The AI ROI Paradox: Why Your Company Isn't Getting More Productive

Hello and welcome everybody. I'm here with Harsh. And Harsh, you and I back in December of last year, you're the one who introduced me to Vibe Coding. And from that moment, you've been vibe coding for a while. We worked together. We entered a hackathon sponsored by Hackforth and Peak 15. We won first place and since then it's been six months we haven't really caught up and a lot's happened since that initial period when I just started learning about vibe coding to today and so I thought we'd talk about
a lot of the lessons that we've learned from vibe coding and why there seems to be a lot of confusion around the vibe coding space and so um the specific point I want to like one of the main points that people are really confused about is a paradox of AI ROI.

So anecdotally we've seen specific people including ourselves who are dramatically more productive.
We can build things that were impossible before but then when you look at a team level when you look at organizations when you look at the economy it's like where is the ROI? Yeah.
And so there's kind of this paradox of like we're we're hearing about people who are dramatically more productive but we're not seeing the results. And so I wanted to talk to you about that like what what why do you think there is this paradox? I have my own hypothesis but I'd like to hear from you. What do you think?
Yeah. So I think there's multiple like reasons or multiple things that are happening at once. Yeah,
one of the first things that I feel is happening is engineers are still figuring out how to best use AI and they still haven't reached a point where they trust AI enough because what I've observed is when you have like access to stop models, what engineers do is they hand off a task that they would do

instead and then they hand off the next task then the next task. So they still are involved in every step of the way and because of that the speed limit is still how much they can do in a day. So the moment you're
talking about human in the loop.
Yeah. Human in the loop sort of a angle. So let's say the engineer has uh gone for let's say lunch or they have just stepped out for the day then the work is stopping.
So in a way even though AI is the one doing it it's still going at a human speed. And I feel for it to actually get to a speed which is more than human uh the human needs to first trust the AI that it'll actually do something then second set up systems so that it can actually do a lot of things and then the human in the loop becomes only something that you observe and then you basically come back to a bunch of things done you review them well and then post reviewing you can then ship all of the things that you know are right okay
so that's like one part of it what do you think like
so I'm I'm going to give you a less charitable interpretation of what's happening

and what I think is that if you look at vi coding at least today and things could change in 3 months to six months
but I still feel that vibe coding is a very high skill endeavor and this is something you taught me so back in December
we were competing against so many other teams in this hackathon
but you actually knew how to leverage AI in better ways that you taught me
and not a lot of people knew how to actually leverage AI.
And so I think it is a skill issue.
Okay.
And I think it's also and and so like the simple way that I think about it is that if you were to take a hundred people
Mhm. How many of those hundred are actually 10x, 40x, 100x,
you know, vibe coders? And I think they're very few who can do it well. Yeah.
Because there's also the the the nuance here is I believe there's a gap. And that gap is

so level one is like you you kind of are able to make a prototype.
Then from the prototype,
a lot of people think they're done.
Yeah. But then it's like then it's like from prototype to something that's usable and useful to you.
Yeah.
And so that you can use in your own applications. And I think the step from prototype to to having something that's usable is a minimum of a 10x
jump
jump in terms of complexity, [clears throat]
you know, time, effort, all of that stuff is a 10x jump.
And then from usability to production
is at minimum another 10x jump. maybe 40x, maybe 100x, but it's significantly more.
Yeah.
And so what you're left with is a lot of people that have prototypes or or kind of usable but very brittle
Yeah.
software.
Yeah. Yeah. [laughter] Exactly.
That just breaks all the time. And we see that today like look at OpenClaw, right? With all the contributors that OpenClaw has, with all the time

investment and energy they put, that thing breaks all the time. I don't even think it's good. like the the app that they launched it was such a shitty UI. I mean I was surprised like how the hell
what you know and compare openclaw to Hermes agent like I use Hermes agent. Why? Because it's just more stable.
Yeah.
Right. And and so this gap and and so going back to like a out of a hundred people I think at least 95 out of the 100
Yeah.
are not able to actually leverage AI in a good way. So this this is my theory right.
Agree. No I completely agree. So I think skill issue is a real thing and I mean please like complete what you're thinking then.
No no no and so go ahead.
Yeah. So I mean skill issues definitely is a real thing and I I mean a very simple example that I keep thinking of is everybody has access to Tik Tok and Instagram everyone has a phone and if they were given the same task like record a 20 second video about something and only a few people will come up with something that's fun to watch
uh everybody else will just miss the mark or they just make something really

boring. So I think you can see that tools don't matter. It's that your skills matter. And I feel that for leveraging an AI to I imagine it like harnessing a Pokémon like do you know when you are a lowle uh you're a like you basically as a trainer are lowle in a game like Emerald or any of those original Pokémon games but you have like a 70 level plus Groudon or like one of these Pokemon. They stop listening to you and then you have to figure out a way to harness them. And I think it's a it's a similar thing with LLMs because people just look at them as chat bots, but if you're able to harness them well enough and play that mind game with them, then you're able to get insane amount of outputs. So an example that I can give of this mind game theory is whenever I use an LLM to wipe code, I [clears throat] act like I am four different personalities with the LLM. So in a single chat, I'll talk to it about how what we are doing it is amazing. In the same chat, I'll talk a little bit

about how what we are doing is wrong or it needs to be better. Then I'll also talk as a third voice which says that what we are doing is like insanely like badl looking. It needs to look so much better. So I'm just one example. So imagine that we are in a room of four engineers or four people and we brainstorm and we come to a conclusion. Yeah.
I try to simulate that but with an LLM.
So it basically becomes like a group project. I have Codeex, Claude Code, and three of my own personalities talking and coming to a conclusion. So that's just one example like people don't go to that depth to build something. They just expect that they'll type something and it's
right. And that that's one of the concepts that I think gets a lot of people stuck, which is essentially, and this is something I've talked about uh quite often over the last year, but like um the fact that when you're vibe coding, it's a probabilistic output.
Yeah. Yeah. And so like all the things that exist in terms of like uh from the from the

harness to all the processes that you put around this.
Yeah. So you give a prompt to an LLM, some kind of output comes out, but all the things in terms of the inputs as far as like you know how you set up your your context, what you tell uh the LLM, your prompt scaffolding, all the things on the input side, how you structure your prompts, things like that
helps optimize what comes out. But then on the output side,
then you have to do a bunch of like eval reviews and things of that nature. And the thing that you're talking about is on the input side, you're you're having multiple LLMs discuss how you plan stuff, but you can do the same thing on the output side where you have multiple LLMs review the code and things like that.
Okay.
So, and and I I think this process and how you optimize the inputs in the outputs all of this is this issue around skill, right?
Yeah. And what you you also like what you sent over in that there was this one sentence that if there is no test or

there's no deterministic test then how do we say that the LM failed or like you know passed the test
right right
so I think this is what it basically is like if you're if you don't have those gates for input output then there's no way you can say that LLM produced slop like it's it open it's it's open-ended right now like you didn't give it parameters so how the hell is it supposed to you know
and and so There are a lot of these skill-based things. Um, what Harsh is talking about is I had put together a presentation that talks about all the things that you need like a a good vibe coder to get from prototype to something usable to production like all the the kind of skills and things you need to do as well as how you increase your outputs in terms of not just like because
so many people I talk to talk about prompting.
Yeah.
But by coding is so much more than prompting, right? It's then you go from prompts then you go to reviews and evals and you go to loops and then you go to

like aentic right
so okay so I thought the next topic we could talk about is like one of the other things that I found to be strange is how back back in March during GDC I talked to a number of uh game studios who were game studios who were saying that they were AI native and and it was easier for them because a lot of them were working on web- based games or working in GDO for example.
But one of the things that they had told me is that their highest output employee from a vi coding perspective was never their engineers.
It was always a product manager. Yeah.
And even at my company, I would say the the employee the kind of employee from a discipline perspective that has had the most difficulty learning how to leverage AI.
Is not the product manager.
Yeah.
Is not even the artist. Yeah.
Right. Like we even have some artists

who are out producing engineers. It's been the engineer. And so I wanted to ask you what have you seen like what are the kinds of disciplines or what are the kinds of people
who have been more successful at leveraging AI versus those who have not been able to and would you agree with me that in the general case engineers for some reason are having a lot of problems?
Oh I completely agree. I think one of the problems is that engineers have been for so long been into like the depths of the code that they are writing. So I think even now they are trying to uh still manually do a lot of things in between or they are trying to check between every step or they're trying to you know uh go across every small edit or every small diff and I think it for a lot of cases is important to obviously check what's happening but in a way that's very different. So it's no longer about uh you know checking each line after every edit. It's more about

running tests. it it's more about uh like having the right plan in place and so on.
Yeah. But to some degree what you're saying kind of the how I'm interpreting what you're saying and kind of what I'm also seeing is that
it seems like engineers are used to a certain way of working.
And I think a general human characteristic is like
when you're used to something you're like oh this is how I code.
Yeah.
And then you're like okay here's a new way of coding.
Yeah.
And then there's just so there's this like defensiveness.
Yeah. Yeah. Exactly. And then there's also this laziness of learning a new way.
Yeah.
But if you're a PM or an artist and you don't have that legacy,
you know, sort of work workflow or ways of doing things and everything's new for you. So you're just like, well, I'm going to just try to do it.
Yeah.
The better way.
No, agreed. It's one of those principles where if you are never told no, then you don't you never think that it's not possible, right? So for a lot of developers a lot of things they feel are not possible or they have been sort of reading about it on Stack Overflow or

getting bashed by their seniors that hey this is just not possible and now it suddenly is but they have heard the no so many times that they kind of are still skeptical but for non-developers they have never heard that this is not possible or
they have never heard a no. So they from the get-go they are happy to try it out and then it just works for them. I think that's one of the reasons why and
but I I would also say there's a little bit of a nuance here too, right? Because
the guys who are the absolute best at VIP coding are probably like the super engineers, right? Like the guys at Anthropic and I've met some of them and their their output is like
ridiculous.
Exactly. I mean one of the things that people say that it's not possible to code something amazing using AI but then all of these people from anthropic and open AAI tell you that I haven't seen a line of code in the last 6 months and everything was shipped using AI
that's when you know that if they can ship such good software without looking at the lines of code then it has to be scale issue if you can't
right and and just for our audience too

I because I I think that people still don't understand the magnitude of the impact
is that so for example at my game studio before we restructured because we like once I saw this we we it definitely became an imperative to restructure our
Oh, I remember you you started wipe coding so much like in December you weren't and then after that anytime I meet you or see you you're doing something like
Yeah. Yeah. Like January through March of this year I was barely sleeping.
How many commits did you say you were doing every month?
So since January I've done over since from January through June. Yeah,
I've done over like 6,300 commits. So, over over a,000 a month. Now, here's the crazy thing, right? Our engineers average 20 a month.
That is crazy.
So, we're talking about this. The magnitude difference is 20 versus a thousand. Yeah.
Right. And even it's not just me at our company, you know, we have a product manager who's
who's going crazy and literally like you

know one of the the the sort of uh philosophies or mantras that we have in our company now is that at this point right now and this is just becoming more possible with things like
you know goal and loop and things like that.
Yeah.
Is that the output to the point that you're making about people about human in the loop?
Yeah. is that the output should actually not be constrained by um the person.
Yeah.
But by your token budget.
Yeah, that that like
So, how many Max accounts do you have? [laughter]
Oh, I mean I actually only have two right now. Uh like one I I actually had two Claude Max accounts. I now switched one of them to Codex Max. I just wanted to try it out for a bit. So, I've been trying it out for the last few days. And I also have Grock Max, the $200 plan, but that's just because I have the X premium. So,
I definitely have to try Grock. I've been hearing a lot of good things about it.
The latest model is decently good. Like,

you can you can give it good tasks and it can actually do it.
And and this kind of would would lead to maybe one of the other topics that we can talk about, which is like for some of the people in the audience who are trying to understand
how do you get more productive? Yeah. How do you actually get to production level code? Like what have you seen in terms of some of the main problems or issues? What are some of the um the complexities or the problems that prevent people from getting from prototype to usability to production? What have you seen?
So I think one of the main things that I've seen like be it friends, colleagues is that they approach it very much as a what needs to be done problem statement. So basically if you want a website to do X they will tell AI that okay do X or they will set up loops or whatever eval etc that it does X but the point is that when you are building something with AI you also have to tell that don't do Y and don't do Z. So I think that's a very big thing that people don't really

understand or it doesn't feel that important but it's very important. So I'll give an example. So let's say you are building a sign-in page and you tell any LLM to build a sign-in page for you and you have eval and everything set up but at the same time you know for sure you don't want sign in with Facebook on your signin flow or when you sign in you want to ensure that a user's user ID is set at two different places on your database and one specific like chron job needs to be kicked off etc. So you will tell it to do three four of these things but then it'll try to also do more things which are generically done by people in a signin flow and then you'll have a website that does 10 things and all of them in a sloppy way. But you need to be able to tell it that okay these are the four things we need these are the four things we don't need or maybe login was not the best example but like imagine there's a screen where you want to make sure that whenever you hover on a button it should pulsate a little bit.
Yeah. But whenever you hover on the

second and third button, it shouldn't pulse it. So that's a detail you need to tell because if you tell it that okay make the first button XY Z, it'll probably then consider it as a memory and then it will use that for the next buttons you create as well. So I'm actually going to think a little bit more about a good better example for this. But the whole point is that you need to act like a QA tester for your own software that you're building and you need to attack it from all types of user behavior. Then you need to think that a user might do XY Z on this page, another user might do ABC and you need to consider all of that and tell the LLM these are the things not to be done or these are the cases for which you're not designing.
Yeah. So some of the problems that I've seen
Yeah.
and there are a few problems but I I would say like the very first problem and this is especially the case again not for like the the S tier engineers but like for the general engineer
is just the problem that we talked about before which is they're used to a certain way of working. So there's

almost this laziness. Yeah. Right. And so I've interviewed a lot of people in India, a lot of tech leads in gaming, a lot of other people who were like, "Oh yeah, I've been using AI for a year and a half."
Yeah.
I'm I'm an expert.
Yeah.
And I'm like, "Really?" [laughter]
And then and and I would ask them, "Okay, show me your your GitHub repo."
Yeah.
And there's like nothing.
Yeah. Yeah.
And I'm like, "Okay, what so I'm like, "What? Okay, so what what's what's the main harness that you use or you know, what's what's the main tool?" They'll say like cloud code. Then I would ask them
some basic questions.
Yeah.
Things like what are the various ways of injecting context?
How do you modify your status line? How do like a bunch of like basic questions in terms of the usability of the tool
and they didn't know anything like how do you set up your prompt scaffolding? All these things they just didn't know. And so like the first problem that I see is that the way that a lot of people approach vibe coding is

completely naively. Oh, I just say make this for me.
Yeah. And we'll expect it to work.
Yeah. And they don't understand. Okay. For the LLM to work properly, you have to have a view in terms of what the LLM knows.
Yeah.
What context does it have? What's in your claw.md or agents file? How do you structure your prompt appropriately? What files do you inject? All those kind of things like they don't even know or think about and they don't know how to use the tool.
So what that's one major problem that I see and then I think the other major problem I see is something that you were alluding to. Now you were talking more to the point on taste, right? Yeah.
How do you make the user experience in a way that would be appealing to users? I think that's one issue.
But I think the other issue is something I call it a little bit more around discernment.

Okay.
But for example, I believe that even the most current frontier models make a lot of logic mistakes.
Yeah. or or mistakes based upon the lack of context.
Yeah.
And so let let's say you're trying to figure out what kind of backend to use and
you're asking for a recommendation from an LM and it keeps like and this is something that just happened last week. It kept saying okay railway would be the best sort of back end to use for this for these reasons.
Then I'm like well what about this? What about this? And then like oh yeah I didn't know about this. Oh yeah I made a mistake. You're right. And so like in my in my experience working in AI vibe coding, if you're not catching like two or three major problems every day,
yeah,
there is a level of discernment that's missing. And so I think if you're going to do vibe coding well, you have to have

two things. One is I think you have to have kind of like high IQ. Yeah. in the sense that there's always some level of logic jumps. There's always some level of things where
the LLM just gets it wrong. Even even Fable
and this problem might get solved later.
But if you don't have that attention to detail and that discernment to try to find where those logic jumps or problems are,
you're not going to wind up with the best output.
Okay? And I think it's not even about how smart the model is. Like imagine the smartest engineer out there. If you go and ask that I want to do XY Z and that engineer will probably say okay use Python for example that's the best language for you to use there's no reason for you to use PHP or like C++ but then you give them additional context that my software needs to run on a very specific kind of a hardware and I need to ensure that my compile time needs to be insanely low
yes
XY Z and then that engine will be like

oh that makes sense I think that's why you should use C++ I was wrong you should not use Python
so That tells you that if you give the entire context then the LLM will you know work for you and that's how you catch mistakes
right because the LM is essentially it's looking at patterns of things that's that it's learned from its training.
Yeah. Yeah. Exactly. It's trying to overorrect a lot of
and similarly like a couple months ago I was working on a GDO project.
Okay.
And I noticed that it was telling me to use GDScript and C. But then I'm like why?
Yeah.
Why use C#?
Yeah.
So if you have a performance hot path, why would you use C++? And it's like oh oops yeah actually you should use. So like there is this thing about how
even today to the point that you're saying that LM may not have that full context and the LM do tend to bias towards certain things. Right. Another thing that that a lot of the models tend to bias towards is understanding that for example if you're just building

a new application, new software green field.
Yeah.
But all of the LMS will always bias towards legacy infrastructure.
Okay. Well, now we need to build all this stuff for Lexus. But and I'm like, no, we don't have any users. Stop putting all this legacy in. Right.
Exactly. Exactly. And then and even you put that in your cloud. MD file, but even then
it will ignore it. Yeah.
It'll ignore it and then you're like stop.
And so this level of discernment where you have that attention to detail.
You're not just fully like
Exactly. I mean I like to say that like you need to see what it's thinking. I literally expand it thinking and I read through it. Yes.
Like what all it's doing and then you tell it. Just today I am working on fixing some bugs in the multiplayer part of October and it's trying to add all of these IM security roles where if you are not a editor then you you can't edit on the canvas and stuff like that. So I'm telling it bro we don't have any users using multiplayer just make it as simple as you can let's get 10 people using it

then let's start adding security and stuff like that.
Right. Right. Right.
So the architecture became so simpler and I was able to get it done in like one day. So
yes. So, so and these are like and for the people in the audience who start vibe coding is you start vibe coding and you and this is why it's so important to have the experience.
Yeah.
Because these are the things that like Harsh and I can think about right now off the top of our heads, right?
Yeah.
And but I would say that there's so many other small things.
Yeah. that if you don't know just based upon experience what to do, you're not going to get be able to make that jump from prototype to usability to production.
Yeah.
Okay. So then maybe moving on to the next topic.
So what what in your mind are the kinds of people whether it's disciplines or the kinds of skills that you need
in order to be successful coding? What do you think?
So I feel at least as the way things stand today models keep changing a lot. I think one thing is important for you as a human is to have a large context

window yourself because the thing is that when you're white coding even a small feature it might touch multiple parts of your project.
Previously you had developers touching small parts of projects and then figuring out merging them and all of those things
but now it's not required. So you end up telling a whole feature and it touches multiple parts of your product. So you need to understand that what all it might have touched and you then need to test all of those other things as well. For example, if you're like changing something in your O and your product is already out there
but you're not testing your login flow after you made a change in your O somewhere else, then it might end up breaking because the LLM is overcorrecting or optimizing for the current task you give it
and it sometimes doesn't care if something else is breaking because of this.
So I think that's one of the core skills. Second is to be able to understand your entire product and all the moving parts of it. Like being able to imagine how your product is designed, how it's moving and how things connect

internally. I think that's something that's very important. And third, taste I guess because now that building is very easy, you need [clears throat] to build something that actually matters and that actually has a reason to exist. Like I have I have gone through this problem for a while. So I can talk a lot about it maybe after I hear your thoughts about this part.
Yeah. And and maybe we can like we can separate kind of two things that are two two kind of related topics. Yeah.
Here. But I kind of think that there's there's one aspect of it which is when we think about AI vibe coding and how that impacts roles and the kinds of characteristics or things that allow you to be successful. Yeah. And then the second is this notion about well then how do you actually become successful using vibe coding and they're kind of two separate things but speaking on the first point the way that I've kind of thought about it and what I tell people at our company today is that I look for these four things so already talked about high IQ

high agency the third is attention to detail because to the point that we had already talked about
you have to find all these small little problems you can't just I mean there are there there's kind of like this this conflict right how do you take yourself out of the loop
but then for some very important things you have to make the right decisions you have to watch what it's doing
and then fourth is having a technical foundation and so I do think that
at least today where AI LM frontier models are today
you still have to understand technology you have to understand whether it's like infrastructure database you have to understand the various kinds of technologies because you have to help and work with the LLM to make the right technology decisions,
right?
And so those are the four things that I generally look for and I I feel like if you have those four things, you can be very successful when it comes to ad cutting. Would you would you agree?
Yeah, I completely agree. I mean like we were discussing point two and three is

something that I like have a lot of opinions on as well. So
yeah. Yeah, please.
Yeah. So point to like the agency part like we discussed like one of the biggest things is that most people aren't incentivized to get more output
and that is why they are not getting that output and that is why overall it looks as if the output hasn't supercharged because of AI but if you look at incentivized teams small teams solo founders so all of these people who are super incentivized to reach the end of the line
they are actually showing that how crazy good level of output you can get using AI.
Yeah. And the last point that you said, the attention to detail, I think that makes so much of a difference. Like the smallest of the smallest things. Now, if you've watched the movie The Interns one of her like warehouses where she packs like one of the boxes herself and they have that moment where as the CEO founder, she makes a very small change. It's not that big of a deal, but it just shows how much tiny attention to detail people who care about it have. Right.

And I think that makes a lot of difference. So when you get a parcel and if it's all messed up inside,
it won't feel as good if it's nicely laid out.
It's the same stuff. But
and and one of the other ways that you can think about high agency is like how much you care, right?
So like
that you give a you really care that you're actually going to be doing things well versus
and I know a lot of people to the point that you're making if you're at a company you're an engineer you have a certain amount of output like your incentive is just to show that output or a little bit more and then you're just going to check out.
Yeah.
So if if you don't have that incentive or if you don't have that agency if you don't care enough
Yeah.
your output's fundamentally going to be limited.
You you are limiting it yourself. Like that's the whole point here. And I think a lot of people get it wrong that give me more incentive, I'll give more output. I think it's I'll give more output that will make you give me more incentive. So I think most people don't understand that and that is why I think

they never push AI to the level that it can actually go.
Right. And then right and so another topic I wanted to talk about is because the other thing that also kind of gets conflated is and to the going all the way back to the original question where's the ROI?
Yeah.
Is kind of like this question around so if you do the things that we talked about it answers the question of can build.
Yeah.
So you now can build
whatever you want. Yeah. And for me, I feel like if it's web, there is nothing that I can build. I can build anything.
Exactly. Yeah.
Right.
But then that doesn't answer the question to the point that you were kind of alluding to before, which is what to build.
Yeah.
And that's where things like taste and discernment and things like that come into play.
Yeah. And so I maybe you could talk a little bit about that as well because I do think that that's the other thing is that even at my company when I see other people building stuff it's like okay

they're building a bunch of crap.
Yeah. [laughter]
Does this thing that you're working on does it help the company? Does it [clears throat] help you get to profitability? Does it help you
really help achieve your goals or lead to something that's going to like make a difference? Right. So what do you think?
I completely agree. So, I mean, I think that there's like types of things that you build. One is like crap. Like, just don't build that. But the second type is something that you're building for yourself or like a tool to help you do something else. And something that you're building, that's like the second type. And the third type of thing that you can build is like a actual product or something that you're actually going to launch. So, very early on in your building journey, you need to figure out what are you trying to build. And if you don't, then you will end up wasting a lot of time. So I have built a lot of things in the last two years like I've built probably over 50 projects and all of them in a very good quality because AI can just let you do things so fast now but a lot of times I approached it in a very prei way where the idea was

that okay this is hard to build it will be a meaningful project
right
but now that it's not hard to build but the dopamine still is the same because our brains probably haven't understood it yet that how easy things are completely so you still get the dopamine and then you end up shipping things that were hard
look amazing and then it's just something that you will never launch or even if you do launch nobody will care about it so I think if you're able to answer that question very quickly that is this something I'm building for myself is it just a project then it will actually get to a point where you can define ROI
and obviously there's a lot of things that people can just build for the sake of it which doesn't really make a lot of sense So I mean
yeah and and I think this is the issue that where the human part right the the AI again at least today cannot answer this question which is
in a world where you can build anything
what should you build

exactly
and then the other nuance there is even if you're building the right thing
are you building it
in a good way because you could have and the two apps that I see everybody building Every company
is building their own like HR recruitment tool and they're all building their own like project management tool, right?
Yeah.
But it's not they're not all the same.
So to the point that you made about taste, it's like you have to now figure out not only what you should build.
Yeah.
But in terms of the details of it, how it's built.
Yeah.
The what and the how.
Yeah. And for what like what problem are you trying to solve,
right? Those become the more fundamental and important questions in the role that humans play versus can I build it which is increasingly and more and more the models are just helping you get there.
Exactly that makes sense.
Okay.
And the taste part also important and

the example I always give is YouTube exists for so long now but not everybody's creating content and a lot of people are creating content but it's still not good. So only a few people have mastered how to create content that keeps viewers hooked. So similarly software has become like content. Everyone can make a video. But if the video is good or bad, it completely depends on how tasteful you are, how creative you are. And I think that's something that you also need to understand very quickly about yourself if you have that person.
Right?
So yeah.
So there's there's two other topics I thought we could talk about before we wrap up this conversation. And the first of which is something that I think is a little bit of a sensitive topic. So we're here in Silicon Valley, right?
Yeah.
San Francisco, which is very close, and you're staying in San Francisco, but like
one of the main things in San Francisco, which is like the center of the universe of AI, is that it's kind of become very sensitive and almost politicized.
Yeah.
When you talk about this topic around

AI's impact on the economy and to job loss.
Yeah. And if you now say something along the lines of of you believe that there's going to be job loss, people get really sensitive about that and they're like, "No, no, Jeff's paradox. Jeans paradox, you know, demand increases blah blah blah blah blah." And so I wanted to get your take because
I actually don't think that just adopting the kind of Silicon Valley narrative on this
and just saying, "Oh, no, it's going to expand jobs." Um, I don't think that's the right mindset or even mentality to have, but I don't know if you disagree with me or not.
No, I agree. I agree with what you say. So, I mean, again, I'll go back to the YouTube example. So, before YouTube or Twitch existed, there were only these large production houses which were able to create movies or content. But now, every kid in their bedroom with their friends can just create content and

start posting on YouTube. So you suddenly have these pockets or like cells of creative groups that can now create and post. So similarly because of this AI has democratized that piece in humanity where it's more and more possible for you and your friends to just pick something up and build something. And I think the type of work is changing a lot. Like rather than 20 organizations employing 10,000 people, it'll probably probably be like a thousand organizations employing that many people and each of them trying to solve different things in the world or trying to build different things in the world. Just like how content has become very broad in terms of what all kind of content can exist. So I think that's how jobs will change like one company won't have a density of people. it'll be multiple companies having a smaller density.
And I think YouTube is an interesting analog because I I think what we've seen with YouTube is that YouTube has put the power of the creation of content into the hands of many creators throughout

the world.
Exactly.
But still,
there's only one Mr. Beast.
That is true. That is true.
And even Mr. Beast would say that building a viral video is a skill issue. He he's famous for saying he could do it again anytime. and he continuously gets hit after hit after hit.
But also what we've seen is that
if you look at the success of people on YouTube,
there is a concentration amongst a small group of creators,
not everyone gets the same reach. And I I think that's also what's going to happen with AI, right? Yeah.
So
what YouTube has done is they've been able to scale reach and kind of like that distribution aspect of it. And what AI is doing is it's scaling the production part of it, the building part of it.
But still I think that in a world where one person can now make 100x
of applications of services of things like

that. The Silicon Valley religion now is that oh no no no Jevans paradox building becomes easy so that's going to expand demand. But what where I push back is that that may be true in certain industries, but there are certain industries where that's not true.
And the reason it's not true is that for example, I come from the games industry.
In gaming, there's a finite set of attention. Right. Exactly.
And so whether you're spending time playing games or watching YouTube videos or movies or how you spend your time.
Yeah. Just because the output increases doesn't mean you have an infinite amount of demand. Demand is not going to be elastic. You're not going to be like, "Oh, there's more content. I'm just going to,
you know, not sleep and just spend more time on that content." Right? And
so that's where I think that there's this danger with this Silicon Valley religion around you cannot talk about job loss, you cannot talk about the negative impacts of AI because I do

think there are going to be negative impacts that come from AI for some people.
Yeah.
And but I also think that the thing that too many people are anchoring on is the downside. Yeah. Right. Because I would say that if you look at every technology transition in history, what has always happened is that there are set of winners and there's a set of losers. And too many people are focusing on the bad part of it, the losers, but there are going to be winners. Now that you can build anything, new opportunities do arise. And so focus on that
and try to like it's it's just the nature of what's happening. It's the nature of the world where there are going to be winners, but there's going to be a smaller number of winners in certain industries. So, you want to try to be in that group of winners rather than in yeah what I believe is going to be a larger group of losers.
I so I think that there is two nuances

to what you said. So, the first part is I'll again go back to the YouTube example for a second. So, even though Mr. beast or like only a few creators hold the power. It's not always about being the creator, but instead these each of these creators have their own media houses now and they employ 20 30 people each. So it's not always about being yourself as a creator but working at one of these creative agencies. So after YouTube democratized content, there suddenly existed a thousand more agencies and each agency employed 30 40 more people. So even though people felt that larger production houses will now cut off employees, they were automatically reaccommodated by these smaller houses. So similarly in AI, even though there will be a few winners, they will end up re-employing or like bringing more of these people to work with them who might not be able to win
in a solo way. I guess
but I I would agree with you. But then I guess where I would push back is still okay. So you let's let's you know, we talked about project management. I think

that these companies, you take a company like Jira, like Elassian with Jira or Monday or ClickUp, like
Monday employs over a thousand people.
I've vibe coded a project management tool which I think is dramatically better and at least for us.
Yeah. Custom
is so much more powerful, capable, and does everything that we need.
Yeah.
And that's replaced a product
that has a company with over a thousand people. Right. So, I do think that there will be some compression, but there will also be new job categories that open and things like that, but I don't think what you're saying will necessarily cover like all
maybe not. No, because I imagine that let's say you let's say for example, you end up making this tool that you built for uh uh project management into a company, then you'll end up hiring 5 10 of these people. Oh, will I?
But if you don't even make a company out of it,
right? But but if if if but let's say let's say and by the way I am open sourcing my my my tool but like I think

what that's going to do is it will take some categories of software to zero
because like I'm open sourcing it right now anyone can use it for free and I I'm not going to hire a thousand people.
That is true.
If anything if I wanted to just break even on it and just like not spend too much time on it maybe I'd hire two people.
Yeah, that that makes sense. Obviously if you make it into a company you want to hire sales people etc. But then that's a different game entirely. I agree. I mean definitely it's going to be very interesting to see how people are reaccommodated throughout this wave but I believe that it's not completely negative like people keep shouting and I feel that it's going to be a very different kind of a world where agency and all of these things will be rewarded much much more and I feel that we are very early in AI right now like the only thing that's changed is software but soon entire food industry ag agriculture industry things will change so much that the kind of opportunities that open up over time I feel would still be very

different but still exist because there's just so much that we've not even imagined because imagine tomorrow if fable 5 is so powerful and fable 9 comes out and fable 9 is probably going to be so good that it can literally tell us how to create a new source of energy for example
and if that happens then suddenly you're going to need a thousand new people to work on extracting that source of energy. So I feel that if AI is able to keep going in the hockey stick growth then so many new things that humans need to do will pop up that
yeah I but I guess like to to the point that you're making right now one of the key open questions I haven't been able to figure this out but an key open question for me is like
how long is the opportunity window open? Yeah. And so another thing that I tell a lot of folks at my company is like I tell them you have to push hard right now.
Yeah.
Because there was a blog post by this

venture capital firm called NFX and I was reading it and they talked about looking back at various technology revolutions and the window of opportunity for certain things like social networking.
Yeah.
And they said that window was 11 months. Okay.
Right. And so the open question I have is like how much more opportunity are we going to have where is it 11 months
okay
to learn AI and then the people who learn how to use it are producing so much that they're going to crowd out everybody else
or are we going to have a sustained period of like a golden age of opportunity where for the next 10 years you can still continue to like use AI and and come up with a bunch of new ideas and and the economic advantages or benefits acrew in a way that's more spread out versus like a power law distribution where only a few people capture all the winnings and it's the early people who win. That's the
No, makes sense. I mean the only thing that I can imagine is that maybe the types of industries will keep changing

rapidly. So the 11month window will exist for software
or for many different things. Yeah.
Well, certainly it's coming for hardware.
Yeah. Then hardware, then it might go to space tech, then it might go to energy. But again, I get it that it's not it's not something we can predict right now because it's going to be very confusing. But I think one bad news for everybody is that you need to as a human being, you need to be way more useful now to be to be employable or to be paid. So that's a bad news for all of the folks that just want to coast by in a job.
Yeah. So like for me and again this is just my current perspective but the thing that I tell a lot of employees in my company is like I think this is the last opportunity for people to be able to work hard.
Yeah. So just push hard now because it may be that if you learn all the things you need to learn now and you're able to get to a point where you're at you're at a point of leverage, right? Like Google search,
there's a certain point where you just have a dominating position in the market

and so you kind of control all the opportunities.
Yeah.
So then
are we at a point in time where now is a time to work? There might be an 11month window and then after that in two years from now you're either working 3 or 4 days a week.
Yeah.
Or zero. [laughter] You know what I mean?
Quite that. That's the thing again. I don't know the answer.
I feel there's enough people in this world who can't sit idle. So no matter how things get easy, we'll find a way to keep working. I feel
something. But if you look back through time, right, through history,
there have always been times of massive economic disruption,
civil unrest,
yeah,
revolutions.
Yeah.
And so I don't know.
Can humans sit?
Maybe that's coming. Maybe that's coming. You know what I mean? Because I will say this. I do think and and now we're kind of going a little bit far field of just AI vibe coding but I I would say that

I believe that in society today there are too many people who own capital who are wealthy
and they take it for granted and they don't understand
that if you are like somebody who lives in the Midwest you know you've got your job you're providing for your family by being a truck driver or you've got some kind of job but then you get displaced.
Yeah.
If you can't feed your family
Yeah.
I think you will I think it's extremely serious.
I'll give you
I think you will do I think you will become violent.
Okay.
I think there is definitely a potential for AI to there there's a path and again I don't believe in being able to predict various scenarios. I generally try to assign a probability like I I believe that outcomes are more of a probability distribution.
I agree.
Okay. So I know we've been talking for a

long while. So one last question which is like okay so now we're in a world where you can almost build anything.
Yeah. And so in that kind of world then how does that impact the world business economics like one simple way of thinking about it is like if you can build anything then production and development yeah
as those costs trend to zero then distribution those costs will increase but how do you view how the world will change given AI
what are the things that people should be thinking about
as we move into this world where again
AI is increasingly just enabling people to build anything they want.
Yeah. So I mean to begin with I still am of the camp that product is more like powerful than distribution and I strongly believe that if you have a great product distribution will happen and right now at least AI can't

understand what humans want. It doesn't understand scalability of a product. It doesn't understand of it can't understand marrying all of these concepts together. So you still need to be the one to figure out what to build and you need to be the one figuring out what needs to be built. So for example like Steve Jobs made the iPhone but back then if somebody was using AI to make the Blackberry better they would have kept going in the same direction by cutting a few things adding a few things they wouldn't have imagined a completely new parallel route or be it like the shift from a carriage to a car. It's s such a parallel shift that the creativity and taste of a human needs to come into picture where you need to figure out a what does a person want b can they pay for it and c can this actually work for a group of people not just like one or two people and that can that actually turn into a income a company and so on so I feel that at least for this window of time until AI can't do that part this remains the most

important thing and we cover all of it and name it taste like as one thing over time. So you need to figure out what people want because people can't tell you what they want. They they think they know what they want but most of the times consumers don't know what they want and how to build it really well that people really understand how to use it. You have amazing user experience. It's simple to use. Everybody can use it and so on. So I think that's where your focus goes into picture.
Yeah. where and if you do that well then then you will win I guess
right and and and I agree with you like a very simplistic way of kind of thinking about the world of AI is in terms of product development and distribution as we're kind of alluding to the point you're making you're saying knowing what to build as well as how you build the product because that's where taste comes in right because in a world of a billion different project management software like what are the

specific ways that you build it that will be more compelling to users.
Exactly.
So that's one thing, right?
But then as the cost of development goes down, I think the distribution cost increases. And so like then you're shifting to how do you actually gain distribution? Because I think those costs are going to go up. Is it by owning a community? Is it by working with influencers? And I do think that the value of influencers of people who own audiences
that starts to increase. The value of that starts to increase. And that's kind of like a simplistic view of viewing like where the world is going with AI. But I think the more general principle that I think people should understand is more around what do the constraints become.
Okay.
So in and so to the for example to the point of product development what are the things that AI is weak in those things like what becomes more valuable? Yeah,
it's the things that the AI and LLMs are bad at. And so those things, how do you

become extremely good at those things so you can bridge the gap? Because ultimately when you're working with AI, it's a partnership. It's human plus LM together.
Yeah.
And then in terms of distribution, like what are the areas where that become hard?
Yeah. in so without going into the specifics of that and I I have some points of view on that but for the purposes of this talk I would say that's the general principle that I think that people should be thinking about is
when development goes to zero then what becomes valuable
and in terms of what to build
what
are the LMS bad at what are the skills that you can double down on in terms of what what we already talked about
such that you can make the right thing Yeah. So I mean I think I have a good example for this. So imagine a world where match three games don't exist.
Okay.
And you have LLMs, you have AI to code whatever you want,
but match three games just don't exist yet. So until you prompt your way to

figuring out that this could be a game mechanic.
LLMs won't tell you that.
So the ability to figure out that this could be a game or this game design could be addictive. I think that becomes such an important mode because LLMs can write amazing code but if your game is not fun no one will play it or similarly if your app is not solving the problem or if your app is not addictive enough it just won't work in the industry. So I think that's what people need to figure out that in game dev for example coding no longer is the important piece but the piece is how do you design it well how do you make sure the mechanic is great and so on. So that's where people need to sort of move on to the step and the reason why I'm taking gaming as an example it's easy to explain
but the same applies for any SAS or like any other software that you're building
right okay so I think we've covered all the topics today maybe the one thing that we can end on is for anyone watching who's interested in Vive coding what would be your last bit of advice

for them what should they do next week if you were to give them advice
so I think the one advice I can give and I give to everybody is just ask a powerful LLM if it can do something for you whatever you have in your mind because most of the times you will just be surprised that it will do it for you like so many people they might just think that how the hell can it do this or how the hell can it do that just ask it to do either it'll just do it or it'll give you three steps to help it do it so for example like a very very random example like my mom she writes a blog and she has like a bunch bunch of images that she converts to webp every single time and uploads it and it's obviously something that cloud code can very easily do but for somebody who hasn't been coding for a long time she didn't even think that it's possible that a LLM can convert images for her and give it to her in a batch right
but she one day decided to ask cloud code can you do it
it's like yeah I can do it or I made a promo video for my app using cloud code for the first time I didn't even imagine that claude can make a video it did it

so I think the main advice is
whatever problems you have the hardest thing you can think of just ask an LLM, right?
Like it's 90% probability that it will just do it
and it changes all the time, right? Like yeah, I remember like somebody had posted like, "Hey, check out this this video that was made by an an AIM." I was like, "Oh man, I didn't even know you could do that." And so to to the point that you're making, there's so many capabilities that AI can do and people don't know what they don't know.
Yeah. Exactly. Exactly. So you need to accept the truth that you don't know what you don't know and keep probing until you figure out what all can be done and what all can't be done. Like I've been using LLMs to wipe code for over a year now but I'm still surprised every single day that how the hell do you do that
right? So
yeah and I think my own advice would just be for the audience like we are in my opinion we are living in an extremely like significant and important time in in in human history right

and I actually do believe that AI is the most significant and impactful technology in all of human history. So my advice would just be like if you have not tried invest now.
Yeah.
Like I don't again I just assign a probability distribution to any potential outcome.
Yeah.
But there are some potentially very dystopian and very negative scenarios that could happen in the next year or two.
Yeah. So, I think that a lot of people may regret it unless you just carve out two to three months right now.
Yeah.
To do whatever you can. Just start building. Just start learning
because you don't want to be a year from now thinking, I wish I would have done that.
Yeah.
And then it's too late.
Yeah.
And then you lose your job. You can't feed your family. [laughter]
And you're part of the civil war.

I hope it never comes to that, [laughter]
right?
Yeah.
Okay. All right. Well, folks, there you have it. Harsh and I have have been vibe coding. I would say they're probably not I mean, I'm sure there are a lot of people out there vibe coding, but
you and I in particular have been doing it a lot. So, these are our perspectives from a couple guys who have done a lot and who have
launched code that's being used by other people. And so, for whatever it's worth,
Yeah. I think yeah that that's what we think and so for the audience uh hope that was helpful for you and we will catch you next time. Thanks.
Awesome. Yeah, thank you so much. Hey