Here’s a bit of little bit of snark from developer John Crickett on X:
Software engineers: Context switching kills productiveness. Also software program engineers: I’m now managing 19 AI brokers and doing 1,800 commits a day.
Crickett’s quip lands completely as a result of it’s not truly a joke. It’s a preview of the following administration fad, whereby we change one unhealthy productiveness proxy (traces of code) with a good worse one (agent output), then act stunned when high quality collapses.
And sure, I do know, no one is doing 1,800 significant commits. But that’s the purpose. The metric is already being gamed, and brokers make gaming easy. If your group begins celebrating “commit velocity” within the agent period, you aren’t measuring productiveness. You are measuring how shortly your workforce can manufacture legal responsibility.
The nice promise of generative synthetic intelligence was that it might lastly clear our backlogs. Coding brokers would churn out boilerplate at superhuman speeds, and groups would lastly ship precisely what the enterprise needs. The actuality, as we settle into 2026, is way extra uncomfortable. Artificial intelligence will not be going to avoid wasting developer productiveness as a result of writing code was by no means the bottleneck in software program engineering. The true bottleneck is validation. Integration. Deep system understanding. Generating code with no rigorous validation framework will not be engineering. It is solely mass-producing technical debt.
So what do we alter?
Thinking appropriately about code
First, as I argued lately, we have to cease fascinated with code as an asset in isolation. Every single line of code is floor space that have to be secured, noticed, maintained, and stitched into every part round it. As such, making code cheaper to write down doesn’t scale back the entire quantity of labor however as an alternative will increase it as a result of you find yourself manufacturing extra legal responsibility per hour.
For years, we handled builders like extremely paid Jira ticket translators. The assumption was that you possibly can take a well-defined requirement, convert it to syntax, and ship it. Crickett rightfully factors out that if that is all you might be doing, then you might be completely replaceable. A machine can do fundamental translation, and a machine is completely joyful to do all of it day with out complaining.
What a machine can not do, nonetheless, is perceive crucial enterprise context. AI can not really feel the monetary value of a compliance mistake or take a look at a buyer workflow and instinctively acknowledge…
