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a16z Podcast

Big Ideas 2026: New Infrastructure Primitives

20 min episode · 2 min read
·
Guy Ouellette

Episode

20 min

Read time

2 min

Topics

Relationships, Investing, Startups

AI-Generated Summary

Key Takeaways

  • On-Chain Credit Origination: Stablecoins function like narrow banks today, but scaling requires native on-chain loan origination that reduces back-office costs by one to three percent annually compared to tokenizing off-chain assets then moving them on-chain afterward.
  • Purpification Strategy: Creating perpetual futures for traditional assets scales better than tokenization, especially for emerging markets like Indian equities where zero-day options already trade higher notional volume than underlying spot assets, enabling global access through derivatives.
  • Autonomous Lab Adoption: AI-driven scientific research advances fastest in markets with established buyers like pharma, life sciences, and chemicals industries. Near-term systems prioritize interpretability and human-AI collaboration over fully closed-loop autonomous experimentation, which remains further out.
  • Greenfield Distribution Model: Startups win by selling to companies at formation through accelerators like Y Combinator, where Mercury captures fifty percent of each batch. New customers have zero switching costs, minimal stakeholders, and grow into enterprise accounts over time.

What It Covers

Three investment partners from a16z present 2026 predictions: programmable money evolving beyond stablecoins into on-chain credit, autonomous labs combining AI reasoning with robotics for scientific research, and AI-native startups selling to other startups at formation.

Key Questions Answered

  • On-Chain Credit Origination: Stablecoins function like narrow banks today, but scaling requires native on-chain loan origination that reduces back-office costs by one to three percent annually compared to tokenizing off-chain assets then moving them on-chain afterward.
  • Purpification Strategy: Creating perpetual futures for traditional assets scales better than tokenization, especially for emerging markets like Indian equities where zero-day options already trade higher notional volume than underlying spot assets, enabling global access through derivatives.
  • Autonomous Lab Adoption: AI-driven scientific research advances fastest in markets with established buyers like pharma, life sciences, and chemicals industries. Near-term systems prioritize interpretability and human-AI collaboration over fully closed-loop autonomous experimentation, which remains further out.
  • Greenfield Distribution Model: Startups win by selling to companies at formation through accelerators like Y Combinator, where Mercury captures fifty percent of each batch. New customers have zero switching costs, minimal stakeholders, and grow into enterprise accounts over time.

Notable Moment

Stablecoins backed by physical infrastructure like solar panels, batteries, and GPUs represent a new category of synthetic dollars that move beyond simple fiat tokenization, similar to how traditional trading strategies evolved into accessible ETFs for retail investors.

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Episode Transcript

Incumbents struggle to sell to startups because they're bound by the rules of P and L. I think taking a more crypto native approach is actually better regardless of whether you wanna 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 twenty twenty six 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. You'll hear how programmable money evolves beyond basic stablecoins, 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, stablecoins going mainstream. Guy Willett argues that stablecoins are only the beginning. The next phase is on chain credit origination and new synthetic products that are easier to scale than simply copying traditional assets onto a blockchain. Here's Guy. My My name is Guy Ouellette, and I'm a general partner on the crypto team here at a sixteen t. This year, my big ideas are origination and purplification. In 2025, we've seen stablecoins 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 stablecoin issuance will be. I think a lot of the existing stablecoins look effectively like narrow 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 three. There's a lot more non bank lending from entities like large private credit funds that have significant equity capital. So banks are doing much more lending to credit funds who are making loans to end depositors. This is very similar today in crypto to how I think stablecoins will effectively end up lending to curators or to asset managers who will end up making loans to end users. And …

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  • Startups win by selling to companies at formation through accelerators like Y Combinator, where Mercury captures fifty percent of each batch.
  • Startups win by selling to companies at formation through accelerators like Y Combinator, where Mercury captures fifty percent of each batch.

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