I agree with your fundamental point. However, I don't think steady erosion of mastery is the only way that these next years have to go, even if it looks the most likely at present. Supposing LLMs or whatever future architecture surpass even the greatest human minds in intelligence, why is that situation fundamentally different to living in a world with Einstein, i.e. a level of mastery I'll never reach before the end of my life? As one interested in the depths, I prefer to live in a world with peaks ever greater than myself---it doesn't prevent me from going as deep as I can, inspired by where they've reached, and doing the things that matter to me.

Turing's view, in fact, is similar: "There would be great opposition [to AI] from the intellectuals [read programmers in the context of this thread] who were afraid of being put out of a job. It is probable though that the intellectuals would be mistaken about this. There would be plenty to do, i.e. in trying to keep one’s intelligence up to the standard set by the machines, for it seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers. There would be no question of the machines dying, and they would be able to converse with each other to sharpen their wits."

[0] Thomas Bernhard's The Loser is a fantastic account of the opposite standpoint---of the second best piano student, who cannot stand existing in a world with Glenn Gould.

Amazon's free cash flow rises year over year (apart from the post-COVID period) [0] while Walmart's doesn't [1] and price multiples are largely determined by expected FCF over time not directly revenue/EBITDA. FCF rain or shine maps roughly onto possibility of paying out dividends/buybacks, which determines the value of an equity in capital markets ("discounted present value of a company's future cash flows").

[0] https://www.macrotrends.net/stocks/charts/AMZN/amazon/free-c... [1] https://www.macrotrends.net/stocks/charts/WMT/walmart/free-c...

Yeah, this is certainly the correct take. There's an alternate timeline where the Scala community focused during the peak on making it a better language for numeric computing/ML rather than the Nth category theoretic framework, but here we are. At a job almost a decade ago, we made some progress on an open source dataframes (and unfortunately proprietary data visualization) library for Scala, but we didn't get far enough before the company closed and the project died [1].

Still my favorite language I had the privilege to work with professionally for over a decade. However, in this post-JVM world, I'm actually excited to see a lot more OCaml discussion on here lately. The Jane Street work on OxCaml is terrific after a long period of stagnation with the language. I'm using it for most of my projects these days.

[1] https://github.com/tixxit/framian

Honestly, I don't think it's irrational: the car industry is just horrible from a business perspective, which is why Tesla had to be financed for so long by crypto scams and most investors wouldn't touch it. Historically (if of course briefly/crudely), it was always a debt-backed gamble on overproduction hoping you could expand forever globally without competition (Ford) or into new market segments through financing (GM).

It's paywalled unfortunately, but [1] is an illustrative Financial Times article discussing car manufacturer behavior in relation to Covid shutdowns and strikes. Many firms found the manufacturing shutdowns to be a boon: the winning strategy to accept it as a cost cut and just raise prices on existing inventory for above average financial performance.

My sense is that Tesla is now just taking that a step further by getting rid of their Fordist aspirations and applying the unarguably successful Apple model to the automotive industry. They don't want to mass produce cars and hope for X% conversion rate to software and services over time: they literally don't want customers who are not able or not going to pay for recurring software services. Software is where free cash flow comes from and free cash flow is where dividends/buybacks come from, which determines the value of an equity. That, of course, is why we get paid well.

I end with the disclaimer that obviously I don't believe the world should be meticulously and exclusively organized for the production of free cash flow, but I do think it's important to understand the logic.

[1] https://www.ft.com/content/4da6406a-c888-49c1-b07f-daa6b9797...

I don't know what it looks like on the ground now, but Scala was the defacto language of data infrastructure across the post-Twitter world of SV late stage/growth startups. In large part, this was because these companies were populated by former members of the Twitter data team so it was familiar, but also because there was so much open source tooling at that point. ML teams largely wrote/write Python, product teams in JS/whatever backend language, but data teams -- outside of Google and the pre-Twitter firms -- usually wrote Scala for Spark, Scalding etc in the 2012-2022ish era.

I worked in Scala for most of my career and it was never hard to get a job on a growth stage data team or, indeed, in finance/data-intensive industries. I'm still shocked at how the language/community managed to throw away the position they had achieved. At first I was equally shocked to see the Saudi Sovereign Wealth fund investing in the language, but then realized it was just 300k from the EU and everything made sense.

It's still my favorite language for getting things done so I wouldn't be upset with a comeback for the language, but I certainly don't expect it at this point.