Synthetic Sciences

Building foundational models for scientific research

[Aayam Bansal @aayambansal]

The long term AI winners will not rent intelligence by the token forever.

The best AI native products start on frontier models. They should.

But if you never graduate, your margins are tied to someone else’s pricing, reliability, and roadmap. You keep paying to generate data you do not fully control.

If you have users, you already have the raw ingredient to graduate: your production data and feedback loops.

That is why we built Flywheel Mode:

• Start with a generalist model
• Store production data and feedback
• Post train a private task specific model on your data with SFT + RL
• Swap that workflow off frontier APIs
• Repeat

Most teams burning cash on frontier LLM APIs are running the same calls on repeat, even when a small specialized model can do it for pennies, faster, and often better.

Frontier models are for discovering the workflow.

Your own models are for scaling it.

[Y Combinator @ycombinator]

.@SynScience is building AI co-scientists for end-to-end scientific research. Scientists can now delegate the full research loop: literature reviews, hypothesis generation, experiment runs on GPUs, analysis, and publication-ready drafts.

Battle-tested on ML research, and expanding fast across domains. Biology mode is SOTA on BixBench Verified (92%).

Congrats on the launch, @ishaangangwanii and @aayambansal!

https://t.co/nUkmQ1dTcN