a16z: 11 Convergence Scenarios of AI and Cryptocurrency
Original Article Title: AI x Crypto Crossovers
Original Article Authors: Scott Duke Kominers, Sam Broner, Jay Drain, Guy Wuollet, Elizabeth Harkavy, Carra Wu, Matt Gleason, a16z crypto
Original Article Translation: Wu Shuo Blockchain
The economic structure of the internet is changing. As the open network gradually collapses into a "prompt bar," we have to consider: Will AI bring a more open internet, or will it lead us into a maze of new paywalls? And who will control the future internet — large centralized companies or a broad user community?
This is where cryptographic technology comes in. We have discussed the intersection of AI and cryptographic technology many times in the past, but in essence, blockchain is a way to redesign internet services and network architecture, building decentralized, trust-neutral systems that are "owned" by users. By reshaping the economic incentives behind today's systems, blockchain provides a counterbalance to the increasing centralization trends in AI systems, driving a more open and resilient internet.
The idea that "cryptographic technology can help build better AI systems, and vice versa" is not new, but it has long lacked a clear definition. Some crossover areas (such as how to authenticate "human identity" in the context of the proliferation of low-cost AI systems) have attracted a large number of developers and users. However, other application scenarios may still take several years or even decades to materialize. Therefore, this article shares 11 crossover application scenarios of AI and cryptographic technology, hoping to stimulate more industry discussions: which are feasible, which challenges remain to be solved, and how the future may evolve.
These scenarios are all based on technologies currently under development — from handling large-scale micropayments to ensuring human ownership in future interactions with AI.
1. Introducing Persistent Data and Context in AI Interactions
Scott Duke Kominers: Generative AI relies heavily on data, but in many application scenarios, the "context" — the state and background information related to the interaction — is often equally important, if not more critical than the data itself.
In an ideal scenario, whether it's an agent, an LLM interface, or another type of AI application, it should be able to remember a significant amount of personalized information, including the type of project you are working on, your communication habits, preferred programming language, and more. However, in reality, users often have to repeatedly rebuild this context — not only starting new sessions within the same application, such as having to rebuild the context when opening a new ChatGPT or Claude window, let alone switching between different AI systems.
Currently, the context within a Generative AI application is almost impossible to transfer to another application.
With blockchain, AI systems can store key context elements in the form of persistent digital assets, allowing them to be loaded at the start of a session and seamlessly migrated between different AI platforms. Furthermore, due to the core features of blockchain protocols such as "forward compatibility" and "interoperability commitment," blockchain may be the only technology pathway capable of systematically addressing this issue.
One intuitive application scenario is in the AI-driven gaming and media sector, where user preferences (such as difficulty level, key layout, etc.) can persist across games and environments. However, the truly valuable scenarios lie in knowledge-based applications—where AI needs to understand the user's knowledge system, learning style, and abilities—and in more specialized applications such as programming assistance. Although some enterprises have built customized AI tools with a "global context" for their own business needs, these contexts still cannot be effectively transferred between different AI systems used within the organization.
Various organizations are just beginning to truly realize this issue, and the closest thing to a universal solution currently is customized bots with fixed, persistent contexts. However, off-chain context portability between platform users has gradually emerged; for example, on the Poe platform, users can rent out custom bots they have created to other users.
If these activities were to be migrated to the blockchain, the AI systems we interact with would be able to share a contextual layer composed of key elements of all our digital behaviors. AI would be able to instantly understand our preferences, enabling better fine-tuning and experience optimization. Conversely, similar to mechanisms like an on-chain intellectual property registration system, if AI is allowed to reference on-chain persistent contexts, it could give rise to a new and more refined market interaction model around prompts and information modules—for example, users could directly monetize their professional skills through authorized means while maintaining data sovereignty.
Of course, as the capability for context sharing improves, it will also breed numerous unforeseen new use cases and possibilities.
2. Universal Identity System for Intelligent Agents
Sam Broner: Identity—specifically, a normalized record of who or what an object is—is the underlying infrastructure that supports today's digital discovery, aggregation, and payment systems. However, because platforms tend to encapsulate this "backbone pipeline" within their systems, users often only experience the identity system in a packaged product interface. For example, Amazon assigns identifiers (such as ASIN or FNSKU) to products, integrates the products for display in a unified interface, and helps users with discovery and payment; Facebook is similar: user identity determines their News Feed content and forms the basis for discovering various content within the app, including Marketplace product listings, organic content, and ad placements.
As AI Agents rapidly evolve, this landscape is about to change. More and more enterprises are using intelligent agents for customer service, logistics, payment, and other scenarios. Their platform will no longer be a traditional "single interface application" but will be distributed over multiple channels and platforms, continuously accumulating deep contextual awareness and performing more tasks on behalf of users. However, if an intelligent agent's identity is only tied to a single platform or a single market, it will be challenging to operate in other critical environments (such as email threads, Slack channels, or within other products).
Therefore, intelligent agents need a unified, portable "digital passport." Without it, it is impossible to confirm how to pay the intelligent agent, verify its version, query its capabilities, identify who it represents to perform tasks, or track its reputation across applications and platforms. The identity system of intelligent agents must simultaneously include wallet functionality, API registry, change log, and social proof to enable any interface (whether it is email, Slack, or another intelligent agent) to parse and communicate with it consistently.
Without this shared "identity primitive," every system integration will require rebuilding this pipeline from scratch; content discovery will remain in a state of ad hoc assembly; and users will continuously lose their critical context when switching between different channels and platforms.
We now have the opportunity to design intelligent agent infrastructure from "first principles." So the question is: How to build an identity layer that is more rich and neutrally trustworthy than DNS records? Instead of rebuilding the kind of monolithic platform that bundles identity, discovery, aggregation, payment, and other functions together, it is better to allow intelligent agents to autonomously receive payments, expose their list of capabilities, and exist in multiple ecosystems without the worry of being locked into a single platform.
This is where the intersection of encryption technology and AI can play a role—blockchain networks provide permissionless composability, enabling developers to create more powerful intelligent agents and a more user-friendly experience.
Overall, vertically integrated solutions like Facebook and Amazon currently offer a better user experience—partly because one of the complexities of building excellent products is ensuring that all components naturally work together from the top down. However, the cost of this convenience is becoming increasingly high, especially against the backdrop of the decreasing software costs required to build, aggregate, promote, monetize, and distribute intelligent agents, as well as the expanding reach of intelligent agent applications.
Efforts are still needed to achieve the user experience of vertically integrated platforms, but once a trustworthy neutral intelligent agent identity layer is established, entrepreneurs can truly have their own passport. This will also drive widespread experimentation and innovation in distribution patterns and interaction design.
3. Future-Oriented "Proof of Personhood" (PoP)
Jay Drain Jr. and Scott Duke Kominers: With the proliferation of AI—whether in the form of bots and avatars running various web interactions or in deepfakes and social media manipulation behaviors—we are increasingly finding it difficult to determine whether the entities we interact with online are genuine humans. This erosion of trust is not a future concern but a current reality. From comment armies on Platform X to automated accounts on dating apps, the line between real and fake is becoming increasingly blurred. In such an environment, "Proof of Personhood" is gradually becoming a critical infrastructure of the internet.
One way to verify "you are human" is through the use of digital identity, including centralized identity authentication systems used by entities like the TSA. Digital IDs encompass all the information a user can use to prove their identity—username, PIN, password, and third-party attestations (such as nationality, credibility, or creditworthiness). The value of decentralization here is very clear: when identity data is stored in centralized systems, issuers can revoke access, charge fees, or even assist in surveillance. Decentralization, on the other hand, disrupts this structure: users, not platform gatekeepers, control their own identity, making it more secure and censorship-resistant.
Unlike traditional identity systems, a decentralized Proof of Personhood mechanism (such as the World's Proof of Human introduced by Worldcoin) allows users to autonomously manage identity data and verify themselves as "human" in a privacy-preserving, trusted, and neutral manner. Similar to a driver's license—issued anytime, anywhere, and valid in any scenario—decentralized PoP can serve as a universal underlying building block that can be reused on any platform, including those that do not yet exist. In other words, blockchain-based PoP has "forward compatibility" as it provides:
Portability: The protocol is an open standard that any platform can integrate. Decentralized PoP can be managed through public infrastructure and fully controlled by users. This means PoP inherently has portability—it can be compatible with any platform now or in the future.
Permissionless Accessibility: Platforms can choose independently whether to support a PoP identity without needing centralized API approvals that may impose discriminatory restrictions on different use cases.
The core challenge in this field is "adoption." Currently, there has not been widespread real-world implementation of "Proof of Personhood" (PoP), but we anticipate that once the user count reaches a critical mass, there are several early partners, and there is a "killer app" that drives user demand, the adoption of PoP will accelerate significantly. Every application that adopts a certain digital ID standard enhances the value of this ID type to users, encouraging more users to acquire that ID, which, in turn, drives application integration of the ID standard to validate "personhood attractiveness." (Additionally, due to the interoperability inherent in on-chain IDs, this network effect can rapidly propagate.)
We have already seen mainstream consumer applications such as gaming, socializing, and social media announce partnerships with World ID to ensure that users are indeed interacting with real humans during gaming, chatting, or transactions — even with their expected specific counterpart. At the same time, new identity protocols have emerged this year, such as the Solana Attestation Service (SAS). While SAS itself is not a Proof of Personhood (PoP) issuer, it allows users to privately associate off-chain data (such as compliance-required KYC results, investor accreditation qualifications, etc.) with a Solana wallet, thus building a user's decentralized identity. These signs all indicate that the inflection point for decentralized PoP may be approaching.
The significance of Proof of Personhood goes far beyond just "preventing bots." It aims to establish clear boundaries between AI agents and human networks, allowing users and applications to differentiate between interactions of "human vs. machine," thereby creating conditions for a more premium, secure, and authentic digital experience.
4. AI-Centric Decentralized Physical Infrastructure (DePIN)
Guy Wuollet: Although AI is a digital service, its development is increasingly limited by physical infrastructure. The decentralized physical infrastructure network (DePIN) — a new paradigm for real-world system construction and operation — is poised to democratize the computational power infrastructure that supports AI innovation, making it more cost-effective, resilient, and censorship-resistant.
Why? The two primary bottlenecks to AI development are energy and chip access. A decentralized energy system can provide more abundant electricity, and developers are also leveraging DePIN to integrate idle chips from gaming PCs, data centers, and other sources. These computing devices can collectively form a permissionless computational marketplace, thereby creating a level playing field for building new AI products.
Other applications include distributed training and fine-tuning of large language models (LLMs) and building a distributed inference network (model inference). The significant cost reduction potential of decentralized training and inference lies in their utilization of idle computing resources that were previously unused. Moreover, architectures of this nature inherently possess censorship resistance, ensuring that developers will not be "deplatformed" or have access restricted due to reliance on hyperscalers (centralized cloud infrastructure providers offering massive scalable computing resources).
The concentration of AI models in the hands of a few companies has long been a concern; however, decentralized networks can help build a lower-cost, more censorship-resistant, and more scalable AI ecosystem.
5. Establishing Infrastructure and Security Mechanisms for AI Agent-to-Service Provider and User Interactions
Scott Duke Kominers: As AI tools continue to enhance their capabilities in handling complex tasks and executing multi-level interaction chains, AI will increasingly need to collaborate independently with other AI without direct human control.
For example, an AI agent may need to request specific data required for a computation, or may need to invoke other AI agents with specialized capabilities to perform tasks—such as having a statistical analysis agent responsible for building and running a model simulation, or mobilizing an image generation agent to assist in creating marketing materials. AI agents will also create significant value in end-to-end transaction execution, such as fully replacing a user to complete a transaction process: finding and booking a flight based on preferences, or automatically discovering and purchasing a new book that aligns with the user's preferences.
Currently, there is no "generalized agent-to-agent market." Such cross-agent requests are typically only achievable through explicit API calls or are limited to within specific closed AI agent ecosystems for internal use only.
More broadly, most AI agents currently operate in isolated ecosystems from each other: APIs are relatively closed, lacking unified architectural standards. Blockchain technology can help establish open standards for protocols, which is crucial for short-term adoption; in the long term, this also helps achieve forward compatibility: as new types of agents continue to emerge, they can all plug into the same underlying network. Due to blockchain's interoperable, open-source, decentralized, and usually more easily upgradable architecture, it is more adaptable to the changes brought about by future AI innovations.
Several companies are already building on-chain infrastructure for agent interactions. Taking Halliday as an example, the company recently launched a protocol that provides a standardized cross-chain architecture for AI workflows and interactions, while incorporating protection mechanisms at the protocol level to ensure that AI does not act beyond user intent. On the other hand, projects like Catena, Skyfire, and Nevermind use blockchain to support automated settlement between agents, enabling AI-to-AI payments without any manual intervention. Similar systems are continuously emerging, and Coinbase has also begun providing infrastructure support for such developments.
6. Keeping AI "Atmospheric Encoding" Applications in Sync
Sam Broner and Scott Duke Kominers: The revolution of generative AI has made building software unprecedentedly easy. The speed of coding has increased by orders of magnitude, and more importantly, coding can be done directly through natural language, allowing inexperienced developers to replicate existing programs, or even build new applications from scratch.
However, AI-assisted coding, while creating new opportunities, has also brought about a significant amount of "entropy" within and across programs. The so-called "vibe coding" abstracts away the complex dependencies behind the software—yet because of this, when the underlying source code library or input changes, the program may expose risks in terms of functionality and security. Additionally, as people use AI to create highly personalized applications and workflows, interfacing with others' systems also becomes more difficult. In fact, even if two vibe-coded programs perform almost the same task, their operational logic and output structure may be completely different.
Traditionally, the standardization work to ensure consistency and compatibility has been carried out by file formats, operating systems, and later shared software and API integration. However, in a world where software evolves, morphs, and forks in real-time, the standardization layer must have: wide accessibility, continuous upgradability, while also earning user trust. Furthermore, relying solely on AI cannot solve the incentive mechanism problem—i.e., how to incentivize developers to build and maintain these inter-system links.
Blockchain can address both of these challenges simultaneously by providing a protocolized synchronization layer embedded in user-customized software builds, capable of dynamically updating with environmental changes to ensure cross-system compatibility.
In the past, large enterprises might have needed to pay millions of dollars to system integrators like Deloitte to customize a Salesforce instance. Today, an engineer might only need a weekend to build a custom "sales data view" interface. However, with the continued growth of customized software, developers will need assistance to ensure these applications stay in sync and available.
This is similar to today's open-source software library development model, but the difference lies in: the synchronization layer does not rely on periodic version releases but instead on continuous updates—and it comes with an incentive mechanism. Both of which can be more easily achieved through encryption technology. Like other blockchain-based protocols, stakeholders sharing ownership of the synchronization layer can incentivize all parties to continuously contribute resources for improvement. Developers, users (and their AI agents), and other adopters can all receive incentives for introducing, using, or iterating on new features and integration solutions.
Conversely, shared ownership also gives all users a vested interest in the overall success of the protocol, forming a mechanism to deter behavioral deviations. Just as Microsoft wouldn't easily disrupt the .docx file format standard because it would have widespread negative implications for its users and brand; the common owners of the synchronization layer would also be unwilling to introduce clumsy or malicious code in the protocol due to the harm to their own interests.
Similar to all previous software standardization architectures, there is also a strong network effect potential here. As AI-generated software experiences the "Cambrian Explosion," the need for diverse and heterogeneous systems to communicate with each other will grow exponentially. In short: to maintain synchronization of ambient coding, we can't rely solely on the ambient itself; encryption technology is the answer.
7. Micro Payment System Supporting Revenue Sharing
Liz Harkavy: AI agents and tools like ChatGPT, Claude, Copilot, among others, have provided a more convenient way for people to access information in the digital world. But for better or worse, they are also shaking the economic structure of the open internet. This trend is already evident—e.g., as students increasingly use AI tools, educational platforms are experiencing significant traffic declines; at the same time, several U.S. media outlets have sued OpenAI for copyright infringement. If the incentive system cannot readjust, we may see further internet closures, more paywalls, and a continuous decrease in content creators.
Policy mechanisms certainly exist, but during the judicial process, some technical solutions are also emerging. The most promising (and simultaneously the most challenging technically) solution is to embed a "revenue-sharing mechanism" into the underlying architecture of the internet. When an AI-driven operation eventually leads to a sale, the content creator providing the information source for that decision should receive a revenue share. The affiliate marketing ecosystem is already doing similar attribution tracking and revenue sharing; more advanced systems can automatically track all contributors along the entire information chain and reward them. Blockchain can clearly play a key role in tracking the "source chain" of information.
However, to implement such a system, new infrastructure is needed—especially: a micro-payment system capable of handling tiny amounts between multiple sources; an attribution protocol that fairly evaluates the value of different contributions; and ensuring a transparent and fair governance model.
Many existing blockchain tools show potential, such as various rollups, L2 networks, AI-native financial institution Catena Labs, and the financial infrastructure protocol 0xSplits, all of which can achieve near-zero-cost transactions and finer-grained payment splits.
Blockchain can enable AI-dominated advanced payment systems through various mechanisms:
Nano Payments: can be split among multiple data providers, enabling a single user interaction to automatically trigger micro-payments to all contribution sources, executed by smart contracts.
Smart Contracts: can automatically trigger enforceable "post-payment" after a transaction is completed, providing transparent, traceable compensation for content sources that influence purchase decisions.
Programmable Payment Splitting: Enables revenue distribution to be enforced through code rather than relying on centralized entities, thereby establishing trustless financial relationships between automated agents.
As these emerging technologies continue to mature, they will build a new media economic model that captures the entire value creation chain from creators to platforms to users.
8. Using Blockchain as a Registry System for Intellectual Property and Traceability
Scott Duke Kominers: The emergence of generative AI has made it urgent to establish an efficient, programmable system for intellectual property (IP) registration and tracking — aiming to ensure both the accuracy of traceability and to support new business models arising from access, sharing, and derivative creation around IP. Existing IP frameworks rely on costly intermediaries and post-enforcement mechanisms, which are clearly insufficient in an era where AI can instantly consume content and generate variants at the touch of a button.
What we need is an open, public registry system that provides creators with clear proof of ownership and is user-friendly and efficient — allowing AI and other web applications to interact directly with it. Blockchain is well-suited to take on this role: it enables creators to register IP without relying on intermediaries and provides tamper-proof traceability; it also allows third-party applications to easily identify, authorize, and interact with these IP assets.
Of course, there is still skepticism about whether technology can truly protect intellectual property as a whole concept. After all, the first two eras of the internet — including the current AI revolution — are often associated with a weakening of IP protection. One reason is that many existing IP business models emphasize "excluding derivative works" rather than incentivizing and monetizing derivative creations. Programmable IP infrastructure not only allows creators, licensees, and brands to clearly establish their IP ownership in the digital space but also fosters new business models centered around "sharing IP for generative AI and digital applications." In a sense, it transforms one of the threats of generative AI to creative work into a new opportunity.
In the early stages of NFTs, we have seen creators trying out new patterns, such as building a brand network effect through the CC0 method on Ethereum to achieve value capture. Recently, we have seen infrastructure providers starting to build standardized, composable IP registration and licensing protocols and even launching dedicated blockchains (such as the Story Protocol). Some artists have begun using protocols like Alias, Neura, Titles to license their styles and works to support creative remixing. Meanwhile, Incention's sci-fi series Emergence allows fans to participate in co-creating the universe and character design and records every creative contribution on-chain via Story's registry system.
9. Web Crawlers That Can Compensate Content Creators
Carra Wu: The most product-market fit AI agent at the moment is not the kind used for programming or entertainment, but web crawlers—they can autonomously browse the internet, collect data, and make judgments on which links to follow.
According to some estimates, nearly half of today's internet traffic comes from non-human sources. Robots often ignore the robots.txt file—a standard that should inform automated crawlers whether a website allows its access—but in reality, it has little to no enforcement—and use the scraped data to strengthen the core moats of the world's largest tech companies. Even worse, websites ultimately bear the cost of these "uninvited guests," expending bandwidth and CPU resources to deal with an endless stream of anonymous crawlers. In response, companies like Cloudflare and other CDNs (Content Delivery Networks) offer blocking services. All of this constitutes a "patchwork" system that should not exist.
We've previously pointed out that the original contract of the internet—content creators create content, and platforms are responsible for the economic coordination of content distribution—is gradually eroding. This trend has been reflected in data: over the past twelve months, website operators have started massively blocking AI-facing crawlers. In July 2024, only about 9% of the top 10,000 global websites were blocking AI crawlers, and today that percentage has reached 37%. As more website operators mature their technology and users become increasingly dissatisfied, this percentage will continue to rise.
So, what if instead of paying CDNs to "one-size-fits-all" block suspected robots, we try an intermediary approach? That is, AI crawlers no longer "freeload" but pay for the right to access data. Here, blockchain can come into play: in this scenario, each web crawler agent holds a certain amount of encrypted assets and engages in on-chain negotiation with each website's "gatekeeper agent" or paywall protocol via the x402 protocol. (Of course, the challenge lies in the robots.txt, or the "robot exclusion standard," which has been deeply ingrained in the operational model of internet enterprises since the 1990s. To change this, it would require large-scale cooperation or support from CDNs like Cloudflare.)
At the same time, human users can prove themselves to be real using the World ID (see earlier), thus gaining free access. This way, content creators and website operators can be compensated at the moment when data is collected by AI, while human users can still enjoy the free flow of information on the internet.
10. Precise yet Non-"Creepy" Privacy-Preserving Advertising
Matt Gleason: AI has already begun to influence how we shop online, but what if the ads we see every day could truly be "helpful"? People dislike ads for many reasons: irrelevant ads are pure noise; at the same time, not all "personalization" is a good thing. Highly targeted ads driven by massive consumer data can feel invasive; other apps try to monetize through practices like "forced ad viewing" (such as unskippable ads on streaming platforms or in-game levels).
Cryptographic technology can help address these issues and provide an opportunity to rethink the advertising ecosystem. When AI agents are combined with blockchain, they can customize ads based on user-set preferences, making ads both relevant and not overly "creepy". More importantly, in this process, users' data will not be globally exposed, and users willing to share data or interact with ads can be compensated.
To achieve this model, several technological foundations are required:
Low-Cost Digital Payment System: To compensate users for ad interaction (views, clicks, conversions), companies need to send a large number of small payments. To achieve scalability, this requires a system with high speed, high throughput, and almost zero fees.
Privacy-Preserving Data Validation: AI agents need to validate if consumers meet certain demographic characteristics. Zero-Knowledge Proofs (ZKPs) can perform such validations without revealing specific privacy information.
New Incentive Models: If the Internet adopts a micropayment-based monetization model (e.g., each interaction < $0.05), users can choose to view ads to receive compensation, transforming the current "data extraction model" into a "user engagement model".
For decades, people have been trying to make ads more "relevant" — online and offline. Re-examining advertising from the perspective of cryptographic technology and AI can truly make ads useful, controllable, and optional. For builders and advertisers, this means a more sustainable, more consistent incentive structure; for users, it provides a richer way to discover information and explore the digital world.
Ultimately, this will not only make ad space more valuable but may also shake up the deeply ingrained, "exploitative"-centric advertising economic model today, replacing it with a more human-centered system: where users are no longer the "product being sold" but true participants.
11. An AI Companion Owned and Controlled by Users
Guy Wuollet: Today, many people spend more time on devices than in offline interactions, and this online time is increasingly spent interacting with AI models or AI-driven content. These models have already provided a form of "companionship" — whether for entertainment, information retrieval, niche interest fulfillment, or as a child's educational tool. It is easy to imagine that in the near future, AI companions geared towards education, healthcare, legal advice, or even daily emotional support will become one of the primary ways humans interact.
Future AI companions will have infinite patience and be deeply customized for individuals and their use cases. They will not only be assistants or "robotic servants" but may also become highly valued relational objects for users. Therefore, the question arises: who truly owns and controls these relationships — the users or the companies and other intermediaries? If you have been concerned about content curation and moderation issues on social media in the past decade, this question will become exponentially more complex and personalized in the future.
The idea that "blockchain or similar censorship-resistant hosting platforms may be the best path to building an uncensorable, user-controlled AI" has been extensively discussed. While users could run local models and purchase GPUs themselves, for most people, this is either too costly or too technically challenging.
Although widespread adoption of AI companions is still a ways off, relevant technologies are rapidly maturing: text-based AI chat is already extremely lifelike; visual virtual avatars are also advancing; and blockchain performance continues to improve. To make an "uncensorable AI companion" truly user-friendly, we need to rely on better encryption app user experiences (UX). Fortunately, wallets like Phantom have made blockchain interactions simpler, and technologies such as embedded wallets, passkeys, and account abstraction enable users to easily achieve self-custody without having to manage their own mnemonic phrases. Additionally, high-throughput, trustless computation systems based on optimistic and ZK coprocessors will allow us to establish meaningful and sustainable long-term relationships with digital companions.
In the near future, the focus of public discourse will shift from "when will realistic digital companions and virtual avatars appear" to "who will control them, and how will they be controlled".
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