Tags: cognitive

Working with agents doesn’t feel like flow — Bill de hÓra

Related to Matt’s thoughts:

…working with agents feels much less like classic deep work, and much more like playing a game. Not to say the work is frivolous—it’s just because it feels like I’m in a game loop.

Flow, at least in the usual sense for me, feels smooth and continuous. The work and your attention starts to line up so cleanly that the experience becomes frictionless. You disappear into the work and meld with it. One notable aspect of flow has been I lose track of time. Working with agents on the other hand, is not like that at all. It’s highly engaging, but in a more jagged, reactive way. I’m focused, but not settled. I’m absorbed, but not merged with the task. I’m paying close attention the whole time, but the attention is dynamic and tactical rather than continuous. I don’t lose track of time at all.

I work, I think? - Annotated

This is about something that’s already happening, that doesn’t show up in employment figures: the quiet destruction of the feedback loop that turns inexperienced people into competent ones. The process by which you get something wrong, feel it, understand why, and become slightly less wrong next time. It’s unglamorous and it’s slow and it’s the only way it’s ever worked.

AI short-circuits that learning completely. Not maliciously. Just structurally. When you can generate something that looks right without doing the thinking, you will (most people, most people being me, will, most of the time, under pressure, with a deadline) and the muscle that thinking would have built never develops.

If you wanted to make a really crude approximation of project management, you could say there are two main styles: waterfall and agile.

It’s not as simple as that by any means. And the two aren’t really separate things; agile came about as a response to the failures of waterfall. But if we’re going to stick with crude approximations, here we go:

• In a waterfall process, you define everything up front and then execute.
• In an agile process, you start executing and then adjust based on what you learn.

So crude! Much approximation!

It only recently struck me that the agile approach is basically a cybernetic system.

Cybernetics is pretty much anything that involves feedback. If it’s got inputs and outputs that are connected in some way, it’s probably cybernetic. Politics. Finance. Your YouTube recommendations. Every video game you’ve ever played. You. Every living thing on the planet. That’s cybernetics.

Fun fact: early on in the history of cybernetics, a bunch of folks wanted to get together at an event to geek about this stuff. But they knew that if they used the word “cybernetics” to describe the event, Norbert Wiener would show up and completely dominate proceedings. So they invented a new alias for the same thing. They coined the term “artificial intelligence”, or AI for short.

Yes, ironically the term “AI” was invented in order to repel a Reply Guy. Now it’s Reply Guy catnip. In today’s AI world, everyone’s a Norbert Wiener.

The thing that has the Wieners really excited right now in the world of programming is the idea of agentic AI. In this set-up, you don’t do any of the actual coding. Instead you specify everything up front and then have a team of artificial agents execute your plan.

That’s right; it’s a return to waterfall. But that’s not as crazy as it sounds. Waterfall was wasteful because execution was expensive and time-consuming. Now that execution is relatively cheap (you pay a bit of money to line the pockets of the worst people in exchange for literal tokens), you can afford to throw some spaghetti at the wall and see if it sticks.

But you lose the learning. The idea of a cybernetic system like, say, agile development, is that you try something, learn from it, and adjust accordingly. You remember what worked. You remember what didn’t. That’s learning.

Outsourcing execution to machines makes a lot of sense.

I’m not so sure it makes sense to outsource learning.

The nature of the job

Large language models help you build the thing faster, which is the primary end goal for your company but only sometimes for you. My primary goal might be to build the thing faster, but it also might be to learn something durably, to enjoy the work, to look forward to Monday.

I don’t like the mental fragility of not fully understanding how my own code works, where AI-generated code is “mine” in that it’s attributed to me in the git blame and I’m its maintainer going forward.

Training your replacement | Go Make Things

I’ve had a lot of people recently tell me AI is “inevitable.” That this is “the future” and “we all better get used to it.”

For the last decade, I’ve had a lot of people tell me the same thing about React.

And over that decade of React being “the future” and “inevitable,” I worked on many, many projects without it. I’ve built a thriving career.

AI feels like that in many ways. It also feels different in that non-technical people also won’t shut the fuck about it.

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

I recently wrote:

The issue isn’t with the code itself, but with the understanding of the code.

That’s the difference between technical debt and cognitive debt.

John has written lots more on this.

The Jeopardy Phenomenon – Chris Coyier

AI has the Jeopardy Phenomenon too.

If you use it to generate code that is outside your expertise, you are likely to think it’s all well and good, especially if it seems to work at first pop. But if you’re intimately familiar with the technology or the code around the code it’s generating, there is a good chance you’ll be like hey! that’s not quite right!

Not just code. I’m astounded by the cognitive dissonance displayed by people who say “I asked an LLM about {topic I’m familiar with}, and here’s all the things it got wrong” who then proceed to say “It was really useful when I asked an LLM for advice on {topic I’m not familiar with, hence why I’m asking an LLM for advice}.”

Like, if you know that the results are super dodgy for your own area of expertise, why would you think they’d be any better for, I don’t know, restaurant recommendations in a city you’ve never been to?

Your brain does not process information and it is not a computer | Aeon Essays

We don’t store words or the rules that tell us how to manipulate them. We don’t create representations of visual stimuli, store them in a short-term memory buffer, and then transfer the representation into a long-term memory device. We don’t retrieve information or images or words from memory registers. Computers do all of these things, but organisms do not.

Phenological Mismatch - e-flux Architecture - e-flux

Over the last fifty years, we have come to recognize that the fuel of our civilizational expansion has become the main driver of our extinction, and that of many of the species we share the planet with. We are now coming to realize that is as true of our cognitive infrastructure. Something is out of sync, felt everywhere: something amiss in the temporal order, and it is as related to political and technological shifts, shifts in our own cognition and attention, as it is to climatic ones. To think clearly in such times requires an intersectional understanding of time itself, a way of thinking that escapes the cognitive traps, ancient and modern, into which we too easily fall. Because our technologies, the infrastructures we have built to escape our past, have turned instead to cancelling our future.

James writes beautifully about rates of change.

The greatest trick our utility-directed technologies have performed is to constantly pull us out of time: to distract us from the here and now, to treat time as a kind of fossil fuel which can be endlessly extracted in the service of a utopian future which never quite arrives. If information is the new oil, we are already, in the hyper-accelerated way of present things, well into the fracking age, with tremors shuddering through the landscape and the tap water on fire. But this is not enough; it will never be enough. We must be displaced utterly in time, caught up in endless imaginings of the future while endlessly neglecting the lessons and potential actions of the present moment.

Cognitive Overload - daverupert.com

From Scott McCloud to responsive design, Dave is pondering our assumptions about screen real estate:

As the amount of information increases, removing details reduces information density and thereby increasing comprehension.

It reminds me of Edward Tufte’s data-ink ratio.