One foot in the AI-forward world, one foot in the before times. Most engineering organizations are stuck somewhere between the two, and getting them unstuck is the job.
A specific problem with an outcome we agree on before it starts: a review process that is not holding up, a stalled adoption effort, an assessment before a decision gets made.
Advisory retainerStanding time each month for the work that does not end: sitting with leadership as decisions come up, coaching managers, keeping a change from unwinding after the first quarter.
In the not-so-distant future my ideal day working in a software team is jumping on a recorded call for 2-4 hours and discussing everything under the sun: product direction, research spikes, architecture, designs, prototypes, business strategy. Everything.
People keep saying one model or another is really good at long-running tasks, and I still don't fully get what they mean. If you are running an agent for hours and hours, a single context window explodes. So this has to be a harness, right?
There is no written artifact I can ask for anymore that proves the person responsible actually understands the requirement. Once a model can generate every asynchronous artifact, the artifact no longer proves anything about the human attached to it.
A senior engineer opens a PR from a more junior teammate and finds code that does the right thing the wrong way. They know exactly what is wrong, write the review comment, and then have to decide whether it is worth writing again next week.
Every organization operates at two speeds. There is a slow-thinking layer where you figure out what to build, and a fast-thinking layer where you execute.
I have spent my career inside engineering organizations rather than around them, which means I show up as a working operator, not a slide deck. I work with organizations that know they are behind on AI and are not sure what to do about it, and I start from where they actually are rather than where the hype cycle says they should be.