/Tech4h ago

Aaron Levie argues global AI regulation is a prisoner's dilemma where unilateral US restrictions risk ceding the frontier lead to China

METR's Chris Painter agreed, citing critical US compute decisions

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#195

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Aaron Levie@levie#195inTech

AI regulation is far less simple than it looks. It’s prisoners dilemma at insane scale.

In theory if all leading AI labs globally agreed to the same process of review and slow down, then we’d get frontier intelligence at similar rates and it diffuses relatively evenly.

If the US remains at the frontier at all times, and has heavy regulation on the release of intelligence then we end up with an economic and geopolitical edge because we can control who has access to frontier intelligence.

If we delay model releases, however, and another player - specifically China - doesn’t slow down and has equally strong models (not now but soon?) then our delays end up advantaging their models and eventually their tech stack.

Now, the US could ban these models, but that actually only puts the US at a steeper disadvantage because other countries won’t have those bans. Then, from a relative competitiveness standpoint the US has now fallen behind even though it started in front.

So, none of this is as simple as it looks. At some point it’s a simple bet of can closed models remain at the frontier in perpetuity or is there a risk of any other player or market catching up or just not falling behind.

9:55 PM · Jun 25, 2026 · 28.5K Views

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Negative users criticized AI regulation in the prisoner's dilemma for enabling government corruption, overreach, and lost US leadership like Europe's slowdown, while positive users endorsed acceleration and open-sourcing instead.

Pos

36.1%

Neg

63.9%

21 comments with sentiment.

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Beff (e/acc)@beffjezos

This is why you simply cannot stop the acceleration.

Aaron Levie@levie

AI regulation is far less simple than it looks. It’s prisoners dilemma at insane scale.

In theory if all leading AI labs globally agreed to the same process of review and slow down, then we’d get frontier intelligence at similar rates and it diffuses relatively evenly.

3h5.9K468

Mr. House@USArmyPhoenix

@levie The frontier models aren’t being regulated nor slowed down, just the frontier models’ public access is.

HUGE DIFFERENCE

4h25

Quinn Slack@sqs

@levie Yeah, and in any situation where the US imposes heavy regulations or bans, there are two other costs: government encroachment on our freedom, and the very real risk that the government won't get it 100% right.

4h1365

Eric Pate@Eric_P8

Tomorrow, the government could unilaterally declare all (non-regime) and open-weight models CP.

An "internal legal opinion" that "any machine capable of generating CP is CP" could be floated.

Then, they upload hashes of all non-regime/open models to NCMEC

Overnight Microsoft, Google, Apple, Drop Box, figma, whoever all flags your use of {model} back to the mothership via the censorship and mass surveillance engine you allowed them to build "for the children".

4h69

Chris Painter@ChrisPainterYup

@levie Good summary! I think what the U.S. “spends” it’s lead on could matter a lot. And so long as you’re in the lead, how you handle the models, what you allocate compute to, and what kinds of research you incentivize, might matter a lot

Aaron Levie@levie

AI regulation is far less simple than it looks. It’s prisoners dilemma at insane scale.

In theory if all leading AI labs globally agreed to the same process of review and slow down, then we’d get frontier intelligence at similar rates and it diffuses relatively evenly.

4h40520

X Finds@X_Finds

@levie Libs don’t care about this, just like they don’t care about, women, black people, trans or even Democrats, as we’re seeing now. Whatever they can leverage to gain power is the only thing the strive for. Identity politics, AI style.

4h30

Rick Gigger@rgigger

@levie The simple part is that there’s no reason to assume this administration will get it right because they are both corrupt and incompetent.

3h11

Mr. House@USArmyPhoenix

The claim is accurate:

US policy as of June 2026 does not impose mandatory regulation or slowdowns on the training and development of frontier AI models; labs continue scaling rapidly without government-mandated pauses or compute limits.

• Focus is on release and access: A recent Trump administration Executive Order creates a voluntary framework for developers to grant federal agencies up to 30 days of pre-release access to certain “covered frontier models” for cybersecurity review, with explicit disclaimers against any mandatory licensing or pre-approval for development, publication, or distribution.

• Key distinction holds: Public deployment is more tightly managed through APIs, safeguards, tiered access, and voluntary government coordination, whereas core model training faces no equivalent restrictions—aligning with the “huge difference” noted in the post.

4h5

Mathew Richards@mathewtweetsai

I replied something similar on your earlier post comparing lab-to-lab releases/mobility. I think the same applies here in the U.S. vs China comparison. What of the companies already allowed into these Mythos-class-type programs and their competitors who are excluded?

3h135

Grant Rowberry@grantrowberry

@levie Pedal to the metal is the only real option right?

4h301

Colin Hayhurst@ColinHayhurst

@levie It is simple if you frame the problem for all humanity, instead of a problem for a country. Call me naive if you like, but sometimes it takes one leader to stand up and start a movement.

3h75

Michiel van Geel@MFvGeel

AI regultation is worth shit as long as everyone keeps putting garbage in. Years ago I made the prediction that it's not about the AI that's fastest, it's about the AI we can trust. Making your data AI-ready through labeling (metadata) and separating scientific grade information from random fluff is key to creating that AI we can trust. Citations will ultimately reduce the hallucinations and reduce the need for review for other purposes than scientific discovery and increased accuracy. RAG agents are the future and the market is still slow to catch up to that idea.

4h181

Hendrik Krack@HKrackDev

@levie The hard part is that every lab can agree with the principle and still be punished first for acting on it.

4h56

Hendrik Krack@HKrackDev

@levie I don't believe the U.S. will keep frontier models regulated for long (at least not under this administration) -the benefits are to big and the pressure to high. I hope that we are conscious enough to think about the consequences for society, especially on those who can't keep up

4h52

The Count of Monte Sisqó@_Billdozer_

@levie what happened to scaling? should the U.S. be decades now from all this recursive improvement talked about on here, guess not

4h37

Jess Fields@jessalanfields

We cannot shoot ourselves in the foot. That’s the answer.

For innovation to happen there must be consistency in rules and regs. Why haven’t these AI companies hired experienced local yokels like me (not advertising but why not) to guide them through the “this will piss off the votes you need to keep at bay” process.

Once you’ve whipped up the hornets’ nest, it’s not going to be easy to get them calmed down again. Right now, I see VERY limited forward-looking clarity for data center expansion and model innovation, and I’ve been ringing this bell since last year.

If CEOs don’t pay attention to anyone but board members and institutional shareholders, we’ll never get out of the atmosphere on AI and effectively become slaves to Chinese tech in half a generation.

4h101

Weeb@CryptoWeeb808

@levie What makes this so difficult is that every solution creates a different bottleneck.

Reducing one risk often increases another.

The challenge isn’t eliminating tradeoffs, it’s deciding which tradeoffs are the most acceptable.

4h91

EJ Campbell@ejc3

@levie But if we don't release models, how will China ever catch up? /s

https://arstechnica.com/tech-policy/2026/06/anthropic-claims-alibaba-defied-trump-to-attack-claude-and-steal-capabilities/

4h29

Anthr@X@0xAnthraX

@Eric_P8 @levie Except legal opinions get challenged in court and u only change 1 byte the whole hash will change.

3h81

Luis Lozano@luislozanog86

China distilled from Claude and GPT 5.5 to bootstrap GLM, based on different studies and accusations that they caught the models identifying as Claude 50% of the time.

Distillation only solves the cold start and that's where the pipeline is proven to have worked and ready to distill Mythos/GPT 5.6 once its available, and from there RL compounds on its own.

That's why gating matters now: if China gets this tier, they close the gap permanently. The government isn't slowing US labs, it's protecting what the believe is the last leading position.

I don't agree with the approach and this is an educated read.

3h25