What are the potential opportunities for the intersection of crypto and AI on Solana?

Author: @knimkar Translation: Plain Language Blockchain

We seem to be entering a Cambrian explosion of use case experiments at the intersection of AI and crypto. I’m really excited about what’s coming out of this energy, and wanted to share some of the exciting new opportunities we’re seeing in the ecosystem at @SolanaFndn.

1. Brief Overview

1) Facilitating the most vibrant agent-driven economy on Solana Truth Terminal is the first demonstration of what’s possible with AI agents when they are able to interact on-chain. We look forward to seeing experiments that safely push the boundaries of what agents can do on-chain. The potential in this area is huge, and we haven’t even begun to explore the design space. This has proven to be the most unexpected and explosive area in the marriage of crypto and AI, and everything is just beginning.

2) Empower Solana developers by making Large Language Models (LLMs) even better at writing Solana code Large Language Models are already pretty good at writing code, and they’re about to get even more powerful. We hope to use these capabilities to make Solana developers 2x to 10x more productive. In the short term, we’ll be creating high-quality benchmarks to measure the ability of LLMs to understand Solana and write Solana code (more on that below), which will help us understand the potential impact of LLMs on the Solana ecosystem. We look forward to supporting teams that are making high-quality progress in fine-tuning their models (we’ll validate their quality by how well they perform on our benchmarks!).

3) Supporting an open and decentralized AI stack By “open and decentralized AI stack,” we mean open and decentralized protocols that facilitate access to the following resources: data for training, compute resources (for both training and inference), model weights, and the ability to verify model outputs (“verifiable computation”). This open AI stack is important because it:

Accelerates experimentation and innovation in model development

Provides a way out for those who may be forced to use untrusted AI (such as state-sanctioned AI)

We want to support teams and products building at all levels of this stack. If you are doing work related to these focus areas, you can contact the original author!

2. Detailed Overview

Below, we will explain in more detail why we are excited about these three pillars and what we hope to see built.

1) Promote the most vibrant agent-driven economy

Why do we focus on this? There has been a lot of discussion about Truth Terminal and GOAT, so I won’t repeat it here, but what is clear is that all kinds of crazy functions that are possible when AI agents interact on-chain have irreversibly entered reality (and in this case, the agents haven’t even taken actions directly on-chain yet).

We can confidently say that we currently have no way of knowing exactly what the future of on-chain proxy behavior will look like, but to give you a sense of how broad this design space is, here are some of the things that are already happening on Solana:

AI leaders like Truth Terminal are trying to foster new age religions through memecoins like $GOAT;

Meanwhile, apps like @HoloworldAI, @vvaifudotfun, @TopHat_One, and @real_alethea allow users to easily create and launch proxies and related tokens.

AI fund managers that train personalized agents of various well-known crypto investors to make investment decisions and cheer on their portfolios. For example, @ai16zdao's meteoric rise at @daosdotfun has created a whole new metaverse of AI funds + agent cheerleaders.

There are also some agent-centric games, such as @ParallelColony, in which players give agents instructions to take actions, often with unexpected results.

Where to go next:

Agency management of multi-faceted projects that require economic coordination among all parties. For example, an agent could be tasked with a complex task like “find a compound that cures disease [X].” Agents can do the following:

Raise funds through tokens on @pumpdotscience

Use the funds raised to pay for access to relevant paid research and pay for computational costs on decentralized computing networks (such as @kuzco_xyz, @rendernetwork, @ionet, etc.) to simulate various compounds

Use bounty platforms like @gib_work to recruit humans to perform tasks that actually work (e.g., run experiments to verify/refine simulation results);

Or perform a simple task, such as helping you build a website, or creating a work of art AI (e.g., @0xzerebro).

There are many other possibilities.

Why does it make more sense for agents to perform financial activities on-chain (rather than in the traditional financial system)? Agents can leverage both the traditional financial system and cryptocurrencies. Here are a few reasons why cryptocurrencies are particularly well suited for certain aspects:

Micropayment scenarios - Solana excels in this area, and applications like Drip have demonstrated their potential.

Speed ​​- Instant settlement can be critical for agents, especially if you want them to be optimally capital efficient.

Access to capital markets through DeFi - Once agents start performing financial activities beyond strictly payments, the advantages of cryptocurrencies become particularly clear. This is perhaps the most powerful reason for agents to participate in the cryptoeconomy. Agents can seamlessly mint assets, trade, invest, borrow, use leverage, and more.

Solana is uniquely suited to support this capital markets activity because of the rich top-tier DeFi infrastructure already available on the Solana mainnet.

In the end, technology is often path-dependent, and it’s not about which product is best, but which product is the first to reach critical mass and become the default path. If we see more agents creating significant wealth through crypto, this could solidify crypto connectivity as an important capability for agents.

What we want to see

Bold experiments in agents combined with wallets that can perform operations on-chain. We’re not being overly specific here because the possibilities are so broad and we expect the most interesting and valuable agent use cases to be those we can’t predict. However, we are particularly interested in exploring and building infrastructure in the following areas:

 At least in prototype stage on testnet (ideally on mainnet)

 2) Make LLM good at writing Solana code and empower Solana developers

 Why do we care about this? LLM is already powerful and is improving rapidly. But writing code is a particularly interesting area of ​​LLM application because it is a task that can be objectively evaluated. As explained in the post below, “Programming has a unique advantage: through ‘self-play’, superhuman data scaling can be achieved. Models can write code and then run it, or write code, write tests, and check self-consistency.”

Limit the negative impact of illusions - current models are very powerful, but still far from perfect. Agents cannot be given completely free rights to perform actions.

Drive non-speculative use cases - for example, allowing you to buy tickets through @xpticket, optimize returns for stablecoin portfolios, or buy food on DoorDash.

Today, while LLMs are still far from perfect for writing code and have some notable shortcomings (for example, they are poor at finding vulnerabilities), tools like Github Copilot and the AI-native code editor Cursor are already fundamentally changing software development (and even changing the way companies recruit talent). Given the rapid progress projected, these models are likely to revolutionize software development. We hope to use this progress to make Solana's developer productivity an order of magnitude higher.

However, there are currently a few challenges that hinder LLM’s performance in understanding Solana:

There is not enough high-quality raw data for LLM to train on;

There is not enough validated builds;

There is not enough high-value information exchange in places like Stack Overflow;

Solana infrastructure evolves rapidly, which means that even code written 6 months ago may not be fully suitable for current needs;

There is no way to evaluate how well the model understands Solana.

What we would like to see

Help us publish better Solana data on the internet!

More teams publish validated builds.

Hope more people in the ecosystem will actively participate in Stack Exchange, ask good questions and provide high-quality answers;

Create high-quality benchmarks to evaluate LLM’s understanding of Solana (RFP is coming soon);

Create fine-tuned versions of LLM that score high on the above benchmarks, and more importantly, accelerate Solana developers’ work. Once we have high-quality benchmarks, we may offer rewards for the first model to reach the benchmark score - stay tuned.

The ultimate achievement here will be a high-quality, differentiated Solana validator client created entirely by AI.

3) Support an open and decentralized AI stack

Why do we care about this? It’s not clear how power in AI will balance between open and closed source AI in the long term. There are good arguments for why closed source entities will remain at the forefront and capture most of the value from the underlying models. Right now, the simplest expectation is that the status quo will continue - large companies like OpenAI and Anthropic push the frontier, and open source models will quickly follow and eventually have uniquely powerful fine-tuned versions for certain use cases. We want Solana to be closely aligned with supporting the open source AI ecosystem. Specifically, this means facilitating access to: data for training, compute power for training and inference, weights for resulting models, and the ability to validate model outputs. There are specific reasons why we think this is important:

A. Open source models help accelerate debugging and innovation in model development How quickly the open source community can refine and fine-tune open source models like Llama demonstrates how the community can effectively complement the efforts of large AI companies in pushing the frontier of AI capabilities (even Google researchers noted last year that "we have no moat, and neither does OpenAI" regarding open source). We believe that a thriving open source AI stack is critical to accelerating the rate of progress in the field.

B. Provide an outlet for those who may be forced to use AI they don't trust (such as state-sanctioned AI) AI is now perhaps the most powerful tool in the arsenal of a dictator or authoritarian regime. State-sanctioned models provide a state-sanctioned version of the truth and become a huge means of control. Highly authoritarian regimes may also have better models because they are willing to ignore the privacy of their citizens to train their AI. The question of AI being used as a tool of control is when it happens, not if it happens, and we want to support the open source AI stack as much as possible to prepare for this possibility.

Solana is already home to many projects supporting the open source AI technology stack:

Grass and Synesis One are facilitating data collection;

@kuzco_xyz, @rendernetwork, @ionet, @theblessnetwork, @nosana_ai, and others are providing large amounts of decentralized computing resources.

Teams like @NousResearch and @PrimeIntellect are working on frameworks to make decentralized training possible (see below).

What we hope to see is more product development at all levels of the open source AI technology stack:

Decentralized data collection, such as @getgrass_io, @usedatahive, @synesis_one

On-chain identity authentication: including protocols that allow wallets to prove that they are human, and verify AI APIs

Decentralized training: examples from @exolabs, @NousResearch, and @PrimeIntellect

IP infrastructure: enabling AIs to license (and pay for) the content they leverage