okay. So I listed what four and then the fifty one is privacy security regulation around social media at so these five areas of technology.

Now there's a spotlight on them to martis point where lands and ends up that's purely a policymaker domain. Our job, though that's tRicky. And many of these situations is that these are, by definition, emerging tech areas.

I mean, five g and seventy conductors are scaled industries, but the rest of these industries are emergent industries. And emerging industries is having been in the industry, he in startups, we don't know where the sus going to land. And so all of a sudden becomes really hard to understand who to talk to, who to believe.

Do we forecast do we not forecast where we think is gonna? And there no solid answers to any of these questions because every six months is going to be get something new. And so how do you build policy making on top of emerging tech areas? That is an art.

And I mean, again, it's up to lawmakers and policymakers to figure this sound, which is where then you end up with things like, for instance, executive orders rather than loss, right? It's very interesting how the policy making apparatus works where you end up with hundred pages of executive order stuff that outlines generally some ideas and thoughts and questions. And I think there's gonna this leaning in and convergence that happens between industry and regulators and stuff because this helps moving.

The tech is moving. Policymakers are learning more. They learn more. They ask more questions. The tech industry moves this way. So it's an iterative long term process, but it's up to the players, including the folks with the money in the investors having seated the table to play this. The good news is the agencies playing very much where friends with everyone, we talk to everybody, that's our job, gather a lot of intelligence, analyzed IT, hopefully with the eye, and then how bar policy makers. So very .

much appreciate. What do you do? Thank you for that.

Thank you.

Now, if you have made of this far, don't forget that you can get an inside look, a six senses american dynastic summat, a six sense e docs flash A D summit. There you can catch several of the exclusive stage talks each and policymakers activity, secretory of fence casting hix or governor west mall of marland, plus the founders from companies like android and cobs and founders like Marvin, all building toward american dynamism. Again, you can find all the above at a 76E点 com flash eighty summer， and one could a link in the show notes.