They’re trying to do something genuinely useful for everyone. It’s not really their fault that the markets are so eager for growth that expectations, and the money pouring in, are way over the top. At some point, there’ll be a correction and a lot of investors will probably be disappointed, but honestly, that’s just how capitalism works.

Many breakout successes in gaming happened due to a platform change. Think AJAX from 2005, then social networks from 2008, then mobile from 2010.
Anything tried since -- VR, blockchain, smartwatch -- failed to gain traction so far. So yes he is looking for a new platform, in part because his company is losing ground on the PC and consoles. And he's also right about the tiktoks of the world eating games' breakfast, especially in the mobile part.

Personally I have high hopes about AR gaming, but the hardware is not there yet.

What makes you think so?

still unsure what is so different from having an LLM de jour tab open and asking it my questions there.
okay sure Comet has a somewhat easier UI for answering questions about the page I'm visiting but I just don't seem to have a need to do that.
so far for me this falls into the "solution looking for a problem" category. I acknowledge I might be not the right audience for this though.

But the reason here is money. (Now who would've thunk!)

The more people are diagnosed with whatever, the more money there is to be made by producing remedies. Remember that if you exhibit symptoms of a disorder, doctors are obliged to treat that -- if they don't, you can sue them for negligence, even if they disagree with the diagnosis or with the whole approach.

Throw in the mix here the interests of the big pharma and medical insurance businesses, et voila, on a systemic level you have a recipe (pun not really intended) for a disaster. (Scientific integrity? No, haven't heard of that one, does it mean we make less money?)

the major reason enshittification happens is because of how software development is financed.

basically, as a company creating a product, you front the dev costs to build something that usually can’t justify those costs on its own. in other words, if you just put that money into an investment fund instead, you’d probably end up with a higher NPV.

but you still do it, because you expect the project to eventually consume itself through enshittification... and in the process, recoup the costs and hopefully make enough extra to keep the bean counters happy.

this dynamic has also created a warped job market for developers, who got too used to being paid a lot thanks to the speculative value of those future enshittified products they build. it does look like that market is now going through a pretty major correction.

oh, and let’s not forget that enshittification works because people still click on those ads and whatever else. it really DOES make more money for the company, at least in the short term. public corporations aren’t stupid, they’re merely myopic by design.

and backdoors for law enforcement and intelligence and whatnot. teh irony

My experience as a CTO hiring developers is very similar: there just aren’t many fresh graduates worth hiring anymore. I’m not sure exactly what changed: maybe universities got worse at teaching? maybe students got worse at learning? maybe a bit of both.

Ten years ago I had a steady supply of promising graduates to choose from. Now, most fall into two categories: a small handful with multiple offers (hard to compete for), and many others who are net-negative for at least two years. They require so much time and effort from more senior colleagues before becoming productive that the investment rarely pays off. Especially if you consider that often once they finally reach that point, they leave before they even break even. So what’s the point of hiring them in the first place? That's why we're only hiring folks with 5 or more of experience now.

(For context, I’m in the EU, so YWMV.)

I would surmise that advancements in AI have also filled much of the demand for simple tasks that can be done for a fiver.

The Spotifies and Apple Musics of the world are also better positioned on the market to sell their own AI "music". They are both tech-savvy AND they also know what the general public likes (and they probably have some impact on the said general public's tastes in music as well).

As a heavy metal fan, I, for one, welcome new AI overlords, and would totes listen to "new" Beethoven or Bach music, once it gets good enough. The purists can eat their hearts out for all I care.

In my macro economy classes, it has been repeatedly "proved" that having less population is beneficial for the wealthy. My inner conspiracy theorist is 100% sure that the whole LGBTOMGWTFBBQ propaganda is mostly there because THEY decided that promoting anything other than a traditional family, which known to be the most children-friendly, will make them richer in the end. My inner skeptic is unsure, but acknowledges that the conspiracy theorist has a point.

which qualities should a programming language / platform have so that it can benefit more from LLM-based tools?

I asked ChatGPT and here is how it ranked TIOBE's top 20:

Top tier -- AI-friendly (9 - 10/10)
These languages get the biggest immediate productivity boost from AI: large corpora, great tooling/IDEs, REPLs or declarative style, and stable ecosystems.
* Python (TIOBE #1) -- huge training corpus, ML ecosystem, REPL + notebooks -> excellent NL-> code and copilots.
* JavaScript (TIOBE #6) -- massive web ecosystem, short idiomatic snippets that LLMs do well at.
* C# (TIOBE #5) -- Roslyn + mature tooling give strong static-analysis signals for safe code generation and refactors.
* SQL (TIOBE #12) -- declarative queries map exceptionally well from natural language; high precision generation.

Strong tier -- Very good fit (7 - 8.9/10)
Large ecosystems and/or excellent tooling -- AI helps a lot, but either low-level detail or domain specifics require caution.
* Java (TIOBE #4) -- huge corpus + static typing reliable refactors, test generation; boilerplate can be an issue.
* C++ (TIOBE #2) -- massive codebase; templates and patterns help, but low-level UB and build/ABI complexity reduce autonomous use.
* C (TIOBE #3) -- plentiful examples, but pointer/UB and platform specifics make fully automatic changes risky.
* Go (TIOBE #8) -- simple, opinionated style and gofmt = AI produces readable, idiomatic code reliably.
* Rust (TIOBE #18) -- excellent compiler diagnostics let AI produce correct-by-construction fixes; steeper semantics but very promising.

Mid tier -- Useful but with caveats (5.5 - 6.9/10)
AI helps productivity (snippets, prototyping, migration), but domain-specific tooling, legacy idioms, or small corpora limit scope.
* Visual Basic (TIOBE #7) -- large legacy base and lots of examples; useful for migrations and automation but inconsistent modern tooling.
* Perl (TIOBE #9) -- expressive one-liners & CPAN help, but varied idioms make safe generation harder.
* Delphi / Object Pascal (TIOBE #10) -- legacy GUIs and embedded uses -- AI aids porting and snippets.
* PHP (TIOBE #15) -- huge web examples/frameworks; dynamic quirks can trip up blind generation.
* R (TIOBE #14) -- great for data tasks; AI helps plotting/analysis code but less so for large engineering systems.
* MATLAB (TIOBE #16) -- numeric/algorithm prototyping benefits from AI, smaller ecosystem for broader tooling.

Lower tier -- Niche / harder for LLMs (3.5 - 5.4/10)
Specialized domains, niche audiences, or small indexed corpora reduce immediate AI impact.
* Fortran (TIOBE #11) -- legacy scientific code -- good for modernization tasks but limited general tooling.
* Ada (TIOBE #13) -- safety-critical focus and niche community; compiler checks help, but corpus is small.
* Kotlin (TIOBE #19) -- modern language with good IDEs, but smaller training footprint vs the biggest languages.
* Scratch (TIOBE #17) -- educational/block language: AI can create lesson content, but not much production automation.

Legacy / very low-benefit tier (0 - 3.0/10)
Very context-sensitive, architecture-specific or educational-only languages where generic LLM assistance is least useful.
* Assembly language (TIOBE #20) -- highly architecture- and context-specific; AI can suggest patterns but needs deep hardware context.

would you agree with the LLM's "logic"?

I get that part, it's always worked this way. But using layoffs to meet targets only really started a couple of years ago (at least in this cycle), and it wasn’t just Microsoft -- it happened across the IT sector. The recent advances in AI alone don't explain the scale of these layoffs. The only explanation that makes sense to me is that shareholders have become more demanding. They may not micromanage layoffs directly, but they can easily look at some metrics and say, "Looks like there’s too much fat there, so go and trim it." Whether those metrics are accurate or not, I have no idea.

So what does this have to do with the executives? They are doing what board told them to do. If board told them "we need to cash out immediately because a financial crash is coming", that's what happens. It's not like only Microsoft is affected by this.

Banal or not, Candy Crush Saga is a fantastically profitable game. If what you describe as drek is what customers like and are willing to pay for, why would it be surprising that someone is happy to provide?

As for the layoffs, it appears that the general trend is to remove the money from businesses and into the hands of the wealth managers, possibly in anticipation of the coming financial crash. And King most certainly does have fat to lose, from various SVPs of Talking to Other SVPs to copywriters that have become as obsolete as typists in the age of laser printers. Sic transit..