It's worth asking whether LLMs are actually making us more productive at all—and if so, what we might be sacrificing in return.
LLM-generated skills work, if you generate them afterwards
LLM “skills” are a short explanatory prompt for a particular task, typically bundled with helper scripts. A recent paper showed that while skills are useful to LLMs, LLM-authored skills are not. From the abstract:
Harnessing Frustration: Using LLMs to Overcome Activation Energy
One of my biggest weaknesses as a software engineer is procrastination when facing a new project. When the scope is unclear, I have a tendency to wait until I feel I’ve “felt out” the problem to start doing anything. I know I’ll feel better and work much faster when I get “stuck in” but I still struggle with that first step, overcoming the “activation energy” required to engage with the details. LLMs have been a game-changer for me in this respect: I can just throw a couple of sentences at them with the shape of the problem. This leads to one of two outcomes: The LLM comes up with a good solution, usually in a slightly different way than what I was thinking. I realize “oh wow the solution is much simpler than I thought”. Straight away I start thinking about the consequences of implementing and improving what the LLM suggested. The LLM comes up with a solution that I intuitively recognize as “wrong”. My immediate reaction is frustration (“How could it get it so wrong”) which leads me to go back and forth with the model, explaining to it why its solution could not possibly work. But in the process of arguing with the model, my brain is churning away and generating variations or different approaches that could work. After a while, even if the AI is still on the wrong track, the debate will trigger a moment of inspiration where suddenly the solution will come to me. I’ll excitedly start up a new conversation and start working through it with the model. The key is the emotional reaction I have immediately to the LLM’s response, either excitement or frustration. By harnessing this immediate feedback loop, I get my brain out of its passive, procrastination mode. It’s almost like a jolt: either I’m thrilled because it’s simpler than I thought, or I’m spurred to action by the urge to correct a perceived ‘wrong’ answer. This forces me to engage with the problem in a meaningful way.
Rotating the Space: On LLMs as a Medium for Thought
An essay on LLMs as a cognitive medium
Artificial
An LLM is a computer program. We should talk about it like a computer program.
LLMs reward expertise
In the 2010s, if you had technical gaps (say, you couldn’t write CSS), you had to either rely on a skilled colleague or just hope that the answer to your exact problem was out there on the internet. Today, everyone can write sort-of-okay CSS by delegating the task to an LLM. LLMs make everybody into a generalist.
AI Doesn’t Reduce Work—It Intensifies It
One of the promises of AI is that it can reduce workloads so employees can focus more on higher-value and more engaging tasks. But according to new research, AI tools don’t reduce work, they consistently intensify it: In the study, employees worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day, often without being asked to do so. That may sound like a win, but it’s not quite so simple. These changes can be unsustainable, leading to workload creep, cognitive fatigue, burnout, and weakened decision-making. The productivity surge enjoyed at the beginning can give way to lower quality work, turnover, and other problems. To correct for this, companies need to adopt an “AI practice,” or a set of norms and standards around AI use that can include intentional pauses, sequencing work, and adding more human grounding.
Extensible Software in the age of LLMs | Jeremy Morrell
Solid core + capability-based sandboxes + LLMs = Users with superpowers
Doing nothing at work
Many engineers should be doing less work. I don’t necessarily mean producing less code or fewer changes, but literally working fewer hours in the day. When they do work, they should be working at a slower pace. I like to aim to be running at 80% utilization by default: unless I have a high-pressure project going on, I spend 20% of my workday away from the computer.
Guardian Angels: LLM Personalization for Productivity and Security
I propose an approach for highly personalized LLMs, for near-future productivity gains and personal info/cybersecurity against increasingly powerful LLMs: they should, in the spirit of uploading, try to emulate the user’s values and preferences in order to amplify the principal—not replace them. I discuss a package of techniques and proposals to accomplish such ‘guardian angels’; dynamic evaluation of LLMs combined with active learning and elicitation and heavy inner-monologue search/data-augmentation.
We could be building a better world with our students. Why are we disempowering them instead?
And why did our constructionism impulse die when it was most needed?
Against False Indulgences in LLM Alignment
Unnecessary moral concern, and the false indulgences that drive it.
Why craft-lovers are losing their craft
Les Orchard made a quiet observation recently that I haven't been able to shake. Before LLM coding assistants arrived, the split between developers was…
How LLMs Actually Work
A from-the-ground-up walkthrough of how modern LLMs work, from tokens to transformer blocks to the next-token loop
Build agents, not pipelines
There are only two ways to use LLMs in a computer program: as part of a pipeline, or as an agent. In other words, either you express the control flow of the program in code, or you give a LLM tools and allow it to manage the control flow itself1.
LLM Neuroanatomy: How I Topped the LLM Leaderboard Without Changing a Single Weight
ML, Biotech, Hardware, and Coordination Problems. Sometimes I write about hard problems and how to solve them.