Every time there's an opening eye product release now,
it feels like there's a bunch of startups waiting with bated breath to see whether opening eye is going to kill their startup.
This is actually a really crazy moment for all startups, adding more types of modalities and more capabilities per model, the better option.
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 Lightcone.
I'm Gary,
this is Jared,
Harge, and Diana, and 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-4-0,
it's Gemini 1.5.
Harge, 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 YC startups?
And when I was watching the open AI 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 chat GBT was, 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, how will they compete?
open AI for these users.
What did you think?
Even we take it back, like do consumer products win from like first principles?
Like is it more about the product or the distribution?
And how do you compete with open AI on either of those things?
Yeah, that's a great question.
I 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 4.0, you basically just, fault, every generation.
And that's really, really powerful.
It that I think we're entering this moment where 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 Cloud 3 really is at 100 or, you know, the next few models end up being closed.
through the 110, 120, 130, this is actually a really crazy moment for all startups.
And most interesting thing is 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, that reasoning alone could not do.
So adding more types of modalities and more capabilities per model, the better off every startup is.
I mean, the cool thing about 4.0 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 opening 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 to do as 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 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 coincident, or not, that OpenAI's announcement was two days before, one day before Google.
What's the difference between the sort of under the hood the way that GPT-40 works and
then Gemini 1.5 works and do you have any opinions on...
Their relative strengths.
Yeah, so the thing about 4.0 why was so interesting it was adding the speech modality and also video processing on top of a text and the way they do that is still primarily
a text based transformer model underneath basically GPT-4 and
What they've done is bootstrap and added modules so that it has different copads to handle this different type of data.
OpenAI famously also implemented and launched Whisper, which 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-4, and they also bolted Dali and they combined these.
And that became 4.0.
So this is why in terms of the reasoning capabilities 4.0 isn't Better per se than 4 by any margin.
So it's how it works It's kind of adding modules how to describe it on the white paper the difference versus Gemini 1.5
Which actually at on the technical aspects and merits.
I'm actually more excited by the Gemini one Huh,
I know it's counterintuitive because 4.0 and opening eye 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 Morgan happy birthday to Jordan
Google IO kind of
missed but in terms of reading the white paper was interesting about Gemini 1.5 is that it's actually
a true mixture of experts 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 whole network activates a path for these different data types.
So instead of the OpenAI model that has kind of modules, this one truly is a one all model.
And it does is,
The 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 I 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
image and video and train this giant thing in a distributed cluster they have
TPUs like they're I think it's their fifth generation now and it's pretty cool done.
Is that the first big model release that's using make sure experts?
I think they talked a bit about it on the previous one,
but everyone was a bit disillusioned after the demo of the duck was not real.
It is a duck.
But this one would describe better.
I mean, the interesting thing is that I think this time they learned their lesson and I think it's actually working.
And the other cool thing about Gemini is it has a context window of a million tokens, which is huge.
The GPT-40 is 128,000.
So imagine what you can do with that, because that's about...
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 million token window which brings a question for all of you what does that
mean for startups especially a lot of the startups that were funding with
infrastructure that do a lot of 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 whether the data is stored are still going to want some sort of RAG system, right?
Like they want the data stored somewhere, they 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 I guess as people just...
a massive context window, because then you can start building the kinds of consumer apps people are excited about, right?
Like 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 Ollama 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 hobbyist and Reddit are doing a lot of it's just trying get like your personal AI
That's got all the information on you, but if you had like a infinite context window, you would 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 kind of see some version of this even now in a chat GPT,
if you go into the settings under 40, it actually now has a memory.
And so you can actually see a inside chat GPT.
I was just using it to sort of generate some like, where's Waldo images for my son.
And I 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, 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 sort of repeated it four or five times.
And then I went and looked in my settings.
And it was like, Gary really doesn't want deformed faces in his generated images.
because we should also try not to use red.
It was interesting to see that literally from even maybe 10 or 15 different chat interactions.
I was getting frustrated, but it was definitely 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 sort of pulled out from your interactions thus far,
and you could sort of delete it as necessary.
Maybe an infinite window doesn't necessarily mean that the retrieval is actually accurate.
And is more anecdotal in practice from what founders have told.
what the actual research paper benchmark gets, which a very kind of lab setting.
So in practice,
I do tend to agree that a rag 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 licked out over the wire.
and the other thing is, yeah, maybe they're still more accurate to do it on your own when you really want that very precise information.
I think you still need RAG.
And I think the analogy I like to think about this is sort of like processors.
Back in the day in the 90s as when Moore's law was actually Moore's law is scaling
It was not just a CPU processing speed getting faster, but also memory cache levels.
We're also getting bigger and bigger But now more than 30 years later
We still have a very complex architecture with how we do different kinds of caching for retrieving data out of like Databases at our databases.
You maybe like a fast memory store with like red is for high availability And then you still have things store in your browser cache is still very much lots of layers of how things will
be catch 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 thing about the context window I mean Gemini may have the team may have already fixed this by now, but certainly a lot
of 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 you'd rather have 128k context window that you knew was pretty rock solid rather than a system where 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 half a million of the tokens.
And like...
probably fixable.
I can't imagine that that's a permanent situation for a million or 10 million token context window,
but something that we're seeing from the field for now.
Also in enterprises, in business use cases, people a lot about what specific data is being retrieved, who's doing it, logging all of this.
stuff and permissioning around data.
So you can imagine having some kind of, yeah, a giant context window is not necessarily what you want in enterprise use case.
You probably want empty other 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 stay.
I was really encouraged what you said actually about how the Google technology is maybe better than the OpenAI itself.
It feels very googly, actually.
They've got the best technology, but they just don't know how to get the polish around it, correct?
That means OpenAI does not have this leap forward, unassailable tech advantage.
Google has something comparable, then should expect to see, like, anthropic come in, we should expect to 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
system of model routers and observability ops software around this stuff just keeps progressing really quickly.
It's funny,
my initial reaction whenever I hear like the model releases is not to worry for the startups actually so much because they're all right.
Like we never talk about how reliant they are in 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 non-monopoly pricing,
which means that you know a thousand flowers can actually bloom.
Like startups can actually make choices and have gross margin of their own and I'd much rather say 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 course that is yet TBD.
We don't know when llama three with 400 billion 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, internships.
who has paid Nvidia more money in the past year.
Meta apparently is number one by 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 of 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 nets to actually compete with tech talk because to good recommendations on Instagram reels.
That's just like very classic tech innovation and disruption, right?
Like they're basically worried about competing with TikTok.
They stop pile a bunch of GPUs and their turns out the GPUs are just really
valuable for this like completely different use case that's going to change the world.
Jared, like on that, you know, 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 open AI, maybe.
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 multi-product company.
Do you see any similarities or differences?
Yeah, it 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 going to 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 three of us in the like 2005 to 2010 era,
there were all these companies who were innovating in the same idea space as Google and Facebook building like related products and
services where the big question was always like what happens if Google does this?
And when investors, that was like the main like a big question that they'd always get from investors is like oh.
people going to do this.
The best response to that, by the way, was like, well, what if Google gets into VC, which he did?
And that's a great ECL.
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 competing with Google in a very head-on way, which was, hey, we're going to build a better set.
And YC definitely funded a lot of companies trying that.
And feel like the approach people would go after was the vertical.
In January or so, we're going to build a better Google for real estate, for example.
Some of those made it.
Did they?
I mean, which one?
Well, you I mean, argue that something like a Redfin or Zillow clearly did.
have vertical access to data, and for a kayak for travel, I guess.
Or Algolia for company enterprise search.
For yeah.
That's true, OK.
There's, yeah, I hadn't thought of, yeah, I thought of Zilow as a search engineer, but yeah, it's essentially that.
It's exactly that.
It's vertical search.
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, or have to basically make money in all these other ways.
You completely different.
It look at all Google, yes.
And the data integration is very different.
You have to really poke and connect to MLS, and a regular search engine wouldn't just work with that.
PageRank wouldn't necessarily work with MLS.
Yeah.
Redfin is 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 that respect like...
Those are interesting consumer scenarios.
Ultimately, a 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 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 on against Google for like the better general purpose search engine just got crushed.
And in general, most of the vertical surgeons nothing that looks anything like Google worked.
The ones that I remember the most were more ones that were in the vein of Google Apps.
Like Google expanded beyond search and started launching Google Docs and Sheets and Slides and Maps and Photos and all these like separate apps,
there were a lot of companies that we funded.
Yep.
That worked.
were either going to be crushed or not by the next Google product.
Yeah, that's like the Santa case that when you can bundle software in, I mean, 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 and surprises often on that I mean famously a drop box was in that row
Potential rope kale right because and drew because Drew actually talked about it when he comes back and give the dinner talks
About the fear when with Google Drive and Google has his 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 yet,
it called GDrive,
it like the secret project inside of Google,
and news of it leaked to the press,
and the whole world just decided that Dropbox's goose was cooked,
like was over,
Google was gonna launch GDrive,
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 it start to possibly compete with Google, spending of dollars to give away infinite storage for free?
That was infinite tokens.
Yeah, and now it's infinite tokens.
big companies trying to do right now that maybe you should avoid doing.
And super obvious one is, well, OpenAI seemed to have released 4.0, 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.
on the web experience.
But if you put two and two together, surely, it's going to look a lot more like her.
I they've been really shooting them.
Dental Scarlett Johansson voice.
And just pretty loud, right?
Yeah, they're like, oh shoot, you who knows?
Are they getting sued?
Who knows?
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.
You know,
we're going to interface with computers and using potentially voice and certainly,
like, we will have the expectation of a lot of smarts and that seems like 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 100%
predictable, like was like, it was like unclear if Google would win that space, but like a lot of them were actually
pretty obvious 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 release is going to be.
And you can easily imagine that what you're building is going to 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 the 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 do 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 going to be like the core.
Because those are early signs of why what chat GPT is being used for as well.
Just like a very, very rudimentary, right?
Yeah.
They always wanted to own products where billions of people would all use the same product.
Anything that was like that.
that was gonna be really tough as a startup.
Yeah.
When I think of it for products I use,
like perplexity, nor do I see 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 Diana who told me about it actually.
I've been using it a lot as a replacement for actually my regular search.
Yeah, that's what I never, I was trying to use perplexity for a while and I couldn't get it.
And use it in the same way I would use like the open AI,
the GBT app,
and was oh,
but chat GBT 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 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.
or it works much, much, much better.
It capture the imagination, right?
Like, OpenAI is not gonna release a model
that they demo the,
oh look, like if you search it, it gives you the links back, or it shows you the YouTube videos that it's referring to.
The is not as cool.
Actually, Gemini 1.5 has that feature, and nobody really talks about the demos from that.
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 going to 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 Instaculture.
or DoorDash's business,
or Ubers,
so all of that was fair game, and all of those turned out to be deck of corn, or even Airbnb, like $100 billion company.
The other thing people always underestimate is just size of new markets.
I for a long time, people didn't believe LinkedIn could be a big company, because they're like, well, why?
Because Facebook, why?
social networking, LinkedIn's just a social network.
It's just going to 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 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 status updates or something and just like Twitter's gonna be done is 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 as 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 open AI one.
And a huge like meta category that is basically almost anything that's B2B.
Like Google basically never built anything B2B.
They like basically only built mass consumer software.
And so if you look at the YC unicorns,
like a ton of them built,
you know, some like B2B thing, like, you know, segment or something that like Google was never gonna build segment.
That's just like not interesting to them.
I'm wondering because I think in B2B, people already underestimate the human part of it.
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 usually like tweet this, this, and this, and this, and all these details, right?
And build lots of like really detailed software to like handle all these obscure edge cases.
Like, I 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.
And just for individuals, but like big construction companies now as well.
And it's like, yeah, like really hard to imagine that being the next OpenAI release, right?
Like, hey guys, we a feature for filing your construction permits.
Can you imagine turning up for your first day work as an OpenAI engineer?
And they're like, okay, you're going to work on the construction permit workflow feature.
I 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,
assuming the open AI,
her digital assistant comes out and then it's going to be on your desktop,
it will actually know everything about you,
it'll know what you're doing and it'll know minute to minute what task you're trying to complete and then it's conceivable if you match that with a launch that I think
they probably didn't invest enough into, which was like the GPT store.
You could sort of imagine that might extend into B2B as well, and then they would sort of charge that VIG.
But I think the thing that I don't think is going to work for B2B actually is I think
there's a lot of sensitivity around the workflows and the data because they're highly proprietary, especially with spaces with FinTech and health care.
I 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, B2B applications has been in FinTech.
We found that.
Greenlight is 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?
The-doc with greenboard.
That was also doing a lot of the compliance things for banks as well.
Bronco AI is doing it in AR, and there are a bunch more companies doing things in payments.
the boring day-to-day that someone,
I mean, is sort of rote doing it, this can just basically supercharge that and have one person do the work of 10.
Yeah.
We call this episode better models, better startups.
I think that is literally true for B2B companies, where it's like the underlying models, a B2B software business model.
models are so much about how do I upsell?
How do I make more money per customer next year than I did this year?
And it's just,
hey, 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.
They care about what the software can do for them.
And so I think I think a world where the models keep getting better,
you've got your choice of which one to use and the additional function of how to use is charge more customers for and you
make more money.
Yeah, that's definitely what we're seeing at YC.
Last batch,
people were making $6 million a year right at the beginning of the batch and it ended up being north of $30 million 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 a few people working on B2B software,
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, to your point, as hard people often underestimate how big these markets are, like, using LM to
automate various jobs is probably as large an opportunity as SaaS, like all the SaaS combined, right?
Because SaaS is basically the tools for the workers to do the jobs.
The AI, like, 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 billions to trillions of dollars per year going into transactional
labor revenue that's on someone's sort of cash flow statement right now.
But turn into software revenue at 10x.
which will be interesting for market caps over the next 10, 20 years.
I was doing office hours with a startup this morning that asked me this question about, hey, you probably saw the GPT-40 launch.
Should we be worried about it?
My reply was,
you should be worried about it,
but you should be worried about the other startups that are 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
So I just think the meta thing as a startup now is you have to be on top of these announcements
and we kind of know what you're going to build in anticipation of them before someone else
does versus being worried about OpenAI or Google being the ones to build them.
It's talking a little bit about a consumer because we did talk about
about what could be potentially rokal for consumer startups if you're going against basically assistance,
some sort of assistant type of thing, opening eyes, hinting strongly direct and they're going in that direction.
What opportunities for consumer AI companies?
What about 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.
I when image models,
an image diffusion models first came out at Google, they were not allowed to generate the human form for PR and legal risk reasons.
This is a large part of what created the opportunity
for OpenAI in the first place is Google was too scared to jeopardize the golden goose by releasing this technology to the public.
This same thing could probably be true now.
things 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 LLM's were a thing, still one of the top companies doing the AI boyfriend or girlfriend.
And wild thing about Replica is that they've been in touch with their sort of AI boyfriend or girlfriend for many years.
And 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 it's going to look weird like that, and people might not be paying attention.
I character AI has really, really deep retention, and people are sort of spending hours per day sort of using things like that.
you know, whatever happens in consumer, it might be non-obvious and it might be very weird like that.
So there's a lot of kind of more edgy stuff around deep fakes that are applied in different different spaces.
So there's a company that you work with Jared, Infiniti AI, right?
Yeah, Infiniti AI lets you turn any scripting.
and that movie can involve famous characters and so it like it enables you to make famous people
say whatever's in your mind which is edgy which is part of what makes it like interesting and cool
Google would never launch that you would never launch that and i think even you know the the
same move that opening i did to google which is being willing to release something that's really
edgy well opening i is now the incumbent guys they now can't release super edgy stuff like that
anymore we're going to 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 make this is explicitly
like a famous person or this is explicitly using the like list of a famous person for profit is
is going to get shut down on the other end you have like and if i make a meme with will smith and
some like a caption like no one's going to sue me And 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, a joke or a satire, like where does that fit?
And yeah, you can't see, you can't imagine Facebook or is going to roll this out on Instagram and Instagram anymore.
And you don't want times, they want to stay well clear of that.
You're already seeing this version of meme 2.0 that are basically defixed that are making the rounds and they're becoming viral tweets.
Why don't we close out by going to a question that one of our audience asks us on Twitter.
So thank you Sandeep for this question.
The is, what specific update from OpenAI, Google, Meta excited each of you and why?
I'll give one.
The 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.
until I heard the open AI voice?
Oh, a bedtime story about robots and love?
I you covered!
Once upon a time, in a world not too different from ours, there was a robot named Bite.
It's amazingly better compared to the income in Texas East Malances because it actually knows what it's saying.
The existing ones by contrast sounds so robotic.
They they're totally understandable, but they're just very boring to listen to.
And the open AI 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's personal for me because
my wife is Brazilian and her parents don't speak English.
And I've been learning Portuguese but it's coming along very slowly.
The 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, has it been going?
Have you been up to anything interesting recently?
It's a massive idea.
I it could change the world.
You go live in a foreign country where you don't speak the language.
It huge consequences.
Yeah.
Douglas Adams, Hitchhiker's Guide to the Galaxy, Made Real is a pretty cool one.
I guess for me, What's funny about 4.0 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 $114,000 version of that robot.
But I think unified models means more and more likelihood practical robotics is 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 bit more of a technical one.
I know it doesn't sound.
too fancy, but really the half the cost is like a huge thing.
And you extrapolate that,
what that means is probably a lot of these models are hitting some kind of asymptotic growth of how much better they can get,
which means also that 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 kind of missed those days when I tech demo was like very exciting.
Now it's just like kind of like a feature.
We 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.
