Big models quietly swing the knife-the AI Agent middle layer is being cleared out in batches.
Some still wrap a ChatGPT shell or bolt a workflow onto a big model and call it an AI startup. Last month Anthropic released Claude Managed Agents-native multi-agent orchestration, managed deployment, tool calls all in one-and this should send chills down their backs. Sounds like a tech update, but look closely: it's a clear-out. The piece Anthropic steps in, another batch of Agent startups die puts it plainly: the selling points of low-code Agent platforms like Dify are perfectly covered by official features, and the orchestration value of LangChain and CrewAI has become optional. The thin middle layer living on "saving devs some engineering" suddenly finds its moat is just the upstream vendor's next changelog line.
This isn't the first time. In 2023 GPT-4 killed a batch of text-gen wrappers; in 2024 GPTs wiped out lightweight Agent platforms; in 2025 Operator took down browser automation. Now 2026: the Agent middle layer clears in batches. Predictions say 90% of AI Agent startups will exit in the next 6-12 months; only vertical scenarios survive.
Why Are You Always "Tying a Rope to a Lobster"?
YC's recent AI Startup School sent a signal: 90% of Agent projects simply don't run-landing execution is far more fatal than model capability. But many founders keep making the same mistake, mistaking "position misalignment" for a track.
"Tying a rope to a lobster" means what you do is essentially patching the big model's gaps. The platform lacks a translation interface, you build one; the platform hasn't done sync yet, you ship it. When the platform makes that feature a default entry next month, you won't even have the energy to cry. Sora shut down, Jasper's valuation recalibrated, Chegg's revenue plummeted-all these cases say the same thing: a middle layer without a moat has a lifespan of 60 to 90 days, exactly one big-model iteration cycle.
Deep-Sea Reefs vs Sand Castles
But don't despair yet. What dies is basically sand castles; some things the platform wave can't wash away.
What survives are species built on deep-sea reefs. They don't compete with big labs on model capability; they bite hard on what platforms can't do, dare not do, or won't do. Like a financial-risk Agent with proprietary risk data, a medical Agent holding a private case library, an industrial Agent connected to real-time production-line IoT. These are strong-scenario, strong-data, strong-outcome-delivery closed loops. The platform has general intelligence but not your private data, and won't bear misdiagnosis or compliance risk. That's your moat.
Non-technical regular people also have a path. Stop competing for tool-use rights; switch to a "foreman mindset": you don't need to lay bricks, but you must know why the wall goes here, what style the client wants, then direct AI to do it. Target the non-standard, communication-heavy dirty work-because AI can't handle complex human motives. Upgrade from a brick-mover to a foreman directing AI, one person acting as a whole team, earning on business-understanding ability.
Don't Ask What to Do-Ask Where You Stand
AI entrepreneurship has passed the brute-force model-tuning wild era; now it competes on ecological niche. Stop fantasizing about building a world-changing AI track; first ask yourself five questions: Is your product a must-pass entry for clients, or a forget-after-use toy? Is your service something the platform can't do, or just hasn't had time to do? If the platform bakes your thing in for free tomorrow, can you still collect money?
If you can't answer three of those, you're not building a track-you're in position misalignment. While there's still time, stop tying ropes to lobsters and go find your own deep-sea reef.