Author: Karen Shen
Compiled by: Block unicorn
In this article, we will explore the potential opportunities for collaboration between cryptocurrencies and consumer-level AI (Artificial Intelligence). The article is divided into three parts:
- Why choose Crypto x Consumer AI?
- Overview of the traditional consumer-level AI market
- Opportunities for Crypto x Consumer AI
Why choose Crypto x Consumer AI
Over the past year, the intersection of AI and cryptocurrencies has become a hot topic of interest for consumers, driving the launch of numerous new projects. The majority of the focus and capital has been concentrated on the infrastructure layer of AI, such as computing power, training processes, inference techniques, intelligent agent models, and data infrastructure.
While many of these projects are ambitious and may yield large-scale results, the technology is not yet at a production-ready level (currently), and the likelihood of widespread commercial adoption in the near term is low. This has left a gap in the market for more directly impactful technology applications, particularly at the consumer level.
Consumer-level AI refers to AI products designed for everyday users, rather than enterprise or business-specific applications. These products include AI-driven general assistants and recommendation systems, generative tools, and creative software. As AI technology has advanced rapidly, consumer-level applications are becoming more intuitive, personalized, and easier for the average user to utilize.
Popular consumer-level AI applications today
Unlike enterprise-level AI, which typically requires precision and deterministic results, consumer-level AI benefits from flexibility, creativity, and adaptability - areas where current AI is excelling.
While still in its early stages, the combination of Bit technology and consumer-level AI is undoubtedly an intriguing topic. It is rare to see two technologies simultaneously progress towards maturity, making it worth exploring - although the outcomes are difficult to predict.
In the Bit space, there is a pressing need for more consumer-facing applications that provide novel and engaging ways to interact with the underlying technology. Over the past decade, Bit investments have driven significant advancements in infrastructure, including faster Bit generation, lower Gas fees, better user experience (UX), and a substantial reduction in the user onboarding barriers that were common a few years ago.
You can simply try joining applications like Moon, and immediately purchase MEME Bits using Apple Pay, to get a tangible sense of how much progress the industry has made. However, there is still a lack of founders and developers willing to tackle interesting consumer Bit problems.
Meanwhile, consumer-level AI is already market-ready, providing a mature opportunity for developers to combine these two technologies and build applications that shape how we interact with, own, and participate in digital assets and AI systems.
Overview of the traditional consumer-level AI market
First, let's leverage two resources to help us quickly understand the experimentation happening within the traditional (non-Bit) consumer-level AI space:
- a16z's "Top Consumer Products by Web Traffic" (3rd edition)
- The latest Y Combinator (YC) W24 batch of projects
a16z's "Top Consumer Products by Web Traffic"
a16z's report ranks the top consumer-level AI web pages and mobile applications by web traffic every six months, analyzing the data to identify trends in how consumers are actively engaging with consumer-level AI technologies, which categories are gaining traction, which are declining, and the early leading projects in each category.
Here are the top 100 consumer-level AI products as of August 2024, categorized into web and mobile applications.
Clearly, content generation and editing tools are leading the consumer-level AI space.
These applications occupy 52% of the top 50 web applications and 36% of the top 50 mobile applications. Notably, this category is expanding from text generation to image generation, and now includes video and music generation, further expanding the potential of AI-driven creative expression.
Popular categories like general assistants, companion tools, and productivity tools maintain a steady presence in the top 100, reflecting ongoing demand. The a16z report's third edition also introduced a new "Aesthetics & Dating" category, with three projects making the list.
Interestingly, a cross-category Bit project has also successfully cracked the rankings. The anime companion app Yodayo (now Moescape AI) ranks 22nd on the web application list.
Moescape AI
Comparing a16z's latest report to their previous ones, while the core consumer-level AI categories remain stable, about 30% of the top 100 projects are new, highlighting the ongoing evolution of the space.
The latest Y Combinator (YC) W24 batch
Next, let's review the latest YC W24 batch as a resource to help identify emerging consumer-level AI projects and categories that may have entered the market but may not have gained enough traction to appear in a16z's top 100 web traffic rankings.
The idea here is that while the actual consumer demand for these products may be uncertain, this information can help us anticipate consumer-level AI trends over the next 6-12 months.
In the recent 235 projects, 63% are focused on the AI domain, with 70% of those being application-layer projects. Only about 14% of the application-layer projects were identified as consumer-centric.
Here is our attempt at categorizing the consumer-level AI projects.
Similarly, content generation remains the most popular category among founders, with new projects continuously pushing the boundaries of creative possibilities.
Mirroring the trends in the a16z report, the latest batch of YC entrepreneurs is exploring more advanced content types, including storytelling, script-to-film generation, music, video, and presentation-focused content.
In addition to content generation, founders are also focused on search, productivity, and edtech. These three categories align with the a16z report, although most of the YC companies in these areas are developing more targeted, industry-specific solutions.
Finally, categories like gaming, automation, marketplaces, and streaming appear in this cohort, signaling some new directions that were not present in the a16z report.
Opportunities for Crypto x Consumer AI
Now that we've provided some background on the trends in the traditional consumer-level AI market, let's turn our attention to consumer-level Bit AI.
First, it may be helpful to briefly outline how AI can be useful for Bit products, or how Bit can be useful for consumer-level AI products.
Bit and AI offer very different value propositions.
One could argue that the values of these two technologies are in conflict - Bit emphasizes decentralization, privacy, and personal ownership, while AI often centralizes power and control in the hands of those who develop and own the most advanced models.
However, with the emergence of decentralized and open-source AI, these boundaries are starting to blur.
The core innovation of AI in consumer products is the ability to generate novel content, mimic and expand human creativity, while learning from massive datasets, leveraging advanced neural network architectures to simulate complex relationships, and produce high-quality outputs.
Early signs indicate that AI applications have strong potential for user retention and monetization. However, they also face a "visitor problem" where user traffic is high but the conversion rate from free users to paying users is lower than usual.
On the other hand, cryptography is a design space that includes decentralization, crypto-economic incentives, and hyper-financialization. It is a distributed ledger that allows the value of any digital object to be stored in a transparent and traceable manner.
Cryptography is highly effective in coordinating activities, aggregating decentralized infrastructure, and creating frictionless markets, easily creating markets where none existed before. However, beyond financial infrastructure, cryptography has yet to create a compelling and sustainable consumer-level application.
AI may be one of the key factors in unlocking the broader consumer potential of cryptography. Recent research has highlighted the rapid adoption of generative AI, with adoption rates exceeding those of PCs and the internet - about 32% of US residents use AI weekly. Given this pace of development, developers of consumer-level cryptography applications who can synchronize their experimentation and innovation with the accelerating adoption of AI will be at a significant advantage.
We believe that through innovative consumer-level applications, combining the powerful capabilities of AI with the unique capabilities of decentralization and financialization networks afforded by cryptography, breakthrough results will emerge.
Market Overview
The number of consumer-centric projects operating in the intersection of cryptography and AI is still relatively small, with our research estimating around 28, although this is not a definitive number.
In this crowdsourced decentralized AI market map, the consumer-level category accounts for only about 13% of the total decentralized AI market, indicating that we still have significant room for growth. As a quick comparison, in the technology market, approximately 60-70% of products are at the application layer, of which about 70-80% are consumer-facing applications.
Although we only cover a small portion of projects in this report, we are still able to identify some early insights.
We have identified some early thoughts from teams on integrating cryptography and AI. These insights have been distilled into the following broader use cases, some of which show promise, while others may be less sustainable.
- Incentive Mechanisms: Cryptocurrencies as a way to incentivize and reward users for activity on AI platforms/applications. For example, one use of the native Wayfinder token is to reward agents and participants for creating valuable on-chain paths as the AI agent traverses. For Botto, the automated AI artist, the community is required to provide feedback on its art creations. Botto distributes a portion of its art sales revenue in the form of $BOTTO tokens to reward this participation.
- Financialization: The ability to trade, own, and generate income from AI assets on the blockchain. For example, Virtuals Protocol provides a platform where anyone can purchase and own a share of AI agents and benefit from the income generated by the AI agents they trust. Ownership is represented in the form of tokens.
- Attribution: Allowing intellectual property holders to track, verify, and claim royalties on the blockchain. For example, uncensored companion projects like Oh.xyz are using cryptography to create digital twin NFTs of creators on their platform to verify the authenticity of content and claim royalties in the future.
- In-App or In-Game Economies: Cryptocurrencies as in-app/in-game currencies. For example, games like Parallel and Today will have in-game economies where players and their AI agents will be able to trade resources using their respective tokens.
- Decentralization: Decentralized networks, services, and models. For example, BitMind is a subnet on Bittensor, building the first decentralized deep fake detection system. Leveraging Bittensor, they are able to incentivize open competition among AI developers to contribute to building the best deep fake detection model.
- Censorship Resistance: Removing censorship of generative AI content creation. For example, Venice is a private and permissionless generative AI assistant built on top of Morpheus' decentralized universal agent network. Unlike traditional AI assistants, Venice does not censor the AI's content or your conversations.
- Membership: Cryptocurrencies as a means to access premium features. For example, MyShell's ecosystem token has multiple use cases, one of which is granting holders access to premium features.
- Assistants: AI as a way to make interactions with cryptocurrencies more seamless. For example, Wayfinder, Fere AI, Fungi, and PAAL AI are vertical-specific assistants or bots for the cryptocurrency industry, aiming to make the end-user's crypto experience more convenient.
- Contextualization: AI as a way to contextualize and personalize content on the blockchain. For example, Unofficial aims to use zkTLS and RAG to build a discovery engine for on-chain social on Farcaster.
After examining the current cryptocurrency and consumer AI landscape - including the applications of cryptography and AI, as well as the state of established and emerging categories in the traditional consumer-level AI domain - the next section will explore the most promising design spaces in this intersection for developers to consider.
Games and Agents/Companions
There are reasons why games and agents/companions have emerged as the two most popular categories for founders in this intersection. They provide the most suitable environments for experimenting with AI and cryptocurrencies.
Games and agents typically operate in fictional realms, with the purpose of entertaining consumers. Their outcomes often do not need to be definitive, and they usually have minimal real-world impact. This provides the perfect conditions for experimentation.
The hyper-realistic gaming environment of today
So far, games like Parallel Colony and Today have used AI as the core experience of their product, with AI NPC characters behaving like real humans, exhibiting autonomy and the ability to engage in dialogue.
Cryptocurrencies are being used as financial channels for in-game payments, agent-to-agent payments, or unlocking character ownership.
Crucially, this new digital economy is the advantage these crypto games have over the many AI games that are about to launch.
AI is a transformative technology, and there is no doubt that it will be a key part of future game development and gaming experiences - but we believe that teams building AI games with a focus on native digital economies will have the greatest competitive edge.
AI agents in games are interesting, but cryptocurrencies unlock the ability to introduce economic systems that mimic human experiences for the first time in games. In-game NPCs simply cannot open their own bank accounts, make transactions, and make real economic decisions. So many unprecedented behaviors and opportunities may emerge.
As Kalos, the founder of Parallel, said on Twitter:
This is best exemplified in fictional environments like games today.
Projects building AI agents and companions share similarities in their use of AI and cryptocurrencies - AI as the core experience, cryptocurrencies as the financial infrastructure. However, while agents in games operate in a constrained environment, allowing for more complex interactions with minimal real-world consequences, agents and companions are currently limited to one-to-one or one-to-many relationships.
For example, using MyShell, Virtuals Protocol, or MoeMate, end-users interact with AI chatbots through chat or voice - the interaction is limited to just you and the chatbot (or other medium). The chatbots are LLM wrappers with limited features that can be customized by the bot's creator, such as the tone of communication, the appearance of the agent, etc. So your interactions with these chatbots are also limited in creativity.
The experience of the Draco Malfoy AI chatbot in MoeMate
Although similar to its competitors, ai16z takes an open-source, bottom-up approach, focusing on building on-chain AI agent infrastructure to provide tools for a multi-agent future.
There is still much to explore in the realms of gaming and agency, such as multi-agent experiences or infinite game modes. Consumer experiences involving multi-agent AI and human interaction, though complex, may bring more dynamic and engaging interactions, as well as more sophisticated crypto-economic systems. This area remains largely unexplored outside of gaming environments.
We still believe this is one of the areas that founders are most interested in, and we are eager to see what kind of innovations the future will bring.
General Assistants and Content Generation Tools
General assistants and content generation tools dominate the traditional consumer-facing AI landscape. However, fierce competition makes entering this market challenging and costly, which also explains why these categories are not as strongly represented on the crypto market map as they are in traditional AI.
Nevertheless, the demand for these tools remains strong, consistently ranking high in a16z's network traffic analysis. For crypto and AI founders, these categories still hold promise, especially for products tailored specifically for crypto users. By focusing on the specific needs of the crypto domain, it may be possible to create unique value without having to compete in the saturated traditional markets.
Here are some examples:
AI-Powered Crypto Assistants: It is well known that crypto is difficult to navigate. Whether you are trying to buy or swap tokens on-chain, or meet the requirements for participating in games or social experiences, there are many hurdles.
Are you on the right network? How do you switch networks? Do you have the right Gas tokens? How do you move funds to the target network?
The learning curve is steep for newcomers. Even for those familiar with cryptocurrencies, these tasks can still be time-consuming.
While the industry has primarily focused on improvements in account abstraction, intents, and other UI/UX aspects so far, AI is more likely to integrate these developments and drive these changes forward. Some teams, such as Wayfinder, Fungi, PAAL AI, and Fere AI, have already been exploring solutions, though no team has yet made significant progress - leaving room for more competitors and specialization.
A glimpse of Wayfinder's crypto assistant
The needs of experienced Solidity developers may differ from those of newcomers. We believe teams that consider specific user segments (customizing the experience based on their specific problems), provide a polished user experience (leveraging advancements in account abstraction and intents), and offer personalized services (based on users' previous on-chain activities) are most likely to succeed.
AI-Powered Asset Generation: In the crypto domain, content generation can be viewed as asset generation. These assets can be tokens and digital assets in the form of ERC20, ERC721, ERC1155, or other standards, with almost limitless ways of generating them. Similar to how Midjourney and DALL-E generate images, or SUNO creates music, AI can also play a crucial role in crypto asset generation.
Early examples of AI-driven crypto asset generation include the $GOAT token by Truth Terminal, Wayfinder's asset deployment proxy, the upcoming gamified asset generation marketplace by Swan, and the AI agent launch platform by Virtuals Protocol.
Beyond just generating assets, AI can also shape narratives, market assets, and give them a "voice". For specific asset types like MEME coins (with no external dependencies), AI can efficiently streamline the end-to-end asset development process.
In a world where AI agents can frictionlessly generate countless crypto assets, the opportunity for developers lies in identifying the flow of value and attention. For example, the strategy taken by Virtuals Protocol is to shift the speculative behavior to the creator level, allowing consumers to bet on the AI agent's ability to attract attention and create interesting assets.
We are currently in the early stages of a crazy new reality where AI can generate real financial value in the form of crypto assets, and humans can enjoy and speculate on the evolution of these assets. While the future of this trend is difficult to predict, this area has a vast experimental space, and we will be closely watching its trajectory.
Miscellaneous
There are many other categories in the intersection of crypto and consumer-facing AI that remain largely unexplored. As AI continues to advance rapidly, these categories are likely to grow and evolve quickly. While many categories may be fleeting, and fewer may be well-suited for crypto collaboration, there is still ample experimental space in this domain - which we welcome!
One way to think about it is to consider crypto-native versions of traditional consumer-facing AI projects, especially those that currently have no crypto intersection. For example, we will apply crypto technology to two categories from the a16z and YC lists, and add an additional category for discussion.
Edtech is a popular consumer-facing AI category that can benefit from crypto technology at different layers of the stack. Education spans regions, subjects, languages, education levels, and teaching methods. In this case, rather than taking a centralized approach, progress in Edtech can be driven by open-source development and collaboration with global contributors. In this context, Edtech-centric subnetworks on Bittensor can help build these models.
Crypto technology can also be applied to the incentive layer of Edtech applications. Beyond traditional gamification strategies (like Duolingo's daily streak mechanism), crypto can enable teachers and students to be rewarded for their contributions and efforts on both the supply and demand sides.
For self-help, the potential of cryptocurrencies in enabling data ownership and monetization may be particularly compelling. Due to cost, social stigma, lack of awareness, and shortage of professionals, it remains inaccessible for many. Projects like Sonia and Maia (both recent YC batch companies) have shown the preliminary possibility of affordable AI-driven mental health counseling solutions. Traditionally, therapists' notes have been stored in physical or digital files in their offices, with the data inaccessible. However, for AI therapists, data can be privately stored online, unlocking new use cases from an individual's mental health data.
Imagine if you could truly own the data from your AI therapy sessions. You could choose to keep it private, monetize it, or even anonymously contribute it to a health data network to support meaningful research. Crypto-native projects like Vana are making this possible, giving people a stake in their own data.
In the entertainment domain, projects like Unlonely are experimenting with crypto-native live streaming, where users can bet on and influence the outcomes of live events using the platform's tokens. Currently, this is limited to real-world activities, but it can be extended to AI-generated content. This could enable 24/7 live streams with greater user control over the narrative. MineTard AI is a recent early example, where an AI agent live-streams Minecraft on Kick, with the agent being influenced by $MTard token holders.
The viral TikTok trend of creators playing NPCs
Last year, a viral TikTok trend emerged where creators played NPCs, executing specific actions based on the "gifts" they received. While this content type was short-lived, it clearly demonstrated consumer interest in interactive live experiences. As AI-driven NPC technology advances, similar gamified interactions may be well-suited for crypto-native live streaming, where AI NPCs can respond in real-time to user inputs.
These are just some rough ideas on how crypto and AI can be applied to consumer-facing applications. In this report, we have not covered all the possible applications, and we expect to see more such innovations as the industry continues to evolve rapidly.
Closing Thoughts
You may have gathered that we are very excited about the possibilities in the intersection of crypto and consumer-facing AI. The projects currently being built in this space only represent a small fraction of the potential.
As these two technologies develop in parallel, founders have a unique window to create a new wave of consumer-facing applications that may change the way we interact with and engage in digital assets and synthetic intelligence.
For those building in this space, we encourage you to continue pushing boundaries and exploring unconventional applications of these technologies. We also hope that for some, this resource can provide assistance on their journey.
