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

“It's awesome. Listen, we're running out of time, man. There's a million more things I want to ask you, but listen, I really appreciate your time to come in. It was a great kind of surveying like what's going on and particularly like teasing apart horizontal versus vertical in this page. Yeah. Which I really want to do. So thank you so much. Yeah. Thank you.”
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 that is exactly the problem I think we were trying to solve here, right? It's just like if you do not give it any of this, like it can just kind of go off and do whatever and the other like regular regulatory concerns around this. And that is the exact UCS that I think we're trying to target with the Asian builder.”
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. Very interesting. Yeah. I'm just saying it's actually like if you want, if you want to really guarantee what happens, there's like a set of techniques that you do. There's some situations where you want to constrain what they do. It could be from a regulatory standpoint. It could be because you want it to run for a long time. And it also could be because I actually have game logic and my game logic is a traditional program. Like I have a monetary system. I have an item system. I have a battle system. Like you can't describe that in English. You have to kind of give it to them so it can behave within that. 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, well, the logic is in there. So it could have a normal conversation, but like in as much as you're trying to guide the logic for like game design or game logic. So you see this with NPCs, but you also see this with regulated industries. I literally can't have it like.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Describing that game logic in English just doesn't work. Yeah. Actually, if you try and do it, and then actually scripting the output doesn't work either if you need to use it in a game context. Like, you would have to know, like, if like a specific direction or specific this or that. So, how do you make these things behave in a more constrained way? People pass in functions. They'll like to describe the logic in Python. So my prompt will be like, you're an NPC in a video game. The user just asked you a question. Here's the logic you should go through. If the user says this, then do this. It's like the pseudocode. Like if the user has this, you know, in the belt, do this, like whatever, whatever, whatever. And then here are the set of valid responses. And so you're almost constraining.”
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 not the code is not being generated by the model. It's the prompt has the code. So let's say that I have an NPC and I want the NPC Like, let's say you're the gamer, yeah, and so you're coming in and you're talking to my NPC, but my NPC has some logic that it needs to do. Like if you say a certain thing, I'll give you a key. Maybe it'll barter.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“It actually has a response catalog as part of it, and it has the logic to apply. Interesting. And so the model takes the language in from the human user. And then, well, like The logic of how to respond is I can Python code because it just turns out that there's been a lot of code written for these types of things and then it'll actually includes the responses that you would send out. Does that make sense? Actually, a lot of NPCs are done this way. Like video game NPCs. So the way that I think about it is like,”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“So, it was part of the prompt. They're like, here are the viable things you can say choose which one to say. So the language reasoning has happened by the model, but nothing generated comes out.”
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 what they do, and I think it's so interesting, they'll like either pass in like a conversation tree and like you can choose something from here.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“I got to say, there's a pattern that's similar to this. I'm wondering if you've seen it that I've seen where some regulated industries actually can't let any generated content go to a user.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Huge need on that side to have determinism here, of which an agent builder with nodes helps enforce this thing ends up being very, very helpful. But I think a lot of us, especially in Silicon Valley, don't really appreciate that there's like a ton of work that actually falls into this camp.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“The people running these teams just really want these SOPs to be followed. And this Pattern actually generalizes on a different work, a standard operating procedure. Yeah, sorry. So she's like the way in which you need to operate the support team. But this extends to marketing, this extends to sales, this extends to a bunch, way more than it has any right to. And what we realized is there's.”
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 there's a practicality piece. There's another piece which is like when we were talking to our customers, we've realized that there's like, because at the end of the day, a lot of this, the agent work is just trying to automate work and like what people do in their day-to-day jobs. There's like actually like two different types of work. There's the work that we think about, which is like maybe what software engineers do, where it's like, it's very undirected. There's like a high-level goal. And then you have your cursor and you're just like writing code. And you're kind of exploring things and going towards an objective. That's like, I don't know, more like knowledge-based work, like data analysis, maybe like that, like coding's kind of like this. But then there's another type of work, which is actually what we realized is maybe even more prevalent in industry than software. We're just not aware of it, which is work tends to be very procedural, very like SOP oriented, like customer support is a good example of this. Like customer support, there's like very clear policy that these agents and people have to follow. And it is actually not great for them to deviate from this and try something else. It's like the team.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“I will say just anecdotally from kind of my perspective. People love it. But I also saw the dissonance, too. Like I saw when it came out, people were like, wait, what is this? Yeah, exactly. No code, low code. Yeah, exactly.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“To do, yeah. And so I think there's like two things at play here. One of them is there is a practicality component. And then the other thing is I think there are actually different types of work that exist out there that could be automated into agents. And so on the practicality side is, yeah, like the models today just like maybe in some future world instruction following would be so good that you just like ask it to do this. Force that process and it like always does the four step process exactly. We're still not there yet. And in the meantime, this entire industry being born and a lot of, you know, People still want to use these models. What can you build for them? So there's a practicality component of it.”
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 people are like, it's too constraining. It's not like AGI Forward. Again, at the end of the day, the AGI will be able to do everything. And so, like, why not? Why have nodes in this node builder thing?”
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 so at DevDay this year when we launched our Asian Builder, I got a bunch of questions around this because the Asian Builder is like a bunch of different nodes and it's like the deterministic thing. And I was like, oh, is this really the future of agents?”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Inference level, they're separate, right? Because you got to inference them differently So it feels like we've been evolving our thinking as an industry on a bunch of stuff, right? Like one of them for sure is like the models like we've talked about. The other one is like context engineering. It seems to me that like actually how you build agents and expose them has evolved too. So maybe you can talk a bit about that.”
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 there's, yeah, I'd say on the API side, a lot of the infrastructure is shared for those, but once you reach the inference level, they're separate, right? Because you got to inference them differently. And it is that team that is just like been really laser focused on making that side particularly efficient. And work well separate from the text models. But yeah, we have ImageGen. We have VideoGen. And we'll continue adding more to the API there.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“It was There's like the simplest thing now Huge But the amount of use cases. And then from your standpoint, you can converge that the API infrastructure probably like that. 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. That was actually the model that got me to go to OpenAI because it was the summer when I was looking for, I was thinking about something new. It's when Dolly 2 came out and it just completely blew my mind. And I distinctly remember I was like asking it to do the simplest thing, like draw a picture of a duck or something. It was like the simplest thing now. And it's like it generated a picture of a white duck. And so that was actually the thing that kind of got me to open the first place. But yeah, we have a bunch in our API. The image gen model, as well as in our API. And then Sora2 is in our API. We launched it at DevDay. It's actually been a huge hit. I've been very, very surprised. Need more GPUs for that. But the amount of use cases.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“And I think that's part of the reason why we're able to kind of do this. Are Maybe you can educate this on me. Like, so I think about APIs as mostly text based from open AI. Do you guys do actual? Do you do actual pixel-based stuff?”
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. So it's actually like pretty separate. And I think that's part of the reason why we're able to kind of do this. Well, it's like one is like the team needs to be extremely strong, which they are. And then two is they're run very separately. They're kind of like thinking about their own particular roadmap. They think about productization very separately as well, right? Which is how the SORA app kind of came out of that as well. And then, yeah, even the inference stacks are slightly different, are kind of like different. They own a lot more around their inference stack and they optimize their inference stack pretty separately. And so I think that contributes to helping us run things in parallel. But it's pretty hard to pull off for sure.”
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 you're totally right, it's an antependence. It's pretty tough to pull off. I think honestly props to Mark on our research team for structuring things in a way we're able to do it. From my perspective, I think the biggest thing is I think our image called the world simulation team, like the team that builds Sora and all that under Ditya is just extremely solid. It's like the highest. Concentration of like talent that I've seen in a while”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“On that one specific model, whereas they I got to say it is a bit of an anti pattern to do both languages, like language-based models and diffusion, like pixel models in the same company. Most that have tried, it found it very clunky to do it, but I mean, you and Google are the two kind of counter examples for this. And so like, Is it possible to even like converge the infrastructures on these things? Is it totally different orgs? Is it shared infrastructure? How do you operationalize?”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“And then you build a product around that. And it's like, yeah, you can just kind of put all these resources into and iterate on that one specific model. Whereas it's a much heavier motion on the tech side.”
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 I actually think that's one of the bigger bottlenecks because I think you are right that on the image side, yeah, you can fine-tune a image diffusion model to be extremely good at editing faces or something very.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Also because the image models tend to be way smaller and like you can iterate on it a lot faster. That's why you get that crazy cool proliferation of image models side. Whereas I don't know for the text models, there's always going to be this really big fat free training step that you have to invest in here. And then even the post training side is like, you know, it's not that it's not like the easiest thing. Just from a compute perspective, obviously it's much smaller. But like it's still pretty heavy to do like a full midrain or like a post-training run.”
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 that one I think is more of an open question. Yeah, so a lot of my, I mean, a lot of my mental model of this comes from the pixel space, which is like you, you know, you can Laura a bunch of image models, right? And you can do a bunch of stuff to make it better and more suitable for some products. For example, but like these open source. Models are really, really good. And like you would believe that you could verticalize a model for editing or cut and paste or this or that, you know, like that's actually part of this, but you actually don't see that happen.”
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 we're like basically starting to move in that direction I think there's a question of how deeply you verticalize it. I think most of what we've done is mostly at the post training, like the tool use level. Like, Codex is particularly good at using the, sorry, GB5 codex is particularly good at using the Codex Harness. But there's even deeper verticalization you can do. That one I think is more of an open question”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“We have a really cracked inference team at OpenAI My sense is like even if we just open source them like if we just literally open sourced DPD5 or something, it would be really, really hard to inference it at the level that we are able to get it to do. There's also, by the way, like feedback loop between the inference team and the training team too. So we can kind of like optimize.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, the other Yeah, so I'd say the way that I personally think about open source in relation to the API business in particular is, well, one, it hasn't shown cannibalization risk, so I'm not particularly worried about that. But also, especially for all these major labs, there are usually like two or three models where that is where you're making all of your impact, all of your revenue. And those are the ones where we're throwing a bunch of resources into improving the model. And these tend to be the larger ones that are extremely hard to inference.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, of course not. And by the way, it turns out inference is super hard to actually have available, fast performant. That's a hard, hard problem. Yeah.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Not like they could recreate it, right? Yeah. I mean, to be clear, like, we have not seen cannibalization at all. Yeah, of course not. It seems like a very different set of use cases. The customers tend to be slightly different. The use cases are very different. And by the way,”
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 all really helpful for us Also, what people And, like, you don't really enable competitors a lot because, I mean, when we say open source, you really mean open weights, right? It's not like they can recreate it, right? You know, and like if I can distill your API as well as I can distill like you giving me the weights in some way. And so like it doesn't really change that dynamic a lot. But yeah.”
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 also particularly true for OpenAI because, as you said, we are vertical and a horizontal company. It's like we want to continue investing in the ecosystem. And just from a brand perspective, I think it's good. But then also. I think from open AI's perspective, if the AI ecosystem grows more and more, it's like a rising type of really helpful for us. And if we can launch an open source model and helps unlock a whole bunch of other use cases in the other industries, I think that's actually not good for us.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“I've lost sense of time. AI time is so good. Yeah, I was like, was it last year? No, it was this year. Yeah, when GPO was.”
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 was earlier this year. I lost sense of time. AI time is so good. Yeah, I was like, was it the last year? No, is this year? Yeah, when GPOSS came out. And so I was just really glad that we did that. The way that I generally think about it is, one, I think as a”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. Nothing would exist without it. Different, maybe Windows. And so it was interesting because I felt like over the last years before we launched the open source model, I know Sam feels this way as well. It's like there's this weird, like, Mindset where because OpenAI hadn't launched anything, it just seemed like it was superi was like super anti open source.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, mostly smaller models on their side. Yeah, that's right. So, how do you think about open source visit cannibalization, you know, like what's the strategical, what's the complexity?”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“So, how do you think about open source? I mean, you know, I think you're the only big lab that's releasing open source. Is that good?”
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, exactly, exactly. And so, like, maybe at the end of the day, like Eustace-Based Pricing is all you need. And it's like, we're just going to live in this world forever. But yeah, I don't know. It's constantly evolving. I think our thinking has evolved here as well. I personally am keeping track of if the outcome-based pricing setups can actually work here, but at least on the API side, I think it's such a usage-based setup. We have the Git infrastructure around this. And so I think we'll probably stay with that for a while.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“The thing is just like thinking quite a bit Thinking for so long, it's like highly It's adding more value. Yeah, yeah, exactly. And so, like, maybe at the end of the day, like”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“This It's not a problem anymore. Yeah, yeah. At some point, it'll be solved. It's so much like the front engineering and the single AGI, I think, from before. Yeah, it's like when you reach that level of, when you push it that far, everything's kind of solved on outcome-based pricing. It sounds very appealing. Like if it can work. But one thing that we've started realizing is it actually ends up correlating quite a bit with usage-based pricing, especially with test time compute. Like if the thing is just thinking quite a bit, like actually, you know, if you charge just by usage race, usage-based and not outcome-based, you're like basically approximating outcome-based at this point. If the thing is thinking for so long, it's like highly correlated with what it's doing.”
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 this is a problem with AI conversations because, like, at any point in time, you're like, but it could get good enough. It's not a problem anymore. Yeah, yeah. At some point, it'll be solid. It's so much like the front engineering and the single AGI, I think, from before.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“At some level, you need to like. But there could be a world where the AI is like, I don't know where I can actually, you know, make judgments of these and do it in an accurate enough way where we can tie it to billing.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Maintain all his infrared and to like get it to work well Yeah, but that's very hard. I mean That's hard because you end up having to price and value non computer science infrastructure, right? Like you're literally going into verticalization now. You're I mean, listen, if it's like porting a code base, maybe you'd have some expertise. But if it's like, whatever, like increasing crop yield.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“He's a legend. Anyways, I was talking to him about pricing as well. And his take is that pricing is kind of like a one-way ratchet. And like basically once you get a taste of usage-based pricing, you're never going to go back to the per se per deployment type pricing. And I think that's definitely true. And I think it's just because it gets closer and closer to your true utility. You're getting all this thing. The main pain point is you have to maintain all this infra to get it to work well. But if you do have it, he thinks it's like a one way ratchet where there's just like no going back. And then, and I think the hot new thing now is like, oh, with AI, you can now kind of measure outcomes. And so that's like another step forward. And if that works, like maybe it's one where you ratchet. So we thought about that is like, you know, is there some type of like outcome-based pricing? This is more on the first party side on an API is kind of.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“He's one of the best. Then kind of your listening, we're huge fans. I'm a huge fan. He's going to, he's going to love it.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source
“Oh, I see Max photos that we don't let people go over. That would literally be like one of the most complex systems somebody's ever built. If you would do usage base at that scale, I mean, these are very, very, very, and like you have to be correct. Like these are very hard systems to scale.”
2025-11-28 · a16z Podcast · How OpenAI Builds for 800 Million Weekly Users: Model Specialization and Fine-Tuning · IDENTIFIED FROM THE TRANSCRIPT · source