Why does AI model selection matter in commercialization systems?
Different model choices affect cost, speed, quality, reliability, workflow and scalability.
project / connected work
MAS.Ai
A project view across the questions, things built and field notes that belong to the same work.
Problems
Can law firms detect demand before the buyer starts looking for legal help?
MAS.Ai is now delivered only to existing paying customers. Further development has stopped, and new development work is focused on Entriva.ai.
Built
MAS.Ai
MAS.Ai is now delivered only to existing paying customers. Further development has stopped.
Field notes
I am moving from consulting to autonomous commercialization
I have decided to stop further development of MAS.Ai and focus new development on Entriva.ai. MAS.Ai will continue to be delivered only to existing paying custo
Where will the money come from?
AI can already perform 80–90 per cent of the tasks involved in much of my own commercialization work.
The next task must exist before the current one closes
Today we received another completed research package from one of the people helping us test a distributed commercial operating model.
We gave everyone a workspace, but kept one commercial memory
We are preparing MAS.AI to meet a much larger market.
When the people building AI ask us to slow down
Over the last few days, some of the people closest to frontier AI have argued that development may need to slow down.
HAAH started to feel like an operating model
Today was one of those days where an experimental way of working started to look less like an experiment.
The extra round before the meeting
Tomorrow, we are meeting a law firm for a discovery conversation about MAS.Ai.
AI keeps fixing the margins because it can
I noticed something uncomfortable while working with AI on a long product build.
The work got better when the AIs stopped trying to agree
I have been building parts of a commercial intelligence system together with another team and another AI. What has surprised me is that the value does not prima
A safeguard turned out to be stronger than we expected
We wanted to verify that cross-tenant learning could not accidentally become approved global learning. The running system turned out to have no functioning appl
We tried to prove tenant isolation before tenants existed
We thought the next step in the learning architecture was to prove tenant isolation. Looking at the running system showed something more basic: there was no ope
A declined meeting does not necessarily mean the need was wrong
A declined meeting could mean wrong timing, wrong relationship, incumbent supplier, poor outreach, low priority or no need.
Model cost changed how we structured the work
AI usage increased without a proportional increase in useful progress. We changed the workflow from many small review loops toward larger coherent work packages
One opportunity should not become five observations
A single opportunity can produce several outcome records. If every event becomes a calibration observation, one real situation can create artificial evidence.
Signal volume became less interesting than signal convergence
More events did not necessarily make an account more interesting. Several independent observations pointing toward the same underlying change appeared more usef