The internet has undergone profound shifts—from static HTML pages to dynamic social platforms to decentralized protocols. In parallel, Artificial Intelligence has evolved from rule-based automation to deep neural networks and now to intelligent agents with memory and reasoning.
But what’s most exciting?
Web and AI — are now intertwining and reshaping how we access, filter, and understand information. These two evolutions happened. And we can use the fruits of them now.
This article explores how AI is not just adapting to the next web; how it’s powering it; and how new architectures like Retrieval-Augmented Generation (RAG) make it possible to find and synthesize information faster than ever before.
Let’s briefly revisit the phases of the web:
Each generation of the web unlocks new possibilities. Each time AI evolves with it.
The Rise of AI That Thinks
In early Web2, AI was about predicting your next click (mostly in ads and sales).
Now? It's about answering your question even before you finish asking it, or automatically connecting data across platforms in real-time.
As AI agesnts built with technologies like vector databases, semantic search, and long-term memory, they enables systems to act more like researchers, not just responders.
A core technology behind this is RAG:
RAG allows an AI to search through external and internal knowledge (structured or unstructured) and then generate responses based on both the user query and the retrieved context.
In contrast to traditional AI pipelines for new era of web, RAG:
- Understands semantic meaning (not just keywords) - that helps to search quicker, show more precise results;
- Finds contextually relevant info across massive datasets - that range results differently than in Google, Yahoo or Bing
- Synthesizes personalized, high-quality answers - no need to go thru 5 first websites to find your answer or product
- Enables modular and reusable AI components across Web3/Web4 apps - this is about components or micro-frontends that will be used in responses to requester.
Let’s imagine a user interacting with a Web3 dashboard for DAOs and tokens. They ask
“What are the trending governance proposals in DeFi this week?”
A traditional system might return a list of links. This list requires filtering by hand, discovering and making research of each item.
A RAG-powered AI agent does this instead:
- Searches vectorized DAO forum content from IPFS or Arweave
- Retrieves proposals semantically similar to governance and DeFi
- Generates a summary using LLMs like GPT-4
Outputs an up-to-date answer:
There are 3 key proposals active this week in Aave, MakerDAO, and Curve, all focused on yield delegation and cross-chain governance…
The general idea is a simplicity of making analysis to get the final results. AI will do everything for you and you would be able to consume result. Isn't it cool?
Not that fast, rabbit...
The most problematic part here hides in which data will be consumed by AI; how frequently it will be updated; and how AI will prioritize or rank that data.
At first glance, this sounds like a technical detail. In reality it’s a fundamental risk for the next generation of intelligent systems.
What does this mean for users?
It means that if someone creates a new token that suddenly goes viral— even if it's a scam — AI may surface it as a top recommendation or trend. Reason is simple - because the data signals (mentions, volume, velocity) suggest it's important.
And here's the problem:
AI doesn’t understand truth. It understands patterns. That's it for now.
If the training data shows massive engagement, rapid trading, or a flood of social mentions, be sure the AI may interpret that as relevance or value. Even if the token is malicious, unverified, or manipulative, for AI in current iterration it's just enough.
This happens because:
RAG and vector search prioritize semantic relevance, not factual correctness.
Language models are non-opinionated unless explicitly tuned or filtered.
Attention = priority, unless you introduce trust-weighted signals.
So a scam with good marketing can exploit AI's data ingestion the same way it manipulates human psychology.
Moreover, it's not just about tokens in web3, it's about everything!!!
Why This Matters More in Web3
In Web2 scams are filtered by central gatekeepers - app stores, SEO penalties, community reporting (in social media, articles, etc.).
But in Web3, data is decentralized, fast-moving, and often unverifiable. AI agents working across these environments must:
Decide what to trust
Evaluate how much weight to give a source
Possibly cross-reference on-chain vs off-chain data
This becomes even more critical when these agents are acting on your behalf—recommending protocols, approving transactions, or giving financial summaries.
The Fix: Explainability and Signal Hygiene
To prevent AI from blindly promoting noise or scams, we must:
Introduce source ranking layers in vector databases (reputation, historical accuracy, verification).
Add metadata weighting to embeddings (e.g., verified contributor flag).
Use counter-signals (blocklists, anomaly detection) to detect hype vs trust.
Trust only verified Smart Contracts (that can be scanned and parsed to find even hidden scums)
Eventually, we’ll need AI agents that explain their reasoning:
This token is trending due to a large volume of posts in the last 12 hours, but it lacks verified smart contract audits and is flagged by 3 DAO reputational feeds.
Until then, users must remember that AI agents are only as good as the data it eats, and the signals they learn to trust.
What is the next stage?
We’re entering a phase where the web is no longer a place, but a conversation between you, your data, and intelligent agents that live across platforms.
These agents will help us navigate decentralized worlds. Also they will help to extract meaning from fragmented ecosystems and act as companions, advisors, and co-builders
But this is just the beginning... It requires more itterrations to become smarter and trusted.
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