When every team calls the same frontier API, the model improves for everyone at once and gives no one an edge. The fix is to own the loop that turns your own corrections into a better model.
Data used to be the moat.
For the last decade of machine learning, the winning formula at companies like Google and Instacart was proprietary data. Running a model produced more data, more data produced a better model, and a better model produced outcomes that generated still more data. The gap widened the longer a company operated.
Generative AI broke that pattern. Most teams now skip training and call a frontier model's API instead. The intelligence they buy is identical to what their competitors buy.
Everyone is renting the same model.
When intelligence is billed per token, nobody owns it. What remains to set one product apart is prompts, retrieval and interface design, and a competitor can copy each of those in weeks.
When an AI feature fails, teams have two levers. They switch to a pricier model or they patch the prompt. The feature itself never learns. It waits for the next release from OpenAI or Anthropic.
Meanwhile, every API call adds to the labs' own data advantage. Each new generalist release lifts every customer by the same amount, so the gain is a commodity rather than an edge.
Your team's corrections are the asset.
The lessons that should belong to you show up in everyday work:
A sales agent picks the wrong CRM action and a rep fixes it.
A support reply comes out sounding off, so a reviewer redrafts it.
A coding agent produces an overbuilt helper function, and an engineer requests a leaner one.
Each correction records how your business defines good work. Sending those signals to a shared frontier model gives them away. Capturing them lets you build a model that gets measurably better at your work the more work it does.
How the loop works.
Cost is usually where the conversation starts. Specialist models cost far less than frontier models, and many workflows do not need a generalist forever. The larger payoff is the loop itself:
1. Capture the work your AI systems already do.
2. Find the calls where the expensive model really made a difference.
3. Train or tune a lower-cost model to handle the repeatable parts.
4. Keep feedback flowing so the system keeps improving for you.
The technique at each step, whether prompt tuning, fine-tuning, reinforcement learning or distillation, matters less than running the cycle. Test, observe, measure, train when the evidence supports it, promote, and repeat. The full essay explains why the frontier labs are proof that this loop works.
Where Orchestra comes in.
Owning data was the game for a decade, and generative AI briefly limited it to a handful of labs. Orchestra exists to give every company that loop back, so edge cases, corrections and judgment build up inside your company instead of disappearing when a session ends.
Evaluate your workflows to see which parts of your AI work could start compounding.