Original title: AI Cryp to Projects That Aren’t Completing Bullsh*t
Original author: 563, former Bankless researcher
Original translation: TechFlow
Navigating the intersection of crypto and AI.
In the search for new alpha information, we inevitably encounter some junk information. When a project can quickly raise 5-6 figures with just a semi-clear introduction and some decent branding, speculators will seize on every new narrative. And as traditional financial sectors have jumped on the AI bandwagon, the "crypto AI" narrative has exacerbated this problem.
The problems with most of these projects are:
1. Most crypto projects don’t need AI
2. Most AI projects don’t require cryptocurrency
Not every decentralized exchange (DEX) needs an AI assistant built in, nor does every chatbot need an accompanying token to facilitate its adoption curve. This hard-wired marriage of AI and crypto almost broke me when I first dug into this narrative.
The bad news? Continuing down the current path of further centralizing this technology will only end in failure, and the proliferation of fake “AI x Crypto” projects will prevent us from turning the tide.
The good news? There is light at the end of the tunnel. Sometimes AI can actually benefit from cryptoeconomics. Likewise, there are real problems that AI can solve in some cryptocurrency use cases.
In today’s article, we’ll explore these critical intersections. The overlap of these niche innovative ideas forms a whole that is greater than the sum of its parts.
A high-level view of the AI stack
Here’s how I think about the different verticals in the “Crypto AI” ecosystem (if you want to go deeper, check out Tommy’s post ). Note that this is a very simplified view, but hopefully it helps lay the foundation.
At a high level, here’s how it all works together:
Collect data at scale.
Process that data so that machines understand how to ingest and apply it.
Train a model on this data to create a general model.
Can then be fine-tuned to handle specific use cases.
Finally, these models are deployed and hosted so that applications can query them for useful insights.
All of these require massive computing resources, which can be run locally or sourced from the cloud.
Let’s explore each of these areas, focusing specifically on how different cryptoeconomic designs can actually improve upon standard workflows.
Encryption gives open source a fighting chance
The debate over "closed source" vs. "open source" development approaches dates back to the Windows-Linux debate and Eric Raymond's famous "Cathedral and Bazaar" theory. While Linux is widely used among enthusiasts today, about 90% of users choose Windows. Why? Because of incentives.
Open source development has many benefits, at least from the outside. It allows the largest number of people to participate in and contribute to the development process. But in this headless structure, there is no unifying directive. The CEO is not proactively trying to get as many people as possible to use their product to maximize their bottom line. In the open source development process, there is a risk that the project will evolve into a "chimera" that splits off in different directions at every intersection of design philosophy.
What is the best way to align incentives? Build a system that rewards behaviors that advance our goals. In other words, put money in the hands of actors that get us closer to our goals . With cryptocurrency, this can be hard-coded into law.
We’ll look at some projects that are doing just that.
Decentralized Physical Infrastructure Networks (DePINs)
“Oh come on, this again?” Yes, I know the DePIN narrative is almost as over-the-top as AI itself, but hang on for a second. I want to believe that DePINs are a crypto use case that has a real chance to change the world. Think about it.
What is crypto really good at? Removing intermediaries and incentives .
Bitcoin’s original vision was a peer-to-peer currency designed to exclude banks. Similarly, modern DePINs are designed to exclude centralized power and introduce provably fair market dynamics. As we’ll see, this architecture is ideal for crowdsourced AI-related networks.
DePINs use early token issuance to increase the supply side (providers) in the hope that this will attract sustainable consumer demand. This aims to solve the cold start problem of new markets.
This means that early hardware/software (“nodes”) providers earn a lot of tokens and a little cash. As users leverage these nodes (in our case, machine learning builders) the cash flow starts to offset the decreasing token issuance over time until a fully self-sustaining ecosystem is established (which could take several years). Early adopters like Helium and Hivemapper show how effective this design is.
Data Networks, the Case of Grass
GPT-3 was allegedly trained with 45TB of plain text data, the equivalent of about 90 million novels (and it still can’t draw a circle). GPT-4 and GPT-5 will require more data than exists on the surface web, so calling AI “data-hungry” is the understatement of the century.
If you are not a top player (OpenAI, Microsoft, Google, Facebook), getting this data is very difficult. The common strategy for most people is web scraping, which is all well and good until you try to step up. If you use a single Amazon Web Services (AWS) instance to try to scrape a large number of websites, you will quickly run into rate limits. This is where Grass comes in.
Grass connects more than two million devices, organizes them to crawl websites from users' IP addresses, collects, structures and sells them to AI companies that are hungry for data. In return, users who participate in the Grass network can earn a steady income from AI companies that use their data.
Of course, there is no token yet, but a future $GRASS token could make users more willing to download their browser extension (or mobile app). Although they have already attracted a large number of users through an extremely successful referral campaign.
GPU Networking, the Case of io.net
Perhaps more important than data is computing power. Did you know that in 2020 and 2021, China spent more on GPUs than on oil? That's crazy, but it's just the beginning. Goodbye Petro, make way for Computecoin.
Now, there are many GPU DePINs on the market, and their working principles are roughly as follows.
1. Machine learning engineers/companies that are in urgent need of computing.
2. On the other hand, there are data centers, idle mining machines, and hobbyists with idle GPUs/CPUs.
Despite the huge global supply, there is a lack of coordination. It is not easy to contact 10 different data centers and have them bid for your usage. A centralized solution would create a rent-seeking intermediary whose incentive is to extract the most value from each party, but crypto can help.
Crypto is very good at creating a market layer that efficiently connects buyers and sellers. A piece of code does not need to be accountable to the financial interests of shareholders.
io.net stood out because it introduced some cool new technology that is critical for AI training - their clustering stack. Traditional clustering involves physically connecting a bunch of GPUs in the same data center so that they can work together to train models. But what if your hardware is distributed all over the world? io.net has partnered with Ray (used to create ChatGPT) to develop cluster middleware that can connect non-co-located GPUs.
Moreover, the AWS sign-up process can take days, while a cluster on io.net can be launched permissionlessly in 90 seconds. For these reasons, I can see io.net becoming the hub for all other GPU DePINs that can plug into their "IO Engine" and unlock built-in clusters and a smooth onboarding experience. All this is only possible with the help of cryptography.
You’ll notice that most aspiring decentralized AI projects (like Bittensor, Morpheus, Gensyn, Ritual, Sahara) have explicit “compute” requirements — this is exactly where GPU DePINs should come in, decentralized AI requires permissionless compute.
Use of incentive structures
Back to the revelation of Bitcoin again. Why do miners keep calculating hashes quickly? Because that’s how they get paid — Satoshi proposed this architecture because it prioritizes security. The lesson? The incentive structures built into these protocols determine the end product they produce.
Bitcoin miners and Ethereum stakers are the participants who absorb all of their native tokens because this is what the protocol wants to incentivize - participants to become miners and stakers.
In an organization, this might come from the CEO, who defines a “vision” or “mission statement.” But people are fallible and can lead a company off course. Computer code, on the other hand, can keep a company focused better than the roughest wage slave. Let’s take a look at a few decentralized projects that have built-in token effects that keep participants focused on lofty goals.
AI builds networks and discusses Bittensor
What if we let Bitcoin miners build AI instead of solving useless math problems? That’s how you get Bittensor.
Bittensor aims to create several experimental ecosystems to experiment with, with the goal of producing “commoditized intelligence” within each ecosystem. This means that one ecosystem (called a subnet, or “SN” for short) might focus on developing language models, another on financial models, and still more on speech synthesis, AI detection, or image generation (see currently active projects).
For the Bittensor Network, it doesn’t matter what you want to do. As long as you can prove that your project is worth funding, the incentives will flow. This is the goal of the subnet owner, who registers the subnet and adjusts the rules of the game.
The participants in this "game" are called miners. These are ML/AI engineers and teams who build models. They are locked in a constantly audited "Thunderdome" and compete with each other to get the most rewards.
Validators are another side that is responsible for conducting reviews and scoring the miners’ work accordingly. If a validator is found to be colluding with a miner, they will be expelled.
Remember the incentives:
Miners earn more when they beat miners in other subnets — this drives AI development.
Validators earn more when they accurately identify high- and low-performing miners — this maintains the fairness of the subnet.
Subnet owners earn more when their subnet produces more useful AI models than other subnets — this incentivizes subnet owners to optimize their “game”.
You can think of Bittensor as a perpetual bounty machine for AI development. Emerging machine learning engineers can try to build something, pitch to VCs and try to raise some money. Or they can join one of the Bittensor subnets as a miner, make a killing, and earn tons of TAO. Which is easier?
Some of the top teams are building on the network:
Nous Research is the king of open source. Their subnets break the mold in fine-tuning open source LLMs. They make the leaderboard impossible to manipulate by testing the model on a continuous stream of synthetic data (unlike traditional benchmarks like HuggingFace).
Taoshi’s proprietary training network is essentially an open source quantitative trading company. They ask ML contributors to build trading algorithms that predict asset price movements. Their API provides quantitative-level trading signals to retail and institutional users and is rapidly moving toward significant profitability.
· Developed by the Corcel team, Cortex serves two purposes. First, they incentivize miners to provide API access to top models (such as GPT-4 and Claude-3) to ensure continued availability for developers. They also provide synthetic data generation, which is very useful for model training and benchmarking (which is why Nous uses it). Check out their tools - Chat and Search.
If nothing else, Bittensor reaffirms the power of incentive structures, all enabled by cryptoeconomics.
Intelligent Agents, Exploring Morpheus
Now, let's look at two aspects of Morpheus:
Cryptoeconomic structures are building AI (Crypto helps AI)
AI-enabled applications enable new use cases in encryption (AI helps encryption)
“Smart Agents” are simply AI models trained on smart contracts. They understand the inner workings of all the top DeFi protocols, know where to find yield, where to bridge, and how to spot suspicious contracts. They are the “auto-routers” of the future, and in my opinion, they will be the way everyone interacts with blockchain in 5-10 years. In fact, once we get to that point, you may not even know you are using crypto. You will just tell the chatbot that you want to move some of your savings into another investment, and everything will happen in the background.
Morpheus embodies part of this “incentivize them and they will come” message. Their goal is to have a platform where intelligent agents can spread and thrive, each building on the success of the last, in an ecosystem that minimizes externalities.
The token inflation structure highlights four main contributors to the protocol:
Code — Agent builder.
Community — Building front-end applications and tools to attract new users to the ecosystem.
Compute — Provides computing power to run the agent.
· Capital – provides their earnings to fuel Morpheus’ economic machine.
Each of these categories receives an equal share of $MOR inflation rewards (a small portion is also saved as an emergency fund), forcing them to:
Build the best agents — Creators get paid when their agents are consistently used. Unlike OpenAI plugins, which are provided for free, this approach pays builders instantly.
· Build the best frontends/tools — creators get paid when their creations are used consistently.
Providing stable computing power — providers are paid when they lend out computing power.
Providing liquidity to projects - Earn their share of MOR by maintaining liquidity for projects.
While there are many other AI/intelligent agent projects, Morpheus’s token economic structure is particularly clear and effective in designing incentive mechanisms.
These smart agents are the ultimate example of how AI is truly removing barriers to crypto adoption. dApp UX is notoriously bad (despite many improvements over the past few years), and the rise of LLMs has ignited the passion of every would-be Web2 and Web3 founder. While there are a ton of for-profit projects, great projects like Morpheus and Wayfinder (see demo below) show how easy it will be to conduct on-chain transactions in the future.
Putting it all together, the interactions between these systems might look a bit like the following. Note that this is an extremely simplified view.
How to tell if a project is completely useless
Keep in mind our two broad categories of “Crypto x AI”:
1. Encryption helps AI
2. AI helps with encryption
In this article, we have mainly explored the first category. As we have seen, a well-designed token system can lay the foundation for the success of the entire ecosystem.
Category 1 - Encryption helps AI
DePIN architectures can help jumpstart markets, and creative token incentive structures can coordinate open source projects toward once-elusive goals. Yes, there are several other legal intersections that I didn’t cover due to space limitations:
Decentralized storage
Trusted Execution Environment (TEE)
Real-time data acquisition (RAG)
Zero-knowledge x machine learning for inference/provenance verification
When deciding whether a new project is truly valuable, ask yourself:
If it’s a spinoff of another established project, is it different enough to be eye-catching?
Is it just a wrapped version of open source software?
Does the project actually benefit from crypto, or is it shoehorned in?
Do we really need 100 crypto projects like HuggingFace (a popular open source machine learning platform)?
Category 2 - AI-assisted encryption
I personally see more fake projects in this category, but there are some cool use cases. AI models can remove barriers in the crypto user experience, especially intelligent agents. Here are some interesting categories worth paying attention to in the field of AI-powered crypto applications:
Enhanced intent system — automating cross-chain operations
Wallet infrastructure
Real-time alerting infrastructure for users and applications
If it's just a "chatbot with a token", it's garbage to me. Please stop promoting these projects for my sanity. Also:
Adding AI won’t magically make your failing app/chain/tool gain product-market fit
No one will play a bad game just because it has AI characters
Labeling your project “AI” doesn’t make it interesting
Where are we headed?
Despite all the noise, some serious teams are working hard to realize the vision of “decentralized AI” and it’s worth fighting for.
In addition to incentivizing projects to develop open source models, decentralized data networks open new doors for emerging AI developers. While most of Open AI’s competitors can’t strike large-scale deals with Reddit, Tumblr, or WordPress, distributed scraping can even the gap.
One company may never have more computing power than the rest of the world combined, but with a decentralized network of GPUs, it means that anyone else has the ability to rival the top companies. All you need is a crypto wallet.
Today we are at a crossroads. If we focus on those truly valuable "crypto x AI" projects, we have the ability to decentralize the entire AI stack.
The vision of cryptocurrency is to create a hard currency that no one can interfere with through the power of cryptography. Just as this emerging technology began to gain popularity, a more formidable challenger emerged.
In the best-case scenario, centralized AI will not only control your finances, but will also impose biases on every piece of data we encounter in our daily lives. It will enrich a very small number of tech leaders in a self-perpetuating cycle of data collection, fine-tuning, and model injection.
It will know you better than you know yourself. It will know which buttons to push to make you laugh more, get angrier, and spend more. Despite what it may seem, it is not responsible for you.
Initially, crypto was seen as a force to counter the centralization of AI . Crypto has the ability to coordinate decentralized individuals to work together to achieve a common goal. However, this ability is now facing an enemy more powerful than central banks: centralized AI . This time, time is running out, and we need to act quickly to resist the centralization trend of AI.
