When consumer-grade AI applications meet crypto, which projects and directions are worth watching?
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When consumer-grade AI applications meet crypto, which projects and directions are worth watching?
With the rapid advancement of AI technology, consumer applications have become more intuitive, personalized, and easier for ordinary users to use.
Navigating Web3 tides with focused insightsAuthor: Karen Shen
Translation: TechFlow
In this article, we explore the potential opportunities at the intersection of cryptocurrency and consumer AI. The piece is divided into three sections:
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Why Crypto x Consumer AI?
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An Overview of Traditional Consumer AI
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Opportunities in Crypto x Consumer AI
Why Crypto x Consumer AI?
Over the past year, the convergence of AI and crypto has gradually become a focal point for consumers, driving the launch of numerous new projects. Most attention and funding have flowed toward infrastructure-level AI—such as compute, training, inference, smart models, and data infrastructure.
While these projects are ambitious and may yield large-scale outcomes, the technology is not yet mature enough for widespread deployment, making short-term commercialization unlikely. This leaves a gap at the consumer level—one that urgently needs applications with direct impact.
Consumer AI refers to artificial intelligence products designed for everyday users, rather than enterprise or business-specific use cases. These include AI-powered general assistants, recommendation systems, generative tools, and creative software. As AI advances rapidly, consumer applications are becoming more intuitive, personalized, and accessible to non-technical users.
Currently popular consumer AI applications
Unlike enterprise AI, which demands precision and deterministic outputs, consumer AI emphasizes flexibility, creativity, and adaptability—areas where AI excels.
Though still early, the combination of crypto and consumer AI is highly compelling. It's rare to witness two transformative technologies maturing simultaneously. Thus, this space deserves deep exploration, even if its outcomes remain uncertain.
The crypto ecosystem urgently needs more consumer-facing applications that offer novel and engaging ways to interact with its underlying technology. Over the past decade, blockchain investment has driven leaps in infrastructure—resulting in faster block times, lower gas fees, better UX, and significant progress on many onboarding barriers we faced just years ago.
You can already feel the industry’s advancement by using apps like Moonshot, which lets you instantly buy meme coins via Apple Pay. However, there remains a shortage of founders and developers willing to tackle interesting consumer crypto problems.
Meanwhile, consumer AI is now ready for market entry, offering developers a golden opportunity to combine both technologies and build applications that reshape how we interact with, own, and participate in digital assets and synthetic intelligence systems.
An Overview of the Traditional Consumer AI Market
First, we’ll leverage two resources to understand recent developments in the traditional (non-crypto) consumer AI landscape:
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a16z’s “Top 100 Gen AI Consumer Products” (Third Edition)
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Y Combinator’s Winter 2024 startup batch
a16z’s “Top 100 Gen AI Consumer Products”
The a16z report ranks the most visited consumer AI web and mobile products every six months based on web traffic data.
By analyzing this data, they identify trends in how consumers actively use consumer AI—what categories are gaining traction, which are declining, and which players are emerging as early leaders in each category.
Below are the top 100 AI consumer products as of August 2024, categorized by web and mobile platforms.
Clearly, content creation and editing tools dominate consumer AI.
These apps occupy 52% of the top 50 web apps and 36% of the top 100 mobile apps. Notably, this category is expanding from text and image generation to video and music, broadening the scope of AI-driven creative expression.
Popular categories like general assistants, companions, and productivity tools maintain stable positions in the top 100, reflecting consistent market demand. The third edition of the a16z report introduced a new category—"Aesthetics & Dating"—with three entries.
Notably, one crypto project stands out across categories. Yodayo (now Moescape AI), an anime companion app, ranked #22 among web apps.
Moescape AI
Comparing a16z’s latest report with previous editions reveals that while core consumer AI categories remain stable, about 30% of the top 100 apps are new—highlighting the sector’s ongoing evolution.
Y Combinator’s Winter 2024 Batch
Next, we reviewed Y Combinator’s Winter 2024 batch to identify emerging consumer AI projects and categories that may not yet have sufficient traffic to appear on a16z’s top 100 list.
Our goal here is to use this information to forecast consumer AI trends over the next 6–12 months, though actual consumer demand for these products remains uncertain.
Among the 235 startups in this cohort, 63% focused on AI, with 70% building at the application layer. Only about 14% of application-layer projects were identified as consumer-facing.
Below is our attempt to categorize these consumer AI projects.
Again, content generation remains the most popular category among founders, with new projects continuously pushing creative boundaries.
Similar to trends seen in the a16z report, YC’s latest batch explores advanced content types—including storytelling, script-to-film generation, music, video, and presentation-focused content.
Finally, categories like gaming, self-help, marketplaces, and streaming appear in this batch—marking new directions not yet visible in the a16z report.
Opportunities at the Intersection of Crypto and Consumer AI
Having understood background trends in the traditional consumer AI market, we now turn to consumer crypto AI.
First, let’s briefly discuss how AI can add value to crypto products—or vice versa—how crypto can benefit consumer AI products.
Crypto and AI offer fundamentally different value propositions.
In some ways, the two technologies hold conflicting values: crypto emphasizes decentralization, privacy, and individual ownership, while AI tends to concentrate power and control in the hands of those who develop and own the most advanced models.
Yet, this boundary is blurring with the rise of decentralized and open-source AI.
In the context of consumer products, AI’s core innovation lies in simulating and extending human creativity through novel content generation—learning from vast datasets, modeling complex relationships via advanced neural networks, and producing high-quality outputs.
Early signals suggest AI apps have strong user retention and monetization potential. However, they also face the so-called “tourist problem”—high visit volume but lower-than-expected conversion from free to paying users.
On the other hand, crypto represents a design space defined by decentralization, cryptographic economic incentives, and hyper-financialization. It’s a distributed ledger capable of transparently and traceably storing the value of any digital object.
Crypto excels at coordinating activities, integrating decentralized infrastructure, and frictionlessly creating new markets. However, beyond financial infrastructure, crypto has yet to produce a compelling and sustainable consumer application.
AI could be a key component in unlocking broader consumer potential for crypto. A recent study shows generative AI adoption is faster than that of personal computers or the internet—around 32% of U.S. residents now use AI weekly. Given this pace, developers of consumer crypto would benefit greatly by experimenting and innovating in parallel with AI’s rapid adoption.
We believe breakthrough consumer applications will emerge by combining AI’s capabilities with the unique strengths of decentralized and financialized networks enabled by crypto.
Market Analysis
Within the intersection of crypto and AI, the number of consumer-focused projects remains relatively small. Based on our research, there are approximately 28 such projects—but this number is uncertain.
In a crowdsourced map of the decentralized AI market, the consumer category accounts for only about 13% of the entire decentralized AI market—revealing substantial growth potential in this area. By contrast, around 60–70% of tech markets consist of application-layer products, of which roughly 70–80% are consumer-facing.
While this report covers only a fraction of projects, we’ve already identified some early insights.
We summarize initial approaches teams are taking to integrate crypto and AI. These insights are distilled into broader application scenarios—some showing promise, others potentially less sustainable.
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Incentives: Using crypto to incentivize and reward user activity on AI platforms or apps. For example, Wayfinder uses its native token to reward agents and participants who create valuable paths for AI agents on-chain. Botto, an autonomous AI artist, rewards community feedback on its artwork and distributes a portion of art sale proceeds in $BOTTO tokens.
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Financialization: The ability to trade, own, and generate income from AI assets on blockchains. For instance, Virtuals Protocol offers a platform where anyone can purchase and own shares of AI agents and earn revenue from agents they support. Ownership is represented via tokens.
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Copyright Attribution: Enabling IP holders to track, verify, and claim royalties on blockchains. Projects like Oh.xyz generate digital twin NFTs for creators using cryptography to verify content authenticity and claim future royalties.
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In-App or In-Game Economies: Using cryptocurrencies as in-app or in-game currencies. Games like Parallel and Today feature in-game economies where players and their AI agents can trade resources using their respective tokens.
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Decentralization: Decentralizing networks, services, and models. For example, BitMind, a subnet on the Bittensor network, is building the first decentralized deepfake detection system. Through Bittensor, they encourage open competition among AI developers to collaboratively build the best detection model.
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Anti-Censorship: Removing restrictions on generative AI content creation. Venice, built on Morpheus’s decentralized general agent network, is a private, permissionless generative AI assistant. Unlike traditional AI assistants, Venice does not censor AI output or store user conversations.
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Membership Systems: Using crypto tokens to access premium features. For example, MyShell’s ecosystem token serves multiple purposes, including granting access to advanced features for holders.
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Smart Assistants: Leveraging AI to simplify user interactions with crypto. Examples include Wayfinder, Fere AI, Fungi, and PAAL AI—domain-specific general assistants or bots designed for the crypto industry to streamline end-user experiences.
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Content Contextualization: Using AI to contextualize and personalize content on-chain. Unofficial, for example, plans to build an on-chain social discovery engine on Farcaster using zkTLS and RAG.
After analyzing the current crypto x consumer AI market—including methods of integrating crypto and AI, and the state of established and emerging categories in traditional consumer AI—the next section explores the most promising design spaces within this convergence.
Gaming and Agents/Companions
Gaming and agents/companions are the two most popular directions for entrepreneurs in this intersection because they provide ideal environments for experimenting with both AI and crypto.
Games and agents typically operate in fictional worlds whose primary purpose is consumer entertainment. Outcomes don’t need to be deterministic, and real-world impact is minimal—making them perfect testbeds for experimentation.
Today’s game environment
Currently, games like Parallel Colony and Today are integrating AI as a core product experience—featuring in-game AI NPCs that behave like real people, with autonomy and conversational abilities.
Crypto is used as financial infrastructure for in-game payments, inter-agent transactions, or unlocking character ownership.
The key point is that this new digital economy gives these crypto-native games a competitive edge over the many AI games soon to hit the market.
AI is a transformative technology—undoubtedly becoming a critical part of game development and future gameplay. But we believe teams that bake digital-native economies into their AI game designs from the start will have the ultimate advantage.
AI agents in games are fascinating, and crypto introduces, for the first time, a system that simulates human economic behavior. NPCs in games can't open bank accounts, conduct trades, or make real economic decisions. This could unlock unprecedented behaviors and opportunities.
As Kalos, founder of Parallel, noted on Twitter:
This concept is most intuitive today in fictional environments like games.
Projects developing AI agents and companions similarly use AI and crypto—AI as the core experience, crypto as the financial foundation. However, unlike games, agents operate in constrained environments allowing richer interactions with minimal real-world consequences. Currently, agents and companions are mostly limited to one-on-one or one-to-many relationships.
For example, through MyShell, Virtuals Protocol, or MoeMate, users interact with AI chatbot characters via text or voice—interactions confined between the user and the bot (or other medium). These chatbots are wrappers around large language models with limited traits customizable by creators—such as tone of voice, appearance, etc. Thus, user interaction with these bots is also creatively constrained.
MoeMate’s Draco Malfoy AI chatbot experience
While similar to competitors, ai16z takes an open-source, bottom-up approach to building on-chain AI agent infrastructure, providing tools for future multi-agent systems. You can explore their projects on GitHub.
There are many unexplored directions in gaming and agents—like multi-agent interaction experiences or infinite game modes. Consumer experiences involving many-to-many AI agent-human interactions are complex but could lead to more dynamic and engaging experiences, along with richer crypto-economic systems. These remain largely unexplored outside gaming environments.
We still believe this is one of the most promising areas for builders, and we’re excited to see what comes next.
General Assistants and Content Generation
General assistants and content generation tools dominate the traditional consumer AI market. However, due to intense competition, entering this space is challenging and costly—explaining why these categories are underrepresented in crypto markets despite their strength in traditional AI.
Nevertheless, demand remains strong, consistently ranking high in a16z’s web traffic analysis. For entrepreneurs at the crypto-AI intersection, these categories remain promising—especially for products tailored to crypto users. By focusing on niche needs within crypto, unique value can be created without direct competition against mainstream AI players.
Here are some examples:
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AI-Powered Crypto Assistants: The crypto space is often seen as hard to navigate. Whether buying or swapping tokens on-chain, or meeting requirements to participate in games or social activities, numerous barriers exist.
Are you on the right network? How do you switch networks? Do you have the correct gas token? How do you transfer funds to the right chain?
The learning curve is steep for newcomers. Even for experienced users, these tasks can be time-consuming.
Although the industry has invested heavily in account abstraction, intent-based design, and other UI/UX improvements, AI is more likely to integrate these advancements and drive change. Teams like Wayfinder, Fungi, PAAL AI, and Fere AI are already exploring solutions, though none have gained significant traction—leaving room for more competition and specialization.
Wayfinder’s early crypto assistant prototype
The needs of an experienced Solidity developer differ vastly from those of a beginner. We believe teams that focus on specific user segments—tailoring experiences precisely to their problems—deliver refined UX (leveraging latest advances in account abstraction and intent design), and offer personalization (based on users’ on-chain history)—will be more likely to succeed.
AI-Driven Asset Generation: In crypto, content generation can be viewed as asset generation. With standards like ERC20, ERC721, ERC1155, and others, nearly infinite assets can be created. Just as Midjourney and DALL-E generate images, or SUNO creates music, AI can play a major role in generating crypto assets.
Examples include Truth Terminal’s $GOAT token, Wayfinder’s asset deployment agent, Swan’s upcoming gamified asset generation marketplace, and Virtuals Protocol’s AI agent launchpad.
Here’s a demo video showing how to create assets using Wayfinder.
Beyond asset creation, AI can shape narratives, promote assets, and give them a “voice.” For certain asset types like memecoins (which lack external dependencies), AI can effectively streamline the end-to-end asset development process.
In a world where AI agents can seamlessly generate infinite crypto assets, the opportunity lies in identifying where value and attention might flow. For instance, Virtuals Protocol believes speculation will shift to the creator level—allowing consumers to speculate on an AI agent’s ability to attract attention and create compelling assets.
We are in the early stages of an emerging reality where AI can generate tangible financial value in the form of crypto assets—meant to be enjoyed and speculated upon. While the trajectory of this development is unpredictable, there’s ample room for experimentation—and we’ll be watching closely.
Other Areas
At the intersection of crypto and consumer AI, many areas remain unexplored. As AI advances rapidly, these domains may quickly expand and evolve. While some may be short-lived or poorly suited for crypto integration, there’s still plenty of room for experimentation—and we welcome it!
One way to think about it is to consider crypto-native versions of traditional consumer AI products that currently don’t incorporate crypto. For example, we apply crypto to two categories from the a16z and YC lists—and add one extra.
Educational Technology (EdTech) is a popular consumer AI category that could benefit from crypto at various layers of its tech stack. Education spans regions, subjects, languages, levels, and teaching methods. Rather than centralized approaches, EdTech could benefit from open-source development by global contributors. In this case, a Bittensor subnet focused on education could help build such models.
Crypto can also enhance incentive layers in EdTech apps. Beyond gamification tactics like Duolingo’s daily streaks, teachers and students could be rewarded cryptoeconomically on both supply and demand sides.
In the self-help space, crypto’s potential for data ownership and monetization may be appealing. Due to cost, stigma, lack of awareness, and therapist shortages, mental health services remain inaccessible. Projects like Sonia and Maia (both recent YC startups) show early promise for affordable, AI-powered therapy. Traditionally, therapist notes are stored in paper or digital files in offices—data that’s hard to access. But with AI therapists, data can be securely stored online, unlocking entirely new use cases from your mental health data.
Imagine truly owning your AI therapy session data. You could choose to keep it private, monetize it, or anonymously contribute it to a health data network for meaningful research. Crypto-native projects like Vana are enabling this at the network level—giving people control over their data.
In entertainment, projects like Unlonely are experimenting with crypto-native live streams, where users can speculate on and influence outcomes using platform tokens. Currently limited to real-life events, this model could extend to AI-generated content—enabling 24/7 streams with greater user control over narrative. MineTard AI is an early example: an AI agent livestreaming Minecraft on Kick, where $MTard holders can influence the agent.
Last year, a viral trend on TikTok saw creators扮演 NPCs, performing actions based on “gifts” they received. Though short-lived, this clearly signaled consumer interest in interactive live experiences. As AI NPC technology improves, similar gamified interactions may suit crypto-native live streams, where AI NPCs respond in real-time to user input.
These are just preliminary ideas on how crypto and AI can be applied to consumer apps. Many more ideas not covered here will likely emerge as the industry evolves rapidly.
Conclusion
As you may have noticed, we are (extremely) bullish on the intersection of crypto and consumer AI. The projects currently being built represent only a fraction of its potential.
With both technologies maturing in parallel, entrepreneurs have a unique opportunity to create a wave of new consumer applications that could transform how we interact with digital assets and synthetic intelligence.
We encourage innovators in this space to keep pushing boundaries and exploring unconventional applications of these technologies. We hope this article serves as a useful resource for those beginning this journey.
If you're a builder working at this intersection, we’d love to hear from you!
Disclosure/Disclaimer: At the time of publication, Collab+Currency or its members may hold some of the assets mentioned in this article. The author and Collab+Currency do not endorse or recommend holding any of the projects or collections mentioned herein.
The information provided is for general reference only and should not be considered investment advice. While we have made efforts to verify the accuracy of the information presented, no guarantees can be made. Investors should recognize that investing in digital assets involves high risk and is suitable only for those willing to bear such risks. Any forward-looking statements are based on specific assumptions, analyses, historical trends, current conditions, and expectations about future developments. These statements do not guarantee future performance and involve risks, uncertainties, and unpredictable assumptions.
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