There’s a big, under-appreciated reason why people may have very different experiences and opinions about using AI for work — are they using it for tasks they’re already an expert at, or tasks they can’t do themselves? The former leads to a growth cycle and the latter leads to a dependence spiral. When I use AI to do something I’m an expert at, like coding, I treat it as a tool. I can build quickly, maintaining an understanding of the code, knowing that if necessary, I can fix the code myself. It feels empowering. It frees up my time to think about the complex, judgment-oriented parts of software engineering that I can’t or won’t delegate to AI. That means my own skills improve rapidly, and I get to climb the ladder of complexity and develop higher-level skills, much more so than when I write the code myself. I feel in control. I can lock in and achieve a flow state — when AI is working, I’m reviewing, building understanding, and planning the next steps. I never get the feeling that the tool is about to replace me. This is the growth cycle. (Of course, the growth cycle is not automatic. I still need to exercise agency to use AI responsibly. But it’s the same challenge with any productivity-enhancing technology, and those who’ve navigated such transitions before are well-equipped to navigate it with AI as well.) On the other hand, if I use it for tasks I don’t understand and haven’t learned to perform myself, I have no choice but to treat it as a superintelligence. If something breaks, the best I can do is ask AI to fix it and hope for the best. I generally can’t evaluate the quality of the output myself. The only way to find out if it's any good is if and when the work is ultimately reviewed by an actual expert. The experience is confusing, unsettling and disempowering. And forget about flow state. By over-relying on AI, I risk losing whatever skill I had at the task in the first place, even if it boosts productivity in the short term. This is the dependence spiral. It’s no wonder that entry-level workers and students preparing to enter the workforce find themselves in a bind. To compete with the AI-enabled productivity of more seasoned workers, they must adopt AI themselves, but doing so risks the dependence spiral. I have some thoughts on solutions that I will share in later posts, but I think having a clear diagnosis of the problem is a useful first step.
Stop saying that AI is just a tool and it only matters how it is used
I’m tired of this phrase and this simple way of thinking about tools. This blog post is a wandering train of thought on the topic of what tools are and why it matters to be even slightly more mature in how we think about them.
The antidote to AI fatigue — Answer.ai Solveit
Are we offloading too much of our thinking to AI?
Reflections on autonomy and the value of thinking for ourselves
I work, I think? - Annotated
How AI may quietly dismantle the feedback loop that turns inexperienced people into competent ones, and why my work matters to me.
Software engineering may no longer be a lifetime career
I don’t think there’s compelling evidence that using AI makes you less intelligent overall1. However, it seems pretty obvious that using AI to perform a task means you don’t learn as much about performing that task. Some software engineers think this is a decisive argument against the use of AI. Their argument goes something like this:
No Vibes Allowed: Solving Hard Problems in Complex Codebases – Dex Horthy, HumanLayer
X : What are you interested in? Me : Wow, that varies constantly. Right now? There are a number of topics that I'm actively pursuing … a) When we talk about software engineering we typically think… | Simon Wardley
X : What are you interested in? Me : Wow, that varies constantly. Right now? There are a number of topics that I'm actively pursuing … a) When we talk about software engineering we typically think about the active part of creating code but software development is currently practised as a craft not an engineering discipline. The only engineering discipline in software engineering is testing. This creates a flaw in the comparison with using AI to code because development itself has never been optimised. If all we have to do is write code and we can automate that part then we can just replace those expensive typists with LLMs but development should be, and has the capability to become an engineering discipline. It's just not that for now. https://lnkd.in/eSRprhbf b) Most people talking digital sovereignty are doing so with good intentions but they literally have no idea what they are talking about. This is not because they are daft or foolish but because they cannot see the environment they are talking about. They are like generals talking about territorial sovereignty with no idea of what territory is or how you represent it. https://lnkd.in/eku2X_Ea c) Architectural decisions are made in code and not in the diagrams we create. Those architectural diagrams are more like prompts, wishes and beliefs of what a system should be but rarely reflect the actual system. This creates additional problems when the real architectural decisions are made by coders but coding itself is a craft not an engineering discipline. d) The current crop of LLMs / LMMs are driving us towards a new theocracy. We can counter this through diversity, critical thinking and open approaches but that does mean we have to get to the point of all symbolic instructions being open. That includes the training data. Copyright is a distraction from the real issue that we don't know what the systems are being trained on. Guardrails are a post event kludge. https://lnkd.in/exJVmNvD e) The medium we use in conversational programming environments such as cursor and lovable (or what we call vibe coding when not looking at the code or Software Engineering + AI when looking at the code) appear to be flawed. We are focused on text not images. The change of medium changes the conversation, the corollary is the conversation we have around the screen and the one we have around the whiteboard. Same problem, different medium, different discussion. https://lnkd.in/e_W6b6z3 f) "Value" in consulting land is mostly theatre rather than something meaningful. There are many forms of value but often we fail to identify this, quantify it or even measure it. https://lnkd.in/eeNbR_VY g) Rewilding Software Engineering. To change software development into an engineering practice, we need to introduce two wolves - one that software engineering is a decision making process and secondly that we need to build tools for problems we are facing - https://lnkd.in/epyUnqgh
When Using AI Leads to “Brain Fry”
As firms increasingly incentivize employees to build and oversee complex teams of agents—for example, by measuring and rewarding token consumption as a proxy for performance—people are finding themselves pushed to their cognitive limits. Participants in a recent study described a “buzzing” feeling or a mental fog with difficulty focusing, slower decision-making, and headaches. The authors call this phenomenon “AI brain fry,” defined as mental fatigue from excessive use or oversight of AI tools beyond one’s cognitive capacity. This AI-associated mental strain carries significant costs in the form of increased employee errors, decision fatigue, and intention to quit. The findings also show how AI-driven workflows can be designed to diminish burnout and point toward specific manager, team, and organizational practices to avoid mental fatigue even as AI work intensifies.
The Friction is Your Judgment — Armin Ronacher & Cristina Poncela Cubeiro, Earendil
AI tools are 'deskilling' workers, philosophy professor says
A philosophy professor warns that AI reliance is weakening workers' judgment, creativity, and problem-solving.
I don't know if I like working at higher levels of abstraction
AI tools push us to higher abstraction. I'm not sure I like what that costs us.
How AI Impacts Skill Formation
AI assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively supervise AI remains unclear. Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that AI-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation -- particularly in safety-critical domains.
How AI Impacts Skill Formation
AI assistance produces significant productivity gains across professional domains, particularly for novice workers. Yet how this assistance affects the development of skills required to effectively supervise AI remains unclear. Novice workers who rely heavily on AI to complete unfamiliar tasks may compromise their own skill acquisition in the process. We conduct randomized experiments to study how developers gained mastery of a new asynchronous programming library with and without the assistance of AI. We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average. Participants who fully delegated coding tasks showed some productivity improvements, but at the cost of learning the library. We identify six distinct AI interaction patterns, three of which involve cognitive engagement and preserve learning outcomes even when participants receive AI assistance. Our findings suggest that AI-enhanced productivity is not a shortcut to competence and AI assistance should be carefully adopted into workflows to preserve skill formation -- particularly in safety-critical domains.
Embracing Gen AI at Work
Today artificial intelligence can be harnessed by nearly anyone, using commands in everyday language instead of code. Soon it will transform more than 40% of all work activity, according to the authors’ research. In this new era of collaboration between humans and machines, the ability to leverage AI effectively will be critical to your professional success. This article describes the three kinds of “fusion skills” you need to get the best results from gen AI. Intelligent interrogation involves instructing large language models to perform in ways that generate better outcomes—by, say, breaking processes down into steps or visualizing multiple potential paths to a solution. Judgment integration is about incorporating expert and ethical human discernment to make AI’s output more trustworthy, reliable, and accurate. It entails augmenting a model’s training sources with authoritative knowledge bases when necessary, keeping biases out of prompts, ensuring the privacy of any data used by the models, and scrutinizing suspect output. With reciprocal apprenticing, you tailor gen AI to your company’s specific business context by including rich organizational data and know-how into the commands you give it. As you become better at doing that, you yourself learn how to train the AI to tackle more-sophisticated challenges. The AI revolution is already here. Learning these three skills will prepare you to thrive in it.
AI is getting too advanced