You’ve been doing this public service for so long (well, for so long in “AI hype” years anyway) that it’d be fascinating to see the evolution of this artifact across time.

The fact that they are not tells me there must be some other financial incentives at work that are unclear to me.

I’m also with you on the junior / mid-level engineer framing (a “brilliant” junior engineer perhaps, one who graduated from at the top of their class from the best CS program in the country) with a big caveat: AI is like a junior engineer who doesn’t know how to learn.

It’s like you’re working with the guy from Memento. Every day your LLM reports to work and they’ve learned nothing from your work so far. Every day is the first day!

Now like the Memento guy you can help them to scatter their workspace with sticky notes and reminders everywhere. With some effort you can start to approximate that thing called “learning” which is LITERALLY the most important trait of every single software developer on a team.

But I confess it’s a struggle for me and the available tooling isn’t there yet. The best I’ve done looks closer to the “second brain” people use tools like Obsidian for. Sadly I don’t think a second brain is a substitute for a first brain. And to be 100% honest any engineer who exhibited the same inability to learn and grow as an AI agent would be sacked after their first month on the job at any company I’ve ever worked at.

I’m actually reasonably optimistic that either the main AI providers or someone else will improve on this in the coming years. It certainly feels like a decent memory paired with a well architected thinking system that’s better at contextually injecting memories (I find LLMs today don’t know what they don’t know unless you force them to put metaphorical sticky notes all over the place) as well as capturing real learnings without supervision shouldn’t be an impossible task requiring novel technical structures.

Anyhow I’d love to be wrong about some of the above and I’m always reading articles like this one hoping that someone has solved these problems already and that I’m just slow on the uptake. But as of today, I’m only modestly better at architecting such agents than I was when I started.

But would behavior change in the long run? Countries like Spain where solar noon differs wildly from clock noon just end up aligning their rituals accordingly (e.g. eating dinner at 9pm).

Note I'm not arguing that this is a good strategy. But given that so many people follow it I imagine it's not as bad as it appears on the surface.

What I hate far worse than what this article complains about is just blatant AI writing in articles, comments, video narration you name it.

Way more insidious, way bigger problem!

It’s unfortunate because many of us are going “full AI” when it comes to coding. And there are some true gives and takes that are interesting to explore.

Sadly, this piece reads like pure hype.

Put another way: no matter how detailed or “perfect” you make a map, it will never be the territory, ie the thing that is mapped.

Computers and AI are like a map in this regard —- just ones and zeros that we have assigned meaning to arbitrarily. No matter how “good” AI gets, it’s still just a map of the thing not the thing itself.

So AI saying “I feel sad” is never more than a representation of sadness that should not be confused with the subjective experience of sadness itself.

You can learn to bake good bread. It’s not _that_ hard. And it’ll probably taste better than store bought bread.

But it almost certainly won’t be cheaper. And it’ll take a more more time and effort.

Still, sometimes you might bake your own bread for kicks. But most of the time, you’ll just buy the bread someone else has already perfected.

I recently chose to take the train vs driving and the factors behind that decision were:

* Time, yes. Train was approximately the same but an actually a bit slower.

* Cost. Train was slightly cheaper when looking at the true cost of driving. Also significantly cheaper than flying.

* Experience. This is entirely overlooked in the timetable centric approach. Train is simply the most pleasant way to travel long distances (maybe ferry is competitive there). I was able to move around and get work done and enjoy the view. If the train company had swapped my train for a bus I would NOT have been a satisfied customer.

* City center to city center (vs airport to airport). Had the train company said “we swapped the arrival location to the airport but technically we still got you to the city” I would NOT have been a happy customer.

The “trains as timetables” hypothesis would imply that the train could meet my needs via something other than rail travel and would definitely lose me as a customer.

On the other hand, improvements such as better wifi service (it was terrible and not sure why cell service is also poor on a train) or a route that was more scenic but did not impact my arrival time significantly would positively affect my likelihood of choosing train.

So the better lesson is know your customer needs and know their specific jobs to be done and center your hypothesis around this.

I’ve previously had the above thought and applied it to the end of sentences, but the idea of introducing them at the level of semantic thought had not occurred to me. But if this is where we’re going I’d start to wish for indentation possibilities. I’ve do this frequently with SQL statements, introducing both line breaks and indentations to provide a visual structure that mimics the semantic structure of clauses and the details they contain.

IIRC they did actually require that he wear contact lenses that replicated the effect for some amount of time (a month or so I believe) because there are people who are not happy with the arrangement. So they sort of force you to try before you buy.

In this agentic AI utopia of six months from now:

* Why would developers — especially junior developers — be assigned oversight of the AI clusters? This sounds more like an engineering management role that’s very hands on. This makes sense because the skill set required for the desired outcomes is no longer “how do I write code that makes these computers work correcty” and rather “what’s the best solution for our customers and/or business in this problem space.” Higher order thinking, expertise in the domain, and dare I say wisdom are more valuable than knowing the intricacies of React hooks.

* Economically speaking what are all these companies doing with all this code? Code is still a liability, not an asset. Mere humans writing code faster than they comprehend the problem space is already a problem and the brave new world described here makes this problem worse not better. In particular here, there’s no longer an economic “moat” to build a business off of if everything can be “solved” in a day with a swarm of AI agents.

* I wonder about the ongoing term scaling of these approaches. The trade off seems to be extremely fast productivity at the start which falls off a cliff as the product matures and grows. It’s like a building that can be constructed in a day up to a few floors but quickly hits an upper limit as your ability to build _on top of_ the foundational layer of poorly understood garbage.

* Heaven help the ops / infrastructure folks who have to run this garbage and deal with issues at scale.

Btw I don’t reject everything in this post — these tools are indeed powerful and compelling and the trendlines are undeniable.

Also is there some level of deterministic behavior here or might every test run result in a different underlying command if your wording isn’t precise enough?