The State of AI: Macro, Apps, and Consumer

Anish Acharya joins Jen Kha to break down the next frontier of AI, from the evolving model landscape and open-source AI to why the application layer, and consumer AI in particular, may be entering a new phase. Anish explains why he believes there will be multiple winners at the model layer, why traditional moats like network effects, scale, and brand still matter, and how companies can choose between frontier and open-weight models depending on the economics of the task. They also explore why models are increasingly specializing, and how applications can combine different types of intelligence to create products that are more valuable than any single model. The conversation then turns to consumer AI: personal agents that can shop and manage your inbox, coding tools enabling a new generation of small businesses, and why Anish thinks we're seeing a renaissance for consumer builders. They also discuss the changing economics of AI software, the rise of "luxury software," and why the biggest risk for today's founders may no longer be thinking too big, but thinking too small.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Erik Torenberg?

  For the last few years, the biggest question in AI was which model would win? The next phase may be less about the models and more about what gets built on top of them. In this episode, Jen Ca sits down with Anish Acharya to unpack where AI goes next. From an increasingly competitive model landscape to the explosion of applications turning raw intelligence into products people actually use. They discuss why AI models aren't becoming commodities, where open weight models have an advantage, and why Anish believes the application layer can capture significant value even as frontier labs continue to grow. Then they turn to consumer AI, where personal agents are beginning to shop,

• 0:42Erik Torenberg?

  manage inboxes, and take action on our behalf. Anish explains why this could be a renaissance for consumer builders, and why a new generation of founders may need to think much bigger about what AI makes possible.

• 0:58Jen Kha?

  To help me break down all things around this incredible abundance stuff, I'm going to bring up Anish Acharya.

• 1:04Anish Acharya?

  Good morning.

• 1:05Jen Kha?

  Thanks, Jen.

• 1:05Anish Acharya?

  Hi.

• 1:06Jen Kha?

  Awesome, awesome, awesome. Hey, Anish. Anish and I were at a GP offsite earlier this week, and he shared with me that he's already running GrokBots, and has purchased a bunch of jeans for him. So, Anish, do you want to drop what you purchased?

• 1:20Anish Acharya?

  True story, true story, yes. I'm going to reveal an important, secret, protected IP, which is that I mostly wear Frame jeans. Frame is a great brand. And GrokBots is an awesome product, actually. I'd say the kind of defining characteristic of GrokBots is sort of resourcefulness. I went to bed a few nights ago and said, hey, buy me a pair of jeans that are inspired by these. I took a photo of my current jeans. I said, don't spend more than $500 and get it done. I woke up in the morning, and it had researched, found a pair, same fit, different wash. Used my credit card, purchased them, and they're on the way. So I think that is going to be something that we see more and more of. We already have the capabilities, and now a lot of the kind of unlock will come from resourcefulness and also the kind of product architecture delivered in a way that most consumers can understand.

• 2:03Jen Kha?

  Awesome, awesome, awesome. Yeah, I told my team that I'm going to set my bot to finally take care of the pile of things I've been promising my husband that I'm going to sell for the last two years. That is the project for this weekend. So, Anish, we asked the question earlier. Which one of today's AI leaders will be the clear winner in three years from now? What's your take?

• 2:22Anish Acharya?

  I'm a many-winners guy. Yeah, I'm in good company with many of you. I mean, if you look at what's happened in the last two weeks, actually, I went from not even being a real contender on the model side to being one of three. So we extraordinarily went from a two-horse race to a three-horse race. And even more broadly, over the course of the year, we went from Anthropic feeling like they were so dominant they could do no wrong to OpenAI, who's just had an excellent three months. The new models are exceptional. The new Codex harness and ChatGPT desktop app is very well done. And we're seeing the sort of specialization in different directions of these labs. They're both growing like crazy, despite each other's continued successes at XAI and at OpenWeight as well. So I'm definitely in the many-winners camp.

• 3:04Jen Kha?

  Yeah, it's interesting to see the sentiment also on X, which is not always a perfect, you know, weather rain for the future, but oftentimes a early indicator of at least where developer sentiment is. And there's been a lot of pushback from Claude, it seems like, recently on people in terms of token usage, et cetera. And so developers tend to be fair-weather friends on these things. They will go where the latest and greatest and very best model is. And particularly the last six to eight weeks, I think we're going to see some very interesting traction in terms of the flow of activity. But obviously Anthropic's going public later this year, and also there's a lot of keen interest on this. So with that, that actually brings us straight into the topic of discussion today. So where and what is next in the next frontier of intelligence?

• 3:45Anish Acharya?

  Amazing, thank you, Jen. So let me tee this up for everybody, and please hop in if you've got questions. So let's first cover the kind of macro and what's happening at a market level. Then we're going to hop into the application layer broadly and sort of talk through why applications are the productization of the intelligence primitive. And then finally, let's talk about consumer. You know, with the launch of GrokBots and a few other products, it's actually been a very fun couple of weeks in consumer. Okay, hopefully our dear friend Leopold doesn't mind me poking a little fun at him here with situational awareness, please, next. All right, look, I think that the kind of case for this being a bubble is over sort of discussed or at least fully discussed. I think actually the out-of-distribution topic that's less discussed is what if we're insufficiently optimistic?

• 4:27Anish Acharya?

  And if you look at some of the underlying indicators, what they point to is essentially infinite demand and highly constrained supply. Things like B200, which is a non sort of cutting-edge GPU prices going up on a per-hour basis, that is very strange. Normally, we see these things be highly deflationary, and it sort of points to very constricted supply and essentially infinite demand. So we're thinking and talking a lot about what's the kind of informed case for optimism here, given some of these second-order indicators. The SaaS bubble was a very, or the SaaS sort of whipsaw was an interesting peek into market psychology. Back in February, when we saw this 30% to 40% drawdown on a bunch of SaaS names, we said that the market is oversold software.

• 5:10Anish Acharya?

  Lo and behold, here we are, and many of those names are back up 40%. So I'm not quite sure what we collectively accomplished. But I'll tell you what we said then, which is still true today, which is for the enterprise, software spend is 8% to 12%. It's just not a huge proportion of spend. So the upside to vibe code your own payroll or CRM is not particularly high. The downside is essentially unlimited. Obviously, there's all kinds of sort of compliance implications of not getting things like payroll right. So most enterprise software today demands a level of precision that just isn't afforded by coding agents. The one thing that has happened, though, is the sort of tide has receded. So for a lot of SaaS companies, had a ton of SBC and things that distorted their economic performance.

• 5:52Anish Acharya?

  I think that's very much visible now, and they're going to have to sort of accelerate or die. So let's leak for the SaaS sort of market than perhaps we all collectively thought for a few months there, but still some sort of existential questions to address. There's been a huge sort of discussion of moats. Are there any moats? There's no more moats. And it's very funny, because if you actually study moats, which I think are most famously codified in the book Seven Powers. It's one of my favorites. The vast majority of moats actually are not affected by abundant, low-cost intelligence. You know, when you think about network effects, scale effects, which shows up in distribution, brand effects, which we tend to discount in Silicon Valley, these things are as good as they've ever been. No amount of coding agents is going to make Nike not Nike. The power of Instagram was never the complexity of building the Instagram app.

• 6:37Anish Acharya?

  Of course, it was the kind of network behind it. So you actually think the majority of moats are as good as they've ever been, and of course are still critical to building compounding value. There are a couple of moats that are exposed. For me, the integration mode is the most obvious one. SAP is so famously complex to integrate into and out of that it's a sort of existential risk to even migrate from one version of SAP to the next. Coding agents makes this dramatically better. I think there's a bit of an existential question actually for SIs and GSIs as to what will their value be when they've historically been this sort of point of integration. So I do think this mode is a little bit at risk, but for the other traditional moats, they persist, and they're as important as they've ever been. And I think this is a really important concept. As you start to think about what are the job functions in the enterprise that are alpha creating?

• 7:21Anish Acharya?

  It's typically product, sales, engineering, research. And conversely, what are the job functions in the enterprise that are sort of, maybe administrative is too bleak, but they are supporting other functions, legal, HR, finance, et cetera? We really think that the kind of rational architecture and the one that is emerging is that for jobs that have unlimited upside, like sales or product, you always want to use frontier tokens. And the reason for that is you just don't know what the value of the new product feature or closing a customer account is. It's effectively unbounded, and therefore it's economically rational to pay almost any price for a model that's even one IQ point smarter, your Fable 5, or your Grok, or your GPT-5.6. Conversely, when you talk about something like finance,

• 8:06Anish Acharya?

  the best way to close the books is accurately. You can't close it 10x better than accurately. So as a result, you kind of have this bounded upside problem, where it makes sense to use open weight models with reinforcement learning for the kind of Pareto efficient cost curve.

• 8:19Jen Kha?

  Maybe before we go out this one, because this is a big great debate. And again, when Kimmy dropped a few weeks ago, there was a lot of consternation about this topic, just given the relative cost, which was the focus of the topic of discussion. But our founder, Jesse Tsang from Decagon, dropped this great post around the fact that in some respects, and for a lot of companies like Decagon, open source is actually the only option. It's not just cost, it's that they can actually localize it, train, fine-tune it. And so maybe unpack a little bit of that configuration, talk through the nuances there, and why folks shouldn't be concerned, even though that is the case for startups, that there's a lot in the way of abundance around this topic.

• 8:58Anish Acharya?

  Yeah, I mean, one of the big topics that we're seeing, or one of the big trends, is that there are just, one, there are sort of comparative advantages of different models. So, and the models often have sort of areas of focus that are almost at tension with each other. So you see a certain set of models that have a high degree of neuroticism, like they're sort of autistic models. GLM-5.2 and GLM-5.3 are great examples of this, where they're very literal and they'll only do exactly what you told them to do and nothing more. Then we're seeing models like a K3 that are just much more sort of open, and they're very presumptuous, and they're creative. And there are roles for both types of models in the organization. And often the sort of shapes of those minds, if you will, are at odds with each other. So that is like one reason you actually want to have multiple models. The reinforcement learning is a really important point.

• 9:42Anish Acharya?

  If you actually have a problem that you can specialize the model around with your reasoning traces, you can start to create this compounding advantage in your domain for your customer base, where you're able to kind of shape the intelligence to be better than any general intelligence for your problem. I know Harvey's had some great results with this as well. Now, the trade-off of that kind of reinforcement learning is you lose generality. So if you have the best sort of model that's fine-tuned for solving legal problems, it may not be great at solving sort of theoretical math problems, and that's okay for Harvey's uses, or in the case of Decagon, customer support. So this sort of open weight specialization property is something that's very unique, and one of the reasons our startups are selecting them. This is also a big topic. We've learned so much since January. We should really do this

• 10:27Anish Acharya?

  monthly, Jen, or yeah, I mean, honestly, we weekly. There's just so much changing. So in January, February, there was a lot of discussion and it's very idiosyncratic and interesting. You know, Anthropic Quad released what is called a legal plugin. You know, plugins are just collections of skill files. You can think of it as a zip of skill files. Skill files are just prompts. They're just long prompts. And there was this huge panic and all of a sudden Thomson Reuters and a bunch of other sort of, you know, big legal names traded down dramatically. But those were really just prompts. And there was a lot of discussion about if labs were going to integrate vertically integrate up into the application layer. Instead, we've seen the very opposite, which is yes, they are vertically integrating, but they're vertically integrating down into inference and compute. It's actually

• 11:08Anish Acharya?

  logical now in hindsight, because the workloads for inference are very homogeneous. So you can build enormous scale in one part of the value chain. Whereas when you think about the application layer, you know, you've got so many idiosyncrasies and unique needs in terms of pricing, packaging, sort of productization, how the market wants to buy. So it's actually a much more challenging and OpEx heavy proposition to move into the application layer versus moving down into the inference layer. And this is the point I alluded to earlier, which is sort of this discussion of model commoditization. You know, if you use the models every day, which I do, I sort of hold myself to a standard of making something either small or big with every model that comes out, you start to appreciate the fact

• 11:51Anish Acharya?

  that these things are not commodities, that they have comparative advantage at a domain level. So a great example is OpenAI with their new GPT models are just so, so good at knowledge work. The harness is also very well set up for knowledge work. You know, if you use the chat GPT desktop app, you know what I mean? If you haven't, please install it. It's very, very cool and interesting. And it's the perfect sort of, when I say harness, I kind of mean kind of product container, like a browser. It's the perfect product container to do spreadsheets and slide presentations and written documents and all of that type of work. If you look at Quad Code, which many of you I'm sure have used, it's just so oriented towards software engineering. You know, it's in a terminal UI, everything

• 12:32Anish Acharya?

  from the small design decisions to the areas in which it specializes, like code planning and code testing is oriented towards the software engineer. And there are many trade offs both products are making for that sort of respective specialization. So one, you've kind of got this domain level specialization that's already occurring. And then two, as I mentioned earlier, you've got the sort of, I think of it as the big five sort of personality traits, if folks have studied that, you know, you can't be both highly open and highly neurotic. And, you know, sometimes when you have an intelligence you're applying to an accounting problem, you want neuroticism. When you're applying it to a design problem, you want openness. So you actually have a need for both types of minds in the organization, which is why you would select something like a GLM-5.3 versus a

• 13:15Anish Acharya?

  Kimi K3. So definitely not commodities in our view. This is an important point. You know, there are many product categories in which model aggregation delivers a greater than sum of parts outcome. And, you know, a good metaphor for this is Expedia. You know, it's so much more useful to use Expedia than it is to go to United, than to go to Delta, than to go to Southwest. You just want a single place where you can benefit from seeing every airline's inventory. Similarly, you know, in coding, we're actually seeing this with Cursor a ton, where you want to do a very frontier model for planning, for example, but then you can use a lesser model for execution. And you really need to have one product harness or sort of product architecture that lets you use multiple models. Creative Tools is another great example, where you've got, you know, models that specialize in

• 14:00Anish Acharya?

  different modalities. So you've got something like an Eleven Labs, which of course is incredible at voice, music as well. And then you've got something like Black Forest, which is doing such an excellent job in kind of video and creative direction. And the correct product is to bring all of these together into one shell. And then finally, research and decisions. We see this all the time where, you know, the models are trained with sort of non-overlapping data sets often. So you're able to just get more information by running the same query through many models adversarially, and then having a separate model sort of help you converge. This is a place where the application layer really shines, because labs, of course, are both incentivized and structurally only able to provide their own in-house models. You, as an application sort of aggregator, can provide the best of breed. Okay, let's

• 14:43Anish Acharya?

  jump into the ops layer. The key point about the application layer is that, you know, intelligence is a primitive, just like buying cloud is a primitive. And what does Salesforce do? It sort of takes the, you know, AWS cloud primitive and turns it into CRM software that delivers an economic outcome for all of their customer segments. The same thing is true of the AI application layer. You know, it's great to have the raw intelligence primitive, but you really need Harvey to turn that into an economic outcome for the legal industry. Similar for somebody like credit unions, is a really interesting market segment where they're so idiosyncratic in how they want to buy products, how they want the product sort of productized, and the shape of the ambition for their market. You know, most credit unions don't want to decrease their headcount by half. They want to double

• 15:28Anish Acharya?

  it, right? And they want to double it while having an economically performing business. So it's just a very specific way that they see the intelligence primitive playing out in their market segment. And the application layer's opportunity is to be the one that kind of delivers that. This is a bit of an advanced concept, but I think an important one. If you look at the kind of way that the evolution of AI use has gone, it's gone from prompting models to putting models in loops. You know, the term agent is overused, but agent is just a model in a loop with sort of tools and memory and a few other things. A great example of this is coding. You know, we've all seen this from software companies, which is a bug gets reported, it gets reproduced, a fix gets generated, it gets verified. If it's a low risk fix, it gets integrated and shipped, and maybe

• 16:12Anish Acharya?

  the customer gets an email saying your bug was fixed. If it's a high risk change, perhaps a human reviews it. But that way, every bug that actually gets reported to the enterprise now gets autonomously fixed through this coding loop. As you start to take that idea and apply it to other parts of the business, things like price optimization, things like procurement, these are very natural sort of business loops that occur that can be fully automated by these models. And then perhaps the most ambitious type of loop is the business loop, which is, hey, you make a change that's very cross cutting to the business and the model comes back and says, hey, I think we need to open a branch in Tijuana. Now, the model can't do that autonomously, but it can make a change at the sort of surface level of the entire business, which is extraordinary. This is how enterprise automation is going

• 16:55Anish Acharya?

  to occur through AI. And I think for me, coding has just been over and over again. And illustration, legal is another great area of industries, not markets. This is something that Mark says, and he's so right, which is if you look at intelligence as a primitive, let's think now about coding intelligence as a primitive. All of these products are working in the sort of respective areas of the stack. You know, Quad Code does such an excellent job of kind of exposing the raw hardware, so to say, to the developer, all the way up to Replit, which is a great abstraction layer for the average small business owner that's unfamiliar with code. These are variations of sort of pricing, producting, productization, packaging for the coding primitive and intelligence. And all of them are working as a result. So I think a big mental model shift for us is ensuring that

• 17:39Anish Acharya?

  we're assessing these as industries, not necessarily simple markets. Okay, and consumer. Consumers had a really cool couple of weeks. You know, we've been saying for, you know, for three years that this is going to be consumers' quarter, but I think that this might be consumers' quarter. Let's go into it. The things that have actually held back consumer so far have been a couple of things. You know, the first is, consumers don't love paying for software. We've learned this lesson over and over again. And unfortunately, unlike the sort of magic of software in the past, AI software has marginal costs of distribution and engagement. And the marginal cost can sometimes be very high. You know, I built an app I use to help me browse my X timeline, and it costs $250 to onboard a new user. So if I'm a startup founder looking at that, looking at a kind of $250, even with a $0

• 18:23Anish Acharya?

  tax onboarding cost, it's very hard to make a mass market free product work. That is changing now because of open weight models, dramatically cheaper and more performant. You know, the second is, we've never had an AI native distribution channel. There's no App Store for AI. So this actual product cycle for consumer looks more like Web 2.0, where you have to kind of build the channel alongside the product, and less like mobile, where you actually have this central point of distribution for the entire ecosystem. Then the final point, I think, is an important one. You know, command line is, we're sort of in the DOS era of AI. And for this technology and its capabilities to sort of fully be embraced by consumers, we're going to need the Windows, so to say. So you think there's just a ton of work to be done around product and design craft to ensure that consumers know how to consume all this magical new capabilities.

• 19:09Anish Acharya?

  Two things are working. So coding agents are extraordinary, I know, have been discussed. I think it's interesting to think about how they work for consumers. Now, if you think of this concept of the digitally native entrepreneur, if you're not a programmer, the way that's historically shown up is you're a YouTube creator. And there was a whole moral panic that we had, you know, 10 years ago about the kids want to be YouTube creators, not astronauts. But I would interpret that instead as the kids actually who grew up on the internet want to build businesses on the internet. And the only way to do it, again, is being a creator. Now with coding agents, you can build a software product that generates $100,000 of revenue a year, $1 million of revenue a year. Now, these are not venture backable businesses, but it's a sort of mom and pop SaaS opportunity, which is emerging and I think very, very cool for the country.

• 19:53Anish Acharya?

  Personal agents, we had this collective moment of excitement around OpenClaw in January, and it was an extraordinary sort of composition of primitives, but it never really crossed over into consumer. You know, it was sort of a developer oriented thing, more of the homebrew computing club kind of energy. We're starting to see with the emergence of GrokBot and ChatGPT work, personal agents being turned into software that consumers can use.

• 20:17Jen Kha?

  Anish, actually, do you mind just pausing on this before we go to the town demo? Because you know, you were a founder building in the last era of the the consumer app experience. And when I even think about it, I was like, gosh, how do you even define consumer today? Because, you know, the plumber that utilizes now GrokBot to completely turn around their business end to end, like, is that consumer or is that enterprise? Because like, it's very like, it's almost like a like PLG led

• 20:44Anish Acharya?

  Yeah.

• 20:44Jen Kha?

  movement, but but it's coming from as a consumer that then cross over into enterprise. And particularly like the last era of consumer application is more towards entertainment as a way to monetize. And so maybe unpack some of that and particularly where you've been spending time as a part of that.

• 21:00Anish Acharya?

  I mean, our simple rule is if you cannot justify acquiring a customer through sales, which usually means a 15K ACV, you have to acquire them through marketing. We think of them as a consumer, which is most small business owners. So I think that the plumber is definitely the consumer in our sort of investing mind. Entertainment is huge, and there's going to be a bunch of AI native entertainment companies. You know, I would argue Character was kind of an entertainment company. There's been a huge trend around short form drama, mostly in Asia, and that's starting to come over here. Many of those are generative or sort of generative assisted. So I look, I think entertainment is going to be massive. Most people want to spend time, not save time, and consumers not that interested in productivity. So that's definitely going to happen and probably worth a separate deep dive.

• 21:43Anish Acharya?

  Okay, and I think Town, for folks who have used it, it's just such a magical experience. And, you know, this is like the number one sort of piece of advice I give to everybody, friends, family, folks in the industry is like, please just use the products, because it's so easy to build intuition when you see how they change day to day. And Town is an investment our partner Alex Rampell made. It's a really extraordinary productivity product. And you sort of see how the compounding improvement of the product through memory advantages it over time. So the first day you use a product, it doesn't know you that well. It's sort of like an employee, a new hire who's just getting up to speed. By day 30, it's able to make excellent assumptions on your behalf, because it just has soaked in 30 days of sort of context, memory and skills.

• 22:27Anish Acharya?

  And this is a pattern that we're seeing more and more, the sort of compounding value being delivered to the end customer, showing up as retention in the business and sort of showing up as pricing power on a per customer basis.

• 22:38Jen Kha?

  Yeah, this is a great one, because folks can utilize Town for their personal use case. And it's a free, you know, trial they give you, I think, something like 40 credits to start or something around there. And so you can kind of see it, once you plug into your personal email, how productive it actually is. On the professional front, I'm always inbox zero. On the personal front, my inbox is like 20,000. David George is probably cringing on the inside here, just because it's unacceptable. However, you know, personal life things are common. So if you email me on my personal, I will never respond to you. However, I plugged Town into it and like, I don't even check it anymore. If there's something important, Town will surface it to me. And also, it does all the scrubbing of like subscriptions and all the things that it can optimize.

• 23:21Jen Kha?

  And it's starting to now self improve upon itself. So like, it'll send you emails where it says like, hey, this routine is costing this much. Like, here's how you could actually save your credits by this. So it's sort of this this unlock into what starts on the productivity side. And to your point, maybe people won't pay for that personally. But once it starts to get locked in and then expand in terms of the remit, you're like, okay, I'll pay the whatever X bucks, you know, just because it helps to manage my life and I can put it on autopilot.

• 23:48Anish Acharya?

  Yeah, it's such a great point, Chad. And like, my mental model for this is just an experienced employee, a tenured employee versus a new hire. You know, the new hire may be brilliant and may even cost less than tenured employee. But we all know the value of a tenured employee. They're just able to make great assumptions on behalf of the organization and you. And, you know, this is a little philosophical, but I think this is where it all goes. Just as we talked about kind of coding loops and business loops for the enterprise, we think there's a set of loops that are informally defined that really sort of lay out a consumer's life. Think of family, friendships, money, health. These are all areas where you're sort of changing information, decisions, agency, execution, and then the loop continues.

• 24:30Anish Acharya?

  So we're starting to see some of these sort of loops emerge around self improvement, kind of health and finance are the two areas that OpenAI is focused on. We've seen a bunch of startups working on shopping. But we think that like the kind of way that this ends up playing out is dramatic quality of life improvement for the consumer. And and that really follows the shape of past product cycles where 80% of the surplus is delivered to the vast market.

• 24:53Jen Kha?

  Do you think, Anish, that all of these, sorry, maybe just going back to the last one, there's a question here. You know, when you think about these personal agent examples, whether it be Town or Ethos, et cetera, all point to, you know, kind of one assistant having context, but it seems like there's many different options. Do you think it'll end up being, you know, sort of one dominant platform for this personal aspect of your life as as time management? Or will it be like an operating system where you have many kind of talking to each other and kind of configuring on the back end?

• 25:22Anish Acharya?

  I think comparative advantage point kind of comes to mind. You know, I think the the characteristics you want from your CFA are different from the one that you want from your sort of party planner. And then just the surface area is so broad that I think that, yes, there's overlapping bits of context. And I think GrokBots has done a nice job of kind of illustrating this in product, where you have many bots that are pointed in slightly different directions and all coordinate to deliver a globally optimum optimal outcome.

• 25:48Jen Kha?

  There's a few questions. I'm going to go back to topics you've covered earlier. So if the application layer captures economic outcomes, how do you think about the competition from the model companies? And what will they allow value accretion to happen downstream? And, you know, are companies of the app layer able to compete with the frontier labs going after that particular market?

• 26:11Anish Acharya?

  I mean, I think so. Again, I think that we're underestimating the kind of complexity of product, pricing, packaging, and how the end customer wants to buy. You know, the way that, you know, teenager wants to consume the intelligence primitive is different than the way a marketing executive at Credit Union wants to actually consume it. And it's very heterogeneous. So to me, it just makes less sense for the labs to move up to the apps layer than to move down to inference. So, you know, and the kind of permission point's an interesting one. I think if we lived in a world of 2023, when it was one model to rule them all, it wouldn't even matter if you had permission, because the labs would just take 100% of your gross margin over time. But now, because you've got many options at all points in the Pareto frontier,

• 26:53Anish Acharya?

  you know, the labs have a harder time actually doing things like that.

• 26:57Jen Kha?

  Awesome. There was a question just on traction. So do you fund anything where there's there's no revenue at this point? Just given how quickly people have been making a progress, or is it extremely difficult?

• 27:08Anish Acharya?

  We try not to. I certainly have spent less time on that strategy. Look, I think that the basket is majority investments that are showing some signs of working. Certainly from a product velocity perspective, that used to be something we measured pretty carefully. Like, it's disqualifying to not be showing a live product in a pitch at any stage these days, because it's so trivial to build stuff. So almost everything we we're seeing are showing signs of, you know, sort of some sort of breakout. I mean, my model is somewhat simplistic, where I just sort of look at, once you have stats, sigs, sales and product, if we extrapolate from there, do we kind of like the price that we have to pay to be a part of it? And the risks that we're taking implicitly? And that's, I'd say, the majority of the work that we do. Look, for very talented, experienced folks, we do kind of take a small call option,

• 27:53Anish Acharya?

  which looks like a pre everything round, but that's not the majority of what we do.

• 27:58Jen Kha?

  Yeah, yeah. But when you think about the the kind of competitive landscape on this, consumer has been unloved for so long. Are you seeing now this reversion, just given it's clear that apps is sort of this next layer of value accretion? Like, the model sort of layer has been somewhat set, and I say that with a huge asterisk, because there might be new algorithmic breakthroughs, you know, kind of kind of folks coming out from left field as we have in the portfolio as well. But do you feel like the shift from the competitive dynamic shifting more towards application?

• 28:28Anish Acharya?

  100%. I mean, it's sort of a renaissance for being a consumer builder, because you've got this extraordinary primitive that you can work with. By the way, we now have a primitive that can kind of operate in the, you know, emotional interpersonal domain. You know, you could like have a conversation with Claude or OpenAI or K3 and feel feelings. And we've had 40 years of technology that really boosted our intellect and productivity, but nothing that kind of spoke to our humanity. So it's a whole different technology surface. It's very wide. I think there are a set of products that labs are just culturally not set up and big tech not set up to go after. You think about launching, you know, a companion product at Google that may disagree with you, that may have sexual innuendo in it. Like, these are things that there's 1,000 committees at Google are designed to prevent.

• 29:13Anish Acharya?

  So startups have areas where they're kind of uniquely capable. And then, look, finally, the consumer sort of excited to download new software, excited to pay for it. It's like Christmas 2009 with the iPhone. People want to try new apps, but unlike the 99 cents days, they're willing to pay 200 a month. So it's sort of a renaissance for consumer builders. And yeah, I think that things have changed.

• 29:32Jen Kha?

  Trying to come up with a joke, the autists in San Francisco are kindly, kindly waiting for this moment down for very, a very long time. There was a, there was a good question from Michelle here. You know, how should we think about the new economics of AI apps companies? Because there's a great, there's a debate around unit economics of of apps companies, right? Like, for example, they may, they have lower gross margins. They're just getting more pressure just because they don't have as much compute access. Capital is such a moat in this environment, it's it's hard to be competitive. So how do you think about the economics of of underwriting returns in in companies today?

• 30:07Anish Acharya?

  I mean, David wrote a great post on this. I think that the kind of the margin topic is a lot more nuanced than it once was. I think it's actually rational in many cases to trade away margin to have wider product surface. I think the very positive part of what's happening in this product cycle is the willingness to pay is extraordinary. And that's why the exercise that we often do with founders is like on the consumer side, for example, is if $20 was the historic ceiling, what's the $200 a month skew of your product? And in fact, what's the $2,000 a month skew? Like, what's the Birkenstock of software? I think we're going to have this luxury software. We're already seeing willingness to pay for it. So the margin topic is more nuanced, but the willingness to pay and buy is higher than ever. So, you know, it's a little bit of fog of war, but we're, we're thinking about all those topics.

• 30:49Jen Kha?

  And you're dropping Birkenstock frame jeans? Like, I had no idea you were such a fashionista. This is like, your butt is helping you get up to, to seat here, my friend.

• 30:58Anish Acharya?

  Your customer is a good steward of capital. Okay, that's all that I know.

• 31:04Jen Kha?

  For a guy I only see in quarter zip ups. I'm just saying. Okay, maybe one question for you on just on the, on the founders, because I don't know if you remember this conversation. This was probably five years ago or so, where most of the founders, you saw, saw more diversity in their background, in part because the software and technology was way more sophisticated. So you had a lot of program managers spinning out of Google, for example, and starting a company, et cetera. What are the type of founders you see building an app today? Are they, do they tend to lean, you know, more technical, more researcher derivatives? Are they product managers? Like, what kind of archetype are you seeing at least the early innings of apps come out from the woodwork on?

• 31:46Anish Acharya?

  Yeah, yeah. Less MBAs, more researchers. And they both have their kind of strengths and weaknesses. I think the business sophistication of the founders we're seeing today is lower, but the kind of technical sophistication is dramatically higher. And the technical sophistication is kind of upstream of all the good things that happened. You know, business sophistication can be kind of taught and observed, but technical sophistication typically not. So definitely seeing a much more technical kind of earlier career founder. But the things they're doing are extraordinary because they don't have any sort of preconceived notions about what's possible. And so much of what holds back senior founders that don't quite get to the other side of this product cycle is, you know, they're not close enough to the technology and they've got an idea that's rooted in the past of what the ceiling is. And I think

• 32:28Anish Acharya?

  the best thing about these young founders is they assume everything is possible. You know, we are at Navside where Ben was saying that the, the biggest risk in the past with the ideas were too big. And now the biggest risk is that the ideas are too small. But I think that's sort of illustrative of the different founder archetypes.

• 32:43Jen Kha?

  Yep. Yep. And maybe on that similar thread, it used to be that, that if you gave a founder too much money, it would wreck the company because the founder almost always has way too many ideas and is a visionary and doesn't have the talent to actually commensurately land with all those ideas. And we're seeing a whole new paradigm on that. Maybe unpack that idea a little bit more, just because it was such a huge theme at that offsite.

• 33:07Anish Acharya?

  Yeah, I mean, for sure, this was a historical wisdom. You know, why didn't we give every seed company 20 or 50 or $100 million? You know, it wasn't just the kind of risk reward, but rather typically the constraining factor was they just didn't have enough talented people to work across $20 million of product surface at the same time. They really had to focus on one idea at a time. And the capital was a great way to enforce that focus. What we're now seeing is you could make different sort of product and model trade offs through more or less capital. And there is a case for a company that raises $100 million, uses it productively and in a focused way, and is able to deliver a different value proposition than the very same team would be able to do with 20. So I think that, again, like the sort of, just as we talked about, sort of fog of war around margins, I think this

• 33:50Anish Acharya?

  question of what is the optimal seed around size and how much capital can you put to work effectively is a much more nuanced topic. I mean, it's sort of this embarrassment of riches, but I'd rather have this problem than the problem we had five years ago, which is, hey, my fintech company is indirectly subsidizing their customers through week underwriting and we don't know the path home.

• 34:09Jen Kha?

  Yeah. Yep. Yeah. The Chris Dixon model, which is you always want the problem of supply, not of demand, right? Right now, we have to fix the supply part, right? What the demand is like, so abundantly there that, that undoubtedly that will, the supply part will, will get fixed. Maybe I'll close on this one last question from Mosfa. So double clicking on the SME sector adoption of AI. So unlike large enterprise, the friction of adoption is much less because they require less change management. I agree with many of that, but not all. Small, small media businesses sometimes have more habit change that you got to work through. But the question is, how do you see the go to market playbook for startups targeting SMEs? And has that changed in the age of AI?

• 34:54Anish Acharya?

  Well, a lot of it for existing SMEs, I think it's the same channels with which you historically reached them. I actually think that one of the interesting things about marketing in the age of AI is that all of the sort of existing networks have been so trained on the methodology of building new networks that they're very careful to ensure no one does it on their network. So Instagram, TikTok, X, it's very hard to build a new sort of distribution channel off the backs of an existing one. So what founders have to do is actually build a product that has the original network effect, which is word of mouth. So we're definitely seeing more of a focus on word of mouth. Yes, the kind of old channels for reaching SMEs are still there. Actually, I think the most interesting segment of the market, though, is sort of new business formation, which is, by the way, at an all time high. I think it's the highest it's been outside of a peak sort

• 35:37Anish Acharya?

  of moment during COVID. These are people who would have never otherwise been SMEs. It's not the sort of 55 year old plumber. It's a 25 year old who previously would have been a YouTube creator and now is building SaaS for their, you know, neighborhood or their city or their high school or whatever else it is.

• 35:52Jen Kha?

  Yep. Awesome. Well, thank you so much for listening. It's always great to have you on. Now I know you're a fashionista and we're going to be clipping that endlessly on the socials. But, but thank you for that. And if folks have any questions, you know where to find Denise and we'll follow up here for some of the questions we weren't able to get to as well.

• 36:12Erik Torenberg?

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our Substack at A16Z.substack.com. Thanks again for listening and I'll see you in the next episode.

• 36:37Erik Torenberg?

  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.