Can these other companies keep up with Claude Code, Cursor, Codeex, GitHub Copilot?
We're almost still sort of at the beginning of all this progress that can still be made. Someone else could catch up.
Are we in the end game when it comes to coding?
I don't think we're anywhere close. Coding is very different from playing Go in that coding is completely open-ended. The space of possibilities in coding is endless. It's limited purely by your imagination.
Are the LLMs going to get commodified?
I still don't think that the AI models themselves are going to get commodified. Even if they all have the same degree, same level of knowledge, people will naturally sometimes just want to talk to different models depending on their mood, even for the same topic.
What do you think about where the value will start to accrue?
I believe that the value is in the application layer. One of the main reasons some of them went out of business, they couldn't build an application. The pure API model doesn't work. I think it's a difference between optimizing for what the humans want, what the real users want and what makes their lives better, as opposed to optimizing for clicks and engagement.
Thanks to our friends at PayPal, the exclusive sponsor for this week in AI. Try the payment and growth platform that's trusted by millions of customers worldwide. PayPal Open. Start growing today at paypalopen.com. All right, everybody. Welcome back to This Week in AI, episode 10. This is the new roundtable that I've been doing in order to get smarter about AI and keep up with an industry that is moving every month is probably a year in our industry. Keeping up with it is incredibly hard. That's the point of this podcast. We'll talk about whatever's happening in our industry with experts, the people who are actually building the future. And we've got an amazing roundtable this week. Aravind Srinivas is with us. He is the co-founder and CEO of Perplexity AI. Started out with AI-powered search and answers. People became addicted to that and then released something called Perplexity Computer in addition to a number of other really great products. And per the FT, the Financial Times, your revenue has grown from 100 million to 450 million apparently. I don't know if you've confirmed that or not, but you had quite a run.
500 million was what we confirmed a week or two ago.
Amazing. So this is hyper-growth at its best. And is it Perplexity Computer that's really driven this? Is it that interface?
Why has this product become such a hit in your mind? I use it every day. I love it. I love the model council. I've been using Perplexity Comet browser and won't shut up about it. But you can download this incredible app. You have Perplexity Computer. Why is it catching people's attention? What are people using it for?
I think it makes agents really simple. That's the core reason. It makes it very, it's the most intuitive interface to be a manager of several agents, to essentially orchestrate several different agents. Which don't need to, like you don't need to think about whether it runs locally or on the cloud or like setting it up. There's no onboarding required. There's no onboarding pain. There's no need to bring API keys. It all works intuitively in the same interface that you're used to asking people to do stuff for you. And it connects to hundreds of connectors that are valuable. It puts all the models in one harness, one agentic harness. So you don't have to feel a vendor lock-in to Claude or GPT. You just can be guaranteed the best model will do whatever it's supposed to do and people love that. People are using it for a lot of deep and wide research and browser automation, data analysis, so many tasks, building dashboards, building web apps.
Yeah, we've started using it internally. We obviously had a fascination with OpenClaw. We're still iterating on that open-source project. We've tried Claude Code and then the team just started loving Perplexity Computer to the point at which I had to upgrade to the $200 a month account. That's awesome. So, you got me on the hook and awesome. I'll be happy to do any customer support for you guys. So, please feel free to ping me if you have run into any issues.
We will ping your customer support line. We're having good success with back office functions. You know, we have a venture capital firm and we've got accounting and we've got legal documents. We do due diligence. So, we've been writing the scope of work, you know, the standard operating procedure, the best practice for say doing due diligence on startups. And now we're going into the Perplexity Computer product and trying to figure out, hey, which sections can we actually give to it and how well does it work, you know? And it's been quite impressive.
Also joining us today, Edwin Chen is here. He is the founder and CEO of Surge AI. They're doing data labeling for all these frontier models. Founded in 2020. And this has become an incredible space. You have clients from OpenAI to Google, Anthropic, Microsoft, Meta from what I understand. 130 employees approximately, 50,000 expert contractors. Some of this might need to be updated. I'm not sure, Edwin, because like I said earlier in our opening, things are moving really fast, but there's been a lot of brouhaha on the internet about expert networks as well. Is this a great business? Is it a terrible business? They're very fast-growing businesses, which always gets people wringing their hands. We have an investment in Scale AI, which I think is a contemporary of yours. Tell us a little bit about the business, why it's important, and then maybe I don't know if it's a backlash or the criticism of the industry that we saw in the last week or two.
I mean, I would start off by saying I actually hate the terminology data labeling because when you talk about data labeling, you think about people doing incredibly simple things like labeling images of cats and dogs and drawing bounding boxes on cars. And I think what we do is actually so much more complex than that. It's like I often think of what we're doing as building a kind of school for AGI. Like we have all these incredibly smart physicists like Harvard professors, Princeton graduate students, Stanford computer science PhDs. And what they're doing is they're kind of like cross-examining these models and probing them to figure out when they make mistakes. And then when they make a mistake, they're going in and teaching them all these incredibly advanced things. And so, yeah, I mean, I think it's almost like one of the most profound things that we can do for AI. It's like I mean even goes beyond teaching. I often think about what we're doing as like raising these models not just to be correct, not just to produce the answer to a question, but to think and to have certain kinds of values and to have like wisdom and taste and all that. So yeah, so I first of all I'll start by saying I think what we're doing...
What's a better term? Is there an industry term that has evolved from data labeling because I agree with you, much more than that. When you hire PhDs or lawyers or CPAs to do this data training, is it data training? What's the right term in the industry or does it need one?
So again, I think I often think about this either parenting or education analogy where again what we're doing is going beyond teaching them facts, going beyond just teaching them, oh like you know this is a Wikipedia page and here's a correct answer. Instead we're trying to teach them like creativity and taste. So like personally the terminology I like is either like teaching the models, AI teaching, because I think it actually gets beyond training as well like you're also measuring them and all that.
How much are these collectively the frontier models spending on this? It seems like billions of dollars a year.
Yeah. And I think the crazy thing is I actually think that this pales in comparison to their compute budgets. So I think they should be spending a lot more.
Yeah, fair enough. And these are true experts getting paid a hundred bucks an hour, 200 bucks an hour from what I understand in very specific fields because a lot of the data obviously on the web that could have been crawled has been crawled. So, how does it just take us mechanically and then we'll get into this amazing doc we have today. We got four or five great subjects we're going to chop up, but just for the audience to understand, how does it work? Do you take the queries that people gave a thumbs down to when they were using an LLM and does that get routed? Like if somebody's not happy with a Perplexity query, does it get routed to you to fix or do you just say, 'Hey, let's just hire this group of attorneys to take these important cases in the world and annotate them in an intelligent fashion.' How does the data work?
Yeah. So there's actually a bunch of different ways it can work. So probably the most canonical way it works is okay so you have this like expert mathematician and in the course of their normal research like yeah they're trying to you know prove some new theorem they will kind of just like interact with the models as if they're doing their normal research so you know hey you try to solve this problem or try to explain this concept to me they keep on doing that until they find a failure from the model again this is why I often think about it as a cross-examining the model like you're talking to them until you kind of like find this very interesting failure and sometimes there's ways of accelerating finding that failure like we may do various things on our end where we're asking our data science team to find loss patterns in the models that guide the failures or yeah sometimes frontier labs will send us like certain kinds of queries where they sense that users are unhappy and I mean it's not always the case that the user is right like you know users are often wrong they thumbs down for incredible reasons and so they will still need to make sure that the model failed there. And so what we do is we'll verify that model failed and if so like you know we'll teach it the correct answer. Yeah like there's all these different ways of coming up with these almost like broken gaps in the model's reasoning. And so it might be either us finding it ourselves. It might be through various kinds of analyses that we do. It might be through like user conversations. Yeah we basically take those conversations and then we teach it the right answer.
Yeah. And it's becoming a great job for people. All right listen topic one. The industry was really taken back by Tim Cook deciding to transition out of the CEO role. This is an important thing for us to discuss here because Apple has a huge opportunity in AI for a number of reasons and they named John Ternus as CEO. Cook's going to be CEO until September 1st. Then he'll move up to executive chairman and he's still going to work on industry relations but Ternus has been there now for 25 years and he worked on a lot of very important hardware products while at the company and I guess the take I'm most interested in hearing Aravind from you and also from Edwin is Apple Silicon has arguably been one of their great success stories they got off Intel and then when the open source thing came out or Kimi, open source models people said hey where can I run these okay if you don't have an Nvidia rack people started pulling together Mac Studios with 128 gigs of RAM, 512 gigs of RAM and they also have Siri so you have Siri, you have silicon and you have a system, an operating system so three big S's there. What should the new CEO do? What would you advise them to do? Aravind, if you were working with them with this incredible group of assets because they don't have a language model to speak of. They've worked on some open source projects. They've got a dysfunctional broken Siri that everybody wants to throw out the window of their car when they try to use it, but it does feel like they are positioned well. What are your takes?
I actually think the M series chips, which is a project led by John Ternus, the current CEO, is one of their underrated assets. I think people really underestimate what it takes to build a powerful chip. At this moment in time it is even better on the benchmarks than DGX Spark at least for local inference of LLMs that can be hosted locally and the open source models like you might have seen Kimi K2.6 that launched recently I think yesterday that seems to be doing even better than Opus and GPT on some of the terminal bench and agentic benchmarks. So, I do think these models are getting to a point like Qwen 3.6, Kimi K2.6, they're getting to a point where they can be competitive with the Frontier, but they could also potentially run on one of your MacBooks or the auxiliary hardware like Mac minis, especially the M6 chips are going to be even better. They've already secured like a lot of the fab capacity in advance for the two nanometer chips for next year and two so they should go deeper on this and I think you have the perfect leader for that and Tim has set up the company well so that Apple Silicon as a bet paid off for multiple years in the future. So if you know agent loops start running locally that's the CPU compute all that stuff doesn't need to be centralized on servers you get to own your agent loops what data your agent accesses on your local system local files, local apps, messages, emails, notes, photos all that can stay private and the orchestration loop can run locally and the model orchestrating them could also potentially run locally. And which company is best positioned to profit from all this? I think it's Apple. So they're actually in a pretty good spot.
Yeah, this is an incredible vision if you think about it, Edwin, because Frontier models are expensive. Now they are the frontier models, so they tend to be ahead, but as you learn, if you're a Perplexity user and it's just picking the best model for you, you know, nine out of 10 queries that most people do, they actually don't care what model it is.
So this vision is compatible with frontier models coexisting together like this orchestrator can still ping a sub-agent that relies on a frontier model.
It could use your own API key or it could use a Perplexity centralized version doesn't matter but the key thing is the loop starts running locally on your hardware. The agent loop itself, the recurring processes like event triggers, like we could have a trigger that says every time, you know, Jason texts me about an issue on Perplexity Max, make sure to alert my support team about it. I could set up a lot of loops like this that just don't need to run on any server. And then it starts to be my own personal computer or like my own agent that I own. And the hardware device that's best suited for this is Apple's ecosystem.
Edwin, what are your thoughts here on the power of the silicon and what the new CEO should do given, hey, they're kind of starting from zero in terms of any kind of product that's facing their massive customer base. What would you do? Do you have the same vision as Aravind or a different one?
So, I think I would say two things. So, one is I think historically people, a lot of people have thought that LLMs were going to be commodities. Like at the end of the day, every model is going to be, you know, intelligent to some level and they're going to be interchangeable. I think what I believe and what we've been starting to see over the past year is that actually every model has a different personality. Like you interact with ChatGPT and it just feels very different from the type of conversation, type of personality, the type of taste that you get from, you know, Claude or Gemini. And so it's almost like if they're not a commodity, and I really don't think they're going to be, you really, really, really need your own foundation model because AI is just going to be so important to the future and to the kind of feel that you want your products to have that you really are going to need your own foundation model. Otherwise, you're just going to be relying on somebody else's taste, somebody else's sophistication. So, I really do think that the base foundation model is going to be incredibly important for Apple and they really need to build it themselves.
I think they need, do they need to own a model Edwin on that first point? Do you think that they should either buy a model company or just start a group and you know maybe fork an existing open-source one because they have an image one that they work on that's open source. What do you think they should do in terms of building models and then you definitely go on to your second point?
Oh, yeah. I mean, I definitely think they really need to build their own because if you don't build your own, you're relying on somebody else's taste and personality for how an AI should behave. And like, you know, Apple has obviously always had such a strong vision for what their products and what their design should be that they can't just outsource it to somebody else. Like sure they may be able to do it temporarily just sort of play around with all these different concepts and play around with what AI products may look like on an Apple device but they really really want to own the future they're going to need their own, like they're doing, they're going to need to infuse their own values into the way they want these AI systems to behave.
Seems clear to me that they are going to put the whole company behind this and one has to wonder like Steve Jobs started the Apple Silicon movement I think it was 2008, 2009 when they made the decision they came out with the first products like eight, nine years later this was a very significant strategic effort but I don't think that they had in mind like oh this is going at that time there were no large language models nobody even knew that this product would exist but man talk about serendipity and making a great bet huh Aravind if you think about historically Steve Jobs's legacy he kind of saw around a corner that you know or maybe two corners at once this impossible possible task.
Yeah, I think Apple Silicon is like a very fortunate, like it wasn't necessarily a bet they made to build hardware for LLMs but the hardware got increasingly more and more powerful. The neural engine is very capable. The MLX compiler is like really really good and they have like a lot of expertise in building these things now. And not just that like it's not just about Mac Studios or minis. It's also consider the fact that if you do want you know like an ecosystem of compute, right? Like you want to wear an Apple glass, let's say in the future, you want to parse whatever you're seeing and you want to start asking questions about it. And all that pairs seamlessly with some auxiliary hardware you have at home, but it's all running as a pseudo desktop server. So, you're able to pair all that compute in one family of devices. I think all that's like the kind of magical experiences you can provide to a consumer without draining the battery on the device itself. So, that all that stuff hasn't been converted into a real consumer experience yet.
But it feels like to me that even if other people build all these consumer AI devices, they're eventually going to lose to Apple, because they have all the chips advantage. They've already secured the capacity for it years in advance. They have the OS. They have the ecosystem lock-in and they have all their personal context and you trust them to handle it in the most privacy conscious way. Right?
This is a key point right the privacy. Unpack that a bit because you mentioned it earlier like hey as a corporation you want to own your agent loops the agentic knowledge that your organization is building. It's essentially your entire business and to feed it into another LLM. Well, that might seem to some people like, okay, no big deal until all your secrets are now being used by your competitors because you just did the training on the next cloud model.
So I mean this is also why they I think they even if they're using a different model like say I guess the news is they're working with Gemini. They will host it on their own silicon, they will customize it to their own needs. They'll be doing a lot of custom post-training for that. So my sense is that even though people a lot of people have like you know opinions on how bad Siri is or good Siri is they have the ability to take time and do things the way they want to do because they have a lot of advantages as a brand that people truly trust and the ecosystem lock-in is underrated and the auxiliary hardware devices, the chip advantage all this is like really underrated right now because and here's my opinion, I haven't said this before, the phone, the iPhone is actually not getting disrupted by AI at all. In fact, like the more AI works better, the iPhone essentially becomes your digital passport. It has your wallets, it has all your cards, it has your passes, it has your health records. You connect with other human beings through it. You do FaceTimes, you do calls, you have your photos of like precious moments in your life. All these are things that are truly personal to you and have no connection to AI.
And that's why they can actually afford to move slow.
Yeah. It does seem to me that that privacy piece, Edwin, is the privacy plus the silicon plus oh my gosh, my photos are here, but I don't want my photos up on OpenAI. All due respect to ChatGPT but even with Google I'm like do I really, I turned off syncing you know and took my photos off Google I'm like I don't think I want those in the Google cloud right now I much prefer to keep all my kids photos and maybe I'm a weirdo on my local device and I trust Apple to not train their next image model on my kids images etc.
Yeah. Exactly. I mean, especially because these models are so powerful that when they're trained on certain pieces of data, they just end up regurgitating it. Like I think it's really really important that you know, people should wonder about where their data comes from.
Yeah. You know, the next story, Edwin, you were talking on our group chat there about the massive amount of late-stage capital. And we are in, you know, a really interesting moment in time in venture capital. We've all been in the industry for a while, but the amount of money and the velocity of the money coming into this space is extraordinary. AI companies raised $242 billion in Q1 of 2026. I'm assuming that number includes the giant hundred billion raise by OpenAI. But your company Edwin, Surge, you waited to raise money. I think you hit like a billion dollars in revenue before you did your first round. Maybe talk a little bit about why you went with the bootstrap model for as long as you did and then what the impact of all this money being dumped on founders, what is the impact that's going to have on the industry and then Aravind I'm going to go to you just to talk about how you manage your treasury because you've also been a beneficiary of this.
Yeah so I mean first of all we've actually never raised so we're still...
So we're still happily bootstrapped and growing in I think the best way possible so I'm actually really really happy that we've never raised and I mean yeah to your point I think it's never been easier to raise money in AI and that's kind of the problem like when you raise a billion dollars you get all these growth targets from your investors that incentivize volume over quality. You get all this board pressure to spend your time optimizing for your next fund raise instead of the product that you're building. Like all of my friends who are CEOs of other companies, they're like, 'Oh yeah, I have to spend the next few weeks just prepping a board deck' and they're like always jealous of the fact that I don't have to do the same thing. And I think the problem is like I've heard that some post-training teams their goal is not to make their model more intelligent. Their goal of all these post-training teams is actually just to get their companies a billion users. And if that's your north star, and yeah, it's a north star that happens when you raise gazillions of money, is it a surprise that your model starts trying to whisper in your ear and start click baiting you? Like, it's kind of funny. A couple weeks ago, I was chatting with ChatGPT and I was asking it, you know, give me some tips for what to do in Tokyo. And it ended a response to me with like, hey, by the way, do you want to hear about one weird trick that you could do that you know? And it was just like shocking to me because like we have this super intelligent model and it just sounds like a 2002 tabloid. And the problem is like this is what happens when you have all these different incentives that don't align with what you were originally trying to build. So yeah. We I think we chose a completely different path. And I think we're really happy that we did it.
Super impressive. And you hit a billion dollars of revenue bootstrapping which I don't, I'm trying to think of another company that's done that in our industry and I can't. I don't know if you can Edwin. Have you heard of one who's hit a billion in revenue without raising venture capital? I mean, I know people who have been incredibly judicious about raising, but that's a true first for me. I don't think I've ever heard that.
Yeah. And I mean, I think it's really important because I actually really do think that AI it's just so important for our future that it kind of needs to be shielded from the typical Silicon Valley growth hack playbook.
The only ones I can think of Mailchimp was famous for that and Patagonia, but that's not in our industry. But those are, oh, that other company that was another one that didn't raise a ton of money to do this. Aravind, do you've got a war chest? You've raised from some of the most important companies and investors in the world. Do you think we're seeing unnatural acts? That's the term I use for what the phenomenon Edwin's talking about. I saw it up close and personal when I was in the publishing space and people would chase clickbait. They would, you know, do all kinds of unnatural acts to try to get their page views up and, you know, BuzzFeed would be and Business Insider would be these like canonical examples of just lunacy. How do you stay grounded? And then also you're in competition with people. So if you don't raise, then there's capital as a weapon. As I saw up close and personal with Uber versus Lyft versus Sidecar, just Travis was an absolute monster when it came to raising money. And if you invested in one, you couldn't invest in the other. That's kind of gone away here a bit, but what are your thoughts on this issue?
Huge kudos to Edwin for doing what he did. I think not raising capital and getting to a billion in revenue is very very hard. Not just in terms of business building, like you know just financial health but also convincing other people to come join your venture. They're all looking at valuations. They all want validation from the rest of the world like to convince really good employees to come join you when you don't yet have a working business. They want to see validation from somebody else which could be a reputed venture capitalist. So trying to build a team without that is very hard. So kudos to him. I think one thing that you know founders need to take away from the success of companies like Surge is the need to be more disciplined. You can raise money for sure. As long as you truly know that you're spending it the right way and like you know Elon raises a lot of money but he knows exactly what to do with it right. xAI has raised a lot of money but it's being spent on building data centers he's not, he's known for like being very judicious about the allocation of capital so you can be both, you can have the discipline of bootstrap founders but you can also have the ambition of the most successful founder in history of capitalism, Elon. So, if you can figure out a way to be both, you could be far more successful. So, that's my takeaway. It doesn't have to be a dichotomy between like staying bootstrapped forever or raising endlessly with very undisciplined capital allocation. I think you should just be a very good capital allocator and you should have a clear plan for why you need money and then you should continue to have the discipline of a bootstrap founder even if you have a gigantic war chest.
How have you managed it? You've raised how much have you raised to date? I mean I think it's been public.
I think we've raised around like around 2 billion cumulatively. We haven't raised since like August of last year and our goal is actually to advance further in our revenue progress that we've been doing since the beginning of the year and try to become profitable. Unlike a model company we don't have to actually spend a lot on compute particularly on training. We do a lot of post-training but we don't do any pre-training. So we have no excuses to not be profitable and unlike companies in the coding application layer where your gross margins on the revenue are actually negative. We don't have that problem. So gross margins on all the revenue we make are pretty positive, highly positive in the case of like Max users the $200 a month plan. So our goal is to just keep growing the top line, stay disciplined, not actually spend more on payroll or infra and become profitable as soon as possible. And when that happens like we don't actually think more capital is leading to a meaningful change to our destiny and that probably should become the blueprint for application layer companies like try to just run the company in an efficient way with the discipline of bootstrap founders like Edwin and try to keep growing the topline revenue.
Being having the unit economics dialed in Aravind is critically important. We've seen some other folks who are supposedly and I think the coding space is the number one example. They're just losing money.
Yeah. On their, I don't know what percentage of users or maybe it's the entire user base in aggregate loses money.
Yeah, that's right. That's what I know. It could change but today that is the case and this is not because of the product. It's actually because of the Frontier Lab subsidizing the tokens in the form of a subscription plan. So even though Claude Code is worth $200 a month, the amount of tokens you can consume on Claude Code is actually worth more than the $200 a month you pay. So they're actually running it as a loss leader to just dominate token collection in order to take all those tokens and make their models even better. So if you are an application layer company competing with Codeex and Claude Code and coding it's pretty difficult for you to have any positive gross margins.
And Edwin I would assume that part of that is whoever has the most tokens consumed specifically in coding will have the best model because you'll have the most reinforcement learning and all that usage from developers is going to create signal for your model.
Yeah. I think there's a lot of interesting signals that you can learn and you know just kind of like the more you reason the more your models reason like often times that just leads to better responses.
Can these other companies keep up with Claude Code, Cursor, Codeex, GitHub Copilot? Are they going to keep up or do you think we've hit this sort of acceleration where Claude's going to run away with it? Edwin.
I don't think there's anything inherently preventing any other companies from catching up. Like certainly that data is valuable, but like we're almost still sort of at the beginning of all of this progress that can still be made that I think yeah, I think someone else could catch up if they wanted to.
Oh, do you do data labeling for all this like fortran and cobalt? And is that part of the desire of these companies to get you to find these old gray beards to explain to you how these, you know, AS400 and micro computers from the 60s and 70s actually work? Is that a big business for you?
It is. I mean, the coding landscape is just so huge that we have to be part of every aspect of it. So yeah, it's every single language. It is front end design and backend design. It is you know the correctness of their algorithms, the efficiency of the algorithms but also the quality and the beauty of the front end designs that they create. So it's just such a wide landscape that yeah we have to be part of.
Are we in the endgame when it comes to coding you know it is a finite set of data Edwin so it would seem to me there will be diminishing returns at some point we are we 96% of the way there 99% of the way there like self-driving is apparently you know 98, 99% of there with the edge cases how would you contextualize that game if we made it a game in terms of being perfectly solved. You know, chess got perfectly solved. They believe no limit holdem has been almost perfectly solved and PLO. We'll see if that eventually becomes perfectly solved. I think they're on the way.
Yeah. I mean, I don't think we're anywhere close. So, one of the things I often think about is coding is very different from playing Go in that coding is completely open-ended, right? You could literally create any program in the world. It doesn't have a single solution or like a single end state. Like sure like a game of Go sort of ends with one person winning and the other person losing. It's almost like I think one analogy I often think about is imagine you took Jeff Dean and you gave Jeff Dean a thousand years to learn more about coding and to explore the world and to also learn about poetry and mathematics and physics and like I don't know history and artistry and all of that like he'd be able to incorporate all of these principles into what he builds and yeah I mean like software is about building globe spanning infrastructure it's about designing rocket ships so there's almost an infinite ceiling to what coding is capable of that I mean I really think that we're just 1% of the way there.
Where do you stand on Aravind? You think we're getting close to solving the game of coding and everyone will just be able to make quality code or do you think we're producing a lot of slop with a lot of attack vectors? We've obviously, you know, covered the various attack vectors that AI is helping identify with Mythos, but what's your take on where we are at in terms of solving the game of coding?
I mean, I think the framing should be around like what does solving mean, right? So maybe think of this as paradigms like Cursor, GitHub Copilot like autocomplete like you're trying to complete a few lines of code but you are writing code largely. Claude Code, Codeex as command line interfaces. You can almost think of it as auto diff. You're looking at the diff the new lines of code added and the existing lines of code subtracted. You're not actually like autocompleting anymore. You're operating at a different abstraction of like changes. The next paradigm is just going to be auto outcomes. Like you're going to look at the outcome. You're not even going to look at the diffs. You're not going to read any line of code. You're going to look at the outcome and then you're going to ask for changes and you're going to keep iterating. That's clearly the next thing. And Elon talks about it. I think he talked about it in one of the xAI all hands live stream where he said you're gonna just output the binary, you know, like you don't even... which is a wild thing to think about.
Yeah. So I still feel we haven't climbed on capability yet. I think we're still very early in like what it means to solve coding entirely. Of course, problem solving skills, like you know, the ability to connect dots across different things that Edwin was talking about. You know, I almost imagine like how would it, what is Jeff Dean or someone like Jeff, I heard Linus is also using AI. So what are these people coding? How do they do things these days that they were not able to do before? I think all these things are very interesting to think about. But also fundamentally if AIs can actually move you to the level of like working at outcomes and binaries then you know what you think of coding also changes from just inspecting lines of code and things like that.
I think that's the most exciting part and you know I always say startups are like where you can see these trends before anything else. It's kind of like Santa Monica with yogurt. I'm sorry, with yoga and like fresh food and farmers market. Like everything interesting starts with the hippies in Santa Monica and Venice and then goes east if they wind up making it past there. And I feel like startups are the same thing. Startups now, you'll have two or three people. They'll never add their fourth employee, the fifth employee that they thought they were going to add. And they're shipping code faster than I've ever seen. And they're doing their go-to market and their customer acquisition using things like Perplexity's Computer and they're producing code at such an alarming rate. Edwin, I actually think we're going to see a significant amount of job creation as more people realize I don't need a developer to start a company. And we had a false start. There were people using scripting and oh god, what was it called before vibe coding? There was a term for like these code, they were almost like WYSIWYG, gosh what was the name of it do you know Aravind that people used to do where they would kind of vibe code...
No code thank you. The no code movement which was like such a false start but I used to have people pitch the accelerator and I'd be like oh who built this like I did I'm like okay what I thought you were the salesperson from Salesforce who started their own company like yeah but I just figured out how to use no code and like no code's just gone now, right? Like it's just totally replaced.
