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Category: Startup Insights
Tags: AIcompetitioninnovationstartupstechnology
Entities: AIAI startupsCampfireCoinbaseDoorDashFlock SafetyNetsuiteOpenAIPeter ThielPlaidSpaceXUberYC
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If you only want to work on things that are hot, you're going to find yourself working on derivative ideas that end up being obvious, that end up having 5, 10, 100 competitors. It's great for that number one, number two, but guess what?
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Like number three through number 98 of all the people in that market, their startups are going to die. Nine out of 10 people might tell you you're stupid or crazy, but then one out of 10 people might be exactly the person who believes what you believe.
run out and try to find things that humans really
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desperately want and need and then you'll figure out the rest. [Music] Welcome back to another episode of the light cone.
As Peter Teal says,
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competition is for losers. And as we look at all the AI startups we're working with, increasingly AI competition is back.
So how do you actually deal with it? You know, well, I think if we go back to 01 again, uh what
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we would say is we're looking for how do you think from first principles, how do you actually deal with that competition by being contrarian and right? Har, how do you think about it?
Yeah, something I've been thinking about recently is um probably just over a year ago, we talked
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about how we were finding it um easier than ever to fund companies that were looking for a startup idea and that they could pivot and find an idea. And it felt like the two causes of that were one there was just so much green field like AI was new.
Um there were so many
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verticals to go after that hadn't been picked over yet. And the models themselves were changing like there was such a there was a step function increase in new models every few months that just caused the idea space to expand.
So you could both there was green field and you could always count
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on a ch big change coming that would shake things up um and create more ideas. And I think clearly we've seen the benefit of that like there are so many vertical AI agent companies that are doing tremendously well.
But I kind of feel like the vibe is shifting a little bit now where when I'm talking to founders doing office hours trying to
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help them find ideas, it's not as easy as hey like go figure out like a a vertical where there's like a workflow to automate like insurance or banking because there's multiple startups in each of these verticals now. Um and there actually hasn't been like a model that's shaken things up um for a while
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now. And so I think it's becoming more important to think about what's your actual like unique insight that is going to enable you to find a good idea and what's like the contrarian bet you're going to make um so that you can actually stand out from all of the competition.
>> So how do you actually discover a secret
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if you will, you know, something that you know and believe that nobody else believes yet? >> Maybe we could do some case studies.
So maybe like why don't we talk about some of the companies we've seen um that made a contrarian bet when they were coming up with their idea initially and like maybe that will shake out some things
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people can learn from. I think a helpful comparison to the previous technology shifts that we've had in in the world.
Like if you look at the history of when we invented the internet, when we invented uh the smartphone, each time
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there's a, you know, roughly two-year window where it was really easy. There's there's essentially like a like a modern day gold rush, right?
Where like the a whole new class of startup ideas like just opened up. Everybody rushed in, launched all the obvious ideas and then
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you know roughly two years after that the obvious ideas begin to have been picked over and you have to like look deeper for a secret. >> I mean I think it's deeper than merely like is it obvious or non-obvious.
Non-obvious sounds like in your body might feel like you know neutral but
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actually non-obvious feels dangerous and scary like I could devote my life to this waste 10 years on this and have no outcome. And you know, I guess the way that manifests, I think, is that um there's just like ways of thinking that
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are out there that we pick up in media or in like sort of the conversations we have with the people around us that um we don't examine. So, you know, I was doing office hours recently with uh someone in the marketing space and they came to me and said, you know what, like
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nobody has ever made a company large enough doing like just what we were doing. The weird thing was you we have AI now.
Like this is sort of the perfect moment where no one's doing it. That means there's no competition.
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In fact, everyone else who has been in the space uh you know prior to the AI revolution has failed. So there just so many dead bodies stacked up around this idea.
You how do you know that you're not the right person to go and actually
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blow this wide open? Especially because there is still this giant new capability.
You know, we have age of intelligence. The rocks can talk, they can think, and they can actually do the work of real human beings.
You know, you might be one of the only people who has
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the guts to go into that space and actually apply this. And his numbers are going up.
The customers are sort of pulling it out of him already. Um, sometimes I'll talk to people about his startup and people say, "Oh, I need that tomorrow." So, you know, this is like
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the perfect example of, you know, sometimes the market will be giving you indications of product market fit, but then you'll be using these mental models from, you know, are people on X talking about it or is Techrunch talking about it or, you know, what will my friends
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say at a party? They'll say like, oh, you're working on a tarpit idea.
So, it sort of interacts with this idea that like competition is back and competition is bad. It's that like the obvious things uh you know you're just going to pile up 10 20 like you know some of these spaces seem like they're like five
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or six or maybe a dozen or like two dozen different startups all of which have a real shot at it and then now's sort of the moment to again focus on the secret again. Yeah, Jared, to your point, the like new tech platforms create this like two-year window.
And
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so, we were talking about this earlier, like both mobile, like iPhone launches, Android comes soon after it. Um, in the immediate like year or two before that, like there were a lot of like sort of obvious ideas to go after like photos, Instagram sort of grew out of that era.
But like the actual big winners turned
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out to be companies like Uber, Door Dash, and Instacart. >> Those were so nonobvious.
It's like folks who weren't around then don't remember how nonobvious that was. Like when the iPhone came out, there was like a million articles, a million like social media posts about like what kind
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of companies you could build with the iPhone. And like I don't think a single person thought that like Uber would be the consequence.
I think the one in this context is really interesting to talk about actually might be Door Dash because um sort of essentially what we're saying is right now competition is back. There is more competition to find a good idea AI idea than ever.
Door Dash
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entered a very crowded space. Like food delivery, food delivery in general had been around for a while.
Mobile was probably like um a catalyst for even more food delivery apps. So by the time Door Dash launched, you already had Postmates.
Um iterations of it. I was just talking and Seamless were huge
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companies. >> There's actually a YC company, Order Ahead, that Gary and I worked with that was actually doing quite well at the time.
Um wasn't actually delivery. It was um let you pick food up from the restaurant which at the time actually pick up.
>> It seemed like a bigger market maybe than food delivery at the time. >> They were farther ahead like they had
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way more locations for instance. >> Yeah.
>> I mean interestingly like it I think it happened even before that with uh it was uh Lyft before and they were called Zimride before and then funny enough YC had a company called Ridejoy >> that was exactly a competitor against
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Zimride >> and they were like neck and neck. >> Neck and neck.
Yeah. And um Zimride decided to try to do uh peer-to-peer local uh rides.
You know, Zimride and uh Ride Joy were sort of both picking off people from Craigslist. And the idea
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there was like, oh, I'm driving to LA this weekend. You know, this platform would match you with other people and help you get, you know, a ride to Tahoe from SF or LA.
Uh and then suddenly Zimride realized well wait a second like what if we actually did this at a much
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smaller scale because we have smart everyone you know 70 80% of people out there started having smartphones. So instead of having this like long drownout email back and forth to like meet at this time so we could get a ride and then you know I'll pay up for gas money.
It became something that could be
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used every single day uh for short hall rides. And that was like sort of the first moment that people thought, well, maybe you could have a lot of people more or less like a a mobile workforce um entirely driven by the phone.
And then I think that's that happened like
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sort of contemporaneously to Uber with their black cars. But the funny thing was, and maybe this rhymes with uh the lot often the office hours that we have to do all the time.
It's like I remember meeting with the Ridejoy guys. I was like, "Hey, this Zimrite thing seems like it's working pretty well.
We're
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seeing um a lot of other companies that you know I think we had uh just met a poor Vamea at Instacart and he was doing it for grocery delivery. Basically the market was sort of pulling it out of all of these startups and that was exactly the moment where um interesting
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interestingly I think those founders said we're worried about uh the laws. It seems illegal and we don't want to do anything illegal.
>> And they weren't wrong. I remember talking to the Lyft founders um the week before they launched Lyft and they were extremely worried that they would go to
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jail and they decided to like roll the dice and launch this thing anyway. I think a big reason why other people didn't launch Lyft and Uber before was that it's like it was basically illegal to do that and they were worried that they would go to jail.
>> What's funny now is uh it does seem like the world will sort of change the laws
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as it were if it turns out that um the end user, the end consumer wins by that much. >> Totally.
I mean, a lot of great startup ideas are sort of in this gray area of like the law is not totally clear. It's a little bit murky whether it's legal or illegal.
Even open AI is like that,
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right? I mean, they they crawled the entire web without permission.
You could argue that that's fair use or you could argue it's like massive copyright theft. I guess all of these sort of buttress this idea that like non-obvious is not merely like, you know, purely intellectually, you know, not clear that
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this is going to work. It actually is a little bit more subtle than that.
It's like, oh, I feel like that's might be a little dangerous. Like there might be something that, you know, I don't feel comfortable doing.
Um, and then really really great founders sort of sense that as actually uh signal. >> Coinbase was like this as well.
Were the
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Coinbase folks like I mean that's always operated in a gray area of legality. >> Coinbase to me feels a little bit less I don't feel like crypto was well understood enough for it to be clearly legal.
actually they they actually couldn't take that approach because they actually needed to get a banking partner
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um in order to even be like have the service launched, right? >> I would say that uh Brian started off recognizing that a lot of the use cases maybe way at the beginning part you had um cipher punks like people who were like radically into like libertarianism
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and this idea that you should not have centralized banking. Uh but they took it to an extreme where they wanted totally anonymous identities.
I mean it was more you know Bitcoin really very early on was a lot more Silk Road than what it is today. So I actually think that Brian
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Armstrong early was like the exact opposite of >> you I mean he was contrarian in another way. Like if you hung out with a lot of people who were really really into Bitcoin uh in, you know, 2010, 2011, 2012, the majority of people you ran across were cipher punks who said, "F
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the state, uh, you know, f the laws and we're going to, you know, have sort of this radical freedom through Bitcoin." >> And there's Brian Armstrong like doing deals with banking companies and working with regulators. >> Yeah.
That's what his contrarian bet
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was. it was that it's worth doing all of this like extra work at a for a time where it wasn't clear the market even wanted it.
Like it was very clear the cyber punks and like Silk Road all wanted like crypto and anonymous payments. It wasn't clear that regular people would.
And so why it didn't seem
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like a valuable thing at the time to go and talk to a bank and do a partnership and go through all the like KYC and AML laws because like you'd only do that if you thought that regular people would want to trade crypto at some point. >> And those things actually make your product worse, right?
like like forcing
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people to go through KYC like greatly increases the friction >> and beyond that they're like they're the exact opposite of what the current market what like the current market could not have been more enraged about like Brian and Coinbase's approach to crypto at the time. >> Yeah.
I mean, you're just going to run across people who say like it'll never
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work. But when markets are brand new and nent like, you know, often when you're that early, the thing that passes as, you know, obvious is uh clearly wrong actually.
So that might might have been like a very profound version of that.
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before anyone cancels us for telling saying that uh you know founders might get something out of doing illegal things like I actually don't think that people should by default go out and try to do illegal things like I think the through line is not that you should do illegal things or you know it's that you
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should think about from first principles what are the things that markets and people need >> totally >> and then I think basically ina in the case of you know Uberx and uh it became very clear within like sort of the first few months of that uh coming out
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especially in San Francisco where literally we have some of the worst taxi cab like sort of infrastructure and really difficult transit. I mean, basically like that came out of necessity.
And then the quality of life inside San Francisco went from here to
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here the second that you could actually just freely move around very easily without the uncertainty of like call a taxi cab and they would just literally not show up, >> not show up like half the time. I know.
>> And it's like how do you actually like live in a city and get around like you know that I I think that you know these
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services literally made San Francisco 10x more livable in a lot of ways. So you know I I think it's like don't just run out and do illegal things like that's actually clearly net bad like it's more what are things that uh people users want and then thinking from first principles also does actually involve
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thinking about well what are the downsides right um and how do we mitigate those things >> I think I think the through line is that the laws were dumb and didn't make sense and if you thought about it from first principles you'd realize that these laws were actually holding these particular laws were actually holding back society
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rather than helping it because they ridden in an era before the smartphone when like illegal taxi services were actually kind of a scourge of society because you'd have like random people driving around like claiming to be a taxi but then they'd like kidnap people and like there was no accountability and like tracking. But now that everyone is
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in like a smartphone based system, there actually is accountability and tracking and Uber is was like is actually able to operate it safely and the taxi medallion system that like ruled the country just didn't make any sense anymore in a smartphone era. >> Yeah, I think that's the key point.
The key point is, yeah, don't go out and do
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things that the law explicitly says you cannot do. But finding laws that were written in a time before some big tech shift that changes everything and just don't reflect reality can be really valuable.
I mean, again, going back to crypto, crypto is a prime example of this. Like you have lots of securities
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laws that were really well-intentioned and designed to like protect consumers from scams and um and buying things they didn't understand. And so like a big part of the SEC rule is like you have to break things up into like brokers and intermediaries and clearing houses, but a lot of the rules in the crypto world just don't make sense like um and so
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they almost have to be like rewritten why there's a lot of legislation going through now. But I think that's if you can find things where like the laws don't really cover the world as it exists today and you sort of brave enough to see what happens if you try that can be >> that's our ground.
The reality is like
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uh actually like that's the role of government like this is why we have a legislature. You know one of the things we're trying to fight for right now is for instance open banking.
So, you know, I think all of us sitting here and most of the people watching, I think, would take things like Plaid for granted. Like, of course, I want Plaid.
Like, you
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know, that's my data. That's my money.
And so, you know, what do you mean like a giant bank has the right to charge exorbitant fees uh to get access to that data. But, you know, that's being adjudicated right now.
like the Trump administration actually has to make some decisions and is an open comment period
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right now around uh whether or not you know a giant bank can prevent tiny startups like you know YC startups or the startups that people are going to start here. Can they charge a crazy amount or use their terms of service to actually block access to this stuff?
And
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so my hope is that you know a functioning government will sort of think about all these things. you know what the banks might be saying in order to get regulatory capture right now is that oh it's not safe we're thinking about the safety of the consumer but when you really dig into it it's like
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they just don't want people to be able to switch off of their banks you know they they don't want the money to go to a lower fee place like this is their moat right regulatory capture absolutely is one and then the thing that is cool about um a modern democracy I think
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people would debate whether we're we have that on the one hand. On the other hand, like you know, I think on in individual cases, that might be, you know, the wrong thing happens here and there.
But in aggregate, like over a long enough time period, if you can get your product and your service into the hands of enough people, you know, we do
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still live in a dem democratic society where, you know, representatives will vote and will like change the laws and that's very much good news. So first principles plus democracy equals open markets and freedom which like what that's what we're fighting for but it
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does happen. >> I guess when different question is what are some of the current contrarian things that founders should be looking at right now since what we're saying is there's more competition now as opposed to a year ago.
There hasn't been to your point harsh a big model improvement or
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one preview has been all about a year or so. That was the big stepping function for model capabilities and there hasn't been anything thus far.
I mean there's been incremental things with 03 and all that but nothing like that that makes it
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harder for startups to find new new ideas. What are the new things that are also in this gray area that founders can look into >> potentially one framework for finding them at least is not like a prescription for exactly what is to look at what are
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like emerging playbooks for ways to build startups that might be wrong or it might be time to flip them. Like to give you a concrete example like Door Dash I think was a prime example of this because when Door Dash started there was actually another YC company Spoon Rocket um doing food delivery where they would
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actually like cook the food um in these kitchens that were spread around the city. >> Yeah.
They operated ghost kitchens all over >> SF. Uh Sprig was another one right and actually in that moment those companies I think came out of this meme of like a full stack startup.
There was this period where it was seen as just
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building software is not ambitious enough and like the big opportunities are going to be in going full stack like don't just build a food delivery app actually have the kitchen and make the food and that's where all the opportunities were and I think that was becoming more established as like the playbook around 2014ish era and so in a
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way that like Door Dash is contrarian bet was actually say we're just going to do deliver we're just actually going to have like an app and a marketplace and we're not trying to be a full stack startup which was obviously the right bet in hindsight and So what might be some of the the sort of consensus playbooks for building AI companies that
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have emerged over the last year that like might be wrong might be worth taking the other side of? >> I guess when I can think of that has come up there's the compound startup notion that Parker Conrad from Ripling really made it popular.
I think it
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hasn't been as adopted. I think people try but it's actually in practice really hard.
I think it's one of those that for certain AI startups, it is actually possible to execute on it and not have to wait two years to ship the product, which is what I think Ribbling roughly
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took for the first version. >> Maybe an example of a startup is uh Campfire.
This is a YC company that's building basically a AI native version for CFOs to compete versus Netswuite. And it turns out that Netswuite is a
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pretty big piece of software. It's very hard to compete.
can't just do like um kind of a point solution in order to be adopted. So one thing to reevaluate instead of doing the standard SAS approach of let's just build the best point solution that's actually built the
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whole thing which is not something typically done as an advice that we give for early stage startup because you just end up not shipping for a long time and not getting customer feedback. maybe in cases like this is the right answer and campfire they've been closing a lot of
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now big accounts and it's taking a bit of time to get there but it seems to be working. >> Yeah, they're killing netswuite which is wild like how can a startup that you know has a dozen people kill Netswuite.
This is like hard to believe on the one hand on the other hand like this is the timeline we're on. So the other thing that's cool about campfire that we're
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seeing in a lot of enterprise startups is uh you know with codegen increasingly you can actually bring the switching cost closer to zero and that was actually the you that's how you can how you know how can campfire switch someone off of Netswuite on a time frame that
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matters like even with the idea of a forward deployed engineer like before codegen like it'd be like six weeks of like writing custom like scripts to like convert one data a schema to another and you know often very specific like if if
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it's dynamic schema on one end that's a lot of custom work that's painstaking and if you get something wrong like the end result doesn't actually work and then you have a churned customer. So that's a a very like that's some good news for people especially doing very complex enterprise projects with like
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conversion like we're seeing more and more examples of enterprise sales that just wouldn't work or it would be you know 6 months to actually get someone to say yes and sign and then another 6 months to get a data uh a data conversion or data integration done. If
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the demos are very good, like that six months could be like two weeks. And then if you can codegen and get very very good at a suite of tools to convert like data from the schema to yours, you could have like time to value in like less than a month when it used to take a
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year. So that's like very good news for startups out there that are doing enterprise.
>> I think for deployed engineers, we've spoken about a lot, but like that might be one that's worth wondering if that's going to shift back as well. Like if you think like Palunteer obviously was like invented it was an incredibly contrarian
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thing at the time that they did it. This sort of like blur the line between consulting and and software.
It's now certainly become the adopted playbook. We talked about it with Bob Mcgru a few episodes ago.
Um >> and he was actually fairly skeptical of it himself even though he's one of the people who invented it which is interesting. remind us how say so when
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we see >> well he was saying essentially that like he thinks it's like being greatly overused and he would like thinks it should be used very sparingly and very unusual situations as opposed to being like a default playbook which is interesting. >> It has certainly become the default playbook.
It is actually working incredibly well. I think we're seeing
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like the companies are growing at like aggressive growth rates because they're employing the forward deployed engineer model. But yeah, if if I had to if I had to guess one thing that if I were trying to just purely pick what's like the contra like what's the one thing to be contrarian about like judged by just what's become the most entrenched
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default playbook it might be the forward deployed engineer approach >> actually yeah you have a good company that is flipping it on its head this gig ML >> yeah I think gigger is taking the concept of the forward deployed engineer which is everything you said that you
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you want to basically transform sort of like your customer schema and business logic into sort of your schema and and logic. >> It's like consulting work basically, >> right?
But instead of a human forward deployed engineer they they have they're just using codegen to do it actually they they've built their own AI forward
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deployed engineer and a big part of the reason they can win deals faster than their competitors is that the human deployed engineer still takes weeks to get the thing going which is like really fast compared to like historic enterprise consulting arrangements but like the AI FD can do it in minutes and
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so I think that's like the kind of the flipping that plate like it's not really an FD at all really it's actually just product um just like your customer inputting in specs and you like deliver them a product instantly. But I think that could be an example of a contrarian bet that will pay off really well.
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>> So I'm hearing a request for startup which is uh AI for AI FD >> maybe >> meta. It's just abstractions all the way down.
>> Flock safety could be a really interesting one to talk about. >> So the story of flock safety I remember
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I was still at initialized. We were looking at companies at demo day and then funny enough that morning um all the cars on my street in Noi Valley were broken into and you know it was a a professional crew.
They came in and uh broke into every car on our street. They
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took all the bags out the back of the car and then you know my house at the time had like a little al cove that was dark and so they brought all the bags into our driveway and then rummaged through them all and I you know had to cancel my meetings that morning to talk to the police and you know I had a Nest
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camera that was like captured the whole thing and I was like you can't do anything like it was a pro crew like they were three people it was like with mechanical like military style precision and the police basically said sorry you know unless you have a license plate. uh we can't do anything and so uh that
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made the first principles investment very easy for me because uh you know Garrett Langley came in he's founder from Atlanta he had a successful exit previously uh but this was hardware so they were selling a uh camera about this
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basically had a Raspberry Pi with a camera in it and then a solar array and uh you know computer vision had you know with imageet had been around long enough that you could run it at the edge in the device and solar had gotten progressed just to a point where it was just good
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enough that you could run these like you know sort of in perpetuity. And so that was his pitch like we're going to go to neighborhood groups like neighborhood associations and you know they already had started I think in Piedmont and in Atlanta in the Atlanta larger greater
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Atlanta area. There's a lot of things that um from a VC VC standpoint like VCs didn't like hardware.
Uh they don't like things that you know sell to potentially small markets. I mean I think in the investment memo I put together like I just called that out directly.
It was
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like, well, if you multiply out the number of neighborhood groups and the ACV for those ne neighborhood groups, uh, there's no way that I mean, maybe the max was like 50 or $60 million a year or something >> like that the actual TAM of this thing was like only $50 million a year.
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>> I mean, speaking as a VC, like maybe the best exhortation I can give to the people listening is like do not use that like you know, it's merely uh an indicator. It's like useful to know, but like neither investors nor founders
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should use that as like cross it off the list, right? >> And he was being run out of Atlanta, Georgia.
Right. >> Right.
So it's like three things that make it made it like basically unfundable. But, you know, I mean, that's why like I think I was hanging out with Brian Singerman at Founders Fund for a little bit and you know, one
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of the things we always talk about is the more rules you have about investing, uh, the more ways you can basically talk yourself out of making a lot of money in ventures. So, I don't know.
I mean, to the extent that that is useful for founders, like I think that that's true. Like, you know, we did a whole episode
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last episode about how, you know, you should be aware of the seven powers and moes, but it would be the stupidest thing in the world to use that as your only criteria for whether or not you should work on a company. And likewise, you know, I think increasingly even in our office hours, we find ourselves
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giving that advice with uh founders. It's you know it is much better to go from first principles from uh what does society need what do users need you know what ideas look could only happen now and then the rest like you can kind of figure out right like the same calculus
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you could do for Coinbase very easily like the you know total market for Bitcoin you know was not trillions of dollars it was like probably on the order of tens to hundreds of millions of dollars at the moment that Coinbase really started thinking and the coalesing around that. Some of this is
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the good news. It's that like the people who actually change everything are the founders actually.
And so if you only want to work on things that are hot, you're going to find yourself working on derivative ideas that end up being obvious that end up having 5, 10, 100
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competitors. And then, you know, it's great for that number one, number two, but guess what?
like number three through number 98 of all the people in that market are going to die. Like their startups are going to die.
So yeah, this is just a little bit of a profound version of that story. I mean, flock
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safety today solves 10% of all reported crime in the United States. It's >> crazy.
I feel like that's a generalizable lesson of flock safety, which is um at any time if you have some startup idea and you go to talk to a bunch of VCs about it, a lot of them will give you feedback. And I could
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imagine that if Garrett had gone and talked to a bunch of VCs, they would have told him like, "Oh, this isn't VC fundable, selling to local governments, hardware, like you should do B2B SAS." It's cool that he didn't like he ended up in a market where like he had very little competition perhaps because everybody else like thought it was too
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weird. >> I think if you're just really razor focused on your customer and the actual need, like it just became so obvious.
I mean, I literally got a flock safety in front of my house and even though it did not end up catching um that crack band of thieves, I felt way safe when I had
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it in front of my house. And um I think that that's what happened with their communities, you know, especially like I remember doing office hours with Garrett and he's like, "This is insane.
We actually caught um you know, someone who had kidnapped a kid."
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>> Wow. >> Right.
And so when you look at the actual human impact, I mean, going back to what we were saying earlier, it's like don't run out and try to do illegal things. Run out and try to find things that humans really desperately want and need and then you'll figure out the rest.
Honestly, like if you're just
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really really focused on people's problems and how severe those problems are, like you know, you'll figure out the rest. It's like you'll figure out the business model, you'll figure out the distribution.
And like you know, Flux Safety later had to figure out that these crimes that they were solving,
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they would be on the evening news. And all it took was, you know, a media team that would reach out to the evening news anchor and say, "Hey, by the way, this was uh solved by Flock Safety.
Here's some video that you can run that's B-roll of like, you know, this this
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crime was solved by this, you know, new technology." And then it started spreading virally. like one town would have a crime solved often like sometimes like pretty intense like violent crime and then literally the neighbor the the city next door like the police chief would say like what is this I need it
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right now and so you can't learn that from like reading blog posts or like going on X and like sort of or even just like asking Chachi PT about it like you actually just have to try a bunch of stuff and then everyone's story is very very different um but you I Garrett and
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the flock team are really unique in that respect. It's like just like first principles in terms of what you're building, first principles in how you get more customers, first principles in terms of what your business model should be.
I think that that's just a much better way to think about it. And then
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the the difference is that you can't sit in your chair sit sitting in front of a computer and uh divine these things per se. You actually have to get out of the house.
You actually have to talk to customers. You know, that's where like I think the goal setting at YC helps a
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lot, right? It wasn't like from uh a VC meeting.
I think Garrett sat and figured out that he needed to change his go to market. You know, I think that he probably looked at basically how do I work backwards from my growth goal?
And
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then he realized that uh selling in neighborhood groups wasn't going to be good enough. So one way or another, they really needed to get over to sell to city government, which sounded impossible, but they could definitely do it.
And that's that's been like one of the big engines of their growth. >> And just fast forward, what's their
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current valuation? >> Yeah, it's worth $7.5 billion now.
And um they're making way way more than $60 million a year. Um and it actually required a number of uh business model pivots.
But you know the core of what they built like you know what they built
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on demo day and what they show showed me was exactly I mean pretty similar like the like the tech itself is the same. Uh what they did have to do is figure out that they couldn't just sell to neighborhood groups.
They still sell to neighborhood groups but what really
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cracked it for them was selling it to um you know police departments and actually officially being used for it. Diana, do you have another example of places to look for contrarian bats?
>> I think one category is sort of the sci-fi founder is really going after
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ideas that most people are scared to build because they're just so freaking hard. And sometimes science and physics are laws may need to be rediscovered to be possible.
One example that really comes to mind is uh open AI. It wasn't clear
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whether AI was going to be a thing when it got when Sam started out of YC and it took many years. It seemed to be mostly a tinkering project for researchers.
They were publishing papers. They were doing kind of side quests.
I mean they had the uh Rubik's cube solver. They
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were solving Dota. And it was unclear how all these would come together to what OpenAI became today.
And also something that people forget is that when open AI launched it got mostly negative press. There were some people like a small like group of like techno
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optimist people who thought it was really cool but by and large most people especially the AI researcher establishment in academia and in other companies mostly just like was extremely negative on the idea that a bunch of
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like >> 20 and 30 something Yeah. could create AGI.
They're like, you know, we're the experts in this. We've been doing this for like 50 years.
If there was a way to do it, we would have already done it. These kids don't know anything.
They haven't published any papers yet.
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There's no peer review here. Yeah.
Not publishing papers was a really big critique. And you know, especially around the scaling laws like I think there was this aspect of they're spending how many millions of dollars on GPUs on projects that uh will not cause
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more papers in the world to arise. papers were like the thing that uh they were trying to you know sort of you know paperclipip optimize for which was totally the wrong thing like the thing that uh really great builders optimized for is like outcomes for customers and users is the same thing when Elon
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started SpaceX. He wasn't the first billionaire to start a spaceflight company.
And I think he was like the fifth billionaire to like try to start a spaceflight company. And so all the press was like, "Oh, look, another billionaire like there to squander his fortune on like rockets." >> And the whole idea of building reusable rockets was blasphemous.
Right.
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>> Yeah. >> I think he went and talked to um rocket scientists and they were like this is not possible.
>> Yeah. >> And after many years and many launches that didn't work out.
>> Yeah. And and then every time a rocket blew up like there would be another huge wave of negative press.
So like for both of those companies like it required the
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founders to like stick to their guns in the face of most people telling them that they were like stupid or crazy for like a long time. >> I mean nine out of 10 people might tell you you're stupid or crazy but then one out of 10 people might be exactly the person who believes what you believe and then you're contrarian and you become
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right because it's necessary to actually attract and be a magnet in the world for all the people who agree with you. Uh I hope you all take a moment to really think about how do you know what is real and correct in the world and re-examine all the sources of these things and if
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it's coming from users coming from your own personal experience or the experience that of people who you directly talk to uh great like that's probably pretty good verifiable stuff that you should use as a substrate of your reality. Uh but if you're doom
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scrolling on X, you're you know sort of listening to famous people like you know to be frank even including us you know we're all N equals 1. The only people that matter are the people who you care about who have certain problems and your ability to solve it and your ability to
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attract all the other people who want to solve those problems too. So with that we'll see you next time.
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