YouSaid · the spoken record

Raghu Raghuram

lines on the record: 126
first: 2025-11-28
most recent: 2025-11-28
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“I Yeah, well, 800 million wow is a little easier because it's not usage-based pricing, it's subscription. So it's like way easier. But I mean, there's still a lot of users on the API that we need to manage all the billing side.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“We're actually just like trying to. From a margin perspective. By the way, this is a huge shift in the industry in general just because I remember the shift from on-prem to recurring. That was a big, big deal. Like, that created Zora. Like, it created whole companies. On how you do this, it changed You know, and like, I think the shift to usage is as big or bigger. And it's also even a really hard technical problem. Yeah. Like, I can't even imagine 800 million. Wow, like how do you build? Yeah.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, yeah, yeah Yeah, and then there's also the strategy of how we price it. And internally, one thing we do is we always make sure that we actually price our usage-based pricing from a cost plus perspective. We're actually just trying to make sure that we're being responsible from a margin perspective. By the way, this is”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think the honest truth here is like it's evolved over time as well. And I actually think the simplest, like the reason why we've done usage-based pricing on the API, honestly, is because it's been closest to how it's actually being used. And so that's kind of how we started. I actually think usage-based pricing on the API has surprisingly held strong. And I actually think this might be something that we'll keep doing for quite a long time, mostly because the cognizance.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“I mean, how have you evolved your thinking, and how do you price these access to intelligence where you don't know how many people are going to use it? It almost certainly usage-based billing, not something else. Like, can you? You talk just a bit about philosophy around pricing on these things. Is it different for product versus API?”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“To a form where it can actually That's another interface to deploy it It is so remarkable how much of this entire economy is basically just token laundering I can do to get like like English it or like a natural language in and then like you know the intelligence out yeah and I mean and it's because these things are so resistant to layering it's so hard to layer a language out like you know like yeah I could even do it pretty easily with like codecs I could just They're actually quite different than what we're used to. Like the COGS is different, the defensibility is different, like all of this. So we're kind of rewriting it. And so that's kind of like, you know, you came from a Kind of pricing background. I mean, you're working on a model for pricing. Now you have the API. So I just love your thoughts on like.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“And so there's no single teams like, this is thinking about agents. I would say the way that it manifests itself more is like each product area thinks about what is this intelligence is actually turning into a form where it can actually agentic behavior is more possible. What would that look like in a first-party product like ChatGPT? What would that look like? This is actually why Codex ended up becoming its own products. What would it look like in a coding style product? We explored it in ChatGPT, like kind of worked there, but actually the Kly interface actually makes a lot more sense. That's another interface to deploy it. And then if you look about the API itself, it's like, this is another interface to deploy it. You're thinking about it in a slightly different way because it's a developer first mindset. We're helping other people build it. The pricing is slightly different. But it's all these like different manifestations of this core. intelligence that is the Asian behavior.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“And so agents are just like one way in which this intelligence kind of be manifested. And so the way that I'd say we actually think about internally is all of our different product lines, Sora, Codex, API, ChatGPT, are just different interfaces and different ways of deploying this.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Starting company and billing it around. I actually think that would be great. We released the Codex SDK and we want people to be able to build it and hack on it. Yeah. Actually, I think this might be what you're getting at, which is. And this is like a kind of a unique thing about OpenAI and kind of reflects on how it's run, which is at the end of the day, OpenAI is like an AGI company. It's like an intelligence company.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“And, like, as a user, and then you just talk to it, or I could build in a way kind of embed it in my app. And so, like, but then that means something to you as far as like, you know, how do you price it and what does it mean for ecosystem? Like, for example, like, would you be fine if I started a company and just like built it around codecs? Is that a thing?”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, let me just try and kind of give you a sense of where this question is coming from. I know how to build a product and we know how to go-to-market for products. We know how to do You know, we know the implications of turning them into platforms. It's just we've been doing this for a very long time, right? We know how to do the same thing for APIs, right? We know how to do billing. We know like the tension of people build on top of it and all of that stuff. And what I've been trying to, and this is just maybe a personal inquiry. It's just not clear for me for an agent if it sits in one of those two camps. Is it more like the product camp? Is it more like the because it's kind of both. Like I could like literally give you code of. Yeah.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“So I actually don't even know if it would be helpful for me to share about my general take on agents it's an AI that will take actions on your behalf that can work over a long time horizons I think that's the that's the most pretty general utilitarian yeah yeah definitely but like if you think about it that way yeah I mean maybe this is what you mean by modality but it is just a like way of like”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Maybe a better question is what is an agent to you? Yeah, yeah, yeah, yeah, yeah. Even getting a language is important for this conversation.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“An API is a dev thing. You kind of give it to a developer. And like a CLI is kind of somewhere in between to me. It's like, is it a product? Is it like it is horizontal? How is it handled internally? Is it a totally separate team that does agents”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Like they feel both vertical and horizontal to me in a way. Like to me, ChatGPT is a product. Right? It's like it's a product, and like my mom uses it, right?”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“And it's like really the intelligence itself Okay, so you have the API, which is horizontal. You've got ChatGPT and other products which are vertical. We haven't even talked about Pixels. This is all just language. Are agents in new modality? Is that something else? Like, you know, like... Codex”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“It would just like do all these tool calls, and it's like really the intelligence itself trying to do the tool calls or reg or anything like that, or write the code to execute. And so the paradigm has shifted there. But yeah, because of that, I think conduct engineering, prompt engineering, what you give the model is extra important.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“It's like insulting all the time. I'm going to randomly grab this thing based on. Exactly. And to be fair, I think Rag was kind of introduced when the models were pre reasoning models. It was like you only had kind of like one shot to do this and it wasn't that smart. But now that we do have the reasoning models, now that we have, I mean, if you like, One of my favorite models is actually 03 because it was like one of the most diligent models”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's like skilling laws or whatever something. Really need to. Where it's Well, it's just very interesting. I mean, to reduce it to like an almost absurdly simplistic level, like the weird thing about rag, for example, the classic use of rag is like you're using like cosine similarity to choose something that you're going to feed into a superintelligence. It's like insulting all that. I'm going to like randomly grab this thing based on fucking embedding space. And then when you want the superintelligence to decide the thing to do. And so it's like pushing intelligence in that retrieval clearly is something that makes a lot of sense. And to me, pushing the intelligence out in a way.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, but like, I don't know. Maybe people forget it, but like that was a very common belief back then because the scaling laws or whatever something like scaling laws and like you'll just mind mel with the model and like you just like like prompting and like instruction falling will just will be so good that you won't really need to and if anything like yeah it's like clearly been wrong and but it is interesting because i think it's a slightly different world that we're in now where the models have gotten really really good at instruction following relative to the you know like gb35 or something yeah but i think the name of the game now is is less on like prompt engineering as we had thought about it two years ago it's more of like it's like the context engineering side where it's like what are the tools you give it what is like the data that it pulls in when does it pull in the right data that's just very interesting i mean i mean”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I mean, I think the prevailing view this is back in 2022. I remember I was talking to so many people and they're basically, I mean, this is similar to like the single model AGI view as well, which is like prompt engineering is just not going to be a thing and you're just not going to have to think about what you're putting in the context window in the future. Like the model would just be good enough and it'll just know what you need to do.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so the Yeah, we, I mean, we've talked about this, and we've actually been piloting some pricing here, too, where it's like, because this data is really helpful and it's kind of hard to get. And if you actually build with a reinforcement fine-tuning API, you can actually get discounted inference and potentially free training too if you're willing to share the data. It's always kind of up to the customer there. But if they do, it is helpful for us. And there will be benefits for the customer as well.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“In process, mostly That you know about Less compelling, but And it becomes a lot more. And will you ever, or maybe do you? Will you ever find ways to get access to that data? Yeah. So if I had the data and I wanted cheap GPUs, I'd trade you for it. I don't know.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Treasure tove or the dream with a fine tuning API was that we should be able to handle both, right? It's like we actually had this dream and we have this whole Laura set up with the fine tuning inference where we should just be able to scale to like millions and millions of these fine-tuned models, which is usually what would happen if you have like this online learning thing. In practice, it's mostly been the form, right? In practice, mostly been like the offline data that they've already created or they are creating with experts or something and like using their product that they're able to use here. But the main thing I was trying to say around the reinforcement fine-tuning API is it kind of changes the paradigm away from just like small incremental improvement, like tone improvements, which is what SFT did, to actually improving the model to potentially soda level on a particular use case that you know about. Like that's where people have really started.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“This is just a naive question for me, which is it feels from just my understanding from my own portfolio, it feels like there's two modalities of use. One of them is I've got a treasure trophy of data that I've had for a long time and I create my model on that treasure trove of data and all that happens offline and then I deploy that. There's another one which is like, I actually have the product being used in real time. I've got a bunch of users and I can actually get much closer to the user. I can kind of A-B test and decide which data. And like, it's kind of more of a near real time thing. Is this focus on Like more product stuff or more treasure tove. So the dream”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“It's honestly just like instruction following plus plus. You like kind of change the tone and you're just really constructing it. But I think the big unlock that has happened recently with the reinforcement fine-tuning model because”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so I mean, maybe even taking a step back. The main reason why we even invested in a fine tuning API in the very beginning is one, there's been huge demand from people to be able to customize the models a bit more. It kind of goes into like prompt engineering and also like, I think the industry's changed their mind on that as well. It's evolved. But the second thing is exactly what you said, which is the companies just have giant treasure troves of data that they were sitting on that they would like to utilize in some fashion in this AI wave. And the simple thing is to put it in some like vector, like do rag with it or something. But there's also, you know, if they have a more technical team, they do want to see how they can use it to customize the models. And so that is actually the main reason why we've invested in this. The interesting thing was way back, kind of back in like 22, 23, our fine-tuning offering was, I'd say, like too limited so that it was very difficult for people to tap into and use this data. So it was just like an SF, like a supervised fine-tuning PI.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Like interest in almost a tit for tat where you can like expose, you know, the ability to get product data into fine-tuning. And then you also benefit from that data because the vendors provide it to you versus like this is 100% they keep their own data and there's kind of no interest in that because it feels to me like the next level of scaling this is kind of where we're at and so just kind of curious how”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Because we want to I want to talk about that in just a bit because the open source is actually very interesting. I mean, actually, I thought the open source model was great, but clearly it's something that a company has to be careful with. But before that, I want to talk a little bit about the fine tuning API. So I've noticed that you are moving towards kind of more sophisticated use of things like, you know, like fine-tuning, which in a way you could read that as a bit of a capitulation, not like... There is product specific data and there's product specific use cases that a general model won't do to your point, right? So like as opposed to proliferation model, you do that. It seems like a lot of that data is actually very, very valuable, right? And so to what extent is there”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, and as the ecosystem grows, it generally is helpful. This is one thing we actually think about a lot too system grows, OpenAI just stands a benefit a lot from this. And this is also why some of our products we've even started opening up to other models, right? Like our ethol's product now allows you to bring in other models to all of this. We think it's like any rising tide generally helps us here. But yeah, I think as we move into a world where there would be a bunch more models, this is why we've kind of invested in our model customization product with fine-tuning API, with the reinforcement fine-tuning, opening that up as well. It's also part of why we open sourced GPT OSS as well, because we want to be able to facilitate.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Not have like you know win or take all consolidated dynamics right I mean you just have to healthier ecosystem a lot more solutions you can provide a lot”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Itself, we have like GPD 4.1 and 4.0 and 5 and all of this. And so I don't think there's room for all this. I don't think that's bad for what's worth. If anything, I think as we've tried to move towards AGI, things have just been very unexpected. And I think the market just evolved and the product portfolio evolves because of that. So I don't think it's a bad thing at all. What I do think it means.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“And yeah, it's like definitely completely changed since then. I think one, but then the other thing to keep in mind is it might continue to change, like even from where we are today, but it's like becoming increasingly clear, I think, that There will be room for a bunch of specialized models. There will likely be a proliferation of other types of models. I mean, you see us do this with like the Codex model”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think the crazy thing about all this is just like how everyone's thinking has just changed over time. I distinctly remember this. And the crazy thing is not that long ago. It's just like two or three years ago. I remember even within OpenAI, the thinking was that there would be one model that rules them all. And it's like, why would you, I mean, like this kind of goes to fine tuning API product? There's like, why would you even have a fine tuning product? Why would you even want to iterate on it? There's going to be this one model that subsumes everything. And that was also kind of the, that is also the most simplistic view of what the AGI will look like.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Maybe two questions, maybe too blunt or too crass, but the first one is what does that mean to ape for AGI? And the second one was, what does that mean for open AI? Like, does that mean that You end up with a model portfolio. Do you select a subset? Do you think this all gets superseded by some god model in the future? Like, how does that play out? Because it's against what most people thought. Most people thought this is all going towards one large model that does everything.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Like first pass. Exactly. Keep you in flow kind of thing I literally sit down at SGP5 to help me plan something out, and it's really good at that. And then, you know, like when I'm coding, you know, I'm doing like the quick chat thing, then I'll use Composer. And then if there's like whatever, there's like some crazy bug or something like that. So, you know, do you remember in the early days of all of this where there's going to be one model? Even like investors, we will never invest in a model company because there will only be one model and it's going to be AGI. But like the reality, it feels like there's this massive proliferation of models, like you said before. They're doing many things. And so”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Like, I've talked to a bunch of people who've used the new Composer model, and it's just really good for first pass to keep you in flow kind of thing. And then you kind of bubble out to another model if you want deeper thinking.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“So I use cursor. And just for a lot of stuff, like writing blogs and we're investors and I use it for sometimes for coding.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah. I think it's both. So I think there's definitely an end user piece here, which is what we've heard from some of our customers. They just get familiar with the model itself. But I also think there's a technical piece, which is also as a developer, especially with startups, you're like really going deep with these models and like really iterating on it, trying to get it really good within your particular harness. You're iterating on your harness itself. You're giving it different tools here and there. And so you really do end up like building a product around the model. And so there is a technical piece where, you know, as you kind of keep building with a particular product like GPT-5, you're actually like building more around it so that your product works uniquely well with that model.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“And do you think that is because of a relationship between the user and the model, or do you think it's more of a technical thing, which is like my evals work for like open AI and like the correctness maintains?”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, yes. And I think, if anything, I think the models are like. Almost like diverging in terms of what they're good at and their specific use case. And I think there's going to be more and more of this. But yeah, basically it's been surprisingly hard for the retention of people building on our API is surprisingly high, especially when people thought you could just kind of swap things around. You might have even tools that help you swap things around. But yeah, the stickiness of the model itself has been surprising.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“To disintermediate you, but like you don't see that happening because it's so hard to put a layer of software between a model and a person. You almost have to expose the model. Yes.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah. And then you see a lot of more successful products like Cursor do this directly, especially the coding products where users want more control. We've even seen some more general consumer products do this. And so it's definitely been true on the consumer side. The interesting thing is I think it's also been true on the API side. And that's also something that I think.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Very utilitarian. It's Yeah, I've definitely come around, so you know, but I actually felt a distance when it changed. It's like there's this emotional thing that goes on, but it's almost like it's an anti-”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. And the interesting thing is, I think the entire industry kind of has slowly changed their mind around this, too. I think in the beginning, we kind of thought like, oh, these are all going to be interchangeable.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“The real value is in the models. It doesn't really matter how you get it to them because it's going to be very tough for someone's going to abstract it away in the classic sense of computer science, of like they don't know that they're using the model. Like you always know you're using GPT-5.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“The user doesn't know, like, whatever. I build on top of the cloud, but I just remediate from the cloud and then I can switch to another cloud or whatever. And it occurs to me that that's kind of hard to do with these models because the models are so hard to abstract away. Like they're just unruly, right? You try to have traditional software drive them. They just don't kind of manage very well. So part of me thinks that it's almost like this. Like antidisintermediation technology that you kind of have to expose it to the user directly. Does that make sense? And so I'm wondering if like, so even if I think ChatGPT is really just trying to expose them all to the user, the API is kind of just trying to expose the model to the user. So I think there's almost this argument that's like,”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“themselves, right? That story is as old as the computer system. There's never not been a computer platform that didn't have that problem. So, okay, so I kind of go back and forth on this one. I want to try one out on you, which is the problem historically with offering a core service as an API is you can get disintermediated, right? And so I can build on top of it, but then”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah. Growth solves so many different things. And the other way we think about it is everyone's kind of building around AGI, building towards AGI. Of course, there's going to be some overlap here. But I would say, at least in my position, I feel more of this tension from the customer, like the API customers themselves, right? Like, oh my gosh, you know, are you going to build this thing that I'm working on?”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“That's the tale is the old, the cloud or operating systems or whatever. So, like, that's, you know, I think it's more like.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source