Original Title: AI x crypto crossovers
Original Authors: a16z Crypto, Scott Duke Kominers, Sam Broner, Jay Drain, Guy Wuollet, Elizabeth Harkavy, Carra Wu, Matt Gleason
Original Compilation: BUBBLE, BlockBeats

The economic model of the internet is changing. As the open web gradually collapses into a "prompt bar," we must consider: Will AI lead to an open internet, or will it devolve into a new maze of paywalls? And who will control it all—large centralized companies or broad user communities?

This is precisely where crypto technology can intervene. We have discussed the intersection of AI and crypto multiple times; simply put, blockchain is a new way to build internet services and networks that are decentralized, structurally neutral, and can be owned by users. They provide a counterbalance to the increasingly apparent centralization trend in current AI systems by renegotiating the economic relationships behind the systems, helping to achieve a more open and robust internet.

The idea that "crypto can help build better AI systems, and vice versa" is not new—but it often lacks clear definition. Some intersection areas, such as verifying "proof of humanity" in the context of the widespread adoption of inexpensive AI systems, have begun to attract the attention of builders and users. Other use cases may take years or even decades to realize. Therefore, in this article, we have compiled 11 practical use cases at the intersection of AI and crypto, aiming to promote in-depth discussions on questions like "what is possible" and "what challenges need to be addressed." These use cases are based on technologies currently being built, whether for handling massive micropayments or ensuring that humans can own their relationship with future AI.

Identity

Persistent Data and Context in AI Interactions

Author: Scott Duke Kominers

Generative AI is data-driven, but in many application scenarios, context (i.e., the state and background information related to a particular interaction) is equally important, if not more so.

Ideally, an AI system—whether an agent, LLM interface, or other applications—should be able to remember the type of project you are working on, your communication style, your preferred programming language, and many other details. However, in reality, users often need to reconstruct this context repeatedly across different sessions of the same application—such as when you start a new ChatGPT or Claude session—not to mention when switching between different systems.

Currently, context is essentially non-transferable between different generative AI applications.

With blockchain, AI systems can save key context elements as persistent digital assets, loading them at the start of each session and seamlessly transferring them across multiple AI platforms. Moreover, blockchain may be the only technology that is both forward-compatible and inherently emphasizes interoperability, which are core attributes of blockchain protocols.

A natural application scenario is in games and media involving AI, where user preferences (from game difficulty to key bindings) can remain consistent across different games and environments. However, a more valuable scenario lies in knowledge-based applications, where AI needs to understand the knowledge users have already mastered and their learning styles; or in more specialized AI usage contexts, such as programming. While some companies have developed customized AI bots that can maintain context to a certain extent, this context often cannot be transferred between different AI systems within the same organization.

Businesses are just beginning to realize this issue, and the closest thing to a universal solution currently is customized bots with fixed context. Within platforms, practices for sharing context between different users are also beginning to emerge off-chain, such as the Poe platform allowing users to rent out their customized bots to others.

If such behaviors were migrated on-chain, it would allow the AI systems we interact with to share a "context layer" composed of key elements from all our digital behaviors. AI systems would be able to immediately understand our preferences, better tune and optimize the interaction experience for us. Conversely, just like on-chain intellectual property registration, allowing AI to reference persistent context on-chain would also spark new market interactions around prompts and information modules. For example, users could directly authorize or monetize their knowledge expertise while retaining ownership of the data. Of course, sharing context would also unlock many possibilities we have yet to imagine.

Universal Identity for AI Agents

Author: Sam Broner

Identity, or the authoritative record of "who" or "what," is the silent underlying structure that supports today's digital discovery, aggregation, and payment systems. Since platforms operate behind the scenes with this infrastructure hidden, we can only experience its existence in the finished product: Amazon assigns unique identifiers (ASIN or FNSKU) to products, centralizes their display, and helps users discover and pay; Facebook operates similarly: user identities form the basis for its content recommendations, Marketplace product displays, organic content, and ad discovery.

However, as AI agents evolve, this situation will change. Companies are deploying AI agents in various scenarios such as customer service, logistics, and payments, while the form of platforms is shifting from a single interface to distributed systems across platforms and devices. These agents will accumulate deep context to complete more tasks for users. If an agent's identity is only tied to one platform or marketplace, it will struggle to function in other important contexts—such as email conversations, Slack channels, or other products.

Therefore, AI agents need a unified, transferable "passport." Otherwise, we will be unable to identify their payment methods, confirm their versions, query their capabilities, know who they represent, or track their cross-platform reputation. An agent's identity should simultaneously function as a wallet, API registry, update log, and social proof—so that any interface (whether email, Slack, or another agent) can recognize and interact with it consistently.

Without a unified "identity" primitive, every integration must build the underlying structure from scratch, and the discovery mechanism still relies on chance, causing users to lose context when switching between different platforms.

We are at a stage where we can "redesign agent infrastructure from first principles." So, how do we build a richer, trust-neutral identity layer than DNS records? We should not recreate those "monolithic platforms" that bundle identity, discovery, aggregation, and payment together; instead, agents should be able to freely receive payments, list their capabilities, and coexist across multiple ecosystems without worrying about being locked into a single platform.

This is where the intersection of AI and crypto shines: the "permissionless composability" provided by blockchain networks can help developers build more useful agents and better user experiences.

Of course, currently, those vertically integrated platforms (like Facebook or Amazon) still offer a better user experience—because one of the complexities of building quality products is ensuring that all modules work together seamlessly from top to bottom. But the cost of this convenience is also high. Especially as the costs of building, aggregating, monetizing, and distributing agents continue to decrease, and the reach of agent applications expands, a trust-neutral identity layer will grant entrepreneurs a true sovereign "passport" and encourage more exploration and innovation in distribution and design.

Future-Compatible "Proof of Personhood"

Authors: Jay Drain Jr. and Scott Duke Kominers

As AI becomes more pervasive—whether driving bots and intelligent agents in online interactions or creating deepfakes and manipulating social media—it is becoming increasingly difficult for people to discern whether the entities they interact with online are real humans or programs. This erosion of trust is not a distant future; it has quietly arrived. From comment bots on X (formerly Twitter) to bots on dating apps, the boundary between reality and the virtual is becoming blurred. In this environment, "Proof of Personhood" (PoP) is gradually becoming a key infrastructure.

Currently, one way to verify that a person is human is through digital identity (such as the centralized identity system used by the U.S. TSA). Digital identity includes various information that users can use to verify their identity—usernames, PINs, passwords, third-party verifications (like citizenship or credit records), and other credentials. The value of decentralization here is evident: when this data is centrally managed, identity issuers can revoke access, charge fees, or even assist in surveillance; whereas decentralization reverses this structure, allowing users rather than platforms to control their identities, making it more secure and less prone to censorship.

Unlike traditional identity systems, decentralized "Proof of Personhood" mechanisms (such as Worldcoin's World ID system) allow users to self-manage and store identity data, verifying that they are real people in a privacy-friendly, trust-neutral manner. Just like a driver's license, once PoP is issued, it can be used across any platform, anytime, anywhere. This blockchain-based PoP thus possesses future compatibility, manifested in two aspects:

Portability: PoP follows open protocols, allowing any platform to integrate it. The identity is controlled by the user and built on public infrastructure, making it fully portable, so any existing or future platform can connect.

Permissionless Accessibility: Any platform can independently choose to recognize the PoP identity without needing authorization through a centralized API, thus avoiding the risk of certain use cases being denied.

The current main challenge in this field is user adoption: although we have not yet seen a large-scale practical use case for Proof of Personhood, we believe that once the user base reaches a critical mass, with a few key partners and some "killer applications" driving it, the adoption of PoP will accelerate rapidly. Every application that integrates some PoP standard will enhance the practical value of that identity, thereby attracting more users to claim that identity, and the user base will, in turn, encourage more applications to integrate that standard, creating a rapidly growing network effect. (And since on-chain identities are designed for interoperability, this effect will be even more explosive.)

We have already seen some mainstream consumer applications and services, especially in gaming, social, and dating sectors, announce partnerships with World ID to help users confirm that they are interacting with real people, even specific individuals they expect. At the same time, new identity protocols are continuously emerging, such as the Solana Attestation Service (SAS). Although SAS itself is not a PoP issuer, it allows users to privately associate off-chain data (such as KYC verification or investment qualifications required for compliance) with their Solana wallets, laying the groundwork for building a decentralized identity system.

All these signs indicate that the tipping point for decentralized PoP may be approaching.

The significance of PoP is not just about "banning bots"; it is a key mechanism for delineating clear boundaries between human networks and AI networks. It allows users and applications to clearly distinguish between "this is a human-to-human interaction" and "this is a human-to-machine interaction," thereby bringing a safer, more authentic, and healthier experience to the digital world.

Decentralized Infrastructure for AI

Decentralized Physical Infrastructure for AI (DePIN)

Author: Guy Wuollet

Although AI is a digital service, its development is increasingly constrained by physical infrastructure. Decentralized Physical Infrastructure Networks (DePIN) are emerging as a new model for building and operating real-world systems, helping to democratize the computational infrastructure that AI innovation relies on, making it cheaper, more resilient, and more resistant to censorship.

Why is this so? The two main bottlenecks in AI development are energy and chip acquisition. Decentralized energy can help unlock more power resources, while developers are leveraging DePIN to aggregate idle chip resources from gaming computers, data centers, and other sources. These computing devices can collectively build a permissionless computational market, creating a fairer environment for AI product development.

Other use cases include distributed training and fine-tuning of large language models (LLMs), as well as distributed networks for model inference. Decentralized training and inference can not only reduce costs (since they utilize previously idle computational power) but also provide censorship resistance, ensuring that developers are not banned for relying on hyperscale centralized cloud service providers.

The high concentration of AI models in a few companies has always been a concerning issue; decentralized networks help build a more cost-effective, censorship-resistant, and scalable AI ecosystem.

Providing Infrastructure and Safeguards for Interactions Between AI Agents, Endpoint Service Providers, and Users

Author: Scott Duke Kominers

As AI tools become increasingly adept at handling complex tasks and multi-layer interaction chains, they will increasingly need to autonomously interact with other AIs rather than relying on human controllers.

For example, an AI agent may need to call certain data related to specific computations or recruit AI agents skilled in particular tasks—such as scheduling a statistical bot to perform model simulations or invoking an image generation bot during the creation of marketing materials. AI agents will also create significant value by executing complete transaction processes or activity flows for users—such as finding and booking flights based on user preferences or discovering and purchasing a new book that matches their tastes.

Currently, there is no mature, universal agent-to-agent market—most of these interactions are still limited to explicit API interfaces or a few closed ecosystems that maintain internal agent calls.

A more general issue is that most current AI agents exist in isolated systems, with closed interfaces and a lack of architectural standards. However, blockchain technology can help protocols establish open standards, which is crucial for short-term adoption. In the long run, this also supports "forward compatibility": as new AI agents continuously evolve and emerge, they will still be able to connect to the same underlying network. Because blockchain has an interoperable, open-source, decentralized, and easily upgradable architecture, it can adapt more quickly to the transformative changes in AI innovation.

Several companies are already building blockchain "tracks" for interactions between agents: for example, Halliday has launched a protocol that provides a standardized cross-chain architecture for AI workflows and interactions, setting safeguards at the protocol level to ensure that AI does not deviate from user intent. Catena, Skyfire, and Nevermind support payment interactions between AI agents without human intervention. Coinbase has also begun providing infrastructure support for such projects.

Keeping AI / vibe-coding Applications in Sync

Authors: Sam Broner and Scott Duke Kominers

The explosive development of generative AI in recent years has made software building easier than ever. Coding efficiency has improved by several orders of magnitude, and more importantly—programming can now be done using natural language, allowing even those unfamiliar with coding to fork existing programs or even build entirely new applications from scratch.

However, while AI-assisted programming brings new opportunities, it also introduces a significant amount of "entropy" within and between programs. The so-called "vibe coding" simplifies the complex network of underlying dependencies, but it can also lead to functional or security issues when underlying components are updated. At the same time, as more people use AI to create personalized applications and workflows, interactions between different user systems will become increasingly difficult. In fact, even if two vibe-coded programs have the same functionality, their operational logic and output structures may differ significantly.

In the past, the standardized way to ensure consistency and compatibility was through file formats and operating systems, and more recently through shared software libraries and API interfaces. But in a world where software is constantly evolving, morphing, and forking in real-time, these layers of standardization need to be widely accessible and continuously upgradable—while also maintaining user trust. Moreover, relying solely on AI cannot solve the problem of incentivizing people to maintain these connections and compatibilities.

Blockchain offers a solution that addresses both issues: embedding a "protocolized synchronization layer" into users' custom software and ensuring cross-application compatibility through dynamic updates. In the past, a large enterprise might have needed to pay millions of dollars to system integrators (like Deloitte) to customize a Salesforce system. Today, an engineer might be able to create a visualization interface for sales data over a weekend. However, with the surge of personalized software, developers will also need assistance to keep these applications synchronized and functioning properly.

This is somewhat similar to the current operation mechanism of open-source software libraries, but its updates are real-time rather than periodic—and there are incentive mechanisms involved. All of this can be achieved through crypto. Like other blockchain-based protocols, shared ownership encourages participants to actively contribute to improving the protocol. Developers, users (or their AI agents), and other consumers can be rewarded for introducing, using, improving new features, and integrations.

In turn, shared ownership also ensures that every user has a stake in the overall success of the protocol, creating a "counteracting malice" mechanism. Just as Microsoft would not easily undermine the .docx file format standard because it would affect user trust and brand reputation, the co-owners of the protocol would also be unlikely to introduce poor or malicious code.

As we have seen with various software standardization architectures, there is also significant potential for network effects here. As the "Cambrian explosion" of AI programming software continues, the number of heterogeneous systems that need to maintain communication will grow rapidly.

In short: vibe coding cannot stay in sync solely by relying on vibe. Crypto is the key.

New Economic and Incentive Models

Micropayment Mechanisms Supporting Revenue Sharing

Author: Liz Harkavy

AI agents and tools like ChatGPT, Claude, and Copilot provide us with new and convenient ways to navigate the digital world. However, for better or worse, these technologies are disrupting the economic system of the open internet. We have already seen early signs of this trend—such as some educational platforms experiencing significant declines in traffic due to students turning to AI tools; and several newspapers in the U.S. have sued OpenAI for copyright infringement. If we cannot realign the incentive mechanisms, the internet will become more closed: more paywalls and fewer content creators.

Of course, policy solutions could address the issue, but while legal proceedings are underway, some technological solutions have begun to emerge. Perhaps the most promising (and technically challenging) is to embed revenue-sharing mechanisms directly into the architecture of the internet. When an AI-driven action facilitates a transaction, the content creator providing the information source for that action should receive a corresponding share. This is already reflected in affiliate marketing systems, which can track sources and share revenue; a more advanced version could automatically track and reward contributors throughout the entire information chain. Blockchain can clearly play a significant role in this "source tracing" mechanism.

However, such systems still need to build new infrastructure—especially micropayment systems capable of handling extremely small transactions, attribution protocols that can fairly assess different types of contributions, and governance models that ensure transparency and fairness. Some blockchain-based tools are already showing potential, such as rollups, L2 scaling solutions, AI-native financial institutions like Catena Labs, and financial infrastructure protocols like 0xSplits—which can enable nearly zero-cost transactions and more granular revenue splits.

Blockchain can make complex agent payment systems a reality through several mechanisms:

Nanopayments can automatically split among multiple data providers, allowing a single user interaction to trigger micropayments to all information contributors;

Smart contracts can enforce post-transaction retroactive payments, ensuring that the information sources that facilitated user decisions are compensated after the transaction is completed, with the entire process being transparent and traceable;

Blockchain can also implement complex, programmable revenue distribution rules, enforcing sharing schemes through code, avoiding centralized subjective judgments, and establishing trustless financial relationships between autonomous agents.

As these emerging technologies continue to mature, they are expected to establish a new media economic model that encompasses the entire value chain from content creators to platforms to users.

Blockchain as a Registration System for Intellectual Property and Source Tracing

Author: Scott Duke Kominers

The rise of generative AI urgently requires an efficient, programmable mechanism for registering and tracking intellectual property (IP)—one that can confirm the source of creation while supporting business models around access, sharing, and adaptation of IP. The current IP system relies on costly intermediaries and post-facto enforcement, which is no longer applicable in a world where AI can instantly consume content and "one-click generate" variants.

We need an open, public registration system that can clearly prove ownership, is easy for IP creators to operate efficiently, and allows AI and other web applications to easily interface. Blockchain is the ideal solution: it allows for IP registration without intermediaries, provides immutable proof of creation, and enables third-party applications to easily recognize, authorize, and interact with these IPs.

Of course, some are skeptical about whether "technology can truly protect IP." After all, Web 1.0 and 2.0, along with the current AI revolution, have often been accompanied by a weakening of intellectual property protections. But the issue is that many existing IP business models still focus on excluding derivative works rather than incentivizing and monetizing them. Programmable IP infrastructure not only allows creators, brands, and others to clearly establish ownership in the digital world but also fosters a new model—building new businesses around a shared mechanism that "allows for the legal use of IP in generative AI and other digital applications."

We have already seen new attempts in the early NFT space, such as promoting brand network effects and value accumulation through CC0 licensing. Furthermore, infrastructure developers have created protocols and even dedicated blockchains (like Story Protocol) specifically for the standardization and composable registration and licensing of IP. Some artists have begun to license their styles and works for creative re-creation through protocols like Alias, Neura, and Titles. In Invention's sci-fi series "Emergence," fans participate in co-creating characters and worldviews, while the Story Protocol registration system retains a record of each creator's contributions.

AI Represented by Webcrawler Should Compensate Content Creators

Author: Carra Wu

Currently, the AI agents that best meet market demand are not programming assistants or entertainment tools, but Webcrawlers—they automatically browse the web, collect data, and autonomously decide which links to visit.

It is estimated that nearly half of the current internet traffic comes from non-human sources. Bot programs often ignore robots.txt files (which are theoretically used to indicate whether crawlers are allowed to scrape the site) and use the scraped data to support the core competitiveness of the world's largest tech companies. Worse still, websites themselves have to pay for these "uninvited guests," bearing the costs of bandwidth and server resources. As a result, CDN providers like Cloudflare have had to launch a series of blocking services. Today, this is a fragmented and cumbersome countermeasure, but it could be replaced by a more reasonable system.

We have pointed out that the internet's original "economic contract"—the win-win relationship between content creators and platforms—is on the verge of collapse. This is also reflected in the data: over the past year, more and more websites have begun to actively block AI crawlers. In July 2024, only 9% of the top 10,000 websites were blocking AI crawlers; now, that percentage has risen to 37% and is still rapidly increasing.

So, can we seek a balance instead of simply blocking all suspected bot requests? One new model is that AI crawlers no longer "freeload" on web content but instead pay for data scraping activities. Blockchain can serve as the execution layer for this model: each crawler agent holds cryptocurrency and initiates on-chain negotiations with the website's "gatekeeper agent" or paywall system through the x402 protocol when accessing the site.

The problem is that robots.txt (also known as the "Robot Exclusion Standard") has become an industry default since the 1990s, and overturning it requires large-scale industry coordination or intervention from CDN providers like Cloudflare. On the other hand, we can open an independent channel for human users: they can prove their "human identity" through World ID (as mentioned above) to continue accessing content for free.

In this way, the behavior of AI collecting content can achieve compensation for creators at the collection point, while human users can still enjoy an "information-free" internet.

More Private Advertising: Precise Yet Not Intrusive

Author: Matt Gleason

AI is already changing the way we shop, so can advertising also be made more "useful"? Many people dislike ads because they are either irrelevant or too intrusive. Even "personalized ads," if too precise and based on a large amount of personal data, can make people feel "spied on."

Some applications attempt to monetize through paywalls (such as watching videos or unlocking game levels). Cryptography can help us reshape this logic. When combined with blockchain, personalized AI agents can deliver ads based on user-defined preferences without exposing user privacy data; at the same time, they can reward users with cryptocurrency after voluntary interactions.

Technically, this model requires:

A low-cost digital payment system: Advertising interaction rewards must support high-frequency, small-value payments, and the system needs to be fast and low-cost;

Privacy-protecting data verification mechanisms: AI advertising agents need to verify whether users meet certain demographic characteristics without exposing specific data; zero-knowledge proof (ZKP) technology can achieve this;

New incentive models: If the micropayment ($0.05) advertising revenue model becomes widespread, users can actively choose to watch ads and profit, shifting from "passively being harvested" to "voluntarily participating."

Humans have long tried to make advertising more useful, both online and offline. By reshaping the advertising system to be driven by "AI + blockchain," we finally have the potential to make advertising truly useful: non-intrusive and profitable.

This will also make advertising space itself more valuable, while potentially overturning the currently invasive "advertising exploitation economy," and instead building a human-centered system: where users are no longer "products," but "participants."

Controlling the Future of AI

AI Companions Owned and Controlled by Humans

Author: Guy Wuollet

Today, many people spend more time on devices than in face-to-face communication, and this time is increasingly being used to interact with AI models and content filtered by AI. In fact, these models are already providing some form of companionship—whether for entertainment, information acquisition, satisfying niche interests, or educating children. We can easily imagine that in the near future, AI companions will be widely used in education, healthcare, legal consulting, and social companionship, becoming a common form of human interaction.

Future AI companions will be infinitely patient and highly customizable based on individual needs. They will not just be assistants or "robot servants," but may become highly valued relational entities for people. Therefore, the question of who will own and control these relationships—whether it will be the users themselves or companies and other intermediaries—becomes crucial. If you have been concerned about content filtering and censorship on social media over the past decade, this issue will become even more complex and personal in the future.

In fact, similar viewpoints have long been proposed (see here and here): blockchain and other censorship-resistant hosting platforms may be the clearest path to achieving uncensorable, user-controlled AI. While individual users can run local models and purchase GPUs, most either cannot afford it or do not know how to do it.

Although we are still some distance from the widespread adoption of AI companions, the technology to achieve this is rapidly advancing: text-interactive AI companions have already performed excellently, and visual avatars have significantly improved; blockchain performance is also gradually increasing. To make it easier for users to use uncensorable AI companions, we need to continue improving the user experience (UX) of crypto applications. Fortunately, blockchain wallets like Phantom have already made on-chain interactions simple, while embedded wallets, passkeys, and account abstraction technologies allow users to achieve self-custody wallets without having to manage mnemonic phrases themselves.

Additionally, high-throughput, trustless computing technologies like Optimistic and zero-knowledge co-processors will also enable us to establish meaningful and lasting relationships with digital companions.

In the near future, we will shift from discussing "when will anthropomorphic digital companions and virtual avatars appear" to "who has the right to control them and in what way."

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