AI+Crypto's Three Misalignment Insights: Don't force it at the wrong time, the real breakthrough point is hidden at the margins.

When AI applications become truly complex and require cross-platform, cross-ecosystem collaboration, the opportunity for distributed Crypto solutions will arise.

Written by: Haotian

Whenever AI + Crypto is mentioned, there are always those who subconsciously react with the question: Does AI need Crypto? The implication is that if you're doing Crypto, you should just focus on issuing tokens and not ride the coattails of AI's popularity. Even web2 AI practitioners often express disdain when it comes to the combination of Crypto. Why?

The reason, I believe, comes from the three misaligned developments of web3AI and web2AI. It's not that the direction is wrong, but rather that the timing is off!

——First Misalignment: The Ants Shaking the Big Tree, Attempting to Join the Computing Power Arms Race

Last year, when OpenAI, Google, and Meta were crazily competing in computing power, a bunch of "decentralized computing power" projects emerged in the crypto space. The logic is simple: use tokens to incentivize retail investors to contribute GPUs, which lowers costs and aims to disrupt traditional cloud computing.

So, what's the result? OpenAI burns tens of millions of dollars for a single training session, and you want a "militia" made up of RTX4090s to go head-to-head with an H100 cluster? Isn't this like a mantis trying to stop a car? The timing is completely off; at this stage, web2 AI is all about centralized efficiency and financial strength, and the distributed advantage of crypto cannot be leveraged at all.

——Second Misalignment: The infrastructure is not yet in place, but there is a rush to roll out the application experience

When Deepseek makes a comeback with the perfect performance of R1, Anthropic unveils the MCP protocol to break the island effect, and the backend performance of web2AI's LLMs, along with the frontend Agent applications, are overwhelmingly leading, the Crypto circle, however, presents the grand narrative of "asset issuance", vying to become a Launchpad, competing to deploy the one-click Agent technology stack, eagerly hoping to give the web3AI industry a Cambrian "token issuance wave".

The question is, what is the use of so many coins that have no practical application value? Is your infrastructure ready? For example, the MCP protocol itself is essentially a USB interface, where a programmer can code in Cursor, and with just one authorization, it can be linked to GitHub. The applications are all mature, and as long as access permissions are granted, they can be seamlessly connected.

However, if you try to let the AI Agent read on-chain data, you will find that most of the on-chain data is noise. The workload required to filter and analyze the data, the storage costs for nodes to crawl the data, the maintenance barriers after RPC calls go down, the complexity of inconsistent data standards in cross-chain environments, and so on, every link is a technical deep pit.

On the one hand, it is plug-and-play for mature applications, and on the other hand, it is the hard work of infrastructure from scratch. Desperately imitating web2 to do application-layer innovation under such infrastructure conditions is like driving an F1 car on a muddy road, and in the end it can only be a large number of low-quality agents flooding the market, the user experience is so poor that it stinks, and it also makes the reputation of the entire AI + Crypto stink.

——The Third Misalignment: Multimodal Complex Tasks are a Trend, but Far from Simple Lego Blocks

When GPT-4V can understand images and generate code, Sora can create videos based on text, and Claude can analyze complex documents and generate interactive applications, web2 AI has entered the deep waters of multimodal complex tasks. What lies behind this? Years of model training, massive data annotation, complex algorithm optimization, and countless iterations and debugging.

However, the Crypto circle saw the breakout performance of Manus and thought about giving the Agent modularity, attempting to combine analysis, decision-making, execution, and other Agents to construct application scenarios such as DeFAi and GameFAI.

The question is, do you think complex tasks are just simple module splicing?

The strength of web2AI's multimodality lies in the deep semantic alignment between various modalities, precise attention mechanisms, and complex feature fusion. This is not a simple combination of several independent modules, but an end-to-end systematic engineering.

But looking at the execution agents of Crypto, they essentially just package existing APIs into different agents, some of which haven't even been sufficiently fine-tuned. The market analysis agent calls CoinGecko, the trading execution agent calls DEX interfaces, and the risk control agent sets a few simple thresholds, then claims this is a "decentralized AI investment system," which is far from the mark.

Ultimately, you say that applications like DeFAI, GameFAI, etc. are indeed the way out for web3AI. That's right, the application scenarios are indeed limitless, but when it comes to practical implementation right now, they are completely failing and are extremely disappointing.

Finally, you must be wondering, what is the right timing? A good idea is to lay out your plans in advance along the evolutionary path of web2AI.

When web2 AI moves towards edge computing, small models, and offline inference, the distributed infrastructure of crypto will have its place.

It's not about going head-to-head with cloud computing giants right now, but rather about solidly building the infrastructure for edge computing scheduling, data synchronization, and cross-device collaboration. By the time AI truly needs distributed architecture, crypto will already be ready.

When the memory bottleneck of LLMs, authentication, and multi-agent collaboration become necessities, the old expertise of blockchain in decentralized storage, identity management, and token incentives will come into play.

Currently, web2 AI is still immersed in the efficiency dividends of centralization. Issues like privacy, data incentives, and verifiable collaboration seem to be overlooked. However, when AI applications become truly complex and require cross-platform and cross-ecosystem collaboration, the opportunity for Crypto distributed solutions will arise.