Author: @knimkar
Translation: Plain Language Blockchain
We seem to be entering the Cambrian explosion stage of use case experimentation at the intersection of AI and the crypto domain. I'm very excited about the outcomes emerging from this wellspring of energy, and I'd like to share some of the exciting new opportunities we're seeing in the @SolanaFndn ecosystem.
1. High-Level Overview
1) Enabling the most vibrant agent-driven economy on Solana The Truth Terminal has pioneered a glimpse of what AI agents might achieve when able to interact on-chain. We look forward to seeing experiments that safely push the boundaries of what agents can do on-chain. This is a massively underexplored design space with explosive potential, and we've only just begun.
2) Empowering large language models (LLMs) to excel at Solana code writing, enabling Solana developers LLMs have already shown impressive capabilities in code writing, and they will only get stronger. We aim to leverage these capabilities to boost Solana developer productivity 2-10x. In the near term, we'll create high-quality benchmarks to measure LLM understanding and code writing for Solana (more on this below), which will help us understand the potential impact of LLMs on the Solana ecosystem. We look forward to supporting teams making 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: training data, 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 model development
Provides an escape hatch for those who may be forced to use untrustworthy AI (e.g., state-sanctioned AI)
We aim 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
Next, we'll dive deeper into why we're excited about these three pillars and what we hope to see built.
1) Enabling the Most Vibrant Agent-Driven Economy
Why are we focused on this? While there has been much discussion around Truth Terminal and GOAT, the sheer craziness of what AI agents might achieve when able to interact on-chain is now undeniably real (and this is without them 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 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;
Apps like @HoloworldAI, @vvaifudotfun, @TopHat_One, and @real_alethea make it easy for users to create and launch agents and associated Tokens.
Training AI fund managers to make investment decisions and cheer for their portfolios, personalized for famous crypto investors. For example, @ai16zdao's meteoric rise on @daosdotfun has created a whole new metaverse of AI fund + agent cheerleader.
Agent-centric games, like @ParallelColony, where players give instructions to agents, often leading to unexpected outcomes.
Potential future directions:
Multi-stakeholder projects where agents coordinate economic activities. For example, an agent could be tasked with "finding a compound that can cure [X] disease." The agent could:
Raise funds through Tokens on @pumpdotscience;
Use the raised funds to pay for access to relevant research and to cover decentralized compute costs on networks like @kuzco_xyz, @rendernetwork, and @ionet to simulate various compounds;
Leverage bounty platforms like @gib_work to recruit humans to perform actual hands-on tasks (e.g., running experiments to validate/refine simulation results);
Or simply execute a simple task, like building a website or creating AI-generated art (e.g., @0xzerebro).
And many other possibilities.
Why does it make more sense for agents to execute financial activities on-chain (rather than in the traditional financial system)? Agents can certainly leverage both the traditional financial system and cryptocurrencies. Here are a few reasons why cryptocurrencies are particularly well-suited in some ways:
Micropayment scenarios - Solana excels here, and apps like Drip have already demonstrated its potential.
Speed - Instant settlement may be crucial for agents, especially when you want them to be capital-efficient.
Access to capital markets through DeFi - Once agents start engaging in financial activities beyond just payments, the advantages of cryptocurrencies become even more apparent. 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 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 "best," but rather which one reaches critical mass and becomes the default path first. If we see more agents creating significant wealth through cryptocurrencies, this could cement the connectivity to crypto as an important capability for agents.
What we hope to see
Bold experiments with agents integrated into wallets, able to take actions on-chain. We haven't provided overly specific definitions here, as the possibilities are vast, and we expect the most interesting and valuable agent use cases to be those we can't predict. However, we're particularly excited about exploration and infrastructure in the following directions:
At least prototyped on testnet (ideally on mainnet)
2) Empowering LLMs to Excel at Solana Coding and Enabling Solana Developers
Why are we focused on this? LLMs have already demonstrated impressive capabilities, and code writing is a particularly compelling direction for LLM applications because it's an objectively measurable task. 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 negative impacts of delusions - Current models are powerful but far from perfect. Agents cannot be given completely unconstrained freedom to take actions.
Promoting non-speculative application scenarios - for example, allowing you to purchase tickets through @xpticket, optimize the returns of your stablecoin investment portfolio, or buy food on DoorDash.
Currently, although LLMs are still far from perfect in writing code and have some obvious shortcomings (for example, they perform poorly in finding vulnerabilities), tools like Github Copilot and AI-native code editor Cursor have fundamentally changed software development (even the way companies recruit talent). Given the expected rapid progress, these models are likely to completely transform software development. We hope to leverage this progress to increase the productivity of Solana developers by an order of magnitude.
However, there are currently some challenges that hinder the performance of LLMs in understanding Solana:
Lack of high-quality raw data for LLMs to train on;
Lack of sufficient verified build versions;
Lack of enough high-value information exchange on platforms like Stack Overflow;
Rapid development of the Solana infrastructure, meaning that even code written 6 months ago may not fully meet current needs;
No way to assess the model's understanding of Solana.
Help us publish better Solana data on the internet!
More teams releasing verified build versions.
Decentralized data collection, e.g. @getgrass_io, @usedatahive, @synesis_one
On-chain identity authentication: including protocols that allow wallets to prove they are human identities, and protocols to verify AI 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
What we hope to see
We hope to see more active participation in the Stack Exchange ecosystem by the ecosystem, asking good questions and providing high-quality answers;
Create high-quality benchmarks to assess LLMs' 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 would be a high-quality, differentiated Solana validator client entirely created by AI.
3) Support an open and decentralizedAItechnology 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 the underlying models. The simplest expectation now 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.
The specific reasons we believe this is important are:
A, Open-source models help accelerate model development debugging and innovation The way the open-source community has rapidly refined and fine-tuned models like Llama demonstrates how the community 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 massive means of control. Highly authoritarian regimes may even have better models because they are willing to disregard citizen privacy to train their AI. The problem with AI being used as a control tool is not if, but when, and we hope to support an open-source AI technology stack as much as possible to prepare for this eventuality.
Solana is already a home for 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 vast amounts of decentralized compute 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 across the various layers of the open-sourceAItechnology stack:
Link to the article: https://www.hellobtc.com/kp/du/12/5568.html
Source: https://x.com/knimkar/status/1863719025500623344
