Pioneers of AI: OpenAI’s Tibo Sottiaux doesn’t want humans to be an ‘afterthought’

SPEAKER_1 (0:01)

We've all spent money on something that seemed incredible, but didn't live up to the hype. A kitchen gadget, a car, when it doesn't deliver, the disappointment is real. Marketers suffer from this too, chasing metrics that look great, but don't translate into revenue. LinkedIn calls that bull spend. Invest in what matters and reach the right buyers with LinkedIn ads. You can target by company, industry, job title and more. LinkedIn ads also generates the highest ROAS of all major ad networks. Spend $250 on your first campaign on LinkedIn ads and get a $250 credit for the next one. Just go to linkedin.com/scale.

That's linkedin.com/scale.

Terms and conditions apply.

The very best founders I know are brilliant at building systems. They connect teams, they remove bottlenecks, and they eliminate single points of failure. And yet, when it comes to their own wealth, most are running a disconnected stack. A tax accountant here and a state attorney there, a wealth manager who doesn't talk to either one of them. Creative planning was built to fix exactly that. One integrated team of tax professionals, state planners, investment specialists, all coordinated by a dedicated wealth manager who sees your full financial picture and keeps every piece working together. Proactive tax efficiency, state strategy, investments, all under one roof. Creative planning, where wealth works together. Learn more at creativeplanning.com/masters of scale.

SPEAKER_2 (1:41)

AI is full of possibilities. Enterprises run on realities. That's why Pega combines the world's most powerful AI with the governance, transparency and control enterprises demand. So you can move faster, adapt faster, and scale AI with confidence.

All the power of AI, none of the uncertainty. Learn more at pega.com/ai.

Thibault Sottiaux (2:11)

It's a privilege to be working on these things at this moment in time. It's something that we all feel like a sort of like a deep collective responsibility.

And so there's this deep sense of, hey, we really must get this right. And this is the time to just put in the effort so that this technology ends up putting humans and humanity at the center of it. And not humans being an afterthought, because relentlessly just pursuing whatever automation. It's like, that's not why we're here, that's not why I'm here. I want this to just benefit all, benefit humanity. And so there is this incredible mission behind it, which is super motivating.

Rana el Kaliouby (2:50)

Tibo Sottiaux leads product at OpenAI. He joined the company two years ago and led the team building Codex, the coding agent, and now oversees OpenAI's core products like ChatGPT. Today, I'm sitting down with Tibo to talk about OpenAI's newest releases, what it's like to work inside a frontier lab during this extraordinary moment, and his approach to safety, trust, and alignment. I'm Rana el Kaliouby, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution.

Hi, Tibo. Welcome to Pioneers of AI. I'm so excited for our conversation.

Thibault Sottiaux (3:36)

Thanks for having me.

Rana el Kaliouby (3:38)

All right. So you lead the product team at OpenAI. Help us understand the scope of your role, how big is your team, all that.

Thibault Sottiaux (3:45)

The biggest part is Chatchabitie that everyone knows about.

And then we also have like specialist things such as Codex, which is like for coders and technical people out there. And then there's also a whole part around like our API and our platform to support building a whole bunch of products out there when they rely on our models. And so like all together, that's the majority of the products we have here at OpenAI.

Rana el Kaliouby (4:12)

Am I right in understanding that like basically the researchers at OpenAI are building the models and then at some point they hand off this model to you and you productize it? Is that a right way to think about it?

Thibault Sottiaux (4:24)

Almost. So we collaborate quite early on on the next type of capabilities that we want to have in the model. So for example, say that we want our models to be able to handle secure payments, and then we would go and work with researchers specifically on these kinds of capabilities, figure out the right kind of data we need to craft for it, the right kind of evaluations we need for it, and then how to bring it into a product. And so the collaboration starts quite early on. Then we train the model and then the researchers hand it over and then we serve it and then we package it into the product.

Rana el Kaliouby (5:00)

Very cool. Okay, so we are talking at the start of October. Dev Day just happened. For those who are not familiar with Dev Day, give us a sense of what it's like.