AI Hype Cycle: Investment Trends and Market Concerns

AI-driven startups are thriving despite fears of a hype cycle, showcasing real utility and revenue growth.

### The AI Hype Cycle: Opportunities and Risks in the Startup Landscape
* 🚀 Y Combinator (YC) is launching its first-ever fall batch with applications due by **August 27th**, offering **$500,000 in funding** for innovative startups.
* 💡 Nidia has emerged as the **most valuable company globally**, raising questions about the **sustainability of AI investments** amidst concerns of **overinvestment**.
* 📈 The current market is experiencing a **strong focus on AI**, with significant funding flowing into AI companies and public markets heavily influenced by **AI-driven gains**.
* 🤔 Founders are questioning whether to pursue AI ventures, reflecting fears reminiscent of **past tech bubbles**, particularly the **1999 dot-com boom**.
* 💰 Despite skepticism, there is a growing consensus in Silicon Valley that this is a **pivotal moment for AI**, with a noticeable disconnect between AI enthusiasm in startups and traditional college startup ideas.
* 🔍 The rapid evolution of AI models has led to **increased competition**, with open-source models **catching up**, challenging the notion that **only a few players will dominate the market**.
* 💻 The startup ecosystem is witnessing **significant revenue growth**, with YC companies generating **impressive revenue figures**, indicating that **real value is being created** despite concerns of a hype cycle.
* 📊 Ultimately, while some companies may appear overvalued, the focus on **sustainable growth and solving real problems** positions many startups well for **long-term success** in the AI landscape.

Hey everyone, I have some pretty crazy news to share with you today. YC is doing the first-ever fall batch. Applications are due August 27th, and we fund you for $500,000. All you have to do is apply on ycombinator.com. Now let's get on with the episode. Nidia became the most valuable company in the world. Who would have thought? There's been a lot of concerns with the different articles online as are saying is AI is overinvestment. [Music] Hey everyone, welcome back to another episode of the Lite Cone. I'm Gary, this is Jared Harge, and Diana. And collectively, we have funded companies worth hundreds of billions of dollars, but when they were just, you know, one or two people, sometimes just an idea. One of the things that we're doing a lot of these days is funding AI companies. These are some of the things that people are saying about AI now, that it's a hype cycle, that nobody's ever going to make money out of this. Can you look at how

much money is being put into Nvidia, into Data Centers. There's just no way numerically that this whole space could possibly make money. This is do all over again. This is the Crypt boom and bust all over again. You know, Doom and Gloom, deceleration is type stuff. One of the memes that comes to mind for me when I think about Market hysteria is this hilarious cartoon. It's uh pretty famous. I've got a stock here that could really Excel, sell, Excel, sell, sell, sell, sell. And then the next frame is This is madness. I can't take it anymore. Goodbye, goodbye, bye, bye, bye. It's just like the sort of Madness that overtakes the market. We're also really familiar with the Gartner hype cycle or we have our own version of it, which is sort of the life cycle of a YC startup, which is sort of the Wiggles of false hope and then like long trough of sorrow and then you know finally actually getting to the promised land. Where are we right now? You know, a lot of people uh who we saw who

were just starting out in their careers, asked us at startup school just a few Just A Week Ago, should I actually even be working on AI right now, which is like the craziest question from what I can tell. Where's that fear coming from, from all the found Founders that are looking for an idea. They're like looking at this, is this real or is this a hype. When you're early in your career, you actually, you know, have maybe read about the hype Cycles in the past, the $200,000 parties every Friday or Saturday nights, just like free-flowing booze and just crazy bacchanal in San Francisco in 1999, 1998. Like, you read about it and you hear, oh, like all of those companies died, and you sort of worry, is that, you know, where we're at now. Yeah, I'd say it's been kind of a surprising experience for me because where we live in Silicon Valley with like our set of like friends and peers who we hang out with, like people are

basically just talking about AI like all the time, and there's a strong consensus that like this is an incredible moment in history. But when we all went to Cambridge a few months ago and met with college students for Startup School East, not that many of them were working on AI. Very few of them were actually thinking about AI. They were just like going about doing the same like regular old startups that we've seen college students start for like 20 years. And I was surprised at how big a disconnect there was between those two worlds. The thing that's unusual to me or novel about this current hype cycle is that I feel like the startup world always goes through periods of like ideas are hot, and you feel like everyone's like - it's this sort of that meme you just talked about - it's like people start getting into a particular genre of idea, and then suddenly everyone's working on like Uber for X or their social mobile local app. This time around, though, that's both happening for AI and the startup world, but then if you look at like public

stock market. It's all like AI is having a huge impact there as well. Like the stock market is up this year, but all the gains are coming from like the big tech companies, The Magnificent Seven. Yeah, magn 7. It's never been this concentrated in history, I believe. And if you think of what's driving all of the gains in like the magn and seven big tech companies, it's essentially all like AI hype. So I think like, like I've never seen like those two things being in sync to this degree before where like the startup stuff and the YC batches are increasingly becoming like trending towards 100% AI at this point. And then the public market returns are essentially 100% like AI-driven as well, which is why I think this has captured like everyone's sort of imagination. But there's also just fear that it's unsustainable and everything's going to pop and crash at some point. Well, is it going to pop and crash at some point? There's been a lot of concerns.

with the different articles online that are saying AI is overinvestment on AI chips. I mean, Nvidia became the most valuable company in the world. Who would have thought, right? And then that's like the bottom layer of the infrastructure. And then a lot of folks are wondering, Okay, you invested in all this infrastructure, then something needs to happen to pay dividends for it, right? Sort of like in the early days, the analogies with railroads. So, you lay down the roads, are there trains going to come? And I mean, one very extreme version of whiplash that I feel like is just racking all of us right now is sort of remember about maybe a year ago when it seemed like there were just a few Foundation models that were going to run away with it, and there was the threat of becoming not just AGI but as artificial superintelligence. There's sort of this idea that, Oh no, like what if there is no opportunity at all left for literally anyone else? It might destroy society. And then I

think now with 405b out and thropic uh claw you know sort of 3.5 sonnet is really quite competitive. You actually have choice and we're sort of swinging to this other moment where we're sort of wondering like well how does value acre to Foundation models versus um sort of the hosting companies. I think they're the big winner here and then we hope that actually the software companies themselves whether they're startups or incumbents are uh also just getting the benefit of these Foundation models. Yeah I feel like because everything is moving so fast it's it's easy to underestimate how important that point is like if we roll ourselves back to the start of 2023 a few months after chat GPT had just launched and we were starting to see the first trickles of like AI ideas in the batch. The the chat gbt rapper meme was really out there right like everybody was talking about how all these startups were going to get crushed because chat gbt and open AI would just own everything. Fast forward a

year and a half later, it's like it's clearly not going to be the case. There's multiple models. You've now just got like the first true Frontier open-source model coming out of Facebook, which we would never have predicted. That's cool, right? Who would have thought the beating and best model was going to be the open-source because it was trailing six months to a year behind what OpenAI was doing, right? I remember before this last llama release, like the four of us were just talking about it as, Wouldn't it just be great if just the lag between the frontier models and open-source, like every time there was a new release, that open-source could catch up like within X months, and if we could just make X smaller and smaller, it would be very exciting. But to be at like essentially parity, I don't think any of us even saw that coming a month ago. Yeah, we're there. I mean, there's that graph that the open-source models were on an exponential, and ostensibly the frontier models look like they're on an S-curve.

and what you're saying, Gary, about the difference in terms of what the current batch right now in summer is using for models versus, let's say, six months to a year ago, is very different. I do remember the numbers roughly in the previous batches. I want to say like 90-ish percent, 80% of folks were using OpenAI models because that was the best and that's simply what was working. And now, we did a casual survey and saw that 3.5, there's actually quite a lot of folks using it. Back then, it was only like one or two folks, companies, and now there's like a couple dozen of them using it. And also, Lama is like a lot more. So, I think we're seeing the OpenAI model usage because all of these are becoming comp of losing market share in the current batch as we speak. Yeah, Gary, I think you make a really good point about it being not clear where the value URS, like even if you believe that AI is going to be

massive, and it's going to be like trillions of dollars of value created. There's still a great deal of uncertainty over who will capture the lion's share of that. Is it the GPU makers? Is it the hosting providers? Is it the model developers? Is it the application developers? Like, which pieces get commoditized and which pieces become incredibly valuable? And it reminds me a lot of both web 1.0 and web 2.0. You had the same phenomenon where there were a lot of people who were very bullish about the overall space, but it wasn't clear where you wanted to live in the space. Like, even if you go back to web 1.0, there was a massive hype over owning the browser. Like, for a long time, it was believed that the way to become super rich on the internet was to own the internet browser because that was the gateway to the internet, right? And Netscape was valued at like, it was like, uh, you know, I don't know, billions of

dollars at the time. Turns out that wasn't the place to play, but that wasn't obvious for years. I think the time is an important part because if you think of, um, again in our own world and our careers, some of the biggest companies YC's funded would be DoorDash and Instacart being two examples, um, which were really driven by us all having smartphones and being wanting to do things on our phone. Uber, obviously, like another huge company, um, but those were all released, or at least like the first versions of those came out like almost, I think around four years post the iPhone, like coming out. Like these things take a while before you really know which ideas are going to take off and where all the value is going to go. I think, uh, one really important fact is that there are all of these other parts of the value chain. Obviously, there's the foundation model, there's the hosting provider, there's a chipmaker, and then way over here are the startups we're funding, which is the application layer and the

the important thing to note is that you do not need a $100 million to start an application layer company. You just need you, and sometimes just you, usually a co-founder, and then if both of you know how to code, you can literally take sort of these hyper-powerful things that are now basically off the shelf and then go into some other market, and you can create a product, solve a real problem, literally get money from people who are willing to pay you forever if you can actually solve their problems using the technology at hand, and you can do it all from the desk that, or you know, literally the computer that you're looking at this video from. Like, you don't need any permission other than literally a working internet connection and the laptop you have, which is exactly the story of Instagram, Card, and DoorDash, right? Those are application layer companies enabled by like the mobile phone technology ship, but they didn't need to like make their own phones. It was just exactly what you said, Gary, yeah, and every other part of it, it's like

yeah, maybe you do need, you know, 50 hundred million dollars to get your foundation model company off the ground. Maybe you do need that much for Fabs or hosting or all these other things. But even then, like, I guess that's not entirely true. Like, it's just that much harder, I think. Also, so if we go back to this, like, are we in an AI hype cycle question and try and, like, if we define it more precisely, no one's saying that AI has literally zero value. Like, it's pretty clear that it does. I think usually when people talk about hype cycles, what they're reacting to is they're seeing the price of things go up very, very quickly. Like, whether that's like public market stocks, like Nvidia, like you mentioned, or even in startup world where you see like, you know, companies get to a billion-dollar valuation within like 6 to 12 months of starting, um, which has happened with some of these, uh, very famous AI researcher teams that used to work at DeepMind.

let's say or at OpenAI they leave and start and without product market fit. Six months later they have this giant valuation. I don't know, right? It kind of reminds me of Professor coins back in the crypto boom. Like, if you had distributed systems experience and you know, you could just walk into even like the first person you meet who is a crypto and you would walk out with you know a billion to 5 billion dollar market value. Like just without having even a line of code, even like a white paper, like nothing. Crypto is the perfect example, right? Like that, that was the last time. It hasn't even been that long ago, it's been two years. The last time it felt like there was a hype cycle, it specifically defined as hey, it seems to us like the value of these companies is increasing at like an unsustainably fast pace. That was all these crypto companies that were like doing their token launches and just raising equity rounds and seeing valuations like double and triple every 3 to 6 months. So I

think there's definitely some scar issue from that. On the flip side, that is also just the startup world. Like, I mean, there have always been startups that at every phase feel like they're overhyped and the valuation is too large and they'll never grow into it. But like, I remember people saying that about Stripe when it first launched because Stripe had raised this big round from Sequoia, valued at like $100 million, and I don't think they'd even publicly launched yet. It was all belief in the founders and the idea and the market. And so that is partly just the business. Investors invest like hoping to make money, and the way they make money is that whatever price they pay today is like there's profit to be made by how much the company's going to grow in the future. But there's a big difference that is maybe a bit nuanced between how to value a technology company versus things that are more like asset speculative, right? Because in the crypto world, there's a lot of that. So maybe we can speak to that versus true.

technology. I wouldn't say that it's purely speculation, though. Like, I would say that when you have investors with billions of dollars to deploy, you have a hammer, and all you see are nails. And what you're trying to do is put as much money as possible behind people and teams that seem like shelling points for other intelligent people. So, that's probably what happened with Stripe. You could argue that it is actually somewhat rational, maybe not to like the billion-dollar market values that people got, but it is somewhat rational that if you are a professor in distributed systems with a certain network, the other people you need to actually create a working cryptocurrency that people trust and build apps on, that's actually the set of people who could do it. And then the money closes the door behind you. And I would argue that that's some of the reason why you have things like Cognition Labs and Devon. You have things like Harvey. That is the market trying to figure out where

is the talent shelling point for really smart people. And like if we all sort of go all in, it actually, you know, sort of buffers that company from, uh, frankly competition. Like and you, you know, it's sort of like the classic Peter Thiel, uh, you know, sort of speech about competition is for losers. I think it's literally on this Y Combinator channel actually. And so it's not totally irrational, but it is, uh, worth lampooning at some level. I think that's a fair way to look at the crypto stuff. Inherently, I think during that period you could divide it out into the asset speculation, which was essentially people who had really focused on launching these tokens very, very quickly and trying to get the price of tokens up. But there were also legitimately credent, like very intense technical teams that were trying to work on building new protocols and trying to take like existing services and build decentralized versions of them. And I think the investor mindset was kind of what you're saying. It's like, Hey, actually like we shouldn't bet against

smart technical people trying to work on really hard technical problems. And there were lots of those within crypto. And so, let's invest ahead of product market fit because, yeah, like these people will attract the next, like, smart people. And if there's something here, these are the people that will figure it out. So, I think the crypto analogy is really good because when Diane and I spoke with a lot of the best students at Harvard and MIT, one of the consistent things that we heard from them was that a lot of them felt burned by the crypto hype cycle, either personally or they had friends. Like, they were old enough to have been around for the 2020, 21, 2022 crypto hype cycle, and a lot of them were now a bit skeptical of AI and, like, whatever the new hotness thing is as a result. And so, like, you two were the founding investors of Coinbase and some of the most successful crypto investors in the world. What how would

you compare what happened with crypto in 2021 with what's happening with AI now. How is it similar? How is it different? Coinbase, I think, was remarkable in that it was not actively a cryptocurrency per se. It was the enabling technology that you would need in order for it to actually happen, and they're still on that journey, I would argue. I mean, Brian Armstrong and earnings, you just this week started talking about, you know, we are actually working with or talking to every financial institution in the world to incorporate blockchain technology into like the actual norms of finance. And I think that that was always sort of the promise that I was excited about when I met Brian first when he was, you know, an anti-fraud engineer coming out of Airbnb actually. Yeah, like Coinbase was essentially a marketplace, and I think it just, it didn't look too different to any other startup where it was like, oh hey, there's this thing that people want to buy and sell to each other, there's no great marketplace for doing that, so why don't we just build

like a trusted Marketplace and it was Fringe and Niche. It was. I don't think people at the moment thought it was going to go the way it did. That was it. The unknown with Coinbase was how big is the market. But it very clearly had product-market fit with the people that cared about it early on. Like, there were absolutely people who wanted to buy and sell Bitcoin that needed a better way to do it. So, there was never any doubt that Coinbase had utility in the sense of, Hey, there's this group of people that want to do something that Coinbase is making easier for them to do. I think what feels different about AI now versus crypto is just that sniff test of when you look at the products. A lot of the Web 3 stuff, in particular, for most people, just never really passed the sniff test of, Like, I don't get why I would use this. But when you look at AI products, it's very, very clear that being able to, like, summarize a 50-page PDF market.

analysis report and actually pluck out like the three key points is clearly like utility that someone will pay for. I mean, there's a company in my current POD at YC right now, and they can do accounts receivable. Like, they took a 12-person team and turned it into one person working on accounts receivable, and 11 other people could work on all the other things in finance. And that's like about as tangible as you can get. Like, literally, you're taking something that it's like not better than literally being a human passing butter, and we're replacing them with butter-passing robots. And there's actually, you know, it's kind of, it's a Rick and Morty joke, but on the other hand, it's kind of serious, actually. Like, how much better is the world that software is doing that thing that, you know, actually is not the most awesome thing that a human could be doing, which is like reconciling like an email and a, you know, bank record to what needed to be paid. Like, that's not

invigorating like work that you know turns on your creativity, right? It's something that is sometimes maybe torture for people, you know, and we can give people other things that they can do, and it's that kind of thing that we get to see right now. We're literally seeing it right, like you, Danana, looked up some numbers here. I mean, the cool stat is we did mention this in a previous episode on Batch by the Numbers. The total revenue that if we aggregate all the revenue that companies come in when they apply to YC, it was 6 million by the end of the batch, about 3-4 months later. If we add up all the revenue that they grew into, it was 20 million, which is like huge over just 3-4 months. And it succeeds, exceeds a lot of the advice that we give. That's still ambitious, like try to grow your company 20% month over month. That's still like an exponential growth. And if you use that benchmark, 20% month over month from

six to from 6 million over 3 4 months is only about like 12 million so like 20 million. We're seeing real revenue that's actually getting acur. And when companies land on a good idea, that value that you're talking about, Gary, their customers recognize it. I mean, customers are discerning, they're paying for it, and there is that kind of value aspect of it, yeah. And then I think the trickiest thing is it can't just be the, you know, the first renewal or the second renewal. Like, you actually have to survive all the renewals. Like, the only way there's enterprise value in a company is, believe it or not, discounted cash flows from the future. So, that means retention has to be like every customer you get, you better have that customer forever. And that's the only way you can build a business of any value. And that also seems to get to the heart of this, um, are we in a hype cycle argument or not? Because I feel like at YC, all of us are on the ground.

floor seeing examples of companies that are ramping up to like a million dollars of AR faster than we're used to seeing with companies during a batch and even in like the 6 to 12 months post the batch. That Revenue ramp is impressive. Gustaf's been talking about Leia, who just closed like their series A from Benchmark, doing like legal AI to automate legal work. There's a company that I worked with a year ago that is probably going to end at 10 million by the end of this year. This is just 12 months later after they landed on the idea, man, 10x after that. You could IPO, and I think this is so different to like Jared and I when we moved to San Francisco in like 2007, right? 'Cause there were the thing that everyone was chasing. You had this with Posterous too, like the thing everyone was chasing was like page views, yeah, page views, and then like active users, and just, I know, registered accounts, whatever it was, but vanity metrics is what you're saying.

like things that make you feel good. But well, I want to jump into a point that Harge made earlier, which is like if you define a hype cycle as a situation in which a lot of assets are overvalued, that might be the case right now. Like Nvidia is the most valuable company in the world right now. I don't know if it should be. Like maybe Nvidia is overvalued and like TPUs are going to start working and like, you know, like all those things could change. There's a bunch of startups that raised a billion dollar plus valuations. In hindsight, like some of those deals will probably look really silly. The good news for our business is we don't care. Like, you know, if we were public market investors, you'd be very worried about, you know, whether Nvidia was overvalued or not. But given like where we play, it literally doesn't matter. Yeah, I mean, I would argue for founders, yeah, it's actually good. And even if some things are overvalued, yeah.

like I mean, it's actually sort of free money for the whole ecosystem. Like, Nvidia being overvalued equals if they needed to raise money, they could raise it very cheaply. It's more capital that allows them to drive the future sort of much faster, and then everyone in the rest of the ecosystem basically benefits from that. And this is where there is a huge difference between where we and the founders are and public market companies. Public market companies have to deliver quarter after quarter. Like, if they miss an earnings report, the stock tanks, employees start getting itchy, it's a massive distraction. But when, like, you know, as YC, we're investing in people when they're just ideas, like, we don't expect the things, we don't expect to know if something's working or not for like 10 plus years, right? Which is the same for the founders, and viewed on like a 10-year horizon, doesn't really matter if things are overvalued today. You just care about directionally, is this going to be worth more in 10

years than it is today. Well, I think the businesses are actually quite a bit more healthy, or can be, not always. Like, you know, I think maybe that's one of the more dangerous things about whether it was Professor coins that raised hundreds of millions of dollars and like never launched, or, you know, frankly, some of the AI Darlings today that might have already been blessed by the, you know, world's biggest VC funds, but they're looking at a balance sheet that has $100 million or $200 million or $500 million and absolutely zero revenue. Like, how do you actually, it's sort of like looking straight up at Mount Everest and saying like, how am I going to actually climb this? And it's a weird contrast to a bunch of the YC companies that we see where, you know, yeah, they might have only raised a million to $2 million out of demo day, but they're starting to hit their milestones of $5 or $10 million, and that actually because they were profitable from day one or relatively quickly.

bank account has only gone up for them, and they look down and say, Well, I raised the seed round. I don't have any board, you know. I don't have to sell any more of my company. So why don't I just build the company the way I want? And, you know, I think maybe 10 years ago, there were not that many examples of it in the YC world. You know, there was sort of Weebly that only raised a seed round and then sold for, you know, a massive sum to Square later. And they could do it because they were profitable in our era of social software. Even today, Zapier is probably one of the most dominant pure software companies that only ever raised a seed round. And, you know, they're driving hundreds of millions of dollars in revenue. It's sort of like this insane moment for them, and they're making a huge bet on AI. Yeah, and so it's such an interesting moment. Like, do you really need the mega round? In fact, the mega round might be like such a

giant load Stone around your neck. You'll never actually overcome that. And the fascinating thing is with these companies that we kind of mentioned. They are hitting profitability or break even, and they don't need to raise, and they have this revenue that is coming in and growing exponentially, and they don't have to. So, perhaps you don't hear as much about them because they don't go public and try to do the mega rounds because they're really playing the long game. And if I think about the categories of things that we're starting to see now that we have a bit more data, I think in every single area for AR, for AI, we are seeing early signs that things are working, even for some things that people think are overhyped, like, uh, think of generative AI to generate images. I think it's hard to imagine, oh, is this just for toy or just for art or just for fun? But there's a company, uh, photo room, that actually is making real revenue, um, and I think they recently announced they're valued at half a billion dollars.

and they're generating images for e-commerce. So when you go in a vertical specific like them, they figure out something, and they got generative AI to be very profitable because it's very hard for brands to take pictures of images of products, and product placement and all that is very hard, and they've done it very cheaply. You don't need a team of photographers, right, or editors. That's like one example I can think of. The other category we talked a lot about this as well is kind of this AI agent workflow, right, where you're replacing a team of ops. You mentioned Y like Greenlight. Greenlight's a great example. Permit Flow is another company that's doing essentially an AI agent that uses AI to just fill out construction permit flows. And again, if we're trying to put like the devil's advocate, argue the other side about these things, it would be, I think there's two attacks you make. One, you say, Oh, these things are like automating, but they haven't fully got

the human out of the loop yet and so like you know to unlock these valuations and to be worth this much amount of money. They really have to get humans out of the loop. Um, and the second would be that just enterprises are never going to trust these things, and so you're not going to sign up like six-figure or million-dollar contracts with big 14500 companies relying entirely on AI. Oh, I thought the second one they were going to say har is that like they're going to be commoditized, so they're just like gbt rappers, and like all the real value is going to queue to the foundation models, and there's going to be like 100 permit flows, and so how will they capture any of the actual value on that one though? I just feel, I feel like that indeed was the big criticism, that was the main attack like a year ago, and I maybe I'm just, this is based because I so strongly believe it's just been debunked at this point, but just everything we talked about like multiple

models open source like being permit flow being the winner in your space is as far as I can tell all about figuring out how to sell, getting the details of the UI correct, like owning all of the little details to get the product perfect. I agree with you, Har. I was just, yeah, that's fair. I was making other people's argument. Yeah, it seems, if anything, that the opposite is likely to be true, that the value is going to intuit to the permit flows, this even, like, even to the, it's not just a wrapper, even at the technical layer. I know they do a lot of work to really fine-tuning to a domain-specific. There's a lot of power to private data in these spaces with permits, with banking data that cannot be done and replicated, and there's actually a lot of finesse there that they actually, it's not just a wrapper, there's like a lot of really thought-out work. That is a great point. I mean, there's a Mark Zuckerberg quote. I think he made, he said this maybe a week or

two ago about how even if all the model development progress froze today, there would still be five years' worth of innovation to go on, just like the application level, um, or the application layer of building point solutions on top of the models, and even, uh, on the side of a copilot, right? Even with a GitHub copilot, the most famous example, is apparently the fastest-growing product for GitHub's revenue. It accounted for apparently 40% of the growth recently, and it's been rumored, reported, that they're making in the hundreds of millions of revenue. That's still to be verified, but around that much, which is significant, over just when was it released, a couple of years ago, right? Another anecdote I could throw out on the point of this needs to get humans completely out of the loop to really be as valuable as everyone says it is. Anecdotally, what I've heard from some startups is, uh, they're building it into the workflow so that the AI, like, does the work, but then you can sort of spot, have a UI to spot check, or just like

have a human do the review process, but increasingly like the customers are not even using the feature anymore. They're just like, I heard that too, I heard that too. And I have this other company that's basically replacing a lot of the call centers, and they are processing hundreds of thousands of calls. And same thing, they're like all these BPOs, offshore call centers in the Philippines or Mexico. They fired that whole center, and this big enterprise is using this startup for like 20 times, 100 times cheaper and faster. And same thing, and to your point, like even if it's not perfect right now, and there is not like another 10x Frontier Model coming out anytime soon, all of the work, like the fine-tuning, gathering the data, like the private data repos, like it's very plausible that alone will just squeeze out and get to like the right level of quality where big enterprises will just start like spending millions and tens of millions of dollars on all of these solutions. That's actually the fourth

category that I think is also working out while yes there's a lot of tooling right now to build LM to do evaluations fine-tuning etc etc. There are a couple of our company, a number of them that actually are having success with doing fine-tuning specifically this tooling for Enterprises that are working with their private data. If you want to skip the specific application, even the tooling is valuable. I also think there's just like a lot more places to apply LMS to that people haven't even thought of. Like Diane and I have a company in our group that just two weeks ago went to this conference of like a particular industry conference and they had this like Eureka moment where they realized that there's an entire industry that is a perfect application for llms that literally nobody who's a technologist even knows about or knows exists. And so like there's no one who's even trying and they're just like you know like off off to the races with this like billion dollar opportunity in an obscure corner.

of the world that's a perfect fit for this technology. So, as you're saying, Harge, I think there's like years' worth of just unlocking of the current technology. I think G, you had a very good framework to kind of think about this in terms of that war and Buffet popularity, and we machine you kind of tell us about that. Yeah, I think ultimately that's what's playing out here. I mean, it's played out in every hype cycle, boom-bust cycle, in not just technology but sort of in the world. There's sort of this Mania that begins like the world is going to change, and then it's so hard to actually understand what's going on that a lot of it is in sort of hearsay or in the media, and this is what x says. Like, you know, on product hunt, there's this sort of a fog of War around what's going on, and then what that results in is a popularity contest. It's basically a, in the short term, all businesses are sort

of voting, subject to the voting machine right. Like, it's not what is actually happening yet, like it's happening so quickly that humans are sense-making machines, and it takes time for us to make sense. And so, in the short term, we are tricked by very fast-talking hustlers. We are tricked by fancy credentials or, you know, I worked at such and such company. There's sort of the clinkle of the world, like the fast-talking Stanford dropout that scams very smart investors out of millions of dollars, right? Like, you get a lot of that, and that's just like the vagaries and the madness of the voting machine, right? Like, it's not like people are trying to put money into things that are scams. It's more that, um, just by purely social effects, we cannot make you sense of the world fast enough. And, you know, basically, the mob gets it wrong. And then, on the flip side, like in the long run though, like ultimately, the value of every company is discounted cash flows from

the future. You know you need your customers to actually have a problem be solved. People pay them, and then, you know, your customers stick with you forever. That's why when you look at a Google or a meta or, you know, any of the Magnificent 7, those companies are the most valuable companies in the world because people feel that there, you know, those companies may well make money forever, right? And there's safety in that. That's sort of like what people, you know, believe now. And then the public markets ultimately are sort of, you know, they themselves are both totally crazy voting machines as well, but they eventually resemble weighing machines. Like, ultimately, you actually have to make money. Ultimately, you actually have to have customers. And at that point, you need to have made something of actual heft and weight that actually works. Well, that's all we have time for today. We'll see you guys next time. [Music]

Are We In An AI Hype Cycle?

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Video Team:
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Y Combinator

YC is launching its first fall batch, offering $500,000 in funding, with applications due by August 27th.

The team discusses the current AI boom, noting the significant investments in companies like Nvidia and the strong market impact of AI.

There's a notable disconnect between the startup world's focus on AI and the traditional startup ideas being pursued by college students, leading to concerns about the sustainability of the current hype cycle.

The Impact of AI Investment and Model Competition

AI chip investment has surged, with Nvidia becoming the world's most valuable company.

The rapid evolution of Foundation models has created competition, challenging the initial dominance of OpenAI.

The uncertainty of who will capture the most value in the AI space mirrors the dynamics of web 1.

0, with multiple players vying for dominance.

The Impact of Timing and Technology on Startup Success

The success of startups like DoorDash and Instacart was driven by the widespread adoption of smartphones, with their first versions launching around four years after the iPhone.

Starting an application layer company doesn't require significant funding; a co-founder and coding skills are often enough to leverage powerful off-the-shelf technology.

The current AI hype cycle is characterized by rapid valuation increases, reminiscent of the recent crypto boom, but the startup world has always seen fluctuations in perceived overvaluation.

The Investor Mindset in Tech: Comparing Crypto and AI

Investors with billions to deploy often focus on backing talented teams, creating a shelling point for smart people to collaborate.

The crypto hype cycle burned many, leading to skepticism about AI's potential.

Coinbase's success came from providing a trusted marketplace for buying and selling Bitcoin, while AI products currently offer clear utility, making them more appealing to users.

The Impact of Software on Business Efficiency

Software is transforming business operations by automating tasks like accounts receivable, allowing companies to reduce their workforce and increase efficiency.

The rapid revenue growth of companies applying to YC, from $6 million to $20 million in just 3-4 months, highlights the tangible value of solving customer pain points.

Building long-term customer retention is crucial for creating enterprise value, and the current wave of startups is achieving impressive revenue growth, challenging traditional business metrics.

The Impact of Overvaluation in the Tech Ecosystem

Overvalued companies like Nvidia can raise capital cheaply, benefiting the entire ecosystem.

Unlike public companies, startups have a longer time horizon to prove their worth, allowing for healthier business growth.

YC-backed companies that raise smaller amounts are often more profitable and can drive their own growth, challenging the need for mega funding rounds.

AI's success in enterprises depends on removing humans from the loop, but trust issues may hinder large contracts.

The real value in AI will likely flow to the foundation models, with private data and domain-specific fine-tuning playing a crucial role.

Despite the current challenges, the application layer and workflow integration are driving significant growth and adoption of AI solutions in businesses.

LLMs are gaining traction in enterprises for fine-tuning with private data, presenting valuable tooling opportunities.

There are untapped industries perfect for LLM applications, offering billion-dollar opportunities.

The hype cycle of technology often leads to a popularity contest, but long-term success is driven by solving customer problems and delivering value.