Reformed lawyer; prev. Stanford, LSE. Go Knicks. đŸ‡«đŸ‡ź/đŸ‡±đŸ‡š/đŸ‡ș🇾

Joined August 2016
Miika Weekes retweeted
If you think written human slop is worse than AI slop you have surrounded yourself with the wrong people
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Miika Weekes retweeted
Freedom of Intelligence Anthropic has created a dangerous, destabilizing mess by lobbying for and getting US government restrictions on models like Mythos, Fable, and GPT-5.6. Now the US government is deciding who has access to which models, and the best models are accessible only to a very few and a very rich set of companies. Nobody wants that. Even Anthropic doesn’t like what happened. Now the rest of us need to clean up the mess. How? We need to fight for our freedom of intelligence, the freedom from government restrictions on who can use which AI models. If we allow government to decide what level of intelligence someone can access, no matter how well intended, we’ll be less safe and forever divided. What Freedom of Intelligence means Freedom of intelligence means the government may not restrict which AI models you can use. This means that the government must not require licensing of model labs, or approval of models prior to release. Otherwise government inevitably will use that power to restrict releases to certain favored individuals and companies (as we’ve just seen) and to introduce biases. Freedom of intelligence also means that the government may not prohibit you from downloading and running open models. If someone commits a crime with the use of AI, that already is illegal and should remain illegal. The government must not force a model lab to release a model against its wishes. If a model lab chooses to release their own model to only a few privileged people and companies (as Anthropic did with Mythos), or to keep it internal, that is their right. Other model labs can compete by serving the rest of the market. It shouldn’t be illegal to offer frontier intelligence to small businesses, startups, and individuals. Intelligence is fundamental When people argue against freedom of intelligence, they say: AI is powerful and sometimes dangerous, and we’ll be safer if the right people control AI the right way. They’re right about the first part and naive about the second part. For something as fundamental as intelligence, there is no such thing as the “right people” to control intelligence, nor the “right way” to control intelligence. People will disagree. People already disagree very, very strongly. In a democratic society, the only stable equilibrium for a bitterly divided realm is to grant individual freedom. Intelligence is not the same as speech or religion, but it is every bit as powerful and dear and deserving of freedom. There is no democratic way to regulate access to intelligence Nobody likes the current US government policy on model restrictions. Nobody really knows what it is, even, or knows what it will be next week. Today, Monday, June 29, 2026, the US government is choosing which people and companies can and can’t access Anthropic’s and OpenAI’s frontier intelligence. Who is deciding? Based on what criteria? Nobody knows. Maybe you think that the US government’s behavior in the last few weeks is a blip, and that the “right people” will control AI the “right way” soon. Maybe you hope, like Dario Amodei, that “qualified third-party”[1] regulators shielded from “political favoritism or arbitrary decisions” will swoop in and take control of AI policy. That’s just not how it works in our political system, certainly not for a high-salience, zero-sum issue like access to intelligence. We would never, ever, ever pass a regulatory apparatus where the most important national policy decisions are decided by unelected experts, free from accountability to the voters. Nor should it pass. (Ironically, the only way it might pass is if Anthropic is the politically favored one, which would violate Dario’s own stated proposal.) But suppose Dario gets lucky and his “Federal AI Control Administration” (my name for it) is created. And suppose on day 1, the Federal AI Control Administration approves the release of Claude Mythos 5, but only to ~100 of the biggest corporations in the US, in order to limit the risk. (Dario would support this government action, presumably, since it’s what Anthropic itself deemed optimal.) On day 2, the Federal AI Control Administration starts deciding which companies should get access to GPT-5.6. Suddenly, “AI safety” has turned into “picking winners and losers”, because it’s safer to not give frontier intelligence to everyone. Of course, this is the actual reality today. Does this sound like the kind of thing that voters in a democracy, already distrustful of AI and of corporate power, would support? No. Is this stable? No. Play it forward a bit. What do you think the 101st biggest company, denied frontier intelligence by the US government, does first: sue or curry political favor? What do you think the US executive branch does with this newfound power? What do you think Anthropic’s corporate rivals, like Amazon and Google and OpenAI, do with their newfound powers to summon arbitrary regulatory fury on each other? There’s no way to sustain a stable, democratic arrangement where government controls access to intelligence. The more powerful you think AI is, the less stable is any attempt to regulate access to intelligence. (By the way, I truly believe Dario and AI safety adherents are true believers with good intent. I am not arguing that they are evil or greedy.) Freedom is counterintuitively stable My biggest fear is that we’ll oscillate around bad AI regulation, with daily distractions and growing corruption, not realizing that the only stable equilibrium is freedom of intelligence. While intelligence is not exactly like speech, the analogy to freedom of speech is useful. Both speech and intelligence are powerful and sometimes dangerous. For thousands of years, kings and despots tried just banning bad speech, imposing probably well-intended “speech safety policies” (i.e., jailing and exiling and killing dissenters). This didn’t work. Our smartest minds, trying as hard as they could for thousands of years, having tamed fire, water, animals, wind, and space, never figured out a way to regulate truth. So, after trying literally every other speech policy, we arrived at freedom of speech: just let people speak, even if they’re wrong, even if their ideas are dangerous. This is, overall, the best policy. It’s counter-intuitive that allowing all the bad speech is better than just giving someone the power to decide what is “bad speech”. It’s so counter-intuitive that we call freedom of speech a human right, which is society’s way to say as strongly as possible, “we wrote this rule in blood, don’t mess with it.” I favor freedom of intelligence for the same reasons. Like speech, AI is powerful and sometimes dangerous. But it’s far more dangerous and unstable to give someone the power to decide what intelligence everyone else can use. Speak up now It feels risky to speak up. Friends and business partners share thoughts similar to mine here. I’ve talked to many of them in the past weeks. But these conversations happen in hushed tones, off the record. Why? Because Anthropic is a king and a kingmaker. We all use or have used their models, they’re great, and we’re scared of losing access or being shut out by them after criticizing them. Anthropic can unilaterally dictate the terms of their commercial relationships, including early access to new models, pricing, data retention, and much more. I have many friends at Anthropic. They’re great people and mean well. They don’t know what people truly think of Anthropic and its lobbying because everyone’s too afraid to speak up. But the more we speak up, the more Anthropic might be able to change from within. If you’re still afraid to speak up, feel free to reach out to me privately to chat ([email protected]). If Anthropic retaliates against me or you for speaking up on this grave matter of national policy that they’re also lobbying on, that would do more than anything to prove our point. How to fight for freedom of intelligence First we need to change minds, then we need to change laws. To change minds, go and talk to people in the real world about freedom of intelligence. Use whatever you find memorable from this post, and figure out your own way to convince people. Share what works. If you’re in San Francisco, join us on Tue Jun 30, 2026, at 6:30pm (link [2] in reply) to start discussing and pushing for freedom of intelligence. Otherwise, organize in your own city, to spread the word and normalize this freedom before we lose it. Why I’m hopeful Nobody, nobody wants access to intelligence to be limited to a very few, and a few rich companies. Freedom of intelligence has broad appeal. Let’s build that big tent.
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Miika Weekes retweeted
Agentic coding forces you to design clean interfaces and document them well. An agent cannot read the implicit mental model shared by your engineering team, it can only read your API contracts and docstrings.
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I want to explain why developing frontier models is such an awful business right now. The core problem is that frontier models have close substitutes, and those substitutes have a materially lower cost structure. Let me explain why that is a nightmare combination for profit capture. Most industries deal with substitutes. For ex, Gillette faces online razor brands and private label competition. That limits pricing power, but Gillette still has a superior cost structure that allows it to earn attractive profits. The LLM business is in a much worse position. The market clearing price is set by substitutes. But the frontier labs have a materially higher cost structure bc they must bear the full cost of discovery (R&D, training, failed experiments, infrastructure, etc). Meanwhile, distillation allows their outputs to become the raw material to create slightly inferior substitutes at a fraction of the cost. That's a uniquely brutal competitive dynamic. It might actually be the worst free-rider problem I’ve ever seen. If open weight models can deliver most of the capability at a fraction of the cost, then customers don’t just have to prefer the frontier model. They have to believe the frontier output is worth many, many times more on the marginal task. That is an extremely high bar. The hope is that frontier models will keep their advantage in the highest value tasks. Bc the more valuable the task, the more valuable the superior output becomes. But test-time scaling threatens that assumption too. If performance on long-horizon tasks keeps improving with more test-time compute, then a slightly inferior but much cheaper model can run longer for the same cost. So it can afford to spend more on search, verification, retries, decomposition, and tool use. In that world, cost efficiency becomes a capability advantage. That will matter most in domains where answers can be checked like, coding, math, data analysis, structured research, optimization problems, tool based workflows, and agentic tasks with objective feedback loops. In other words, the frontier labs’ current bread and butter. So with these long-horizon tasks, the cheaper substitute actually becomes even more of a threat. An industry structure where close substitutes have dramatically lower cost structures doesn’t just risk capping frontier model profitability. Once the full costs are factored in, it can prevent frontier labs from earning any economic profit at all. Frontier labs may eventually find a way to differentiate enough to earn real pricing power. But so far the opposite is happening
the substitutes are closing the gap.
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In hindsight very considerate to the UK-based fandom for the Knicks to get it done in only 19 games. Now just have to reacclimate to the time zone I actually live in.
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Miika Weekes retweeted
đŸ˜€đŸ˜€
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Build a museum on his planet and hang it there
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Miika Weekes retweeted
Providing evidence contrary to deeply held beliefs doesn’t always work. When beliefs are entrenched in one’s sense of self, logical claims can backfire. This can lead to a communication breakdown, especially when the logic runs counter to identity-oriented language, since we’re primed to react to perceived threats to our social groups [1]. 1/6
Replying to @JTLonsdale
Joe my friend you need to have better information hygiene about things like this. A minute of research could have cleared this up for you. chatgpt.com/share/6a22e0e7-a

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LOC as a performance metric is like deciding a debate based on who said the most words
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Miika Weekes retweeted
Context alone will not solve the fundamental problems of LLMs. No matter how much context engineering you do, there will still be hallucinations, misinterpretations of instructions, prompt injections and jailbreaks, and toll-call mistakes (e.g., deleting your files or emails). The true solution is to build the right system and scaffolding to make sure the AI works properly. - Always build with the assumption that what can go wrong will go wrong. - Set hard rules on tool calls as opposed to recommendations in the prompt. - Evaluate the sensitivity of each step of an automated workflow. If there is a slight chance that an LLM hallucination error will result in a disaster, put a human in the loop. AI can be a multiplier when you acknowledge and embrace its limitations. It will be a nightmare when you hope it works well every time.
AI at its core is a context problem. Solve context, and you can solve any field.
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Miika Weekes retweeted
"I get rebounds that break teams." This mic'd up moment between Josh Hart and Mitchell Robinson in 2023 😂 Hart finished with 15 REB in under 30 minutes in Game 1 of the 2026 NBA Finals!
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Miika Weekes retweeted
They're not lying. With AI coding agents, they can not only 100x but 1000x their output, which is 1 year's worth of code in 0.365 days. However, 1000x output doesn't mean 1000x productivity. When you defer this amount of your work to the agents, you lose all semblance of control. You are at the mercy of whatever the LLM thinks is useful. There is no way you can fully understand what the AI is doing.
I am so thoroughly convinced that anyone who thinks AI 100x's their output is a liar or a lunatic. You are telling me you can make 1 years worth of decisions in 3.65 days? Let alone describing those accurately and coaxing the result from the AI... (1.8 days european time)?
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Very interesting; time is a useful but imperfect metric. How is quality being measured? Are we comparing to prorated salaries? Feels like an incomplete step in the right direction.
Replying to @cognition
We built a system to estimate whether an agent’s output was actually productive, and if so, how long a human engineer would have taken to do the same work. Validated on real engineers’ time estimates working in enterprise codebases. Read the blog from @ScottWu46: cognition.ai/blog/ai-guarant

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Miika Weekes retweeted
“It’s like hiring Tom Cruise, finding out he charges by the stunt, and you don’t know if you’re making Jerry Maguire or Mission: Impossible” is a hilarious analogy for companies blowing their AI tokens.
Today, Ramp raised $750M at a $44B valuation. Last time we grew this fast, we were 1/20th the size. For 2000 years, business was built on two pillars. Today, a third: intelligence. It’s your least governed cost. It’s also your single greatest opportunity.
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Miika Weekes retweeted
The King of Hart
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Right direction imo; the real problem isn’t model quality, it’s organizational design and transparency. With the right oversight, provenance, and replicability systems OS models should be more than able to handle law firm workflows
We partnered with @FireworksAI_HQ to train open-source models for legal. Here's what we found: 1) Hybrid legal agents can beat frontier models on quality and cost by routing selectively to a frontier advisor. We tested a hybrid setup where GLM 5.1 served as the primary worker, routing tasks to Opus 4.7 as an advisor when needed. GLM invoked Opus sparingly, just 0.83 times per task on average. The hybrid setup beat Opus on both quality and cost: 18% all-pass vs 14%, at $368 vs $954 across the same 100 tasks. 2) Post-training can push open models to frontier-level legal performance. On a 100-task slice of our Legal Agent Benchmark (LAB), SFT moved Kimi 2.6's all-pass rate from 11% to 15%, beating Opus' 14%. But the cost gap was even more striking: $84 vs $954 across the same 100 tasks, or ~11x cheaper. We're excited to continue working with @FireworksAI_HQ on the next generation of open-source legal agents.
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