O1이 창업자와 AI 개발에 미치는 영향

### OpenAI의 O1이 창업자와 빌더에게 미치는 영향: 기회와 도전 과제
* 🤖 OpenAI의 O1 모델 도입으로 인해 빌더들 사이에서는 **AI의 현재, 과거, 미래 가치를 모두 담아내어 독립 개발자들을 소외시킬 수 있다**는 우려가 제기되고 있습니다.
* 💡 반대로, **O1이 더 결정적이고 정확한 것으로 입증**되면 창업자는 신속한 엔지니어링에서 사용자 경험과 소프트웨어 품질 향상으로 초점을 전환할 수 있습니다.
* 💰 OpenAI는 최근 최대 10조 개의 매개변수에 이르는 대규모 언어 모델 개발의 자본 집약적 특성을 강조하며 **기록적인 66억 달러의 투자금**을 유치했습니다.
* 📈 모델의 확장은 AI 스타트업과 경제 성장의 물결을 일으켰던 **GPT-2에서 GPT-3.5로의 전환**과 마찬가지로 상당한 혁신으로 이어질 수 있습니다.
* 🔍 현재 모델은 인간 지능 수준에 근접하고 있으며, 소프트웨어 엔지니어는 다양한 작업에서 **인간에 가까운 성능**을 달성할 수 있어 **인공 일반 지능(AGI)의 도래 가능성**을 암시하고 있습니다.
* 📊 스타트업들은 점점 더 AI 모델 사용처를 다양화하고 있으며, **Claude의 시장 점유율은 불과 6개월 만에 5%에서 25%로 급증**하여 OpenAI의 독주에서 벗어나고 있음을 보여줍니다.
* 🚀 AI 기술이 발전함에 따라 많은 스타트업이 효율성과 수익성에서 획기적인 성과를 거두고 있으며, 일부 기업은 **운영의 상당 부분을 자동화**하고 **현금 흐름이 흑자**가 되는 성과를 거두고 있습니다.
* 🌐 AI 애플리케이션의 미래는 **사용자 친화적인 인터페이스의 개발과 고급 모델을 일상적인 도구에 통합하는 능력**에 달려 있으며, 잠재적으로 다양한 산업에 혁명을 일으킬 수 있습니다.

창업자는 AI 도구의 정확도가 높아짐에 따라 사용자 경험을 향상하는 데 집중할 수 있게 되어 더욱 성공할 수 있습니다.

if o1 is this magical, what does it actually mean for Founders and Builders? One argument is it's bad for Builders because maybe o1 is just so powerful that open AI will just capture all the value. You mean they're going to capture a light cone of all future value? Yeah, there you go. They'll capture a light cone of all present, past, and future value. Oh my God. The alternative, more optimistic scenario is we see ourselves how much time the founders spend, especially during the batch, on getting prompts to work correctly, getting the outputs to be accurate. But if it becomes more deterministic and accurate, then they can just spend their time on bread-and-butter software things. The winners will just be whoever builds the best user experience, gets all these nitty-gritty details. [Music] Correct. Welcome back to another episode of the light cone. We are sort of in this moment where open AI has raised the largest V round ever, $6.6 billion with a B. Here's what Sarah Friar, the CFO of

open AI said about how they're going to use the money. It's compute first, and it's not cheap. Uh, it's great talent second, um, and then, of course, it's all the normal operating expenses of a more traditional company. But I think there is no denying that you are, we're on a scaling law right now where orders of magnitude matter. The next model is going to be an order of magnitude bigger, and the next one, on and on, and so that does make it very capital-intensive. So, it's really about orders of magnitude. Let's live in the future. There's 10 trillion parameters out there, 10 trillion parameter large language models, two orders of magnitude out from the state-of-the-art today. What happens? Like, are people actually going to be throwing queries and actually using these 10 trillion parameter models? Uh, it seems like you'd be waiting, you know, 10 minutes per token. Yeah, for a bit of context right now, the frontier models, I mean, they're not public exactly how many parameters they have, but they're

roughly in the 500s of billions is like Lama 3405 billion. Anthropic is speculated to be 500 billion. GPT 40 roughly around that much. Getting to 10 trillion, that's a two order magnitude, right? I think the type of level of potential innovation could be the same leap we saw from GPT-2, which was around 1 billion parameters. That was released with the paper of a scaling loss, which was one of these SEOL papers that people figured out, Okay, this is Transformer architecture that we figured out. What if we just throw a bunch of engineering and just do a lot of it? Where does this scale and this L logarithmic type of scaling? Then this was proofed out when GPT-3.5 or 3 got released. That was about 170-ish billion parameters, so that's like that two order magnitude. And we saw what happened with that. That created this new flourishing era of AI companies, and we saw it, we experienced this back in 2023 when we started seeing all these

companies building on top of GPT 3.5 that was starting to work, and it created this giant wealth. So, we could probably expect, if this scaling law continues, the feeling will be similar to what we felt from that year of transition, 2022 to 2023. Yeah, that was the moment when everything changed. So, that would be pretty wild if that happens again. I think there's one interesting aspect to this, which is clearly the current generation state-of-the-art models that are available, especially given one chain of thoughts. They sort of basically rival normal intelligence. Like, you could make a strong case that AGI is basically already here. The majority of the tasks that, you know, 98% of knowledge workers do day-to-day, it is now possible for a software engineer, probably sitting in front of a cursor, to write something that gets to, you know, 90 to 98% accuracy and actually do what a human knowledge worker with 120 IQ would be doing all day. And that's sort of RIT large like

there are probably hundreds of companies that each of us have worked with over the past few years that are literally doing that day-to-day right now. You know, the weird interesting question is like at 10 trillion parameters, at you know, 200 to 300 IQ, like sort of ASI beyond what a normal human being normally could do. You know, what does that unlock? Uh, there's a great article in the Atlantic, uh, with Terence Tao, sort of famously this Taiwanese mathematician who is literally north of 200 IQ, and how he uses chat GPT right now, and it's sort of unlocking uh, new capabilities for him. There are some examples of this happening, you know, quite a few times in human history. Like, you could argue that nuclear power was that vision. You had to actually model theoretically that something like uh, nuclear fission was possible before anyone you experimentally tried to do it. Um, for your transforms, yeah, maybe the thing is uh, if we think a lot of the capabilities right now are here but is not evenly

distributed. If you go walk down the street and you talk to the random Joe, they don't feel the AI. They're just living their normal life, and stuff is still just normal. It hasn't changed. But I think the counterexample is just sometimes these discoveries take time for it to really pan out. This is an example we're discussing: Fourier transform was this mathematical representation that Joseph Fourier discovered in the 1800s. That was like a seminal thesis that he wrote about representing series of functions that were repeating in periods. Before Fourier transform, they were written as these long sums and series of some that are very expensive to add them up and figure out how to really model the equation, basically. But he found this very elegant way that instead of just doing sums of series, you could basically collapse all these math functions into sine and cosine waves that only need two variables, basically the amplitude and the period. And you could represent every periodic signal and function. I mean, sound really cool math, which is like how

some of this LLM and use case sounds like, Okay, cool, they can do all this coding, but four years transform took another 150 years until the 1950s when people figured out what to do with this. It turned out that four-year transform was super good at representing signals, and we need signals basically to represent everything in the analog world to be digital because bits are ones and zeros. And how do you compress that? And one of the big applications as well is radio waves and made telecommunication a lot more efficient, image representation, encoding information theory. It just unlocks so much of the modern world like the internet and cell towers work because of this theory. But it took 150 years until the average Joe could feel the four-year transform. Interesting, that's a really powerful idea. I mean, that took a while then. I mean, apparently in the 1950s, that's the moment that color TV happened. So unlock by four Transformers as well. That's right. If you apply to the AI stuff that's happening today though, it's

like one where do you start the clock ticking from. Like, it's not clear if you start it from the ChatGPT-3 moment two years ago or from just like all of the research that's been going on for decades. Like, we might actually just be, we've talked about this before, but we might actually be like decades into this now, and it's starting to hit like the inflection moment potentially. Yeah, for sure. I mean, if we run with Diana's example of fast-for-transforms, like all the math that's underpinning all this new AI stuff is linear algebra stuff that's like 100 years old. It just turns out how far you could push it. Yeah, if you have all the GPUs to compute it, I guess that's one potential way that these 10 trillion parameter models actually alter the face of what humans are capable of. Like, they sort of unlock something about the nature of reality and our ability to model it, and then somehow it leads to either nuclear weapons or the color TV, the other big

thing is just because this is all in software, like, um, compared to, like, foro transforms, like, a lot of the applications are just see in physical devices, right, like record players or telephones, like you said, and so takes a while for the technology to get adopted because you have to, like, buy your updated device and all these things. Now we have, like, Facebook and Google who have, like, you know, pretty decent percentage, just like the world using their software already. Like, as soon as these things start rolling out, and I feel like it's another thing that's starting to be noticed is meta in particular coming out with their meta Ray bands, the consumer-like device. Like, I think consumers, once this becomes, um, something that's, like, visual in your, like, smart glasses, plus, like, a voice app that you can talk to, and it, like, is indistinguishable from a human being, like, that's going to be a real change-the-world moment for. They'll start feeling the AI once they can, like, talk to it all the time. I mean, it seems like there's, uh

really a bifurcation in what we might expect when we have this capability. Um, at the extreme, and you're going to have people like Terren too pushing the edge and boundary of our understanding of our, you know, modelable world. And then, you know, maybe that's actually worth tens or hundreds of millions of dollars of inference to run these 10 trillion parameter models. And then the more likely way this ends up being useful for the rest of us is actually in distillation. So, taking, you know, there's some evidence that, for instance, uh, Meta's 40, uh, 405b was mostly used, useful to make their 70 billion parameter model much, much better. And so, and you actually see this today, there was sort of this moment there where we thought that, um, you know, people might just go to GPD 4 and distill out all the weights. And it seems like there's some evidence that, uh, certain governmental entities are doing that already. But GPT for itself and for, you know, it became 40, OpenAI itself has

now enabled distillation internal to its own API so you can use 01. You can use, uh, even GPT-4 or 4.40 to distill it down into a much cheaper, uh, model that's internal to them, like GPT-4, uh, 40 mini. And that's sort of their, you know, lock-in capability. Yeah, I don't think this is talked much about, but it is interesting that you have these giant models like the 400 or 500, whatever billion parameter models that are basically the teacher models because they're the mega trained with everything and took forever. And they are the teacher model, master model that teaches the student model, which are these smaller ones that are faster and cheaper because doing inference for a 405 billion parameter model, it's very expensive. So we have evidence that all these distillation models are working. Companies in the batch, they're building from the latest and greatest. They're not going for the giant model with all of the parameters and give me the biggest thing to do so that it works the best. We have

evidence that's not the case. People are not going for the big model, and we actually have stats in the batch. I mean, Harsh, we kind of talked about them. Yeah, Jared had run some numbers on this, and it's fascinating. But I think the bigger meta point is even the fact that talking about the startups or the founders building this stuff are choosing the smaller models versus the bigger models. They just have choice. And even like a year ago, when this entire industry started existing, like everything was built on top of ChatGPT, right? There was 100% market share, the ChatGPT rapper meme. And I feel like we've, especially over the last six months, seen people start talking about the other models like Claude and Sonnet being sort of this word of mouth for almost being better at Coen than ChatGPT, and people just starting to use different models. And so the numbers that Jared ran for the Summer 24 batch are fascinating because it seemed to that trend just

continued like we have more diversification of llms and models that developers are building on top of, and some of the stuff that really stood out is Claude has even just in six months from the winter batch to the summer batch has gone from like 5% developer market share to like 25% of companies in the batch, yes, of companies in the batch, which is huge. That's like never seen a jump like that, right? L has gone from zero% to 8%. Like one thing that we know from running YC for a long time is that whatever the companies in the batch use is a very good predictor of what like the best companies in the world are using, and therefore what products will be most successful. A lot of YC's most successful companies, you could have basically predicted which ones they would be based on just looking, basically just running a poll of what the companies in the batch use. If we just take opening eyes latest fundraise off the table and the latest like the o01 model off the table for a second, it

would seem like among developers and builders, OpenAI was losing. Like, they went from being the only game in town to just like bleeding market share to the other models at a pretty rapid rate. The interesting thing, though, is maybe they are coming back. Like, was the stat that you P seem like or 15% of the bat are already using oan even though it's not like fully available yet. It's only like 2 weeks old now. Yep, yeah. And we're seeing some interesting things uh with a one. We're actually hosting right now in person, right now as we speak downstairs a hackathon to give YC company's Early Access to 01. And Sam himself was here, did the K kickoff. There's a bunch of OpenAI researchers and engineers working on it, and it's only been about four hours of hacking. And we already heard of, I already saw actually some demos as I was walking by to see some teams, and they already built things that were not possible before with any other model. Do you do you have some examples? One of the

companies I'm working with is uh freestyle. They're building a Cloud solution fully built with TypeScript, with, if you're familiar with durable objects, with this really cool framework that makes front-end and back-end seamless to develop, and it's really cool to use. What was cool about them is they just been working on it for a couple of hours, and I saw a demo that was mind-blown. They basically got a version of repet agent working with the product. All they had to prompt 01 with was all their developer AI, some of their developer documentation, and some of their code, and they could just prompt it, Build me a web app that writes a to-do list, or this and it would just boom, just work. And it was able to reason and inference with the documentation and took a lot longer, but it arrived and built the actual app. What's interesting for us to talk about is if 01 is this magical, what does it actually mean for Founders and Builders? And one argument is it's bad for Builders because maybe 01 is just so

powerful that OpenAI will just capture all the value and um everything that could be valuable and built on top of this stuff will just be owned by them. You mean they're going to capture a light cone of all future value? Yeah, there you go. They'll capture a light cone of all present, past, and future value. My God. The alternative, more optimistic scenario is we see ourselves how much time the founders spend, especially during the batch, on the tooling around getting the prom, like getting prompts to work correctly, getting the outputs to be accurate, human in the loop, like all of this time spent just getting the call product working, is not deterministic. But if it becomes more deterministic and accurate, then they can just spend their time on bread and butter software things, you know, like better UI, better customer experience, more sales, more relationships, in which case it's like it may be a better time to start now than ever because you don't even have like maybe all of the knowledge you learn around how to get like the prompts accurate and working.

was just temporary knowledge that's no longer relevant. As these things get more powerful, actually we had this conversation with Jake Hel from Casex, where getting the legal copilot to work to 100% was the huge unlock, and it was really hard. Yep, he, like you, hated this whole talk about all the things he had to do to actually get the thing to be accurate enough. Yeah, imagine if he didn't have to do any of that. If they just, on day one, you could be guaranteed 100% accuracy, like as though you're just building a web app on top of a database. The barrier to entry to build these things goes way down. There's going to be more competition than ever, and then it will probably just become look more like a traditional Winner Takes All software market. Jarett has an example. So there's a company at Dry Merch, yeah, that you work with, and they went from 80% accuracy to pretty much 99, or for intents of purposes 100%, using A1 and unlocked a bunch of things.

you want to talk about them. Yeah, just by swapping out GPT-40 for 01. I think there might be even more bullish version hard, which is that there are use cases right now that people are not able to use llms for, because even though they're trying to get the accuracy high enough, they just can't get it accurate enough to actually be rolled out in production. Like, especially if you think about like really mission-critical jobs where the consequences of stakes are dire, like pretty hard to use llms for that. As they keep getting more accurate, those applications will start to actually work. I guess there is a lot of evidence inside the YC greater portfolio. You know, I was meeting a company from 2017. I think I tweeted about them. They were, you know, $50 million annualized revenue at that point, but growing 50% a year, a year or two ago. They were not profitable. They knew that they needed to raise more money, but in the year since they

automated about 60% of their customer support tickets, and they went from something that needed to raise another round imminently to something that was totally cash flow break-even while still growing 50% year on year. That's sort of like the dream scenario for building Enterprise Value because you're big enough that you know you're a going concern, and then you're literally compounding your growth with like no additional Capital coming in. So, it's companies like that that actually end up becoming like half a billion, a billion dollars a year in revenue, and like driving hundreds of millions of dollars in free cash flow. I mean, that's sort of the dream for Founders at some level, and I think that that's one of the more dramatic examples that I've seen thus far, and I think it's sort of not an isolated case. You know how we're sort of talking, it's you know, 2024 now, and we're still in this overhang moment where

uh company sort of on this path raised way too much money at you know 30X or 40x uh you know next 12 months multiple Revenue like seemingly struggling but you know also never going to raise another round like this is actually pretty good news for them because they actually can go from like not profitable to you know Break Even to then potentially very profitable I think that narrative is not out there and I think it's uh really really good news for founders I probably start to catch attention I didn't the cler CEO got a lot of attention a few weeks ago for I mean it's not unclear how much of it is real or not but at least they're pitching that they're just you know replacing their internal systems of records for HR and sales with homebuilt or LM create apps at least was like the insinuation yeah what is it they got rid of workday yeah that was it yeah that's pretty wild honestly I mean so that's good if you treat open aai as the Google

of the next 20 years. You want to invest in Google and all the things that Google enabled, like Airbnb. Google could do Airbnb; it probably won't. Yeah, just from like, I don't know, CO's theorem of the firm. Probably, it's just like too inefficient and too difficult. Requires too much domain expertise to actually pull that off. So, what are sort of the new Googles that are getting built out? There's these vertical agents. What are some examples that we'll have that we could talk about? I loved working with this company called Tax GPT, um, from the last YC batch. They started off actually, uh, really literally a rapper and like, you know, it's in the name Tax GPT, uh, but my favorite example about them is like, you know, it turned out that tax advice, you know, doing basic rag on, um, you know, it's sort of like case text actually. It was, uh, you know, being able to do rag on existing case law and existing policy documents from the IRS or internationally. That was just sort of

the wedge that got them in front of you know tens of thousands of accountants and accounting firms. And now what they're doing is building an enterprise business on document upload. So you sort of, you know, get them for cheap or free for the thing that people are Googling for, and then once they know about you and trust you, you get like this 10 or $100,000 a year ACV contract that then takes over real workflow that, you know, actually extinguishes tens to hundreds of hours of work per accountant. And another interesting thing about the 01 model is we were saying originally Chachi BT was the only thing you could build on top of open AI was the only G in town. Then there were all these models. I think the sort of alpha leak we have here, like right now in this room, is downstairs. People are building at the cutting edge of 01 that even the public does not have access to. And what we're seeing is that this is a real major step forward.

like O1 is going to be a big deal for any program or engineer who is building an AI application. The interesting thing is, will this cycle repeat where it will give OpenAI a temporary lead, the market share will just like go, you know, back up towards 100%, but then within 6 months, Llama will be updated, Claude will come up with its new release, Gemini will keep getting better, and there'll just be like, you know, four different models that have equivalent levels of reasoning, or will this be like the first time OpenAI has like a true breakthrough? And I would just define a true breakthrough as something that's actually defensible. Like, if no one else can replicate it, then that puts them in a really powerful position. But we don't know, and I think that's what's interesting. It's like OpenAI seems like it is continually the one pushing the envelope. Like, they always seem to be the first ones to make major breakthroughs, but they have never been able to maintain the lead so far, I think.

the other thing that's interesting about AI is that it makes a lot of the GPU needs even bigger because it's moving a lot of the computation needs a lot higher for inference because it's taking a lot more time to do a lot of the inference. So, I think it's going to change also a lot of dynamics underneath for a lot of the companies building AI infrastructure as well, which is something food for thought. Seems like there are two different types of use cases. I believe they did just enable distillation from 01 into 40, and so it's conceivable that for relatively rote and repeating use cases, you could just sort of use 01 for the difficult ones, and then you distill it out, and then you pay 40 or 40 mini prices from there. And then there again, there's this other type of problem that is very specific. I mean, I imagine most Co many Cod genen situations are a little bit more like that where you need to pay like for the full 01 experience because it's you.

know fairly detailed and specific depends a few building F too right. If you're an Enterprise software and you can pass the cost on to your customer, and they can tolerate higher latency and don't care as much about it being instant, then you can just use maybe o1 a lot. If you're putting like consumer apps, probably not. But talking of consumer apps, I mean the other thing that was striking about open AI is lace releases like this realtime voice API, um super cool. It's pretty remarkable, and I think the most telling thing to me is that the ongoing usage-based pricing is $9 per hour, and it sort of points to a sort of powerful thing. Like if I were um a macro Trader, I would be very, very bearish on countries that rely very heavily on call centers right now, um because you $9 an hour is sort of right there at uh what a call center would cost. This is another thing we're definitely seeing within the batch right. Like it's clear that voice is a almost like a killer app, like arguably like um

there's a company I just worked with in this batch, um, or in my group at least, do just do um AI voice for deck collection, and their track is just phenomenal. It's working incredibly well. A whole bunch of the voice apps in S24 were just like some of the fastest-growing, like just like explosive companies. It was a clear trend for S24, and I remember working with companies in the prior batches to try to do voice, and it just like wasn't working well enough. Like the latency was too high, exactly like it got confused if like with interruptions and things like that, and it's like just turned the corner where it's like finally working. There was another company I worked with, a happy robot, that landed on this idea that was doing a voice agent for coordinating all these phone calls for logistics. Think of a truck driver that needs to go from point A to point B. These are all like people just calling to check where you are. There's no like, they're not like find my friends for it.

and they started getting a lot of usage on this, and I think we talked a bit about this before, that at this point, AI has passed Turing tests and is solving all of these very menial problems over the phone. That's pretty wild. I guess one thing that is maybe under-discussed is to what degree the engineering teams that are in these sort of incumbent industries. It feels like it's pretty binary. Either, you know, the vast majority of companies and organizations, especially the ones that were maybe founded four or more years ago, they actually don't take any of this seriously. Like, they have literally no initiative on this stuff. And I sort of wonder how generational it is. Like, I'm realizing that managers and VP Avenger, like, they're probably my age now. I'm 43 now. And, you know, if I wasn't here seeing exactly what was happening, I would be sort of tempted to say, like, this is just the same old thing. AI, yeah, yeah, yeah. But I think it's the rate of improvement that

people don't get if they're not as close to it as we are. I just think your average corporate enterprise person is certainly used to technology disrupting things, but over pretty long time frames. And if anything, they become cynical, like, Oh, the cloud. Like, cloud was such a buzzword for a long time. It totally did change our enterprise software's build and delivery, but it took like a decade or so. And so, I suspect everyone's feeling that way about AI. It's just your natural default mode is to be cynical. Oh yeah, like it's not going to be ready for a while. And then probably if you looked at this stuff even six months ago, like we were just talking about, if you looked at an AI voice app six months ago, you're like, Oh, this is years away from being anything that we need to take seriously. It's like actually like three to four months later, like it's like it's some real major inflection point. And I think that's what takes even people within tech. It's

surprising all of us how quickly this stuff is moving. It's the fastest any tech has ever improved, I think. Yeah, certainly faster than processors, certainly faster than the cloud, and it's kind of fun to actually watch. It's been remarkable to actually see another example of this in the batch. So, a lot of the technical founders, sometimes I sit with them, and I just watch how they code, the before and the after. Before all of this wave of AI, just standard, you have your IDE and things on the terminal. People shipped fine, but the demos and products that we're seeing founders build during the batch is like a next level of polish. And when you see them and see them code, it's like they're living in the future. They're really not just like at this point. GitHub Co-Pilot is already kind of old news. They're using the latest coding assistant, a lot of them perhaps using something like Continue or Cursor, right? This is something Jared pulled out as well when we asked the founders the IDEs, right?

Yeah, we surveyed the summer 24 Founders, and half the batch is using Cursor compared to only 12% that's using GitHub Copilot. That was surprising to me. They're not even using the fully agented coding agents like Replit or Hazer. It's still sort of like Copilot phase stuff, but even just going from GitHub Copilot to Cursor, which is like The Next Step Up in terms of how much the actual AI does, is this incredible breakthrough. They showed very quickly. I mean, this is evident today in the hack. I was impressed with what they built. I was looking at their editor like, CER, like cool. It's another sign why the founders have the advantage, right? It feels to me again like when GitHub Copilot first came on the scene. It seemed it's GitHub plus Microsoft. It has all the developers, plus it has all the capital, plus it has all the access to the inside track on OpenAI. How could any coding IDE compete with them? It will just get subsumed and like

cursor has come out of nowhere and is like, you know, according to our numbers, like five times the size of GitHub co-pilot within the batch, which again, like you were saying earlier, like the startup Founders are actually usually The Taste makers on this kind of thing. Um, I think there's certain types of businesses where it doesn't make sense to maybe go after startup Founders as your early customers, but for developer tools, it definitely does. Like Stripe, AWS, both wanted to own YC batches in particular, um, and that worked out really well for them. So, it's probably a really good sign for cursor, honestly, that they have like such good penetration within the YC batch. Yeah, I would definitely say cursor is pretty awesome, but Alta Vista was awesome too. Yeah, I remember using that as a search engine, and there was another version, and the next version was 10 times better, and so this is the way it's going to go. I mean, which the only people who win are actually developers because of all this competition. So, I think again takes us to

like the optimistic view of all of this stuff, which is as the models get more powerful, the winners will just be whoever builds the best user experience and gets all these nitty-gritty details correct. And so, that's why Curser can beat GitHub Copilot that has all the advantages. Alterist is a great example; like, there's still room for someone to keep doing to Curser what Curser has done to GitHub Copilot. So, let's get back to 10 trillion parameters. What world do you think we will live in with this made real with ASI or something approaching it? You know, what will humans actually do, and how much more awesome will it be? Well, I'll give a steel man for a really bullish case, which is that the thing that is holding back the rate of scientific and technological progress is arguably the number of smart people who can actually analyze all the information that we already know about the world.

there's millions of scientific papers already out there, an incredible amount of data. But like, try reading all of it. It's far beyond the scale of any human's comprehension. And if we make the models smart enough that they can actually do original thinking and deep analysis with correct logic, and you could let loose an infinite, a near-infinite amount of intelligence on the near-infinite amount of data and knowledge that we have about the world, you can just imagine it's just coming out with just crazy scientific discoveries: room temperature fusion, room temperature superconductors, time travel, flying cars, all the stuff that humans haven't been able to invent yet. Like, with enough intelligence, maybe we'll finally invent it all. Sign me up for that future, sounds great. I totally agree with you. I think, you know, what this might be is not merely a bicycle for the mind. It might actually be a self-driving car, or even crazier, maybe Rocket To Mars. So, with that, we'll see you guys next time. [Music]

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OpenAI의 성장이 창업자와 빌더에게 미치는 영향

OpenAI의 강력한 성장은 현재, 과거, 미래의 모든 가치를 포착할 수 있기 때문에 구축자에게 우려를 불러일으킵니다.

그러나 OpenAI의 모델이 더욱 결정론적이고 정확해진다면 창업자는 사용자 경험과 소프트웨어 세부 사항을 개선하는 데 집중할 수 있습니다.

AI 모델의 확장 법칙에 따르면 10조 개의 매개변수가 있는 모델과 같이 더 큰 규모의 모델은 GPT-3에서 볼 수 있는 것과 유사한 혁신의 물결을 일으켜 잠재적으로 AI 회사에 새로운 역량과 부를 가져다줄 수 있습니다.

푸리에 변환과 AI가 미치는 영향

1800년대에 조셉 푸리에가 발견한 푸리에 변환은 사인파와 코사인파를 이용한 주기 함수 표현에 혁명을 일으켰지만, 그 진정한 영향은 150년 후인 1950년대에 이르러서야 느껴졌습니다.

이 수학 이론은 디지털 신호 표현, 통신, 이미지 인코딩에서 중요한 역할을 하며 궁극적으로 현대 세계를 형성하는 데 결정적인 역할을 했습니다.

수십 년간의 연구와 강력한 컴퓨팅을 기반으로 한 AI 기술의 급속한 확산은 스마트 글래스와 같은 소비자 기기가 잠재적으로 일상 생활에 AI를 도입하면서 이와 유사한 혁신적 영향을 가져올 수 있습니다.

대규모 AI 모델과 증류가 미치는 영향

10조 개의 매개변수가 있는 모델과 같은 대규모 AI 모델은 상당한 수익과 영향력을 창출할 수 있는 잠재력을 가지고 있습니다.

이러한 대규모 모델을 사용하여 더 작고 저렴한 모델을 개선하는 증류법, 즉 디스틸레이션이 일반적인 관행이 되고 있으며, OpenAI와 같은 회사에서 내부적으로 이를 지원하고 있습니다.

Claude와 Sonnet과 같은 새로운 모델이 시장 점유율을 높이고 ChatGPT의 지배력에 도전하는 등 AI 모델 사용이 다양화되는 추세가 나타나고 있습니다.

OpenAI는 다른 모델에 시장 점유율을 빼앗기고 있었지만, 이제 시장의 15%가 이미 새로운 제품인 01을 사용하면서 재기의 조짐을 보이고 있습니다.

이 회사는 해커톤을 개최하여 YC 기업들에게 01을 미리 체험할 수 있는 기회를 제공하고 있으며, 몇 시간 만에 이미 여러 팀이 01을 사용하여 인상적인 데모를 만들었습니다.

01의 강력한 기능은 창업자와 빌더에게 미치는 영향에 대한 의문을 불러일으키며, OpenAI의 가치 포착과 창업자의 제품 개발 시간을 보다 효율적으로 사용할 수 있는 잠재력을 모두 제공합니다.

LLM이 비즈니스 성장에 미치는 영향

LLM은 미션 크리티컬한 작업에 필요한 정확도를 달성하는 데 어려움을 겪고 있지만, 개선됨에 따라 더 많은 애플리케이션을 지원할 수 있게 될 것입니다.

YC 포트폴리오의 한 기업은 고객 지원 티켓의 60%를 자동화하여 전년 대비 50%의 성장을 유지하면서 현금 흐름 손익분기점을 달성했습니다.

현재 자금 조달의 오버행은 어려움을 겪고 있는 기업이 수익성이 낮은 상황에서 잠재적으로 수익성이 높은 상황으로 전환할 수 있도록 함으로써 도움이 될 수 있습니다.

AI가 회계 및 비즈니스 모델에 미치는 영향

AI는 문서 업로드 서비스를 제공하여 연간 계약을 크게 늘리고 회계사의 업무량을 줄임으로써 회계 산업을 변화시키고 있습니다.

다양한 AI 모델의 등장으로 OpenAI의 지배력이 도전받고 있으며, 향후 시장 점유율 분포에 대한 의문이 제기되고 있습니다.

OpenAI의 획기적인 AI 기술은 GPU 수요를 증가시키고 AI 인프라 기업의 역학 관계를 변화시키고 있으며, 실시간 음성 API는 기존 콜센터를 뒤흔들 수 있는 경쟁력 있는 가격 모델을 제공합니다.

채권 추심을 위한 AI 음성 기술은 폭발적인 성장세를 보이고 있으며, S24의 기업들이 이러한 트렌드를 주도하고 있습니다.

AI 음성 에이전트를 통해 물류 관련 전화 통화를 성공적으로 조율하는 사례가 크게 증가하고 있습니다.

AI의 빠른 발전은 기술 업계 종사자들조차 놀라게 하고 있으며, 제품 개발과 코딩의 새로운 차원으로 이어지고 있습니다.

커서가 코딩 도구에 미치는 영향

Summer 24 창업자 중 절반이 Cursor를 사용하고 있으며, 이는 GitHub Copilot의 12% 사용률을 뛰어넘는 수치입니다.

YC 배치 내에서 Cursor의 빠른 성장과 성공은 개발자 도구에서 초기 고객 확보를 위해 스타트업 창업자를 타겟팅하는 것의 이점을 강조합니다.

방대한 양의 데이터를 분석하는 강력한 AI 모델의 잠재력은 획기적인 과학적 발견과 기술 발전으로 이어질 수 있습니다.