A team I talked to recently was proud of a number: forty-one internal tools shipped this year. Genuinely impressive throughput — agents, integrations, a couple of slick little apps. I asked how many of the last ten were still in use. The room did that thing rooms do now. Nobody knew, and the not-knowing was the answer.
Last week I argued that AI didn’t kill Agile — it moved the constraint: when building got cheap, the bottleneck slid downstream from can we build it? to can anyone actually absorb it? I made the case that Jobs to be Done is how you decide what to point all that cheap capability at. But I left a thread hanging on purpose. Knowing which job to chase tells you where to aim. It says nothing about how fast an organization can actually take the hit.
That second number — the rate — is the whole game now. And a guy in a 1984 business novel already told us how it works.
Goldratt’s uncomfortable little rule
Eliyahu Goldratt’s Theory of Constraints is almost annoyingly simple. Any system has exactly one constraint at a time — one step that sets the pace for the whole line. The throughput of the entire system is the throughput of that one step. And here’s the part people hate: improving anything that isn’t the constraint does nothing for the system. It just feels like progress.
For twenty years, engineering was the constraint, so every productivity gain there was real — it moved the whole line. That’s the muscle memory we’re all still running on. Make the developers faster, ship more, win.
Then we made building cheap, and the constraint moved. It’s now sitting squarely on the organization’s ability to absorb change. Which means most of the effort we’re still pouring into building faster is, in Goldratt’s terms, optimizing a non-constraint. It feels like progress. It produces a graveyard.
Building faster just grows the pile
Here’s the mechanism, because it’s the part that matters. In a factory, when you run a non-bottleneck machine flat out, you don’t get more finished product. You get inventory — half-built stuff stacking up in front of the slow step, going stale, tying up cash, hiding problems.
Swap “inventory” for “unadopted capability” and you’ve described every AI initiative graveyard I’ve ever seen. The forty-one tools. The pilot that never rolled out. The copilot with twelve daily users out of four thousand seats. That pile isn’t evidence of productivity — it’s work-in-progress stacked in front of the constraint, quietly rotting. Goldratt’s line was that an hour saved at a non-constraint is a mirage. A tool shipped past your absorption limit is worse: it’s a mirage with a maintenance cost.
The move nobody wants to make
Theory of Constraints is blunt about what comes next. Once you’ve found the constraint, you subordinate everything else to it. Every other step runs at the constraint’s pace, not its own — deliberately, even when it could go faster.
Translated, that’s a genuinely unpopular instruction: stop pushing capability into the organization faster than the organization can absorb it. Cap how much change is in flight at once — a WIP limit, but for transformation instead of tickets. Let the edge pull the next capability when it has digested the last one, instead of a central team pushing a firehose just because the firehose is cheap now.
This cuts against every instinct we built during the scarcity years, when the cardinal sin was an idle engineer. The idle engineer isn’t the expensive thing anymore. The half-absorbed change is.
Adoption has a speed limit, and it’s human
Why can’t you just turn the rate up? Because the constraint is people changing how they work, and Everett Rogers mapped that curve sixty years ago. A new way of working spreads through a population at a rate — innovators first, then early adopters, then the majority dragged across one reluctant cohort at a time. You can grease it. You cannot skip it. Push harder than the curve allows and you don’t accelerate adoption; you manufacture resistance, which is negative progress.
That’s the thing a dashboard full of shipped-feature counts will never show you. The capability side is now effectively infinite. The human side moves at the speed of trust, habit, and how much disruption a team can hold before it stops doing its actual job.
You can raise the ceiling — slowly
The good news, such as it is: the constraint isn’t fixed. Goldratt’s last step is to elevate it — invest until it’s no longer the bottleneck. But absorptive capacity, the way Cohen and Levinthal described it, is not something you buy in a quarter. An organization’s ability to take in something new depends on what it already knows and how well it’s wired to spread that knowledge around. It compounds. The teams that can absorb fast in 2026 are the ones that built the muscle in 2024.
So elevating the constraint is real work, and most of it is unglamorous: putting engineers at the edge to lower the anxiety and break the habit — the forces I wrote about last time — building the connective tissue that lets one team’s adoption teach the next, and treating can we absorb this? as a capability you invest in rather than a thing you assume. That’s the actual leverage point now. Not another model. The metabolic rate.
Where this leaves you
The first post was about finding the bottleneck. This one is about the discipline that finding it demands: stop optimizing the thing that is no longer the constraint. Building was the hard part for so long that we’ve mistaken it for the point. It isn’t anymore.
The teams that win the next few years won’t be the ones generating the most capability. They’ll be the ones who matched their build rate to their absorption rate, defended that limit when it felt like leaving speed on the table, and spent their real energy raising the ceiling one compounding quarter at a time.
Goldratt would have found the whole thing familiar. We just gave the bottleneck a new address.
Further reading
- Goldratt, Eliyahu M., and Jeff Cox. The Goal: A Process of Ongoing Improvement. North River Press, 1984. — the original Theory of Constraints, delivered as a factory novel.
- Rogers, Everett M. Diffusion of Innovations. Free Press, 1962 (5th ed., 2003). — the adoption curve and how new practices actually spread through a population.
- Cohen, Wesley M., and Daniel A. Levinthal. “Absorptive Capacity: A New Perspective on Learning and Innovation.” Administrative Science Quarterly 35, no. 1 (1990): 128–152. — why an organization’s ability to absorb the new depends on what it already knows.
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