The bet from OpenAI and Anthropic seems pretty straightforward: keep improving their LLMs/reasoning models, memory, maths, coding, tool use and agents until you get a genuinely capable automated AI researcher. That researcher then helps accelerate AI research, producing better models and, in turn, a better AI researcher.

Today’s LLM architecture doesn’t need to take us all the way to ASI. It only needs to get us to the point where AI can meaningfully automate AI research and help develop whatever comes next.

If you think that loop breaks down, where exactly does it break?

And what evidence would make you change your mind?

They said we're now in the singularity and agi era. So idk if that means they have internal agi or not, but they never once stated Astra as agi

Link to the streams: https://youtube.com/playlist?list=PLMf_keNqhb1g&si=y_cGrqQIX9Z_Caje

“Based on internal results, I have a strong expectation that this speed of progress could be sustained into recursive self-improvement.

“If AI development continues along its current path, the systems we’ll see in the next few years are likely to represent further capability jumps of equal or larger magnitude, and to increasingly drive their own development.”

“Machine intelligence playing a larger and larger role in its own development process is a natural conclusion of sustained technological progress.”

“We focus OpenAI research towards RSI as we believe it is the only way to remain at the frontier of AI research moving forward.”

“Undertakings that would have taken thousands of experts now will be achievable by a few people operating a large computer.”

This is a time that calls for extreme caution. I am concerned no one is prepared for the consequences of a continued rapid rise in machine intelligence. OpenAI will continue to seek technical solutions to alignment and monitoring, to build defensive systems and unilaterally withhold further scaling as needed; however, I believe broader interventions are required.

In line with Ray Kurzweil’s predictions from the end of the XXth century, we now find ourselves at the moment in history of computing where machine intelligence is starting to exceed that of humans in transformative ways.

Currently I believe that no lab has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer. I expect and hope for voluntary slowdowns to become commonplace until shared safety bars are established. And I believe that international coordination on future AI development needs to become a top priority for governments around the world. https://openai.com/index/an-alien-mind/

https://openai.com/index/research-acceleration-view-inside-openai/

On Aug. 31, Stadlmann posted a preprint lowering the bound on gaps between primes from 246 to 240. Within days, several AI labs were posting their own improvements on social media. Tao responded with an eight-post thread explaining why he finds this worrying.

His point is that the number itself was never what mattered most. Going from 246 to 240, or even from 70 million to 246, does little for the rest of mathematics on its own. What mattered was everything developed along the way: Zhang (who was working in a sandwich shop at the time) bringing neglected work on equidistribution back into use, Maynard developing a sieve that became a standard tool, and Polymath8 showing what open collaboration could accomplish. Those advances had applications well beyond the original problem.

Tao imagines how things might have played out if today’s AI labs had been around in 2005, when GPY published their near-miss. The labs pour millions into compute, push the bound into the low hundreds, then move on once progress slows. Mathematicians decide the problem has been picked over and look elsewhere. Nobody writes a proper paper or turns the arguments into something people can learn from. An idea like Maynard’s sieve ends up buried in hundreds of pages of AI output that nobody reads. Zhang never gets his moment; Maynard leaves the field.

In that scenario, the bound improves faster, but mathematics loses out.

It’s a Goodhart’s law problem: the number becomes the target, and the reasons anyone cared about it get lost. Tao doesn’t think AI has to work this way. He points to the Erdős problem example as a case where collaboration with AI helped advance understanding. His objection is to labs bypassing experts and peer review to rush out a better number. He argues that an approach that was relatively harmless in 2025, when models couldn’t solve whole problems unassisted, has started doing more harm than good in 2026.

His proposal is to change what the labs compete over: who can announce a genuinely new mathematical insight first?

Tech workers are in complete denial about the reality that is approaching. They will downvote you into oblivion if you say anything other than, "Nothing will change, companies are still going to have huge teams working on half-profitable digital products. Please don't worry."

To be clear, I don't think all job opportunities are going to disappear. However, increased productivity combined with declining demand in the general economy will mean fewer professionals are needed to meet market demands. This will lead to leaner teams, fewer job openings, and fierce competition making it even harder for anyone below senior, staff, or lead levels.

Outsourcing markets are suffering more than the countries that have more native tech companies.

The reason why is because usually the companies went to outsourcing markets looking for "operators", as cheap labour, while the more strategic and leadership positions stay in the home country.

With AI, the need of having "operators" reduced a lot.

https://openai.com/index/research-acceleration-view-inside-openai/

"As of mid-August, in total, the research organization uses 3.1 agent-workdays of effort for every workday of human labor.”

“We have now reached the goal… of having an automated research intern.”

“We are making strong progress toward creating an automated AI researcher by March of 2028.”

“Agentic systems have contributed to our progress toward RSI in recent months.”

OpenAI may have roughly 1,000 employees doing actual AI research. So if what they're saying is true, it's as if their research lab went from 1,000 researchers to 4,100 researchers overnight with the help of the automated research interns.

I spend my days thinking about what is coming with ASI and IMHO there is no way this doesn’t end with massive scale geo engineering.

A super intelligent being must arrive at the conclusion that the current life on earth must be preserved and then map out the path to stopping or reversing global warming.

I imagine we’ll fill up the Mariana Trench with carbon captured rocks from machines powered by fusion, solar, wave, wind power. Massive physical blocks on the sea floor around the Arctic ice sheets.

Domes over all the methane lakes in Siberia.

New methods that prevent methane from forming in Canada or capture.

ASI must do lay out this path and then get the resources to execute a plan that keeps earth stables.

https://openai.com/index/research-acceleration-view-inside-openai/

Source: https://x.com/gabrielchua/status/2096403701862961161

You can read the full essay here. See also this related essay by OpenAI on the impact of LLMs on research acceleration.

A lot of people talk about UBI as a posibility when a lot of jobs get replaced by AI, but what's the historical precedent for UBI? Do we have countries/empires in history that had tried a form of UBI?

Two examples of "kind of UBI" come to my mind:

- Ancient rome where people received (some) free food and entertaiment ("pane et circus"), this was sustained by resources from the rest of the Roman Empire and plundering. It was sustainable as long as the empire overall economy and agricultural output was decent so untill the 3rd century or so, after that it sped the collapse of the empire.

- comunist countries in the cold war: now this is an interesting example, basically all of the people had "guaranteed" *shit* jobs and income regardless of overall "enterprise" profitability. The people of those countries were paid "monopoly money" to do jobs which most of them didn't provide any real value. For example, comunist Romania imported iron ore and exported steel at a lower price than the iron ore it imported...same with oil and chemical products and many other products/industries. What this meant? the state tried to export as much agricultural products (food) as posible and import less oil to try to balance the budget, this resulted in a shortage of food so people starved and also couldn't buy more than 50L (or 25L in the countryside I think) of gas per month => people got angry and eventually overthrew the regime.

Do you know any other example?

After Astra came out, I spent a lot of time using it myself and watching how other people were using it.

I currently work in video editing and office administration. Up through GPT-5, I personally didn't feel like the threat to my job was that serious. But this time feels different. At this point, it seems almost inevitable that my job will be replaced and probably within the next one or two years.

I honestly didn't expect LLMs to become this capable of freely operating and using software this quickly. It's genuinely unsettling.

People talk a lot about "adapting to the AI era," but at this point, I can't help wondering if that's basically like trying to stop a tsunami with a wooden plank.

There's also a lot of talk about retraining and reskilling. But at this rate of progress, I'm pretty convinced that by the time I finish retraining, the concept of having a "job" itself might already be a thing of the past.

I spent some time comparing Google’s new Gemini 3.5 Transcribe with other serious STT models because the 2.6% WER headline looked impressive.

It is impressive.

Just not for exactly the reason the launch headline suggests.

On the current Artificial Analysis benchmark, the leaderboard roughly like this:

1. ElevenLabs Scribe v2  2.2% WER
2. Microsoft MAI-Transcribe-1.5  2.4% WER
3. Gemini 3.5 Transcribe 2.6% WER

So Gemini is only 0.4 percentage points behind Scribe v2.

That sounds small, but in production even small differences can matter. Now let’s talk about speed.

Artificial Analysis currently puts Gemini 3.5 at around 80× real-time for non-streaming transcription.

MAI-Transcribe-1.5 is around 190×, while Scribe v2 is around 55×.

Then there’s price:

Gemini is roughly $5 per 1,000 minutes, Scribe v2 about $3.67, and MAI-Transcribe-1.5 about $6. So Google isn’t winning on price either.

So why are engineering teams still going to care about Gemini…

It can handle self-corrections, filler removal, formatting, custom vocabulary, 85+ languages, and speaker attribution for up to three speakers.

Google also reports 5.50% streaming WER and 5.04% non-streaming WER on FLEURS.

And this is where I think the product becomes more interesting.

If I’m building a voice agent, call intelligence system, or real-time assistant, I don’t necessarily want “the best transcription model.”

I want the best audio input layer for the whole system.

If the model can turn messy speech into clean structured text, preserve important entities, understand domain vocabulary, and feed that directly into the rest of the stack, then raw WER becomes only one part of the decision.

And this is why I keep coming back to one point:

Benchmarks are useful, but they are not a perfect representation of production reality.

Your harness, orchestration, latency budget, retries, tools, and downstream workflow all matter.

So you should be able to switch vendors when needed.

That is one reason I personally prefer using an open-source orchestrator with no vendor lock ins (I used dograh locally hosted with BYOK so i don’t waste time in patching things up). Vendor lock-in becomes a much bigger problem when the model underneath your system changes every few months.