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?

Sonnet 5.5 is reigning in Real SWE benchmark why is that?

So much doom and gloom lately. Let’s take a break and imagine that if AI does indeed intend to get rid of us, what’s the nicest way it could let us go?

I’ll go first.

Personally, I hope our future AI overlords have the decency to let us live out our zombie apocalypse fantasies instead of just wiping us out in 0.003 seconds. Give us a few weeks of societal collapse. Let us raid abandoned grocery stores, build barricades, form questionable alliances, and dramatically betray each other over canned beans and toilet paper.

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?

These scare tactics have to stop.

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

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.

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

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.

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

https://arxiv.org/abs/2610.13354

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

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.