We want chat GBT as a first party app.

First party app is a really great way to get 800 million wows or whatever now.

>> Tenth of the globe, right?

>> Yeah. Yeah. 10% of the globe uses it every week.

>> every week. Every week.

Even within OpenAI, the the thinking was that there would be like one model that rose them all.

It's like definitely completely changed.

It's becoming increasingly clear that there will be room for a bunch of specialized models.

There will likely be a proliferation of other types of model.

Companies just have giant treasure troves of data that they are sitting on.

The big unlock that has happened recently is with the reinforcement finetuning.

With that setup, we're now letting you actually run RL, which allows you to leverage your data way more.

Sherman, thanks very much for joining.

So, we're being joined by Sherman Woo.

Um, it'd be great actually if you provided the long form of your background as we get into this, just for those that that may not know you.

I mean, I I view Sherman as one of the the top AI thought leaders, so I'm really looking forward to this.

>> Yeah. Yeah. Thanks for having me. I'm I'm really excited to be be on the podcast.

Um, yeah. Yeah, a little bit more of my my background. So, uh maybe we can start from present day and go backwards.

So, I I currently lead um the engineering team for the for OpenAI's developer platform.

So, the biggest product in there of course is the the API.

>> is there more for the developer platform than the API?

Um I just kind of assume there was synonymous.

Well, so I I also think about uh other things that we put into our platform side.

So, like technically our government work uh is is also like offering deploying this different areas.

Yeah. Like I've talked about

>> Oh, so you have like a local deployment like

>> Yeah. Yeah. So, we actually do have a local deployment Atlas Alamos National Labs. It's super cool. I went to visit it. It's like very different than what I'm used to.

Um, but yeah, in a like, you know, classified supercomput with with our with our model running there.

That's cool.

Um, so there's that. Um, but like mostly the the API. Um, cuz

>> Did you go to Los Alamos?

>> We did. Yeah, I did go to Los Alamos. It was great. They showed us around. They showed us some of the historic sites.

Real history. Um, yeah.

>> I used to work at Livermore, man. So, I've got like an

>> Oh, yeah. Yeah. My first job out of college. So,

>> Right. Right. Right. You sounded that next.

>> Yeah. Yeah. Yeah. Well, we we hope to we hope to.

Um but uh yeah, so I work on the developer platform. I've been working on it for around uh 3 years uh now.

So I joined in 2022. Uh was basically hired to work on the the API product which at the time was the only product that that opening I had.

Um and I've basically just worked on it the uh the entire time.

Um I've always been super interested in the developer side and kind of like the startup story of this technology. And so it's been really really cool to kind of see see this evolve.

And so um that's my time in OpenAI.

Before OpenAI um I was at uh Open Door uh for around 6 years. I was working on the pricing side. My my general background before

>> I think it's such a dissonant like you know

[laughter] pricing at open door to like running API and

it's such a different uh it's been fascinating actually for me to see the differences between the companies like they're run so differently.

Um they both have open in the name so there's some overlap but like that's bas that's pretty much it. Um

>> but yeah I was there for around six years uh working on the pricing team. So

our team basically would run the ML models.

This isn't actually pricing the assets on Open Door like the inventory.

>> Exactly.

So open so yeah, Open Door would buy and sell homes and their main project was buying homes directly from people selling them for with all cash offers and so my team was responsible for how much we would pay for them. Uh

and so it was a really fun like ML challenge. Uh it had a huge operational element to it as well cuz not everything was automated obviously

but it was a really fascinating technical challenge and um

>> is there any sense of that on the API side like like GPU capacity buying or is it just totally unrelated?

Oh on the on the API side um there's is a small bit of like how we price the the models but uh I don't think we do anything as sophisticated as uh uh as open door.

Open door is just like such a hard problem. It's like such a like expensive asset. It's like like the the holding costs are very expensive. You're like holding on to it for like months at a time. There's like a variability in like the holding time.

>> A massive long tail of like potential things that could

long tail.

Yes. And like you know you have the you try to think about it from a portfolio perspective and like if one of them just like you're holding on it for two years it blows everything.

>> Everything like goes negative.

So it's uh it's a very very different different challenge.

Yeah. Yeah. Six years there. Uh lots of up and downs. uh saw a lot of the booms, saw a lot of the struggles and then we IPOed uh before I before I left.

Um but yeah, just in general it was a very great experience.

I think uh for me it was uh it was also just like had such a very like business operations and like

>> like a very like by the book type of culture whereas open is like very different.

What's so interesting I was just thinking about it now is like even for a company like that like you don't think about it as a tech company but if there is a deep technology problem it actually is the pricing right. It's actually an ML problem.

It's not what it drives. like the website, it's not the, you know, it's not the platform, it's like, it's not the API, it's literally that.

>> Yep. Yep. Yep. And that's what attracted me to it. I think that's was interesting.

Um, uh, it's also a way like lower margin business than, uh, OpenAI. Uh, cuz you're like making a tiny spread on these homes.

Uh, they talk about like basis points like eating bits for breakfast and all that.

So, um, anyways, I was at Open uh, Open Doorf around 6 years. Um, and then before that, uh, was my first job out of college which was at Kora um, with Adam Dans from group there.

Yeah. So I was working on the newsfeed.

>> Uh so worked on newsfeed ranking for a bit. Worked on the product side.

Um but was uh that was actually my first exposure to like actual ML and industry and uh learned a lot from uh uh from from the engineers at core.

We basically hired a lot of the the early like feed engineers.

>> Was Charlie still there when you were there?

>> Charlie was not there when I when I right after that was a really legendary team, you know.

I was still known to be kind of this super iconic founding team.

>> Yeah. Yeah. the the the early founding team was really solid.

I still think that even while I was there, I was I I still like am amazed at the quality of the talent that we had.

Um I think there's like one company's like 50 to 100 people, but like

>> yeah, like a bunch of the perplexity team was there.

Dennis Dennis was on the feed team with me. Uh uh uh Johnny Ho, Jerry Ma,

>> that's right. And then Alexander the scale now, you know, like was there he was he was he was there between high school and college.

Um it was an incredible team. I I don't think I I think I kind of took it for granted while I was there.

It was a good group. But um

>> uh and how did you how did you get to Kora? What did you study in undergrad?

Yeah, so before that I was at MIT for undergrad. I studied computer science.

Um uh did like one of those like computer science and the master's degree kind of like crammed it in.

Um uh I ended up at CORE because I got an uh what we call an externship there.

So like uh at MIT uh you actually get January off.

So there's like the fall semester and then January's off and then you have uh the spring semester.

And so it's kind of this it's called independent activities period.

So some people just like take classes, some people just do nothing,

>> but it's uh some people will do like month-long internships and some crazy companies will like offer a month-long internship to a college student.

And it really is just kind of like a way to get uh people into

>> Did you come out here from Boston or

Yeah, it was crazy.

So um so so so uh you had to apply. I remember uh yeah this is like I think 2013 uh January or something um you had apply and I remember the core internship was the one that just paid the most. They paid I think it was like $8,000 $9,000

[laughter] and I was like wow that's like for a month and you're kind of ramping up like half the time >> I can eat for a year. >> Yeah. Yeah. As a college student like this like great and you get a and yeah they would they would kind of like fly out fly you out here and so I did the interviews and then luckily got an offer

and so yeah I came out for a January. Um that was right when they moved into their new Mountain View office and I basically uh yeah honestly just ramped up for like 2 weeks and then had like two weeks of good productivity working on the feed team.

>> So was that on the was that like userf facing like userf facing product? Yeah.