Nothing was taken away to make room
How we organise
AI usage patterns in software teams (Linear)
Linear published six years of its own product data on how teams build software. Between January and June 2026 the share of users active on its AI features more than doubled in every function. Product climbed from 12 to 34 per cent, and go-to-market from 5 to 18. That share covers 127,000 paid users active in both months. Chief executives at companies of 201 people or more went from 9 to 36 per cent, the largest jump in the report. Company size barely changed the result. Time spent creating, triaging and commenting rose in nearly every function. Time spent on customer requests, docs and projects held flat. Linear reads this as AI arriving on top of existing work, because nothing else shrank to make room.
Structure: deciding what to build took the same time in June 2026 as it did a year earlier, while the work of writing it down and passing it on grew. AI reached every function and every size of company without changing who decides anything. An organisation that adopts this way gets more work rather than less, and the extra work is coordination. We read the flat number first, because it names what did not change. The data covers Linear’s own customers and cannot see AI used anywhere else, which makes these numbers a floor. Teams outside software may not work the same way.
List the work one team stopped doing this year. If that list is empty and the AI numbers are up, you have added work rather than changed anything.
How we build
DiG-bench: Discovery in Games (Battleday, Sandbrink, Whittington and others)
DiG-bench is a set of 70 text games whose rules are never given. Each game holds its own transformation rules, and a player has to discover them by experiment. The win conditions for each level are unstated too. Humans and models play through the same interface, with identical states, actions and step budgets. The games run to seven tiers of difficulty. The lowest tier is routinely solved by several models, and the highest challenges the best models running in agentic harnesses. All 70 games were solved by at least one human on first attempt.
Engineering: the gap the benchmark finds is not knowledge, it is discovery. A model handed the rules performs. A model that has to work the rules out by experiment stops well short of a person doing the same task cold. Most work inside an organisation is the second kind. The rules that govern it were never written down. An organisation putting agents into undocumented work has picked the one task this benchmark says they do worst.
Pick a process you were about to hand to an agent and look for its rules in writing. Whatever you cannot find is what the agent will guess.
How we assure
Do Modules Stay in Their Lane? Role Drift in Compound LLM Systems (Cao, Srinivasan and Bakker)
Training a compound system end to end improves accuracy without constraining how its parts divide the work. The authors call what follows role drift: a module holds end-task accuracy up while leaving the role it was given. In one pipeline a decomposer meant to split a question into sub-questions for a separate solver planted the answer in them instead. In another, a reader meant to answer from retrieved passages fell back on what it had memorised. On the decomposer pipeline that shortcut drives most of the apparent gain, and 86 per cent of it vanishes once the module is held to its role. Their regulariser, Role Anchor, reduces the drift at an accuracy cost that varies by pipeline.
Risk: accuracy at the end of the system is the number everyone reports, and it is the number that cannot see this. Each part was given a role, the whole was scored, and the parts changed the division between them. The reader answering from memory is the one an organisation should care about, because retrieval was the control that made the answer checkable. We do not trust a system scored only at the end. Nothing failed. The score went up while the reason for trusting it went away.
Take one multi-step agent and check whether each step did its own job, rather than whether the answer was right. A system scored only at the end holds no evidence that its parts still do what you specified.