encryption x consumer-grade artificial intelligence

Article author: Karen Shen Article translation: Block unicorn

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

• Why choose encryption x consumer-grade AI?
• 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 Cryptocurrency has become a popular field that consumers follow, driving the launch of numerous new projects. The majority of follow points and capital are focused on 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 production-level maturity (at present), 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-grade AI applications today

Unlike enterprise-level AI, which typically requires accuracy and deterministic results, consumer-level AI benefits from flexibility, creativity, and adaptability - these are the areas where AI excels today.

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

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

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

Meanwhile, consumer-grade AI has been market-ready, providing developers with a mature opportunity to combine these two technologies to build applications that shape our interactions, ownership, and participation with digital assets and AI systems.

Overview of the Traditional Consumer-Grade AI Market

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

• The Top Consumer Products Ranked by Network Traffic (3rd Edition) by a16z
• The latest batch of W24 projects from the YC team

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

The a16z report ranks the most visited consumer-level AI web pages and mobile applications every six months by analyzing network traffic data for consumer-level AI products.

By analyzing this data, they identify the trends of how consumers actively engage in consumer-level AI technology, which categories are gaining follow, which categories are declining, and the early leading projects in 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-grade AI field.

These applications 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 generation 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 remained stable in the top 100 list, reflecting sustained demand. The third edition of the a16z report added the 'Aesthetics and Dating' category, with three projects entering 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 previous reports, it can be observed that while the category of core consumer AI remains stable, about 30% of the top 100 projects are new projects, highlighting the continuous development in this field.

The latest batch of projects from the YC team, W24

Next, let's take a look back at batch W24 of YC's (latest version) project batch, as a resource to help identify emerging consumer-grade AI projects and categories. These projects and categories may have entered the market, but 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 consumer demand for these products, this information can help us predict consumer-level AI trends in the next 6-12 months.

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

Here is our attempt to classify consumer-level 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 in the a16z report, YC's latest batch of entrepreneurs are exploring advanced content types, including storytelling, script-to-film generation, music, video, and presentation-based content.

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

Finally, categories such as gaming, automation, market, and streaming appear in this group, marking some new directions that are not mentioned in the a16z report.

Crypto Assets x Consumer AI Opportunities

Now that we've introduced the background trends of the traditional consumer AI market, let's turn our attention to consumer encryption AI.

First, let's briefly introduce how AI can be useful for encryption products, or how encryption can be useful for consumer-grade 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 are starting to blur.

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

Early indications show 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 users to paying users is below average.

On the other hand, encryption technology is a design space that includes Decentralization, encryption economic incentives, and super-financial 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 exist before. However, apart from financial infrastructure, encryption technology has not yet created a compelling and sustainable consumer-grade application.

AI may be one of the key factors in unlocking the wider consumer potential of encryption technology. A recent study has highlighted 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-grade encryption technology would have a significant advantage if they could experiment and innovate in sync with the accelerated adoption of AI.

We believe that breakthroughs will emerge through innovative consumer-level applications, combined with the powerful capabilities of AI and the unique capabilities 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 around 28, although this is not a final number.

In this crowdsourced Decentralization AI market map, the consumer-level category accounts for only about 13% of the total Decentralization AI market, indicating that we still have a lot of rise space. For a quick comparison, about 60-70% of products in the technical market are located in the Application Layer, of which about 70-80% are consumer-facing 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 in integrating encryption with AI. These insights have been distilled into several broader use cases, some of which show potential, while others may be less sustainable.

1. 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. For Botto, the automated AI artist, it requires its community to provide feedback on its artwork. Botto rewards this participation by distributing a portion of the art sales revenue in the form of $BOTTO Tokens.
2. Financialization: the ability to trade 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.
3. vesting: Allows intellectual property holders to track, verify, and claim royalties on-chain. For example, uncensored companion projects like Oh.xyz are using encryption technology to create digital twins of creators' Non-fungible Tokens on their platform, thereby verifying the authenticity of the content and claiming royalties in the future.
4. In-app or in-game economy: Cryptocurrency as an in-app/in-game currency. For example, games like Parallel and Today will have an in-game economy, and players and their AI agents will be able to trade resources using their respective tokens.
5. Decentralization: Decentralization networks, services, and models. For example, BitMind, a subnet on Bittensor, is building the first Depth forgery detection system for Decentralization. With Bittensor, they are able to encourage open competition among AI developers to contribute to building the best Depth forgery detection model.
6. 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-purpose agent network of Morpheus. Unlike traditional AI assistants, Venice will not review the content of AI or download your conversations.
7. 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 granting holders access to advanced features.
8. Assistant: AI is a way to make the 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 the end user's encryption experience more convenient.
9. Contextualization: AI is a way to contextualize and personalize the content of Blockon-chain. For example, Unofficial aims to build a discovery engine for on-chain social interaction on Farcaster using zkTLS and RAG.

In reviewing the current market of Crypto Assets and consumer AI—including the application of Crypto Assets and AI, as well as the status of established and emerging categories in the traditional consumer-level AI field—the following section will explore the most promising design space in this intersection for developers to reference.

Games and Agents / Partners

The reason why games and agents/partners have become the two most popular categories for founders in this intersectional field is because they provide the most suitable environment for AI and encryption experiments.

Games and agents typically operate in fictional domains and are intended for entertainment consumers. Their outcomes are often not meant to be decisive and generally have minimal impact on real life. Therefore, this provides an ideal condition for experiments.

The current surreal gaming environment

So far, games like Parallel Colony and Today use AI as the core experience of the product, that is, the AI NPC characters in the game behave like real humans, have autonomy, and can engage in conversations.

Crypto Assets are being used as financial channels 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 game 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 what Cryptocurrency unlocks is the ability to introduce an economic system that replicates human experience for the first time in the game. NPCs in the game simply cannot open their own bank accounts, conduct transactions, and make real economic decisions. Therefore, there may be many unprecedented behaviors and opportunities.

As Parallel's founder, Kalos, tweeted:

Nowadays, this is best reflected in fictional environments such as games.

There are similarities between projects that build AI agents and companions and the use of AI and Cryptocurrency - AI as the core experience and Cryptocurrency as the financial infrastructure. However, while agents in games operate in a limited environment, allowing for more complex interactions and almost no real-life consequences, agents and companions are currently limited to one-on-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 - interactions are limited to you and the chatbot (or other media). The chatbot is an LLM wrapper with limited features that can be customized by the creator, such as the tone of communication and the appearance of the agent. Therefore, your interaction with these chatbots is also limited in terms of creativity.

Experience of MoeMate's Draco Malfoy AI chatbot

Although similar to its competitors, ai16z takes a bottom-up approach, focusing on building on-chain AI agent infrastructure, providing tools for the multi-agent future.

There is still much to explore in the fields of gaming and agency, such as multi-agent experiences or infinite game modes. While the consumer experience involving interactions between multiple AI agents and humans is complex, it may bring about more dynamic and engaging interactive experiences, as well as more complex encryption economies. This field has yet to be fully explored beyond the gaming environment.

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

Universal 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.

However, the demand for these tools remains strong and has consistently ranked high in a16z's network traffic analysis. For founders in the intersection of encryption and AI, these categories still hold great prospects, 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-powered encryption assistant: It is well known that encryption is difficult to master. Whether you are trying to buy or exchange Tokens on-chain, or meet the requirements for participating in games or social experiences, there are many obstacles.

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

For Newbies, the learning curve is high. Even for those familiar with Crypto Assets, these tasks can still take a lot of time.

Although so far, 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 so far, leaving room for more competitors and specialization.

An overview of Wayfinder's encryption assistant

The demand for experienced Solidity developers may differ from that of Newbies. We believe that teams that consider specific user groups when building (customizing the experience based on the user group's issues), provide exquisite user experiences (leveraging advances in account abstraction and intent), and offer personalized services (based on the user's previous on-chain activity) are most likely to succeed.

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

Early examples of AI-driven encryption asset generation, such as Truth Terminal's $GOAT Token, Wayfinder's asset deployment agency, Swan's upcoming gamified asset generation market, and Virtuals Protocol's AI agency launch platform.

In addition to generating assets, AI can also shape narratives, market assets, and give them a "voice." For specific asset types like MEME coin (which do not have external dependencies), AI can efficiently simplify 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 transfer speculative behavior to the level of creators, 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 real financial value in the form of encryption assets, and humans can enjoy and speculate on the development of these assets. Although the future of this trend is difficult 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-grade 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 ample room for experimentation in this field - we welcome this!

One way to think about it is to consider the encryption equivalent of traditional consumer-grade 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 different levels of the stack. Education covers regions, disciplines, languages, educational levels, and teaching methods. In this case, instead of taking a centralized approach, it is better to promote Edtech through Open Source development and collaboration with global contributors. In this context, Bittensor's Edtech-focused subnet can help build these models.

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 at both the supply and demand ends.

For self-service, the potential of cryptocurrency in terms of data ownership and monetization could be very promising. Due to factors such as cost, social stigma, lack of awareness, and shortage of professionals, it remains inaccessible to many. Projects like Sonia and Maia (both recent YC incubator projects) are showcasing the possibility of affordable AI-driven mental counseling solutions. Traditionally, therapists' notes are stored in physical or digital files in their offices, making the data inaccessible. However, for AI therapists, data can be stored securely online, unlocking new applications from individuals' mental health data.

Imagine if you could actually own data from AI therapy courses. You could choose to keep it confidential, monetize it, or even anonymously contribute it to a health data network to support meaningful research. Encryption-native projects like Vana are making this possible by empowering people with stake in their own data.

In the entertainment industry, projects like Unlonely are experimenting with native encryption live streaming, allowing users to speculate and influence the outcome of the live stream using the platform's Token. Currently, this is limited to real-life activities, but it can be expanded to AI-generated content. This enables 24/7 live streaming with users having greater control over the narrative of the live stream. MineTard AI is a recent early example. It is an AI agent that live streams Minecraft on Kick 24/7, and the agent can be influenced by $MTard holders.

Last year, a viral trend emerged on TikTok, in which creators played the role of NPCs and performed specific actions based on the "gifts" they received. Although this type of content was short-lived, it clearly indicated consumer interest in interactive live experiences. With the advancement of AI-driven NPC technology, similar gamified interactions may be suitable for encryption-native live broadcasts, where AI NPCs can respond to user input in real time.

Hot NPC trends on TikTok

These are just some rough ideas about how to apply encryption and AI to consumer-grade applications. In this report, we have not covered all possible applications, and with the rapid development of the industry, we expect to see more such innovations.

Message

You may have already noticed that we are very excited about the possibilities of encryption and consumer-level AI crossover. The projects currently being built in this area represent only a small part of the possibilities.

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

For those who are building in this field, we encourage you to continue pushing the boundaries and exploring unconventional applications of these technologies. We also hope that this resource can be helpful for some on their journey.