Public transcript

Can Open-Source Models Distill OpenAI To Death? | Greg Brockman

By: Big Technology
Length: 4 min
Language: English
Text from: the video's captions
Transcribed: 13 October 2026

Read the full transcript Open on YouTube

Notes in English.

Summary

In this short interview, Greg Brockman responds to the argument that open-source model makers can distill OpenAI's leading models and stay only a couple of months behind. He argues OpenAI's real investment is in the "machine that makes the machine" — the end-to-end co-designed system of people and supercomputers — and that distillation cannot fully replicate a model's capability. He also addresses pricing and competition by pointing to steep year-over-year price drops, Jevons paradox, and the returns to intelligence seen in the new model.

Key points

Brockman says OpenAI's real investment is in end-to-end co-design: a system of people and a way of leveraging massive supercomputers to produce models.
He argues distillation is not as simple as taking a model's output and getting the same capability in a smaller, faster package — otherwise OpenAI would do it itself.
OpenAI invests heavily in safeguards and mitigations, including a long-standing public preparedness framework covering areas like cyber and bio.
Brockman notes OpenAI has dropped prices on the same level of intelligence year over year, sometimes by a factor of a hundred.
He describes a real Jevons paradox: lowering the cost of intelligence leads to way more activity.
He says there are returns to intelligence, and a little more intelligence goes a long way in what people use the models for.
He claims the new model's "point one" improvement understates the magic early testers have seen in practical work.

Questions it answers

00:00The distillation pattern

How does OpenAI defend its lead when open-source makers distill its models and stay just months behind?

Brockman reframes the investment as end-to-end co-design: a system of people and a way of leveraging massive supercomputers to produce models, rather than just the models themselves.

OpenAI's real investment is in the system and people that produce the technology.
The interviewer notes open source is consistently a couple of months behind leading foundational models.

00:44Limits of distillation

Can open-source distillation fully replicate a leading model's capability?

Brockman says no — you cannot simply take a model's output, distill it, and get the same capability in a smaller, faster model. If that worked, OpenAI would do it itself to gain an easier-to-serve model.

There is a lot of art behind distillation, but it doesn't match full capability.
OpenAI invests in "the machine that makes the machine."

01:23Safeguards and availability

How does OpenAI approach safety and deployment?

It invests in safeguards and mitigations across many potential misuse areas, including cyber and bio, documented in its public preparedness framework, while aiming to keep models broadly available.

The preparedness framework is public.
OpenAI believes the technology should benefit and lift everyone up.

02:23Pricing and open-source economics

How does OpenAI handle open source delivering almost-as-good performance more cheaply?

Brockman points out OpenAI has dropped prices on the same level of intelligence year over year, sometimes by a factor of a hundred, and describes a real Jevons paradox where lower costs drive way more activity.

The new model's pricing is roughly double GPT-5.4's, per the interviewer.
Price drops of at least an order of magnitude per year are the historical norm, Brockman says.

03:21Returns to intelligence

Why does Brockman think a small intelligence gain matters so much?

He says there are returns to intelligence: for the tasks these models can now do, a little more intelligence goes a long way, so 5.5's seemingly incremental improvement should drive a massive improvement in usage.

The "point one" improvement undersells the magic early testers have seen.
Lower cost plus more capability means far more activity.

Notes

The distillation question

The interviewer describes a repeating pattern: massive models come out, open-source makers distill them, and open source ends up just a couple of months behind the leading foundational models.
With investment now so large and capabilities increasing dramatically, the interviewer asks how OpenAI's lead is defensible long term.
Brockman reframes the question: the real investment is in end-to-end co-design — a system of people producing the technology and a way of working together, including how to leverage massive supercomputers.

Why distillation isn't enough

Brockman argues it's not as simple as taking a model's outputs and distilling them into an equally capable but smaller, faster model.
If that were possible, OpenAI would do it itself and gain a model that is much easier to serve.
He acknowledges there is a lot of art behind distillation, but stresses that OpenAI is investing in "the machine that makes the machine."

Safety and deployment

On the deployment side, OpenAI thinks a lot about safeguards and mitigations for many ways the models could be misused in real situations.
This spans areas like cyber and bio, backed by a long-standing effort documented in the public preparedness framework.
Brockman says every piece of what OpenAI does must connect to making progress while making the models broadly available, because the company believes the technology empowers and lifts everyone up.

Pricing and the open-source economics threat

The interviewer notes the new model's pricing is roughly double that of GPT-5.4, and asks how OpenAI handles open source delivering almost-as-good performance more cheaply.
Brockman responds that OpenAI's history shows prices on the same level of intelligence dropping year over year, sometimes by a factor of a hundred, and at least an order of magnitude.
He cites a real Jevons paradox: when you lower the cost of something, way more activity happens.

Returns to intelligence

Brockman says there are returns to intelligence — for the tasks these models can now do, a little more intelligence goes a long way.
He frames the new model (5.5) as seemingly an incremental improvement, but predicts a massive improvement in what people use it for.
He says the "point one" improvement really undersells the magic that early testers have seen in their practical work.

Text from the video's own captions. Summary and notes written by AI from the transcript, so check anything important against the recording.