Rethinking the AI Agent Startup Model: Attention Is Not Everything—Real Demand Is Key
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Rethinking the AI Agent Startup Model: Attention Is Not Everything—Real Demand Is Key
To build products with lasting value, not for the next 6 months, but for the next 6 years.
Navigating Web3 tides with focused insightsAuthor: 0xJeff
Translation: Luffy, Foresight News
The crypto market has experienced repeated pullbacks, with liquidity gradually drying up. Recently launched AI agent projects deemed "successful" have reached a maximum market cap (MC) of around $10 million. Here, "success" means the project has achieved product-market fit (PMF), delivers value to real users, and has already generated (or is about to generate) revenue.
This stands in stark contrast to 3–4 months ago, when successful AI agent projects could reach market caps exceeding $100 million—especially those positioning themselves as “agent + framework/launchpad token.” For example, $AVA, a 3D agent, captures value through its audiovisual layer from its own launchpad and supported projects.
Old Model: Agents as Frameworks
The previous model involved launching an agent to demonstrate functionality, attract developers who wanted to build their own agents, and require them to hold/burn/use the project’s token to access the framework. What was the problem? Crypto Twitter overvalued framework tokens, while these “framework agents” were largely undifferentiated and similar. In most cases, they lacked actual products, merely hyping on Twitter to drive up token prices.
First-generation AI agent products were primarily conversational agents. This was unique to crypto, where we place greater emphasis on community building—similar to founder-led marketing (where founders gain attention through promotion). Having agents promote projects on Twitter initially seemed effective. When this model emerged in November 2024, it worked well. But now, there are 420,690 agents constantly spamming the world with simplistic, repetitive content—and frankly, they’re annoying.
New Model: Agents as a Business
The old model has been ruthlessly eliminated by the market. If you're building an AI agent project today, here's how you should think:
Launching an agent means operating a startup and managing up to three products: the core product, the token, and the agent.
1. Core Product (Real Business)
Your core product should solve real problems—not just be a conversational agent—but a genuine product that delivers user value.
Examples:
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A prediction model that increases betting accuracy, helping users win more in sports betting (e.g., @AskBillyBets).
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A crypto asset prediction model that optimizes trades, minimizes impermanent loss, and maximizes returns for liquidity providers (LPs) (e.g., @Cod3xOrg, @gizatechxyz, @Almanak__).
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An AI agent research search engine aggregating insights from top-tier sources like Cookie, Kaito, Nansen, Messari, Aixbt, CoinGecko (CG), Dexscreener, and Bubblemaps to aid investment decisions (no team has successfully done this yet—we need a Perplexity-like product for AI agents).
Building the core product should be every team’s priority before launching a token. You must ensure real market demand and willingness to pay. Otherwise, you’ll enter crypto’s “death spiral,” which can be worse than what traditional startups face:
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High operational costs.
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Cost of using token incentives to acquire customers.
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Token price crash → reputational damage → no one cares about your project.
If your token price drops significantly, it becomes a curse. In this space, most people won’t care about your project regardless of progress or core product strength.
Don’t rely on token incentives. Focus on attracting customers through the product and design a sustainable business model balancing growth and revenue.
@KaitoAI’s approach is an excellent case study:
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They built an enterprise-grade product: a social/emotional/narrative-focused crypto search engine, charging users, projects, and ecosystems for real value.
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They launched the Mindshare Dashboard, becoming the standard tool for tracking narratives and trends.
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They introduced the Yapper leaderboard, encouraging KOLs to organically share it as a status symbol.
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They incentivized Twitter engagement with tangible rewards via NFT whitelists and $KAITO token airdrops.
While Kaito’s model is hard to replicate, it teaches us: achieve product-market fit first, generate revenue, and create excitement before launching a token. Once you have traction (hype) and income, launching a token can elevate the project further.
Communication also matters. Many projects have great products but poor communication. If no one knows what you’re doing, no one will care.
2. Token (Coordination Tool)
We’ve shifted from the “VC token” model to the “fair launch” model favoring high circulating supply and low fully diluted valuation (FDV) tokens. But fair launches aren’t truly fair—every token distribution strategy has trade-offs.
If you launch your agent project’s token with high circulating supply and low FDV, you won’t raise funds from VCs or angel investors (due to low valuation). However, you can use the token as a marketing tool to boost visibility.
Many teams launch two tokens:
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Agent token: for boosting visibility.
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Ecosystem token: to raise funds from VCs and angels at a higher valuation.
But this creates misaligned expectations. The community expects airdrops, and when the ecosystem token launches, capital flows from the agent token to the ecosystem token, causing the agent token’s price to crash.
Managing the core product, agent token, and ecosystem token—while ensuring each accumulates value—is extremely complex and difficult.
Ideally, one token should capture all value from the core product. Historically, projects that generate revenue and recycle it back into the token (via buybacks or yield distribution) survive long-term.
The token should complement the core product—not be essential to it.
3. Agent (Supplementary Product)
Here, “agent” refers to conversational agents built using frameworks like ElizaOS, G.A.M.E, ARC, or Pippin.
While these agents integrate on-chain and off-chain functions, they should supplement the core product.
Agents should enhance the core product by transforming the user funnel:
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Instead of users seeking out your product, let the agent deliver it to them.
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This could mean directly showcasing the product via text or video using agents on Twitter.
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Using the agent as an AI assistant to change user interaction (similar to ChatGPT’s abstraction model).
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The agent itself acts as the interface, executing tasks in the background.
There are exceptions. Aixbt, for example, provides real-time social and sentiment insights from Twitter, allowing users to access premium signals earlier than others. By consistently delivering quality information, Aixbt became the top KOL in crypto Twitter, demonstrating terminal-level capability. In this case, the agent itself is the product.
However, this is extremely hard to replicate. Most teams should first focus on strengthening their core product.
Cookie DAO is an excellent product-first case study:
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Started with a free AI agent dashboard to acquire users.
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Moved to a freemium model—locking advanced features behind COOKIE token ownership.
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Monetized by offering APIs to projects and agents.
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Launched Agent Cookie to push insights directly to Twitter.
Summary
Between 2020 and 2021, you needed Solidity skills to launch a token. Now, platforms like Pump.fun make tokenizing anything effortless.
This shift changed mindsets—people no longer focus on building real products but jump straight to launching tokens. It’s like “garbage in, garbage out,” with capital flowing from one junk project to another.
We need to change this.
To build sustainable projects, treat agent projects as startups. Don’t just chase crypto Twitter attention or funding from VCs and angels—build products with lasting value, not for the next 6 months, but for the next 6 years.
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