The Coordination Tax
Sixty-seven percent.
That’s the share of knowledge workers who adopted AI tools in 2025 and were working more hours by year-end. Not fewer. UC Berkeley Labor Center, published earlier this year.
Read that again.
The entire productivity narrative around AI is built on an assumption nobody has verified: that AI tools reduce the total work, not just the time a single task takes.
They don’t.
Here’s what actually happens. An AI tool makes one task faster. The draft takes twenty seconds instead of twenty minutes. The analysis completes in two minutes instead of two hours.
But the task was never the bottleneck.
The bottleneck is everything that happens after the task: the review, the follow-up, the coordination, the decision, the loop that gets opened but never fully closed. AI accelerates task completion and leaves the coordination layer entirely untouched.
So you get faster tasks and the same overhead — except now the overhead compounds because tasks are completing faster. More output, same coordination infrastructure. More loops, same loop-closing capacity.
The math doesn’t work.
BCG published a parallel finding this year. 1,488 workers studied. Fourteen percent of heavy AI users report clinical-level cognitive overload: mental fog, slower decision-making, 39% more major errors, 39% higher intent to quit.
The cause, per the researchers: AI eliminates natural cognitive recovery time.
Tasks that used to take twenty minutes — with natural pauses, transitions, friction — now take twenty seconds. Workers move immediately to the next cognitively demanding task. The slack in the system, which used to function as recovery, is gone.
Harvard Business Review confirmed it separately: AI tool adoption is correlated with increased work intensity, not decreased workload.
This is not a “you’re using AI wrong” problem. It’s a systems design problem. When you increase throughput in one part of a system without redesigning the rest, pressure builds somewhere else.
The somewhere else is the coordination layer.
Here’s the pattern, stated plainly.
AI tools are generators. They produce: drafts, summaries, analyses, schedules, action items, code, responses. That generation is real value. But generation is not the end of the work — it’s the beginning of the loop.
Every generated output creates a decision requirement. Every decision requirement creates a coordination need. Every coordination need creates an open loop. And open loops don’t close themselves.
The Berkeley and BCG data are showing — without naming it this way — that AI has successfully automated the creation of work while leaving the management of work entirely to humans.
That’s why you have more AI and more hours. Not despite AI’s success. Because of it.
There’s a separate number worth noting here: according to research published this year, only 24% of organizations have full visibility into what their AI agents are doing when they talk to each other. Three out of four enterprises are operating agents they can’t fully see or coordinate.
This is the same pattern at the organizational level. Agents are generating. The coordination layer — who’s watching, what’s happening, what needs a human decision — is still manual, still incomplete, still the bottleneck.
This is the problem Deeplica is built to solve. Not: make tasks faster. That race is already running and every major platform is in it.
The problem is: close the loop. Track the coordination requirement that task completion creates. Know what’s waiting, what’s blocked, what needs a decision, and who needs to know what — across every tool, every channel, every workflow — automatically, without requiring a human to maintain the coordination layer by hand.
That’s not a productivity feature. That’s infrastructure.
The research is confirming what we’ve been building toward. The tools are producing. The coordination layer is still human. That gap is real, it’s measurable, and it’s getting worse as the tools get better.
More AI, more hours — until the coordination layer catches up.
