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Raghu Raghuram

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2025-11-28
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2025-11-28
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  1. “Yeah, yeah, 10% of Globe uses it every week, every week. And it's growing and is growing. So, like at some point, you know, it'll hit even higher than that. So, yeah, like obviously the reach there is unmatched. But then also just being able to have a platform where we can reach even more than just that. One thing we talk about internally sometimes is what does our end user reach from the API? It's actually really, really, it's really broad. It's hard because ChatGPT is growing so quickly, but at some points it was definitely larger than ChatGPT. And the fact that we're able to tap into all this and get the reach that we want, I think is really good. But yeah, I mean, there's definitely some tension sometimes. I think it's come up in a couple of places. I think one of them is on the product side. So as you mentioned, sometimes there are competitors kind of like building on our.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  2. “Also, just like, yeah, the amount of time and A tenth of the globe. Yeah, yeah. 10% of the globe uses it every week, every week.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  3. “Unusual for companies to have both of that Yeah, yeah, I completely agree. I think there is some amount of tension. I think one thing that really helps here is salmon, Greg, just from a founder perspective have since day one just been very principled in the way in which we approach this. They've always kind of told us we want ChatGPT as a first party app. We also want the API. And the nice thing is I think they're able to do this because at the end of the day, it kind of comes back to the mission of OpenAI, which is to create AI and then to distribute the benefits as broadly as possible. And so if you interpret this, you want it in as many surfaces as you want. And the first party apps are a really great way to get, you know, it was like 800 million wows or whatever now.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  4. “It's kind Just like absorbs some knowledge from this. Awesome So, one place I wanted to start is something that I find very unique about OpenAI's both a pretty horizontal company. Like it's got an API. Like I would say we've got this massive portfolio of companies, right? And I would say a good fraction of them use the API. And then it's also a vertical company in that you've got full-on apps, right? Like everybody uses ChatGPT, for example. And so you're responsible for the API and kind of the dev tool side. So maybe just to begin with, is there an internal tension between the two? Like, is that a discussion? Like the API may, whatever, it may help a competitor to like the vertical version or is it not, things are just growing so fast. It's not an issue. I'll just love to how you think about that. By the way, it's very unusual for companies to have both of that. These two things this early. It's very unusual.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  5. “Yeah, yeah. That's actually how I ended up at OpenAI, too, kind of fast forwarding from there because Open AI kind of kept a quiet profile ish. I'd always kind of kept tabs on them because a bunch of the core people I knew kind of like ended up there. It's kind of like checking in on it and they were like, yeah, something crazy is happening here. You should definitely check it out. So yeah, I definitely owe a lot to Cora. But yeah, part of the reason why I went there versus other options as a new grad was the team was just”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  6. “Yeah, That's his envelope. I mean, like, by the way, I think it's one of the untold stories of Silicon Valley's like how good that original team is. I mean, a lot of them are still there and still good, but the diaspora from Quora is everywhere. Yeah.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  7. “Yeah, I distinctly remember my externship project for those two weeks was just to like add a couple features to our feature store. And that would make it sway into the model. I remember my mentor there is Tudor, who's now running, I think it's called Harmonic Labs. Yeah, yeah. Crazy team. Crazy team.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  8. “Yeah, yeah. As a college student, it's like great. And yeah, they would kind of like fly you out here. So I did the interviews and then luckily got an offer. And so, yeah, it came out for January. That was right when they moved into their new mountain view office. And I basically honestly just ramped up for like two weeks and then have two weeks of good productivity working on the feed team.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  9. “Yeah, 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.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  10. “Yeah, yeah, it was crazy. So you had to apply. I remember, yeah, this is, I think, 2013, January or something. You had to 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. And I was like, wow, that's like for a month. And you're kind of revamping up like half the time.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  11. “Yeah, so before that, I was at MIT for undergrad. I studied computer science, did one of those computer science and the master's degree, kind of like crammed it in. I ended up a core because I got in what we call an internship there. So at MIT, you actually get January off. So there's like the fall semester and then January's off. And then you have the spring semester. And so it's called independent activities period. So some people just like take classes. Some people just do nothing. But some people will do like month-long internships and some crazy companies will offer a month-long internship to a college student. And it really is just kind of like a way to get people into.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  12. “Yeah, yeah. The early founding team was really solid. I still think that even while I was there, I would still amazed at the quality of the talent that we had. I think there's like one of the companies like 50 to 100 people. But yeah, like a bunch of the perplexity team was there. Dennis was on the feed team with me, Johnny Ho, Jerry Ma.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  13. “It's like an MML problem. Yeah, that's what attracts the website, it's not though. Yeah, yeah. Yep, yep, yep. And that's what attracted me too. I think that was interesting. It's also a way lower margin business than OpenAI because you're making a tiny spread on these homes. They talk about basis points, like eating bits for breakfast and all that. Anyways, I was at Open Door for around six years. And then before that was my first job out of college, which was at Cora, at MDNS. No, I can't. Yeah. So I was working on the news feed. So worked on news feed ranking for a bit, worked on the product side. That was actually my first exposure to like actual ML in industry and learned a lot from the engineers at core. We basically hired a lot of the early feed engineers.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  14. “What's so interesting? I was just thinking about it now. It's like, even for a company like that, 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 ICNML problem. Yeah, that's what attracts the website. It's not the platform. It's not the API. It's literally that.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  15. “Long tail, yes, and try to think about it from a portfolio perspective. And if one of them just holding on it for two years, it blows everything, like goes negative. So it's a very, very different challenge. Yeah. Yeah. Six years there. Lots of ups, saw a lot of the booms, saw a lot of the struggles. And then we IPO'd or far allowed it to.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  16. “On the API side, there is a small bit of like how we price the models, but I don't think we do anything as sophisticated as Open Door. Open Door was just like such a hard problem. It's like such expensive asset. The holding costs are very expensive. You're like holding on to it for months at a time. There's like a variability in the holding time.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  17. “With all cash offers Is there any sense of that on the API side, like GPU capacity buying, or is it just totally unrelated?”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  18. “Exactly. So, yeah, Open Door would buy and sell homes, and their main project was buying homes directly from people selling them with all cash offers. And so my team was responsible for how much we would pay for them. And so it was a really fun ML challenge. It had a huge operational element to it as well because not everything was automated, obviously. But it was a really fascinating technical challenge.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  19. “Yeah, we hope to. Yeah, so I work on the developer platform. I've been working on it for around three years now. So I joined in 2022. I was basically hired to work on the API product, which at the time was the only product that OpenAI had. And I've basically just worked on it the entire time. 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 this evolve. And so that's my time in OpenAI. Before OpenAI, I was at OpenDOR for around six years. I was working on the pricing side. My general background before.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  20. “Yeah, yeah. So we actually do have a local deployment at Los Alamos National Labs. It's super cool. I went to visit it. It's very different than what I'm used to. But yeah, in a classified supercomputer.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  21. “Well, so I also think about other things that we put into our platform side. So technically, our government work is also like offering and deploying this into different areas. Yeah, like I've talked about. So if you have like a limit.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  22. “Yeah, yeah, thanks for having me. I'm really excited to be on the podcast. Yeah, so a little bit more of my background. So maybe we can start from present day and go backwards. So I currently lead the engineering team for OpenAI's developer platform. So the biggest product in there, of course, is the API.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  23. “How they press access to intelligence, and why deterministic workflows might matter more than pure AI agents. Thanks very much for joining. So, we're being joined by Sherman Wu. It'd be great actually if you provided the long form of your background as we get into this, just for those that may not know you. I mean, I've used Sherman as one of the top AI thought leaders, so I'm really looking forward to this.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  24. “That road from Which allows you OpenAI sells weapons to its own enemies. Every day, thousands of startups build on OpenAI's API, many trying to compete directly with ChatGPT. It's the ultimate platform paradox. Enable your competitors or lose the ecosystem. Sherman Wu runs this HighWire Act. He leads engineering for OpenAI's developer platform, the API that powers half of Silicon Valley's AI ambitions. Before OpenAI, he spent six years at OpenDOR teaching machines to price houses where a single wrong prediction could cost millions. Today, Sherwin sits down with A16Z General partner Martine Casado to explore something nobody expected, that the models themselves are becoming anti-disintermediation technology. You can't abstract them away, and every attempt to hide them behind software fails because users already know and care which model they're using, is changing everything about how platforms work. Sherwood and Martin talk about why OpenAI abandoned the dream of one model to roll them all.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  25. “Yeah, yeah, temporary land with globe uses it every week. Every week. Yeah, even within OpenAI, the thinking was that there would be like one model that rules 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 models. Companies just have giant treasure troves of data that they were sitting on. The big unlock that has happened recently is with the reinforcement fine-tuning. With that setup, we're now letting you actually run RL, which allows you to leverage your data way more.”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source

  26. “Million wiles or whatever now, tenth of the globe, yeah, yeah, 10% of the globe uses it every week, every week, even within a”

    2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source