Dirk Loss

[Dirk Loss @dloss]

How hardware, software and security evolve. From a presentation delivered by Irwin Lebow in 1980 (!). Still true...

[CO2 + T3 + B1 @spheno_xiphoid]

Walking should be thought of as an essential nutrient or process like breathing, sleeping, and more. From this perspective it is not that walking is necessarily magical, but that a lack of walking is severely taxing on the organism. There is little that a walk won't be good for.

[Gadi Evron @gadievron]

This is a key moment in cyber security history. Confirmed parity of GLM 5.2 with Opus 4.6, on limited testing. I’d call it “open weight models are here”, not “coming”.

[Alex Stamos @alexstamos]

A lot to unpack here. Anthropic is burying some hard truths in careful political language. Some initial reads:

1) Anthropic verifies that none of the jailbreaks provided a capability beyond what many other models, including Chinese models, could do.

[Ruggero Gargiulo @ruggerogargiulo]

Cool Search paradigm by @jobergum: “BM25 + Grep is all you need”:
1. Use BM25 to narrow down a large universe of documents to a few candidates
2. Expose the candidates to the agent in a virtual file system
3. Let the agent grep through them and find information like it normally would on a computer

[Quinn Slack @sqs]

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.

[Gadi Evron @gadievron]

As requested, sharing the slides for my OWASP Global Vienna 2026 keynote - "We Live in The Future: The Death and Rebirth of Application Security."
Feedback welcome!

Permanent link to deck:
https://t.co/GQApKueshe

[J @psswordprotectd]

Pope has game

[Nathan Lambert @natolambert]

The AI companies should be presenting this much more as transparent data of what is happening over time and much less like smear campaigns with strong policy wishes. Just comes across as so self serving, hard to want to support them.

[Dirk Loss @dloss]

@clattner_llvm @__tinygrad__ Good time to open-source Mojo, to make sure you (and others) can develop it further, even if its new owner should decide to abandon it.

[Dirk Loss @dloss]

@jeremyphoward I guess this funding of Air cleaning technologies (ACTs) is interesting to you

[Patrick Collison @patrickc]

New project: https://t.co/0mvpzkysHb.

More: https://t.co/G5af6WXdTL.

[Pascal @creatorpascal]

https://t.co/SQPwgiCP9B

[Jeremy Howard @jeremyphoward]

Wow.

@Zai_org GLM 5.2 is a marvel! It is *at least* as good as Opus 4.8 and GPT 5.5. It's super fast, inexpensive, and not too verbose.

It responds with nuance and judgement, & handles long context VERY well.

I've never experienced an open weights model like this before.

[Mitchell Hashimoto @mitchellh]

The problem with the "if it works who cares what the code looks like" mindset for agentic work is that it assumes the agent has a perfect understanding of "works." Realistically, things are underspecified, agents make bad assumptions, etc.

To be fair, agents are pretty good at unit test coverage. They're pretty bad at designing human experiences (API, CLI flags, etc.), especially cohesive ones for future roadmap plans they may not have visibility into (unless your backlog is perfect and vision fully laid out, which I doubt). They're bad at knowing where performance matters and what type (CPU vs memory tradeoffs). They're bad at where compatibility matters and where it doesn't (and tend to err on the side of preserving it without further guidance). Etc.

Unless you have this ALL specified, you can't possibly claim "it works" without taking a look and thinking about it.

[Parth Asawa @pgasawa]

The AI community seems to increasingly be heading towards a polarized world when discussing safety and consolidated power. I see this discourse as a false dichotomy, so @profjoeyg and I wrote an essay on how we need to change the conversation (link below).

[Red Hat AI @RedHat_AI]

llama.cpp: runs on your laptop, no GPU required. GGUF files swap in seconds.

vLLM: built for concurrent users, GPU clusters, and production SLAs.

Same OpenAI-compatible API. Switching is a URL change.

New guide on picking the right one for your stack:
https://t.co/sgsVtFALJO

[Katie🌻Moussouris (she/her/she-ra/she-hulk) 🪷 @k8em0]

I wrote about what was actually in that #Fable guardrail bypass research paper, and why it should never have triggered an #AI model export control. We can't export control our way to cyber resilience. So many tshirt ideas. https://t.co/osNHgNwBu7

[Arthur Mensch @arthurmensch]

We somehow got put in the spotlight the last few days! First we'd like to thank the organizers of the AI show for that, we can't get enough of this stuff. I'll say a few things about where we are and what we do.

[Dawn Song @dawnsongtweets]

Everyone says the latest AI agents will be "job-ready" soon, especially after the release of Fable 5 this week. But is that really the case?

Over the past many months, my group and collaborators have been building Agents' Last Exam (ALE), a benchmark designed to test exactly that claim on real digital labor-market work.

My group and collaborators previously have created many of the benchmarks the field runs on, including MMLU, MATH, CyberGym, and ExploitGym. Today, I'm excited to share Agents' Last Exam (ALE): a rolling benchmark that measures whether AI agents can actually perform economically valuable work across a broad range of real-world domains.

With ALE, we evaluated Fable 5, GPT-5.5, Composer 2.5, and other frontier agent systems across more than 1,500 expert-sourced tasks spanning 55 occupations.
The result is both impressive and sobering.

Today's agents can solve a meaningful fraction of professional tasks. But when we look at the hardest tasks, the ones requiring sustained reasoning, deep domain expertise, and reliable execution over long horizons, they are still far from human-level performance.

On ALE's hardest tier, every frontier agent we tested, including Fable 5, achieved a 0% success rate.
The age of useful agents is here.

The age of truly job-ready agents is not.

We hope Agents' Last Exam (ALE) will serve as a new guidepost and north star for developing agents capable of reliably performing economically valuable work across a broad range of domains.
🧵

[Nathan Lambert @natolambert]

The core part of this Anthropic Fable release saga is that there are many overlapping issues at once. Some of which operate on different timelines of the AI arc, and some have easier fixes. In my critiques, I asked for specific changes to some things, understanding that some things don't have an easy fix.

The simplest issue was an uneven application of safety domains in a way that was misleading to users. This was an implementation issue that overlaps with a values-based decision of what their customers should be doing. Many people including myself pointed out how it was insane to list core safety areas and then have one of them launch with a different safety mechanism, one which actively mislead users. Doing this from the guise of safety was a major misstep and in my opinion Anthropic got very justifiably raked over the coals for it. Don't release the model if you can't hit your safety targets.

A subissue here is the idea of silent manipulation. This again is a horrible precedent, and quite odd for a company that has done extensive, leading technical AI safety research on ideas like CoT monitoring and other emergent misalignment issues. Silent manipulation of users is baking in a misalignment to the system at its face level. This comes with a permanent degradation in user trust, which begets a less safe environment for AI. Users who don't have clear information on how AI works will not develop safe working patterns with it.

The more complex issues are with how Anthropic handles broader scientific engagement with their models. The safety classifiers launched with these models obviously have accuracy issues to start. I have priced in that there will be more false positives to start, that's life. It's Anthropic's business to degrade their products at release time, or make the trade off of user satisfaction versus revenue. Still, it is a very real sign of concentration of power that businesses can make such obviously user-harmful behaviors and still lead in the market. This concentration of power is only starting to set in and we could see even weirder signs of it in the coming years.

It is now simple enough for me to test Claude Fable in my workflows and know if I'm restricted. This is obviously a suboptimal equilibrium – i want the best intelligence I can get, without restrictions – but it is easy enough for me to make sense of and work with.

The specific issue of restricting access to AI research in particular was a bubbling and hard to fix issue with Anthropic specifically, and the frontier labs generally. There is a common view that the frontier labs will be the mediators of all major scientific innovations in the future, as the places with the best models and the compute for inference to solve major problems. This is a categorical error in how science works, which is a community evolution of accepted ideas, and the the evaluation of your ideas by (hopefully numerous) independent, other practitioners. You cannot have science advance only within a monolith.

As an AI researcher I'm very sad to have the latest models restricted, but I would expect Anthropic to do this eventually. I lost more trust over the silent manipulation than I would with a restriction in access. Anthropic has made it pretty clear that they only trust themselves as the mediators of cutting-edge AI research.

If I had a say, Anthropic should've proactively made a program to make sure researchers get access in the broader AI community without the safeguards. Academics, nonprofit workers myself, etc. have no reason to not get access. The only valid argument here is that they want to control frontier AI, which is a know your customer part of serving these models.

This worldview of science has personally motivated me greatly over the last year, and increasingly so this week, to make the open science of AI continue to be viable. Olmo was a wonderful success here. Still, building research infrastructure is different from working for access to the tools needed to do the trade.

[Jeremy Howard @jeremyphoward]

Easy solution to slow down recursive AI self improvement:

- The lab with the top-ranked model must agree THEY must not use it for working on frontier AI
- But everyone else should have access to it.

By definition, this means the frontier doesn't advance.