Podcast Summary: The a16z Show – Big Ideas 2026: New Infrastructure Primitives

Date: December 26, 2025
Host: Andreessen Horowitz (a16z)
Guests: Guy Willette, Oliver Xu, James DeCosta

Episode Overview

This episode dives into a16z’s “Big Ideas” for 2026, zooming in on three transformative infrastructure primitives poised to reshape entire markets:

• Programmable money evolving beyond stablecoins
• Autonomy in scientific labs
• 'Greenfield' distribution strategies for AI startups

The unifying theme? Not mere upgrades of current systems, but genuinely new “rails” enabling novel workflows, markets, and compounding growth.

Key Discussion Points & Insights

1. Beyond Stablecoins: On-chain Credit Origination and Synthetic Products

with Guy Willette, General Partner, a16z Crypto

• Second-order effects of stablecoin mainstream adoption:

  • Stablecoins currently resemble “narrow banks,” holding deposits in fiat or treasuries
  • The vision for the future: On-chain native credit origination, not just tokenizing off-chain loans ([01:19])

• Why native origination matters:

  • On-chain origination dramatically cuts back office costs—loan servicing alone often takes 1–3% annually
  • Enables composability across DeFi: "Doing on chain origination will allow for much more composability between different DeFi projects." (Guy Willette, [02:40])

• Perpification:

  • Defined by Guy as: "To make a perp or a perpetual future out of what is today an existing spot asset." ([06:14])
  • Rather than tokenizing assets (copying them on-chain), perpification creates scalable synthetic representations linked to off-chain prices.
  • Seen as especially useful in emerging markets (e.g., India), where derivatives often have more volume than underlying assets.

• The future of synthetic dollars:

  • “Synthetic dollars” are pegged to the USD but collateralized with assets beyond fiat (e.g., physical infrastructure, advanced trading strategies, even GPUs or solar panels)
  • Could expand capital efficiency and financial product diversity globally ([07:50])

Notable quote:

"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 you're going to have much greater back office efficiency in many cases."
— Guy Willette ([07:56])

2. Autonomous Labs: AI Collaboration in Science

with Oliver Xu, Partner, a16z American Dynamism

• From basic automation to AI/robotic collaboration:

  • Labs already use programmable robots, but what’s new is combining them with advanced AI reasoning for experiment planning.
  • The near-term vision: Human scientists collaborating with AI systems and robots—not fully autonomous, but greater speed and feedback loops ([09:31])

• Why interpretability matters:

  • Scientific AI must be explainable: “You want to really understand why the system is doing what it’s doing, why it’s planning on iterating on an experiment in a given way…” ([10:30])

• Adoption depends on market pull:

  • Sectors with strong demand for rapid research (life sciences, pharma, chemicals, materials) will lead adoption due to faster and more valuable iteration.

• Ecosystem examples:

  • Early-stage startups: Medra (life sciences), Chemify/Yoneda Labs (chemistry)
  • Public/private partnerships: DOE Genesis Mission, DeepMind-UK Government collaboration ([14:15])

Notable quote:

“Laboratory automation is something that's existed for a long time ... 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.”
— Oliver Xu ([09:35])

3. Greenfield Distribution: Startups Selling to Startups

with James DeCosta, Partner, a16z Apps

• Solving the ‘distribution before incumbents’ problem:

  • Incumbents update with AI, so startups must win distribution in new ways.
  • Serving other startups at formation sidesteps red tape—less friction, no switching costs, decisions by a handful of founders ([15:14])

• Grow with your customers:

  • Early adoption = built-in scaling: “If you attract all of the new companies at formation and then grow with them as your customers become big companies ... so will you.”
  • Stripe’s early growth is cited as the template ([17:08])

• Accelerators as distribution engines:

  • Y Combinator, SpeedRun, Entrepreneur First offer dense pools of “greenfield” companies needing infrastructure from day one.
  • Example: Mercury banking works with 50% of each YC batch ([17:41])

• Narrow wedge, rapid iteration:

  • Penetrate through a specific use-case (e.g., HR, CRM); rapidly expand the feature set to retain and scale as customers mature.
  • “You also don’t have to be constrained by the existing categories of enterprise software...” ([18:25])

Notable quote:

“One of the most powerful and underrated ways for startups to win distribution is actually to serve companies at formation or greenfield companies.”
— James DeCosta ([15:24])

Memorable Moments & Quotes (with Timestamps)

• Guy Willette:

  • “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.” ([01:41])
  • “I'm much more interested in native on chain loan origination because ... it can significantly reduce the back office costs that are traditionally associated with creating basically asset backed securities or other forms of credit assets.” ([06:34])

• Oliver Xu:

  • “Systems that are purpose built for scientific research are probably going to focus a lot on ... interpretability, on recording what exactly is happening throughout each step of the process as it collaborates with a human scientist.” ([10:45])

• James DeCosta:

  • “Incumbents struggle to sell to startups because they're bound by the rules of P and L. New startups represent very little in the form of new revenue for incumbents, but they cost money to serve...” ([17:13])
  • “Graduation moments also offer the perfect opportunity ... where maybe startups have outgrown a simple solution ... like moving from QuickBooks to Netsuite...” ([18:10])

Timestamps for Key Segments

| Segment | Speaker(s) | Timestamp | |------------------------------------------|------------------|---------------| | Stablecoins & On-chain Credit | Guy Willette | 01:19–08:58 | | Autonomous Labs & AI in Science | Oliver Xu | 09:31–14:47 | | Greenfield Distribution Strategy | James DeCosta | 15:14–18:38 | | Episode Summary and Connection | Host (D) | 18:38 |

Conclusion

This episode unpacks how new “infrastructure primitives”—programmable money, AI-driven science, and greenfield distribution—aren’t just buzzwords, but the rails underpinning future markets:

• Guy reveals the next upgrade for crypto finance is native origination and scalable synthetic products.
• Oliver connects advances in AI/robotics to the coming era of semi-autonomous, explainable scientific research.
• James frames startup-to-startup distribution as a compounding, customer-scaling edge for the AI startup ecosystem.

Takeaway:
New infrastructure primitives are already rearchitecting the foundation for worldwide innovation—they’re the rails on which the next generation of tech will scale and compound.