Better AI Models, Better Startups [Lightcone Podcast Ep. 7]

all startups. Adding more types of modalities and more capabilities,.

per model, the the better off every startup is. You have to be on top of these announcements and be kind of know what you're gonna build in anticipation of them before someone else does versus being worried about OpenAI or Google being the ones to build them.

Welcome back to another episode of the Light Cone. I'm Gary. This is Jared, Harge, and Diana. And we're some of the group partners at YC who have funded companies that have gone on to be worth hundreds of billions of dollars in aggregate.

And today, we are at an interesting moment in the innovation of large language models and that we've seen a lot of really new tech come out just in the last few weeks, whether it's GPT four o, it's Gemini 1. 5.

Hajj, how are you thinking about, you know, what does it mean for these models to be so much better? Anytime I see a new announcement from one of the big AI companies with the release of a new model, the first thing I think about is what does this mean for the startups, and in particular, YC startups.

And when I was watching the OpenAI demos, it was pretty clear to me that they are really targeting consumer. Like, all of the demos were cool consumer use cases and applications, which makes sense. That's kind of what ChatGPT was. It was a consumer app that went really viral. I just wonder what it means for the consumer companies that we're funding.

And in particular, like, how will they compete with OpenAI for these users? What did you think? Like, even if we take it back, like, how do consumer products win from, like, first principles? Like, is it more about the product or the distribution? And how do you compete with OpenAI on either of those things? Yeah. That's a great question. I mean,.

I think ultimately, it's both. And then how I want it to be is that the best product wins. How it actually is is whoever has the best distribution and a sufficiently good product seems to win. Either way, I actually think we're at it's sort of in this moment where the better the model becomes.

If you're already using four and suddenly four, you know, you can change one line of code and suddenly be using four o, you basically just get smarter by default every generation. And that's really, really powerful. It means that, you know, I think we're entering this moment where the the IQ of these things is still, you know, four is arguably around 85. It's not that high.

And then if the next generation, if Claude three really is at a hundred or, you know, the next few models end up being closer to, you know, one ten, one 20, one 30, this is actually a really crazy moment for all startups. And the most interesting thing is, like, adding new capabilities. So having the same model be great at coding, for instance.

That means that, you know, you might have a breakthrough in reasoning, not through just the model reasoning itself, but you could have the model actually write code and have the code do better.

And even right now it seems like there's a lot of evidence that if instead of trying to prompt the model to do the work itself, you have it write code and you execute the code, it can actually do things that reasoning alone could not do. So adding more types of modalities and more capabilities per model,.

the the better off every startup is. I mean, the cool thing about, four o is that you can get better structure output.

In this particular case, they are better getting JSON, which is getting signs of getting large language models, not just outputting English, but more language for computers so that you can build even better applications on top, which is signaling that this better model can be better for startups and make it easier to integrate. Because one of the challenges for startups has been always coercing.

LMs to output the right thing so you can actually process it in regular business logic.

The other thing I kind of thought about when I was looking at the demos is as it relates to startups, if only one of these companies has the most powerful model by some distance, then that is indeed bad for startups because you have to depend on them being friendly and having, like, a nice API for you to build on top of.

If there are multiple equivalently powerful models, you're much safer off as a startup. It was funny, maybe coincidental, maybe not, that, like, OpenAI's announcement was, like, what, two days before? One day. One day before Google's. Right? What's the difference between the so under the hood, the way that GPT four o works and then Gemini 1. 5 works?

And do you have any opinions on their relative strengths?

Yeah. So the thing about four o why it was so interesting, it was adding the speech modality and also video processing on top of text. And the way they do that is still primarily a text based transformer model underneath, basically, GPT four. And what they've done is Bootstrap and added modules so that it has different co paths to handle this different type of data.

OpenAI famously also implemented and launched Whisper, which is one of the state of the art for automatic speech recognition. And probably, that's what they're doing. They took the architecture of Whisper and then bolted it into GPT four, and they also bolted DALI, and they combined these, and that became four o.

So this is why in terms of the reasoning capabilities, four o isn't better per se than four by any margin. So it's how it works. It's kinda adding modules, how they describe it on the white paper. The difference versus Gemini 1. 5, which actually on the technical aspects and merits, I'm actually more excited by Interesting. Gemini one.

I know it's counterintuitive because four o and OpenAI has captured the zeitgeist of everyone, and they're so good at the demos. Right? Singing happy birthday a bit off key, that's like so human. Happy birthday to you. Happy birthday to you. Happy birthday, dear Joel Jordan.

Happy birthday to Jordan. Blah blah blah blah.

Google IO kinda missed the mark in terms of demo.

But in terms of reading their white paper, what's interesting about Gemini one point five is that it's actually a true mixtures of expert, and that is a technique that's new where they actually train from the ground up a giant model with the actual data of text, image, audio, and the whole network activates a specific path for these different data types.

So instead of the OpenAI model that has, like, kinda modules, this one truly is a one all model. And what it does is different parts of the network activate depending on this data input, so it becomes very energy efficient. And I think the reason why Google was able to do it is because they have the engineering hammer.

They have TPUs where they can really afford to put a lot of data because it's very expensive to put not just all text, image, and video and train this giant thing in a distributed cluster. They have TPUs like their I think it's their fifth generation now.

And it's pretty cool what they've done. Is that the first big model release that's using Mature Experts?

I think they talked a bit about it on the previous one, but everyone was a bit dissolution after the demo of the duck was not real. It is a duck. Yes. But this one was described better. I mean, the interesting thing is that I think this time they learned their lesson, and I think it's actually working. Yeah.

And the other cool thing about Gemini is it has a context window of a million tokens, which is huge. The g p d 4 0 is a 28,000. So imagine what you can do with that because that's about, like, five books of 500 words or more. And the cool thing about the Gemini 1. 5 was their white paper has this saying that on research, they proved it to work on a 10,000,000 token window.

Which brings a question for all of you, what does that mean for startups, especially a lot of the startups that we're funding with infrastructure that do a lot of rack? There could be the controversial argument that all these startups building tooling around Rag, which is a whole industrial right now,.

maybe they become obsolete. What do you all think about that? I feel like the people who care a lot about data privacy and where the data is stored are still going to want some sort of rag system. Right? Like, they want the data stored somewhere they control it versus all in the context window. It's not clear that that's gonna be the biggest part of the market.

Like, in general, people who care this much about any behind the scenes architectural thing tend to be, like, early adopters, but not, like, mass market consumer. So my guess is people just want, like, a massive context window because then you can start building the kinds of consumer apps people are excited about. Right?

Like, the a assistant that just has all this context on me that knows everything about me. Like, currently, I think the best way you can do that is you, like, run Olama or one of these open source models, and then you, like, throw a bunch of your, like, personal emails at it. That's like a project that the hobbyists on Reddit are doing a lot of.

It's just trying to get, like, your personal AI that's got all the information on you. But if you had, like, a infinite context window, you wouldn't need to do all of that. I think you'd still need Rag to be able to sort of store everything, and that's, like, sort of the long term permanent memory. And then what you actually want is a separate workflow to pull out the interesting things about.

that user and their intentions, and then you actually have a little, like, summary bullet point of things that you know about the user. You can actually kinda see some version of this even now in ChatGPT if you go into the settings under four o that actually now has a memory. And so you can actually see a concrete version of this inside ChatGPT.

I was just using it to sort of generate some, like, where's Waldo images for my son. And it wasn't quite doing what I wanted. It kept using, like making, like, really deformed faces. So I kept like prompting it back to back. I was like, no, no, no. I really want no deformed faces.

And then for a while it was like I said I wanted a red robot in the corner, and it kept making all of the characters like various forms of red. And I said, no, no, no. I really don't want you to do it. And I, you know, sort of repeated it four or five times. And then, I went and looked in my settings, it was like, Gary really doesn't want deformed faces in his, generated images.

We should also try not to use red. And it was interesting to see that, like, literally from even, like, maybe 10 or 15 different chat interactions. You know, I was getting frustrated, but it was definitely sort of developing some sort of memory based on my experience with it.

And the most interesting thing was that you could see what the machine had, like, sort of pulled out from your interactions thus far, and you could, like, sort of delete it as necessary.

Maybe a infinite window doesn't necessarily mean that the retrieval is actually accurate. Yeah. And this is more, I mean, more anecdotal in practice from what founders have told us versus what the actual research paper benchmark gets, which is a very kinda lab setting. So in practice, I do tend to agree that a RAC pipeline infrastructure is still very much needed exactly for what you said.

Privacy and people wanting to fine tune models on their own data and not getting that leaked out over the wire over the Internet. And the other thing is, yeah, maybe that's still more accurate to do it on your own when you really want that very precise information. I think you still need RAC. And I think the analogy I like to think about this is sort of like processors.

Back in the day in the nineties as when Moore's Law was actually Moore's Law scaling, it was not just CPU processing speed getting faster, but also memory cache levels were also getting bigger and bigger. But now more than thirty years later, we still have a very complex architecture with how we do different kinds of caching for retrieving data out of, like, databases.

Out of databases, you have maybe, like, a fast memory store with, like, Redis for high availability, and then you still have things stored in your browser cache. There's still very much lots of layers of how things will be cached. And I think RAG is gonna be this foundational thing that will stay, and it'll be, like, how we work with databases normally now, just like lots of levels. Yeah. Yeah.

The tricky thing about the context window I mean, Gemini may have the team may have already fixed this by now, but certainly a lot of the founders I talked to, they said.

it's sort of you know, the million token context window sort of lacks specificity. Literally, if you ask for retrieval from its own context window from you know, or the prompt, it actually sometimes just, like, can't seem to recall it or can't seem to, you know, pick out the specific thing that you already fed into it.

And the tricky thing there is, like, you'd rather have a 28 k context window that you knew was pretty rock solid rather than a system where, you know, it's still a bit of a black box. You don't really know what's going on. And then for all you know, it's just, like, sort of randomly picking up, like, half a million of the tokens. And that, you know, again, like, probably fixable.

You know, I can't imagine that that's, like, a permanent situation for, you know, a million or 10,000,000.

token context window, but something that we're seeing from the field for now. Also in enterprises, like, in business use cases, people care a lot about, like, what specific data is being retrieved, who's doing it, like, all of this stuff and permissioning around data. And so, yeah, you can imagine having some kind of yeah.

A giant context window is not necessarily what you want in enterprise use case. You actually probably want, in particular, sensitive data stored somewhere else and retrieve, like, when it's needed and know who's making the requests and filter it appropriately.

Exactly.

I think that will that will stay. I was really encouraged what you said actually about how the Google technology is maybe better than the OpenAI stuff. It feels very googly actually. It's like, hey, they've got the best technology, but they just, like, don't know how to get, like, the polish around it correct. That means OpenAI does not have this, like, leap forward unassailable tech advantage.

If Google has something comparable, then we should expect to see, like, Anthropic come in. We should expect to see, like, Meta come in. And what we're seeing at the batch level is just the models are pretty abstracted out, right, on a day to day basis. Like, our founders are already using different models to prototype versus, like, build and scale.

Like, the ecosystem of model routers and observability ops software around this stuff just keeps progressing really quickly. So it's funny. My my initial reaction whenever I hear, like, the model releases is not to worry for the startups actually so much because they're all red. Like, we never talk about how reliant they are on any one model.

I worry if there's one model that's very, very good, and it'll be dominant and sort of take over the world.

I'm less and less worried if there are many different alternatives because then you have a market, and a marketplace equals, you know, non monopoly pricing, which means that, you know, a thousand flowers can actually bloom. Like, other startups can actually make choices and have gross margin of their own.

And I'd much rather see, you know, thousands of companies make a billion dollars a year each rather than, you know, one or two, let alone seven companies worth a trillion dollars. And I think we have a dark horse that is yet TBD.

We don't know when LAMA three with 400,000,000,000 parameters comes out because that's still being trained. And that's,.

like, one that's like, wow. It could really turn tables as well. Yeah. The interesting thing about Meta is, I mean, they have probably one of the largest clusters. Certainly, I think I was reading, you know, in terms of who has paid NVIDIA more money in the past year, Meta apparently is number one by by a a decent bit, actually.

And the funny thing is they have this giant cluster not because they necessarily have foreseen this whole.

shift that happened recently in the last couple years with large language models. They acquire lots of GPUs because they needed to train their recommendation models, right, that use actually similar architecture with deep neural deep neural nets to actually compete with TikTok because to build these, like, really good recommendations on Instagram results.

That's just a very classic tech innovation and disruption. Right? Like, they're basically worried about competing with TikTok. They stockpile a bunch of GPUs, and it turns out the GPUs are just really valuable for this, like, completely different use case that's gonna change the world. Jared, like, on that note, if you zoom out just like, how does this cycle of, hey. Like, we're worried.

Startups are worried about the elephant in the room. This case is OpenAI, maybe Google competing and crushing them. How does it play out to when we first moved out here even? Like, in that, like, era where Facebook was rising, Google was starting to go from the search engine company to, like, the multiproduct.

company. Do you see any similarities or differences? Yeah. Reminds me of that a lot. Like, every time there's an OpenAI product release now, it feels like there's a bunch of startups waiting with bated breath to see whether OpenAI is gonna kill their startup. And then there's all this Internet commentary afterwards about, like, which startups got killed by the latest OpenAI release.

And it reminds me a lot of when we got to YC, the the three of us in the, like, 02/2010 era. There were all these companies who were innovating in the same idea space as Google and Facebook, building related products and services, where the big question was always, like, what happens if Google does this?

And when startups were pitching to investors, that was like the main, like, a big question that they'd always get from investors, is like, oh, like, but isn't Google going to do this? The best response to that, by the way, was like, well, what if Google gets into VC? Which it did.

A great VC arm.

So, a lot of the people who are building AI apps now, this is the first hype cycle they've ever been in. But we've all been through multiple hype cycles. And so, I think it's interesting, actually, for the people who are in the middle of this hype cycle now where all of this is new, to look back on the past hype cycles and.

see how the history of what happened there can inform their decisions about what to work on. If we take Google as an example, one thing that's interesting is if you look back, there was there was competing with Google in a very head on way, which was, hey, we're gonna build a better search engine. And YC definitely funded a lot companies trying that.

And I feel like the approach people would go after was the vertical engine. Was, hey. We're gonna build a better Google for real estate, for example.

Some of those made it. Did they? I'm assuming which ones? You could, I mean, argue that something like a Redfin or Zillow clearly did have vertical access to data and then Or a kayak for travel, I guess. Or Algolia.

for.

company enterprise search. Enterprise search. Yeah. That's true. Okay. Those yeah. I hadn't thought of yeah. I hadn't thought of Zillow as a search engine, but, yeah, it's essentially that.

It's exactly that. It's vertical search. Yep. But you have to monetize not necessarily through the same way a search engine would. You have to have other services. You have to become a broker. You have to, you know, basically make money in all these other ways once you are the customer. Completely different.

It doesn't look at all like Google. Yes. And the data integration is very different. Like, you have to really poke and connect to MLS, and a regular search engine wouldn't wouldn't just work with that. Like, PageRank wouldn't necessarily work with MLS. Yeah.

Redfin's very interesting because I'm very addicted to Redfin, and it has actually absolutely caused me to buy property that I normally wouldn't buy. So, you know, in that respect, like, those are interesting consumer scenarios.

Ultimately, a great consumer is actually about buying just, like, a little bit of someone's brain such that during the course of one's day I mean, it doesn't have to be every day, but ideally it is. You sort of think to use it. And no one of those companies would have said that they had better technology or they beat Google on technology. Right?

Like anyone who went up head head on against Google for like the better general purpose search engine just got crushed. And in general, most of the vertical search engines didn't work. And certainly, nothing that looks anything like Google worked.

The the ones that I remember the most were more ones that were in the vein of Google apps. Like when Google expanded beyond search and started launching Google Docs and Sheets and Slides and Maps and photos and all all these all these, like, like, separate apps, there were a lot of companies that we funded.

Yep. That were either going to be crushed or not by the next Google product. Yeah. That's like the standard case of when you can bundle software in. I mean, this is this is what Microsoft did to Netscape. Right?

Like, once you can start bundling in software, especially in the enterprise, it's like people don't necessarily want to buy, like, 10 different solutions from 10 different vendors all the time. If you can offer a good enough product across several different use cases and bundle them together, enterprises often want that. I mean, famously, Dropbox.

was in that row potential roadkill. Right? Definitely. Drew because Drew actually talks about it when he comes back and give the dinner talks about the fear when with Google Drive and Google had this other product carousel thing. Right? Yeah. In fact, there is a time when.

Dropbox had launched. This was after the batch. And Google was working on Google Drive, but hadn't launched it. It was called G Drive. It was like the secret project inside of Google, and news of it leaked to the press. And the whole world just decided that like Dropbox's goose was cooked. Like, it was over. Google was gonna launch G Drive.

And because it was Google, they had infinite money. They were gonna do the same move that they're doing now, which is they're like infinite money at the product and give away infinite storage for free. How could a startup possibly compete with Google spending billions of dollars to give away infinite storage for free? Now it's infinite tokens. Yeah. And now it's infinite What.

are the big companies trying to do right now that maybe you should avoid doing? And the super obvious one is, well, OpenAI seem to have released four point zero, which is multimodal. And then it also simultaneously released the first version of the desktop app. But that version of the desktop app is merely sort of a skin on the web experience.

But if you put two and two together, surely, it's gonna look a lot more like her. I mean, they've been really all has that Scarlett Johansky just pulled up. Right? Yeah. They're like, oh, shoot. You know? Who knows? Are they getting sued?

Who knows? That's that's what Twitter says today anyway. But I think if you look at the details of that, you know, you can sort of sketch out what's going to happen with LLMs on the desktop. And the desktop is sort of has access to all your files, has access to not just that, but all of your applications. It has access to your IDE locally. It has access to your browser.

It can do transactions for you. That's starting to look like basically the true personal assistant that is directly consumer. And then that sounds like a whole category. Like, you know, we're gonna interface with computers and using potentially voice, and certainly, like, ex we will have the expectation of a lot of smarts. And.

that, you know, that seems like where that's where they're going, and that's going to be one of the fights. When I was thinking back to, like, this first era of companies, I guess one thought I had is that it was fairly predictable, actually, what Google would build. Not a % predictable.

Like, Dropbox was, like it was, like, unclear if Google would win that space, but, like, a lot of them were actually pretty obvious Yeah. In hindsight. Like, ad tech, for example. Like, all of ad tech just, like, never stuck around because it was, like, too strategic to Google and Facebook, and so they just had to own all of it. And, like, almost all of vertical search just didn't really survive.

It's pretty easy to imagine what the next version of OpenAI, like, product releases is gonna be. And if you can easily imagine that what you're building is gonna be in the next OpenAI release, you know, maybe it will be. Using that framework, it's like OpenAI really wants to capture just, like, the.

imagination, like, sci fi imagination of everyone. So it's like, yeah, it's like the.

general purpose AI system that you just talk to and it figures out what you want and does everything. It seems hard to compete with them on that. That's like competing with Google on search. Yeah. Right. That's clearly gonna be like the the core. Because those are early signs of why what ChatGPT is being used for as well. Just like a very, very rudimentary.

Right? Yeah. Which is the same thing with Google. They always wanted to own products where.

billions of people would all use the same product. Anything that was like that was gonna be really tough as a startup. Yep. When I think of it for products I use, like Perplexity, not a YC company, but Perplexity is a product I use a lot because it's much better for sort of research.

If I need to fix a toaster, it's way easier for me to type in, like, the model of the toaster into Perplexity and get back, like, specific links and YouTube videos and just the whole workflow. It was Diana who told me about it, actually. Yeah. I've been using it a lot as a replacement for actually my regular search. Yeah.

That's why I never I was trying to use Perplexity for a while, and I couldn't get it. And it was because I was trying to use it in the same way I would use, like, the OpenAI, the ChatGPT app. Oh. Yeah. And I was like, oh, but, like, ChatGPT is just so much better because I just, like, type in fuzzy things, and it figures it out, and it comes back with smart things.

And Perplexity just wasn't as good for that use case, but the specific, hey, I have this task that I want, like, source material back and links for, it works much, much, much better. It doesn't capture the imagination. Right? Like, OpenAI is not gonna, like, release a model that they demo the, oh, look.

Like, if you search it, like, gives you the links back or it, like, shows you the YouTube videos that it's referring to. The demo is not as cool. Actually, Gemini 1. 5 has that feature, and nobody really talks about the demos from Yeah. From Google IO.

They're kinda like, So maybe one way to figure out how not to be roadkill is to, like if you can build the valuable but unsexy things that OpenAI aren't gonna demo on stage because it doesn't, like, capture the sci fi imagination,.

you might survive. Yeah. That's definitely a whole line of thinking. Like, Google was never going to do Instacart or DoorDash's business. So or Uber's. So all of that was fair game, and all of those turned out to be, you know, Decacorn or, you know, even Airbnb, like $100,000,000,000.

company. See, other thing people always underestimate is just, I think, the size of new markets. I remember for a long time, people didn't believe LinkedIn could be a big company. Because it's like, well, like, why? Because Facebook won social networking. LinkedIn's just a social network. It's just gonna be a like, you have your work tab on your Facebook profile.

Like, why would you need something else? Same thing with Twitter. I remember when I first moved to San Francisco in 02/2007, some of the first people I met were the early Facebook employees. And they were like, they saw Twitter growing, and they're like, oh, yeah. We're gonna, like, release updates or something, and it's just like Twitter's gonna be done. It's just a feature.

But, yeah, it turned out like Twitter was like a whole other thing. Instacart and DoorDash, I think, are another great example of this. Because, again, I remember iPhone comes out, Android becomes pervasive. It's like, oh, there's it's just gonna be like Apple and Google dominate mobile, but there were all these things that they would never build. Same in this AI world, probably. Right?

There's all these things that the big companies are never gonna.

build, and we probably have more appetite for using multiple AI agent type apps than just like the one OpenAI one. And a huge, like, meta category that is basically almost anything that's b to b. Like, Google basically never built anything b to b. They, like, basically only built mass consumer software.

And so if you look at the YC unicorns, like, ton of them built, you know, some, like, b to b thing,.

like, you know, segment or something that, like, Google was never gonna build segment. That's just, like, not interesting to them. Ironically, because I think in b to b, people really underestimate the human part of it.

Like, so much of it is actually the sales machine, and it's being willing to go out and figure out who you sell to, do the sales, like, listen to someone, like, give you all the things they're unhappy about and note them down and take them back to your engineering team and say, oh, yeah. We used to, tweak this, this, and this, and this, and all these details. Right?

Like And build lots of, like, really detailed software to, like, handle all these obscure edge cases. Like, think of one of our AI companies at YC that's doing really well is called Permit Flow. And they literally just expedite the process for applying for construction permits. Not just for individuals, but for, like, big construction companies now as well.

It's like, yeah, like Really hard to imagine that being the next OpenAI release. Right? Like, hey, guys. We built a a feature for for filing your construction permits. Yeah. Can you, yeah, can you imagine turning up for your first day of work as an OpenAI engineer? And they're like, okay. You're gonna work on the construction permit workflow feature.

They they think it works that way. Well, I guess if you join those two ideas together, something interesting happens, though. It seems sort of inevitable sometime in the next two to five years, you know, assuming the OpenAI HRR digital assistant comes out. And then it's gonna be on your desktop. It will actually know everything about you.

It'll know what you're doing and know it'll know minute to minute what task you're trying to complete. And then it's conceivable, you know, if you match that with sort of a launch that I think they probably didn't invest enough into, which was like the GPT store. Mhmm. You could sort of imagine that might extend into b to b as well, and then they would sort of charge that vig.

But I think the the thing that I don't think is gonna work for b to b actually is I think there's a lot of sensitivity around the workflows on the data because they're highly proprietary,.

especially with spaces with fintech and health care. I mean, for good reasons, they should be very regulated and a lot of privacy data to protect the consumers. So I think the other area that we've been having also success for AI b to b applications has been in fintech.

We funded Greenlight They're doing KYC using AI to replace all the human behind that does a lot of the validation of consumer identities. Or we also have a GreenBoard. Right? That we'll start with Green. GreenBoard that was also doing a lot of the compliance things for banks as well. Yeah.

Bronco AI is doing it in AR, and there are a bunch more companies doing things in payments and just any of the boring.

day to day that, you know, someone, I mean, is sort of rote doing it. Mhmm.

This can just basically supercharge that and, you know, have one person do the work of 10. Yeah. And we call this episode better models, better startups. I think that is literally true for b to b companies where it's like the underlying models like b to b software business models are so much about how do I upsell, and, like, how do I make more money per customer next year than I did this year?

And it just hey. Like, every time the model gets better, you can just pass that along as, an upsell premium feature or an upgrade to the software, and your end user doesn't care. Right? Like, they just care about what the function the software can do for them. And so I think there's a world where the models keep getting better.

You've got your choice of which one to use, and the additional functionality you just charge more to your customers for, and you make more money. Yeah. That's definitely what we're seeing at YC. I mean, last batch, people were making $6,000,000 a year right at the beginning of the batch, and it ended up being north of 30,000,000 by the end of the batch. So that's some really outrageous.

revenue growth in a very, very short amount of time, three or four months. And that's sort of on the back of what, you know, a few people working on b to b software, you know, they can focus on a particular one that makes a lot of money. And then people are willing to fork out a lot of cash if they see ROI.

pretty much immediately. There's not as many founders working in this area as there should be given the size of the opportunity. Like like, to your to your point, Harj, like, people often underestimate how big these markets are. Like, using LLM to automate various jobs is probably as large an opportunity as SaaS, like all of SaaS combined. Right?

Because like SaaS is basically the tools for the workers to do the jobs. The AI equivalent of SaaS is like it it just does the jobs. It's a tool plus the people. Yeah. So like, it should be just as large. And.

yeah, there should be, like, a lot more people working on this. So there might be, you know, billions to trillions of dollars per year going into transactional labor revenue that's on someone's, you know, sort of, you know, cash flow statement right now. Yeah. But it'll turn into software revenue at 10 x,.

which will be interesting for market caps over the next ten, twenty years. I was doing office hours with a start up this morning that asked me this question about, hey. Like, you probably saw the GPT four o launch. Like, should we be worried about it? Yeah.

My reply was, you should be worried about it, but you should be worried about the other start ups that are, like, competing with you because, ultimately, it's all of the stuff we're talking about, it's whoever builds the best product on top of these models with all the right nuances and details is going to win, and that's going to be one of the other startups in the space.

So I just think the meta thing as a startup now is you have to be on top of these announcements and be kind of know what you're gonna build in anticipation of them before someone else does versus being worried about OpenAI or Google being the ones to build them. Let's talk a little bit about consumer.

because we did talk about what could be potentially roadkill for consumer startups if you're going against basically assistance, some sort of assistant type of thing. OpenAI is hinting well, strongly direct, and they're going in that direction. What about opportunities for consumer AI companies? What are those those things that they could flourish?

Well, here's an edgy one. Anything that involves legal or PR risk is challenging for incumbents.

to take on. Microsoft giving money to OpenAI in the first place, you could argue, was really about that. Mean, when image models image diffusion models first came out of Google, they were not allowed to generate the human form.

for PR and legal risk risk reasons. This is a large part of what created the opportunity for OpenAI in the first place as Google was too scared to jeopardize the golden goose by releasing this technology to the public. The same thing could probably be true now for startups.

Things that are increasingly edgy are often the places where there's great startup opportunity. I mean, things like Replica AI, which was AI NLP company working in this space for many years even before LLMs were a thing, still one of the top companies doing the AI boyfriend or girlfriend.

And the wild thing about Replica is that they've been in touch with their sort of AI boyfriend or girlfriend for many years. And earlier, we were talking about, you know, a million token context window. You can imagine that virtual entity knowing everything about you, like, for many, many years, like, even your, you know, deepest, darkest secrets and desires.

I mean, that's pretty wild stuff, but, you know, it's gonna look weird like that. And, you know, people might not be paying attention. I mean, character AI has really, really deep retention, and people are sort of spending hours per day sort of using things like that. So, you know, whatever happens in consumer, it might be non obvious, and and it might be very weird like that.

So there's a lot of kinda more edgy stuff around.

deepfakes that are applied in different different spaces. So there's a company that you work with, Jared, Infinity AI. Right? Yeah. Infinity AI lets you turn any script into a movie,.

and that movie can involve famous characters. And so it, like, enables you to make famous people say whatever's in your mind, which is edgy, which is part of what makes it interesting and cool. Google would never launch that. Google would never launch that. And I think even, you know, the the same move that OpenAI did to Google, which is being willing to release something that's really edgy.

Well, OpenAI is now the incumbent guys. They now can't release super edgy stuff like that anymore. We're gonna see a lot of that during election season in particular. Right? Because it's interesting when you think about it. Like, anything.

that's on the, hey, like, I am this is explicitly like a famous person. This is explicitly using the likeness of a famous person for profit is is going to get shut down. On the other end, have like, and if I make a meme with Will Smith and some like a caption, like no one's gonna sue me for that. And a lot of this content is like right in the middle. Right?

It's like, I'm not trying to build like a video that's literally, I want people to believe that it's like these people saying these things. But what if it's like A joke about a joke or a satire? Like, where does that fit? And yeah, you can't see, you can't imagine Facebook is gonna roll this out on Instagram anytime soon. Right? Like, they'd wanna they wanna stay well clear of that.

But You're already seeing this version of memes sort of two point o that are basically deepfakes that.

are making the rounds, and they're becoming viral tweets. Right?

Hey. Why don't we close out by going to a question that one of our audience asked us on Twitter? So thank you, Sandeep, for this question. The question is, what specific update from OpenAI, Google, Meta excited each of you and why? I'll give one.

The thing that really excited me about the OpenAI release was the emotion in the generated voice. And I didn't realize how much I was missing this from the existing text to speech models until I heard the OpenAI voice.

Oh, a bedtime story about robots and love? I got you covered. Once upon a time, in a world not too different from ours, there was a robot named Byte.

It's amazingly better compared to the incumbent text to speech models because it actually knows what it's saying. The existing ones, by contrast, sound so robotic. They, like they're totally understandable, but they're just very boring to listen to. And the OpenAI one, it felt like you were talking to a human. My one was the translator.

demo. The idea of basically having a live translator in your pocket. It it's personal for me because I my wife is Brazilian. Her parents don't speak English. And so I've been learning Portuguese, but it's coming along very slowly. The the idea of having just like a translator that's always in my pocket that makes it easy for me to communicate with anyone anywhere in the world is really exciting.

Hey, how's it been going? Have you been up to anything interesting recently?

It's a massive idea. I mean, it could change the world. You could go live in a foreign country where you don't speak the language. It it, like, it has huge consequences.

Yeah. Douglas Adams, Hitchhiker's Guide to the Galaxy Yep. Made Real is a pretty cool one. I guess for me, what's funny about four point is it sounds like maybe it was actually just a reorg. Basically, there was a reorg at OpenAI, and they realized they want all of the teams rowing in the same direction.

And then what that means is probably really good things for both their assistant desktop product, but also eventually robotics, which might be a really big deal down the road. This Chinese company called Unitree announced a $16,000 humanoid biped robot, though Twitter warns me that it's another $50,000 if you actually want open API access.

Previously, they made a hundred $14,000 version of that robot, But I think unified models means more and more likelihood that practical robotics is, you know, actually not that far away. Famous last words, of course, we've been saying that pretty consistently for many years in a row, but this time it's different. I think for me, maybe a bit more of a technical one. I know it doesn't sound too.

too fancy, but really the half the cost is like a huge thing.

And if you extrapolate that, what that means is probably a lot of these models are hitting some kinda asymptotic growth of how much better they can get, which means also that they're becoming more stable, and it can open up the space for actual custom silicon to process all of these and enable a lot more low power processing to enable robotics and build a device that you mentioned and actually have it in your packet and not be tethered to the Internet.

So all these things that we could perhaps see excitement of new tech product releases because I kinda miss those day when every product tech demo was, like, very exciting. Now it's just, like, kinda like a feature. True.

We could be excited about new things coming up. Well, we're gonna be really excited to see what you guys all come up with. That's it for this week. We'll see you next time.