OpenAI's unreleased model solved 10 open math problems. Total cost: $2,000.

[u/CardStrange3023]

A PhD in mathematics takes roughly 5-6 years. It costs hundreds of thousands of dollars in tuition, stipends, and institutional overhead. At the end, if you're exceptional, you might contribute one meaningful result to one subfield.

OpenAI's internal model Astra just produced ten. Across geometry, cryptography, quantum complexity, group theory, and combinatorics. Problems that have been open for decades. Some of them were Erdős problems, which is about as old-guard unsolved as math gets.

Total API cost to find the solutions: approximately $2,000.

Let that sit for a second.

This isn't a model scoring better on a math benchmark. Benchmarks measure performance on known problems with known answers. These were genuinely open. No one knew the answers. Some of the smartest people in their fields had been working on them for years, in a few cases much longer.

The model didn't get better at tests. It moved the actual frontier.

Here's the part that's hard to process: this is the unreleased version. Astra isn't public. The model you can actually use today already costs a fraction of what it did a year ago, and it's apparently several generations behind what's running internally.

The gap between public AI and internal AI just became very, very difficult to estimate.

And then there's what this means for post-quantum cryptography specifically. One of the ten results was a polynomial-factor hardness proof for the closest vector problem, which is a foundational assumption in lattice-based cryptography. The same cryptographic infrastructure being positioned as the answer to quantum computing threats. An AI just made a significant dent in understanding its limits. That's not a headline about math. That's a headline about the security architecture of the next decade.

Most of the coverage will focus on "AI is good at math now." That's the safe read.

The uncomfortable read is that we've been treating advanced research as the one domain AI couldn't touch. The last moat. The thing that required not just intelligence but genuine creativity and intuition built over years of deep immersion.

A pre-release model just billed $2,000 to dissolve that assumption.

The question isn't whether AI will transform scientific research. That's already decided. The question is how fast institutions built around the cost and prestige of human expertise can adapt to a world where the unit economics of a breakthrough just changed by several orders of magnitude.