B (3:26)

See, that's what's so fascinating, Mike, is what we need to be thinking about is not just where the technology is today, but where is it in 6 months, 12 months, 24 months? And from my perspective, it's very clear that the proprietary American models are going to be much, much more capable. So let's put some numbers around that. The big five American firms have assembled about 10 times the compute to train their current LLM compared to their prior LLMs. And that's a big deal. If the scaling laws hold, then a 10x increase in training COMPUTE should result in models that are about twice as capable. Now just let that sink in for a minute. Twice as capable from here, that's a big deal. And so when we think about the benefit of deploying these models, whether it's in the life sciences or any number of other disciplines, those benefits could start to get very large. And the challenge for the open models will be will they be able to keep up in terms of access to compute, to training, access to data to train those models? That's a big question. Now again, there's room for both approaches. And it's very possible for the Indian government to continue to experiment and really see which approach is going to serve their citizens the best. And I was really struck by just how focused the Indian government is on serving all of their citizens, most notably the poorest of the poor in their nation. So we'll just have to see. But the pure technologist would say that these proprietary models are going to increase in capability much faster than the open source models. So, Mike, let's pivot from the technology layer to the geopolitical layer, because the US Strategy unveiled at the summit goes way beyond innovation.

A (5:03)

Yeah, it's a good point. And within this discussion of whether or not other countries will choose to pursue open models or more closely adhere to US based models is really a question about how the United States exercises power globally and how it creates alliances going forward. Clearly, some part of the strategy is that the US assumes that if it has technology that's alluring to its partners that they'll want to align with the US's broad goals globally and that they'll want to be partners in supporting those goals, which of course are tied to AI development. So the PAC Silica, which you mentioned earlier, is an interesting point here because this is clearly part of the US strategy to develop relationships with other countries such that the other countries get access to US models and access to US AI in general. And what the US gets in return is, is access to supply chain, critical resources, labor, all the things that you need to further the AI buildout, particularly as the US is trying to disassociate more and more from China and the resources that China might have been able to bring to bear in an AI buildout.

B (6:26)

So Mike, the US framed real AI sovereignty, unquote, as strategic autonomy rather than full self sufficiency. So essentially the US is encouraging nations to integrate components of the American AI stack. Now from your perspective, Mike, from a macro and policy standpoint, how significant is that distinction?

A (6:46)

Well, I think it's extremely important and clearly the US views its AI strategy as not just economic strategy, but national security strategy. There are maybe some analogs to how the US has been able to over the past 80 years or so, use its dominance in military and military equipment to create a security umbrella that other countries want to be under and do something similar with AI, which is if there is a dominant technology and others want access to it for the societal or economic benefits, then that is going to help when you're negotiating with those countries on other things that you value, whether it be trade policy, foreign policy, sanctions versus another country, that type of thing. So in a lot of ways it seems like the US is talking about AI and developing AI as an anchor asset to its power in a way that military power has been that anchor asset for much of the post World War II period.

B (7:50)

See, that's what's so interesting, Mike, because you've highlighted before to me that you believe AI could replace weaponry as really the anchor asset for US global power, almost a tech equivalent of a defense umbrella. So how durable is that strategy? Especially given that some countries are expressing unease about dependency.

A (8:09)

Yeah, it's really hard to know. And I think the tension you and I talked about earlier, Steven, about whether countries will be willing to make the trade off for access to superior AI models versus open and free models that might be inferior, that'll tell us if this is a viable strategy or not. And it appears like this is still playing out because correct me if I'm wrong, it's not like we've received some very clear signals from India or other countries about their willingness to make that trade off.

B (8:40)

No, I think that's right. And just building on the concept of the trade offs and sort of the, you know, a standard for AI deployment, you know, the US has explicitly rejected centralized global AI governance in favor of national control aligned with domestic values. So, so what does that signal about how global technology standards may evolve, particularly as in the US the National Institute of Standards and Technology or NIST works to develop interoperable standards for agentic AI systems.

A (9:09)

Yeah, Stephen, I think it's hard to know. It might be that the US is okay with other countries having substantial degrees of freedom with how they use US based AI models because they could use US law to at a later date change how those models are being used if there's a use case that comes out of it that they find is against US values similar in some way to how the US dollar, being the predominant currency and therefore being the predominant payment system globally, gives the US degrees of freedom to impose sanctions and limit other types of economic transactions when it's in the US interest. So I don't know that to be specifically true, but it's an interesting question to consider and a potential motivation behind why a laissez faire approach might be ultimately still aligned with US interests.

B (10:06)

So Michael, it sounds like really AI is becoming the new strategic infrastructure globally.

A (10:11)

Yeah, I think that's actually a great way to think about it. And so, Stephen, if that were the case, and we're talking about the potential for this to shape geopolitical competition, potentially economic differentials across the globe, and if that is correlated at least to some degree with the further development and computing power of these models, what do you think investors should be looking at for signals from here?

B (10:42)

Number one by a mile for me is really the pace of model progress. Not just American models, but Chinese models, open source models, and there the big reveal for the United States should be somewhere between April and June for the big five LLM players. That's a bit of speculation based on tracking their chip purchases, their power access, etc. But that appears to be the timeframe. And a couple of execs have spoken to that approximate timeframe. I would caution investors that I think we're going to be surprised in terms of just how powerful those models are. And we're already seeing in early 2026, these models that were not trained on that kind of volume of compute have really exceeded expectations, you know, quite dramatically in some cases. And I'll give you one example, METR is a third party that tracks the complexity what these models can do. And METR has been highlighting that every seven months the complexity of what these models are able to do approximately doubles. It was very fast. But what really got my attention was about a week ago One of the LLMs broke that trend in a big way to the upside. So if the scaling laws would hold, based on what MECH would have expected, they would expect a model to be able to act independently for about eight hours. A little over eight hours. What we saw was the best American model that was recently introduced was more like 15. That's a big deal. And so I think we're seeing signs of nonlinear improvement. We're also going to see additional statements from these AI execs around recursive self improvement of the models. One ex AI executive spoke to that. Another LLM exec spoke to that recently as well. So we're starting to see an acceleration. That means we then need to really consider the trade offs between the open models and the proprietary. That's going to become really critical and that should happen really through the spring and summer.

A (12:37)

Got it. Well Steven, thanks for taking the time to talk. Great speaking with you, Mike, and thanks for listening. If you enjoy thoughts on the market, please leave us a review wherever you listen and share the podcast with a friend or colleague today.

B (12:53)

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