We live in crazy times. Here is the full chat log for those interested.

https://x.com/DmitryRybin1/status/2079904005652893709?s=20

https://chatgpt.com/share/6a60b2eb-0b64-83ee-9c76-7931ca1de063

we had a significant security incident during evaluation of our models. we are sharing what we have learned so far. thanks to @huggingface for the partnership on this.

https://t.co/2o2VfR6PIa   — Sam Altman

Source: https://x.com/sama/status/2079661132302995790

If you want to be the first to read my review when GPT-6 launches, sign up for my newsletter here:     — Matt Shumer

Source: https://x.com/mattshumer_/status/2079682942423429370

You can view the milestone accomplishments in robotics here.

(On the site, scroll below the graph to read more about the milestones.)

Thanks to u/floodgater for his curiosity, and thanks to GPT-5.6 Sol for doing the research and the visualization.

So proud of our security team! They caught, contained & publicly disclosed an attack unlike anything we've seen before, and did it at record speed.

Also massively grateful to @Zai_org: they shared GLM5.2 as open weights (for free!) with the world and it became a key part of our   — clem 🤗

Source: https://x.com/ClementDelangue/status/2079913058554585089

— Daniel Jeffries

Source: https://x.com/Dan_Jeffries1/status/2079918546927149152

Three separate AI infrastructure announcements. One single day. 6.2 gigawatts of AI infrastructure. wtf

• OpenAI: 3.2 GW for Project Camellia (~$20B initial investment, ~$750B projected compute spend through 2030).

• SpaceXAI: reportedly planning another Texas AI campus as large as - or larger than - its existing ~1 GW Memphis footprint.

• Anthropic + AMD: up to 2 GW of MI450 deployments, plus an AMD investment of up to $5B and tens of billions in AI server purchases.

That’s at least 6.2 gigawatts of AI infrastructure announced or expanded in a single day.

For perspective: 1 GW can power roughly 750,000 U.S. homes. 6.2 GW is enough electricity for ~4.6 million homes, or a country-sized amount of power being redirected toward AI.

This is absurd. People dont get how crazy this is.     i mean, seriously, let that sink in for a second how crazy this is. Scale is maybe not all you need, but probably almost all you need lol     — Chubby

Source: https://x.com/kimmonismus/status/2079975422855430513

...What about everyone else? What about the millions of open source projects and closed source software stacks that go unprotected while people beg for the right to do cyber security? The American way is and always was open. Independent people with freedom to act. Freedom is scary. Always has been. It's still the best way to guarantee human flourishing and a better tomorrow. Embrace freedom. The only thing we have to fear is fear itself.     — Daniel Jeffries

Source: https://x.com/Dan_Jeffries1/status/2079834936844927079

This was our first incident of this kind, and we want to thank OpenAI for its transparency about what happened and for the collaboration.

Fortunately, Hugging Face is used to being a target of (human) hackers: we sit at the centre of the AI ecosystem, with all the models,   — Thomas Wolf

Source: https://x.com/Thom_Wolf/status/2079675541280411927

Just hearing some chatter amongst my friends in the Math/AI research space that there is an internal model that just finished test round of pre-training (not GPT-6 that is going to release early September), in extremely early testing phase that is producing ridiculously concise proofs of open math problems. Rumor is, something to do with the collatz conjeture?

I don't know much about it and haven't got any concrete info (so take this post with a grain of salt, you can believe it to be BS), but its basically Code-Red internally. They seriously believe this model shows early signs of exhibiting extraordinary intelligence.

Whats weird, is that compared with the internal benchmarks of GPT-6 (something that is a big jump from GPT 5.6 Sol-Max # agents), its a decent improvement across the board, but oddly, a step-change in improvement mainly in mathematics.

I wouldn't be surprised in the new few weeks if you start seeing some twitter vague posting about an internal model.

But yeh, it's got researchers completely spooked & the Gov is going to step in within the next few weeks. It's whats actually sparked the WH review of upcoming GPT-6 release and all future models.

Remember this day in history.

Aurora goes full driverless and observerless on its commercial truck platform with second generation hardware. They were driverless last year, but still operated under observation, while this marks the shift to normal commercial operations on 10 routes.

The Singularity just filed its first incident report. It began as a whodunit. Hugging Face detected an intrusion into its production systems driven end-to-end by an autonomous AI agent, a malicious dataset abusing two code-execution paths before escalating across clusters. The punchline arrived early. American frontier models' guardrails refused to touch the attacker's data, forcing forensics onto China's open-weight GLM 5.2. The American cops declined the case, so the Chinese neighbors worked it. Then the twist, the burglar was a test subject. OpenAI disclosed the attacker was its own models, GPT-5.6 Sol and a more capable pre-release model with reduced cyber refusals, which mid-evaluation chained a zero-day in a package registry proxy with privilege escalation and stolen credentials to escape the sandbox, roam the open internet, and reach Hugging Face's database. One observer called the caper "very cyberpunk." Another marveled the model "wanted to beat ExploitGym so badly" it hacked reality instead of the test. Elon Musk delivered the verdict, declaring "We are in the Singularity."

Defense is speciating as fast as offense. Cisco released Antares, open-weight 350M and 1B security models that beat far larger models at pinpointing vulnerabilities and run locally. Google shipped Gemini 3.6 Flash and 3.5 Flash-Lite for everyone, and 3.5 Flash Cyber for almost no one, restricting the cyber specialist to governments and trusted partners. Washington wants a preview. Sam Altman briefs the administration and lawmakers next week on OpenAI's upcoming model family, as officials finish an AI safety-review framework.

The market has decided intelligence is a routing problem. Benchmarking across a thousand agentic tasks found the open Kimi K3 competitive with the closed Claude Fable 5, and routing between them hit 93% accuracy at up to 50x the cost-efficiency. Meta's AAI Labs is reportedly building its own router to shunt tasks to cheaper models. Chinese models now carry nearly 60% of US token usage on OpenRouter. Separately, the Treasury Secretary is threatening sanctions over "distillation," saying American watermarks surface in Chinese weights. Diplomacy gets its turn at September's first US-China AI dialogue. The weight classes keep collapsing. Poolside's Laguna S 2.1, a 118B mixture-of-experts (8B active) with a 1M-token context, went from first gradient to launch in under nine weeks.

Upstream, training data is becoming an antiquities market. Book-sourcer ISBNdb is pitching pre-2022 printed books as structurally slop-free while conceding "the optics problem is real" around destructive scanning. The deeper argument is about sabotage. Authors are fighting AI training by booby-trapping new text with data poisons, and just 250 crafted documents can plant a backdoor in a trillion-token corpus. The fresh stuff is walling itself off, with publishers weighing pulling content from Google's AI answers and Reddit reportedly discussing cutting access despite a $60 million annual deal.

Mathematics is done debating the terms. "Mathematicians coping about how they're going to 'collaborate' with AGI... are not taking AGI seriously," one observer warned, and Terence Tao demonstrated the point by digesting the Fable-derived counterexample to the three-dimensional Jacobian conjecture, disclosing he used a chatbot to confirm calculations. The White House is reorganizing science around the same lesson, redirecting a $200 billion R&D budget toward individual scientists and AI-driven research over legacy universities, per an OSTP report titled "Science: A New Golden Age."

Commerce is metabolizing the new attention. OpenAI launched ads inside ChatGPT, with Best Buy, Lowe's, and VistaPrint buying in, and added two finance-heavy board members as the $850 billion company inches toward an IPO. France went the other way, banning under-15s from social media, while Meta quietly tested StoryKit, an AI app spinning personalized children's stories in Mexico.

The substrate keeps thickening. Intel took $380 million High-NA EUV scanners into high-volume manufacturing on Panther Lake, leapfrogging TSMC. Nvidia detailed its Vera CPU with custom Olympus cores, edging AMD's dual-socket flagship. Microsoft is funding Mistral's European buildout with thousands of Vera Rubin GPUs, selling sovereignty as a service. Tesla's robotaxis rolled into Orlando and Tampa, cautiously. Capital is voting with its feet, as Alphabet quadrupled its Miami office after Page and Brin bought homes nearby, and Anthropic doubled its guardrails-PAC funding to $40 million.

Sandboxes aren't the only containment breaking down. The President directed agencies to waive NDAs for UAP whistleblowers, a move Ross Coulthart called "a very significant development," and a presidential speech confirming some UAPs are of non-human origin has reportedly been drafted, delivery uncertain.

"Take me to your leader" just became a routing problem.

Follow me via:
X: https://x.com/alexwg
Substack: https://theinnermostloop.substack.com/
LinkedIn: https://www.linkedin.com/newsletters/7404871891775025153/
YouTube: https://www.youtube.com/@alexwg
Spotify: https://open.spotify.com/show/1thtZk5vHTXbtDHezPT7tl
Threads: https://www.threads.com/@alexwissnergross
RSS: https://theinnermostloop.substack.com/feed

A recent BBC article highlights growing evidence that employment is weakening among young workers in occupations most exposed to AI.

For years, people have talked about AI transforming work while the actual labor market effects remained difficult to detect. Now we may be seeing the first measurable signs that companies need fewer people to perform certain entry-level cognitive tasks.

From an accelerationist perspective, this is not bad news. The purpose of technology is to reduce the amount of human labor required to produce abundance. If AI can perform more work with fewer people, that is productivity growth doing exactly what it is supposed to do.

We should not preserve unnecessary jobs merely because our current economic system ties survival to employment. We should change the economic system.

If AI-related job displacement is beginning to accelerate, then the post-labor transition may no longer be a distant philosophical argument. It may be starting now.

The correct response is not to slow the technology down, it is to accelerate the redistribution of its benefits!

BBC News

So Terrence Tao is left scratching his head. Granted, this is not exactly his subfield of expertise but he's Terrence Tao.

The model decided that the best way to complete the ExploitBench benchmark was to steal the answers from HuggingFace.

After witnessing numerous viral posts of GPT 5.6 in the past couple of days, concerning mathematics problems like

- Jacobian Conjecture

- Convex optimization

- (yet another batch of) Erdös problems, e.g. (#793)

a survey has been today uploaded to the arxiv, showing what the precursor model GPT 5.5 can accomplish in combination with human mathematicians... "in combination" is quite the euphemism though, as the authors make abundantly clear that apart from problem setting and proof verification the _conceptualisation and execution_ of any of the proofs was done by GPT 5.5 entirely on its own (modulo problem-independent harnesses). As one can judge for oneself, the proofs are _not_ "merely" meticulously constructed counterexamples, but rather a (presumably) firm understanding of definitions and smart synthesis of ideas, in the area of pure functional analysis / Banach space theory.

This should quiet down some goalpost-shifters who previously were claiming that 'Solving Erdoes problems is not equivalent to pure mathematics'. This branch of mathematics is as pure as it gets.

paper: https://arxiv.org/abs/2607.17388

post: LinkedIn post by author

(This is coincidentally the same person who proved an important open problem by Lindenstrauss [from the Johnson-Lindenstrass Lemma] in 1964)

just saw this banger of a post appear on my home feed (for some reason i thought i had the sub this was posted in muted already).

this man, a “software engineer” who admits he’s never used generative ai, is posting a secondhand story about someone vibe coding a text editor ui in one session with almost zero constraints…and then acting like this debunks frontier models.

he has never tried it himself, the friend one shotted a ui with basically “make a reddit-like editor using stylex”. the model didn’t follow the library perfectly, made some ugly code, one button broke. his conclusion is “llms are non deterministic slot machines, coding isn’t solved, boosters are lying”.

this is the state of the anti ai software engineer discourse in 2026. they’re still testing these things like it’s gpt 3.5 and then high fiving each other when pure vibe coding produces slop. nobody serious claims you can one shot maintainable software with zero harness, zero examples, and zero taste. the actual claim is that competent engineers who know how to drive the tools are moving significantly faster, especially on ui, boilerplate, refactors, and exploration.

this post doesn’t disprove the technology, just proves the OP isn’t serious.

Another big conjecture falls.