CONTENTS · OWNER-CREATED WRITING
The AI Productivity Paradox
Why more parallel AI work can increase throughput while extending completion and supervision time.
CORE ARGUMENT
Generated output is not completed work. Verification queues, context switching, and integration become the new bottleneck.
I once wrote that AI doesn't make us dumber — we misuse it by offloading all of our thinking to it.
Today I'll tell you something that sounds contradictory: my work finishes slower because of AI.
Hold that thought — both claims share the same root cause, and I will show you what that root is.
What my work actually looks like
I run 4–5 AI-assisted tasks in parallel across wildly different domains, while building shared tooling (agent skills, sql-context-pack) alongside. Each project runs the same loop: Prompt → Command → Wait.
- Task A → Prompt → Command → Wait (Project A)
- Task C → Prompt → Command → Wait (Project C)
- Task B → Prompt → Command → Wait (Project B)
It looks like beautiful parallelism. In reality there are two hidden costs.
Cost #1: The Human Brain
During each Wait gap, we don't rest; we context-switch into another project, while anxiously polling every agent for blockers and decisions. That's the Supervision Tax — paid in attention. The more agents you run, the higher the bill.
Cost #2: Shared Resources
Parallelism is real, but tokens and throughput are capped and shared. Every task finishes at roughly the same time; the speed gain gets flattened.
Net result
Throughput goes up, but each piece of work finishes later. Projects start Very Fast, then visibly drag by mid-project.
Thinking itself isn't slower — it loses effectiveness from multitasking across wildly different contexts; back when we produced one task at a time, we never dropped context like this. And it's all compounded by scattered governance, data, context, and boundaries — no centralized store, no AI-first infrastructure.
Takeaway
Doing more with AI doesn't mean letting scope sprawl. Do the opposite: lock scope and define narrow, explicit boundaries — because the thinking-heavy work is still yours, and it's the most exhausting part.
Next part: why this isn't AI's fault — it's infrastructure born Human-First.
EVIDENCE NOTE
Claim boundary
This page renders the original article source from Information while preserving its reasoning and limitations. It does not claim every pattern is used in production and does not invent metrics beyond the source.
