As one of the co-authors of the CERN for AI piece you cite, I've definitely moved away from a publicly-funded effort to directly compete with labs at the frontier, but I'm also sceptical of strategies that rely on open models.
It looks like Chinese models are misreporting their evaluation results and have inflated performance as a result -> when independently evaluated, the gap is increasing between the frontier and open models.
Unclear how much of this is because of intense compute shortages. Regardless, seems like Europe should be building more compute.
As always, very thoughtful—thank you. A few points:
1. After the initial euphoria, most consumers—whose budget constraints are binding, with budget shares adding up to 100% and not fully adjustable—will choose to use the older, less expensive versions, much like they stick to old iPhones and cars.
2. This trend will intensify competition.
3. Additionally, market participants know that tacit collusion is very difficult when n > 2;
4. So the war of attrition will continue, but not forever.
5. In this war, the announcements of future spending signal a determination to outspend rivals. However, if the previous arguments hold, these signals may carry limited credibility.
Thesis 3 is the one that should reshape the investment conversation. If intelligence itself isnt the bottleneck, then the hundreds of billions being poured into producing more of it are being invested in the wrong constraint. The binding constraint is organisational, how companies restructure workflows to actually absorb what the models can already do. That makes the AI value chain look like a barbell: durable margin at the hardware chokepoints above and at the implementation layer below, with the model layer squeezed between customers who can switch and suppliers who can't be replaced.
The EMI parallel is the sharpest diagnosis in the piece. The labs are building in the competitive middle of their own value chain, compelled to reinvest every dollar into staying ahead of a frontier that the open-weight fringe compresses from below. Thats a structurally unprofitable position regardless of how impressive the technology is. The market is pricing the labs as if they occupy the chokepoint. The IO economics say they occupy the squeeze zone.
I feel like the body of your argument goes in the opposite direction of the thesis. Frontier models will be commoditized, making OpenAI-vs-Anthropic into an unprofitable fight. Okay that’s a reasonable theory. Therefore… Europe should spend lots of money to join the fight?
The logical conclusion would be, *don’t* try to join the fight. Instead encourage companies that are complementary. Ie, companies that extensively use the OpenAI or Anthropic APIs, and thus are benefited by competition between the two.
Well, we don't want to join the fight! We are advocating staying several tiers behind, as one of the strategies (together with the ones in the first piece on "Smart Second Mover" along the lines you suggest) to succeed in a world where uncertainty is high and the imperative is keeping some options and some choice.
Well, you already have Mistral. The market of "companies who are forced by regulation to buy a European product" seems critical to them. If anything, it seems more likely that some government funded competitor would hurt Mistral and then fall apart, than that it would have an effect on the broader AI model market beyond Europe.
ElevenLabs and Helsing both seem promising, too - not competing at all in the fundamental model space, but at something slightly different. If I were designed European policy, I would try to be encouraging more companies along those lines, rather than trying to get government entities to write software that takes on OpenAI's market head on.
As one of the co-authors of the CERN for AI piece you cite, I've definitely moved away from a publicly-funded effort to directly compete with labs at the frontier, but I'm also sceptical of strategies that rely on open models.
At the very least, Chinese open models appear to be falling further and further behind the frontier: https://www.nist.gov/news-events/news/2026/05/caisi-evaluation-deepseek-v4-pro
It looks like Chinese models are misreporting their evaluation results and have inflated performance as a result -> when independently evaluated, the gap is increasing between the frontier and open models.
Unclear how much of this is because of intense compute shortages. Regardless, seems like Europe should be building more compute.
As always, very thoughtful—thank you. A few points:
1. After the initial euphoria, most consumers—whose budget constraints are binding, with budget shares adding up to 100% and not fully adjustable—will choose to use the older, less expensive versions, much like they stick to old iPhones and cars.
2. This trend will intensify competition.
3. Additionally, market participants know that tacit collusion is very difficult when n > 2;
4. So the war of attrition will continue, but not forever.
5. In this war, the announcements of future spending signal a determination to outspend rivals. However, if the previous arguments hold, these signals may carry limited credibility.
Thesis 3 is the one that should reshape the investment conversation. If intelligence itself isnt the bottleneck, then the hundreds of billions being poured into producing more of it are being invested in the wrong constraint. The binding constraint is organisational, how companies restructure workflows to actually absorb what the models can already do. That makes the AI value chain look like a barbell: durable margin at the hardware chokepoints above and at the implementation layer below, with the model layer squeezed between customers who can switch and suppliers who can't be replaced.
The EMI parallel is the sharpest diagnosis in the piece. The labs are building in the competitive middle of their own value chain, compelled to reinvest every dollar into staying ahead of a frontier that the open-weight fringe compresses from below. Thats a structurally unprofitable position regardless of how impressive the technology is. The market is pricing the labs as if they occupy the chokepoint. The IO economics say they occupy the squeeze zone.
I feel like the body of your argument goes in the opposite direction of the thesis. Frontier models will be commoditized, making OpenAI-vs-Anthropic into an unprofitable fight. Okay that’s a reasonable theory. Therefore… Europe should spend lots of money to join the fight?
The logical conclusion would be, *don’t* try to join the fight. Instead encourage companies that are complementary. Ie, companies that extensively use the OpenAI or Anthropic APIs, and thus are benefited by competition between the two.
Well, we don't want to join the fight! We are advocating staying several tiers behind, as one of the strategies (together with the ones in the first piece on "Smart Second Mover" along the lines you suggest) to succeed in a world where uncertainty is high and the imperative is keeping some options and some choice.
Well, you already have Mistral. The market of "companies who are forced by regulation to buy a European product" seems critical to them. If anything, it seems more likely that some government funded competitor would hurt Mistral and then fall apart, than that it would have an effect on the broader AI model market beyond Europe.
ElevenLabs and Helsing both seem promising, too - not competing at all in the fundamental model space, but at something slightly different. If I were designed European policy, I would try to be encouraging more companies along those lines, rather than trying to get government entities to write software that takes on OpenAI's market head on.