I am no-where near as concerned by this as I was a year ago, when I was expecting the axe to fall at any moment before the Chinese labs achieved some sort of escape velocity. I now think it's too late, all the cats are out of all the bags, there's no moat except maybe a temporal one of a few months, the genie is out of the bottle.
There is no secret sauce the US labs have that the Chinese ones don't, or won't have soon enough. Deepseek 4 and Kimi 2.5 are not quite Claude 4.5/GPT5.5 but there's no fundamental principle missing - they are strong evidence that there's no real advantage the "frontier" labs possess that isn't related to scale, which they will gain in time (if they even need to). The RL post-training techniques that work are widely known and easily copied. All Deepseek is really lacking is data, which they're getting - and the harder Anthropic/the USG makes it to access claude in china, the more of that precious data they'll get!
I used to sort of entertain the "fast take-off breakaway" scenario as being plausible but not really anymore. The only genuine moat the frontier labs have is their product take-up, which isn't nothing, far from it, but it's not some unbreakable technological wall. Too late guys - it might have been too late for quite some time.
I wish it was true. I would gladly use a GPT 5.2 high model equivalent for coding (6 months old) if it was offered cheaper by Deepseek or Kimi. And I'm sure that's an extremely prevalent opinion by the millions of Claude and Codex users who are bothered by the costs.
However, they just don't perform that well in practice. That's the real issue. You can actually see it when you move away from open benchmarks. Deep seek 3.2 is 4% on Arc-AGI 2 [1], while GPT 5.2 high is 52% and GPT 5.5 pro high is 84.6%. That's the real reason why nobody is using these models for serious work. It's incredibly frustrating.
In addition, I already feel the pain myself on the model restriction. I'll asking my codex 5.5 agent to crawl a website - BOOM, cybersecurity warning on my account. I'll ask it to fix SSH on my local network - another warning. I'm worried about the day my account would be randomly banned and I cannot create a new one. OpenAI already asks you to perform full identification in order to eliminate these warnings - probably exactly for that - so that if they ban you, it's permanent.
I agree the genie is out of the bottle technologically. I'm less convinced that means access stops being politically and economically important. The bottle may be gone but the best lamps are still expensive
But a “good enough” lamp just got a lot cheaper. The cost of tokens on DeepSeek V4 Pro is so low I don’t even think about and currently am trying to figure out useful things for as many agents simultaneously running as I can. What would have cost $150 less than a year ago now costs 35¢.
Likewise Qwen 3.6 absolutely blows me away and that’s on a 35b 6-bit model on a local 5090. Same thing, busy trying to find stuff to do to keep it busy 24/7.
I can still find some niches for Opus 4.7 but being able to attack problems and not worry about consumption is a game changer.
> There is no secret sauce the US labs have that the Chinese ones don't, or won't have soon enough
This is not just about mainland China though. The current US government is extremely selfish and self-centered. Other countries really need to consider for their own long-term situation here.
No mention of open weights anywhere in the piece, which is weird. Qwen, Llama, DeepSeek are months behind frontier, not years. If you're a European startup worried about getting cut off from Anthropic's API in 2027, the real question is what the open-weight frontier looks like then. Probably pretty capable. That undercuts most of the doom scenario.
Also, he concedes Mythos-level capabilities will be cheap next year, then handwaves it with "you need the best AI, not good-enough AI." For most use cases, frontier minus six months is fine.
Affordability of hardware that can run local LLMs is a real factor, too. Not sure when RAM prices are going down, but with everything that’s happening and can happen in the world right now, it doesn’t look like it’ll drop in the near or medium-term
No one is going to run models that are comparable to frontier locally without spending enormous sums for use at scale or in large orgs. Even with cheap RAM, you will still need a very large budget for frontier-level capability.
Open models that are competitive with frontier will be used on shared hosts.
Models have been capped out on training and (active) parameters a while ago, its tooling / harness that is making the big jumps in performance happen. And then you have things like DeepSeek with a pretty small KV cache.
And with the extreme chip shortages for the next two years, there's little appetite for even bigger models anyway.
Barring a breakthrough in scaling, the only direction the models can really go is smaller. Which will inevitably mean better performing local models for same chip budget.
Open weights undercut the absolute cutoff scenario. They don't fully solve the question of who gets the best model first, who gets enough tokens to use it heavily, and who gets to integrate it into sensitive workflows without waiting for permission
Llama is not months behind GPT 5.5 Pro. I don't think Qwen or DeepSeek are either.
edit: I'm specifically referring to the "5.5 Pro" model, not regular 5.5 with Pro tier subscription. Claude has no model available that's comparable to 5.5 Pro either.
I’ve used DeepSeek 4 Pro through Claude. It’s fine. Plans are similar to what sonnet/opus make. Same massage-the-plan -> massage-the-code loop. Maybe the code is a bit worse, but that’s the “months behind” thing.
The thing is, vast majority of code tasks aren’t a venture into the unknown. We as an industry for the most part build CRUD interfaces and dashboards. That can be achieved, with supervision, with frontier open-weights models quite well.
I think maybe you are both right. Perhaps AI coding assistants just don't need to be all that smart in many cases, so open weights models are fine. At the same time, frontier models are advancing in other domains, like mathematics, where raw intelligence is a more important factor.
I can’t compare raw intelligence of these models, and I certainly can’t say anything about their advances in mathematics (without repeating press releases). But, erm, does it really matter? It’s not like some engineer somewhere will vibe-calculate how much weight a bridge can hold.
Well, yes, someone probably will do that. But I’m pretty sure there will be consequences for the engineer errors in this vibe-calculations.
Open models are pretty good at this point but the problem is that they are limited by the tooling and infrastructure that surrounds them. For example, the last time I tried to set up web search with an open model, the experience was pretty bad.
The uncomfortable implication is that "AI sovereignty" may end up being less about training your own GPT-class model and more about securing compute, energy, datacenter security and contractual access
The distillation risk has been brewing for a while now. In a very real sense, the model is the data, so if the data is locked down because of how valuable it is, it was only a matter of time before fully open access to the models would be revoked.
There's also an additional economic concern that rarely gets mentioned: because no one has cracked continual learning, keeping models up-to-date and filling in gaps in performance requires retraining on an ever growing dataset. Granted, you aren't starting from scratch each time, but the scaling required just to stay relevant looks daunting.
I don't know where any this goes on a societal level, but I've believed since the release of deepseek r1 that access to frontier models would eventually be locked up behind contracts, since the only moats protecting the models themselves are purely artificial. It remains to be seen how effective China is at pushing the envelope, and whether they are interested in providing unfettered access. And on top of that, it remains to be seen how well these models actually turn out to scale in the long run.
> “The two AI superpowers are going to start talking. We’re going to set up a protocol in terms of how do we go forward with best practices for AI to make sure nonstate actors don’t get a hold of these models,” Bessent told Joe Kernen on Thursday, on the sidelines of President Donald Trump’s two-day meeting in Beijing with Chinese President Xi Jinping.
> And it doesn’t stop with the security questions: the Trump administration’s signature style of international engagement is to wield American leverage as a bundle. Deadlocks in trade negotiations are broken by threatening to withhold intelligence, tech deals are stalled by reference to food safety standards. And so I don’t know when a U.S. administration would choose to leverage its seemingly inevitable predeployment authority over frontier models to secure its broader interests, but I’m sure it would in due time. That means that even if we do everything ‘right’ on the security and economic side, frontier access is still fundamentally contingent as long as there’ll be divergences between governments’ strategic interests.
The Trump Administration telling the very neo-fascist oligarchs who bought him an election and bought him a ballroom to play nice with their toys? At the expense of rampant capitalism? Lol.
He already showed us the limit of his comprehension of the topic when he made EO 14179 limiting states from regulating AI.
Trump doesn't swing for perfect pitches. He is a madman, a lunatic, and a true moron. Do not give this man any credit. I would be shocked if he could tell you the time on an analog clock.
> very neo-fascist oligarchs who bought him an election
Gotta low how it is ok to question the results of the latest presidential election but not the earlier one which is supposed to be sacrosanct, but again ok to question the one in 2016.
Somehow Trump is owned by capitalists but also starts trade/actual wars that thwart their agenda. I never understood how people come up with simplistic reductionistic views full of inconsistencies. Won't the evil capitalists and Neo fascists be served better with a predictable/controllable president?
I am no-where near as concerned by this as I was a year ago, when I was expecting the axe to fall at any moment before the Chinese labs achieved some sort of escape velocity. I now think it's too late, all the cats are out of all the bags, there's no moat except maybe a temporal one of a few months, the genie is out of the bottle.
There is no secret sauce the US labs have that the Chinese ones don't, or won't have soon enough. Deepseek 4 and Kimi 2.5 are not quite Claude 4.5/GPT5.5 but there's no fundamental principle missing - they are strong evidence that there's no real advantage the "frontier" labs possess that isn't related to scale, which they will gain in time (if they even need to). The RL post-training techniques that work are widely known and easily copied. All Deepseek is really lacking is data, which they're getting - and the harder Anthropic/the USG makes it to access claude in china, the more of that precious data they'll get!
I used to sort of entertain the "fast take-off breakaway" scenario as being plausible but not really anymore. The only genuine moat the frontier labs have is their product take-up, which isn't nothing, far from it, but it's not some unbreakable technological wall. Too late guys - it might have been too late for quite some time.
I wish it was true. I would gladly use a GPT 5.2 high model equivalent for coding (6 months old) if it was offered cheaper by Deepseek or Kimi. And I'm sure that's an extremely prevalent opinion by the millions of Claude and Codex users who are bothered by the costs.
However, they just don't perform that well in practice. That's the real issue. You can actually see it when you move away from open benchmarks. Deep seek 3.2 is 4% on Arc-AGI 2 [1], while GPT 5.2 high is 52% and GPT 5.5 pro high is 84.6%. That's the real reason why nobody is using these models for serious work. It's incredibly frustrating.
In addition, I already feel the pain myself on the model restriction. I'll asking my codex 5.5 agent to crawl a website - BOOM, cybersecurity warning on my account. I'll ask it to fix SSH on my local network - another warning. I'm worried about the day my account would be randomly banned and I cannot create a new one. OpenAI already asks you to perform full identification in order to eliminate these warnings - probably exactly for that - so that if they ban you, it's permanent.
[1] https://arcprize.org/leaderboard
I agree the genie is out of the bottle technologically. I'm less convinced that means access stops being politically and economically important. The bottle may be gone but the best lamps are still expensive
But a “good enough” lamp just got a lot cheaper. The cost of tokens on DeepSeek V4 Pro is so low I don’t even think about and currently am trying to figure out useful things for as many agents simultaneously running as I can. What would have cost $150 less than a year ago now costs 35¢.
Likewise Qwen 3.6 absolutely blows me away and that’s on a 35b 6-bit model on a local 5090. Same thing, busy trying to find stuff to do to keep it busy 24/7.
I can still find some niches for Opus 4.7 but being able to attack problems and not worry about consumption is a game changer.
Virtually no one is going to pay for the best performing lamp if the next best lamp does 90% as good for an order of magnitude cheaper.
I will say, as pointed out by others, DeepSeek and other Chinese providers still lack a bit in the tooling that Claude has, but they'll get there.
If the second-best lamp is 90% as good and 10x cheaper, most people will use the second-best lamp...
> There is no secret sauce the US labs have that the Chinese ones don't, or won't have soon enough
This is not just about mainland China though. The current US government is extremely selfish and self-centered. Other countries really need to consider for their own long-term situation here.
No mention of open weights anywhere in the piece, which is weird. Qwen, Llama, DeepSeek are months behind frontier, not years. If you're a European startup worried about getting cut off from Anthropic's API in 2027, the real question is what the open-weight frontier looks like then. Probably pretty capable. That undercuts most of the doom scenario.
Also, he concedes Mythos-level capabilities will be cheap next year, then handwaves it with "you need the best AI, not good-enough AI." For most use cases, frontier minus six months is fine.
Affordability of hardware that can run local LLMs is a real factor, too. Not sure when RAM prices are going down, but with everything that’s happening and can happen in the world right now, it doesn’t look like it’ll drop in the near or medium-term
No one is going to run models that are comparable to frontier locally without spending enormous sums for use at scale or in large orgs. Even with cheap RAM, you will still need a very large budget for frontier-level capability.
Open models that are competitive with frontier will be used on shared hosts.
Models have been capped out on training and (active) parameters a while ago, its tooling / harness that is making the big jumps in performance happen. And then you have things like DeepSeek with a pretty small KV cache.
And with the extreme chip shortages for the next two years, there's little appetite for even bigger models anyway.
Barring a breakthrough in scaling, the only direction the models can really go is smaller. Which will inevitably mean better performing local models for same chip budget.
Open weights undercut the absolute cutoff scenario. They don't fully solve the question of who gets the best model first, who gets enough tokens to use it heavily, and who gets to integrate it into sensitive workflows without waiting for permission
Llama is not months behind GPT 5.5 Pro. I don't think Qwen or DeepSeek are either.
edit: I'm specifically referring to the "5.5 Pro" model, not regular 5.5 with Pro tier subscription. Claude has no model available that's comparable to 5.5 Pro either.
I’ve used DeepSeek 4 Pro through Claude. It’s fine. Plans are similar to what sonnet/opus make. Same massage-the-plan -> massage-the-code loop. Maybe the code is a bit worse, but that’s the “months behind” thing.
The thing is, vast majority of code tasks aren’t a venture into the unknown. We as an industry for the most part build CRUD interfaces and dashboards. That can be achieved, with supervision, with frontier open-weights models quite well.
I think maybe you are both right. Perhaps AI coding assistants just don't need to be all that smart in many cases, so open weights models are fine. At the same time, frontier models are advancing in other domains, like mathematics, where raw intelligence is a more important factor.
I can’t compare raw intelligence of these models, and I certainly can’t say anything about their advances in mathematics (without repeating press releases). But, erm, does it really matter? It’s not like some engineer somewhere will vibe-calculate how much weight a bridge can hold.
Well, yes, someone probably will do that. But I’m pretty sure there will be consequences for the engineer errors in this vibe-calculations.
Open models are pretty good at this point but the problem is that they are limited by the tooling and infrastructure that surrounds them. For example, the last time I tried to set up web search with an open model, the experience was pretty bad.
The uncomfortable implication is that "AI sovereignty" may end up being less about training your own GPT-class model and more about securing compute, energy, datacenter security and contractual access
The distillation risk has been brewing for a while now. In a very real sense, the model is the data, so if the data is locked down because of how valuable it is, it was only a matter of time before fully open access to the models would be revoked.
There's also an additional economic concern that rarely gets mentioned: because no one has cracked continual learning, keeping models up-to-date and filling in gaps in performance requires retraining on an ever growing dataset. Granted, you aren't starting from scratch each time, but the scaling required just to stay relevant looks daunting.
I don't know where any this goes on a societal level, but I've believed since the release of deepseek r1 that access to frontier models would eventually be locked up behind contracts, since the only moats protecting the models themselves are purely artificial. It remains to be seen how effective China is at pushing the envelope, and whether they are interested in providing unfettered access. And on top of that, it remains to be seen how well these models actually turn out to scale in the long run.
This is a good point, especially the "model is the data" framing
Quote:
> “The two AI superpowers are going to start talking. We’re going to set up a protocol in terms of how do we go forward with best practices for AI to make sure nonstate actors don’t get a hold of these models,” Bessent told Joe Kernen on Thursday, on the sidelines of President Donald Trump’s two-day meeting in Beijing with Chinese President Xi Jinping.
https://www.cnbc.com/2026/05/14/us-china-ai-rules-bessent-us...
OpenAI is already talking openly about gated access to their models (see this OpenAI podcast episode for example: https://openai.com/podcast/#oai-podcast-episode-16)
Separately there's also a very active effort to stop open weight releases.
It's dangerous to those who think access to frontier intelligence is important.
What's the likelihood that universities eventually become open model providers?
So now AI is about apartheid. I am not liking this at all.
Damn. I predicted this last year and got thrashed for it.
Glad to see others catching on.
> And it doesn’t stop with the security questions: the Trump administration’s signature style of international engagement is to wield American leverage as a bundle. Deadlocks in trade negotiations are broken by threatening to withhold intelligence, tech deals are stalled by reference to food safety standards. And so I don’t know when a U.S. administration would choose to leverage its seemingly inevitable predeployment authority over frontier models to secure its broader interests, but I’m sure it would in due time. That means that even if we do everything ‘right’ on the security and economic side, frontier access is still fundamentally contingent as long as there’ll be divergences between governments’ strategic interests.
The Trump Administration telling the very neo-fascist oligarchs who bought him an election and bought him a ballroom to play nice with their toys? At the expense of rampant capitalism? Lol.
He already showed us the limit of his comprehension of the topic when he made EO 14179 limiting states from regulating AI.
Trump doesn't swing for perfect pitches. He is a madman, a lunatic, and a true moron. Do not give this man any credit. I would be shocked if he could tell you the time on an analog clock.
> very neo-fascist oligarchs who bought him an election
Gotta low how it is ok to question the results of the latest presidential election but not the earlier one which is supposed to be sacrosanct, but again ok to question the one in 2016.
Somehow Trump is owned by capitalists but also starts trade/actual wars that thwart their agenda. I never understood how people come up with simplistic reductionistic views full of inconsistencies. Won't the evil capitalists and Neo fascists be served better with a predictable/controllable president?