When the Board Asks About AI: A Practical Answer From Atlassian
Notes from the teams shipping code execution to real users.
There is a version of this story that is mostly hype. There is another version, the one we are interested in, that is mostly engineering.
The cost curve matters here. GPT-5.1 is roughly an order of magnitude cheaper per token than the equivalent model 18 months ago, and that changes which problems are worth automating at all.
Inside Duolingo, the rollout looked less like a moonshot and more like a slow migration. A pilot, a champion, a quiet expansion, a budget line.
What DeepSeek actually shipped with Qwen 3 is less a single capability and more a cluster of small, compounding improvements — the kind that only show up when you put a real workflow on top.
Eval harnesses, once an afterthought, are becoming the most important piece of code in many AI projects. HubSpot's team treats theirs the way an SRE team treats a runbook.
What xAI actually shipped with Mistral Large 3 is less a single capability and more a cluster of small, compounding improvements — the kind that only show up when you put a real workflow on top.
None of this guarantees a clean story. Google DeepMind could ship a model next month that rearranges the assumptions in this piece. But the direction of travel, for now, is clear enough to plan around.