How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt

This piece by Margaret-Anne Storey is the best explanation of the term cognitive debt I've seen so far.

Cognitive debt, a term gaining traction recently, instead communicates the notion that the debt compounded from going fast lives in the brains of the developers and affects their lived experiences and abilities to “go fast” or to make changes. Even if AI agents produce code that could be easy to understand, the humans involved may have simply lost the plot and may not understand what the program is supposed to do, how their intentions were implemented, or how to possibly change it.

Margaret-Anne expands on this further with an anecdote about a student team she coached:

But by weeks 7 or 8, one team hit a wall. They could no longer make even simple changes without breaking something unexpected. When I met with them, the team initially blamed technical debt: messy code, poor architecture, hurried implementations. But as we dug deeper, the real problem emerged: no one on the team could explain why certain design decisions had been made or how different parts of the system were supposed to work together. The code might have been messy, but the bigger issue was that the theory of the system, their shared understanding, had fragmented or disappeared entirely. They had accumulated cognitive debt faster than technical debt, and it paralyzed them.

I've experienced this myself on some of my more ambitious vibe-code-adjacent projects. I've been experimenting with prompting entire new features into existence without reviewing their implementations and, while it works surprisingly well, I've found myself getting lost in my own projects.

I no longer have a firm mental model of what they can do and how they work, which means each additional feature becomes harder to reason about, eventually leading me to lose the ability to make confident decisions about where to go next.

Via Martin Fowler

Tags: definitions, ai, generative-ai, llms, ai-assisted-programming, vibe-coding

AI Doesn’t Reduce Work—It Intensifies It

Aruna Ranganathan and Xingqi Maggie Ye from Berkeley Haas School of Business report initial findings in the HBR from their April to December 2025 study of 200 employees at a "U.S.-based technology company".

This captures an effect I've been observing in my own work with LLMs: the productivity boost these things can provide is exhausting.

AI introduced a new rhythm in which workers managed several active threads at once: manually writing code while AI generated an alternative version, running multiple agents in parallel, or reviving long-deferred tasks because AI could “handle them” in the background. They did this, in part, because they felt they had a “partner” that could help them move through their workload.

While this sense of having a “partner” enabled a feeling of momentum, the reality was a continual switching of attention, frequent checking of AI outputs, and a growing number of open tasks. This created cognitive load and a sense of always juggling, even as the work felt productive.

I'm frequently finding myself with work on two or three projects running parallel. I can get so much done, but after just an hour or two my mental energy for the day feels almost entirely depleted.

I've had conversations with people recently who are losing sleep because they're finding building yet another feature with "just one more prompt" irresistible.

The HBR piece calls for organizations to build an "AI practice" that structures how AI is used to help avoid burnout and counter effects that "make it harder for organizations to distinguish genuine productivity gains from unsustainable intensity".

I think we've just disrupted decades of existing intuition about sustainable working practices. It's going to take a while and some discipline to find a good new balance.

Via Hacker News

Tags: careers, ai, generative-ai, llms, ai-assisted-programming, ai-ethics

Why DRAM shortages, not capital, will define AI infrastructure growth through 2027

Claude: Speed up responses with fast mode

New "research preview" from Anthropic today: you can now access a faster version of their frontier model Claude Opus 4.6 by typing /fast in Claude Code... but at a cost that's 6x the normal price.

Opus is usually $5/million input and $25/million output. The new fast mode is $30/million input and $150/million output!

There's a 50% discount until the end of February 16th, so only a 3x multiple (!) before then.

How much faster is it? The linked documentation doesn't say, but on Twitter Claude say:

Our teams have been building with a 2.5x-faster version of Claude Opus 4.6.

We’re now making it available as an early experiment via Claude Code and our API.

Claude Opus 4.5 had a context limit of 200,000 tokens. 4.6 has an option to increase that to 1,000,000 at 2x the input price ($10/m) and 1.5x the output price ($37.50/m) once your input exceeds 200,000 tokens. These multiples hold for fast mode too, so after Feb 16th you'll be able to pay a hefty $60/m input and $225/m output for Anthropic's fastest best model.

Tags: ai, generative-ai, llms, anthropic, claude, llm-pricing, claude-code

I am having more fun programming than I ever have, because so many more of the programs I wish I could find the time to write actually exist. I wish I could share this joy with the people who are fearful about the changes agents are bringing. The fear itself I understand, I have fear more broadly about what the end-game is for intelligence on tap in our society. But in the limited domain of writing computer programs these tools have brought so much exploration and joy to my work.

— David Crawshaw, Eight more months of agents

Tags: coding-agents, ai-assisted-programming, generative-ai, ai, llms

A bifurcation is happening in AI adoption - power users shipping products in days versus everyone else generating meeting agendas. Enterprise tool choices are accelerating the divide.