AI is changing the fundamental unit of work
For most of modern history, value came from producing the thing itself. You wrote the memo, built the spreadsheet, designed the interface, analyzed the market, or wrote the code. Execution was the work.
Increasingly, execution is becoming automated. The new leverage comes from building the system that produces the output rather than producing the output directly. Our job is shifting from “build the thing” to “build the thing that builds the thing.”
That sounds abstract until you realize how quickly it changes daily decision-making.
Every knowledge worker now faces a constant prioritization problem: do you spend time doing the work manually, or do you stop and automate it? The difficulty is that the world does not pause while you redesign your workflow. Customers still need answers. Products still need to ship. Revenue still matters. You cannot spend six months building a perfect AI factory while ignoring the actual business.
At the same time, standing still is dangerous. Every month, one of the frontier labs releases a model, framework, or capability that invalidates part of last month’s “best practice.” Teams spend weeks refining workflows that are suddenly replaced by a newer model with better reasoning, larger context windows, lower cost, or native agentic capabilities. The pace of improvement is so high that over-investing in optimization can become its own trap.
This creates two failure modes.
Failure mode 1
Refusing to adapt
Teams continue operating manually while competitors gain leverage through automation, orchestration, and AI-assisted decision-making. Eventually the gap compounds.
Failure mode 2
Trapped in meta-work
People spend all their time evaluating models, rebuilding pipelines, redesigning prompts, experimenting with agents, and reorganizing workflows, but very little actual value gets created. They become operators of perpetual infrastructure projects instead of builders of meaningful outcomes.
Ironically, both groups lose for opposite reasons. One ignored the future. The other disappeared into it.
I think the most useful framework comes from product management’s idea of multiple horizons.
Strong product teams balance short-term stability, medium-term iteration, and long-term positioning simultaneously. AI adoption requires the same mindset.
Near term
Do the work that matters today
Sometimes the correct answer is simply execution. Businesses still run on completed tasks, shipped products, signed contracts, and solved customer problems. Not every process should be paused for optimization.
Mid term
Automate yesterday's work
Once a task becomes repetitive, it should begin migrating into infrastructure. The report becomes a template. The template becomes a workflow. The workflow becomes an agent. Over time, repeated labor turns into systems. This is where most real AI leverage will come from over the next several years — not magical autonomous intelligence, but systematic removal of recurring cognitive labor.
Long term
Question whether the work itself survives
This is the horizon most people underinvest in because it requires abandoning assumptions instead of improving them. Many workflows only exist because information used to be expensive, fragmented, slow, or difficult to process. AI changes those constraints. Some deliverables disappear. Some professions compress upward into judgment and coordination. Some industries reorganize entirely around new abstractions.
The long-term winners will not simply use AI to accelerate existing systems. They will rethink why those systems exist at all.
That is why this moment deserves more attention than most people are giving it.
This is not just another software cycle. It is a restructuring of how value is created inside organizations. The abstraction layer is moving upward. Humans are spending less time producing outputs directly and more time designing environments, systems, workflows, and constraints that generate outputs automatically.
The important skill is no longer just execution. It is deciding what should be automated, what should remain human, and what should disappear entirely.
That is a different kind of work than most people were trained for.
And it is probably worth your attention.