The bottleneck was never the code

You see it elsewhere as well. There's now a cottage industry (with visible members like Ed Zitron) who have made a career out of creating and selling anti-AI content. At first they were complaining that AI lies constantly. As AI got better, they shifted to other talking points.

I think it can be easy to look at code as an asset, but fundamentally it is a liability. Some of the "bottlenecks" to new code are in place to make sure that the yield outweighs the increased liability. Agents that produce more code faster are producing more liability faster. Much of the excitement and much of the skepticism about coding agents is about whether the immediate increased productivity (new features) and even immediate yield (new products or new revenue) outweighs the increased long term liabilities. I'd say we won't find out for another 1-3 years, and of course that the answer will differ in different domains.

From this perspective, attempting to build these bottlenecks into the agentic workflow directly makes some sense. Supplying coding agents with additional context that values a coherent project vision and that pushes back against new features or unconstrained processes would be valuable.

Is this what the article is trying to get at? Is this attempting to make some agents essentially take on product management responsibilities, synthesizing as much as possible into a cohesive product vision and reminding the coding agents of that vision as strictly as possible? Should these agents review new proposals and new pull requests for "adherence to the full picture", whether you want to call this "context" or "vision" or something else?

I think these agents might do an exceptionally good job at synthesizing context and presenting a cohesive roadmap that appears, linguistically, to adhere to the team values and vision. But I'm doubtful that they can have the discernment that a quality manager or team can have. Rapidly and convincingly greenlighting a particular roadmap could do more harm than good.

1. Increased velocity makes rituals like daily standup and other comms relatively infrequent compared to how they used to be, so there are fewer touch points now. For example a daily standup might have been occuring several times while someone worked on one feature ticket, but now they can bang out multiple features a day plus some bug fixes, but still only have the daily touch point.

2. AI written code needs to be thought through and planned a lot more than human written, because the machine doesn't go through the same discovery/writing process that a human goes through. It looks superficially similar, but is subtlely and importantly different.

3. Without solid planning and requirements definitions, it's a lot easier for AI to go off the rails and do something you don't ultimately want. That wasn't true for humans writing code because they have a lot of project context knowledge that helps a great deal. AI obviously doesn't have this.

4. With the intense speed of devs now thanks to AI, it's far easier to step on each other and end up with at best merge conflicts, at worst significant deviation in solutions, and often major refactors/overhauls that can make the codebase feel foreign and confusing to devs. Most people have had the experience of stepping away from a project and coming back after a refactor had been done, and realizing that they don't know where basic things even are anymore. It can be unsettling and add a lot of friction.

5. AI can be pretty good (and very fast) at producing documentation and plans, so the "cost" of planning before coding is a lot lower now. That changes the equation of "what is the most important thing to spend my time on to iterate quickly".