The Future of Software Development - Vibe Coding, Prompt Engineering & AI Assistants

0:00It's like faster than light speed travel has just been invented. For many of the developers and programmers I talked to today, they're like going to Disneyland just because how many great tools there are to help them move faster. We do spend a lot of time just trying to be very honest with ourselves or what has the new behavior been created where is this stuff getting used. Infrastructure never goes away, just gets layered. In this case, it's definitely all the infrastructure that we've been using and leveraging in the past are still very relevant, but it's definitely getting layered by having this fourth pillar.

0:28Software was always the destructor. One of the most exciting things about the AI wave is like, Software is being destructed. Like, we're being destructed. This is a pretty big deal. It's by far the biggest thing that I've seen happen sort of in my life. Is AI the fourth pillar of infrastructure? Today on the podcast, we're joined by Martin Casado, Jennifer Lee, and Matt Bordstein from A16Z's InfraTeam to unpack how infrastructure is evolving in the age of AI, from compute, storage, and networking to models, developer tools, and agents. We get into what info actually means today, how it differs from enterprise and why software itself is being disrupted.

1:03We also talk through the rise of technical users as buyers, what makes an infra company defensible, and how past info waves from cloud to covid to the current AI boom have shaped how we invest in build. Let's get into it. 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 company's discussed in this podcast. For more details, including a link to our investments, please see A16Z .com forward slash disclosures.

1:49We're here today to discuss the state of Infra. First, can we get a definition of Infra, where does Infra differ from Enterprise? How do we think about it internally? I would say Infra is basically what makes software work. We'll probably get pretty deep into a set of technical definitions. You mentioned sort of networking storage compute, where does AI fit into that? But I think of the simplest possible level if you want software. Infra is what engineers are using behind the scenes to make all this possible. And our formal definition internally is technical buyer, right? So it's the stuff you use to build the stuff, the stuff you use to build apps.

2:21And if it's used by a technical user, we consider an infrastructure, whereas something like let's say vertical SaaS could be used by a flooring company or by a marketer or by sales, that would not be considered infrastructure. And technical user for the record is developer, data scientist, analyst, cybersecurity, professional. Yeah, right. There's a DevOps, right? there's a wide range of people. These are our people, right? Like the kind of nerds behind the scenes. And in the system terms, I think about it as compute, networking, storage, but also all the tooling that goes around the developer's day of what you're using to build software and what are the tools and products that are operating.

2:59These are growing in more complex software as well, all the way to semi -techno users that may want to either prototype or tinker with beauty applications. where we're very interested in anything in the technical domain and used by technical people. So you mentioned compute, networking, storage. How should we think about models? This is the fourth layer of info out of the interface. How should we think about that? I certainly think of it as a fourth layer of infrastructure. It's certainly leverage and build on top of all the three pillars we're talking about. It has a lot of demand of compute and of course it's trained and also producing a large amount of data.

3:33and to leverage and use these models for our purposes, latency and networking capabilities is also very important. But it's going to be as prevalent as any piece of infrastructure software. I don't know, like the analogy these days anymore. Is it a database? Is it sort of like a new form of a compute? So really to me, it's like a fourth pillar that incorporates everything, but also provides intelligence for the software we're using and doing today. Yeah. So I think this is exactly right. I think it's probably worth asking why a piece of infrastructure is a piece of infrastructure. And generally, a new piece of infrastructure changes the way that you program computers and it changes the stack that's around it.

4:11It's got different memory requirements. It's got different lanes and see requirements. So it just requires rethinking how we build software and how we build infrastructure. I would say in addition to compute networking storage, I would say distributed systems would also be included just because things like state consistency required to think about like proximity and guarantees. I would say databases probably did too because it changed our programming model. We have different guarantees. And these models very much fit in that for a couple of reasons. I think Jennifer is exactly right. Like you just build different data centers and different chips if you want to build these models.

4:43So it has that impact. But programming them is like non -obvious. Like we're just still trying to grapple like how you program them. Like they don't really listen to you. Sometimes they do the coding themselves. And if I were to try and to still like, what is the one biggest difference that these models provide to infrastructure? It's the following. I don't remember ever in the history of computer science where we've, from an application standpoint, we've abdicated logic. Like in the past, we've abdicated resources. Like you're like, give me compute, give me swords. Like these abstracted resources put the logic, the yes or no, like the what it's doing, always came from the programmer.

5:19But in these ones, we're like, come up with the answer for me. And so it's requiring us to rethink what does it mean to be a programmer? What does that mean to be software, et cetera? So it's clearly very fundamental to computer science. And I would say, again, to Jennifer's point, it's very, very much a new piece of infrastructure. And I think a lot of people are trying to reason by analogy. You sort of alluded to this, Jennifer. It's like, oh, is it like a database because it can answer queries? Or is it like a network? Because it's sort of non -deterministic. And we need to handle retries and weird edge cases.

5:48But I think people are really just trying to figure out how to program these things, which you sort of some, right, team. but like we've got a star from a blank sheet of paper, which is what makes our jobs like really exciting right now because there's a lot of people trying to figure it out and coming up with the ideas. Many of us have been in computer science for a long time, right? We've been in our schools and in our operating lives and in our investing lives. And like software was always the disruptor, right? Like we've disrupted taxis and we've disrupt like sales, we've disrupted the back office or we disrupt everything.

6:17Like one of the most exciting thing about the AI wave is like software is being disrupted. Like we're being disrupted, right? I just don't like that. I just don't like that. I just don't like that. I just don't like that. I don't like that. I don't like that. So we have to think of ourselves eating as we're right. Honestly, I think this is the first time I could honestly say that like the profession that I've dedicated my entire life to is being disruptive and it's very exciting because it's eating itself in a way. Yeah. And it's tempting to be a cramudge in, right? Because we've all been doing this.

6:44Oh, it's like really being open to and embracing the new stuff is like the key thing. What still applies is infrastructure never goes away. just gets layered. In this case, it's definitely all the infrastructure that we've been using and leveraging in the past are still very relevant, but it's definitely getting layered by having the support pillar, which is AI models. What's different from past super cycles? First of all, what can we learn as we enter this new one? So there's two things that happen. So one of them is often when you bring the marginal cost of something down, like with compute, we did it for computation and for the internet, we did it with distribution.

7:18it increases the tam a whole bunch. So for one, you almost always see this massive tam expansion. And part of that tends to be because the tam is bigger, you've got new users. And because you have new users, like there's normally like a new behavior that happens, right? This is very much the case with the internet, which is like people weren't used to going to a computer and talking to everybody around the world on top of the internet. And existing companies don't know really how to think about new behaviors. like they've built these sales motions and operating things around like the old behavior.

7:50And so you see Tam expansion, you see new behaviors, those new behaviors provide white space for challengers, new like startup companies to come and to go ahead and fill those models. And I think we're seeing exactly that happen with this one as well. Like clearly this market is massive. If you look at how successful these model companies are. But also you're seeing use cases that like computers just never really have done before and you're seeing that too. And so in that way, I think it rhymes very much with, say, the internet rhymes very much with probably even the microchip. Maybe I'll answer that question just from my personal experience.

8:20I'm always like sort of a tools person and also wanting to like having tools for filling certain creativity because I came to Codian Computer Science as a late bloomer after my 20s and really enjoyed all the tools available for me at that point of just building software and like learning computer science as well. Now we just have massive and massive leverage in trying to create anything as long as you have a good idea. Martín laughs at me about this. I was a bigger like local, no -co -chenthin for let's say the last five, ten years because again these are tools for people who have good ideas but may not be like educated in the computer science term but the retools of the world, the Wix and Squarespace's like you can build applications can be a software easily with these tools but now you're given like the next level of thought partners tools to really as any role in the company prototype, solve our interfaces for your end customer for end user as well as your knowledge they need or they want to see.

9:15Like you can really realize these ideas really quickly in like, your fingertips. So low code's finally happening. It just takes a lot of code. And it just turns out the code is natural language. And it was so funny because when Jennifer joined the team she was very excited about low code, but for my view, low code is like Python. I think it's like this. It's like going to be a violent, like, an anti -M is low -coated. You're right. So we kind of had to bridge that gap. And it was in a way like a bit irreconcilable until AI came out and it's very, very clearly. Like what the promise of low -code was.

9:45And so you're right, it really is disrupting software. So when I was a kid, right, the internet was sort of a new thing. I just remember really vividly, there was this movie with Sandra Bullock called the Net. And she orders a pizza from her computer. Yeah. And this is like completely mind -blowing. And like now, actually, this is a common user behavior. But like, what we're dealing with now is just so much bigger than that. Right? I just think it's really hard to, like I think some of these points about how will the infrastructure evolve, like how will the companies adapt and things like that are probably transferable.

10:14But this is a pretty big deal. It's by far the biggest thing that I've seen happen. So I'm going to slice. Let's zoom out and take this long view. And Martin, you're actually the perfect full circle because, where are you the first info investment ever as a portfolio founder? I think it was either me or Octa, but I will say Neon Todd were like the infreportful. One one LP days when they would trot us out in front of the LP's, it was like me and Todd from Octa. So for sure, I was one of the first two. At what point did we develop clear infrepractice at Aizenzea? So we always had strong infre people, right?

10:46So like Ben Horowitz is an infreq guy. And obviously I would say Mark is, like, he masquerades is a consumer guy, but he is actually I'm a revolutionized way we use computers in this deep infrastructure way. And by the way, many things came from that JavaScript, et cetera. We had Peter Levine, ZenSource, we had Scott Weiss. So there's always been deep infra. But when I joined the firm, we didn't think of it as ZenFra, we thought of it as enterprise, right? And so you're either in the consumer team, you're in the Fintech team, or you're in the Enterprise team. And the thing about just classifying the stuff as enterprise, the go -to -market motions for something that touches technology and being able to reason about that is so different than reaching to the go -to -market motion that's purely through like sales, right?

11:32And we've just learned over time that like, we do deep market diligence as a firm and we do deep diligence on companies before we invest in them and that the type of diligence we do if it was deep infrastructure just required a different type of junior partner and type of analysis. So over time we realized that companies where you can evaluate more by like the business model the market buyer, the unit economics, those kind of class of companies are just sufficiently distinct. So we decided to just pull them apart and then that's why we had the apps fun. And then of course, the InfraFun. If you think about enterprise, there is of course a horizontal piece of it, but also a lot of vertical enterprise where the applications buyers, sectors where infrastructure is almost always horizontal.

12:14And that's another thing we realize along the way if we still want to appeal to these technical audience and technical buyers, but we want to think about sort of this space in a horizontal fashion where these technology can be distributed to all these different sectors and verticals as well. And it impacts how we think about the stack, it impacts how we think about what is going to drive the form of like how the stacks integrate with each other, it's just like different from enterprise generally. Yeah, so it's actually very, very important distinction. I'm glad you brought it up. So like, technical buyers tend to be centralized buyers in a way, right?

12:46Like IT will buy compute network in storage. you've got the compute person, the networking person, but it kind of rolls up the IT. And developers are kind of a centralized buyer. So like you can understand IT, if you can understand the developers, but when I was looking at vertical SaaS apps, early in my investing career, to understand how to sell into a flooring company, you have to understand the flooring market. And that's entirely different than like the pet food market and that's entirely different than the construction market. And so I felt like there was no real central software buyer for these kind of verticals.

13:16So as you get further away from corner infrastructure, you get away from this notion of a centralized educated buyer, and just the level of analysis becomes very different. I think that's exactly right. And there's even sort of like the horseshoe theory of software buyers happening now, where you have like consumers over here, and then you kind of work up to like apps, and like you would think that Infra's way over here, but it's actually that doing back around, like developers are making a lot of the decisions, and a lot of the like marketing and sales to developers looks more like consumer these days than he is to.

13:43That's an incredibly important point. I was researching the stats the other day. When I first joined Venture, or maybe even just started thinking about infrastructure developers do like in the low tens of millions, and they're becoming like the next generation of consumers. Now they're definitely the next generation of consumers. It's going above 50 million on the way to even bigger than that now we're talking about natural language being a program in language for everybody. Like they are making decisions just as a consumer. So we're of course like turning ourselves to also understand how developers as individuals adopt tools, but also like IT buying centers as customers and enterprise organizations, like Evaluating Tools, so there's just a lot to learn and figure out given these are the technical audience we really care about.

14:24Yeah, I wanna get to how we think about our investible universe and what sort of subcategories are mature versus newer or sort of ripe, but before that, I want to better trace the evolution a little bit, so maybe more team we can start with you. If you had to categorize like since you've been investing, like the different waves of information, or like maybe the different inflection points at which it changed how we even thought about it. How would you characterize it? Let's just say for the lifecycle of the firm, so it was sort of 2009, and that was actually pre -cloud. And so a lot of the early investments were right in the cusp of cloud.

14:58And like, for example, my company, like, the cloud was out there, but it wasn't sufficiently deployed. You could use it as a core thesis. And a lot of the early investments were like this. And we talk about it in terms of tech, but it rips through the entire business model. like early software was on -prem with a perpetual license. That's just different economics and different analysis. The pre -cloud, installable software area was one, and then we saw the cloud transition. We went to recurring revenue, totally different deploying model, totally different operating model. During that time, we'd see things like net dollar retention being more important, expansion being more important, gross term being more important.

15:34It's margin. Gross margin being more important. Everything changed to that. By the way, meanwhile, of course, we saw consumer being disrupted by mobile, right, with the rise of the Ubers and the Lifts and the RB and Bs. And then again, we're seeing it happen again during this AI, which I would say, the AI transformation of the last three years has been the most dramatic. I've seen in the last 30 years of being in this industry. And there's one interesting blip along that path, which was COVID. We come from the realm of like enterprise sales and like being on the ground and the point things and like that totally evaporated.

16:07So like actually that was a dramatic shift as well, but it was driven by this kind of force measure as opposed to a secular technical wave like the other two did. I would say one of the benefit COVID brought is there was already the trend going of like developers buying and adopting tools like this bottom -up motion for infrastructure and for dev tools. It got accelerated during the COVID time where lots of people are just like tinkering, trying out products and tools and also more people building dev tools during that time. I feel like there was a flourishing ecosystem system just of these like PLG or product led DevTool companies that are giving birth to now we're seeing a lot of like AI DevTools are coming up.

16:45There's nothing developers like better than being forced to stay home, not attractive to other people. Right. This is like a real deal. That's new software. That's amazing. Okay. That's a good overview. Let's go deeper into the present a little bit. Can you guys share how we think about the different subcategories or landscape that kind to make up in from, we could also plug some examples of portfolio companies, spaces where we made some bets. Here's a few important categories. Developer tools, meaning anything developers use to make their lives better, easier, faster, more efficient. Cursor is probably our top developer tool company right now.

17:22And before that GitHub, right? Yeah, GitHub, we were in GitHub. So we've seen, very much. And Jennifer, you've actually backed a bunch of interesting DevTools companies in the last few years too. Yeah, from Lennie Fie, Stilis. There was a long time where the DevTools was written off by BC. Oh, yeah. The tam is too small. Yeah, it's here. I mean, at the time of GitHub, would you ever have imagined a repository who would be a huge company? Like, I was like, almost a joke, yeah. Right, and people are like, I'm like, business model, all these things. Small tam is the classic red flag for infra investing.

17:50If you're at home listening to this and someone tells you small tam, that they're not an infra investor. Infer creates tam. There's one takeaway from this thing, is tam creative. So yeah, so I think dev tools, you've got core infra, which is compute now, written storage, right? This is like, to IT. And then you tend to actually be quite a bit above the core infrastructure stacks. And maybe you talk through it, kind of the areas you focus on. I think about both like how developers are like easy and tools to improve their efficiency, but also how customers are getting a value out of that as well.

18:18So a lot of packaging, maybe depth tools into SaaS forms, like I mean, the industry is company called Pylon. It is a SaaS company that does customer support, but fundamentally is like doing a data pipeline. So that's like core infrastructure. That's very good at connecting with systems and providing contacts to a lot of like agents and AM models. So to me, that's infrastructure and we're spending a lot of time off course like the cutting -edge AI research So a lot of foundation modeling investing and we can probably numerate for the next 10 minutes of I will say the reason that we're a little bit skittish on this question is early in Supercycle, it's very hard to distinguish between an infrared company and the application companies and the reason is because the Tam is so small and so new the new technology becomes the app, right?

18:58Right, so let me refer to like the original super cycle that started the firm which is the internet like I remember when it came out, it was a consumer thing, or at least a student in school thing, right? And so this is one company, which everybody was downloading from an FTP server, Netscape, and using it as individuals. The enterprise didn't know what to think about it and banned it or whatever. And the same company that built JavaScript, that's building this core technology is also doing the browser. And over time it matures. And then of course you have all of these internet companies and all the applications show up.

19:31We're seeing the same thing in the AI wave. Like, is mid -journey, is that an infra company? They build a model or is that an app company? Well, it's both in this sense. And so I do think that at this stage, it's very hard to distinguish. It's exactly right. It's very hard to answer if OpenAI is an app company or is it an infra company. It already is building infrastructure. There's a cloud running these models for a different sector and different use cases. But at the same time, Build a consumer app that's TGPT. We think of Foundation Model Companies are similar. like 11 Labs, they're a voice app provider, and they have the creator application that can use the Studio2Crate voices.

20:07But at the same time, they're also supplying the voices to these large -scale enterprise use case that are fine -tuning, cloning, or own voice, and distributing that through API. So it's both. Yeah. Another area we've done a lot of work is data systems. And this goes all the way back to Databricks. And there's sort of been these two branches. One is this like kind of back -end data -enge driven, in big data systems, Spark, DUP sort of thing. And the other is this sort of data analyst, more tabular, snowflake kind of thing. And I think it's a firm, we've been super, super active and super aggressive, you know, on investing companies like Databricks, I mentioned five -trane, DBT, which you guys invested in together, Hex, which is doing really, really well, tabular, which was acquired by Databricks.

20:47So we're still, I think, really, really bullish on this. Unfortunately, hey, I had sucked the air out of the room for a lot of data companies from a bunch of different angles, but I think we'll continue to do more of this as well. How do we think about defensibility for AI companies, whether it's the app layer or the model layer, do they all have their respective areas of defensibility or how is it sort of a notion of defensibility evolved? So we once wrote a blog post that there was no defensibility anywhere in the stack. For anything, for anything. And yet people make lots of money. Yeah, and yet.

21:18You know, the argument at the time was like, okay, like Nvidia has sort of a moat because chip designs are hard to copy. But if you go sort of up or down, it's kind of like, okay, they're all sort of manufactured at the same place, right at TSMC. If you go down, if you go up, the cloud providers provide effectively the same product. Like the models are training on the same data and out of similar capabilities, the apps are all kind of like all using the same model. So to those sort of the naive theory, I think when we were just trying to understand this to be, any and I think maybe it was true at the time.

21:47Well, Martino is sort of alluding to a second ago is meanwhile, every company at every layer of the stack is doing like fantastically well right now. And during this initial phase of industry development, which we sometimes call the Brownian motion phase, I actually think it's really hard to make sort of pronouncements like this about what's gonna work, what's not gonna work, where's all you're gonna accrue, et cetera, et cetera. App companies are doing really, really well. And we're pretty clear past the sort of wrapper phase. I don't think there are any wrappers anymore. Building good products with AI is really hard.

22:16And the founders doing it now have really good intuition for how to do it. The models are clearly pushing the whole industry forward and they've built huge companies from that. So it's all working right now. And I think you could actually make a case for how defensibility will work. It's quite different from the way defensibilities work before. And maybe you guys want to add onto that. Yeah. And how defensibility work for infrared companies before largely, it's really hard to let's say, if you're building a new database, like a new framework, it just takes a lot of expertise in the domain of understanding what has happened in the past, where the field comes through, and what are the innovations that needs to happen to polish, let's say, a software into this new abstraction to provide to developers.