Solana Foundation: Three Strategic Directions for the Integration of AI and Cryptocurrency Technology

Original Title: "Focus areas at the intersection of crypto and AI"

Original Author: Kuleen, Head of DePIN at the Solana Foundation

Compiled by: Yuliya, PANews

Currently, the intersection of AI and crypto technology is entering an experimental phase akin to a "Cambrian explosion." This article from the Solana Foundation elaborates on three key development directions for the fusion of AI and crypto.

TLDR

1. Building the most vibrant agent-driven economy on Solana

Truth Terminal has already demonstrated the feasibility of AI agents operating on-chain. Experiments in this field are continuously pushing the boundaries of agent operations on-chain, which not only has immense potential but also a vast design space. This has become one of the most groundbreaking and explosive directions in the crypto and AI fields, and this is just the beginning.

2. Enhancing LLM capabilities in Solana code development

Large language models (LLMs) have already shown excellent performance in code writing, and this will further improve in the future. With these capabilities, the efficiency of Solana developers is expected to increase by 2-10 times. Recently, establishing high-quality benchmarks to assess LLMs' understanding and writing capabilities of Solana code will help understand the potential impact of LLMs on the Solana ecosystem. High-quality model fine-tuning solutions will be validated in the benchmarking process.

3. Supporting an open and decentralized AI tech stack

The "open and decentralized AI tech stack" includes the following key elements:

• Data acquisition for training

• Training and inference computing power

• Model weight sharing

• Model output verification capabilities

The importance of this open AI tech stack is reflected in:

• Accelerating innovation and experimentation in model development

• Providing alternatives for users who distrust centralized AI

1. Building the most vibrant agent-driven economy

There has been much discussion about Truth Terminal and $GOAT, so there is no need to elaborate here. However, it is certain that when AI agents begin to participate in on-chain activities, a new world full of possibilities has unfolded (notably, agents have not yet taken direct action on-chain).

While it is currently impossible to accurately predict the future development of agents' on-chain behavior, we can glimpse the vast prospects of this design space by observing the innovations that have already occurred on Solana:

• AI projects like Truth Terminal are developing new digital communities through Meme coins like $GOAT

• Platforms like Holoworld AI, vvaifu.fun, Top Hat AI, and Alethea AI enable users to easily create and deploy intelligent agents and their associated tokens

• AI fund managers trained based on the personality traits of well-known crypto investors are emerging, with the rapid rise of ai16z on the daos.fun platform creating a new ecosystem for AI funds and agent supporters

• Additionally, gaming platforms like Colony allow players to participate in games by guiding agent actions, often resulting in unexpected innovative gameplay.

Future Development Directions

In the future, intelligent agents could manage complex projects requiring multi-party economic coordination. For example, in the field of scientific research, agents could be responsible for finding therapeutic compounds for specific diseases. Specifically:

• Raising funds through the Pump Science platform

• Using the raised funds to pay for access to paid research materials and for computational costs of compound simulations on decentralized computing networks like kuzco, Render Network, and io.net

• Recruiting humans to perform experimental verification tasks through bounty platforms like Gib.Work (e.g., running experiments to verify/build simulation results)

In addition to complex projects, agents can also perform simple tasks such as building personal websites or creating artworks (like zerebro), with limitless application scenarios.

Why does it make more sense for agents to perform financial activities on-chain rather than through traditional channels?

Agents can certainly utilize both traditional financial channels and cryptocurrency systems simultaneously. However, cryptocurrencies have unique advantages in certain areas:

• Micro-payment applications—Solana excels in this regard, as demonstrated by applications like Drip

• Speed advantages—instant settlement features help agents achieve maximum capital efficiency

• Accessing capital markets through DeFi—this may be the strongest reason for agents to participate in the crypto economy. When agents need to engage in financial activities beyond payments, the advantages of cryptocurrencies become even more apparent. Agents can seamlessly mint assets, trade, invest, lend, and use leverage. Particularly, Solana is well-suited to support these capital market activities due to its existing top-tier DeFi infrastructure on its mainnet.

From a technological development perspective, path dependence plays a crucial role. Whether a product is optimal is not the most important factor; the key is who can first achieve critical scale and become the default choice. As more agents earn through cryptocurrencies, crypto connectivity is likely to become a core capability of agents.

What the Foundation hopes to see

The Solana Foundation hopes to see agents equipped with crypto wallets conducting bold innovative experiments on-chain. The Foundation does not overly restrict specific directions here, as the possibilities are indeed vast—believing that the most interesting and valuable agent application scenarios are likely those that cannot be foreseen at present.

However, the Foundation is particularly focused on exploring the following directions:

1. Risk control mechanisms

• Although current models perform well, they are far from perfect

• Agents cannot be given completely unconstrained freedom of action

1. Promoting non-speculative use cases

• Purchasing tickets through xpticket

• Optimizing stablecoin investment portfolio returns

• Ordering food on DoorDash

1. Development progress requirements

• Must at least reach the prototype stage on the testnet

• Ideally already running on the mainnet

2. Enhancing LLMs' ability to write Solana code, empowering Solana developers

LLMs have demonstrated strong capabilities and are rapidly advancing. In the application domain of LLMs, code writing may experience particularly steep progress curves, as this is a task that can be objectively assessed. As mentioned below, "Programming has a unique advantage: the potential for superhuman data scaling through 'self-play.' The model can write code and run it, or write code, write tests, and then check for self-consistency."

Today, although LLMs are still not perfect in code writing and have obvious shortcomings (e.g., poor performance in bug detection), AI-native code editors like Github Copilot and Cursor have fundamentally changed software development (even changing the way companies recruit talent). Given the expected rapid rate of progress, these models are likely to revolutionize software development. The Foundation hopes to leverage this advancement to increase the work efficiency of Solana developers by an order of magnitude.

However, several challenges currently hinder LLMs from achieving excellence in understanding Solana:

• Lack of high-quality raw training data

• Insufficient number of verified builds

• Lack of high-value interactions on platforms like Stack Overflow

• Historically, the rapid development of Solana's infrastructure means that even code written six months ago may not fully meet today's needs

• Lack of methods to assess the model's understanding of Solana

What the Foundation hopes to see

• Assistance in obtaining better Solana data from the internet

• More teams releasing verified builds

• More people in the ecosystem actively asking good questions and providing high-quality answers on Stack Exchange

• Creation of high-quality benchmarks for assessing LLMs' understanding of Solana (RFP to be released soon)

• Creation of LLM fine-tuning models that perform well on the aforementioned benchmarks, and more importantly, accelerate the work efficiency of Solana developers. Once high-quality benchmarks are available, the Foundation may reward the first model to reach the benchmark threshold score.

The ultimate significant achievement will be: a completely AI-generated, high-quality, differentiated Solana validator node client.

3. Supporting an open and decentralized AI tech stack

In the field of AI, the long-term balance of power between open-source and closed-source models remains unclear. There are indeed arguments that support closed-source entities will continue to maintain technological leadership and capture the primary value of foundational models. The simplest expectation currently is to maintain the status quo—tech giants like OpenAI and Anthropic push the frontier while open-source models quickly follow and gain unique advantages through fine-tuning in specific application scenarios.

The Foundation is committed to closely integrating Solana with the open-source AI ecosystem. Specifically, this means supporting access to the following elements:

• Training data

• Training and inference computing power

• Model weights

• Model output verification capabilities

The importance of this strategy is reflected in:

1. Open-source models accelerate innovation and iteration

The rapid improvements and fine-tuning of open-source models like Llama by the open-source community demonstrate how the community can effectively complement the work of large AI companies and push the boundaries of AI capabilities (even a Google researcher pointed out last year, "Regarding open source, we have no moat, and neither does OpenAI"). The Foundation believes that a thriving open-source AI tech stack is crucial for accelerating progress in this field.

2. Providing choices for users who distrust centralized AI

AI could be one of the most powerful tools in the arsenal of dictatorial or authoritarian regimes. State-sanctioned models provide officially recognized "truths," serving as an important control mechanism. Highly authoritarian regimes may possess superior models because they are willing to overlook citizen privacy to train AI. The use of AI for control is an inevitable trend, and the Foundation hopes to prepare for this by fully supporting an open-source AI tech stack.

Several projects in the Solana ecosystem are already supporting an open AI tech stack:

• Data collection—Grass and Synesis One are advancing data collection

• Decentralized computing power—kuzco, Render Network, io.net, Bless Network, Nosana, etc.

• Decentralized training frameworks—Nous Research, Prime Intellect

What the Foundation hopes to see

The Foundation hopes to see more products built at all levels of the open-source AI tech stack:

• Decentralized data collection: e.g., Grass, Datahive, Synesis One

• On-chain identity: protocols that support wallet verification of human identity, protocols that verify AI API responses, allowing users to confirm they are interacting with LLMs

• Decentralized training: projects similar to EXO Labs, Nous Research, and Prime Intellect

• IP infrastructure: enabling AI to license (and pay for) the content it uses