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Big Technology Podcast
The Pentagon's AI Plan + Behind the Anthropic Fight — With Under Secretary of War Emil Michael
00:59:58
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- [00:00]
A
I worry about other countries using AI to take humans out of the decision making progress. They don't trust their generals.
- [00:06]
B
If you were so close to being willing to work with them, then how could they end up being a supply chain risk?
- [00:13]
A
It's just we don't want them in our supply chain. We don't want to use them. Yeah, President decided that he doesn't want the government to use them. If I went back to my office right now, it's like, how would I order a pizza from outside to be delivered in? I'd have no idea.
- [00:25]
B
So you're not a believer in the Pentagon pizza?
- [00:27]
A
I'm not a believer in the Pentagon pizza index.
- [00:29]
B
We're here at the Pentagon because the AI story that we talk about on this show has escalated quickly, very quickly, into a core national security issue. And you saw that, of course, when the Pentagon banned anthropic earlier this year. So let's talk about it with Undersecretary of War Emile Michael and speak with him about how AI might change the future of warfare and how it might already be doing so. Mr. Undersecretary, welcome to the show.
- [00:55]
A
Thanks for having me.
- [00:57]
B
So AI's capabilities are increasing exceptionally fast, and you're the man tasked with implementing them at the Pentagon. So I want to know from you, how is AI going to change war? How do you hope it will change war?
- [01:09]
A
I think one of the analogies I like to draw is having been at Uber and you look at an autonomous vehicle and people were scared of Uber from taxis, and then they were scared of autonomous vehicles from Uber. But in reality, if you look at FSD from, from Tesla or even Waymo, the safety statistics are amazing.
- [01:29]
B
Self driving.
- [01:30]
A
Yeah. And it's like people are afraid of the change, but the change is better than what we had. The same thing with Uber, people were afraid of the change from taxis, but it made service more reliable. There was less drinking and driving, more availability, more reliability. So if you would apply that to the war context, you could do much more, be more precise, be more specific about what you're going after, what you're defending, how you, you know, and the precision is really what's interesting to me because if you can use AI to detect and discriminate and discriminate, I mean, discern a decoy from a non decoy, you could be more precise. And the example I always give is like a drone swarms coming out of military base. You try and determine are they armed, are they not armed? What are these things? How do I deal with them? Well, some of the visualization. These models can help you do a better job of taking them down or not taking them down because they're not a threat. Where one human can't really absorb multiple hundreds of inputs at the same time and make a reaction that's as precise.
- [02:40]
B
Yeah, I want to make this concrete for folks. And recently the public has been lucky because in a world where sometimes we, we don't get the most transparency into how this technology works. We did get a demo and this came from Cameron Stanley, the Department of War's chief digital and AI officer. And he showed what a program called Maven Smart System, which is the Pentagon's core tech platform, looks like. And I'm pretty sure it was called Target Workbench. And this is where they select targets and then end up going and sending. Seems like they use the word action for them. My understanding is they end up going and trying to. And sending the attacks to these targets through the system. So the way he described it is it's this single unified visualization that allows you to look at live images and then be able to select targets.
- [03:31]
A
Well, that. And then imagine the context around that. Where are my assets? Where are my planes, my boats? What are. What might happen if you took that action? What might be the reaction? Subsuming all that information but still having a human make the decision at the end means that you're increasing the human context window is one way to think about it. When you talk about context windows and AI. Well, think about a human that's trying to absorb all this information and make the best decision they can. If you could synthesize that information so they can make that decision and you're using more sources, by definition, almost the data and choices are going to be better choices.
- [04:10]
B
Yeah. And he showed it in action. You're seeing this data overlaid on this map. And then he says when you find something that you want to target and you'll see the information, he says it's very interesting. He says left click, right click, left click. And then it ends up in a targeting workflow. What's happening in those clicks?
- [04:29]
A
I mean, what's happening in those clicks? Without knowing exactly. I mean, it could be everything from if it's an error, let's say it's an error oriented thing. There's an F35. It could be. What's the weather? What's the drag? What do I have on board the airplane? What am I going after? Where is the collateral potential? Collateral effects? I mean, it's. Again, it's less. Give me one Example of how that might work and more just imagining how much input you could have into that decision when you have a computer basically able to gather that information, help you synthesize so that you can make the right choice.
- [05:08]
B
Yeah, it's interesting. He shows that there are toggles that the military can select whether it's an optimization for how much fuel you want to burn, what munitions you want to use, the distance that you need to travel to hit the target, and then
- [05:22]
A
you can optimize fuel, weather, where are other assets that the adversary might have and where and how might they react? Just the amount of information that you could absorb is almost infinite. So the idea of taking one person and giving them the power of 10 people makes them better at what they do by potentially an order of magnitude.
- [05:47]
B
Yeah. And then within there, once that's in the workflow, the last step is whoever's looking at it can, assuming they have the permissions, can action on that target, which means sends the assets which they're doing anyway.
- [06:00]
A
So you have people whose job it is to do this. So that already without any computers, it could be with paper and pen, it could be with whiteboards, it could be with PowerPoints. And now you're accelerating that and giving this person the power of more tools so that when they do do the right click, left click, right click or left click, right click, left click. Some of those clicks, they're way more informed. And then you're going to lead to. You're going to lead to better outcomes.
- [06:26]
B
Right. And it is interesting to see what's happened with this digitalization. Whereas before this is from pirate wires, they say by the start of the conflict with Iran this year, targeting processes were connected with PowerPoint, email and Excel files. I'm paraphrasing. Target lists were relayed in spreadsheets, sequenced maneuvers sat in Gantt charts and PowerPoint.
- [06:50]
A
I mean, that's probably the case historically because when did AI models start to become generally available and then to consumers, we're the ChatGPT moment in 22. And then you say, when was it available to enterprises? And then when was available to government on the networks that government uses for war fighting? And you're talking about a fairly recent phenomenon where these tools are even available. And then we have to. We went through protocols, safety testing, the modeling and simulation for how would you use this in a complex. There's a lot that leads up to actually using it in a way that we feel responsible for.
- [07:31]
B
What's interesting, I don't see an LLM in there are large Language models or today's generative AI layers baked in that system.
- [07:38]
A
Yeah, I think the genesis of what Palantir does is an orchestration layer on top of data streams, data that we put in it and say, here's the data we would normally use for any battlefield operation, plus an AI to help you synthesize it. So all those things are combined and they provide the visualization, but there's not
- [08:02]
B
like a chatbot on the side window which is like lay out a list of targets that I want to hit here. My objective is to win this war. What are my targets?
- [08:13]
A
It's not a Skynet thing. No, it is a tool like any other tool that you might have on your computer or in your war room or with your team, except it's on your computer visualized. But you still have checks and balances. You still have to get all the authorities you need to do anything. It surfaces the choices in a way that's more consumable, if that makes sense.
- [08:43]
B
Right. And it's good to have this discussion because I think as this is a fast moving technology, it's good to be able to talk about it so everybody understands how this works. And I think that this is again going through like some of the what's actually happening versus misconceptions. There's been some talk and we're gonna get into the anthropic situation in the middle in a bit. But just to talk specifically about what an LLM can do in this process, there's been talk that like the LLM was involved in the kill chain and you know, but that is not exactly what the LLM has been doing.
- [09:20]
A
So people have, I think, like, let's talk about the extremes. And I talk about this in the way we're deploying AI in the department, there's the enterprise, corporate level. Like tons of PowerPoints are generated in this building. Memos you couldn't imagine. It's like nothing you've seen in the corporate world. And that could all be made more efficient. And that's sort of the mundane work that people would prefer to do less of. So they get some more interesting work. Then there's the intelligence layer, which is. Imagine all the intelligence we gather from satellite imagery all over the world. How do you synthesize that? So right now you have to have a human analyst look at everything and make a judgment. Imagine you had the historical data of all satellite imagery. Then you could look at it and say, this is an anomaly and I can learn what it was so it could tell you what the Anomaly detection might be, which is a totally different paradigm for intel. Intel analysis, if you will. And then third is for war fighting, where it could take all the paperwork and modeling and simulation, all those things, not only be able to have you react faster, but react in a more precise way. And those are kind of some more tangible ways of AI. And that's why I think if people understood that better, particularly in Silicon Valley, they say, oh, that makes sense, like any big company would do, or any big organization efficiency. How do you be strategic about what you're doing and allow more analysis and then you know how to use it to execute on whatever operation you have in front of you?
- [10:52]
B
Yeah, and this is. I mean, a big reason why we're here is I wanted to speak with you because I read so many stories and they didn't comport with what I was hearing from people close to what was happening. And I thought, let's lay it flat, clear the air.
- [11:05]
A
That's the way we say here in the department.
- [11:07]
B
That's right. So just to confirm the LLMs, what they're doing is they're summarizing different reports,
- [11:15]
A
synthesizing, interpreting, you know, taking in different forms of data and giving you alternatives. Okay. And most of these are very mundane because, again, you have to imagine that every single thing that the military does is. Has to be audited, has to have the right command, command and control structure, like who's authorized this? And that hasn't been checked through the legal system or has it comply with all our memo or internal memos about ethics and sort of the laws that we follow in conflict. And that doesn't change. It's just the tools to do that, make that better and easier, if that makes sense.
- [11:58]
B
Now, there's an argument among those who watch this tech in action that sometimes a little friction is better. Right. Like, that was the one thing that made me feel somewhat uneasy when I looked at this smart maven Smart system demo is like, maybe we want the Excel spreadsheets and the word docs and the PowerPoints. When it comes to something as serious as making a decision to attack a target, like, maybe you don't want to make it that easy, because the easier you make it, the easier it is just to hit action and send it away.
- [12:32]
A
The friction's there regardless. Again, this is a key point. You have the same rules of engagement, the same approval system. What you now have is better aggregation and synthesis of the data that you would already use to make that decision. So it's partially about speed, but it's more about more data points. Right. So if you think about it as we're taking as many data points as we can to make a better decision, yes, it's going to be faster if you were going to go hunt and peck for all those data points. But that makes. There's no military in the world that doesn't believe in speed. So that's sort of speed wins the game. Look at what happened in Venezuela. The speed at which that execution of that operation happened meant that we didn't have any casualties on our side. That's amazing. If you had to spend way more time, you weren't able to synthesize information as well as one could, maybe you had to be there for 48 hours instead of three hours. So you think about that. Speed has to be one of our prerogatives, but better information is the goal so that the decisions are more precise and, and more consistent with the operational objective we've got.
- [13:53]
B
Is there a limit to what this can do for you? I mean, I'm thinking in the context of the war with Iran, obviously there have been many airstrikes, lots of them, quite precise. An entire echelon of Iranian leadership taken out. But the IRGC is still in control. There's a new Ayatollah with the same last name. So isn't there a limit?
- [14:16]
A
Yeah, there's a limit. I mean, no one, I don't believe that there is some all seeing, all knowing answer to human conflict which has been happening since humans existed. I think that ultimately what you want is clear objectives. You need the manpower and machinery to do it and you want to do it with the least cost, with the least amount of damage and the quickest time. Right. That's the goal. And I don't think, you know, AI or really any technology is sort of the, you know, becomes the answer. It's just one of the tools.
- [15:00]
B
Yeah, and that's sort of one of the fundamental questions here is does AI just become something that is a speed up is a friction remover or can it fundamentally change war?
- [15:12]
A
I mean, I don't think, I don't worry about that from our side because I believe the way the United States has structured our command and control is you have a Commander in Chief in the Constitution, he appoints a Secretary of War who's confirmed by the Senate and you have the generals and all their ranks. So all the procedures to make sure that decisions we're making all are the result of a democratically elected leader and a Congress that finances these things. I worry about other countries who don't have that. Using AI to take humans out of the decision making progress. They don't trust their generals because of graft, because of, because they don't have the expertise. And they start to use machines in place of humans as opposed to using machines to augment humans. Humans. So that's more of a worry for me. And I think that one of the things I've tried to explain to some of these companies is think about the alternative. What would an adversary want to do with AI that we wouldn't because it's not consistent with our values and we have a chain of command, a constitutional government. If another government doesn't and wants to use AI to eliminate risk, human risk, looking to augment human capability, it's a totally different way of thinking.
- [16:34]
B
Which governments are you referencing?
- [16:36]
A
I mean, I think if you think about the biggest military buildup in world history in China, and you think of seeing the purge of the generals and sort of the military hierarchy there, you start to wonder, well, how do you replace all these people? What is the command and control? What would your a high strategy be if you're running that country relative to ours? It's just a different mindset.
- [17:01]
B
And so the uses that we've talked about right now are largely, when we talk about LLMs in this world, largely they're chatbot uses, or I put them in the chatbot bucket. Right? You have information, you synthesize the information, you get something that saves you time to make a decision. But now the AI industry is moving towards agents, which is like the word connotes, letting the AI take some action for you. Do you have a plan for agents here? Is that where this goes?
- [17:34]
A
I think that. Not for things that require human judgment, no. I mean, again, you have to have an endpoint where it ends with human oversight and human discretion on the most consequential decisions. Right. But you could imagine scenarios like I described with a drum swarm coming in at a military base at night. And how do you, how do you deal with that? But again, that's not an agent use case per se. That's like a visual discrimination or discernment use case. And maybe you have a directed energy laser that could take them down. And it's a lot cheaper than the alternative, a lot safer, a lot less collateral damage. But in terms of agents, we've have some agent pilots at our enterprise level. Remember I was talking about the enterprise corporate level. Just to do the mundane things we have to do every day. But those things are not sort of where we're at at the war fighting level.
- [18:28]
B
Okay, so if I'm hearing you right, basically the plan here is not to automate warfare. No, but the question is here, if you have your adversary, who's doing that? Let's say you're in a direct conflict. I mean, maybe it won't be China. Can you really afford to sit still and do it by the book? Because that's the worry, right. Is that these capabilities are out there, they're integrated, and it becomes tempting to like go into let's say a maven smart system and say LM is getting me 99% of the way there. Just finish it off.
- [18:59]
A
No, and I'll tell you why. It's not that I'm advocating for that. I understand. I'll tell you why. It's like number one, that's the reason that us has to be AI dominant so we're never faced in a position where the counterforce AI is better than our AI, and therefore we have to face those choices at all secondarily. People confuse automation with some sort of automated army. Right. And automation, just as I described you in the drone example, what about an automated mine sweeping or mine detection operation? There's no human underwater. Do you want to find the mines? There's no human involved at all. But there's an action you want to take to do that? Well, everyone would say like, well, we don't want mines on our shores. Sounds like a good idea. Or there's a missile coming at you and you want to take it down from space like golden dome, like we've talked about. How do you do that? Right. You have to do that in 90 seconds from when it's launched. So those kinds of things, in the most extreme circumstances, you want humans to be able to rely on some automation capabilities. But in terms of mobilizing a whole army or whole fleet of jets or whole fleet of suites, that's not in anyone's mind. And we've written that there's a 35 page directive at DOD that talks about human oversight and how we manage these systems. And we're constantly updating that and making sure we have the right controls on it.
- [20:24]
B
Yeah, one more thing about lms, one thing that I heard is that they could be useful potentially in being another layer of data on top of strikes before they happen. So for instance, the, the school in Minab, Iran, where there was markings outside of playground and hopscotch outside. Maybe an LLM in the future, if that, something like that becomes a target, can be like basically flag it and say, hey, maybe don't shoot here.
- [20:50]
A
Yeah, this is the point I was trying to make with the driverless cars is like if a driverless car ends up detecting a jaywalker better than a human, isn't that a better option? So when I say it's there to augment human decision, it could be on the front end or on the back end, which is check and make sure this is something that we want to go after or hear warning signs. It works both ways, but ultimately humans have to make the decision that's the end state how that decision is contributed to ethicalms, especially the ones that are trained on visual. Google has your nest cams, it has YouTube, has a lot of human movement. All these things have different data sets that they're trained with to some degree that are proprietary, could be very valuable. So that's why LLMs I think is going to go away as a term because they're not large language models only, they're visual. They're going to be used for robotics. They're going to be used for a lot of things. Yeah.
- [21:48]
B
And that's the general side of the whole AI part.
- [21:51]
A
That's right.
- [21:52]
B
Let's talk about drones briefly. You brought up a few times. I feel like it's worth discussing since it's part of your remit. Very interesting uses of drones in Ukraine right now and we saw, I think, unprecedented uses of drones in the Iran war. Different use cases. One is an air war, one is a ground war. What are the main things that you've learned watching this in action and how do you think it changes again, the way fighting might happen?
- [22:18]
A
Yeah, two different. You're right to point out two different scenarios. So in Russia, Ukraine you have a battle over territory. And so that battle over territory, where the lines are drawn means that with the drone warfare, the robots are the front line and the humans are back. And the idea is, well, why risk a human going front if you could send a machine first and see what, you know, see if you could fight it that way. Still a lot of destruction, death there. That's obviously sad and unnecessary. But I don't know how much more there'd be if you had civil war style thing where you have, you know, humans on humans in Iran, the drones. I think the lesson from that is that the imbalance of costs, right. You have a cheap drone going against very expensive targets. Right.
- [23:12]
B
And also millions of dollars to shoot one of those things down.
- [23:15]
A
And to protect your exquisite targets on your side against a very cheap drone, you have to use expensive countermeasures. And so the Lesson there is how do you turn the dial from maybe we should have more mass attritable weapons like drones or counter drones that are affordable so that the cost ratios are similar as opposed to a country that can afford cheaper stuff being able to threaten expensive assets on our side. And for me, that's been a big push in this department, which is how do I bring. We call it mass attritable weapons that are not exquisite, that can be delivered quickly, that are designed to manufacture for manufacturability, that are cheap, that you can afford to lose, as opposed to the big stuff that we build, that takes 10 years to build, that costs billions of dollars. Yeah.
- [24:11]
B
So let's tackle both of the ways that the US is working to head off these threats. Let's start with this. The bigger drones, shall we say? Yeah, so there's a program here, Lucas, right. Low cost, $30,000 a pop, you can send them out. And do they crash into the other drones? I mean, what's the point of. Or do they do the same thing that the drones do?
- [24:32]
A
They have the same. The idea is that the Shahid drones that the Iranians had, we call it a one way attack drone, long distance, can go fast but cheap to manufacture. These are sort of that. And they could do a lot of things. They could be defensive, take out other drones, or they can be offensive and they're designed to be cheap to manufacture. If you lose a couple, you're okay. Right. Just from a financial standpoint. And you know, they're used in the same way, in theory.
- [25:07]
B
Are we working with the Ukrainians on this project? I mean there was like some headlines that the Ukrainians offered to help, we turned them down. And now what's the story there?
- [25:18]
A
There's two levels of this sort of the grand sort of United States, Ukraine relationship. But we just launched our drone dominance program and I think there was two Ukrainian companies in it that were going to be like onshore manufacturing here and take some of the learnings with them here to help us with our kind of smaller drone, you know, scenario. And so we're sort of agnostic to that, but we want to divest of supply chains from adversaries. So that is one of the requirements is that the drones that we use at the drone dominance program don't have a dependency on adversaries.
- [25:58]
B
Okay. And that touches on the smaller drones are the ones that are being used in the land war, the DJI style drones that.
- [26:05]
A
Yeah, first person view.
- [26:06]
B
Right, exactly. You know, China has been putting on these displays, epic displays of, you Know the drone art in the sky or swarms. Drones.
- [26:18]
A
Swarms. Right.
- [26:19]
B
To call it what they are, what they are. And you know, at a, you know, at first look, it's like, man, like China is really innovating on fireworks. But then you realize this is completely a military simulation.
- [26:31]
A
Could be. I mean, I think that's the scenario that, that I've tried to explain. And I do think it's something that these AI companies understand what's explained to them. And you could say, you see that drone art display that you saw, Imagine those were armed drones, and imagine that they were communicating with each other, and they could therefore form and reform in ways against your defenses. How do you defend against those? And depending on where you are, you may have a fully defendable garrison, let's say. But let's say it's a small military base, let's say it's over the border. How do you deal with these things? And that's like something that's a new challenge that wasn't present or at least we weren't thinking about before the Ukraine, Russia.
- [27:20]
B
So what is the answer? Like, is the US Working on the defense side of that and on the offensive side?
- [27:25]
A
Both. Okay. All the time. Right. Drone dominance has both elements to it. We have a counter uas, Counter Unmanned Systems Task Force that's looking at everything from lasers, directed energy was one of my critical priority areas to how do you do electronic warfare on these things to take them down? They're all run in some way. So there's lots of different measures and countermeasures. That's what makes this technology this time in the department so interesting. Danger of warfare is changing. Technology is getting more capable. The actual ability to access technology is becoming cheaper. The need to have these systems interoperate is never greater. Because what is a drone storm? It's a set of interoperable drones that work together and you could see them in the sky like you're talking about, and you can imagine what their military utility might be. So the tech problems there are super interesting. Right? Right. They're hard, but they're interesting.
- [28:22]
B
Briefly, on the cyber warfare side of things, I imagine AI could really impact that side of warfare, it seems so.
- [28:32]
A
It seems that models that are trained on code learn can learn vulnerabilities in code, is what these companies are saying. And that presents risk and opportunity. But yeah, I mean, they're obviously what you've heard in the news yourself, and it's been released about the, about the cyber capabilities that are almost here are certainly going to come from every frontier model company at some point. Certainly going to be tried to be distilled by the adversaries are going to be the next wave of innovation from these companies.
- [29:13]
B
Okay, so it's clear, I think from the beginning of our conversation that AI is becoming critical in what the Pentagon does. It's helping synthesize information in some areas, it's helping with targeting. Clearly you need it for drones and you also need it if this is going to be a new cyber security front. Yeah, so I want to talk about how you pick the AI vendors. So we're going to talk about the situation with Anthropic and then a few other topics when we come back right after this. Most leaders know how work is supposed to happen, but when it comes to how it actually gets done day to day across tools, teams and handoffs, they're mostly guessing. That's exactly the problem Scribe Optimization was built to solve. Trusted by over 80,000 enterprises, including nearly half of the Fortune 500, it gives leaders a live view into how work is really happening across approved business apps without interviews, manual process mapping, or extra effort from the team. And because it's continuously analyzing real workflow activity, the insights stay current instead of going stale the moment a process changes. You can see which workflows are happening, where time is going, and which tools are involved. It automatically surfaces top issues, explains why they're happening, and even recommends ways to fix them with estimated time savings. And importantly, it's built with privacy in mind, so activity is only captured in admin approved business apps and user level data is anonymized by default. The kind of visibility that used to take months, now it's just always on. If you're ready to stop guessing and start seeing, Visit Scribe. How BigTech that's S C R I B E How BigTech look, if you have a kid in school right now, you know the drill. What you take 20 minutes of homework ends up taking two hours and usually ends in tears. And every good tutor, well, they're fully booked for months. This episode is brought to you by Brainly. Brainly is an AI powered personal tutor built by educators, not a general purpose chatbot. It doesn't just give your kid the answer, it walks them through step by step explanations so they actually understand the material. It learns how your child learns, diagnoses when they're struggling, and builds a personalized learning path in under three minutes. Available 24 7. There's no scheduling headaches and it's just a fraction of the cost of a private tutor. Finals are coming. Build your teen's study plan now. It only takes minutes. Go to brainly.com bigtech to get 50% off your first Brainly subscription with my code Big Tech that's B R A I n l y.com BigTech Insurance isn't one size fits all, and shopping for it shouldn't feel like squeezing into something that just doesn't fit. That's why drivers have enjoyed Progressive's name your price tool for years. With the name your price tool, you tell them what you want to pay and they show you options that fit your budget enough. Hunting for discounts, trying to calculate rates, and tinkering with coverages. Maybe you're picking out your very first policy, or maybe you're just looking for something that works better for you and your family. Either way, they make it simple to see your options. No guesswork, no surprises. Ready to see how easy and fun shopping for car insurance can be? Visit progressive.com and give the name your price tool a try. Take the stress out of shopping and find coverage that fits your life on your terms. Progressive Casualty Insurance company and affiliates price and coverage match limited by state law. And we're back here on Big Technology podcast with Emile Michael, the undersecretary of war for research and engineering. Emil, appreciate you being here with us. Let's talk about anthropic. So I just want to hear from your perspective, describe the culture of anthropic versus the administration.
- [32:55]
A
Well, I would say this, which is they were the first to aggressively try to provide service to the government after the Biden administration's executive order about AI, because they were. And again, you see this in the marketplace too. OpenAI was more focused on the consumer with ChatGPT and the subscriptions. XAI sort of hadn't been started really until 1824 months ago. And then Google was also focused more on the consumer. They were focused more on enterprise. When I say enterprise, I mean enterprise writ large. An enterprise like the Department of War or an enterprise like a big company. And so they were naturally started sooner here. And I think there's a certain portion of people at all these companies that all now have a government division that are all going to start understanding the vernacular a little bit and we can have conversations like you and I were having about what are the meaning of some of these advancing capabilities of the world. But from a culture standpoint, I think we think about we live in the bureaucracy of what we have to do every day to innovate and to reform. And I think the image that they might have and this is not unique to them, of the Department of War or the administration, is that we don't have the safeguards that we do, that we're not paying attention to the risks. We are, if not more so than most Americans would understand, that we do because of the procedures that have built up over decades and decades of being careful and smart about what we do. And so that culture clash, to the extent is what called a lack of understanding, a lack of confidence, lack of trust in us and our ability to do things in a way that's consistent with our values as a country and the laws that are passed. And that's, I guess, how I describe the difference.
- [35:01]
B
Okay, and just to recap what's happened between the Pentagon and Anthropic recently, they were in maven smart system, like we discussed. There were all these provisions in the contract that the team here didn't like. So there was a renegotiation. It almost came together. But there were two things that Anthropic wanted to include in the contract. A provision against mass surveillance, a provision against autonomous warfare. And ultimately there was not an agreement there.
- [35:32]
A
Right. Although I would say the following, which is just sort of clear. The provisions that were in the contracts that ultimately served the Department of War said, you can't use it for planning kinetic actions. You can't use it to develop weapons systems. So all the science, engineering, aerodynamics, all
- [35:52]
B
those were the original stipulations. They agreed to throw that out.
- [35:57]
A
But it took three months, examples, handholding examples, to say, well, what about this example? You can't run a department of 3 million people by exception. You have to have, especially if you think about AI as an intelligence layer, they can apply to many things, from aerodynamics and physics and math to synthesizing information to anomaly detection, whatever. And we run hospitals, we run schools, we run weapons systems, run to defensive systems that protect against all these kinds of things. So to go by exception and try to say, well, how about this scenario? How about this scenario? Became not tenable and took a long time to get there. And that's where you started to say, are they aligned with our mission here? And then the idea that autonomous weapons were an issue was sort of, I think, more marketing than anything because we have our own policy before they showed up, that talks about that. And we affirmed that we will have human oversight on all decisions militarily that are made using their AI. So what else can you do? You're like, we affirm human oversight. We have these directives already. We have the laws, and eventually they Agreed that there was no problem there. But they marketed it as an issue that we were disputing. At the end we saw it on domestic disperse. We are not a domestic law enforcement agency. We do not have authorities to do domestic mass surveillance. So it was sort of like you have Congress that passes laws, the national security Act of 1947, the FISA act, all the civil liberties that are enshrined in law and the Constitution. And I said, affirmed, we will follow all those laws and all future laws. And all the authorities were granted and not granted. We're not the FBI, we're not Homeland Security. But again, that wasn't enough. They wanted us to rewrite the law because they thought Congress was just behind. They weren't understanding that new tech allowed new capabilities. But again, it's not our mission. We don't have the authority to do it.
- [38:15]
B
Okay, but here's the thing, right? And so I think that. And so eventually the contract was, was ripped off.
- [38:20]
A
They called it off.
- [38:21]
B
Yeah, right. And I think deciding not to work together makes complete sense if you have a value misalignment. But then the Pentagon took it a step further, deemed anthropic a supply chain risk. And that one I, I'm a little bit puzzled by. Because if you were so close to being willing to work with them, if they agree to all lawful uses of the technology being used by the Pentagon, then how could they end up being a supply chain risk? Which basically means that the Pentagon won't work with them, any government contractors can't work with them. And the administration took it a step further and said no government agency should work with them.
- [38:59]
A
Well, I'll speak to what Department of War cares about in our supply chain. If Lockheed Martin builds a weapon for me and they're using a technology to help them do some of these science oriented things, physics, aerodynamics and so on. And the vendor has expressed an unwillingness to want that to be part of the use case. Well then what am I getting in that, in that system that's eventually going to come upstream to our war fighters? I don't know. What if they decide to change their red lines? What if the model hallucinates because its values are like, we don't want to cause this to be used in a kinetic way. Those were the things currently in the contract. So you worry about the downstream implications of that on everything that leads to the protecting the war fighter and defending the country. And so it is a legit worry if their alignment with our mission is not real.
- [40:01]
B
But then you also limit yourself In a way to some of the capabilities they might have. I mean, if you think about Mythos, we talked about cyber warfare. Mythos is their new model. It's in preview. There's a project called glasswing that has a bunch of entities that have come together and they're trying it. And one of the things about glasswing and about this Mythos model is that it is convincingly good at cybersecurity and cyber attacks. This is from the AI Security Institute. This week we conducted cyber evaluations of Claude Matho's preview and and found that it is the first model to complete an AISI cyber range end to end, which means it's a 32 step corporate network attack. From initial reconnaissance to full network takeover, we estimate it would take human experts 20 hours to complete.
- [40:51]
A
So you're saying it's an AI cyber weapon? Automated. Well, autonomous cyber weapon.
- [40:55]
B
Well, here's the thing. I'm not encouraging the use, but I'm saying that like you talked about the drones that are meant to hit other drones, wouldn't you want this tool at this disposal? There's an argument to be made, and I'm curious to hear what you think about it, that you sort of put yourself in a corner when you're not taking these capabilities and using the ones that you want.
- [41:16]
A
The original sin was in the past administration, choosing one AI provider and having no options because it is.
- [41:26]
B
That makes sense to me.
- [41:26]
A
At a gargantuan effort to get these software things onto classified networks. A lot of complexity to do that because they're secure networks. Right. This isn't AWS cloud for consumers. So the original sin was not having more than one provider so that you had more options. But I also believe if you talk to every other of these frontier AI companies, they're going to have similar capabilities.
- [41:56]
B
But they don't yet.
- [41:58]
A
If you were to use that. Yeah, but they will soon. If you look at the distillation attacks that our adversaries are using based on our models, how long do they take to show up in Deep Sea Curse or any of these other things?
- [42:12]
B
A couple months.
- [42:12]
A
Yes. So if you think about those timelines, you're thinking about the timelines. And we'll never sacrifice capability for national security or anything. So I think we're cognizant of what's happening and we're working with every model company and we feel good about our posture there.
- [42:33]
B
The other thing that people say about this, and I'd be curious to get your thoughts on it, is that you can look at the history of companies that have been deemed supply chain risk to the Pentagon. It's very rare, if unprecedented for a company like Anthropic to be banned that way. So why do you think it rised to the level and do you think it merits this fairly unprecedented action?
- [42:55]
A
Well, I mean, on one hand you can't say that they have this cyber nuclear bomb, and yet we shouldn't be worried about how those capabilities enter and remain in our supply chain. Those two things are inconsistent. Right? I'm not blaming you. I'm just saying that if you believe they're going to cause 40% unemployment, if you believe that these things have a capability that you put 50,000 geniuses in a data center, they're going to coerce the world, they could create bio and chem weapons. Of course the Department of War is going to want to understand and constrain those things so that they don't do something unintended. On our side, these companies are talking about their things in apocalyptic terms which make it necessary for us to judge the management teams, judge their actions, look at the terms of service, understand how they fit in our supply chain. This technology is like nothing we've ever seen. So you can't compare it to, you know, a chip from a foreign chip manufacturer that gets put in the supply chain. This is a whole different thing because of just what you said is the power of what they're saying it's going to do, the disruption it might cause an American life and we don't. If someone developed a nuclear bomb in their, in their garage, you don't think we'd have anything to say about it? You know, of course we would. Right. Or a biological weapon or any of these things. So I think those are things that heighten the awareness that we have of what these models can do and where they're going.
- [44:35]
B
Okay, I just want to talk about this one more, one more level, which is a practical level, which is, and you've mentioned this in interviews before, that Anthropics models were hosted on Amazon's cloud, their government cloud, and so they upload the weights to the model and then you use it through Amazon. So let's just take the Lockheed example. If Lockheed is designing some systems and Claude is baked in there, Claude, I mean, Anthropic wouldn't have the capability to turn that off if it's hosted somewhere else, maybe upgrade it and in which case you flip them out. But to turn it off. They don't really have that capability.
- [45:16]
A
No, I mean, I think where you understand how this technology Works better than most. The upgrade cycles for these things are now compressing to now 3ish months.
- [45:29]
B
Right.
- [45:30]
A
So every three months you have a new set of model weights, a new set of guardrails, a new set of bugs. The way the model behaves, the way it hallucinates or doesn't hallucinate, the way it does. Refusals, refuses to answer certain questions. And there's an important anecdote which was written about, which was anthropic, is also serving the Centers for Disease Control. And so you have some scientists going there and learning about pathogens.
- [46:02]
B
Right.
- [46:02]
A
And they assumed that was a bad actor and it took them. They refused to undo that refusal.
- [46:08]
B
But that was the off the shelf model or was it?
- [46:11]
A
Yeah, sure, it was the off the shelf model, but what's to stop that from being in the next model? We don't know. So the point is to have a reliable partner, you have to have alignment on these issues, which is we have a national security mission. We want to use it for all lawful use cases. In the HHS case, it would be all lawful use cases. And it's lawful for HHS to be doing pathogen research. Totally. I would hope that that's what they're doing. We hope that's what they're doing. So for someone to have made the judgment to turn that off and they're like, oh, well, it was an old model. This, that, that's not how it has to work. In the future. If you are truly an American company that's trying to protect Americans and do good things for Americans, the government has to be able to use this powerful tool to succeed in its mission. Yeah, but if I'm hamstrung by their choices, that's what gets in the command and control structure.
- [47:06]
B
But haven't you solved this to a degree? Because if you just have Claude, then I totally see it. But if you have Claude and Grok and OpenAI in there, then maybe if Claude makes an update you don't like, you let OpenAI run with the next generation.
- [47:25]
A
If we hadn't had made the original sin, I think you'd have had them competing for the government business, had they competing for the government business. Like in any non monopolistic scenario, power would be balanced between customer and vendor and eventually we'll have that. But we didn't have that, so then they could make those choices on their own.
- [47:49]
B
So I asked about the culture side of things in the beginning and there's also this perception that, well, that and I mean, I have a decent read on the Government and a decent read on Anthropic. They're definitely different cultures. And the other read on this is, okay, maybe, maybe there were some things that the government was uncomfortable about, but this really just came down to a culture clash where like, even, I think, wasn't it Pete Hegseth, when he tweeted about Anthropic, said, you know, we're not going to let any woke company tell us what to do. Is it possible that this is just a culture clash versus the bigger thing that it turned into?
- [48:31]
A
No, because, I mean, I would tell. I would tell the Anthropic guys when they came to see me, this is independent of politics. I just care about having the best system for our war fighters. Why would I spend three months if it was a culture clash? Andrew Ross Sorkin asked me the same thing at cnbc. He's like, you just, you know, you're not buddies with him or you're buddies with him. He's like, I've never met any. I don't know these guys. I know the culture of Silicon Valley. So I did take a lot of time to try to explain as a transplant to the government, here's why this matters, here's some scenarios. And eventually we got to a point where it was just, they wanted control. And you can't have control of the Department of War's actions and activities so long as they're legal and consistent with our guidelines.
- [49:22]
B
And so those on the outside who look at this and they say, okay, supply chain risk designation, no government agency can work with them. This is effectively the federal government attempting to destroy Anthropic because of a procurement dispute.
- [49:36]
A
I mean, the destroy Anthropic, that's tripled in revenue in three months or tripled in valuation.
- [49:43]
B
They're doing okay.
- [49:44]
A
They're doing okay. That's silly, right? So that's silly because the percentage of revenue that we represent of any of these high companies is infinitesimal. It's just, we don't want them in our supply chain. We don't want to use them. President decided that he doesn't want the government to use them. There are great alternatives, and we're going to have to fix past mistakes by ensuring that those alternatives are available. And I have high confidence, if not more confidence, that these other models will be the same or better over time.
- [50:14]
B
Okay, last one on this, and thanks for answering all of these. It's good to get your perspective. The judge in the case or one of the judges that in this case, because Anthropic is suing to have that designation removed. Judge Rita Lynn said the Department of War's records show that it designated Anthropic as a supply chain risk because of its hostile manner through the press. Punishing Anthropic for bringing public scrutiny to the government's contracting position is classic illegal First Amendment retaliation. Did it have anything to do with the press, the press strategy?
- [50:45]
A
I mean, I shouldn't comment on the legal case, but I think the notion that a First Amendment claim is going to hold up is, would be shocking because that means that the government has no choice to make, right? If a vendor, any vendor, says, I don't agree with your term, and they're like, well, that's why we're not going to hire you to do whatever kind of work we do, translation work at the Department of War, and that becomes a First Amendment claim, then it sort of would be so overreaching that it would be not workable. So I feel like that was a throwaway. But I will say that the thing that makes the Department of War different than most other agencies, and I don't mean this to be dramatic, but we really do have lives on the line. And when people talk about government bureaucrats and them not caring, the people here, the career people, they care. They really care. They care about the war fighter, they care about the country. It's a really patriotic place and it is very nonpartisan. In the middle of the pentagon, we have 3 million employees. And so that mission is very sensitive. So, like we are sensitive to the exec, the relationships we have with these companies, because there's a lot of unpredictability in our business, right? So something happens in Iran and we need companies to move fast. You have to have some trust with them. You have to have some shared values. You have to have, you have to understand they have economic interests. And then we have to understand that our needs are going to change based on the threat environment. And so that kind of matters. So you could go. They could litigate in the, in the public all they want, that's fine. But do we have alignment for real? When we get in the room and we are facing a conflict, are we aligned? So I was pretty impervious to that stuff.
- [52:47]
B
There's a website, Genai Mil, that's available to the people in the military here. And interestingly, Google is in there, Gemini's in there. And Google went through something similar, even somewhat more explosive, where the employees protested. And yet here they are, they're working with the Pentagon. Again, forgiven. And to a degree, could that happen
- [53:10]
A
with Anthropic I think so. I mean, I believe that when you combine like, you know, if you Fast forward from 2018 where the Google Maven thing happened to 2026 and you talk to people from Google who are involved in that, I think they regret it. And they regret it because probably the same reason they didn't understand what was ha. Well, what we did here and what we do here in this administration is going to carry forward to other administrations because we're at a crucible moment for AI and that's going to be, could be an administration of either party. So whatever decisions they make for us, it's non partisan and it's for the future. And I think, and I hope that companies that went through that moment in 18, like Google, kind of, as they get more mature and more of an understanding of what it means to work with the government and understand us better, get to a good spot, hopefully sooner than eight years.
- [54:06]
B
Yeah, that did take a while.
- [54:07]
A
Yeah. But I will say Google's been an excellent partner. Before this gen AI got model shifted
- [54:13]
B
in a big way.
- [54:14]
A
And I mean the whole tech industry. When I was there at Uber in 2016, 17, we didn't touch this stuff. Huh. It was just the employees and sort of, I wouldn't call it a little bit of a mob mentality where employees had a lot of say over what their products and services were doing and senior leaders and founders were very sensitive to that. I think that sensitivity has gotten a little more balanced right now. If you don't want to go work at Palantir, don't go work at Palantir. There's a ton of other places you don't want to work at Uber. Don't work at Uber. I think the, the balance is in a better place and I think Silicon Valley because of the fact that we're doing more outreach to them. There's more California companies, both Southern and Northern are going to succeed here. Hopefully that knowledge transfer will happen faster.
- [55:05]
B
Let me bring up one more headline. There's a story this week that also says that you had some Xai stock. Do you have any SpaceX? Is that a conflict?
- [55:15]
A
I sold all my SpaceX. Okay. And I. So what happens when you take one of these jobs? You show your whole sort of list to the Office of Government Ethics, nonpartisan. They go through it and they say, we think these things are, these things are red lines. You sell your defense company stocks didn't have much. SpaceX was on the list, so you have to sell that. And then depending on your role here's the things you have to sell that might be specific to your role. And then based on the kind of connection to that, you could recuse yourself from dealing with a company. So I just recused myself from dealing with Xai until I could sell. And I was pretty active about it because I didn't have the AI portfolio until the fall. So I got the AI portfolio. I was like, hey, I'd like to be involved in this. I'd like to not recuse myself. They said, well, you have to sell. Great. Give me permission. Got permission. Sold. Was recused. In the meantime.
- [56:07]
B
Okay, two more things I want to speak with you about. I'll be quick as we come to a close. First of all, I think it's. Every time we have this conversation, a conversation like this, we have to talk about procurement. And it's like, I know I can tell half the audience is ready to go to sleep now, but it's really important the way that these services are bought, because, like, the Pentagon budget, for instance, has been, I'll say, inflated because some of the vendors have charged more than an arm and the leg. And the leg for services. So talk a little bit about how you're working to reform the procurement process and why. Why that's going to be good for people.
- [56:43]
A
Yeah, so. So in the 80s, during the height of the Cold War, we had about 50 defense contractors. Five. Zero. And they consolidated down to five. So that was one sort of dramatic reduction in the number of competitors for anything. And then we outsourced a lot of the core capabilities to other countries. So the supply chains got brittle, and China didn't have a military buildup till the mid 2010. So you put all these two things together and you said, well, what was happening is we have a small number of competitors. They were taking less risk, so we were paying them for, you know, cost plus. Now, some of this. Well, and I. It's important for me to say this every time I've asked this. Some things are so speculative that no company can economically do that unless you're financing some of their R and D. So there are things that are 10 years out, 15 years out, 20 years out that you have to do that. But because the nature of warfare is changing and because there's defense, the greatest VC boom in defense tech in a country's history, and because you have founders like Palmer Luckey and all these folks who are willing to go into this business, it's made us much more able to do business deals for our audience who's bored with procurement, talking about Business world, it's important we can now do deals where if you deliver a weapon and it works on time, you get paid. And if you don't, you don't imagine that. And guess what, if you do it cheaper so you make a little bit more profit, I'm okay with that. Right. So there's a little bit more risk sharing there. And I think ultimately, especially for things that are easier to produce and quicker, that you're not taking a huge R and D risk. Like you're inventing the next, you know, you know, space shuttle that, that can land on the moon and, and be there for, you know, three years and build a base like all the things are, you know, very speculative, hard things. I think you'll see us moving a lot more toward business, business oriented contracts, which is good for them and good for us.
- [59:00]
B
Definitely.
- [59:01]
A
And better for the taxpayer.
- [59:02]
B
Yeah. Most importantly.
- [59:03]
A
Yeah.
- [59:04]
B
I think we pay enough taxes that we should know where it's going and hopefully it's not wasted. All right, I don't want to leave without asking you about the Pentagon Pizza Index. Are you aware that there are people tracking how much pizza is ordered near this building? We're at the Pentagon and they've used it to predict military action.
- [59:23]
A
I've seen that on X. Honestly, I would have no idea where you'd get a pizza delivery to come into the Pentagon because it's just a card bill.
- [59:31]
B
There's a specific Papa Jazz.
- [59:32]
A
No, I'm not doubting that, but I actually don't know. If I went back to my office right now, it's like, how would I order a pizza from outside to be delivered in? I'd have no idea.
- [59:43]
B
So you're not a believer in the Pentagon pizza Index?
- [59:45]
A
I'm not a believer in the Pentagon pizza index.
- [59:47]
B
We shouldn't take it seriously, huh? We shouldn't take it seriously.
- [59:52]
A
I'm not a believer in it because I literally don't know how you get any food delivered from the outside.
- [59:59]
B
This is the Pentagon. You're telling me the Pentagon can go to war with countries millions or thousands of miles away, but it can't get pizzas in the building?
- [60:10]
A
I'm sure there's a way someone could walk out to the edge of the Pentagon, receive a pizza and bring it in?
- [60:15]
B
This place is the best logistics operation in the world.
- [60:21]
A
Look, I don't know. What if someone's messing with it to mess with the prediction markets?
- [60:26]
B
I wouldn't put it past anybody.
- [60:28]
A
So therefore it's inherently an unreliable measure in my view, because it's easy to corrupt it.
- [60:37]
B
So the pizza around here.
- [60:40]
A
I think there is a pizza place or two inside the building that close at five.
- [60:46]
B
That's why they look at the late night Papa Johnson, apparently. I'll leave it at that. Mr.
- [60:51]
A
Undersecretary, that was a hell of a last question. I wish that. Yeah, my pleasure. All right.
- [60:56]
B
All right.
- [60:56]
A
Thank you. Thanks for coming all the way D.C. my pleasure. Pleasure.
- [60:58]
B
Thanks for having us in person. Thanks, everybody, for listening and watching. You now know the secret to the Pentagon Pizza Index. We'll see you next time on Big Technology Podcast.