The a16z Show - Big Ideas 2026: New Infrastructure Primitives

Starting point is 00:00:00 Incumbents struggle to sell to startups because they're bound by the rules of P&L. I think taking a more crypto-native approach is actually better regardless of whether you want to offer the product to a crypto-native audience or to a more traditional audience. Where you see autonomous labs and autonomous science being adopted first is probably more of a function of the market that it's operating in. If you attract all of the new companies at formation and then grow with them, as your customers become big companies in their own right, so will you. As the year winds down, our investment team looks ahead. Our 26 big ideas highlight the problems, opportunities, and shifts we expect builders to take on next. This episode is about three big ideas about new rails, not incremental improvements to existing systems, but foundational primitives that make entirely new markets and workflows possible.

Starting point is 00:00:50 You'll hear how programmable money evolves beyond basic stable coins, how autonomy starts entering scientific research through labs, and how distribution itself becomes a strategy when startups sell to other startups at formation. We'll start with the rail that's already visible. Stable coins going mainstream. Guy Willett argues that stable coins are only the beginning. The next phase is on-chain credit origination and new synthetic products

Starting point is 00:01:14 that are easier to scale than simply copying traditional assets onto a blockchain. Here's Guy. My name is Guy Willett and I'm a general partner on the crypto team here at A16C. This year, my big ideas are origination and purplification. In 2025, we've seen St. Coins go mainstream with increasing outstanding issuance and payment volume. And one of the things I've been thinking about is what the most important second order effects of outstanding stable coin issuance will be. I think a lot of the existing stable coins look effectively like narrow

Starting point is 00:01:47 banks today, where they hold user deposits in fiat or perhaps in treasury bills. I think it's very unlikely in the long term that we scale on chain finance exclusively through narrow banks. And so I've been thinking much more about how we can facilitate credit and capital formation on chain, there will be a sort of new role or entity that's very important, which is something akin to a private credit fund helping to facilitate loans on chain. If you look at the way things are maturing on chain for crypto, you see, I think, a very similar market structure to what's happening in the traditional financial world. After the great financial crisis, in part because of Basel 3, there's a lot more non-bank lending from entities like large

Starting point is 00:02:29 private credit funds that have significant equity capital. So banks are doing much more lending to credit funds for making loans to end depositors. This is very similar today in crypto to how I think stable coins will effectively end up lending to curators or to asset managers who will end up making loans to end users. And one of the things I've spending a lot of time considering is how we move credit origination on chain. Instead of originating a series of loans off chain, a credit card receivable, for example, then tokenizing that, potentially securitizing it,

Starting point is 00:03:01 and moving that on-chain is a copy of an off-chain asset, how we originate credit natively on-chain. And I think this is important, specifically because it can drastically reduce back office costs like loan servicing, which in many cases can take 1 to 3% of the outstanding credit facility itself every year. So it's incredibly expensive. But I also think doing on-chain origination

Starting point is 00:03:22 will allow for much more composability between different DFI projects. In a very similar vein, when we think about how to move, traditional assets on chain, lots of people focus on tokenizing those assets, meaning creating an on-chain record or copy of the existing asset that exists off-chain. We've seen many more perpetual futures projects purpify or create a perp related to an off-chain price feed or an off-chain asset. So instead of tokenizing something like an equity and putting it on-chain, you could create a perpetual future for that equity.

Starting point is 00:03:52 This, I think, is interesting in the U.S. and in Europe and in robust capital markets, but I think it's particularly interesting for emerging market equities, things like the Indian equities market, for example, where existing derivatives like zero-day options often trade more notional volume than the underlying spot. So I think there's already pretty good product market fit for derivatives on these underlying assets. We could see a lot of those move on chain and in the coming years. The stable coins have gone mainstream today, but I would argue we need more than simple tokenization of fiat dollars or fiat currencies. And I think there are a couple of reasons for this. The first would be I think we need a better way to facilitate credit on-chain than minting stable coins and then using those stable coins for loans.

Starting point is 00:04:36 I think in many cases, stable coins look like narrow banks and we need some way to do credit on-chain. And so I think there's an interesting opportunity for curators that today operate on something like Morpho or private credit funds, someone like Apollo that has put a credit on-chain, take more of an active role in helping to manage existing stable coin collateral. An interesting form of this is there are lots of, call them synthetic dollars, because they're not strictly speaking stable coins today. But that means a dollar representation backed by off-chain traditional assets, where the normal token itself is fully collateralized by fiat dollars off-chain. But the staked representation of it then would be collateralized by higher risk and higher yield credit assets. And Athena popularized this idea. Initially, a synthetic dollar that is backed by a cash and carry trade, a basis trade for perpetual futures. but we're starting to see this proliferate into other asset classes and into other structures, things like currency, passion carry trades, synthetic dollars that are backed by physical infrastructure akin to solar panels or batteries or GPUs.

Starting point is 00:05:38 I think emerging market equities are perhaps a more interesting asset to bring on chain than, let's say, U.S. equities because they have a fundamental product value proposition as opposed to simply access. The thing that most excites me about putting U.S. equities on chain is allowing global access to, you know, truly. American financial services and assets. But when we think about an example like the Indian equities and derivatives market, in many cases, zero-day options trade higher notional volume than the underlying spot assets do. And so I think there are many places where users and retail investors are actually more interested in trading derivatives. And those derivatives could be made much more efficient and given much greater access by moving on chain. So I think the idea of purification is very interesting. And just to say what that means,

Starting point is 00:06:26 very literally, that means to make a perp or a perpetual future out of what is today an existing spot asset. So a perpetual future is a derivative that allows an end user, an investor, to tune their leverage limits in a very intuitive and simple way. And there is a perp price that trades against a mark price. And when the two prices diverge, there are fees or a funding rate that are charged to the holder of the asset. And I think perpification is interesting for a wide swath of traditional assets because it's much easier to create a synthetic representation on chain that can scale to very high notional volume than trying to tokenize existing assets, basically copying them on chain. And so to say it in briefer terms, I think the idea of

Starting point is 00:07:08 creating synthetic representations of traditional assets on chain is more easily scalable than creating literal copies of those assets on chain today. So specifically when we think about the credit markets. Lots of people are interested in tokenization today, but I don't know the tokenization brings as many benefits for credit assets as people would imagine. I'm much more interested in native on-chain loan origination because I think it can significantly reduce the back office costs that are traditionally associated with creating basically asset back securities or other forms of credit assets. I think taking a more crypto-native approach is actually better regardless of whether you want to offer the product to a crypto-native audience or to a more traditional audience because

Starting point is 00:07:48 you're going to have much greater back office efficiency in many cases. A lot of these details are still being figured out, but I think there is great potential there. When we talk about synthetic dollars on chain, meaning a token on a blockchain that is pegged to a dollar-denominated representation, but is collateralized by some underlying set of financial assets or in many cases a specific structured product, I think there are a lot of opportunities to take things like currency cash and carry trades or traditional infrastructure financing for things like GPUs or solar panels or batteries and reflect those on chain. And really, this looks very similar in a crypto-native sense to how traditional trading strategies

Starting point is 00:08:28 have been turned into ETFs in existing markets, providing greater access and legibility to users. I think that will happen much more on-chain. I think there's a real opportunity for builders to create, let's call it, a synthetic dollar factory to take these existing, these interesting trades or investing products and peg them to a dollar, offer them to existing investors, and help collateralize stable coins with those synthetic dollars. Guy's point is that once the new rails exist, you can find financial products that are more native, more composable, and cheaper to operate. Now we shift to another domain

Starting point is 00:09:07 where new rails can change the speed of progress, science. Oliver's shoe explains how advances in AI reasoning and robot learning move us toward autonomous labs. Not fully self-driving science yet, but real collaboration between scientists, AI systems, and lab automation, where interpretability and traceability matter because research requires understanding why, not just what. Here's Oliver. My name is Oliver Shue. I'm a partner on the American Dynamism team, Here at A16Z.

Starting point is 00:09:36 And my big idea is that advances in AI reasoning capabilities and in robot learning will help accelerate scientific progress by moving us closer towards autonomous labs. Laboratory automation is something that's existed for a long time. Like that is not new. This idea of having robots that you can pre-program to assist in some of the motions involved in a lab. What is new and what is emerging right now is the combination of reasoning capabilities and experiment planning and the physical element of lab automation. So, what that might look like in the near term is collaboration between a scientist and a system that involves both an AI application and a robot, and having that be a much more collaborative process

Starting point is 00:10:25 in the near term in many different kinds of labs and many different kinds of scientific processes, whether that's in the life sciences, in the chemicals industry, in the material science research sphere, and so on and so forth. One of the things that I think is important in the near term, though, is around interpretability. So, you know, if you think about AI systems as non-deterministic computers, one of the things that really matters for research is you want to really understand why the system is doing what it's doing,

Starting point is 00:10:57 why it's planning on iterating on an experiment in a given way, why it's planning on doing this particular thing. And I think, you know, systems that are purpose-built for scientific research are probably going to focus a lot on that, on the interpretability, on recording what exactly is happening throughout each step of the process as it collaborates with a human scientist. I think this concept of fully self-driving science, right, like a closed loop where you have AI that iterates on itself and then carries out an experiment and then continues to iterate

Starting point is 00:11:32 without human intervention, I think this is further out. This is what I would consider the destination for this idea of autonomous science. I think where we are right now is that there's a lot of work being done to form the foundations of autonomous science. And if you consider science as, you know, broadly speaking, some combination of theory, of computation and of experimentation, there's work being done in the AI ecosystem across areas like mathematical reasoning, physical reasoning, simulation and world models, and robot learning. And all of these things eventually, as these capabilities improve, can be applied to closing this loop. But progress across all these fields is, of course, uneven. And you kind of have to wait for the capabilities to get to the point where they're ready to be applied to this closed loop. And I think that's the destination.

Starting point is 00:12:31 And in the near term, incrementally, we'll make progress on, you know, lab automation, on the reasoning pieces of this. but ultimately the final destination, I think, would be around this idea of a self-driving lab or of autonomous science. Part of this is going to be driven by the market dynamic in which the research is conducted. So I think there are certain categories of science where there is just a much more mature demand-side market for the outputs of research. And examples include, of course, life sciences and pharma, the chemicals industry, facets of the material science industry, I think these are areas where there is a ready and willing buyer for a lot of the outputs of this research. And the increase in speed and capability, as well as any cost advantage that might accrue, all of these things are going to matter more for markets where there is a well-established buyer of research output.

Starting point is 00:13:27 And so I think where you see autonomous labs and autonomous science being adopted first is probably more of a function of the market that it's operating in. I think Periodic Labs is a great example of a team taking a swing at autonomous science. I think when you look at the early stage startup landscape, there's companies like Medra that are focused on the life sciences and pharma market. There's companies like Chemify and Yoneda Labs that are focused on the chemistry industry. And then zooming out a bit, there's collaborations between government and industry that are really focused on this intersection of AI and science. There is the Genesis mission led by the Department of Energy that brings together, you know, academia, government, and the national labs, as well as leading AI companies to pursue AI-driven science. I think just today, DeepMind announced a partnership with the UK government to collaborate on areas of scientific discovery. So I think there's startups that are working on lab automation, there's startups that are working on building an AI scientist, and that,

Starting point is 00:14:33 Work is happening against the backdrop of a broader collaboration between both the public sector and the private sector and academia to really accelerate AI-driven scientific discovery. Oliver shows what happens when tools become systems. Research gets faster when planning, execution, and iteration begin to close the loop. To close, we focus on a different kind of primitive, distribution. James Acosta introduces the Greenfield Strategy, AI-Native startups selling to other AI-native startups early, when there are a few stakeholders, no switching costs, and the chance to grow alongside customers as they scale into major companies.

Starting point is 00:15:11 Here's James. My name is James DeCosta. I'm a partner on their apps investing team. My big idea for 2026 is the Greenfield strategy. We're going to see AI-native startups selling to other AI-native startups reach scale. One of the biggest issues for startups is whether they can reach distribution

Starting point is 00:15:31 before incumbents reach innovation. And in this software side, incumbents are also adding AI as well. So what can the startup do? One of the most powerful and underrated ways for startups to win distribution is actually to serve companies at formation or Greenfield companies.

Starting point is 00:15:47 The battle between every startup and incumbent ultimately comes down to whether the startup can get to distribution before the incumbent innovates. And in this software cycle, incumbents aren't asleep on AI either. But one of the best and underrated ways startup can win the game of distribution is actually selling

Starting point is 00:16:04 to other startups. Those startups have far fewer stakeholders. You just need to convince a CEO and a couple of founders. Those startups don't need a complete solution, and those startups don't have any switching costs because they don't even have a solution in place today. If you attract all of the new companies at formation and then grow with them, as your customers become big companies in their own right, so will you. This is actually the playbook that Stripe used over a decade ago. Many of Stripe's first customers did not exist when Stripe was founded. But as Stripe's customers grew to become big customers in their own right, Stripe used them as a case study to then sell to other enterprises outside of Silicon Valley.

Starting point is 00:16:43 Incumbents struggle to sell to startups because they're bound by the rules of P&L. New startups represent very little in the form of new revenue for incumbents, but they cost money to serve in terms of marketing spend or sales or building out a new product. As a new startup, you're still figuring it out and you just need to get your initial product in customers' hands. One of the most important things if you're following this strategy is to find a constant source of new customers. And accelerators like Y Combinator or Speed Run or Entrepreneur first offer a perfect opportunity for that. Mercury, for instance, works with 50% of every single YC batch and then grows with those companies over time. Every single new startup still needs a CRM.

Starting point is 00:17:26 Every single new startup still needs a HR and workforce system. If you can fundamentally find a narrow wedge and build a better source. solution for those AI startups, you can use that as a way to get into the market. Once you've found a wedge and made that single wedge much better with AI, one of the really important things is that you have to keep shipping useful features to your customers to actually be able to grow with them and reduce the risk of churn over time. Once you've actually got these new customers, you have to rapidly iterate and ship features and build out a wider product as your customers grow.

Starting point is 00:18:00 Graduation moments also offer the perfect opportunity for startups to apply. the Greenfield strategy. This is the moments where maybe startups have outgrown a simple solution that they have in place, like moving from QuickBooks to NetSuite. And AI companies like, Rillit, are doing exactly this. You also don't have to be constrained by the existing categories of enterprise software. CRM, customer success, and finding pipeline all used to be different software systems. But now with AI, maybe the next new AI Native CRM can find your leads, can track those leads and actually ensure your customers are successful once they're onboarded to your

Starting point is 00:18:34 platform. These three ideas fit together as a single thesis. New rails create new compounding. Guy shows programmable money evolving from stable coins into on-chain credit origination in synthetic products that scale with lower operational friction. Oliver shows how autonomy entering

Starting point is 00:18:53 science through lab collaboration with interpretability as the near-term requirement that makes it usable. James shows a distribution strategy that compounds. When customers at formation, and grow with them before incumbents can catch on. This is what new infrastructure primitives really means here. Not a buzzword, but the rails that let entirely new systems emerge and scale.

Starting point is 00:19:15 Thanks for listening to this episode of the A16Z podcast. If you like 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 A16. substack.com. Thanks again for listening and I'll see you in the next episode. 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

Starting point is 00:19:48 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 slash disclosures.

The a16z Show - Big Ideas 2026: New Infrastructure Primitives

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