OpenAI researcher Michelle Pokrass shares insights on shifting paradigms in frontier model development and training efficiency.
Prediction Not checkable as stated
Pokrass predicts developers will abandon RAG vector stores for direct long-context
“So we do expect a lot of developers to start, you know, uploading their full context more directly to the model. So for smaller tasks, you maybe don't need The whole vector store.”
Insight
Pokrass: Prototype with GPT-4.1, then downscale for latency or upscale for reasoning
“I think the answer is always going to be the fastest model that accomplishes your task, right? So maybe you start prompting 4.1 as a starting point if it does your task super well, Then maybe you could drop down a 4.1 mini and save latency, or even nano. Where…”
Opinion
Pokras: Vision fine-tuning is the most underrated release for bespoke OCR
“Vision fine-tuning is so underrated. For the past, like, two months, whenever I talk to founders, they tell me this is the thing they need most. A lot of people are doing, like, OCR on, on very bespoke formats, like government documents, and vision fine-tuning…”
Insight
Pokrass: Every successful company eventually outgrows Postgres for NoSQL
“At some point, every company gets the scale, every successful company gets the scale where Postgres is not cutting it. And then you migrate to some sort of NoSQL database.”
Insight
Function calling benchmarks like BFCL are largely saturated
“I find that a lot of these evals are mostly saturated, like for BFCL. All the models are near, near the top. Already. And kind of the errors are more, I would say like just differences in default behaviors. I think most of the models on the leaderboard can kin…”
Insight
Multi-step agentic apps fail at 95% reliability due to compounded errors
“Like if something is 95% reliable, but you're chaining together a bunch of calls, if you magnify that error rate, it makes your like application not work. So that's a really exciting thing here from going from like 95% to a hundred percent. I'm very biased wor…”