Smaller teams have more agency to move and usually team members with broader responsibility and understanding of the systems. Also possibly closer to stakeholders, so are already involved in specification creation and know where automation can add value. Add an AI agent and they can pick and choose where they can be most effective at a system level.
Bigger teams have clear boundaries that stop agency - blockers due to cross team dependencies, potentially no idea what stakeholders want, just piecemeal incremental change of a bigger system specified by someone else. If all they can do is automate that limited scope it's really just like faster typing.
> Cognitive Debt, Like Technical Debt, Must Be Repaid
In quite a few circumstances, cognitive debt doesn't entirely need to be repaid. I personally found with multiple projects that certain directions aren't the one I want to go in. But I only found it out after fully fleshing it out with Claude Code and then by using my own app realizing that certain things that I thought would work, they don't.
For example, I created library.aliceindataland.com (a narrative driven SQL course). After a while, I noticed that the grading scheme was off and it needed to be rewritten. The same goes for how I wanted to implement the cheatsheet, or lessons not following the standard format. Of course, I need to understand the new code but I don't need to understand the old code.
With other small forms of code, I just don't really need to know how things work because it's that simple. For example, every 5 minutes I track to which wifi network I'm connected with. It's mostly useful to simply know whether I went to the office that day or not. A python script retrieves the data and when I look at it, I can recognize that it's correct. But doing it this way is sure a lot faster than active recall.
At work, I've had similar things. At my previous job I created SEO and SEA tools for marketing experts. So I remember creating this whole app that gave experts insights into SEO things that Ahrefs and similar sites don't, as it was tailored to the data of the company I worked at. The feedback I basically got was: the data is great, the insights are necessary, but the way the app works is unusuable for us. I was a bit perplexed as I personally didn't find it that complicated. But I also know that I'm not the one using it. Then I created a second version and that was way more usable. The second version assumed a completely different front-end app and front-end app architecture though. All the cognitive debt of V1? No payback needed.
The reason that this is the case, as it seems to me, fall under a few categories:
1. Experimenting with technologies. If you have certain assumptions about how a technology works but it turns out you're wrong, or you learn through the process that an adjacent technology works way better, then you need to redo it. Back when coding by hand was such a thing, I had this with a collaborative drawing project called Doodledocs (2019). I didn't know if browsers supported pressure sensitivity and to what extent it was easy to implement. It required a few programming experiments.
2. It's a small and simple script, not much more to it.
3. Experimenting with usability. A lot of the time, we don't know how usable our app is. In my experience, this seems to be either because (1) it's a hobby project or (2) the UX people have been fired years ago. In these cases, more often than not, UX becomes an afterthought. But with LLMs, delivering a 95% fully working version is usually done within a week for a greenfield project. This 95% fully working version is an amazing high fidelity interaction prototype (95% no less). Once you do that for a few iterations, you then understand what you really need. Once you understand what you really need, then you can start repaying the cognitive debt.
I've found it's usually category 3, sometimes 2 and rarely 1.
The software is necessarily complex due to legislative requirements, and the corpus of documentation the AI has access to just doesn't seem to capture the complexities and subtleties of the system and its related platforms.
I can churn out ACs quicker, but if I just move on to the next thing as if they're 'done' then quality is going to decline sharply. I'm currently entirely re-writing the first set of ACs it generated because the base premise was off.
This is both a prompt engineering and an availability-of-enough-context documentation problem, but both of those have fairly long learning curve work. Not many places do knowledge management very well, and so the requisite base information just may not be complete enough, and one missing 'patch' can very much change a lot of contexts.
I did a live demo in front of the CPAs, using their documentation, and Claude asked clarification questions they hadn't thought of and exposed gaps in the old manual processes.
So the logical next step is to focus on Biological Immortality and short of that Digital Immortality. God speed everyone.
In that situation, coming in cold to a library that you haven't worked on before to make a change is the normal case, not "cognitive debt."
If you have common coding standards that all your libraries abide by, then it's much easier to dive into a new one.
Also, being able to ask an AI questions about an unfamiliar library might actually help?
I think it's great for writing tests and sanity checking changes but wouldn't let it write core driver code(I'm a systems programmer so YMMV). Maybe in a month I'll think differently.
That’s the neat trick kiddo, they won’t. Across the industry, the messaging is clear: use AI and be more productive. Management is salivating at the idea of getting rid of people and keeping a higher share of profits for themselves. Most ICs I talk to are increasingly expressing the feeling of burnout, fear of losing jobs and resentment that AI is being pushed the way it is being pushed. I have more than a few conversations where people have clearly expressed that they are mostly focused on keeping their jobs. They don’t care about cognitive debt and some are looking forward to the time when the debt comes due.
It is depressing, but it is the reality.
Of course, we have had compilers and tooling, but those are the pencil and drafting board of the draftsperson. An ecosystem of packages, dependencies and APIs has evolved, but those are often just spells the software magician invokes after reading the spellbook^H^H^H^H^H^H^H^H^H stackoverflow^H^H^H^H^H^H^H^H^H^H^H^H^H API documentation.
We are going to need to build a new set of boundaries and abstractions with new handover protocols to manage this mess.
Lack of documentation, failed onboarding, poor architectural understanding, missing tests, review fatigue — if all of these are simply grouped together as “cognitive debt,” isn’t that just a failure to build a proper workflow?
The scope is too broad. It reminds me of Stepanov, the creator of the STL, saying that if everything is an object, then nothing is.
When an abstraction tries to cover too many things, that abstraction inevitably fails.
The way AI specifically amplifies this problem is through the difference between direct work and indirect work. The core issue is that “it works” can easily create the illusion that “I understand it.”
Another thing I felt while reading this essay is that it almost seems to go against the direction of modern software engineering. Once software grows beyond a certain size, it is already impossible for anyone except perhaps the original designer to understand the entire system. The goal is not for everyone to understand everything.
The real goal is to make local changes safely, and to ensure that the system keeps running without major disruption when one replaceable part — including a person — leaves.
At this point, many things being described in the industry as “cognitive debt” look to me like rhetorical tools for selling essays.
Reading this, I even wondered: if I write about trendy terms like cognitive debt or spec-driven development on my own blog, will people pay more attention?
To be honest, spec driven development has a similar issue. When you go from a specification down into implementation, information loss is inevitable. LLMs cannot fully solve that. In the end, a human supervisor still has to iterate several times and tune the result precisely. The real question should be: how far down should the specification go? In other words, at what local scope does it become faster for a human programmer to modify the code directly than to keep steering the AI-generated code?
But that discussion is often missing.
As people sometimes say, “when you start talking about Agile, it stops being agile.” In the same way, I think the “cognitive debt” frame may be a flawed abstraction of the current phenomenon.
The moment a living practice is nominalized, packaged, and turned into a consulting product, it loses its original dynamism and context-dependence, becoming a dead template.
It puts various discomforts that emerged after AI adoption — review burden, lack of understanding, fatigue — into a single box.
Then it attaches the economic metaphor of “debt” to emphasize the seriousness of the problem, and subtly injects the normative idea that “this must eventually be repaid.”
Now we all know horrible mangers who didn't keep up to date nor used their thinking. This will happen with AI useage too. What is more we are expecting people who are engineers to have a manager's mindset (by managing AI agents, products requirements, etc). Many engineers are horrible at this and have no desires or ability to become a manager. This is why they went to engineering in the first place.
The funny part is that these are the same people who are upset that these folks up the food chain "do nothing".
That just sounds like everyone is going to be management. Blindly setting goals and demanding features of a black box, formerly the development team, soon to be 'AI' agents.
gdulli•1h ago
The ability to generate code has seemingly transposed what people think of as a "high-performing team" from one that produces quality to one that produces quantity. With the short term gains obviously increasing long term technical debt.