@FinanceYF5: Counterattack of the AI Application Layer 1/ Large model companies are being encroached upon from the other side. Cursor, Decagon, Harvey, Notion are all doing the same thing: moving from API to self-trained models. Not to save money, but to take back the flywheel.

🧵The Counterattack of the AI Application Layer

1/🧙Large model companies are being eaten from below

Cursor, Decagon, Harvey, Notion are all doing the same thing: moving from API to self-trained models.

It’s not to save money—it’s to take back the flywheel. 👇 https://t.co/HgPtoXuHM5

The Counterattack of the AI Application Layer

1/ Large model companies are being reverse-sniped

Cursor, Decagon, Harvey, Notion are all doing the same thing: moving from API to self-trained models.

It’s not to save money—it’s to take back the flywheel.

2/ Cursor’s Composer is a signal

The base uses the open-source Kimi, the magic is in their own post-training.

The feedback signal for whether code completions are good or bad only exists inside Cursor—no one else can get it.

3/ Training your own model is now industry consensus

Decagon, Abridge, OpenEvidence, Hippocratic, Cognition, Lovable, Harvey, Gamma are all self-training.

Every company building long-horizon agents is leaving the frontier API.

4/ Sutton’s bitter lesson no longer applies

Pretraining scaling wins only when the target is fixed.

But “how should this doctor’s medical record be written” is a moving target—only RL against real-world outcomes can approach it, and those outcomes exist only inside the product.

5/ The economics of specialized models already win

At the same capability budget, a specialized open-source model matches or exceeds frontier models on in-distribution agentic tasks.

The longer the task and the more tool calls, the bigger the gap—unit economics are nearly an order of magnitude better.

6/ Big players can’t catch up—it’s an organizational problem

First, frontier labs are structured as “one model serving everyone.” Second, specialization requires “multiple models for segmented customers.” Third, OpenAI just deprecated their fine-tuning API, admitting this isn’t their core business.

7/ The moat has changed

After software costs collapse, the only surviving barrier is the training signal no one else can see.

Every user interaction generates data, every training run produces a better model, and the flywheel spins inside the product—big players are outside the flywheel.

That’s all, original author @oneill_c

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