The Trillion Dollar AI Software Development Stack

AI coding is the first really large market for AI. When do we say this is our agents? We just at the end of the value chain, or like, does this work or not work? A click, yes or no. Agents more than ever need an environment to run these things. Contacts engineering for both humans and agents. Every single part of it is getting disrupted. It's not that, you know, there's just somebody riding code like your classical developers, being disrupted, but everybody along the value chain. [MUSIC PLAYING] So, Yoko, we just launched this, I think, amazing new DevStack for the AI coding environment. And I'm really very excited about this. Yeah. And let me start with a very high order pitch why I think this is so incredibly exciting. I think AI coding is the first really large market for AI. I mean, we've seen there's a ton of investment that has flown in the question now to some degrees of as the value, right? Why are we doing all this? AI coding is, can create an incredible amount of value. If you think about this, we have about 30 million developers worldwide, roughly, right? Let's say each of them generates $100,000 in value. The United States, it may be low because many of them get paid a lot more, but internationally, it might be a little high. But I think the order of magnitude it holds. So an aggregate, the value we're creating here is about 30 million times $100,000, so $3 trillion. I will argue even more because that's just developers, but then there's also people who are development curious. You're not developers. Maybe they're, I mean, design engineering. Now it's a big thing. Every designer-- Product managers. Right code. Doc riders. Exactly. Yeah, I mean, there's only these effects. But if you just take the $3 trillion figure, that's about the GDP of France. So the claim we're making here is crazy as it sounds, is that we're saying the entire population of the 7th or 8th, I think, largest economy on the planet generates about as much value as a couple of startups that are reshaping the AI, software development ecosystem, plus the LLMs underneath. Yeah. And everything we see and touch and use nowadays are all software. That's right. So we've-- software has disrupted everything in the world. And now software itself is getting massively disrupted. Totally. And then what you mentioned in the blog post is really interesting, just because we now are more capable at using LLM2 generate code and coding and produce software. But then as a result, there's no-- it's not like this less jobs. It's actually more and more software is being produced before maybe a SaaS service catering to hundreds of people's needs, 2,000 of people's needs. Now you can really just buy code things, software buy one for one. Yeah. I buy code. I do that, too. Exactly. You do that, exactly. And then I buy code my own email filter. I don't do that so much if using LLM. To reply to my email. But I have a filter where I categorize the labels, you know, things like that. Only to some emails. Only to some emails. So the first question becomes like, how do you think the development loop is shifting? I think the answer is complex. And very frankly, it's so early, I think, in this AI revolution, we don't have the full answer yet. But we have our little stack, our little software development lifecycle post AI in the blog post. And I think the biggest learning from that is probably that every single part of it is getting disrupted. It's not that there's just somebody writing code like your classical developers being disrupted, but everybody along the value chain is getting disrupted. What's the most surprising part for you? What's the most disrupted field today? Encoding? What do you think it will be? Like what AI will come after next? So I think, well, but we've seen the biggest growth, I can say, to say, in the classic sort of coding, IDE integrated coding assistance or more agente coding assistance, the cursors and davens and get up co-pilots and cloud codes of the world. I think that's where we see the most traction, where we see an incredible revenue growth. Possibly, I mean, I want to say that segment possibly has the fastest revenue growth of any startup sector we've seen in the history of startups, which is again an incredible statement. But I think this is currently the Vanguard, right? And everybody's aware of it. We're seeing $1 billion, you know, at quehires or take over office. So that's an incredibly vibrant sector. Now, which one is next? That's a really good question. So to be very specific in a blog, we wrote about the basic loop. The basics is your plan, your code, your review. Where does LM come in? Where do you think more of the loop would be disrupted? Like, do you think the loop will still be the same as what we used to have the basic? Or do you think it will look very different? I think at this point, it's very hard to speculate about the end state. But if you-- let's assume you've seen the first email here. No, no, no, no, no, I'll get there. But if you looked at the first email sent of the internet, right, you can sort of predict that probably we'll have websites and these things. Maybe if you're good, you can, right? But, you know, it's saying like, hey, the net effect of this is that everybody can rent out their house and compete with hotels. And this is going to be the biggest hotel company on the planet. You'd be like, well, that's a little far fetch. But now we have Airbnb, right? So the secondary effects, I think, are really hard to guess. Look, my current hypothesis is I think we'll still have software developers. I think they're not going anywhere, right? I think what they do will look completely different. Right. Yeah. I think the CS education, frankly, any CS class taught today at any major universities, probably best seen as a historical relic from a bygone time, right? I mean, the basic, if you look at the best of breed startups, what they're doing, you know, the loop that the developers in looks so different from what you did before, right? You have multiple agents that you're prompting, that you're telling things, you know, yeah, and pull that back into UI, you're trying to understand what they did, you're trying to get put them back on the rails. So a lot more thinking at a higher level. I mean, all of coding sort of has been higher level of distractions. But I think we're making a huge leap here. So how it's going to look like I have no idea. My gut fingers will probably have more developers. Look, this basic plan execute cycle, it's probably going to be some flavor of that that's still around is my guess. So one of my top questions is that would this be a step-by-step loop or would this musting to just one step? So one example is if I, if my agent is writing a code, like do I still need to review it or do I have another agent? I just reviews it. If it's the same agent, you know, like implementation details one thing you can separate out the agent generating code from the agent, you know, reviewing code. But then if it's all agent, both generating and reviewing code is just the same step. Do we actually disagree, you know, the step-by-step process? I have a human in the loop. Whereas if as a human, I write code and I want agent to review code, that makes sense to me. So I do wonder when do we pull out something? As this is an individual tool and individual staff that agent takes care of. And when do we say this is all agents? We just at the end of the value chain, or like does this work or not work? A click yes or no? I think the time period is over, which an agent can work autonomously. We'll get longer. You know, still, if somebody says like, look, I want to write a complete ERP system, you know, for my, you know, multinational enterprise, go. There's no way I could imagine that that they'll just run and back come software that actually hits the requirements. And in part, I think it's a problem that models us a little very, very far away from being able to run autonomously for that long. But the other problem is, like, let's assume this was an all human team. We wouldn't understand all the challenges yet at the beginning, right? We'd have to revisit the design. We have to visit the architecture. It has cost implications and so on. So at some point, you sort of need to go back to the architects and the product managers and say, hey, we had a plan A. Didn't quite work or we have found new challenges. So here's our updated plan A. Is that the, you know, on a plan B, right? Is this what you want to do? So I think, I think the loop will still be there. The time scales will probably change. But yeah, it's very, very hard to guess when. Yeah. Another thing we start to see more often is contrary to how much humans need to come and intervene the loop. We actually start to give agents tools for them to know what they have to do. One example is this loop I see so often, like the agent wants to implement, say, clerk off in their app. Now they need to go to Metlify and contact seven to say, what is the latest version of clerk? How can I implement it correctly and you want file? I'm not going to copy past it to cursor or give it to the agent because I'm too lazy as a human now. The agent should be able to call the API themselves to put stuff in the context to make it work. This is just one example of what behavior change were seeing. Because before, as developers, we're so used to go back to the docs and refer to the docs and tell the agent what to do. Now agents can obviously-- We cut out the middleman. Right, we cut out the middleman. I don't need to route all these requests for the agents anymore. And then I think there's other examples, which is verification. As a human, before I write code or review others, people's code, I pull out the code. And then the first thing I do is actually not to review because I don't like reading code. I'm not a human compiler. The first thing I do is to fork the change and see if it still works. If it doesn't work, I just do not review it. Nowadays, there are opportunities to give just agents a native environment to first see, does this work? Does the UI still look good? Do all the requests don't check out? - Oh yeah. - Ah, did it. break my build before the human needs to come in a review. Maybe that manifests itself in part of the local development process, maybe manifests itself in the PR review process. But in any case, now agents, more than ever, need an environment to run these things. When I used to write something just for myself, like a little script I need somewhere, the past, it usually didn't include unit tests. Production code is different, but just personal things. It's like, yeah, it's a single developer. I know what I'm doing here. With agents, I now start including unit tests because there's so much easier to write. And they allow an agent, as you said, to understand if the changes that they did, did broke anything else. They may not have the context how this original was built anymore and then easier to just perform. So that's a per valuable. On a grand scheme of how much economic value this generates, wearing the value chain, do you think it's growing the fastest? You see where the agents is producing so much more value than other areas. But what are the areas you feel like will be the next takeoff? So look, I'm talking about 100 or so enterprises about this per year. Just remember, take our portfolio companies to the most potential customers. What I'm hearing from them is that the number one use case in terms of ROI right now is legacy code porting. Right, it's not super surprising. I want the first papers in the space from Google, they wrote a fantastic paper on, you know, where they're detailed on, you know, just doing very mundane things, like replacing a Java library across a very large code base. But not like millions of lines of code, right? So it's a very large code base. What do you consider as legacy stack? What do you consider as new stack? Well, it truly depends on you. But I mean, for the banks, it's often Kobo or Fortran to do Java. Kobo, I haven't heard that word for a long time. It's, you know, I actually wrote Kobo a code once in the 90s. It probably dates me at this point. Okay, actually, how are all ons with Kobo? They're apparently extremely good. So, surprise. So here's the thing, right? One of the hardest thing, if you implement code with LMS, is just getting the specification precise, right? If I can specify something very precisely, then usually the L can do a good job at implementing it. So, one of these companies do is they take legacy code. They have an LLM, right, a specification that fits the legacy code. And then they say, re-implement the specification and you may look at the code if they're, you know, as a tie-break, if something is not clear, right? And that seems to work incredibly well. So I'm hearing that today, I heard from several sources now that you can get, you know, about a 2X speed up versus traditional on processes for that, right? And that's amazing, right? And what this has led to is that actually, of those enterprises that I've talked to, the majority says they're currently accelerating, like at least the majority of those that are sophisticated about this. They're saying they're accelerating the developer hiring, right? We don't know if there's a long-term trend, but right now they're basically saying, "Look, because we found so many low-hanging fruit-type projects where with a little bit of front investment, we can then save infrastructure costs." And that's super exciting. So what this will mean for the, you know, how much longer is the mainframe business gonna be around, or, you know, I don't know, but there's definitely a shift there, where suddenly legacy code migration is much, much easier than it was before. You know, I think that'll change a lot of the dynamics in the class enterprise software space. - That's interesting. I do wonder if we will get new mainframe code because now, 'cause before, no one knows how to program those things. - That could also be, yeah. - Right. And now you realize you can program mainframe using natural language. - Yeah, totally. - So another possibility is that we get renaissance of like the underlying legacy coding languages. - It's crazy to me how versatile these coding assistants are, right? I mean, we're seeing them right, QDoconnels. - Yeah. - Which is like, that is difficult stuff to write, right, by any metric. - Right, totally. - I've tried them with the language, which basically has no usable training data set. And they're still able to sort of abstract, you know, with a couple of examples, you know, of how the code will have to look like. It's not perfect, but so I think it's a very, very broad technology for sure. - Recently, just like what we were talking about before, code reviews, 'cause like to your point, all of us are so good at coding and generating code. Sometimes it's beyond our comprehension. We'll take more time to review the code than the coding agents. Like, that's not our country where you show up, pinion, and just like how it is. - That's the reality. - Yeah, so it does make me wonder how our development chain and steps are gonna evolve from here. How are we going to do PRs when we can't possibly review thousands of lives of code as humans? So does that mean the right abstraction now is still code or does it mean the right abstraction now is for us to review plans? If that's the case, is GitHub review still the right abstraction for that? I think there's still a role for review in general. I think the question is, will humans do the review? Like right now, most of the code then will and generates. - Yeah, unless you're deep in vibe coding territory, just like, oh, this is a one off, I'm just gonna try something out. Maybe then you don't review it, you just sit except and hope for the best. But anything else, you do review the, I still review the code line by line. That said, we're starting to see really good tools that plug into your backend system, your GitHub. Whenever pull request comes in, they analyze it, they comment on it, they point out security vulnerabilities, they point out that the spec is different from the implementation. They point out that this creates dependencies, which may not be desired, they enforce coding guidelines, which is very, very powerful. I haven't met anybody yet, tell me if you have, but I haven't met anybody yet who basically has said, we're gonna rely purely on AI to review code. Anything can go in if the AI checks off on it. But I have seen, for example, companies that are saying before we had two developers review code, and now it's one developer, right? And, or cases where basically just the AI hangs out in the GitHub discussion and comments on things. So you can basically tell you how it's like, can you look at this? I was using this library somewhere else. So basically you can, so I only have somebody who can help with these tasks. My hot take on these is actually, if PR is supposed to give us a context as developers, what did these other coding agents or my colleagues change that I should be aware of? I don't think code, reviewing code is the right abstraction. 'Cause it might be feature level, it might be performance level. So now, now it might just become a wine liner. This agent improved your Cura implementation. And maybe I don't even know Cura, but I know the improvement when I can verify it. So the question for me is, do I still need to go to the PR review every line? Or should I just be given two sentences to know how this works and the environment to test it out? And now it's just-- - If it's the right two sentences that works, will the LM always pick the right two ones? I don't know yet. - Yeah, that's true. I mean, if you're given the environment, the question is like, can you verify the environment against the two sentences? If the answer is yes, that would be easier to solve. If the answer is no, that's harder. - But I think there's a bigger picture here though, which is that LLM is also very good in generating the documentation and description for the code, right? So when I use somewhat cursor for coding, it generates code, I often ask it afterwards and now take the internal documentation up data because the internal documentation is important for me, but also for the coding agents because they want to be able to refer to it. You don't want every single time to take the entire code base and stick it into the context window. It's massively inefficient and slow. So if instead you can just say, like read this document that I explained to you the class hierarchy, right? And then based on that, implement this new subclass, or so it's much, much faster to do. And I think there's a real opportunity there to get to much better documented code than previously, right? And you can almost-- - A compiler is great and then it takes a high-level abstraction translates into a lower level one. But now we have the ability that if somebody had an hand optimizes the lower level one, we can use that to update the higher level one. - That's true. What is the new compiler in the age of AI? - LLM is a sort of a compiler, right? In some way. They take a high-level description and fill it down. I mean, do you think the people-- - I guess what's missing is it doesn't have a natively have an environment compiler give you. Does this worker, does it compile, or does it not compile? It doesn't tell you, does this work, which is the very subject thing. - I think it's right. And a compiler enforces, is a strict enforcement of certain things, right? You can rely on that, I don't know. I mean, using Rust and things will be typed, right? So that's a huge step forward, right? Then I can exclude certain bugs with an LLM, that's not the case, right? (laughs) Now, that's a sort of wonder, is this just an initial thing? Is this just a, I mean, LLM's over time, like for example, we can give LLM's tools that allow them to syntactically parse code, right? And then now suddenly it can start reasoning above code. They can ask, are we sure that the representation of object X is the same across these different modules, even if it gets realized in between or something like that? - I don't mean to make this episode disaggregating GitHub, but by GitHub is so central to so much of our workflow, everything goes through it. From the social aspect of discovering what other developers are doing, distributing the software, you can download things to keeping track of what you have changed. Git. - Or the integration of the build system, the backend, yeah. - Yeah, so now, another interesting thing we start to see is people use Git repost very differently now. Before it was like, oh, humans will make some changes, will commit it, and then other people will see it, And now we open the PR, people see. different revisions. Now agents are doing so many changes. It's kind of counterproductive to commit everything. And then repose usually have very low rate limits because it's designed for humans to use. So now the question becomes what is the new repo abstraction? That handles a distributed infrastructure but also has cater to high frequency commits. And sometimes when agents doing these commits, they don't even want to preserve this forever. It's more like an intermediate step so that they can explore five different paths, but then revert to any of them. And then when they're happy with it, they come back to GitHub. So one thing I've been playing around with is there's this company called Relays. And then their docs has this repose feature where you can give a repo to the agent that agent will come in and stuff. Like very high frequency kind of works really well with bi-coding agents. And that has been such a great experience. And in a while that I start to see people building this internally too. It makes sense. Look, if we completely change how humans write code, or we're shifting from humans to delegating the most of the writing to agents, I think we'll be foolish to assume that the underlying services that were good fit for the human role are still a good fit for this new agent. That's almost certainly not the case where we can do better in our cases. I think you're completely right for source code repositories. We want something that's much more real time. Honestly, let's take this to the limit. Let's assume I have now I've just me personally running 100 agents in parallel that all try to implement features in my code base. We probably need some kind of coordination mechanism between them. All the then all not trying to edit the same file. The rebase only carries you so far. You just get too many so. But we all need shared memory on the repo they're working on. Because we don't want to reinstall the dependencies every time. That's right. Exactly. And so you probably need something that's much, much more flexible and more real time. And we were still figuring out what that is. What we realized is doing this is amazing. And I think it's not just true for GitHub. I think GitHub is one of the big platforms we use. But take the specification writing with a conference or JIRA for example, you could use there. If you would develop these systems bottoms up with AI in mind, they would probably look very, very different. Should my story tracker have a function that looks at code and update stories accordingly? That would be very natural. We've seen this for documentation. It's not like minify. It looks very, very different at the documentation problem than previous generations. We're seeing it for testing. We're seeing it for code for PR reviews. We're rethinking this entire stack. And that's exciting. That's true. Another behavior change we have seen, which is really interesting because humans, like developers who are very lazy. So if we don't have to read it, we do not read it. We only read the relevant parts in documentation. We only skim through things. So the net new behavior we see all the time with documentation, like host it on minify, is that users who are net new just going and ask questions. And agents do not just ingest it anymore. They will actually just do a query against this context. So context engineering for both humans because where our brains are like, oh, I'm so we need the context and agents who need context, which is a very critical part. It's such a critical part of development from here. And then the question becomes, what are the other tools you think agents will need? We had this huge market map from the blog. And then there's a country where all the previous market map suggests, which is like, here are the developer tools. Now there are agent tools to make it better. In some cases, the same tool cators to both. We need to documentations for humans. So look, early days, but I think we're seeing a couple of big categories emerge. Sandboxes are helpful to try out code snippets or build things. How do you define sandbox? I think we can record the whole episode on that. But I think we will at some point. But look, it is a unique environment with certain safety guarantees, right? Where LLMs can be with clever, you know, if they use external sources, they can potentially be maliciously prompted to do bad things. And you just want to have something that basically limits how much that blast radius if anything bad happens. I think we're seeing interesting search tools and parsing tools, like, some like source graph or so, right? If I'm writing a couple of my code and a couple of files, we don't need that. But if you have really large code base, you know, and suddenly the question is, look, we're looking for, we're trying to replace, we're trying to add a parameter to a function in a library that's widely used, whereas this library used. This is a really hard problem, suddenly, right? I mean, it's like, if you're in Python or so, you can import something as something else. So, so, like a simple find operation will no longer find these things. You probably need some tactic parsing to find those. So I think we're seeing documentation tools that are optimized for for agents and allow agents to look up these things. I think it's good for if an agent can do things like web search. So, you know, we're seeing companies and that space. What are I missing here? There's so many more. I think there's the other part of more, we're seeing more specialized models. Yeah, I mean, the for things like code editing or, you know, re-ranking files and things like that, right? That's definitely shaping up as a market. If you take a big step back, if if we assume there's massive value creation here, that probably creates an opportunity to create a very large number of startups. I mean, if you would have asked me 18 months back, I would have asked me 24 months back, I would have said, like, "Look, dev tools, it's the smallest kind of market. How big can this be, right? That's not very exciting." If you ask me today, it suddenly looks like this is, you know, a market that could go in the hundreds of billions of dollars, right? And theory could go to Trillion, I don't know. So, how many companies can you create in that space? I have no idea, but probably hundreds. And over time, it'll consolidate, but I expect this to be an ecosystem, not a business model. I have a fun question, which is going forward. So, when GitHub came out, they have this commit charts. It was on t-shirts, you know, like people compare that commit charts, people commit specific messages. So, the commit charts look like, like, has a good graphics. What? Because before commit charts are so tied to the value, developer spring, like how many commits do you make? How many in lines of code do you change? I mean, we all know it's not the best proxy for value. Yes, not at all. Right. So, what would be the next commit chart on GitHub? What would that look like? What's the next? That's a very, that's a very basic question. But maybe how many tokens you burn? Do you come to the office and I look at a bird like 10 million tokens over the weekend? So, token's burn could also be very ineffective prompting or complex engineering. Yeah, just stack my entire code base in the context window, maybe. Is it the number of agents you use? Is it more game level? Right. What is the unit of value? That's the closest approximation to what you've delivered as value as developer. Not cashed in put tokens. No. I'm not going to complicate it. I don't know. I'm taking this to a high level. The metrics are we evaluate software development or changing. Right. We're potentially a big complex refactoring. Isn't that much work anymore? That's true. Because I can let the LM do this and it's structurally easy. A specific optimization in a failure obscure area where the LM has no training data. I may have to do by hand and it's vastly more valuable. So, yeah, it's complicated. Maybe it's number of apps if I coded. I think something else, which is there's on the market map agent toolboxes. There's actually a box I want to double click on. There's the agent orchestration. You can now use not just one agent but multiple agents, even different copies of the same agent for them to parallel do things. What's the implication of this? What can you do with multiple agents orchestrating them together that you couldn't do before? I mean, we're all very ad-huged. What's the most faster? I think there's a couple of things that work faster, obviously. But you can also try out multiple approaches and parallel and see which one works best. I've seen approaches where people, for example, want to optimize a certain code. They fire a couple of agents with slightly different approaches and then just measure which one works best. I see. And there's some startups proposing doing that in a more automated way, even, where you would just do this without human intervention. You just say, "Optimize this." And then this gets kicked off in the background. And the result takes a crazy amount of token set, the Android. So I mean, I think this is a really another really interesting trend where three months ago, I don't recall anybody talking about the cost of coding assistance. Right. Yeah. Three months ago, honestly, nothing. You go to a Reddit forum on cursor. There was pretty much nothing posted there. Today, there's one of the number one topics in those forums. Maybe the number one topic. Because I think we figured out how with high-powered reasoning models and very large context windows, we can have single tasks that some of they cost dollars. not about tens of dollars, but at least, you know, dollars is very, very doable. And that adds up, right? It depends, you know, if you're a super high-end programmer, maybe it doesn't matter. If anybody else, a couple of dollars an hour, you know, that's a substantial expense. If you're in a low-cost location, it may end up costing more than what you are making. It used to be that if I was writing software, over simplifying slightly, I had one expense which is people. Right? Okay, they needed, you know, a laptop and some connectivity in the office. But the grand scheme of things, the cost that really mattered, you know, at least in the, you know, more high-cost locations was the compensation of the person. That seems to have changed now. We suddenly have infrastructure costs for a software engineer, right? They need the constant feed off of LM tokens to keep them happy, otherwise they're not productive. They'll probably change the industry somehow, right? I think, I'm not sure we understand how, right? We know people will be building more, and that's for sure. That's right. And the question becomes, does building more currently with more tokens burn? To some extent, right? And I met so many great engineers who are burning, you know, like they're the top token burning engineer of the company and they're just so effective. Yeah. I have like two laptops side by side and, you know, around coding agents that way. It's like the shift from digging versus driving the excavator. Right. Press for search instead of that. But that changes the industry. Right. Probably the person slightly happier, right? I mean, that's what happened in that case. You need less of them. People will, but you can also build a lot more, right? So I think it'll change the industry then. I think there's more customization to software to your point about building more. And because there's always one bespoke tool for any business out there. You know, there's HR software. It covers 80% of what every company needs. So 20% like I know this really well because I used to be a PM on these enterprise software. We build APIs. So internal teams build their versions. That's right. And then now we just have turtles all the way down. Like we build the base layer, the internal team builds next layer and then developers are like, us, don't work. Let me build something else. But now with a vibe coding, I actually think customization is just easier and easier than ever. And now I even need a centralized team to build that layer using a commercial solutions APIs. You can just code it up yourself like what I did. Something I've been thinking about is what's the next workflow or automation going to be? Like before we have Zapier and other great RPA tools of the world to make it work. Now with vibe coding, obviously you still need to know code to some extent to make it work. How would that change? I think we'll just end up having more workflows running software somewhere. The question is like how can we unlock the net new like new developers who are not traditionally technical but now are writing code to implement these. Maybe they don't need a graphical interface or if they do need a graphical interface, it can be represented by just not which is more agent. Apparently. Yeah. But we're trying to see almost self-extending software, right? Software where a user with a prompt can add additional functionality. Yeah, that's so true. Yeah, yeah, yeah. Which is crazy. Is that a trend? I think it is a trend. I think it is. Well, the next version of Microsoft Word, not the next version, but maybe a couple of versions on the road have a, you know, add feature button and they help menu. So software, I think the takeaway point is software is having more affordance than before because of the ability to integrate LM. And what I mean by that is before if I'm a marketing company, I ship a feature so people can visualize six charts. Now I ship a chat session with the LM. LM can reach back to my data and the LM's generate code to materialize whatever charts people want to see. So it's more than six. It's like thousands and hundreds and thousands of things that people will want to see. So the interaction model between an user of the marketing software and LM becomes, it can materialize not new features using their own words. So prompt, which is very different from the four software teams. They're shipping feature by feature. So now with all that, what do you think people want to build? What do you think developers should be building? What do you think the world needs? It needs so much. I mean, there's two things I'm sure about. One is this is, let's say over the last three, four decades, probably the best moment in time to start a company in the development space. Right? If you have such a massive disruption, this is what this is what allows us to start up to really grow and scale and pick a battle with the income. And I think we've seen that with the GitHub co-pilot from Microsoft first in the market, we've seen it to the number one model company with an open AI. They have the number one source code repo. They have the number one IDE. They have the best enterprise sales force. And still, we're seeing a swarm of competitors that are all doing very, very well against them. So this is really the time. The second thing that I'm 100% convinced of is that the good ideas are not coming from the VCs, but from the part of the nurse. So if you spot an opportunity to do something better with AI right now, you can probably add value, right? And then it's about fast executions, about building a great team. It's about running very, very fast. But my predictions, I think we will fund dozens of companies this base going forward. Yeah, we are excited to just fund the next wave of startup. But if you're looking for ideas here are two general directions. The first one is, what are the traditional workflows that you can now read even? They might not be one to one. So like a better gift may not be get exactly. It might be get and something else, right? And then if you just map out the value chain, like what we have on the blog post, you can pick a box and decide what you want to do with it. I think it's right. Yeah. That's why way to do it. The other way to do it is very differently from before, like as product people, we used to only build for humans, other developers. Now we actually build a lot for the agents. Are the customers? Does the agent need a better context? That's right. You should build for that. Does the agent want lower latency for certain models? Well, their company is shipping code apply models that operates way faster with higher accuracy. That's also a need. And when you're looking to where agents don't yet work, there are just plenty of things to work on. You know, from just like easily reusable sandbox, then other agreed companies are already building that or from to like how do you enable the agent to kind of smash PR review process with the development process. There's just so much more there to even how agents could work. So tree agents as your customer. Yes. And build for them. The classic infrastructure, right? Yes. Absolutely. No, I think I think this is really an amazing time to start a company in this place. Yeah. Yeah. Thanks for listening. If you enjoyed the episode, let us know by leaving a review. We've got more great conversations coming your way. See you next time. As a reminder, the content here is for informational purposes only. Should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com/disclosures.