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This entire week, we’re running our observations on what’s ahead this year… stay tuned here, and be sure to also subscribe to our weekly newsletter for more trend updates, industry reports, builder guides, news analysis, and other resources.
1. This year, we’ll use AI for more substantive research tasks
As a mathematical economist, it was difficult to get consumer AI models to even understand my work process back in January 2025; yet by November 2025, I could give models abstract instructions in the same way I would to a doctoral student… And they sometimes returned novel and correctly executed answers. Beyond my personal experience here, AIs are being used for research more broadly — especially in reasoning domains, where models are now directly aiding discovery and also autonomously solving Putnam problems (perhaps the world’s hardest university-level math exam).
It’s still an open question which fields this type of research assistance will help most, and how. But this year, I’m expecting AI research to enable, and reward, a new type of polymath research style: one that favors an ability to conjecture relationships between ideas, and quickly extrapolate from even more conjectural answers.
Those answers may not be accurate, but can still point in the right direction (at least under some topology). Ironically, it’s kind of like harnessing the power of model hallucinations: When the models get “smart” enough, giving them abstract space to bounce around can still produce nonsense — but can sometimes crack open a discovery, just like how people can be most creative when they’re not working in a linear, clearly stated direction.
Reasoning in this manner will require a new style of AI workflow — not just agent-to-agent, but more agent-wrapping-agent — where layers of models help the researcher evaluate the earlier models’ approaches and successively synthesize the wheat from the chaff. I’ve been using this approach to write papers, while others are conducting patent searches, inventing new forms of art, or (unfortunately) finding novel smart contract attacks.
However: Operating ensembles of wrapped reasoning agents for research will require better interoperability between models, along with a way to recognize and properly compensate each model’s contribution — both problems crypto can help solve.
~Scott Kominers, a16z crypto research team and professor, Harvard Business School
2. We’ll go from just know your customer (KYC) to ‘know your agent’ (KYA)
The bottleneck for the agent economy is shifting from intelligence to identity. In financial services, “non-human identities” now outnumber human employees 96-to-1 — yet these identities remain unbanked ghosts.
The critical missing primitive here is KYA: Know Your Agent.
Just as humans need credit scores to get loans, agents will need cryptographically signed credentials to transact — linking the agent to its principal, its constraints, and its liability. Until this exists, merchants will keep blocking agents at the firewall.
The industry that built that KYC infrastructure over decades now has just months to figure out KYA.
~Sean Neville, cofounder of Circle and architect of USDC; CEO of Catena Labs
3. We need to solve the ‘invisible tax’ on the open web
The rise of AI agents is imposing an invisible tax on the open web, fundamentally disrupting its economic foundation. This disruption stems from a growing misalignment between the Context and Execution layers of the internet: Currently, AI agents extract data from ad-supported sites (the Context layer), providing convenience to users while systematically bypassing the revenue streams (like ads and subscriptions) that fund the content.
To prevent the erosion of the open web (and preserve the diverse content that fuels AI itself), we need the mass deployment of technical and economic solutions. This could include models like next-generation sponsored content, micro-attribution systems, or other novel funding models. Existing AI licensing deals are also proving to be a financially unsustainable bandaid, often compensating content providers with a fraction of the revenue they’ve already lost to AI-cannibalized traffic.
The web needs a new techno-economic model where value flows automatically. The key transition for the coming year will be moving from static licensing to real-time, usage-based compensation. This means testing and scaling systems — potentially leveraging blockchain enabled nanopayments and sophisticated attribution standards — to automatically reward every entity that contributes information to an agent’s successful task.
~Liz Harkavy, a16z crypto investment team
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