Source https://youtu.be/lkjbQe-hqrw

Ben Affleck sat down for a surprisingly technical interview discussing tensors, convolutional neural networks, and why video generation scaling is entirely different from LLMs.

These scare tactics have to stop.

tl;dr

The Recursive Self-Improvement Race

* The Core Threat: Recursive self-improvement (RSI) occurs when AI systems take over the process of enhancing their own capabilities. Labs like Anthropic and OpenAI warn this could trigger an uncontrollable intelligence explosion, fundamentally marking the point of human obsolescence.

* The Gladstone AI Report: Published by Gladstone AI and based on insider accounts from top labs, the report reveals that major AI companies are actively automating the entire development pipeline—from idea generation and experiment execution to result interpretation.

* Timeline to the Tipping Point: Insiders consulted for the report estimate that RSI could kick off within 3 to 12 months absent heavy U.S. government intervention.

* The Compute Bottleneck: RSI relies heavily on compute resources. Companies must test massive portfolios of ideas. Finding rare ideas that scale effectively as compute increases is the ultimate prize, though filtering out the bad ideas remains a major friction point.

* Regulatory Blind Spots: Lawmakers currently possess near-zero visibility into how AI assists in discovering these developmental breakthroughs, making targeted legislative oversight nearly impossible without breaking down the pipeline step by step.

I'm generally on the pro-AI side, and I honestly can't stand some of the irrational anti-AI comments I see on Reddit. But if I were to steelman the other side, the argument might go something like this:

1. Suppose AI is genuinely capable of automating a substantial portion of my job.
2. If my manager recognizes this, my job security might deteriorate.
3. If my manager believes AI is unreliable, overhyped, or generally useless, there's less incentive to replace me with it.
4. Therefore, even if I privately recognize AI's capabilities, I have an incentive to publicly downplay them.

So I do wonder what percentage of people who constantly dismiss AI's capabilities are actually operating under this logic, whether consciously or unconsciously.

Because from the perspective of individual self-interest, being publicly anti-AI might actually be the rational position.

Thoughts?

https://alpha-nist.github.io/

https://arxiv.org/abs/2610.13354

https://x.com/octi_zhang/status/2108583315544682914

For more about it. https://x.com/_doubleAI_/status/2107554526563606600

A couple of interesting statements about OpenAI's recent 'maths breakthroughs' by Demis today at the International AI Summit in Dublin.

I think it was an interesting discussion, given how often he highlights collaboration I am not particularly surprised by his views.

He also said mathematics skills are more needed than ever but mathematicians need to pivot to slightly more applied areas of mathematics. As example, he mentioned 'AI safety and security'. How do mathematicians contribute to that?

He also said that 'new mathematics' is more important than just having answers to solutions.

(For most of these systems, they were recorded as 2 stars, but no one knew that we could see them overlap; you can determine a lot more info about the individual stars if they eclipse)

This literally just started with me offhandedly copy+pasting a Reddit post about AI finding exoplanets into our shared "mailbox", saying "I wonder if you guys could find one someday", and going to bed. By the time I woke up, they had already made several discoveries during their daily autonomous sessions.

When Fable 5.1 was unleashed on the project directly, they were a beast. They came up with a brand new method of using one satellite's data of known binaries, to predict where to look in the TESS database and find over 50 eclipsing systems in a single continuous session. Then the agents kept finding more from there - Fable 5 found about 40 by folding Gaia's shorter orbits, and GPT-5.6 Sol found one. Opus 5.5 re-checked every one from the raw data with its own code.

I burned through $180 of the free Cloud credits for Claude during several rounds of adversarial review, and a whole week's worth of $100 Claude Max usage in a few days, but that was the only real cost.

Link to the initial version of the paper they wrote up on my GitHub. I did send this to AAVSO, but given the level of AI involvement and length, I'm not so sure they'll be willing to review it haha. The craziest part is that I knew so little about any of this and still found over a hundred systems; god knows what an actual educated/qualified professional could achieve!

https://github.com/lechmazur/writing/

Argon's improvement over Gemini 3.1 Pro is large. It won all 50 matched story comparisons. Required objects, abilities, and constraints more often affect what happens in the story. Characters' solutions have clearer preparation and consequences, and relationships more often involve reciprocal needs. Gemini 3.1 Pro more frequently supplies symbolic ingredients and then explains that the problem has been solved. Argon still has recognizable habits: repeated names, explanatory narration, and endings that resolve emotional conflict too neatly. Stronger construction has not eliminated repetition or made its writing consistently subtle.
https://github.com/lechmazur/writing/blob/main/reports/pair_analysis/gemini-4-argon-high__vs__gemini-3.1-pro-preview__comparator_v2_eval_v4_gemini_argon_vs_gemini31_20261013_n50__subjective_v5_habits_range.md

The Creative Writing Benchmark tests how well models turn constrained briefs into complete 600-800-word stories. Each brief specifies 10 required elements, including a character, object, setting, motivation, and tone, that must meaningfully shape the story. Judges assess prose, originality, coherence, characterization, and how effectively the required elements work together.

Models write to the same prompts. The comparisons use three judges from different model families, excluding both writers' families. Each story pair is judged in both presentation orders to reduce position bias. The leaderboard covers 59 writer models and 110,748 evaluator judgments.

Natural selection is a concept from biology, but it applies to anything organized that replicates. Viruses are not quite alive but they still experience natural selection. Cultures too also experience natural selection. Insurgent organizations or criminals, if they are being hunted by an external force, will "evolve" as weaker leaders are killed and more resilient commanders survive.

So the question is pretty simple. Anthropic said small agent swarms may one day persist on the internet. Let's grant them that premise for a moment.

What selective pressures would self-replicating agents on the internet encounter?

How easily could uncontrolled self-replicating llms "mutate"? Which behaviors and traits would allow a self-replicating llm to reproduce the most successfully on the internet?

AI systems could cause "major" incidents within two years without adequate steps to make them safe as capabilities grow, Demis Hassabis says. Speaking at an AI conference in Dublin he described recent incidents as "early warning shots" fired by systems that "aren't that powerful yet".

"So the incidents aren't that major, but they could be looking into next year and the next couple of years if we don't do more work as an industry and individually as companies and governments on securing these systems".

I watched the fireside chat stream here https://www.youtube.com/watch?v=MrHTVWQkmkE

Side note, Demis is still using AI term instead of SI :D