nobody can agree how many ai models shipped this month.

the condition is a market that cannot count itself. llm-stats, benchlm and digitalapplied all publish dated release ledgers. for 1 to 14 september 2026 they report six, seven and sixteen releases. two date gpt-6 astra to the third and one to the fourth, and each lists models the others do not carry at all. these are not opinion pages, they are the trackers people cite when they tell you the pace is accelerating. the cost is that a company waiting for clarity before it acts is waiting for something that does not exist, and the wait is not neutral: every quarter spent evaluating is a quarter a competitor spent shipping something mediocre and learning from it. the evaluation is the delay, the delay is the decision, and it is being taken by default rather than by anyone in the room.

the fake fix is a longer evaluation, a bigger matrix, another vendor bake-off, all of which price a field that will have moved twice before the document is signed off. stop evaluating the field and evaluate one workflow: take the handoff that hurts most, put something on it this month, and measure the handoff rather than the model. the model will be replaced twice before your process changes once, which is the entire argument for fixing the process first.

do this: pick one broken handoff and fix it this month. not this: wait for the landscape to settle, because it does not.