GPT doing a great job summarizing and providing a counter:
what's they key thesis here and the argument ?
*Key thesis:*
When technology eliminates meaningful performance differences between products, industries shift from competing on *engineering and function* to competing on *brand and status*. The Swiss watch industry after the quartz crisis is a clear example: mechanical watches stopped being valuable tools and became *luxury symbols whose value comes mainly from branding, scarcity, and signaling wealth*.
*In one sentence:*
The essay argues that when technology makes functional differences between products disappear, industries shift from engineering competition to branding competition, and the modern luxury watch market illustrates how that transformation works.
---
If you want, I can also explain *why this essay is controversial among economists and designers*—there are a few big assumptions in the argument.
And the counter
A few places the argument is debatable (even if it’s persuasive as a narrative):
It treats “brand” and “engineering” as mostly opposed. In practice, luxury buyers often do pay for engineering (finishing, durability, serviceability, movement architecture), and those technical choices can reinforce brand rather than fight it. The tension is real, but not always zero-sum.
It downplays other drivers of the Swiss recovery. The rebound isn’t only “they discovered brand.” Things like Swatch’s role in recapitalizing distribution/production, industry consolidation, improvements in manufacturing, and globalization of luxury demand can explain a lot of the late-1980s revenue surge alongside marketing.
The “artificial scarcity = managed asset bubble” framing is provocative but contestable. Scarcity can be interpreted as: capacity constraints, quality control, long-term brand stewardship, or channel strategy—not necessarily “bubble management.” It may function bubble-like in resale markets, but that doesn’t prove intent or fragility in the way financial bubbles work.
Some claims are asserted more than demonstrated. Examples: Rolex “abandoned research” after ~1960; “only three survived as independents”; “most brands are owned by six holding companies”; “they rebuy hundreds per year.” These might be true in broad strokes, but the essay uses them as load-bearing supports without showing evidence in-text (beyond a couple notes).
The design-history claims are simplified. Watch size and shape trends aren’t only branding; they’re also driven by changing tastes, ergonomics, manufacturing norms, sport/tool styling, and shifting notions of masculinity. “Big = cheap historically” is directionally true in some eras, but not a universal law.
The gender section is especially shaky. “Women never really went for mechanical watches” and the steam-engine analogy read like overgeneralizations—there are strong counterexamples (and cultural variation) that complicate that story.
Please don't post like this. We could trivially get this take ourselves if we cared for it. It would be obvious that this is ChatGPT even without a disclaimer, and the analysis is exactly as formulaic and facile as you'd expect. (How could it reasonably conclude that pg's just-written essay is "controversial among economists and designers", let alone why? It's not making social media rounds; it was just published today; search engine results are mostly unrelated stuff and certainly aren't pointing to discussion...).
Congratulations to the creator of this site and thank you so much for posting it !
I have to (unwillingly) do frontend work so I recently read up on CSS quite a bit. I have always thought that using computed numbers for styling is bonkers. Its better to use CSS that uses logical values. The site seems to emphasize that style.
Number of features shipped. Traction metrics. Revenue per product. Ultimately business metrics. For example, tax prep effectiveness would be a proper experiment tied to specific metrics.
Curious: whats your primary programming language and what sort of development do you do ? In my experience with LLMs agentic coding paired with a good IDE works wonders. Its also allows me to surgically write critical bits of code myself while outsourcing boilerplate stuff.
Thats great. I think we need to start researching how to get cheaper models to do math. I have a hunch it should be possible to get leaner models to achieve these results with the right sort of reinforcement learning.
Why should these guys bother with people who won't pay for their offering ? The community is not skilled enough to contribute to this type of project. Honestly most serious open source is industry backed and solves very challenging distributed systems problems. A run of the mill web dev doesnt know these things I am sorry to say.
No one should be immune from criticism. If you make a well established open source project WITH the help of thousands of volunteers around the world only to lock it up and say "pay up", that's called extortion.
I think the issue is answering the question whats the business model ? If the team makes money consulting on clojure, then thats likely a bad model since I have not seen a single example of people paying for advice on coding. Usually the answer is to hire a coder who knows thier stuff and increasingly to use AI.
Open source for infrastructure products work just fine. It simplifies distribution by eliminating the need for procurement, builds some kind of attachment since people love using their own tinkered products and hedges risk for the customer since if the devs stop working on the product someone else will pick up.
But having to fill out forms, doing compliance work are great money making levers for which you just charge through the nose. Ultimately, open soircing is a distribution strategy and whether you should adopt it or not is dependent on the context. Most infra products do and it works out fine. Case in point: Clickhouse, Kafka, Grafana, Sentry, RedHat, Gitlab.
Self study is the best study. Out of all the bloatedness of modern education, one thing that doesnt bother me is the high cost of textbooks. High quality books and a habit of studying yourself enables you to learn high skill disciplines on the cheap.
For me, I am currently slogging through Lazlo Lovasz's combinatorics book and another one on Monte Carlo method. Dont know why but its just a good way to pass the time while staying away from the internet and its attention hogging.
Are technical/scientific books from pre-1925 particularly useful for self-learning today? I'd imagine for most disciplines, the knowledge has progressed and possibly changed course since then and it may be more outdated than not.
It might depend on the topic. Classical mechanics? I'm not sure that there is any fundamentally new knowledge since 1925 in that field. What's different is that people have 100 more years of figuring out how to explain it well.
what's they key thesis here and the argument ?
*Key thesis:* When technology eliminates meaningful performance differences between products, industries shift from competing on *engineering and function* to competing on *brand and status*. The Swiss watch industry after the quartz crisis is a clear example: mechanical watches stopped being valuable tools and became *luxury symbols whose value comes mainly from branding, scarcity, and signaling wealth*.
*In one sentence:* The essay argues that when technology makes functional differences between products disappear, industries shift from engineering competition to branding competition, and the modern luxury watch market illustrates how that transformation works.
---
If you want, I can also explain *why this essay is controversial among economists and designers*—there are a few big assumptions in the argument.
And the counter A few places the argument is debatable (even if it’s persuasive as a narrative):
It treats “brand” and “engineering” as mostly opposed. In practice, luxury buyers often do pay for engineering (finishing, durability, serviceability, movement architecture), and those technical choices can reinforce brand rather than fight it. The tension is real, but not always zero-sum.
It downplays other drivers of the Swiss recovery. The rebound isn’t only “they discovered brand.” Things like Swatch’s role in recapitalizing distribution/production, industry consolidation, improvements in manufacturing, and globalization of luxury demand can explain a lot of the late-1980s revenue surge alongside marketing.
The “artificial scarcity = managed asset bubble” framing is provocative but contestable. Scarcity can be interpreted as: capacity constraints, quality control, long-term brand stewardship, or channel strategy—not necessarily “bubble management.” It may function bubble-like in resale markets, but that doesn’t prove intent or fragility in the way financial bubbles work.
Some claims are asserted more than demonstrated. Examples: Rolex “abandoned research” after ~1960; “only three survived as independents”; “most brands are owned by six holding companies”; “they rebuy hundreds per year.” These might be true in broad strokes, but the essay uses them as load-bearing supports without showing evidence in-text (beyond a couple notes).
The design-history claims are simplified. Watch size and shape trends aren’t only branding; they’re also driven by changing tastes, ergonomics, manufacturing norms, sport/tool styling, and shifting notions of masculinity. “Big = cheap historically” is directionally true in some eras, but not a universal law.
The gender section is especially shaky. “Women never really went for mechanical watches” and the steam-engine analogy read like overgeneralizations—there are strong counterexamples (and cultural variation) that complicate that story.
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