The first wave of AI productivity gains came from one place: accelerating execution against known work.
Every team has a backlog: bugs nobody has time for, refactors that keep getting punted, integrations tagged for “next quarter” for three quarters in a row. Output is increasing because the constraint is hands, not ideas. Some of the work has been queued for years. Some of it has been waiting long enough to lose its value entirely.
Backlogs don’t empty. Business partners keep generating new work: new products, regulatory shifts, competitive pressure all refill the queue. What changes is the relationship between arrival rate and execution rate. When AI-assisted teams can work through new work faster than it arrives, the pump that ran continuously to stay ahead of the flood now runs intermittently. The flow didn’t change. The pump’s capacity did.
Once execution stops being the bottleneck, what’s missing becomes obvious. Most backlogs are incremental: another column on the report, another tooltip, another preference toggle. None of them ask whether the underlying product is still solving what the customer actually needs. Bolted-on parts let teams defer that question for years. AI lets teams bolt on parts faster than ever, which means the deferral continues faster too—until something breaks.
What the next phase looks like in practice isn’t subtle. Teams running forced ideation under deadlines that weren’t there before. Customer research compressed into weeks instead of quarters. Strategic bets made with less data because waiting costs more than guessing wrong. Cross-functional debates over product direction that used to happen once a year, now happening monthly.
There’s a name for this in energy: fracking. When the easy reservoirs are tapped, you don’t stop drilling: you switch to harder techniques that extract under pressure. The gains come slower, cost more, and produce disruption the easy phase didn’t. You don’t have to like fracking for oil or for ideas to accept the prediction. Extracting novel ideas under competitive pressure is coming whether we like it or not, and like its energy counterpart, it’ll be aggressive and produce consequences nowhere near where we expect.
Two responses are possible from here. One treats AI as a way to do the same work with fewer hands, with productivity gains absorbed into cost reduction. The other treats it as a way to do more ambitious work with the same hands, with productivity gains channeled into work that wasn’t possible before. Both are valid choices, but competitive pressure will favor the more ambitious one.
The first wave was clearing the queue. The second is harder, costlier, and unevenly distributed. The companies set up for it will pull ahead. The companies hoping it’ll go away will spend the next year arguing it isn’t real.