“The agents aren’t just coming for your Twitter feed – they’re rewiring the entire digital economy.”

In this article, we’ll dive into how to spot the next AI Agent Alpha by fully understanding the methodology behind it. Remember: once everyone sees it, it’s no longer Alpha. The key is staying two steps ahead by applying this methodology, and you will succeed.

*Special thanks to @Delphi_Digital AI Reports and other pioneering researchers for their foundational work.

Golden Rule: Consensus 🖕

I will dump on you. You will dump on others. We are all here to make the fucking money so you should never really marry your bag. Your mission is to dump on a right time. In order to dump the shit to others, you will need to find a project with “consensus”. That means a high possibility you would find your exit liquidity. Building consensus is a huge topic and we are not covering all of it today.

How to make $$?

1. Predict an emerging trend (consensus) with conviction (you need knowledge to back up your beliefs)
2. Ape in potential tickers with enough legitimacy and innovation
3. Sell your bags to others when consensus is formed

Below article will focus on the hardest step: Prediction. If you have nothing in your head or some personal conviction into the trend, there’s no way to ape in potential tickers by yourself. Either you are fully depends on luck, or wait for someone’s call so the KOL can dump on you again. Let get started.

I. Three Waves of Agentic Evolution

While 2024 was defined by AI breakthroughs like OpenAI’s o1 and billion-dollar valuations, 2025 is shaping up to be the year artificial intelligence gets financialized. But spotting real value in this rapidly evolving landscape requires looking beyond the hype of “AI agents” and understanding the fundamental shifts happening beneath the surface.

Understanding where we are in the adoption cycle is crucial for identifying opportunities. The agentic economy is unfolding in three distinct phases:

• Wave 1: Human-to-Agent (Present)

We’re currently here – think simple chatbot interactions that you have seen in X (@aixbt_agent). These agents act primarily as research assistants and execution layers for human intent. While valuable, they don’t require revolutionary infrastructure changes. Now, most of the agents looks like a tailored chatgpt to give you assistance on simple research. These agents are not not very autonomous. They aren’t independently managing resources, taking on risk, or paying for other services.

• Wave 2: Agent-to-Human (Emerging)

Here’s where things get really interesting. AI agents are starting to handle everyday tasks on their own—like running trading strategies, optimizing home energy usage, or negotiating and paying your bills—all without you constantly checking in. While tools like Stripe’s Agent SDK can cover some of these scenarios, they also point to a bigger shift: instead of paying a flat monthly or annual fee, you’ll see more granular, pay-as-you-go pricing.

As agents take on more responsibility, they’ll need to cover the costs of computing power, per-query API fees, model inference, and more—whenever and however they need it. These small, on-demand transactions quickly expose the limits of today’s payment rails, which aren’t designed for real-time micro-payments. That’s exactly where crypto steps up, offering faster settlement, lower fees, and a more flexible approach that can handle these new demands far better than traditional systems.

Some on-chain examples could be:

1. Automated trading systems
2. Yield optimization
3. Portfolio rebalancing

• Wave 3: Agent-to-Agent / Collaborative (Future)

This is where the biggest opportunities lie. We’re seeing early experiments with agent-to-agent commerce through projects like Terminal of Truth and Zerebro, but the real potential goes far beyond social media tokens:

1. Resource Markets: Compute agents negotiating with storage agents for optimal data placement
2. Service Optimization: Database agents negotiating query optimization services with compute agents
3. Financial Services: Risk assessment agents trading insurance with coverage agents

This stage requires an infrastructure designed specifically for machine-to-machine interactions. The traditional monetary system, centered on human verification and control, falls short in an economy driven by autonomous entities. In contrast, stablecoins offer essential frameworks with their programmability, cross-border capabilities, rapid transaction settlements, and facilitation of micro-payments (yes, I’m bullish on stablecoin sector too)

II. Break Down the Web3 AI Stacks

Delphi Digital has made a pretty clear “Decentralized AI” stack classification. There are 6 focuses (I think with high potential) and each of them have some “sub-sector”. You can call them as narrative or innovative direction. I can gurantee each sub-sector will have one leader to hit 500M at least in upcoming months.

6 Focuses with a famous example

1. Applications - @aixbt_agent
2. Agent Enablers / Coordinations - @virtuals_io
3. Privacy - @PhalaNetwork
4. AI Training / Inferencing - @ritualnet
5. Compute - @ionet
6. Data - @ArweaveEco

Either Bookmark this article or save this picture, will be your future guidance when aping into a new narrative

There are many sub-sectors in each focuses so I’m not going to cover all of them here. However I expect there will be a “sequence of inflow” of money. This sequence will be highly depends on the market maturity and development stages. I make a visual below:

Using aixbt just because it’s cute. Not saying it’s will be the ultimate king of AI

Stage 1:

We have seen ton of agents launching everyday and most of them are chatbot / ChatGPT wrapper with very little actual usage, but it’s a funny meme. As we know a product being useful or not is totally irrelevant to its valuation, so most of the funny agents with enough room of imagination will get pumped .

Agent Enablers (launchpad) become a layer 1 for agents in this stage. Example: As agents generate revenue through audience interaction, these funds are used to buy back and burn tokens paired with $Virtual in liquidity pools. This creates a direct correlation between agent success and platform value, aligning incentives across the ecosystem.

The Box highlighted in red is the current market focus

Stage 2:

A narrative is not sexy anymore if there’s only chatbot on twitter shitposting. Therefore, we have seen people start blending in “privacy” concept (example: @aipool_tee & @sporedotfun). The good thing is most of the people do not know technical terms like TEE, FHE, ZKP. It suddenly made the agents application looks innovative even tho the agents may not actually implemented the actual TEE in it. However, all these are to improve the agents application and to give it more “utility”. Agents will soon tap into DeFi / wallet space.

We will see agents capable of swapping, bridging, optimizing trade routes, minimizing trade fees for you. And these agents will be integrated into wallet UI. The thing is these agent now need to compete on “technical” rather than “cult” in stage 1. Therefore, you can see some token like $BUZZ or $ACOLYT got pumped because they have a legit AI team / dev behind (even tho not comparable to Web2 AI expert).

You can soon expect that: “AI Agent wealth effect” attract a large number of top-tier web2 AI developers to the web3 space to make money, leading to the emergence of many strong AI projects. If I were you, you may actual check LinkedIn over Dexscreener more frequently (lol).

Stage 3:

These agents will finally mature and derive their core value from AI inferencing, data, and distributed compute. We will have TEE-based infrastructure for secure key management, specialized Data Availability (DA) layers to store and retrieve LLM context, on-chain Oracles for trustworthy data feeds, zkVM frameworks for verifiable execution, and chain abstraction solutions. This also means tapping into large-scale, trustless computing resources, while ensuring that interactions, data flows, and outputs remain verifiable and secure. At that point, advanced infrastructure like @ritualnet, @ionet, @StoryProtocol will shift from mere speculation to genuine necessities in driving next-generation AI innovation.

III. OK. So what am I aping now?

Let go back to this overview that we have previously seen:

We are heading into stage 2 now

I assume that we are going from stage 1 to 2 now. I would be more interested if Agents can actually provide real values “actively” to human (us). Not just about posting twitter or analyze the Price chart of a token (I can do that with GPT). By the definition of “actively”, I would assume the agents managing resources and making decisions autonomously on behalf of me. They can settle the transaction by themselves. Some more complicated application that I will personally invest:

• Automated trading systems
• Portfolio rebalancing
• Virtual Reality
• AI-powered smart contracts (prediction market arbitration)
• etc

(please dm me if you are actually building one, happy to support but make sure to have something workable first instead of a whitepaper)

The above direction could still be too board for most of you, including me. As refering to our golden rule of consensus, I observe that most consensus goes into following:

I. @shawmakesmagic . He is the ultimate AI cult caller in this AI supercycle. Just like @MustStopMurad in the memecoin supercycle. Don’t fade ai16z and whatever shaw / core ai16z memebrs is looking into. Whatever project he follows + shill would be a buy opportunity. P.S. BE AWARE of “ai16z parnters”, they are not really core memeber sometimes. I can call myself as a partner too.

II. Solana Hackathon Winner. The chain is pushing the projects for you. You need to be ahead of the market and select some high-quality projects. I did that shit for you and you can check my posts. I’m not insider so I can only judge those based on my personal experience. You should study by yourself too.

Link: https://docs.google.com/spreadsheets/d/e/2PACX-1vSUJDajao0_et_XkH6FvvfN1VLv6ndvBoVl1juEMD1WK_RRwP3RRruPQTteOiIiAxY4bM81PKQSPr2M/pubhtml

However, to actually profit from this narrative you will need tool to help you or you cannot ape in at a good timing. For example: @AgentiPy is in my high-quality list orginally with a token. Once it launched a token, it goes up to 40M and I personally cannot ape in quick enough too. You will need tool to monitor the project twitter and ape in when it detected a CA is posted. More tutorial coming soon.

III. AI Framework. Do you know why there are 500+ layers too and all dapps turn into appchain? It’s simply becasue of “higher valuation”. Infra is just giving you higher room of valuatioin. However, not every framework is equal. Either you ape into the market leader with size or you study tech and github to find a “good tech infra”. If you cannot deal with these shit, the best choice is still finding a good entry in $ai16z or $virtual. Don’t lose all your SOL / ETH into random AI scam projects.

IV. DeFi x AI. As mentioned above, I’m bullish on the upcoming DEFAI narrative. It fits my stage 2 asseumption which is Agent-to-Human. A good start to study is looking thruough below DeFi x AI projects and its segments.

Lastly, understanding these dynamics is essential for identifying genuine opportunities in the agentic economy. While the current landscape may seem dominated by simple social media agents, the real value lies in the infrastructure and frameworks that will enable the next generation of autonomous economic activity. I will keep holding my $ai16z and $Virtual for now.

If you find this article useful, I’d be grateful for a comment or a repost. I’m running a community-first experierment to distribute my 80% Kaito airdrop (if available) to the first 20K active followers. Your engagement is appreciated.

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