From Collab+Currency by Karen Shen (Collab+Currency)

In this article, we’ll examine the potential opportunities for collaboration between crypto and consumer AI. The article is broken up into three sections:

Why Crypto x Consumer AI?

Over the past year, the intersection of AI and crypto has gained prominence as a key area of consumer interest, fueling a surge of new project launches. Much of the attention and capital has been directed toward the foundational infrastructure of AI, such as compute, training, inference, agentic models, and data infrastructure.

While many of these projects are ambitious and could lead to large-scale outcomes, the technology isn’t production-grade ready (yet), and broad commercialization in the short term is unlikely. This has left a gap in the market for more immediately impactful applications of the technology, particularly at the consumer layer.

Consumer AI refers to artificial intelligence products designed for everyday users rather than enterprise or business-specific applications. These range from AI-driven general assistants and recommendation systems to generative tools and creative software. With the rapid advancement of AI technology, consumer applications are becoming more intuitive, personalized, and accessible to the average user.

Popular consumer AI applications today

Unlike enterprise AI, which often requires precision and deterministic outcomes, consumer AI benefits from flexibility, creativity, and adaptability–all attributes where AI currently excels.

While still early, the intersection of crypto and consumer AI is intriguing. It’s rare to witness two technologies advancing toward maturity at the same time. For this reason, it’s worthy of exploration — albeit difficult to predict outcomes.

On the crypto side, there is a pressing need for more consumer-facing applications that can introduce novel and engaging ways to interact with its underlying technology. Over the last decade, blockchain investments have led to leaps in infrastructure, faster block times, cheaper gas, better UX, and success in abstracting away a lot of the onboarding friction we faced just a few years back.

You only need to try onboarding to apps like Moonshot where you can buy meme coins instantly with Apple Pay to reflect on how far the industry has come. Yet, there remains a lack of founders and developers willing to solve interesting consumer crypto problems.

Meanwhile, Consumer AI is market-ready, offering builders a ripe opportunity to combine these two technologies and build applications that can shape the future of how we interact, own, and participate with digital assets and synthetic intelligence systems.

Traditional Consumer AI Market Overview

To start, let’s leverage two resources to help get us up to speed with experiments inside the traditional (non-crypto) consumer AI space:

a16z’s Top Consumer Apps by Web Traffic

The purpose of a16z’s report is to review web traffic data and rank the most visited consumer AI web and mobile products every six months.

By analyzing this data, they identify trends in how consumers actively engage with consumer AI technology, which categories are gaining traction, which are dropping off, and which projects are the early winners in each category.

Here are the top 100 AI consumer products (as of August 2024), broken down by category across both web and mobile apps.

Clearly, content generation and editing tools are leading the field in consumer AI.

These applications now represent 52% of the top 50 web apps and 36% of the top mobile apps. Notably, the category is evolving beyond text-to-image to include video and music generation, broadening the potential for AI-driven creative expression.

Popular categories like general assistants, companions, and productivity tools maintain a consistent presence in the Top 100 list, reflecting stable demand. A new addition to the 3rd edition of a16z’s report is “aesthetics & dating,” which saw three projects make the list.

In a notable cross-category appearance, a crypto project joined the ranks as well. Yodayo (now Moescape AI), an anime companion app, reached #22 on the web apps list.

Moescape AI

Comparing a16z’s most recent report with the previous one reveals that while core consumer AI categories remain steady, approximately 30% of the top 100 projects are new, highlighting the ongoing evolution of this sector.

YC’s W24 Cohort

Next, let’s review YC’s W24 cohort (their latest cohort) as a resource to help identify emerging consumer AI projects and categories that are entering the market but may not have sufficient traction to appear on the a16z’s top 100 web traffic list.

Here, the idea is that this information can help us forecast consumer AI trends over the next 6–12 months, despite there being uncertainty around real consumer demand for these products.

In the most recent cohort of 235 projects, 63% were focused on AI, with 70% of these building on the application layer. Only approximately 14% of the application-layer projects were identified as consumer-focused.

Here’s our attempt at categorizing the consumer AI projects.

Again, content generation remains the most popular category among founders, with new projects pushing the boundaries of creative possibilities.

Similar to trends seen in the a16z report, YC’s latest cohort is exploring advanced content types, including storytelling, script-to-movie generation, music, video, and presentation-focused content.

In addition to content generation, founders are also focusing on search, productivity, and edtech. These three categories align with a16z’s report, though most YC companies within them are developing more niche, vertical-specific solutions tailored to particular industries.

Lastly, categories such as gaming, self-help, marketplaces, and streaming appeared in this cohort, marking new directions not present in the a16z report.

The Crypto x Consumer AI Opportunity

Now that we’ve covered background trends in the traditional consumer AI market, let’s turn our attention to consumer crypto AI.

To start, it may be useful for us to briefly run through ways in which AI can be useful to crypto products or alternatively, how crypto can be useful to consumer AI products.

Crypto and AI offer very different value propositions.

You can go as far as to say that the two technologies have conflicting values–crypto focuses on decentralization, privacy, and individual ownership, while AI tends to centralize power and control in the hands of those who develop and own the most advanced models.

These lines are beginning to blur with decentralized and open-source AI.

AI’s core innovation, in the context of consumer products, is the ability to mimic and extend human creativity by generating novel content, while learning from massive datasets and using advanced neural architectures to model complex relationships and produce high-quality outputs.

Early indicators suggest that AI applications demonstrate strong user retention and monetization potential. However, they also face a “tourist problem,” characterized by high user traffic but lower than usual conversion rates from free to paid users.

Crypto, on the other hand, is a design space that includes properties of decentralization, crypto-economic incentives, and hyper-financialization. It’s a distributed ledger that allows *any* digital object’s value to be stored, transparently, and with provenance.

Crypto is highly effective at coordinating activity, aggregating decentralized infrastructure, and creating markets frictionlessly where they did not previously exist. However, outside of financial infrastructure, crypto has yet to build a compelling and sustainable consumer app.

AI may be one of the missing pieces to unlock crypto’s broader consumer potential. A recent study highlights the rapid adoption of generative AI, with its uptake surpassing that of both PCs and the internet–about 32% of US residents now use AI weekly. Given this pace, it would be highly advantageous for consumer crypto builders to experiment and innovate in tandem with AI’s accelerating adoption.

We believe that breakthroughs will emerge from innovative consumer applications that harness the power of AI alongside the unique capabilities of decentralized and financialized networks enabled by crypto.

Market Breakdown

The number of consumer-focused projects operating at the intersection of crypto and AI remains relatively small, with our research estimating around 28, though this is certainly not definitive.

In this crowd-sourced decentralized AI market map, the consumer category represents only ~13% of the total decentralized AI market, illustrating how much room we have to grow. As a quick comparison, ~60–70% of the tech market is made up of products on the application layer, of which ~70–80% are consumer applications.

Though we’re only covering a small sample size of projects in this report, we were able to identify some early insights.

We’ve identified some of the early ways that teams are thinking about integrating crypto and AI. These insights are distilled below into broader use cases, some of which show promise, while others may be less sustainable.

After examining the current crypto x consumer AI market–including how crypto and AI are applied and the state of established and emerging categories in traditional consumer AI–this next section explores the most promising design spaces for builders at this intersection.

Gaming & Agents/Companions

There’s a reason why gaming and agents/companions are two of the most popular categories for founders at this intersection to build in. It’s because they offer the most conducive environments for the experimentation of both AI and crypto.

Games and agents often operate in the realm of make-believe with the purpose of entertaining consumers. The outcomes rarely need to be determinative and usually have minimal real-life consequences. For this reason, it makes for the perfect conditions for experimentation.

Today’sother-worldly gaming environment

So far, games like Parallel Colony and Today are using AI as the products’ core experience i.e. in-game AI NPC characters that behave like real humans, that are autonomous and conversational.

Crypto is being applied as the financial rails to make in-game payments, agent-to-agent payments, or to unlock the ability to own a character.

Crucially, this new digital economy is the edge that these crypto games will have over the multitude of AI games that are bound to come to market.

AI is a transformative technology that is no doubt becoming a crucial part of game development and the gaming experience going forward — but we believe it’s the teams that are building AI games with digitally native economies in mind that will have the ultimate edge.

AI agents in games are interesting, but what crypto unlocks is the ability for games to, for the first time, introduce an economic system that replicates the human experience. NPCs in games simply cannot open their own bank account, transact, and make real economic decisions. There are a lot of unprecedented behaviors and opportunities that may open up as a result.

As Kalos, founder of Parallel tweeted:

And this is best conceptualized today in make-believe environments like games.

Projects building AI agents and companions use AI and crypto similarly–AI as the core experience and crypto as the financial rails. However, unlike games, where agents operate in a confined environment, allowing for more complex interactions with few real-life consequences, agents and companions are restricted to one-to-one or one-to-many relationships (for now).

For example, with MyShell, Virtuals Protocol, or MoeMate, end-users are engaging with an AI chatbot character via a chat or voice function–the interaction is simply between you and the chatbots (or other mediums). The chatbots are LLM wrappers with limited characteristics that can be customized by the creator of the bot i.e. the tone of communication, what the agent looks like, etc. As a result, the interactions you have with these chatbots are also limited in creativity.

MoeMate’s Draco Malfoy AI chatbot experience

While similar to its competitors, ai16z takes an open-sourced, bottom-up approach to building onchain AI agent infrastructure, providing the tools for a multi-agent future. You can check out their Github here.

There is still a lot to be explored in both the gaming and the agents category like multi-agent experiences or infinite game modes. Consumer experiences that involve many-to-many AI agent-to-human relationships are complex but could lead to more dynamic and engaging experiences, with more sophisticated crypto-economies. This is yet to be explored outside of gaming environments.

We continue to believe that this is one of the most interesting areas for founders to be building in and we can’t wait to see what’s to come.

General Assistants & Content Generation

General assistants and content generation tools dominate the traditional consumer AI space. However, intense competition makes entry into this market challenging and costly, explaining why these categories are less represented in the crypto market map when compared to their strong presence in traditional AI.

Yet, demand for these tools is strong, consistently ranking high in a16z’s web traffic analysis. For founders at the intersection of crypto and AI, these categories remain promising, especially for products tailored specifically for crypto users. By focusing on crypto-specific needs, there’s potential to carve out unique value without competing in the saturated traditional market.

Here are some examples:

AI-enabled Crypto Assistants: Crypto is notoriously hard to navigate. Whether you’re trying to buy or swap a token onchain or meet the requirements needed to participate in a game or social experience, there are many hurdles.

Are you on the right network? How do you switch networks? Do you have the right gas token? How do you move money to the correct network?

For newcomers, the learning curve is steep. Even for those familiar with crypto, these tasks can still be time-consuming.

While the industry has focused heavily on account abstraction, intents, and other UI/UX improvements to date, AI is more likely to package these developments and drive these changes forward. A few teams, such as Wayfinder, Fungi, PAAL AI, and Fere AI, are already exploring solutions, though none have gained significant traction yet–leaving room for more competition and specialization.

A sneak peek into Wayfinder’s crypto assistant

The needs of a seasoned Solidity developer likely differ from those of a newcomer. We believe that teams that build with specific user groups in mind (tailoring the experience entirely to that user group’s problems), with refined user experience (leveraging advancements in account abstraction and intents), and personalization (based on your previous onchain activity) are best positioned to succeed.

AI-enabled Asset Generation: Content generation in the crypto space can be thought of as asset generation. There are a near-infinite number of assets that can be generated in the form of ERC20s, ERC721s, ERC1155s, and other standards. Similar to how Midjourney and DALL-E generate images or how SUNO creates music, AI can play a key role in generating crypto assets too.

Projects like Truth Terminal’s $GOAT token, Wayfinder’s asset deployment agent, Swan’s upcoming gamified asset generation market, and Virtuals Protocol’s AI agent launchpad are early examples of AI-driven crypto-asset generation.

Here’s a demo video of how you can use Wayfinder to create an asset.

Beyond generating assets, AI can shape the narrative, market the asset, and provide its “voice.” For specific asset types like memecoins (no external dependencies), AI can effectively streamline the end-to-end asset development process.

In a world where an infinite number of crypto assets can be generated frictionlessly by AI agents, the opportunity for builders is to identify where the value and attention may flow. For example, Virtuals Protocol took the stance that speculation will move to the creator level, allowing consumers to speculate on an AI agent’s ability to garner attention and create interesting assets.

We’re only in the first innings of a wild new reality where real financial value in the form of crypto assets can be generated by AI and then enjoyed and speculated on by humans. While the future of this development is difficult to predict, there’s plenty to experiment with here, and we’ll be watching closely to see where it leads.

Miscellaneous

A wide range of categories remain unexplored at the intersection of crypto and consumer AI. With AI advancing rapidly, this list is likely to grow and evolve quickly. Many categories may be short-lived, and fewer may suit crypto collaboration, but there’s still ample room for experimentation in this space–and we welcome it!

One way of thinking about it is to consider what the crypto-equivalents would be for some of the traditional consumer AI projects with no crypto overlap. As an example, we applied crypto to two categories from a16z and YC’s list and threw in an extra one too.

Edtech is a popular consumer AI category that can benefit from crypto across different layers of the stack. Education spans regions, subjects, languages, educational levels, and teaching methods. Rather than a centralized approach, edtech might thrive from open-source development with global contributors. In this context, an edtech-focused subnet on Bittensor could help build these models.

Crypto could also be applied to the incentivization layer of edtech applications. Going beyond traditional gamification tactics like Duolingo’s daily streaks, teachers and students could be rewarded for their contributions and efforts both on the supply and demand side through crypto.

For self-help, crypto’s potential to enable data ownership and monetization may be compelling. Mental health remains out of reach for many reasons such as cost, stigma, lack of awareness, and the shortage of professionals. Projects like Sonia and Maia (both recent YC cohort ventures) offer early glimpses of affordable AI-enabled therapist solutions. Traditionally, therapist notes are stored on paper or digital files at offices, where the data is inaccessible. However, with AI-enabled therapists, where data is stored privately online, entirely new use cases could be unlocked from your mental health data.

Imagine if you could actually own the data from your AI therapy sessions. You’d have the choice to keep it private, monetize it, or even contribute it–anonymously–to a network of health data that powers meaningful research. Crypto-native projects like Vana are beginning to make this possible on the network level, giving people a stake in their own data.

In entertainment, projects like Unlonely are experimenting with crypto-native livestreaming, where users can speculate on and influence a livestream’s outcome by trading the platform’s token. Currently, this is limited to IRL events but it could extend to AI-generated content. This could enable 24/7 streaming, with users having much greater control over the livestream narrative. MineTard AI is an early example that recently surfaced. It’s an AI agent livestreaming Minecraft on Kick 24/7 where the agent can be influenced by holders of $MTard.

Last year, a viral TikTok trend featured creators acting as NPCs, performing specific actions based on the ‘gifts’ they received. While this content type was short-lived, it showed clear consumer interest in interactive live-stream experiences. With advances in AI-driven NPCs, similar gamified interactions could be a fit for crypto-native live streaming, where AI NPCs could respond to user input in real time.

Viral TikTok NPC Trend

These are just a few scrappy ideas for how crypto and AI can be applied to consumer applications. There are plenty more that haven’t been covered in this report and as the industry evolves rapidly, we expect to see plenty more.

Parting Thoughts

As you can probably tell, we are (very) excited about the possibilities at the intersection of crypto and consumer AI. The projects currently building in this space only represent a very small portion of what is possible.

With these two technologies maturing in parallel, founders have a unique window to create a new wave of consumer applications that could transform how we interact and engage with digital assets and synthetic intelligence.

To those building in this field, we encourage you to keep pushing boundaries and explore unconventional applications of these technologies. And we hope that for some, this was a useful resource to begin that journey.

If you’re a builder at this intersection, we’d love to connect with you!

Disclosure / Disclaimer: At time of publication, Collab+Currency or its members may have exposure to some of the assets described in this piece. The author and Collab+Currency do not endorse or recommend ownership of any project or collection described in this article.

The information provided is for general informational purposes only and should not be construed as investment advice. While attempts have been made to verify the accuracy of the information provided we cannot make any guarantees. Investors should be aware that investing in digital assets involves a high level of risk and should be undertaken only by individuals prepared to endure such risks. Any forward-looking statements made are based on certain assumptions and analyses and perception of historical trends, current conditions, and expected future developments, as well as other factors. Such statements are not guarantees of future performance and are subject to certain risks, uncertainties, and assumptions that are difficult to predict.