With all the emphasis on the speed of modern AI tools, we often seem to forget that velocity is a vector quantity. Increased speed only gets us where we want to be sooner if we are also heading in the right direction. If we’re far enough off course, increasing speed becomes counterproductive and it ends up taking longer to get where we want to be.

I’ve been noticing that this simple reality explains almost all of both the good and the bad that I hear about LLM-based coding tools. Using AI for research or to spin up a quick demo or prototype is using it to help plot a course. A lot of the multi-stage agentic workflows also come down to creating guard rails before doing the main implementation so the AI can’t get too far off track. Most of the success stories I hear seem to be in these areas so far. Meanwhile, probably the most common criticism I see is that an AI that is simply given a prompt to implement some new feature or bug fix for an existing system often misunderstands or makes bad assumptions and ends up repeatedly running into dead ends. It moves fast but without knowing which direction to move in.

Awesome article, I feel a lot of people have also forgotten that good projects take iteration not 100 new features. To get few features to an excelent state it requires multilpe iterations at multiple stages. 1) The developer who does a task validates that their thinking was the correct one, they see how they changes impact the system, is it scalable? Does it need to be scalable? While you are working and thinking on it you get more and more context which simply wasn't there at the begining. 2) A feature done once (even after my perfect ClaudeCode plan) is not done forever, people will want to make it better/faster/smoother/etc. But instead of taking the time to analyze and perfect it we go onto the next feature, and if we have to iterate on the current one, we don't iterate we redo...

Really like the article I think it is awesome, and I strongly believe AI for coding will stay, but I also beleive that we need to still have a strong understanding of why we are building things and what they look like.

One of my favorite ideas from Nietzsche [1] is that civilizations take millennia to “digest” or integrate concepts. It seems a little obvious, maybe, until you look at the modern world and realize the baseline assumption is something like, “every problem is just a question of resources.”

An example being the common attitude that [advanced tech] is just a math problem to be solved, and not a process that needs to play itself out in the real world, interacting with it and learning, then integrating those lessons over time.

Another way to put this is: experience is undervalued, and knowledge is overvalued. Probably because experience isn’t fungible and therefore cannot be quantified as easily by market systems.

1. Probably not his original idea, and now that I think about it this is kind of more Hegelian. I’m not familiar enough with Hegel to reference him though.

I've been working on a clone of Sid Meier's Pirates but with a princess theme (for my daughters).

I've been using AI to help me write it and I've come to a couple conclusions:

- AI can make working PoCs incredibly quickly

- It can even help me think of story lines, decision paths etc

- Given that, there is still a TON of decisions to be made e.g. what artwork to use, what makes sense from a story perspective

- Playtesting alone + iterating still occurs at human speed b/c if humans are the intended audience, getting their opinions takes human time, not computer time

I've started using this example more and more as it highlights that, yes, AI can save huge amounts of time. However, as we learned from the Theory of Constraints, there is always another bottleneck somewhere that will slow things down.

Some of the items listed in the "takes time" list say the beginning are not great examples. They are better emblems of artificial scarcity, especially Hermes bags.

> We pay premiums for Swiss watches, Hermès bags and old properties precisely because of the time embedded in them

Lost me in paragraph three. We pay for those things because they're recognizable status symbols, not because they took a long time to make. It took my grandmother a long time to knit the sweater I'm wearing, but its market value is probably close to zero.

> everybody who is like me, fully onboarded into AI and agentic tools, seemingly has less and less time available because we fall into a trap where we’re immediately filling it with more things

You fill a jar with sand and there is no space for big rocks.

But if you fill the jar with big rocks, there is plenty of space for sand. Remove one of the rocks and the sand instantly fills that void.

Make sure you fit the rocks first.

We value human ingenuity and effort. If there was a button "create an Oscar-worty movie" anyone could press it would make a paradox. The trick is that this won't render film industry useless, since we watch movies only when we believe they're worth our time, which is not true for zero-effort content.

I've been hearing similar things from a lot of different directions. The underlying issue about "you cannot replace time" is one that is good to internalize early. A number of people I know who "missed" their kids growing up because they were working hard to make lots of money. You can't go buy "time with my kids when they were growing up."

Agentic coding very much feels like a "video game" in the sense of you pull the lever and open the loot box and sometimes it's an epic +10 agility sword and sometimes its just grey vendor trash. Whether or not it generates "good" or even "usable" code fades to the background as the thrill of "I just asked for a UI to orchestrate micro services and BLAMMO there it was!" moves to the fore.

Sounds familiar, for most of my life I have tried to remove all "friction" from life – applying that engineering mindset to make everything as efficient as possible. Only then I realized that life somehow is about that "friction".