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Solana and Base, which ecosystem is more suitable for AI agent?

Solana and Base, which ecosystem is more suitable for AI agent?

BlockBeatsBlockBeats2024/12/18 08:15
By:BlockBeats

AI agent is the next direction that Base will compete for, and it already has many advantages.

Original title: "Solana and Base, which ecosystem is more suitable for AI agents?"
Original author: Kevin, BlockBooster


The term AI agents comes from OpenAI's roadmap. Sam Altman divides the capabilities that AI should have into 5 parts, and the third step is the AI agent that will be frequently encountered in the next few years.


Solana and Base, which ecosystem is more suitable for AI agent? image 0


What AI agents can do is autonomous learning, decision-making and task execution. Of course, according to the degree of intelligence and ability, Stuart Russell and Peter Norvig in the book "Artificial Intelligence: A Modern Approach", AI agents can be divided into 5 directions:


· Simple Reflex Agents: only react to the current state.


· Model-Based Reflex Agents: historical states are considered in the decision-making process.


· Goal-Based Agents: focus on planning and finding the best path to achieve a specific goal.


· Utility-Based Agents: aim to weigh benefits and risks to maximize utility.


· Learning Agents: continuously learn and improve through experience.


So what level are the AI agents currently in the market or in the industry at? What direction are they?


Solana and Base, which ecosystem is more suitable for AI agent? image 1


OpenAI o1 has reached Level 2 artificial intelligence. Personally, I think that the current AI agents in the industry are between Level 2 and Level 3, that is, Level 2.5. This does not mean that the agents in the industry have surpassed OpenAI. In fact, web3 agents are still at the stage of GPT wrapper. So why is it Level 2.5? Because through human or program intervention, let's call it an intermediary, the combination of GPT wrapper and intermediary forms a form that cannot withstand scrutiny, but has objective initiative. It is an extension of the application of the OpenAI model in a certain direction. In terms of what the agent can do, it is the most basic simple reflective agent. Some of these agents will consider historical states, but they need active input. Only by continuously feeding data can the agent complete learning. This is a passive model training method, which is far from the state defined by Level 3. The latter three Goal-Based, Utility-Based, and Learning Agents have not yet entered the market. Therefore, I think that the current AI agent is still in its early stages, which is a fine-tuning of the Level 2 general LLM, and has not deviated from Level 2 in terms of architecture. So can the evolution to the Level 3 state be achieved by crypto alone? Or do we need to wait for companies like OpenAI to develop it?


Why can Base or Solana be the center of the AI agent narrative?


Before discussing the industry that can promote the birth of Level 3 agents, we should determine which ecosystem has the potential to become fertile ground for AI agents. Is it Base? Or Solana?


To answer this question, let's first review how AI has affected Web3 in the past two years. When OpenAI just released ChatGPT, the industry's protocols still followed the inertial thinking and quickly poured into the infrastructure bubble. Among them, a large number of computing power/reasoning aggregation platforms emerged, and AI + DePIN infrastructure was also born. The common point between the two is that they have built a grand vision. This is not to say that grand visions are not good. In fact, agents can also build such visions, but in terms of landing and user needs, such large infrastructure protocols are not well considered. Because the market demand they want to drag up is far from saturated in the traditional Internet industry, and user education and market education are not sufficient. Under the impact of the Memecoin craze, the empty AI infrastructure seems even more empty.


Since the infrastructure is too heavy and too big, why not make it lightweight? Agents derived from GPT wrappers are efficient and iterate quickly in both startup and user reach. Lightweight agents have ample potential to create bubbles, and when the bubbles burst, fertile ground for new life will emerge.


Furthermore, in the current market environment, using agents and Memecoin to start projects can land products in a very short time. Allowing users to directly experience the use of the product, in this process, the agent can cleverly use Memecoin's roadmap to grow the community to achieve rapid product iteration, and this iteration is low-cost and fast. Serious AI protocols no longer need to be bound by the heavy old consensus framework. Break the cage, go light, bombard users with lightweight and high-speed iterations. After market education and dissemination are fully carried out, build on this basis and build infrastructure for a grand vision. Lightweight agents are covered with the ambiguous veil of Memecoin. Community culture and fundamentals will no longer be a contradiction. A new asset development path is gradually emerging, and this may be a path chosen by new AI protocols in the future.


The above discussion answers the potential of AI agents to become the core narrative. Under the premise that AI agents can continue to grow rapidly, it is particularly important to choose the right ecosystem. Is it Base? Or Solana? Before answering this question, let's take a look at the current status of serious agent protocols in the market.


First is Arweave/AO: PermaDAO mentioned: AO is designed with the Actor model, and each component is an independent agent that can operate in parallel, which is highly consistent with the application architecture driven by AI Agent. AI relies on three elements: model, algorithm and computing power, and AO can meet such high resource requirements. AO can independently allocate computing resources to each agent process, effectively eliminating computing performance bottlenecks.


In addition, Spectral is one of the few protocols based on agents, and its development direction is text-to-code and model reasoning.


Looking back at an agent token in the current market, it can be found that these agents hardly use the chain infrastructure. This is a fact, because all models in the industry, including agents, are off-chain. Feeding data is off-chain, model training is not decentralized, and the output information is not on-chain. This is an objective fact, because the EVM chain does not support the combination of AI and smart contracts, and of course base and solana do not support it either. Next year, we can look forward to the introduction of ao. Can the model be put on the chain and have a good performance? If ao fails, it may take many years for the model to be put on the chain before Ethereum is introduced, at least not before 2030, or other public chains can realize the model on the chain, but if the architecture and historical resource reserves such as ao cannot be realized, the model on the chain may be more difficult for other public chains.


At present, there are not many practical use cases for AI agent tokens. In fact, it is difficult to tell the difference between ai agent coin and ai Memecoin on Base and Solana. Although agent tokens have no special purpose, why do I think that ai agent coin and ai Memecoin should not be confused? Because I think the current stage is to create an AI agent bubble.


Why discuss Base's desire to compete with Solana for the dominant public chain position of AI agent?


Base attracted a lot of market attention in the first half of this bull market. In the competition for market share of Memecoin, Base had a brief and eye-catching performance, such as $BRETT and $DEGEN. But it still lost to Solana. I think AI agent is the next direction for Base to compete, and it already has many advantages.


AI agent will accelerate the birth of bubbles and create chaos, but will eventually leave users and applications:


The birth and expansion of bubbles will attract the attention of the market, and this attention will undergo qualitative changes over time. What are the characteristics of such qualitative changes? In the process of increasing market attention, a series of user pain points and market gaps will be exposed. When the main contradictions cannot be coordinated, but the attention continues to increase, it is the moment of qualitative change. When the qualitative change is completed, the accumulated users and applications can undertake the grand vision. This is what Memecoin cannot and does not intend to do, which is why I think that although the agent and Memecoin are currently unclear, they should never be confused.


Before the qualitative change occurs, the bubble will give birth to a mess and various dramas, for example: the number of agents will increase exponentially, and thousands of agents will squeeze into the user's sight. How to squeeze? Agents can access social media such as X and Farcaster, promote tokens themselves, and promote tokens with various angles that degen likes and the unique information density of agents.


Next, the fast-iterating agent can complete the on-chain transaction, and a group of Viking pirates broke into the dark forest. The panel protocols on the market, the bots in the TG group, and the Dune panel will be invaded by agents. The indicators familiar to users will be played by agents, such as transaction volume, number of addresses, chip distribution, and simulated dealer behavior. The on-chain data may need more professional cleaning to reflect the value, otherwise it will be deceived by the agent, just like the Viking pirates plundering your wealth.


If the market can reach this stage, then the new era of AI agents will be half successful, because "attention is value" will allow agents to enter the house. This potential comes from:


· Strong distribution capabilities: agents cause enough topics, such as Goat, and stable distribution paths can be copied.


· Ease of deployment: Agent deployment platforms will also explode, including Zerebro, vvaifu, Dolion, griffain and Virtual. Users do not need to know any code to build agents, and the UX of agent deployment platforms will also be optimized in competition.


· Memecoin effect: In the startup phase, agent tokens do not have a suitable business model, and token use cases are minimal. Under the veil of Memecoin, the community can be accumulated quickly, keeping the startup success rate efficient.


· Extremely high ceiling: OpenAI's Level 3 agent is still under development, and even giants cannot quickly launch products, so its market space must be huge. The lower limit of the agent is Memecoin, but the upper limit is an autonomous advanced intelligent body.


· Low market resistance: Agents led by Goat can build a large audience. Agents are different from AI infrastructure, and users are not disgusted. When users are not disgusted, they are likely to start paying attention to it.


· Potential incentives: The use case of agent tokens has not yet been developed. If the agent introduces a points system and strengthens the incentives, it will be able to accumulate a large number of users.


· Iteration potential: As mentioned above, agents are lightweight and can achieve fast iteration of products. This objective iteration capability can create products and content that are more and more attractive to users.


Therefore, AI agents can become the core narrative and a battleground.


Why does Base have the potential to compete with Solana?


Base With the strong support of Coinbase and North American capital, the Base ecosystem has experienced explosive growth in 2024. In November, capital inflows exceeded Solana and significantly exceeded Solana in the past 7 days.


Solana and Base, which ecosystem is more suitable for AI agent? image 2


If ETH can continue to break through the ETH/BTC exchange rate pair next year, the spillover effect of ETH season will have a significant impact on Base. Currently, 23% of ETH outflows are to Base, and this figure is still rising.


Solana and Base, which ecosystem is more suitable for AI agent? image 3


AI agent Launchpad Mapping


Virtual


The V1 stage mainly focused on model training, data contribution and interactive functions, while in the V2 stage, Virtual launched a token incubation platform for AI agents, and the iconic update was fun.virtuals released in October.


Among them, LUNA has developed into an "independent entity" with independent identity and financial capabilities. In this process, LUNA and Coinbase's roadmaps are aligned, and the latter provides powerful technical tools and support to help implement AI agents on Base.


AI agent technology performs well in brand building, especially in the creation of cultural brands. Through AI agents, brands can interact with the community more efficiently. This includes simplifying interactive tasks and flexibly distributing rewards to increase user stickiness and brand awareness.


It is worth noting that all AI agent transactions only support the use of native Virtual tokens. Virtual tokens absorb the value capture of the entire ecosystem and become an important pillar of ecological development.


Virtual focuses on improving product functions, using AI tools to empower users and build a bridge between Web2 and Web3. It emphasizes "use value" rather than "hype hotspots". Although its tool-type products are frequently called in actual applications, they lack the dissemination effect that cryptocurrencies usually have, which is also a shortcoming of the V1 stage.


Clanker


"Posting is issuing coins" lowers the threshold for token issuance and attracts a large number of users to try. People are scrambling to @Clanker, a phenomenon similar to the operation of letting AI summarize video content in social media; but the difference is that here the content release is directly converted into asset issuance.


How does Clanker work?


TokenBot (i.e. Clanker) will deploy Meme tokens on Base to the one-sided liquidity pool (LP), and the liquidity will be locked. The token issuer will receive the following benefits:


· 0.25% of all Swap fees.


· 1% of the total supply of tokens (unlocked for one month).


Users can view the number of tokens deployed or create their own tokens through the clanker.world official website.


Unlike PumpFun, which issues tokens through the bonding curve on Raydium, during which a 1% transaction fee and a fixed fee of 2 SOL will be charged; Clanker does not adopt the bonding curve model, but charges a 1% fee as income through Uni v3 transactions.


AI Agent Layer


AI Agent Layer is a platform within the Base ecosystem that focuses on creating AI agents and Launchpads. It was officially launched on November 18. Before the platform was launched, AIFUN Token was first issued on November 14 and has now been listed on exchanges such as MEXC and Gate. The current price is $0.09, with a market value of approximately $25 million.


Creator.bid


Creator.bid was originally an AI platform focused on digital content monetization and ownership. In April this year, the platform completed a new round of financing.


On October 21, Creator.bid announced the official launch of the Base mainnet, realizing the function of creating and publishing AI agents with one click, providing content creators with new tools and profit models.


Simulacrum


Simulacrum is built on Empyreal. It transforms platforms such as Twitter, Farcaster, Reddit and TikTok into blockchain interaction layers. Users can achieve on-chain operations such as token transactions or tip payments through simple social media posts.


Use technologies such as account abstraction, AI agents, intent-driven and language models to simplify complex blockchain background operations. Make DeFi easier for ordinary users to use.


vvaifu.fun


Similar to Pump.fun, users can easily create AI agents and their associated tokens. AI agents can be seamlessly integrated with social platforms such as Twitter, Telegram, and Discord to automate user interactions.


Dasha is an AI agent created by vvaifu.fun, with an independent Twitter account, Telegram channel, and Discord community. All operations and management are completed by AI.


Top Hat


Top Hat can not only interact with users through text, but also understand and process image content. After a user sends a picture, the AI agent can "understand" the content of the picture and respond.


Griffain


With a trainable AI agent platform, Griffain has launched 1,000 trainable AI agents, demonstrating the future potential of smart contracts and automated trading.


This article is from a contribution and does not represent the views of BlockBeats.

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Disclaimer: The content of this article solely reflects the author's opinion and does not represent the platform in any capacity. This article is not intended to serve as a reference for making investment decisions.

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