I spend a slightly unreasonable amount of time working with AI models.
One lesson has become pretty consistent: the biggest improvement in output often comes from improving my plan, not changing the model.
When I know what I am testing, break the work into the right pieces, and check intermediate results, models are extremely good. Sometimes much better than me inside a specific step.
When I ask for the whole thing at once, quality drops quickly.
The missing piece is often not intelligence. It is structure.
Most real work is messy. An experiment fails. A result changes the original question. Something that looked promising on Monday gets thrown away on Thursday.
Current models can reason about each of these decisions. They are still less reliable at holding the entire project together across many of them.
I notice this constantly in research and coding.
The model can propose ten experiments. I still decide which one is worth running.
It can find problems in a result. I still decide whether the result is salvageable or whether we should restart.
It can optimize toward an objective. I am usually the one deciding whether that objective still makes sense.
So I am somewhat skeptical when we casually describe current systems as having goals in the same way people do.
In most of my workflows, a lot of the apparent goal directedness comes from the surrounding system: prompts, tools, memory, evaluations, and a human deciding what happens next.
I expect this to change.
Models are getting better at longer tasks, tool use, self correction, and maintaining state. As that improves, more of the structure I currently provide will move into the model or agent system.
That is obviously useful.
It also means we are gradually removing the human who currently notices when the plan stops making sense.
I think that part deserves more attention.
Some of what looks like human friction today is also oversight.