encryption x Consumer-level Artificial Intelligence

In the past year, the intersection of AI and Crypto Assets has become a hot area of interest for consumers, driving the launch of numerous new projects.

Author: Karen Shen

Compilation: Block unicorn

In this article, we will explore the potential opportunities for collaboration between Cryptocurrency and consumer-grade AI (Artificial Intelligence). The article is divided into three parts:

Why choose encryption x consumer-grade AI?

An overview of the traditional consumer-grade AI market

encryption x Opportunities for Consumer-Grade AI

Why choose encryption x consumer-level AI

In the past year, the intersection of AI and Crypto Assets has become a hot area followed by consumers, driving the launch of a large number of new projects. The vast majority of follow points and capital are concentrated in the infrastructure layer of AI, such as computing power, training processes, inference techniques, intelligent agent models, and data infrastructure.

Although many of these projects are ambitious and may bring about significant results, the technology has not yet reached a production level (currently), and the possibility of achieving widespread commercialization in the short term is low. This leaves a gap in the market for more impactful technological applications, especially at the consumer level.

Consumer-grade 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, generation tools, and creative software. With the rapid development of AI technology, consumer-grade applications are becoming more intuitive, personalized, and easier for ordinary users to use.

Popular consumer-level AI applications today

Consumer-level AI, unlike enterprise-level AI, which typically requires precision and deterministic results, benefits from flexibility, creativity, and adaptability—areas in which current AI excels.

Although still in its early stages, the combination of encryption technology and consumer-level AI is undoubtedly a fascinating topic. It is rare to see two technologies advancing towards maturity at the same time, so it is worth exploring - although the outcome is difficult to predict.

In the field of encryption technology, there is an urgent need for more consumer-oriented applications that provide new and interesting ways to interact with underlying technology. Over the past decade, investment in blockchain has driven significant progress in infrastructure, including faster block generation speed, lower gas fees, better user experience (UX), and greatly reduced entry barriers that were common a few years ago.

By simply trying to join applications like Moonshot, you can use Apple Pay to buy MEME coin instantly, and you can intuitively feel how much progress the entire industry has made. However, there is still a lack of founders and developers willing to solve interesting consumer encryption issues.

At the same time, consumer-grade AI is ready for the market, providing developers with a mature opportunity to combine these two technologies, build applications, and shape applications that interact with and have digital asset and AI systems in a way that we own and participate in.

An overview of the traditional consumer-grade AI market

First, let's use two resources to help us quickly understand the experiments in the traditional (non-encrypted) consumer-grade AI field:

a16z's Top Consumer Products by Web Traffic (3rd Edition)

YC team's latest W24 project batch

a16z's "Top Consumer Products Ranked by Network Traffic"

a16z's report ranks the most visited consumer-grade AI web pages and mobile applications every six months by analyzing network traffic data of consumer-grade AI products.

By analyzing this data, they identified trends in how consumers are actively engaging with consumer-level AI technology, which categories are gaining followers, which categories are declining, and early leading projects within each category.

Here are the top 100 consumer AI products as of August 2024, categorized into web and mobile applications.

Obviously, content generation and editing tools are leading the way in the consumer-level AI field.

These apps now account for 52% of the top 50 web applications and 36% of the top 50 mobile applications. It is worth noting that this category is expanding from text-to-image to include video and music generation, further expanding the potential of AI-driven creative expression.

Popular categories such as general assistants, companion tools, and productivity tools have remained stable in the top 100 rankings, reflecting sustained demand. The third edition of the a16z report added the category of 'Aesthetics and Dating', in which three projects made the list.

It is worth mentioning that a cross-category encryption project has also successfully made it to the list. The anime companion app Yodayo (now Moescape AI) ranks 22nd on the web application list.

Moescape AI

Comparing a16z's latest report with the previous ones, it can be found that although the core consumer-level AI categories remain stable, about 30% of the top 100 projects are new, highlighting the continued development in this field.

YC team's latest W24 project batch

Next, let's review the W24 batch of YC (latest version), as a resource to help identify emerging consumer-grade AI projects and categories, although these projects and categories have entered the market, they may not have enough appeal to appear on a16z's top 100 web traffic list.

The idea here is that although there is uncertainty about consumers' actual needs for these products, this information can help us predict consumer-level AI trends in the next 6-12 months.

Among the recent 235 projects, 63% are focused on the AI field, of which 70% are built on the Application Layer. Only about 14% of the Application Layer projects are identified as consumer-centric.

Here is our attempt at categorizing consumer-grade AI projects.

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

Similar to the trends mentioned in the a16z report, the latest batch of entrepreneurs from YC are exploring advanced content types, including storytelling, script-to-movie generation, music, video, and demonstration-focused content.

In addition to content generation, the founders also focus on search, productivity, and education technology. These three categories align with a16z's report, although most companies in YC develop more targeted, industry-specific solutions in these areas.

Finally, categories such as gaming, automation, markets, and streaming are emerging in this group, indicating some new directions that were not mentioned in the a16z report.

Opportunities of Cryptocurrency x Consumer AI

Now that we have introduced the background trends of the traditional consumer-grade AI market, let's shift our focus to consumer-grade encryption AI.

First, let's briefly introduce how AI can be useful for encryption products, or how encryption can be useful for consumer-level AI products, which may be helpful.

encryption and AI offer very different value propositions.

It can be said that there is a conflict of values between these two technologies - encryption focuses on Decentralization, privacy, and individual 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 Decentralization and Open Source AI, these boundaries begin to blur.

The core innovation of AI in consumer products is to generate novel content, imitate and extend human creativity, and learn from massive datasets, using advanced neural network architectures to simulate complex relationships and produce high-quality outputs.

Early signs indicate that AI applications demonstrate 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 to paid users is lower than usual.

On the other hand, encryption technology is a design space that includes Decentralization, encryption economic incentives, and hyper-financialization characteristics. It is a Distributed Ledger that allows the value of any digital object to be stored in a transparent and traceable manner.

encryption technology is very effective in coordinating activities, aggregating Decentralization infrastructure, and creating frictionless markets where markets did not previously exist. However, apart from financial infrastructure, encryption technology has not yet created a compelling and sustainable consumer-level application.

AI may be one of the key factors unlocking the wider consumer potential for encryption technology. A recent study highlights the rapid adoption of generative AI, which has surpassed that of PCs and the internet - about 32% of US residents use AI every week. Given this pace of development, developers of consumer-level encryption technology could have a significant advantage if they experiment and innovate in sync with the accelerated adoption of AI.

We believe that breakthroughs will emerge through innovative consumer-level applications, combining the powerful capabilities of AI with the unique abilities of Decentralization and financialized networks empowered by encryption technology.

Market Overview

There are still relatively few consumer-centric projects operating in the intersection of encryption and AI, with our research estimating about 28, although this is not a final number.

In this crowdsourced Decentralization AI market map, consumer-level categories account for only about 13% of the total Decentralization AI market, which indicates that we still have a significant room for rise. As a quick comparison, about 60-70% of products in the technology market are located in the Application Layer, of which about 70-80% are consumer-oriented applications.

Although we only cover a small portion of the projects in this report, we are still able to identify some early insights.

We have identified some early ideas from the team's integration of encryption and AI. These insights have been distilled into several broader use cases, some of which show potential, while others may not be sustainable.

Incentive Mechanism: Cryptocurrency serves as a way to incentivize and reward users for their activities on AI platforms/applications. For example, one use of the Wayfinder native Token is to reward agents and participants for creating valuable on-chain paths when AI agents walk on-chain. In the case of Botto, an automated AI artist, it requires its community to provide feedback on its artistic creations. Botto rewards this participation by distributing a portion of its art sales revenue in the form of $BOTTO Tokens.

Financialization: the ability to transact on-chain, own, and generate income from AI assets. For example, Virtuals Protocol provides a platform where anyone can purchase and own a portion of AI agents and benefit from the income generated by AI agents they trust. Ownership is represented in the form of Tokens.

vesting: Allows intellectual property holders to track, verify, and claim royalties on-chain within Block. For example, uncensored projects like Oh.xyz are using encryption technology on their platform to create digital twins of creators as Non-fungible Tokens, in order to verify the authenticity of content and claim royalties in the future.

In-app or in-game economy: Cryptocurrency as in-app/in-game currency. 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: Decentralization networks, services, and models. For example, BitMind is a subnet on Bittensor, building the first Decentralization Depth forgery detection system. By using Bittensor, they are able to encourage open competition among AI developers and contribute to building the best Depth forgery detection model.

Anti-censorship: Cancel the review of generative AI content creation. For example, Venice is a private and unlicensed generative AI assistant built on the Decentralization general agent network of Morpheus. Unlike traditional AI assistants, Venice does not review the content of AI or download your conversations.

Membership System: Cryptocurrency serves as a means to access advanced features. For example, MyShell's ecosystem Token has multiple use cases, one of which is to grant holders access to advanced features.

Assistant: AI is a way to make interaction between people and Crypto Assets easier. For example, Wayfinder, Fere AI, Fungi, and PAAL AI are vertical general assistants or robots for the Crypto Assets industry, aiming to make end users' encryption experience more convenient.

Contextualization: AI is a way to contextualize and personalize the content on-chain. For example, Unofficial aims to build a discovery engine for on-chain socialization on Farcaster using zkTLS and RAG.

After examining the current Cryptocurrency and consumer AI market, including the application of Cryptocurrency and AI, as well as the status of established and emerging categories in the traditional consumer-grade AI field, the next section will explore the most promising design space in this intersection area for developers to reference.

Games and Agents / Partners

There is a reason why games and agents/partners are the two most popular categories among founders in this intersectional field. It's because they provide the most suitable environment for AI and cryptocurrency experiments.

Games and agents typically operate in fictional domains with the aim of entertaining consumers. Their outcomes are often not meant to be decisive and typically have little impact on real-life. Thus, this provides the perfect conditions for experimentation.

The current hyper-realistic gaming environment

So far, games like Parallel Colony and Today have used AI as the core experience of the product, that is, the behavior of AI NPC characters in the game is like real humans, with autonomy and the ability to engage in conversation.

Cryptocurrency is being used as a financial channel for in-game payments, agent-to-agent payments, or unlocking character ownership rights.

It is crucial that this new digital economy is the advantage of these encryption games over the many AI games that are about to be launched.

AI is a transformative technology, undoubtedly, it will become a key part of future game development and gaming experience—but we believe that teams building AI games with a focus on the digital native economy will have the greatest competitive advantage.

The AI agents in the game are interesting, but Crypto Assets unlock the ability to introduce an economic system that replicates human experiences for the first time in the game. NPCs in the game simply cannot open their own bank account, conduct transactions, and make real economic decisions. Therefore, there may be many unprecedented behaviors and opportunities.

As Parallel's founder Kalos said on Twitter:

Now, in fictional environments such as games, this point has been best exemplified.

There are similarities between projects that build AI agents and companions and those that use AI and Cryptocurrency - AI as the core experience and Cryptocurrency as the financial infrastructure. However, unlike agents in games that operate in a limited environment, allow for more complex interactions, and have almost no real-life 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 functions - interaction is limited to you and the chatbot (or other media). Chatbots are LLM wrappers with limited features that can be customized by the creator of the bot, such as communication tone, agent appearance, etc. Therefore, your interaction with these chatbots is also limited in creativity.

Experience with MoeMate's Draco Malfoy AI chatbot

Although similar to its competitors, ai16z has adopted an Open Source, bottom-up approach, focusing on building on-chain AI agent infrastructure to provide tools for the multi-agent future.

In the fields of gaming and agency, there are still many areas worth exploring, such as multi-agent experiences or infinite game modes. While the consumer experience involving multi-agent AI interaction with humans is complex, it may bring about more dynamic and engaging interactive experiences, as well as a more complex encryption economic system. This field has not been fully explored beyond the gaming environment.

We still believe this is one of the areas that the founder is most interested in, and we can't wait to see what kind of innovation the future will bring.

General Assistant and Content Generation Tool

General assistants and content generation tools dominate the traditional consumer-level AI field. However, intense competition makes it challenging and costly to enter this market, which also explains why these categories are not as dominant in 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 founders in the intersection of encryption and AI, these categories still hold great potential, especially products tailored specifically for encryption users. By focusing on the specific needs of the encryption field, it is possible to create unique value without competing in saturated traditional markets.

Here are some examples:

AI-supported encryption assistant: It is well known that encryption is difficult to master. Whether you are trying to buy or exchange Token on-chain, or meet the requirements for participating in games or social experiences, there are many obstacles.

Are you on the right network? How to switch networks? Do you have the correct Gas Token? How to transfer funds to the target network?

For Newbie, the learning difficulty is very high. Even for those familiar with Crypto Assets, these tasks may still take a lot of time.

Although the industry has mainly improved in account abstraction, intents, and other UI/UX aspects, AI is more likely to integrate these developments and drive the transformation forward. Some teams, such as Wayfinder, Fungi, PAAL AI, and Fere AI, have been exploring solutions, although no team has made significant progress at present - leaving space for more competitors and specialization.

An overview of Wayfinder's encryption assistant

The requirements of experienced Solidity developers may differ from Newbie. We believe that teams that consider specific user groups during construction (customizing the experience based on the user group's needs), provide refined user experience (utilizing advancements in account abstraction and intent), and offer personalized services (based on the user's previous on-chain activities) are most likely to succeed.

AI-supported asset generation: In the field of encryption, content generation can be regarded as asset generation. These assets can be ERC20, ERC721, ERC1155, or other standard forms of tokens and digital assets. The ways of generating them are almost unlimited. Similar to the way Midjourney and DALL-E generate images, or SUNO creates music, AI can also play a key role in asset generation in encryption.

Early examples of AI-driven encryption asset generation include $GOAT Token from Truth Terminal, asset deployment agents from Wayfinder, a gamified asset generation market to be launched by Swan, and an AI agent launch platform from Virtuals Protocol.

In addition to generating assets, AI can also shape narratives, market assets, and give them 'voice'. For specific asset types like MEME coin (with no external dependencies), AI can efficiently streamline the end-to-end asset development process.

In this world where AI agents can generate countless encryption assets without friction, the opportunity for developers lies in identifying the flow of value and attention. For example, the strategy adopted by Virtuals Protocol is to shift speculative behavior to the creator level, allowing consumers to predict the ability of AI agents to attract attention and create interesting assets.

We are currently in the early stages of a crazy new reality, in which AI can generate true financial value in the form of encrypted assets, and humans can enjoy and speculate on the development of these assets. Although the future of this trend is hard to predict, there is a huge experimental space in this field, and we will closely follow its direction.

Miscellaneous

In the intersection of encryption and consumer-level AI, there are still many categories that have not been fully explored. With the rapid development of AI, these categories are likely to rise and evolve quickly. Although many categories may be short-lived, and there may be fewer categories suitable for encryption collaboration, there is still plenty of room for experimentation in this field - we welcome it!

One way of thinking is to consider the encryption equivalent versions of traditional consumer-level AI projects, especially those that currently have no encryption intersection. For example, we apply encryption technology to two categories in the a16z and YC lists, and add an additional category for discussion.

Edtech is a popular consumer AI category that can benefit from encryption technology at various levels of the stack. It covers education regions, subjects, languages, education levels, and teaching methods. In this case, instead of taking a centralized approach, it is better to advance Edtech through Open Source development and collaboration with global contributors. In this context, Bittensor's Edtech-centric subnet can help build these models.

The encryption technology can also be applied to the incentive aspect of educational technology (Edtech) applications. Beyond traditional gamification strategies (such as Duolingo's daily consecutive login mechanism), through encryption technology, teachers and students can be rewarded for their contributions and efforts on both the supply and demand sides.

For self-help, the potential of Cryptocurrency in terms of data ownership and monetization may be very appealing. Due to reasons such as cost, social stigma, lack of awareness, and shortage of professionals, it is still difficult for many people to access. Projects like Sonia and Maia (both recent YC incubated projects) have shown the possibility of affordable AI-driven psychological counseling solutions. Traditionally, therapists' notes are stored in paper or digital files in the office, making the data inaccessible. However, for AI therapists, data can be stored securely online, unlocking new applications from personal mental health data.

Imagine if you could actually own data from AI treatment courses. You can choose to keep it confidential, or monetize it, or even anonymously contribute it to a health data network to support meaningful research. Projects like Vana are making this possible, allowing people to have a stake in their own data.

In the entertainment sector, projects such as Unlonely are attempting native live streaming with encryption, where users can speculate and influence the outcome of the live stream using the platform's Token. Currently, this is limited to real-life events, but it can be expanded to AI-generated content. This could enable 24/7 live streaming, giving users greater control over the live narrative. MineTard AI is a recent early example. It is an AI agent that live streams my world on Kick 24/7, and the agent can be influenced by $MTard holder.

Last year, there was a viral trend on TikTok where creators played as NPCs and performed specific actions based on the 'gifts' they received. Although this type of content was short-lived, it clearly indicated consumers' interest in interactive live streaming experiences. With advancements in AI-driven NPC technology, similar gamified interactions may be suitable for native encryption live streaming, where AI NPCs can respond to user inputs in real time.

Hot NPC trends on TikTok

These are just some rough ideas on how to apply encryption and AI to consumer-grade applications. In this report, we didn't cover all possible applications, and we expect to see more such innovations as the industry develops rapidly.

Greetings

You may have noticed that we are very excited about the potential of encryption and consumer-grade AI crossover. The projects currently being developed in this field represent only a small fraction of the possibilities.

With the parallel development of these two technologies, the founders have a unique window to create a new wave of consumer-level applications that may change the way we interact and participate in digital assets and synthetic intelligence.

For those who are building in this field, we encourage you to continue to push boundaries and explore unconventional applications of these technologies. We also hope that this resource can help some people on their journey.