As Electi reaches it's 9th birthday, we have gone major shifts in the industry.
Last year marked the end of scarcity in code, now everything revolves around leveraging LLM's and AI to create something.
However building something of value in a controlled environment with such immence speed requires having absolute control of the process. Since 2017 we have worked with banks, regulators, insurers and shipping groups on the buying side of AI.
Follow one product from first pilot to scale inside organisations like these, and the same foundations appear in order.
1. It starts with a job that has an owner, a deadline and a budget, like closing the month-end books. Jensen Huang points to the same place: the biggest opportunities sit inside real-world problems. The best teams stay in that one segment until their first 100 users cannot work without them. Those users outweigh 100,000 who tried the demo once.
2. Then the buyer asks the first hard question: how do you know it is right? Evals answer it, so they come before features. They also make the architecture safe to change. Plan to swap models twice a year. With a test set that shows within a day whether the new model performs better on real tasks, a swap takes two weeks. Without one, it takes a quarter. Next to evals sits the trust layer: permissions, audit logs, human in the loop. Enterprise buyers pay for control.
3. Next comes money. Inference is COGS, so measure the cost of each completed task before you set a price. Then price the outcome. Seat pricing shrinks as agents replace headcount, which means revenue falls just as the product starts working. Outcome pricing holds up when both sides share a definition of done they can audit. Covey's advice applies: begin with the end in mind.
4. After that, the product starts to compound. Every completed task feeds a loop of usage data that only you hold. The same usage earns trust across your client base, and that trust becomes your distribution. A competitor can rebuild your product in 6 weeks. Earning those relationships takes years. Eric Ries put the discipline plainly: the only way to win is to learn faster than anyone else.
Read the journey again. Every stage is an operating discipline: focus, measurement, control, pricing, learning. Grove, Covey and Horowitz wrote that playbook decades before the first LLM, and it still applies.
build the loop <-> rent the model
