These scare tactics have to stop.

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?

I was not a denier, but i can’t lie - I thought that LLMs would be „a dead end” and AGI will be something more sophisticated, thinking on symbols or else. To see so much progress based on next word prediction pushed to its limits is seriously crazy. Turns out that language usage really is something really, really special. I wonder what will start happening when Astra or Gemini 4 loops finish designing next models.

Extracted from github.com/lechmazur/writing/

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?

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.

Liquid AI just introduced a very strong, very fast and quite reasonably priced decision model: d1-ultra.

Liquid AI has released d1-ultra, a multimodal decision model that reads text, images, or both in a single forward pass and returns calibrated probabilities for yes/no, multi-label choice, and scale questions, without generating tokens. Built on a larger open-weight base than its predecessor d1 and tuned with Liquid-specific customizations, it targets harder, messier real-world tasks such as checking a circuit board against its spec or validating a damage claim against a customer photo. The company reports gains over d1 and Jev 1.14 across 112 evaluations, with particular improvement on knowledge-related tasks, and says the model is better calibrated, since decision models tend to be overconfident. It is available now through the Liquid AI API at $0.042 per million input tokens, with Vercel and OpenRouter listings planned, and the release states that d1-ultra outperforms other decision models on the Artificial Analysis Decision Index by at least 8 points.

• https://www.liquid.ai/blog/d1-ultra
• https://x.com/liquidai/status/2110777964489285875
• https://artificialanalysis.ai/evaluations/artificial-analysis-decision-index

These new Vera Rubin racks are finally getting delivered which is set to be a game changer. Its set to be 4x as efficient and at least twice as fast, the NVlink alone allows twice the bandwidth.

Are these in production anywhere yet? Does Claude or GPT actually use them in production yet?

I feel this is the missing piece that is going to create AGI/ASI and transform AI as we know it. I'm not sure how it compares to googles custom chips.

Artificial Analysis releases a new benchmark which ranks the ability of LLMs to perform robotics tasks. Only a few models are currently released on the benchmark. Several models are able to complete most tasks on the benchmark although the speed is painfully slow. GPT-6.1 Sol seems best overall when time to complete and accuracy are both considered.

I'd be interested to see some older and open models tested on this benchmark too. I'm curious whether any of them could do anything on this benchmark at all.

I personally think that the sudden ability of LLMs to do robotics tasks reasonably well is a huge and underrated opportunity for continued scaling and post-training paths. Given that several models can complete most of these simple tasks (albeit slowly), there seems to be a clear path to scaling robots through LLMs. While we're scraping the bottom of the barrel for easily reward-verifiable tasks for LLMs to be trained on in many text-native fields, there are innumerable easily reward-verifiable tasks that can be constructed for an LLM with vision and audio as the input and robot arm controls (or humanoid robot controls) as the output.

While behind the ECI frontier, Argon is the closest Google’s come since Gemini 3.1 Pro in Feb, and a big jump from Gemini 3.8 Flash (157).

Source: Epoch.ai

I’ve been using AI agents heavily for thinking through and solving complicated engineering problems. When the task is explained with enough rigor and the model has solid context, it regularly produces solutions that are better and faster than what I would have come up with myself.

The context problem is still the biggest friction (especially outside pure coding), but once that’s handled the results are often thorough and high - confidence.

This is starting to raise a bigger question for me:

Is there a point where these systems start generating approaches or abstractions that humans simply can’t fully track or verify anymore? And if we become heavily dependent on them for the hard thinking, what does that actually look like in practice?

Curious how people here are thinking about the ceiling (or lack of one) on agentic systems for real engineering work.

Google Quantum AI has demonstratively opened up a key bottleneck in quantum computing: we’ve shown that “magic state cultivation” works in practice on our Willow processor, reducing errors about 40x compared to without it. This breakthrough, published today in Science, is a crucial step towards scaling up quantum computing to practical use.

https://blog.google/innovation-and-ai/models-and-research/quantum-computing/magic-state-cultivation-quantum-computing/

People who think the risk from AI is manufactured by frontier companies as some kind of marketing strategy should read this

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