AI Roundup: Anthropic, OpenClaw, layoffs, and more

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2:18One reason Department of War wanted to take this action in the way that it did is specifically to intimidate other companies into compliance. I think it really wanted to send a message here like you either get on board with doing things the way we want or the consequences are going to be very severe. Are companies really cutting jobs because of the productivity gains of AI? In some cases, companies are saying that these cuts are due to AI when actually, you know, they're using that as an excuse to cover up for poor business decisions. A lot of these Gulf countries have big ambitions to be AI hubs.

2:53How does this instability threaten or reshape these ambitions? Iran did launch some drone attacks and missile attacks against data centers in the UAE and in Bahrain. It was really the first time that we saw deliberate targeting of data centers. Jeremy Kahn is the AI editor at Fortune and my co-chair at Fortune's Brainstorm AI conference. He's my go-to person to unpack what is happening in the AI space beyond the headlines. And there's a lot happening right now. There's a standoff between Anthropic and the Pentagon, the rise of AI agents, and of course, the instability in the Middle East and how that could reshape the AI industry.

3:32Even for someone like me who's in the AI space, day in and day out, it feels like a lot. It's so hard to keep up. There's a lot of uncertainty. I don't know, just a lot of confusion. So let's try to make sense of it all. I'm Rana El-Khalyubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.

4:03Hi, Jeremy. So good to see you again. Thank you for joining us again on Pioneers of AI. Oh, Rana, it's great to see you and thank you for having me back again. All right. So let's dig into it. Let's start with Anthropic. This all kind of started to unfold over the last few weeks. It's definitely still important, Rana. And it is an ongoing situation because Anthropic has now sued the Department of War to try to overturn this decision to label them a supply chain risk. They have filed for an injunction, actually, to stop the ruling from going into effect. So Anthropic filed two lawsuits, actually, one in California and one in the DC area.

4:44And there have been some hearings, initial hearings in both of those cases. And we will see how, you know, what happens there. A lot of legal experts, you know, I've talked to think Anthropic has a very good case. Whether they will get an injunction is sort of another matter. I want to kind of rewind a little bit. So this has been brewing for many months, really since the U.S. government has been working more closely with the big kind of AI companies. But it really escalated when Anthropic demanded that the Department of War not use their technology for domestic surveillance or for fully autonomous weapons, which then the Trump administration deemed Anthropic to be a supply chain risk, which I guess this is the first time in U.S.

5:25history that an American company is deemed a supply chain risk. Why is this such a big deal? Yeah, so this is really an unprecedented decision by the U.S. government. The supply chain risk designation was actually developed in order to prevent foreign suppliers that might pose a sabotage risk from entering the supply chain of defense products. A lot of legal experts say it's really inappropriate for the Department of War to have used the supply chain risk designation essentially in a retaliatory way. They were quite happy to buy in Anthropics technology other than these two red lines. And it was really a contract dispute that went awry.

6:06They couldn't reach contract terms that they could agree on. So a lot of people said, OK, fine, you cancel the contract. But to go further and try to label them a supply chain risk, which means that any defense department contractor cannot use Anthropics clawed models in the fulfillment of a Department of Defense contract, a lot of people feel like that's going too far. And actually, the Department of War, the Secretary of War, Pete Hegseth, had tried to interpret this supply chain risk ruling even broader than that. He had tweeted out on X that the designation meant that anybody who had a Defense Department contract had to cease all commercial relationships with Anthropic, which legal experts say there's just no basis for that in the statute.

6:48Even if the supply chain risk designation holds, it should apply only to the work that companies do in fulfilling Defense Department contracts. It should not mean that they have to sever all commercial relationships with Anthropic and not use Anthropic's models for any purpose. Yeah, because that would also mean, like, for example, major providers like AWS or Google Cloud, for example, both use Cloud, right? Technically, they would have to stop that. Yes. Not only does AWS and Google have this close relationship with Anthropic, they're also investors in Anthropic. Some people said if you really take the Hezgeth interpretation, they'd have to divest from those investments.

7:30It really would be, as some people said, if you take that very broad interpretation, an existential threat to Anthropic. It probably would be the end of them as a company. You know, I'm personally really fascinated by this because, as you know, when I had my company Affectiva, we had very strong, very similar language, actually, in our terms and conditions when we were licensing our technology. Obviously, this is at a very different scale, but it's perhaps the most prominent example where AI ethics and alignment is coming head to head with government use. What do you make of that? Yeah, no, it's fascinating.

8:03It's one of the reasons this is such an important issue. It speaks to this wider question about who is going to control advanced AI and who is going to decide what guardrails should be in place around this technology's use. A lot of AI policy people I talk to say, you know, we really need Congress to act here. There should be laws around this. It should really be up to democratically elected representatives to make these decisions and set these high-level rules. How do we want AI used in war within the U.S. military? What do we think about the use of AI for potentially the surveillance of U.S.

8:39citizens? Those sorts of decisions need to be made really by Congress, but Congress hasn't acted. As you know, Congress has yet to pass any real federal AI regulation. And in the absence of that, we're falling back on things like a contractual negotiation between one part of the government and Anthropoc. There's also some questions about what the whole industry does. I mean, it's very interesting in the Anthropoc case. Another reason it's so important is if you look at the number of amicus briefs that other people have filed in support of Anthropic, it includes a number of very prominent AI researchers and computer scientists who work at other AI companies.

9:17It also includes Microsoft itself as a corporate entity. They filed an amicus brief supporting Anthropic's position. So I think it's very interesting. This is a case that's going to have really big ramifications for these big questions about how do we govern AI? What does it mean for the AI industry, but industry at large, if a company can be penalized for saying no to the government? Yeah, that's a great question. A lot of people feel that, okay, if you can't reach a contractual agreement, is it fair that the punishment for that is that the government can essentially try to destroy your business?

9:51And a lot of people are saying no. That's very un-American. But it's going to have this chilling effect. I think the one reason the Department of War wanted to take this action in the way that it did is specifically to intimidate other companies into compliance. I think it really wanted to send a message here like you either get on board with doing things the way we want or the consequences are going to be very severe. And this is kind of we've seen this play out several times with the Trump administration where they've taken very punitive action against people who have disagreed with them on some particular policy decision or who they even just perceive, you know, is politically not aligned with them.

10:30they have sought to punish those businesses. You saw it with the law firms that they felt had done work, you know, for the Biden administration or had opposed them in the past on things. You've seen it with the universities where they've stripped funding from universities that they feel are not complying with the policies that they want. And in some cases, those actions have been illegal, but they've had this tremendous chilling effect. And that seems to be kind of the playbook that they're using here with the Anthropic case. Okay, so as all of this was unfolding, the Department of Work worked out a deal with OpenAI that has also a lot of confusion around it.

11:03So where do we stand with that? Right. So, yeah, it was a pretty amazing story. Right in the middle of this very public dispute, while the negotiations were still ongoing, you had OpenAI CEO Sam Altman first of all come out and say that he agreed with the red lines that his rival, Anthropic was trying to impose, that basically OpenAI had the same red lines. And then you have just hours later him announcing, oh, by the way, we're talking to the Department of Defense too, and we have reached a deal. And our deal included, according to Sam Altman and according to the national security policy executives at OpenAI, it included language that was attempting to establish the same red lines, but with one big exception.

11:51So the exception was that OpenAI agreed to contract language that says, it's okay for the Department of War to use our technology for, quote, all lawful purposes. And then it tries to define further in the contract, critics have pointed out that there's a lot of loopholes here that the Department of War could could step through to essentially do the things that it wants to do and could essentially do a mass surveillance, could essentially use autonomous weapons and still comply with the contract. In particular, on the surveillance issue, what Anthropic was most worried about was the idea that you could buy a lot of commercially available data.

12:34This is data that's available for sale to anyone. And then you could stitch that together using AI to really create quite an accurate picture of the activities of a lot of specific American individuals. And the question is, is that, you know, mass surveillance? And the government is trying to argue it isn't. And a lot of other people say, well, it feels a lot like mass surveillance. We're going to take a short break. More in a minute.

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15:49So NVIDIA just wrapped up GTC, which is their annual global AI conference. What are some of the headlines that came out of that? So the biggest headline that came out of the event was Jensen Wang, NVIDIA's CEO, saying that he saw visibility to a trillion dollars of GPU and NVIDIA product sales through the end of 2027. So that was like a big headline number. That was twice the amount he had predicted that NVIDIA would sell through 2026. So he's saying basically a year later, I can see that we're going to double that number, which is already massive,$500 billion. We're going to have a trillion dollars worth of revenue, which is a pretty astounding number, although it's interesting that NVIDIA's stock is priced so highly that the market did not respond that much.

16:35It kind of shrugged it off. And in fact, there was a little bit of a bump up when he first came out with the number. But by the next day, the stock was actually a bit down from where it had been. So that was the big headline. But then sort of under that, there were some very interesting other announcements. One of the most interesting, I thought, is that they said that their next generation server rack that's going to come out by 2027 is going to include both NVIDIA's latest generation Vera Rubin GPUs, but also these things called LPUs or language processing units. And those are being made by a company called Grok, which was an AI chip startup that NVIDIA did this partnership deal with earlier in the year.

17:16One of these weird, they're often called reverse aqua hires, where they licensed Grok's technology and then hired in to NVIDIA Grok's co-founder and CEO, Jonathan Ross, and also some other members of the founding team. I think what's interesting here is it's an acknowledgement that you might need other chip architectures, particularly when it comes to AI inference. And this was an acknowledgement really for the first time by NVIDIA that maybe the GPU is not the ideal architecture for inference. And maybe we're going to want to use some other kinds of chips. All right. This is also kind of hot off the press.

17:55Jeff Bezos is apparently raising a$100 billion fund. It's an AI manufacturing fund to basically buy manufacturing companies and AI-ify them, implement AI to speed up their automation. And he's targeting aerospace, chip making and defense. What do we know about this? Yeah. So this is a brand new announcement from Bezos. They haven't started to make any investments yet, but it is very interesting. And it follows a playbook you've seen a lot of other private equity players thinking about in the past two years. and you've actually seen a number of new sort of PE funds be launched with the idea around, it's sort of a similar concept or similar thesis.

18:34We're going to buy up a lot of old line companies in existing industries. We're going to add a bunch of AI to them. They're going to be so much more productive. And then we're going to sell them basically back off and we're going to make a lot of money. We'll see what happens here. It probably is true that some manufacturing in some industries is really backward or really using very legacy processes. But it's also true that a lot of manufacturers have invested a good amount over the past several decades in automation, in robotics. I wonder sort of where the gains are still and what companies he is going to buy.

19:11Where is he going to go to find these kind of, especially with a hundred billion dollar fund, you're going to be making some pretty big investments. So these have to be fairly large manufacturers, and yet you have to find large manufacturers that have not already implemented a good amount of automation. So I think it's going to be interesting to see what he invests in. Yeah. One of our investment theses at my fund, Blue Tulip Ventures, is that there's a huge opportunity in these antiquated, kind of, yeah, like these huge industries that are maybe not AI forward and coming in and reimagining the workflows.

19:45So I do believe that this is a huge business opportunity, but I also wonder if we're underestimating kind of the cultural, like the actual like workforce challenges to bring people on board and what that looks like. Yeah, no, I think it's a fascinating area. It is going to be one of the big differentiators. It's like, can you actually get your workforce to think differently? Okay. So next I want to ask you about these AI neolabs, right? So these are not companies in the traditional sense, they operate really more like private research institutions. And they're often founded by former OpenAI, DeepMind, Anthropic, and Google Brain researchers.

20:20And the idea is that they're not really fixated on building a product or shipping product, but it's more like, let's explore what is possible. And I'll just share, you know, HumansAnd is fairly new. I just saw news about a company called Mirandal that is former Anthropic researchers. They're raising$175 million at a billion-dollar valuation, specifically doing AI, R &D for biology and material science. Again, as an investor, I am really curious about these companies because, A, they're raising massive rounds of funding at crazy valuations. Pre-product, pre-revenue, what is the commercial viability of some of these labs?

21:01That's a really good question. I think the hope is that they all become like OpenAI or Anthropic, or really maybe OpenAI is the best example. Because OpenAI started out, you know, it was doing blue sky research. It was playing around with lots of different ideas around how you build more intelligent AI models. It was originally doing a lot of work in reinforcement learning. And then it started playing around with these transformer-based language models kind of on the side. And then suddenly, you know, those took off. It hit it big and reoriented the whole company around that and came out with a way to productize it almost by accident.

21:38It was, yeah, it was sort of an accidental company in some ways. And I think investors may be hoping that, you know, lightning strikes twice and you can do the same thing with another lab that they'll, you know, you're going to fund all this blue sky research. These guys are really smart. They're going to find something and then we'll figure out how to productize it and the money will flow in. So I think that's kind of the bet, but it is a high-risk bet, and it's quite possible that it won't work out. One of these Neo Labs, called the Thinking Machines Lab, which Mira Marati, the former CTO of OpenAI, founded with a bunch of other ex-open AI alums, they've really struggled.

22:18So they have put out a small product that helps other AI researchers optimize training. That's their product that they've put out so far. But other than that, it's not really clear what they're working on. And they've started to suffer a lot of staff defections to other AI labs. They lost some people to Meta. They've lost some people who've gone back to OpenAI. They lost some people who went to do their own startup. They lost someone to Google. It's like you wonder what's going on there. And I think part of, you know, from the reporting we've done on it so far, it does seem like part of the issue has been the lack of a product strategy.

22:53There's been some disagreement among the co-founders about which way they want to build and what is their product going to be. And I think that kind of lack of direction or lack of agreement has caused problems. And you can see maybe that some of the other Neo Labs may have some of the same issues. But I am interested to see what happens because they are all working on interesting kind of outstanding problems in AI. A lot of them are trying to solve issues around continuous learning, or they're trying to build world models, which better understand cause and effect, or they're trying to work on problems around common sense reasoning.

23:29Like there's all these sort of outstanding issues in the field. All right. So, Jeremy, there's been massive layoffs at companies like Amazon, Oracle and Meta. Are companies really cutting jobs because of the productivity gains of AI or is AI really just becoming a scapegoat for a volatile market? And how can we know? Yeah, that's a really good question. And I think the answer is it's hard to tell what's really happening here. In some cases, there's clearly a little bit of what's being called AI washing going on, where companies are saying that these cuts are due to AI and due to productivity gains from AI.

24:08because it makes the company sound very cutting edge and like they're doing something really smart and wow, they've really figured it out when actually, you know, they're using that as an excuse to cover up for poor business decisions. And in a lot of cases, they're still trying to unwind tremendous amounts of overhiring that took place really during the pandemic years. You know, some of these companies, you know, tripled their workforces in just three years and then they're still trying to sort of slim down. But in some cases, I think we really are seeing big productivity gains from AI, particularly around software development.

24:41And some of these companies are saying, we just don't need as many developers. And the other thing you think you're starting to see, which is AI related, but is not quite about the productivity gains of AI, has to do with the cost of AI and the cost of the AI capital spending, particularly in the case of like Oracle and Meta, which are among the companies that are spending tens of billions of dollars to build out AI infrastructure, to build out data centers. I think in those cases, there's some sense that we need to find savings somewhere else, at least some savings, and one place to find it is labor.

25:12And I think in some cases, companies are betting not on present productivity gains, but on hoped for future productivity gains, that they're saying, well, we have to find cuts to compensate for this spending somewhere. We are going to assume that AI is going to enable us to save on product, you know, increase productivity in the future and save on labor costs. So we're going to make some of those cuts now. And then one other kind of technical thing that's going on, some of these software companies have a tremendous amount of stock-based compensation and also have been trading at really high multiples.