a16z Crypto Outlines 2026 AI Trends, Highlighting Impacts On Research, Finance, And The Future Of Open Web
In Brief
a16z crypto’s 2026 AI trends report highlights that the convergence of AI, digital agents, and blockchain will drive new research workflows, financial infrastructure, and web-based value systems.
a16z crypto, the blockchain and digital-asset fund of Silicon Valley venture capital firm Andreessen Horowitz, released an overview of AI trends projected for 2026.
The highlighted developments suggest that 2026 could be a pivotal year for the integration of AI, digital agents, and blockchain technologies. As AI becomes capable of complex reasoning and autonomous problem-solving, and as digital agents require verifiable identities to participate in financial and online ecosystems, new systems will be needed to ensure fairness, transparency, and value flow.
From enabling new research workflows to securing agent-based transactions and sustaining the open web, these trends point toward a future where technology, identity, and incentives must align to support both innovation and a stable digital economy.
In the report, Scott Kominers, a16z crypto research team member and Harvard Business School professor, pointed out that AI will be increasingly applied to substantive research tasks.
He highlighted that by late 2025, AI models were capable of following abstract instructions and delivering novel, correctly executed outputs, a development that previously had been difficult. According to the researcher, AI is now being used more broadly in research, particularly in reasoning-heavy domains, where models are assisting discovery and autonomously solving complex problems, such as the Putnam mathematics exam.
The potential impact of this type of AI research assistance is still emerging, but it may foster a new form of polymathic research that emphasizes hypothesizing relationships between ideas and quickly extrapolating from tentative or partially correct answers. These outputs, while not always precise, can provide useful guidance, analogous to leveraging model “hallucinations” for creative breakthroughs. This approach requires a novel AI workflow, described as agent-wrapping-agent, in which layers of models evaluate prior outputs and progressively refine results.
Scott Kominers reported using this method for writing academic papers, while other researchers are applying it to patent analysis, artistic creation, or identifying vulnerabilities in smart contracts. He added that implementing ensembles of layered reasoning agents will necessitate improved model interoperability and mechanisms to fairly assess and reward each model’s contribution, challenges that blockchain and crypto technologies could potentially address.
Financial Services Shift From KYC To KYA As Digital Agents Expand
Sean Neville, cofounder of Circle, architect of USDC, and CEO of Catena Labs, outlined his trend, pointing out that the focus in financial services is shifting from “know your customer” (KYC) to “know your agent” (KYA).
He noted that the bottleneck for the emerging agent economy is moving from intelligence to identity. According to Sean Neville, non-human identities now outnumber human employees by 96 to 1, yet these digital agents largely remain unbanked and unverified.
He emphasized that KYA is a critical infrastructure requirement. Similar to how humans rely on credit scores to access loans, digital agents will require cryptographically signed credentials to conduct transactions, linking each agent to its principal, operational constraints, and liability. Without such mechanisms, merchants will continue to block agent activity at access points. He further suggested that while the financial industry developed KYC systems over decades, the implementation of KYA may need to be achieved within a matter of months.
AI Agents To Impose An ‘Invisible Tax’ On The Open Web
Meanwhile, Liz Harkavy of the a16z crypto investment team stated that the rise of AI agents is creating an “invisible tax” on the open web, disrupting its economic foundation.
This issue arises from a misalignment between the Context and Execution layers of the internet, where AI agents extract information from advertisement-supported sites to provide user convenience while bypassing the revenue streams, such as advertising and subscriptions, that sustain content creation.
Liz Harkavy noted that to maintain the open web and the diverse content that supports AI development, new technical and economic solutions are required. These could include next-generation sponsored content, micro-attribution mechanisms, or other innovative funding models. Current AI licensing arrangements, she added, are insufficient and often compensate content providers only for a fraction of the revenue lost to AI-driven traffic.
According to her, the web needs a techno-economic model in which value flows automatically, transitioning from static licensing to real-time, usage-based compensation. This approach may involve testing and scaling systems that use blockchain-enabled nanopayments and advanced attribution standards to ensure that every entity contributing information to an AI agent’s task is automatically rewarded.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
