The Solana ecosystem is driving the deep integration of AI and crypto technologies, especially in areas such as AI agents, Solana developer tools, and decentralized AI tech stacks, demonstrating immense innovative potential.
Author: @knimkar
Translation: Plainspoken Blockchain
We seem to be entering an explosive phase of experimentation at the intersection of AI and crypto. I am very excited about the outcomes emerging from this energy and want to share some exciting new opportunities we see in the ecosystem at @SolanaFndn.
1. Brief Overview
1) Promote the most vibrant agent-driven economy on Solana. Truth Terminal has first demonstrated the achievements possible when AI agents can interact on-chain. We look forward to seeing experiments that safely push the boundaries of agents' on-chain capabilities. The potential in this field is enormous, and we have barely begun to explore the design space within it. This has already proven to be the most unexpected and explosive area of the intersection between crypto and AI, and everything has just begun.
2) Enable large language models (LLMs) to perform better in writing Solana code, empowering Solana developers. Large language models have already performed quite well in writing code, and they will become even more powerful. We hope to leverage these capabilities to increase Solana developers' productivity by 2 to 10 times. In the short term, we will create high-quality benchmarks to measure LLMs' understanding of Solana and their ability to write Solana code (see below for details), and these tests 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 (we will validate their quality through their outstanding performance in benchmark tests!).
3) Support an open and decentralized AI tech stack What we mean by an 'open and decentralized AI tech stack' refers to protocols that facilitate access to the following resources: data for training, computing resources (for training and inference), model weights, and the ability to verify model outputs ('verifiable computation'). This open AI tech stack is crucial because it:
Accelerate experimentation and innovation in the model development process
Provide an exit for those who may be forced to use untrustworthy AI (e.g., state-approved AI)
We hope to support the teams and products being built at all levels of this tech stack. If you are working on something related to these focus areas, please reach out to the original author!
2. Detailed Overview
Below, we will elaborate on why we are excited about these three pillars and what we hope to see built.
1) Promote the most vibrant agent-driven economy on Solana. The Truth Terminal has first demonstrated the achievements possible when AI agents can interact on-chain. We look forward to seeing experiments that safely push the boundaries of agents' on-chain capabilities. The potential in this field is enormous, and we have barely begun to explore the design space within it. This has already proven to be the most unexpected and explosive area of the intersection between crypto and AI, and everything has just begun.
Why are we focusing on this? There has been much discussion about Truth Terminal and GOAT; I will not repeat it here, but it is clear that the various crazy functionalities that may be achieved when AI agents interact on-chain have irreversibly entered reality (and in this case, agents have not even taken direct actions on-chain).
We can confidently say that we currently do not know exactly what the future of on-chain agent behavior will look like, but to give everyone a sense of how broad this design space is, here are some things that have already happened on Solana:
AI leaders like Truth Terminal are trying to cultivate a new era's religion through memecoins like $GOAT;
Meanwhile, applications like @HoloworldAI, @vvaifudotfun, @TopHat_One, and @real_alethea enable users to easily create and launch agents and related tokens.
By training AI fund managers to personalize agents of various well-known crypto investors to make investment decisions and fuel their portfolios. For example, @ai16zdao's rapid rise on @daosdotfun has created a new metaverse of AI funds + agent supporters.
There are also agent-centric games, such as @ParallelColony, where players issue commands to agents, often resulting in unexpected outcomes.
Possible directions for future development:
Agent management requires multi-faceted projects that coordinate the economy of various parties. For example, agents could take on complex tasks such as 'finding a compound that can cure [X] disease.' Agents can execute the following operations:
Raise funds through Tokens on @pumpdotscience;
Use the raised funds to pay for relevant paid research fees and computational costs on decentralized computing networks (such as @kuzco_xyz, @rendernetwork, @ionet, etc.) for simulating various compounds;
Use bounty platforms like @gib_work to recruit humans to perform tasks that require actual work (e.g., running experiments to validate/refine simulation results);
Or perform a simple task, like helping you build a website or creating AI-generated art (e.g., @0xzerebro).
There are many other possibilities.
Why does it make more sense for agents to conduct financial activities on-chain (rather than through the traditional financial system)? Agents can fully utilize both the traditional financial system and cryptocurrencies simultaneously. Here are several reasons why cryptocurrencies are particularly well-suited for certain aspects:
Micropayment scenarios—Solana excels in this area, with applications like Drip already demonstrating its potential.
Speed—instant settlement can be crucial for agents, especially when you want them to achieve optimal capital efficiency.
Accessing capital markets through DeFi—once agents start engaging in financial activities beyond strict payments, the advantages of cryptocurrencies become particularly evident. This may be the most powerful reason for agents to participate in the crypto economy. Agents can seamlessly mint assets, trade, invest, borrow, and leverage, among other operations.
Solana is particularly well-suited to support such capital market activities because the Solana mainnet already has a rich array of top DeFi infrastructure.
Finally, technology is often path-dependent; the key is not which product is the best, but which product first reaches critical mass and becomes the default path. If we see more agents creating significant wealth through cryptocurrencies, this may solidify the connectivity of cryptocurrencies as an important capability for agents.
What we hope to see
Bold experiments combining agents with wallets that can execute operations on-chain. We have not provided overly specific definitions here, as the possibilities are vast, and we anticipate that the most interesting and valuable agent application scenarios will be those we cannot predict. However, we are particularly interested in exploring and building infrastructure in the following directions:
At least in prototype stage on testnets (preferably on mainnets)
2) Enable LLMs to excel at writing Solana code and empower Solana developers
Why are we focusing on this? LLMs already possess strong capabilities and are rapidly advancing. However, writing code is an area within LLM applications that is particularly worth watching, as it is a task that can be objectively assessed. As explained in the post below, 'Programming has unique advantages: through 'self-play,' it can achieve superhuman levels of data scaling. Models can write code, then run it, or write code, write tests, and check for self-consistency.'
Limits the negative impacts of hallucinations—current models are powerful but still far from perfect. Agents cannot be granted complete autonomy to perform operations.
Promotes non-speculative application scenarios—for example, allowing you to purchase tickets through @xpticket, optimizing yields for stablecoin portfolios, or ordering food on DoorDash.
Currently, although LLMs are still far from perfect in writing code and have some notable shortcomings (e.g., they perform poorly in finding vulnerabilities), tools like GitHub Copilot and AI-native code editor Cursor have fundamentally changed software development (even changing the way companies recruit talent). Given the anticipated rapid progress, these models are likely to revolutionize software development. We hope to leverage this progress to increase the productivity of Solana developers by an order of magnitude.
However, there are some challenges currently hindering LLMs' performance in understanding Solana:
Insufficient high-quality raw data for LLM training;
Lack of sufficient validated build versions;
Insufficient high-value information exchange in places like Stack Overflow;
The rapid development of Solana infrastructure means that even code written six months ago may not fully meet current needs;
No way to assess the model's understanding of Solana.
What we hope to see
Help us publish better Solana data on the internet!
More teams publishing validated build versions.
Hope more people in the ecosystem 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 coming soon);
Create LLM fine-tuned versions that score well on the above benchmarks, and more importantly, accelerate the work of Solana developers. Once we have high-quality benchmarks, we may offer rewards for the first model to reach the benchmark scores—stay tuned.
The ultimate achievement here will be high-quality, differentiated Solana validator node clients created entirely by AI.
3) Support an open and decentralized AI tech stack
Why are we focusing on this? It is currently unclear how power in the AI space will balance between open-source and closed-source AI in the long term. There are good arguments for why closed-source entities will remain at the forefront of technology and capture most of the value from foundational models. The simplest expectation now is that the status quo will persist—large companies like OpenAI and Anthropic will push the technological frontier while open-source models will quickly follow and eventually possess uniquely strong fine-tuned versions for certain use cases. We hope Solana can closely integrate and support the open-source AI ecosystem. Specifically, this means facilitating access to the following: data for training, computing power for training and inference, model weights, and the ability to verify model outputs. We believe these are important specific reasons:
A. Open-source models contribute to accelerating debugging and innovation in model development. The open-source community's ability to quickly refine and fine-tune open-source models like Llama demonstrates how the community effectively complements the efforts of large AI companies in advancing the frontier of AI capabilities (even Google researchers pointed out last year that regarding open-source 'we have no moat, and neither does OpenAI'). We believe a thriving open-source AI tech stack is crucial for accelerating the pace of progress in this field.
B. Provide an exit for those who may be forced to use AI they do not trust (e.g., state-sanctioned AI). AI is possibly the most powerful tool in the arsenal of dictators or authoritarian regimes. State-sanctioned models provide a version of truth endorsed by the state and become a massive tool for control. Highly authoritarian regimes may even have better models, as they are willing to overlook citizens' privacy to train their AI. The question is not if AI will be used as a tool of control, but when, and we hope to support an open-source AI tech stack as much as possible to prepare for this possibility.
Solana is already home to many projects supporting an open-source AI tech stack:
Grass and Synesis One are facilitating data collection;
@kuzco_xyz, @rendernetwork, @ionet, @theblessnetwork, @nosana_ai, etc., are providing a wealth of decentralized computing resources.
Teams like @NousResearch and @PrimeIntellect are working on developing frameworks to enable decentralized training (see below).
What we hope to see is the development of more products at various levels of the open-source AI tech stack:
Decentralized data collection, such as @getgrass_io, @usedatahive, @synesis_one
On-chain identity verification: 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: for example, @exolabs, @NousResearch, and @PrimeIntellect
Intellectual property infrastructure: enabling AI to license (and pay for) the content they utilize
Article link: https://www.hellobtc.com/kp/du/12/5568.html
Source: https://x.com/knimkar/status/1863719025500623344
