Is this actually correct? I don't see any evidence for a "airflight bubble" or a "car bubble" or a "loom bubble" at the technologies' invention. Also the "canal bubble" wasn't about the technology, it was about the speculation on a series of big canals but we had been making canals for a long time. More importantly, even if it was correct, there are plenty of bubbles (if not significantly more) around things that didn't have value or tech that didn't matter.

Before AI, we were trying to save money, but through a different technique: Prompting (overseas) humans.

After over a decade of trying that, we learned that had... flaws. So round 2: Prompting (smart) robots.

The job losses? This is just Offshoring 2.0; complete with everyone getting to re-learn the lessons of Offshoring 1.0.

Nice.

1. Routinely some task or domain of work that some expert claims that LLM’s will able to do, LLM’s start being able to reliably perform that task within 6 months to a year, if they haven’t already

2. Whenever AI gets better, people move the goalposts regarding what “intelligence” counts as

3. Still, LLM’s reveal that there is an element to intelligence that is not orthogonal to the ability to do well on tests or benchmarks

This works on people as well!

Cops do this when interrogating. You tell the same story three times, sometimes backwards. It's hard to keep track of everything if you're lying or you don't recall clearly so you can get a sense of confidence. Also works on interviews, ask them to explain a subject in three different ways to see if they truly understand.

I just want to point out that this answer implicitly means that, at the very least, the profession is at least questionably uncertain which isn't a good sign for people with a long future orientation such as students.

The claim in the blog post that all technology leads to speculative asset bubbles I find hard to believe. Where was the electricity bubble? The steel bubble? The pre-war aviation bubble? (The aviation bubble appeared decades later due to changes in government regulation.)

Is this an AI bubble? I genuinely don't know! There is a lot of real uncertainty about future cash flows. Uncertainty is not the foundation of a bubble.

I knew dot-com was a bubble because you could find evidence, even before it popped. (A famous case: a company held equity in a bubble asset, and that company had a market cap below the equity it held, because the bubble did not extend to second-order investments.)

> Maybe LLMs mark the point where we join our engineering peers in a world on non-determinism.

Those other forms of engineering have no choice due to the nature of what they are engineering.

Software engineers already have a way to introduce determinism into the systems they build! We’re going backwards!

A junior engineer can't write code anywhere nearly as fast. It's apples vs oranges. I can have the LLm rewrite the code 10 times until its correct and its much cheaper than hiring an obsequious jr engineer

For any macOS users, I highly recommend an Alfred workflow so you just press command + space then type 'llm <prompt>' and it opens tabs with the prompt in perplexity, (locally running) deepseek, chatgpt, claude and grok, or whatever other LLMs you want to add.

This approach satisfies Fowler's recommendation of cross referencing LLM responses, but is also very efficient and over time gives you a sense of which LLMs perform better for certain tasks.

I like this article, I generally agree with it. I think the take is good. However, after spending ridiculous amounts of time with LLMs (prompt engineering, writing tokenizers/samplers, context engineering, and... Yes... Vibe coding) for some periods 10 hour days into weekends, I have come to believe that many are a bit off the mark. This article is refreshing, but I disagree that people talking about the future are talking "from another orifice".

I won't dare say I know what the future looks like, but the present very much appears to be an overall upskilling and rework of collaboration. Just like every attempt before, some things are right and some are simply misguided. e.g. Agile for the sake of agile isn't any more efficient than any other process.

We are headed in a direction where written code is no longer a time sink. Juniors can onboard faster and more independently with LLMs, while seniors can shift their focus to a higher level in application stacks. LLMs have the ability to lighten cognitive loads and increase productivity, but just like any other productivity enhancing tool doing more isn't necessarily always better. LLMs make it very easy to create and if all you do is create [code], you'll create your own personal mess.

When I was using LLMs effectively, I found myself focusing more on higher level goals with code being less of a time sink. In the process I found myself spending more time laying out documentation and context than I did on the actual code itself. I spent some days purely on documentation and health systems to keep all content in check.

I know my comment is a bit sparse on specifics, I'm happy to engage and share details for those with questions.

This is a completely wrong assumption and negates a bunch of the points of the article...

One thing I've done with some success is use a Test Driven Development methodology with Claude Sonnet (or recently GPT-5). Moving forward the feature in discrete steps with initial tests and within the red/green loop. I don't see a lot written or discussed about that approach so far, but then reading Martin's article made me realize that the people most proficient with TDD are not really in the Venn Diagram intersection of those wanting to throw themselves wholeheartedly into using LLMs to agent code. The 'super clippy' autocomplete is not the interesting way to use them, it's with multiple agents and prompt techniques at different abstraction levels - that's where you can really cook with gas. Many TDD experts have great pride in the art of code, communicating like a human and holding the abstractions in their head, so we might not get good guidance from the same set of people who helped us before. I think there's a nice green field of 'how to write software' lessons with these tools coming up, with many caution stories and lessons being learnt right now.

I used to avidly read all his stuff, and I remember 20ish years ago he decided to rename Inversion of Control to Dependency Injection. In doing so, and his accompany blog, he showed he didn't actually understand it at a deep level (and hence his poor renaming).

This feels similar. I know what he's trying to say, but he's just wrong. He's trying to say the LLM is hallucinating everything, but Fowler is missing is that Hallucination in LLM terms refers to a very specific negative behavior.

Reminds me of a recent experience when I asked CC to implement a feature. It wrote some code that struck me as potentially problematic. When I said, "why did you do X? couldn't that be problematic?" it responded with "correct; that approach is not recommended because of Y; I'll fix it". So then why did it do it in the first place? A human dev might have made the same mistake, but it wouldn't have made the mistake knowing that it was making a mistake.

No, they're like an extremely experienced and knowledgeable senior colleague – who drinks heavily on the job. Overconfident, forgetful, sloppy, easily distracted. But you can hire so many of them, so cheaply, and they don't get mad when you fire them!