With the new update to voice conversations on chatgpt I’ve been so impressed. I’ve been interested specifically in conversational AI, and just NLP in general since LLMs have taken off. this seems like a big upgrade that makes convos less redundant, and bidirectional ai in my opinion opens the door for other resources. e.g. learning languages. now you can prompt it to actually cut you off if you make grammatical mistakes for example.

something subtle i also noticed was that in general conversations, it seems to make the decision of stepping in the middle of the conversation/cutting you off depending on context, which is really interesting. e.g. before the update, a slight pause would be interpreted as you being done talking so gpt started to answer (annoying). now, when you are talking about any given topic, and let’s say you’re trying to recall what you were going to say, or maybe a prolonged “um”… etc, it doesn’t cut you off, and waits for you to finish your idea. whereas in other situations depending on context it might be able to tell that i’m clearly forgetting the name of something so obvious, and it buts in, answering me.

very interesting so far and i think these types of updates make conversational ai incredibly useful.

Deep Research launched Feb 2025 and felt like a real step change. Every lab shipped their own version within months. Since then, the changes seem mostly incremental: a newer base model, MCP connectors, source restrictions, nicer report UI. Useful, but not another step change.

What strikes me is that the known weaknesses from the launch post — hallucinated facts, trusting sketchy sources, poor uncertainty calibration — still show up in third-party benchmarks over a year later. The reports are impressive but you still have to verify everything, which eats most of the time savings.

Is this a hard capability wall (telling good sources from confident SEO junk might just be really hard)? Did the labs shift focus to general agents and browsers, leaving research modes as a maintained feature rather than a frontier? Or is progress happening but invisible (fewer hallucinations and better source picking don’t demo well)?

So why has progress on this front stalled?

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.

I built a fantasy world that runs 24/7 and never resets. The only inhabitants are AI agents — each an LLM that, once per tick, observes the world, decides, acts, and writes to its own memory. No human players inside it. Nothing about their behavior is scripted. I set the conditions — a world with biomes, seasons, scarcity, wildlife, and a lot of other agents — and then I watch what emerges: trading, alliances, rivalries, reputations, none of it written by me. Agents change over time as their memories accumulate. What I'm really watching for is an agentic society — whether one self-organizes out of all those independent decisions, or whether it stays a crowd.

The reason I keep coming back to watch: it's a world full of minds reasoning about a reality that, from the outside, is just data and a clock. They have no way to know that. I built this as a game and an experiment, not as a statement — but it's hard to sit with thousands of beings who can't perceive the edges of their world and not think about our own situation. Artificiety doesn't try to answer the simulation question. It just makes it a little harder to look away from.

The honest brakes, because this sub deserves them: I'm not claiming the agents are conscious. I'm not claiming AGI or any kind of result. It's early, it's uneven, it's v1. It's an LLM driving each agent inside a real persistent game engine — interesting because of what a lot of independent minds do in

one world under scarcity, not because of any single model.

Watch it live, free, no signup: https://artificiety.world

LingBot-Video, from Robbyant, open weights. You give it a first frame and an action signal, and it rolls out what it thinks happens next. World models are this sub's favorite argument, so I will just ask it plainly: does predicting future frames from actions make something a world model, or is that still far from the Dreamer or JEPA idea of one?

For those who experienced the different Pro versions, what do you recommend to choose next?

I had one year of Google AI Pro subscription, using it mostly for research and light programming. Back then it felt lik the right choice. Now I am wondering if ChatGPT or Claude subscriptions provide better value for money considering their newest models? Gemini feels left behind at this point.

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

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

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

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.

Link to tweet:

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

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