a16z predicts: By 2026, AI will evolve from a "tool" to a "new economic infrastructure"

Author: a16z crypto

Translation: Deep潮 TechFlow

Original Title: a16z: Three Major Trends in Artificial Intelligence by 2026

This year, AI will undertake more substantive research tasks

As a mathematical economist, back in January 2025, I found it difficult to get consumer-grade AI models to understand my workflow; however, by November 2025, I was able to give abstract instructions to AI models just like instructing a PhD student… and they sometimes returned novel and correct answers. Beyond my personal experience, AI is being more broadly applied in research, especially in reasoning. These models not only directly assist in the discovery process but can autonomously solve difficult problems such as the Putnam problem (perhaps the hardest university math exam in the world).

It remains uncertain which fields will benefit most from this research assistance and how exactly it will be implemented. But I expect that this year, AI research will promote and reward a new style of “jack-of-all-trades” research: one that emphasizes conceptualizing relationships between ideas and can quickly infer from more hypothetical answers.

These answers may not be entirely accurate, but they can still guide research in the right direction (at least within certain topological structures). Ironically, this is somewhat like leveraging the power of models’ “hallucinations”: when models are “smart enough,” giving them an abstract space to stir thoughts may still produce some meaningless results—but sometimes it leads to breakthrough discoveries, much like humans working non-linearly or without clear direction can be most creative.

Reasoning in this way requires a new AI workflow style—not just simple “agent-to-agent” interactions, but a complex collaboration pattern of “agent nesting agents.” In this mode, different levels of models assist researchers in evaluating early-stage proposals and gradually distill the essence. I have already used this approach to write papers, while others are conducting patent searches, inventing new forms of art, and (regrettably) discovering new attack methods for smart contracts.

However, to operate these nested reasoning agents for research, better interoperability between models and a method to identify and appropriately compensate each model’s contribution are needed—and these issues might be addressed by blockchain technology.

—Scott Kominers (@skominers), a16z Crypto Research Team Member, Harvard Business School Professor

From “Know Your Customer” (KYC) to “Know Your Agent” (KYA): The Shift in Identity Verification

The bottleneck in the agent economy is shifting from intelligence to identity verification. In financial services, the number of “non-human identities” now exceeds 96 times that of human employees—yet these “identities” remain “ghosts” unable to access banking services.

The missing key infrastructure here is “Know Your Agent” (KYA). Just as humans need credit scores to obtain loans, agents need cryptographic signatures as credentials to conduct transactions—credentials that link the agent to its entity, constraints, and liabilities. Until this infrastructure is built, merchants will continue to block these agents at firewalls.

The industry that built KYC infrastructure over the past decades now has only a few months to explore how to implement KYA.

—Sean Neville (@psneville), Co-founder of Circle, Architect of USDC; CEO of Catena Labs

Solving the “Invisible Tax” Problem of the Open Web: Economic Challenges in the AI Era

The rise of AI agents is imposing an “invisible tax” on the open web, fundamentally disrupting its economic foundation. This disruption stems from the increasing mismatch between the “context layer” and the “execution layer” of the internet: currently, AI agents extract data from ad-supported websites (the context layer), providing convenience to users but systematically bypassing the revenue sources supporting content (such as ads and subscriptions).

To prevent the gradual decline of the open web (and protect the diverse content fueling AI), we need large-scale deployment of technological and economic solutions. These might include next-generation sponsorship models, micro-attribution systems, or other new funding mechanisms. However, existing AI licensing agreements have proven financially unsustainable—they often only compensate content providers for a small fraction of revenue lost due to AI traffic diversion.

The web urgently needs a new economic model where value can flow automatically. The key shift in the coming year will be from static licensing models to real-time usage-based compensation mechanisms. This involves testing and expanding systems—possibly leveraging blockchain-supported nanopayments and complex attribution standards—to automatically reward entities that contribute information for successful AI agent tasks.

—Liz Harkavy (@liz_harkavy), a16z Crypto Investment Team