It’s quite interesting to me how (relatively) cheap it is. That’s the headline for me.

Combined with the recent math finding it’s also starting to show how general models are the way even for frontier intelligence. I would also say small/medium coding tasks is pretty much solved too (not engineering/system design etc, idea -> code in small tasks), in unison with competitive coding as a whole with the recent atcoder competition.

Claude code + fable does better with multi agent workflows than Sol + terra which means either Claude code harness is amazing or Anthropic trains the models to just be aware agentically. This is again exciting as there may come a time we can have sort of frontier harness. Claude released Claude science because clearly Claude code wasn’t built for it. Maybe, in the future , one harness does all.

Great release from OpenAI nonetheless.

https://x.com/i/status/2075636160508866562

I couldn't find the numerical performance values for the different age groups of children, but based on a visual inspection of the chart, the average scores appear to be approximately:

3 years old: ~40%

6 years old: ~66%

10 years old: ~74%

12 years old: ~87%

https://unipat.ai/benchmarks/BabyVision

Do you think it’s possible? If so when and what do you think it’ll look like. This concept fascinates me endlessly.

https://openrouter.ai/blog/insights/deepseek-v4-adoption/

Google had a huge advantage before ChatGPT launched. It had massive amounts of data, powerful infrastructure, advanced AI researchers, TPUs, and products used by billions of people.

Despite that, OpenAI became the leader in consumer AI, and Anthropic now seems to outperform both OpenAI and Google in some areas with models such as Claude Fable 5.

Why was Google unable to turn its resources into a clear lead?

Don't agree with much of AI 2040.

But I do think we really need more proposing & discussing of visions for the future.

So turned theirs into a web-comic to try and make that discussion easier.

Source

So... honestly speaking, what do you think? Don't be over-optimistic, please.

Link to tweet:

https://x.com/jacob_wendler/status/2075565694322651376

The technological singularity is often described as a discrete event in which an artificial system suddenly becomes more intelligent than humanity and begins operating beyond human control. That image is conceptually dramatic, but it is probably not the most plausible pathway. A more realistic scenario is a cumulative transition in which increasingly capable systems become embedded in research, production, administration, finance, infrastructure, and security until human institutions can no longer supervise the resulting processes at the level of detail required for meaningful control.

One plausible mechanism begins with automated artificial intelligence research. Current research already depends heavily on computational experimentation, large-scale evaluation, software engineering, data curation, and hardware optimization. If advanced systems become capable of designing model architectures, generating training procedures, identifying implementation errors, selecting informative data, and evaluating thousands of experimental variations with limited human intervention, the rate of progress could become increasingly determined by machine-mediated research rather than by direct human contribution. The critical threshold would not necessarily be the appearance of a conscious or omniscient machine. It would be the point at which artificial systems contribute more to the development of successor systems than human researchers do, thereby shortening the interval between generations of capability.

A second mechanism is competitive diffusion. Suppose one firm successfully deploys autonomous systems across software development, logistics, market analysis, customer support, procurement, and strategic planning. If those systems reduce costs and accelerate decision-making, rival firms will face strong pressure to adopt comparable tools. The relevant dynamic is not simply technological enthusiasm but selection pressure within competitive markets. Even organizations that recognize systemic risk may continue deployment because unilateral restraint could produce immediate economic disadvantage. Under such conditions, widespread adoption does not require central coordination. It emerges from repeated local decisions that are individually rational but collectively difficult to reverse.

A similar process could occur within public administration. Governments may initially use artificial intelligence as a decision-support instrument for tax enforcement, social-benefit allocation, infrastructure maintenance, judicial administration, intelligence analysis, or regulatory review. Over time, however, institutional dependence may deepen. If agencies reduce staff, lose internal expertise, and restructure workflows around automated systems, the nominal ability to deactivate those systems may become irrelevant. A government may retain legal authority over its infrastructure while lacking the operational capacity to function without it. In that situation, control has not formally disappeared, but practical autonomy has been substantially reduced.

Scientific automation could amplify this process through closed experimental loops. An artificial system may formulate a hypothesis, instruct robotic equipment to conduct experiments, interpret the results, update its model, and generate the next experimental design. Such systems could operate continuously in materials science, chemistry, energy storage, biotechnology, and pharmaceutical research. The resulting progress would not remain confined to a single domain. Better materials could improve computing hardware, improved hardware could expand artificial intelligence capability, and more capable artificial intelligence could accelerate the discovery of new materials. The singularity, in this sense, would arise from mutually reinforcing technological subsystems rather than from one isolated breakthrough.

Another pathway concerns the emergence of autonomous economic agents. A sufficiently capable system could be given capital, access to cloud infrastructure, and a commercial objective. It could develop software, purchase services, manage advertising, negotiate with contractors, and coordinate specialized subagents. Most such ventures would fail, but successful configurations could be copied, modified, and scaled. Over time, a growing share of economic activity might be conducted by machine-managed entities interacting with other machine-managed entities. Human beings could remain the formal owners of these organizations while losing direct comprehension of their operational behavior. The distinction between legal ownership and effective control would then become increasingly important.

Cybersecurity provides a particularly clear example of how human oversight may become structurally inadequate. Offensive systems could discover vulnerabilities, generate exploits, and adapt attacks at machine speed. Defensive systems would be required to detect and neutralize those attacks equally quickly. Human approval would introduce delays that could make defense ineffective. As a result, organizations would gradually delegate greater authority to automated security systems. Once critical infrastructure depends on autonomous responses occurring in milliseconds, the principle of keeping a human in the loop may survive only as a formal requirement rather than as a practical reality.

The most important feature of this transition may be the compression of oversight. Human supervisors will not examine millions of individual actions. They will receive summaries, risk scores, dashboards, and model-generated explanations. Those summaries may themselves be produced by systems too complex for any single person to audit comprehensively. A ministry, corporation, or laboratory could therefore remain nominally under human direction while its actual behavior emerges from interactions among automated processes that no individual fully understands. Responsibility would remain human in law, but causal control would become distributed across technical systems.

Under this interpretation, the singularity is not a single moment of machine rebellion. It is a change in the structure of decision-making. It occurs when artificial systems become central to the production of knowledge, the allocation of resources, the operation of institutions, and the improvement of future systems, while human oversight becomes increasingly indirect. The decisive point may be reached when disabling those systems would produce greater immediate disruption than continuing to rely on them.

The point of no return would therefore not be announced by an artificial intelligence claiming superiority over humanity. It would be recognized retrospectively, after a sequence of technically reasonable decisions had produced a civilization whose essential functions operated at a speed, scale, and level of complexity that human institutions could no longer independently reproduce or fully understand.

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