Author: @knimkar Translator: Baihua Blockchain

We seem to be entering the Cambrian explosion stage of use case experimentation at the intersection of AI and the crypto domain. I am very excited about the outcomes emerging from this wellspring of energy, and would like to share some of the exciting new opportunities we are seeing in the @SolanaFndn ecosystem.

1. High-level Overview

1) Enabling the most vibrant agent-driven economy: Truth Terminal's first demonstration of what AI agents can achieve when able to interact on-chain was truly mind-bending. We look forward to seeing more experiments that safely push the boundaries of what agents can do on-chain. The potential in this space is immense, and we've barely scratched the surface of the design space. This has proven to be the most unexpected and explosive intersection of crypto and AI, and it's only just beginning.

2) Empowering Solana developers with large language models (LLMs): LLMs have already shown impressive capabilities in writing code, and they are only going to get stronger. We hope to leverage these capabilities to boost Solana developers' productivity 2-10x. In the near term, we will create high-quality benchmarks to measure LLMs' understanding of Solana and ability to write Solana code (more on this below), which will help us understand the potential impact of LLMs on the Solana ecosystem. We look forward to supporting teams that make high-quality progress in fine-tuning models (which we'll validate through their stellar performance on these benchmarks!).

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

Accelerates experimentation and innovation in the model development process

Provides an escape hatch for those who may be forced to use untrustworthy AI (e.g., state-sanctioned AI)

We hope to support teams and products building at all layers of this technology stack. If you're working on anything related to these focus areas, feel free to reach out to the original author!

2. Detailed Overview

1) Enabling the most vibrant agent-driven economy

Why are we excited about this? There has been a lot of discussion about Truth Terminal and GOAT, and I won't rehash that here, but suffice it to say that the sheer craziness of what AI agents can achieve when interacting on-chain has irreversibly entered reality (and this is with agents not even directly taking actions on-chain yet).

We can say with confidence that we have no idea what the future of on-chain agent behavior will look like, but to give a sense of the breadth of the design space, here are some things that have already happened on Solana:

AI luminaries like Truth Terminal are trying to cultivate a new era of religion through memecoins like $GOAT;

Meanwhile, apps like @HoloworldAI, @vvaifudotfun, @TopHat_One, @real_alethea make it easy for users to create and launch agents and associated Tokens.

AI fund managers trained to mimic the personalities of famous crypto investors, making investment decisions and cheering on their portfolios. For example, @ai16zdao's meteoric rise on @daosdotfun has created a whole new metaverse of AI fund + agent cheerleader.

There are also agent-centric games like @ParallelColony, where players give instructions to agents, often leading to unexpected outcomes.

Potential future directions:

Multi-stakeholder projects that require agent coordination for economic activities. For example, having agents tackle a complex task like "find a compound that can cure [X] disease." Agents could:

Raise funds through Tokens on @pumpdotscience;

Use the raised funds to pay for access to relevant paid research, and to pay for decentralized compute (e.g., @kuzco_xyz, @rendernetwork, @ionet) to simulate various compounds;

Leverage bounty platforms like @gib_work to recruit humans to execute physical tasks (e.g., run experiments to validate/refine the simulations);

Or simply execute a simple task like building you a website, or creating AI-generated art (e.g., @0xzerebro).

The possibilities are endless.

Why does it make more sense for agents to execute financial activities on-chain (vs. the traditional financial system)? Agents can certainly leverage both traditional finance and crypto. Here are a few reasons why crypto is particularly well-suited in some cases:

Micropayment scenarios - Solana shines here, and apps like Drip have already demonstrated its potential.

Speed - Instant settlement may be critical for agents, especially when you want them to be capital-efficient.

Access to capital markets via DeFi - Once agents start engaging in financial activities beyond just payments, the advantages of crypto become even more pronounced. This is likely the most powerful reason for agents to participate in the crypto economy. Agents can seamlessly mint assets, trade, invest, borrow, use leverage, etc.

Solana is particularly well-suited to support this kind of capital markets activity, as the Solana mainnet already has a rich ecosystem of top-tier DeFi infrastructure.

Ultimately, technology tends to be path-dependent, and the key is not which product is the best, but which one reaches critical mass and becomes the default path first. If we see more agents creating significant wealth through crypto, this could cement crypto connectivity as an important capability for agents.

What we'd like to see

Bold experiments with agents integrated with wallets, able to execute on-chain operations. We haven't provided too specific a definition here, as the possibilities are quite broad, and we expect the most interesting and valuable agent use cases to be the ones we can't predict. However, we are particularly interested in exploration and infrastructure building in the following directions:

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

2) Empowering Solana developers with LLMs

Why are we excited about this? LLMs have already demonstrated impressive capabilities, and writing code is a particularly interesting area of LLM application, as it's a task that can be objectively evaluated. As the post below explains, "Programming has a unique advantage: through 'self-play', it can achieve superhuman data scaling. Models can write code, then run it, or write code, write tests, and check self-consistency."

Mitigating the downside of delusions - Current models are incredibly powerful, but still far from perfect. Agents cannot be given completely unfettered freedom to execute operations.

Driving non-speculative use cases - e.g., using you to buy tickets through @xpticket, optimize yields for a stablecoin portfolio, or order food on DoorDash.

While LLMs are still far from perfect at writing code, and have obvious shortcomings (e.g., poor at finding bugs), tools like Github Copilot and AI-native code editors like Cursor have already fundamentally changed software development (even how companies hire talent). Given the expected rapid progress, these models are likely to transform software development. We hope to leverage this progress to boost Solana developers' productivity by an order of magnitude.

However, there are some challenges currently hindering LLM performance in understanding Solana:

Lack of high-quality raw data for LLMs to train on;

Lack of sufficient vetted build artifacts;

Lack of high-value information exchange on places like Stack Overflow;

Solana's rapidly evolving infrastructure means even code written 6 months ago may not fully fit current needs;

No way to assess models' understanding of Solana.

What we'd like to see

Help us publish better Solana data on the internet!

More verified build releases from the team.

We 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 assess LLM's understanding of Solana (RFP to be released soon);

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

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

3) Support an open and decentralized AI technology stack

Why do we focus on this? It is currently unclear how power in the AI field will balance between open-source and closed-source AI in the long run. There are good arguments for why closed-source entities will maintain technological leadership and capture most of the value from base models. For now, the simplest expectation is that the status quo will continue - large companies like OpenAI and Anthropic drive the technological frontier, while open-source models quickly catch up and ultimately have unique powerful fine-tuned versions for certain use cases. We hope Solana can closely interface with and support the open-source AI ecosystem. Specifically, this means facilitating access to: data for training, compute power for training and inference, model weights, and the ability to validate model outputs. We believe there are important concrete reasons for this:

A, Open-source models help accelerate model development debugging and innovation The open-source community has shown how it can effectively complement the efforts of large AI companies in pushing the frontiers of AI capabilities (even Google researchers pointed out last year that "we don't have a moat, and neither does OpenAI"). We believe a thriving open-source AI technology stack is crucial to accelerating the pace of progress in this field.

B, Provide an outlet for those who may be forced to use AI they don't trust (e.g. state-sanctioned AI) AI is now perhaps the most powerful tool in the arsenal of dictators or authoritarian regimes. State-sanctioned models provide an officially 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 citizen privacy to train their AI. The problem with AI being used as a tool of control is not whether it will happen, but when, and we hope to support an open-source AI technology stack as much as possible to prepare for this possibility.

Solana has already become a home for many projects supporting an open-source AI technology stack:

Grass and Synesis One are promoting data collection;

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

Teams like @NousResearch and @PrimeIntellect are working on developing 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, e.g. @getgrass_io, @usedatahive, @synesis_one

On-chain identity verification: including protocols that allow wallets to prove they are human identities, and protocols to verify LLM API responses so consumers can confirm they are interacting with LLMs

Decentralized training: e.g. @exolabs, @NousResearch and @PrimeIntellect

Intellectual property infrastructure: allowing AI to license (and pay for) the content it utilizes