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# Google's Astra solved 10 open math problems for $2,000 and the singularity crowd is already running with it

**[Trends](https://daily.dev/sources/trends)** · 2 min read · 2 upvotes · 1 comments

## Summary

Google's internal Astra model reportedly solved ten open problems in mathematics and theoretical computer science at a total cost of around $2,000 using Sol API pricing. The claim comes from a former colleague of Greg Brockman and has sparked debate: one camp sees it as a genuine inflection point toward AI-driven research breakthroughs and even singularity-adjacent feedback loops with quantum computing, while the other acknowledges the result but reminds that the underlying mechanism is still next-token prediction. Key caveats remain — Astra is still internal, the specific problems haven't been disclosed, the methodology is opaque, and 'solved' is unverified by peer review. The most striking angle is the economics: $2,000 per ten mathematical advances reframes the cost-per-insight argument for AI in research in a way that's hard to ignore, even if the claims ultimately need scrutiny.

## Content

Google's internal Astra model — the next major release after Gemini — apparently solved ten significant open problems in mathematics and theoretical computer science, at a total cost of around $2,000 at Sol API pricing. That's the claim from Greg Brockman's former colleague gdb, who posted the list without much fanfare.

The reaction split pretty cleanly into two camps.

One side is treating this as a genuine inflection point. Tejas Kumar connected it directly to quantum computing: if AI can crack problems that have stumped researchers for decades, and quantum computing is one of those problem spaces, then AI improving quantum and quantum improving AI starts to look less like a thought experiment and more like a near-term feedback loop. The singularity framing came out fast.

The other side is more measured, but still impressed. Hesamation's take cuts both ways: "these stochastic parrots are predicting very smart next tokens." It's a compliment wrapped in a reminder that the underlying mechanism hasn't changed. The model isn't reasoning in some new way — it's just doing what language models do, at a level that happens to clear problems mathematicians couldn't.

What's actually notable here isn't the philosophical debate. It's the price tag. $2,000 for ten advances in math and theoretical CS is the kind of number that makes you do the math on what research institutions spend per breakthrough. If even a fraction of these results hold up to peer scrutiny, the cost-per-insight argument for AI in research gets a lot harder to dismiss.

The problems themselves haven't been detailed publicly yet — Astra is still internal. So the claims are unverified, the methodology is opaque, and "solved" is doing a lot of work in that sentence. Math has a way of humbling announcements like this once the proofs get checked.

Still, the pattern here is real: AI keeps landing in territory that was supposed to require human intuition, and the goalposts keep moving. Whether that's the singularity or just a very expensive autocomplete getting very good at a specific task probably depends on which camp you were already in.

## Community discussion

Top comments from developers on daily.dev.

**@agustinbarrientos** · 0 upvotes

> Where can we see the ten problems and the full method?

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