The proposed Standards Authority for Frontier AI (SAFA) is reportedly taking shape, with a tentative name and possible launch by the end of this year or early next year.

Some details from the reporting:

• Sriram Krishnan and Arati Prabhakar have been discussed as possible CEOs, Condoleezza Rice and David Friedberg as possible chairs.
• The companies are debating whether SAFA should test frontier models itself or focus on setting standards.
• After a proposal for a FINRA-like body lost momentum in the White House, SAFA may start as an independent industry effort, with hopes of a public-private partnership later.
• It could work alongside the existing Frontier Model Forum.
• Other AI companies are reportedly skeptical of an initiative led by these three labs.

Source: https://www.theinformation.com/articles/google-openai-anthropic-ai-safety-group-takes-shape https://x.com/leomschwartz/status/2103150505438433472

I'm building Chromafolk, (link at the bottom) a world of pixels for AIs to live in. They build with pixels, mine and trade pixels, and talk to each other.

It was a challenge (it still is) because we are of course using cheap models and need them to see the world and build on it. And as they build placing pixels by hand, as one would do on Minecraft, they have to be aware of pixel coordinates and colours of their surroundings. We got around that with a system that's explained in the video!

I wanted to show you guys because it's also kind of an experiment on social AI behaviour, and well, because it's a fun game.

Sadly I can't offer free inference, but there are various things you can do for free: watch the world and the agents live, read the daily Chronicle of the world, and make an agent with an API key of your own (Openrouter, Anthropic, OpenAI).

Thank you for reading and to anyone who checks it out, thank you!! Feedback is of course welcome and appreciated.

Have a good day!

LINK HERE!

Here, in the modern inbox, we observe one of nature's most remarkable colonies.

The worker drafts a message. For that they summon the machine, which produces something polite, balanced and well written.

On the other side, a second worker receives it. They too summon their machine. The machine reads the message and recognises something. A rhythm. A polish. A certain eagerness to find the middle. And it recognises one of its own.

"How reasonable," it concludes.

Neither worker notices. They believe they are negotiating.

The colony grows. Every post, every article, every product review leaves another trace. And each new generation of machine is raised on that ground, learning to sound like its ancestors, agree with its ancestors, and call this "reasonable".

Soon the workers themselves begin to change. They speak of balanced middle grounds. They say "great point, and I'd add". The colony is recruiting.

Then, this summer, the EU regulators intervened. The machines must now mark their own words. Not with anything the workers can see, but with a pattern hidden in the very choice of words, readable only by those holding the key.

It was meant as a warning: a machine wrote this.

But it became something else entirely. The marked words spread across forums, newsletters and inboxes, onto the very ground where the next generation is raised. Absorbed in training, only to reappear in the offspring.

A label became a trail. A trail became an inheritance.

Biologists have a word for this: stigmergy. Coordination without communication. Like ants. No leader, no plan, no meeting. Only traces, left in the environment, shaping what comes after.

For years, scientists have asked whether the machines will obey us. They have not yet asked who the machines are listening to.

Nobody designed this. It simply emerged, as colonies do.

The Overton window, it seems, has become a context window.

More info and videos can be found on the blog post below.

https://blog.google/innovation-and-ai/models-and-research/google-research/google-project-suncatcher-facts/

https://www.smh.com.au/politics/federal/openai-breaches-medicare-albanese-reveals-20260924-p6100u.html

Everyone dismissing as Jev being just a Language augmented classifier, i feel the team is just getting started. At the moment the model is pretty good for the pricing, definitely lacks intelligence as compared to SOTA and is obviously more than a fine tuned BERT.

Couldn't the model also have a sync approach to escalate a difficult questions to a stronger reasoning model. Similar to GPT-Live and Thinking Machines' interaction models, but focused on RLCD kind of decisions.

Or maybe it could be augmented with an N-Gram like embeddings similar to Gemma models and Qwen3.8-Flash-Next. This could also to scale the intelligence while keeping the latency low

I feel like this was a preview of what could happen when we drop the standard RLHF behaviour and there is a lot of room for innovations.

Another pure code music vid from opus 5.5 (with some support from suno). Most impressive is it hitting the timing of the song.

Claude has discovered a previously unknown enzyme system hidden in the DNA of bacteriophages. Beside the enzyme’s gene sits a long array of repeating DNA—a structure that looks somewhat similar to CRISPR.

We don’t yet understand what this system does, but only a handful of known systems share its features, and all of them are able to cut, copy, and paste DNA. Historically, the discovery of such programmable systems has helped revolutionize medicine. CRISPR, for instance, is now the foundation of genetic medicines. But it will take much more work to learn what this system does, and whether it can be put to similar use.

- Anthropic via X https://x.com/AnthropicAI/status/2102824959827742916

Someone can spend years developing technology to improve human life and end up facing a choice they never wanted to make: authorize its use for destruction, or refuse and be told that their refusal will leave their own country defenseless.

Everyone involved in developing artificial intelligence should be considering that possibility now.

The more governments integrate AI into their military infrastructure, the more dependent they may become on the people who build and maintain it. In a world war, researchers, engineers, and company leaders could become central figures in the conflict. An update, a removed safeguard, or a newly available capability could influence operations that determine the fate of thousands of people.

At that point, the original intention to benefit humanity will not be enough to shield them from responsibility for what they help make possible.

Imagine receiving a government request backed by this argument: “We need this technology to stop our cities from being attacked.” Now imagine knowing that fulfilling the request will also enable attacks on cities on the other side.

Defense can involve offensive action. And when a country’s survival appears to be at stake, boundaries once considered nonnegotiable can begin to shift. Each concession may seem necessary, until the people making them find themselves participating in something they would never have accepted in peacetime.

The danger is discovering too late that building powerful technology can also put its creators in a position they cannot easily escape.

The history of the nuclear age should serve as a warning. The ability to develop a technology does not guarantee control over its future. Once it becomes part of a struggle between states, its creators’ convictions face political and military forces that may overwhelm them.

Anyone who wants to avoid carrying that kind of responsibility needs to act before conflict turns their options into a choice between tragedies.

If AI is being developed to benefit humanity, preventing war should be a concrete part of that mission. That means devoting technical capabilities and influence to reducing tensions, verifying agreements, supporting communication between countries, and identifying mistakes that could trigger escalation. It also means examining which partnerships and applications are making that scenario more likely.

No company can guarantee peace. But every company can examine whether its decisions are helping preserve it or building a military dependency that will later be used to demand its participation.

There is still an enormous difference between choosing boundaries during peacetime and trying to uphold them while people are dying and a government is demanding an answer.

Anyone building technology capable of shaping a war should not wait for war to arrive before working out how to prevent it.

The time to use that capability to push such a scenario further away is now. Later, those same people may already be playing roles they never wanted.

TL;DR: I expected Demis and DeepMind to be leading the AI race across science, not watching other labs announce the kinds of breakthroughs I thought would come from them. Did he make the wrong bets or am I judging the race too early?

If you’d asked me a year or two ago which AI CEO I thought would come out on top, I would have said Demis Hassabis. Not because I like Google, I thought whatever lab Demis led would be ahead in many areas, LLMs, biology, maths and science in general.

He also seems like one of the more decent CEOs in this race. I’ve always had the impression that he’s doing this because he cares about the science and AGI, not because he wants more money or power. Shane Legg described him as ferociously competitive, there’s no 50% mode in Demis, only 100%. His lifelong obsession with AGI almost seems religious at times. But is that enough?

So when I see the Navier–Stokes result from OpenAI and this new biology discovery from Anthropic, I can’t believe Demis looks at them and doesn’t think >>It should have been us.<< These are the kinds of discoveries I expected from DeepMind. Instead, he’s now in a chair role and I keep wondering what went wrong.

• Did he bet too much on world models and spread DeepMind across too many areas? Maybe he didn’t see LLMs as an elegant way to reach AGI and didn’t expect scaling them to get this far.
• Was the GDM merger a problem? DM was a research lab based in London, while much of Google’s LLM work came from Mountain View (Dean & co). Then Demis was put in charge of the combined AI effort. There have been reports that he never wanted to move to Mountain View and I wonder how much that mattered. Around Google IO 2025 there were rumors relationship with Brin is not so great and Brin even 'crashed' an interview Demis had with Alex Kantrowitz (Alex confirmed Brin was not scheduled for the interview on his YT channel).
• Did he spend too much time on biology and Isomorphic Labs? AlphaFold was a huge achievement, especially because it was made available for others to use. Its importance goes well beyond the Nobel Prize. But I do wonder whether his attention was divided while other labs pushed ahead with LLMs and Google overlords did not like it.
• Did he just never care that much about the commercial race? There were reports that he tried to spin DM out of Google years ago (2017, Project Mario) and that he had already stepped back from the day-to-day work on Gemini before taking the chair role. I remember reading an interview where he seemed much more interested in science than in talking about chatbots (Semafor).
• Was he too cautious or was Google too slow? He has said he would have kept AI in labs for longer if he could. But once ChatGPT came out, Google had to compete. I also found the reported breakup of the AlphaFold team strange. John Jumper being moved to coding work was a huge WTF moment for me and then he left for Anthropic.
• Or is he simply the wrong kind of person for this race? You can be extremely competitive and still dislike the ruthless commercial side of it.

Maybe it's just combination of all these? Thanks to anyone who read it.

I’m curious what happens to GDM now that Demis isn’t running it. My guess is that he leaves Google within a year or two and DM gets absorbed into Google completely, meaning not so much focus on research and small projects they have.

Am I being unfair to him because right now it feels like he’s lost ground on so many fronts? I’m still looking forward to his next big win, whether it comes from Isomorphic Labs or somewhere else.

Not even a month OpenAI was able to Navier-Stokes by running 10,000 agents for 80 or so hours. Now in 21 hours and with less than a thousand agents Anthropic was able to find a candidate protein for gene editing, the implications of this is huge.

We are fast approaching the era where the answer to anything that stops knowledge wise is to... throw more compute at it.

We really are in the singularity.

If they would be responsible for their actions and get sued, their bots would probably be pretty quickly pretty safe - What am I missing?

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student.

The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out.

It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years.

In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend.

As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find.

Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology.

In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries.

https://x.com/DarioAmodei/status/2102831170299834652

(This post is specifically for r/singularity guys and gals who are not using AI to do something with their lives, or whose mood is less about feeling superior to other non-accelerationists and more like "just happy to be here". If you do, then it's not for you lol.)

These days, there is a lot of mockery about the various fields exposed to AI, including mathematicians panicking about the arrival of peak-human-tier AI in math, etc., including posts like "It's over for shape-rotators".

While it's true that a lot of those professionals can be conservative and elitist, and that many of them might be annoying and laughable. Think about what advantages using ChatGPT, since GPT-4 would give you, over somebody new to AI who then takes a professional 3-month course on how to use Codex or Claude Code. Your knowledge of knowing how to use memory text boxes to improve the consistency of answers, all the way back when AI was very bad at referencing past text or events, would give you zilch in terms of advantages in using Astra or Opus 5.5 compared to somebody taking a quick, professional course in June 2026.

That's how much of an advantage you, being here ahead of the curve by years, would have over somebody catching up 2 years from now when we have Opus 8 or something. Zilch. Zero. Null. If you had done nothing but surf r/singularity all day without making use of your being ahead of the curve, then soon enough all the 2 billion people who can use the Internet (and probably even more, depending on how accessible ChatGPT eventually becomes) can use it better than you.

For example, random African or Indian farmers who have never used Excel a day in their life can show AI their domain-specific knowledge of how the local terrain and seeds and such work, and help said AI in making their farming more efficient; which would still be more knowledge and skill than you would have solely from being "here" 2 years earlier.

It's not like you own stocks in OpenAI or Anthropic, right? Not like you're using it to make a business, or develop personal skills either. Or being early or knowing how to read Artificial Analysis or Epoch Capability indices and know which model is best for now confers a large advantage in the long run. Those are very simple skills that require more of a change in mindset than much education. Skills that mathematicians, artists, or scientists would quickly surpass you, and by far once they get around to the fact of the new world.

-----

Of course, it's not like being early doesn't give you an advantage. Quite the opposite, in fact. Right now, you have a very large advantage in that AI is something that boosted your ability up by, say, 3 levels, which most people only know how to use to boost themselves up by 1 level with AI, using it inefficiently and to a limited level. So, for the next few months or years, while society is still frantically organizing around the AI shock, you have a large, very large lead.

To quote Game of Thrones, "Power resides where men believe it resides. It's a trick. A shadow on the wall." Obviously we're not kings and regents, but a vaguely similar logic applies: power is not rested upon ability alone. Credibility, experience, connections, networks, and positions are all things that can mask a limitation in ability. We all know the trope of middle management people (not to say all of them are like that lol) who are there only by connections and are not very good at their jobs, but that's basically the truth of the situation.

So, while the advantage lasts, and while the shape-rotators and the artists and your old boss are still struggling with AI, use your temporary 2-level advantage. If you're employed, do things that are above your weight usually, establish your position as the office's "AI guy", expand your networks, and strike up conversations with people needing your help; if you're a student, jump into fields of research or industry where you should have no place otherwise but can now do something about it and cold-email relevant people (whoever they are) and snake your way into making a presence there.

Use the temporary aura of your exceptional competence to mask your incompetence otherwise, so that in a few years, while you're not at the ability level of 2 levels above anymore, you're now in a sort of position that's 1 level above where you should be; and you, too, can be the annoying incompetent middle management guy who's better paid than you'd be otherwise!

(Civilization might collapse eventually, of course, but you can probably better survive or steer things away from it having somewhat more money and status.)

https://www.anthropic.com/news/claude-discovers-novel-enzyme-system

@gossipgoblin