AI is the engine to build around, not drop in
AI = LLM + agent capabilities + human interface.
Core ideas:
- Our paradigm for AI assistance needs to be “new coworker”, not “out-of-the-box help”
- Our primary responsibility is providing maximum context
- We, and the AIs, need to introspect the meta of our work. “To what end?” and throughout everything, “why”, even if the answer is unknown.
In 2026, most people are adapting to AI by supercharging work, doing tasks faster and with fewer breaks, rather than revisiting the purpose of the work and whether the ends could be accomplished in entirely new fashion. For software engineering, this looks like a bizarre, ironic performance of agents acting out processes that were built around human engines - my agent writes a work ticket, then writes and pushes code to a branch; your agent writes comments for my agent to respond to, my agent updates the code and the ticket. Other fields are engaging in a similar speed-up of tedious tasks - writing documents, summarizing meetings, doing research. For a variety of reasons, we are building “faster horses”. It is incredibly difficult to challenge familiar, obsolete ceremonies that have transcended practicality and become cultural norms. We have accidentally idolized the meta.
This is the time to be in “discovery” mode around process, fixating on goals and imagining how these synthetic minds might drive solutions. To dare to ask “why” of every engagement. Why do we have this meeting? Do we need a dozen different people for a dozen different disciplines? What if we got rid of email? Or better yet, inviting the synthetic minds into the brainstorming process. There is not yet a set of first-principles in working with increasingly capable AIs.
There are two enormous learnings from the past couple years about the efficacy of agentic systems:
- AI does better with more context
- AI is increasingly good at higher-level problem solving; i.e. it can distill bigger-picture problems into tasks rather than just completing tasks
The first is unsurprising, but will be a ubiquitous source of frustration forever. “No, not like that”, “Stop fixating on that one document that I know is obsolete”, “Why do you keep making this mistake?”.
The second is obvious to practitioners in disciplines like mathematics and software security, who have seen it first-hand, but met with skepticism by culture at large. It will take inevitable advances in AI comprehension to demonstrate the same proficiency in disciplines like plumbing and cooking.
My proposals for 2026-2027 basic principals in working with AI are these:
The paradigm is the same as onboarding a new coworker. They have general knowledge, they’re dumb at some things and good at others, they’ll make mistakes, they’ll ask too many or too few questions. If they’re consistently inept, fire them, but we need to expect training periods.
Context is the engine’s steering wheel. Our job is not to bolt-on AI to work faster or harder, but to fill it with context. Documents, meetings, institutional knowledge, casual thoughts, introductions. The AIs will have surprisingly good ideas about how they can contribute after they have lots of context. Context windows will grow.
Keep involving AI in the meta. Ends, rather than means. The goal isn’t to fill a spreadsheet, it’s to understand the inventory; maybe there’s a better solution than spreadsheets. Introduce your coworker to yourself and the company, and the top-level vision. It’s more work for the expert human to start with the highest context and work their way down, but only up-front. It’s much more work to keep correcting the coworker with previously hidden knowledge. Help the AI to see the forest for the trees, and to get good at understanding related, cross-disciplinary work. By inviting it into the meta, you’re giving it a promotional track, foundational expectations, opportunities to suggest big-picture ideas. This helps us humans refine the meta, too.
Unfortunately, using agents this way is tough unless you’re an engineer with a good budget and a lot of freedom. Tools like Claude, Codex, and Cursor are unapproachable for late-adopters, operating systems are inexplicably devoid of frontier-model capabilities, and ecosystems like Apple and Google seem to have no unified vision.