Michelle Pokrass, Tech Lead at OpenAI, discusses infrastructure scaling bottlenecks across high-growth engineering teams like Coinbase, Clubhouse, and OpenAI.
“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.”
quote is from the automated transcript, cleaned for reading:
filler sounds and stutters are removed, nothing is rephrased. names can be misheard
(the analysis reads context, assessments check outside sources). how →
More from Michelle Pokrass
PredictionNot 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.”
Michelle PokrassApr 15, 2025▶ 15:21GPT 4.1: The New OpenAI Workhorse
Insight
Pokrass: AI model gains now driven by post-training, not larger pre-trains
“We find that actually a significant amount of the gains come from new post-training techniques. So I think in the past the narrative is that you need to pre-train these larger and larger models to get better performance, and we're finding that we're able to sq…”
Michelle PokrassApr 15, 2025▶ 7:57GPT 4.1: The New OpenAI Workhorse
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…”
Michelle PokrassApr 15, 2025▶ 27:58GPT 4.1: The New OpenAI Workhorse
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…”
Michelle PokrassOct 4, 2024▶ 56:21Building AGI in Real Time (OpenAI Dev Day 2024)
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…”
Michelle PokrassSep 17, 2024▶ 23:54Building AGI with OpenAI's Structured Outputs API
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…”
Michelle PokrassSep 17, 2024▶ 28:10Building AGI with OpenAI's Structured Outputs API
Made with StarZero
Turn any episode into a week of clips.
This entire site, over 200 episodes transcribed, diarized, checked and made playable,
runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the
moments worth sharing, cuts them, captions them, and reframes them for every feed.
We use essential cookies to make the site work. With your permission we
also use analytics cookies (Google Analytics and Mixpanel) to understand
usage and improve StarZero. See our Cookie Policy.