I guess it depends on your definition of "too much", but also on your definition of "thinking".
What I've found is that LLMs are excellent tools for:
- Spitting out white collar corporate speak that sounds plausible to other corporate speakers
- Hugely accelerating the process of going through academia which, except for a few specific cases, is essentially years of copy/pasting
- Writing code as long as such code as been written many times before
- Figuring out which one of the 10 ads and 12 misleading links in a web search is the one you're looking for
- Summarizing a book in ten paragraphs so you don't have to read it
...which turns out a lot of people defined as "thinking". I don't agree with that. But in either case, not "too much" is being offloaded: it's all of it.
Those metrics are hugely misleading because they account for current fiscal year revenue and margins.
For Xbox being what it is today, which is mostly about the subscription and not the hardware console or the exclusives, you have to compound their acquisition frenzy of 2018-2020 or so, which totals about 75 billion+.
They didn't want the developers nor the catalog. What Microsoft wanted is to change the economics and dynamics of the entire games industry, to make it Netflix-like (play what's in the catalog today, pay monthly even if you don't play anything) vs. what Steam offers (purchase once, own "forever", even if it's de-listed).
But that didn't play out. Optimistic estimates put total revenue for Xbox since then in the 20B ballpark. At a ~5% margin (as other commenters have pointed out) the profit is about 1B dollars.
It means that after almost a decade, the entirety of Xbox is in the red for about 74 billion dollars, which is 74 billion away from breaking even.
Steam still dominates PC gaming. Xbox consoles can't be more irrelevant today.
This isn't about over-hiring or AI. It was a bet at the executive level that went horribly wrong. They can still do things like selling IPs at a bargain to compensate, but still. Horribly wrong.
Note: Microsoft doesn't publish hard factual data so the numbers above are somewhat speculative (e.g. "analysts data")
Matches my experience. Hard science may not have that many maximalists. Applied science and technology (electrical, electronics, computer science, even mechanical), I've found plenty.
What got me out of academia (yup, I was a professor) was:
Do you have vast field experience and want to get into the classroom to teach how it's really done? Tough luck, you should've spent your time writing papers.
No matter how much you know or how good you are, everything is about feeding the maximalist machine, if you're an outlier, worse people with better "scores", more papers and never leaving faculty will forever beat you until they retire.
I took a good look at the publishing process. Absolutely everything about it was back-channeling to carefully select the topic, scope and reviewers of a paper to get it through the process. Goodhart's Law at its finest.
Advice given to me: "aim for a lower rank and be happy with teaching the whole thing while the old professor takes a nap in the corner".
AI or not AI, anything destroying that self-perpetuating bureaucracy is welcome.
And I welcome the change. In my long experience in academia, I've only found two types of practitioners:
1% are the absolutely brilliant minds that academia was originally created for. People that, without a doubt, leave their mark in the vast corpus of human knowledge. I consider myself fortunate for meeting and learning from them, and I thank the academia ecosystem for that.
But the remaining 99% are the maximalists, as described in the article. More papers/students/grants, then repeat. Worse enough, they're absolutely useless outside of academia, as they never did anything at all outside that bubble.
An embarrassing lot of CS professors would stumble around your average production codebase.
I think AI is just the final nail in the coffin for the latter bunch, as they have been dogs eating their own tail for decades already.
Most of the general, watered down tone of Corporate America that we love to hate comes from the legal department, usually at the late-stage point when they make all the decisions in a company: product, business, launches, strategy, direction, etc. Everything needs to run through legal, and they have a final say on everything, including every public communication.
That's why I'd love an interview with Steam's legal head. Sounds like they'd have some wild stories to share.
Your guess is not wild at all, and the article implies that (at least until the payment popup shows up)
My grandmother used to grow her own vegetables and fruits and had a minimal chicken farm for eggs until the early 2000s, all in her regular backyard, it's not ancient history or something that required a lot of real state.
Now there's a 15-story building and no land whatsoever where her house used to be.
I agree it's not "entitlement" specifically but there's something there. I guess by now everyone has experienced that type of person that "tries to help" by copy/pasting a bunch of AI slop and expecting you to work through the cognitive load of validating it.
The original post sums it pretty well, such big output inherently meant big effort, which was a proxy for good faith. Now that's gone.
Once upon a time there were engineers that used software. Just like any other tool, and usually in combination with electronic, electrical and mechanical equipment, all of them being very well aware of the laws governing it all.
But it was so great as a tool that some engineers didn't want to deal with the burdens and limitations of the physical world, and started focusing on software more and more.
Then the software engineers came, for whom the physical and mathematical aspect of the whole thing was just a distant history lesson (and preferably a problem in someone else's computer).
And after software engineers, the only constant in the entire ordeal will remain: engineering, in a shape or form that very likely nobody can predict right now.
I'm from the times when you had to purchase a separate chip to perform floating point math. It was called a math co-processor. [1]
After a few generations (and over a decade) that was indistinguishable from the CPU chip itself.
It's a long hyperbole, I know, but I think local inference is inevitable; and the big fishes know it.
Will that be a complex technical setup? An appliance? An additional chip in your motherboard? So transparent it's burned right into the CPU? Those are just implementation details. We're probably just one generational breakthrough away from it.
If you were there, you'd remember the UTF8/Unicode fiasco (how long it took, how many people was both relying and struggling with it, and how they needed to cover it up after even some hosting providers attempted a beta upgrade and had to roll it back).
There was a PHP 6 (I'm including the non-updates in PHP 5 "waiting" for it), they just had to rewrite history as of PR damage control back then. That's what the article you linked describes, pretty much.
I agree, as I was one of them. Rightfully so, because PHP 20 years ago was the prime example of a complete disaster. Not just occasionally, there were long years of incompatibility, missing implementations, incoherent errors, security issues, fragmentation, etc.
Paid my share of dealing with those problems with PHP 5 and 6 (after coming from PHP 4). I think it became a more sane ecosystem around very late 7.x to 8.
I won't touch PHP ever again, but I'm glad (no irony) that they finally were able to pull it off. There were some good ideas there, then they quickly became victims of their own success.
Nowadays, there's places (Amazon) where PHP is just forbidden at a company-wide level (not joking) because of their early, long-standing reputation of being a mess. Or places where they just gave up and re-implemented their own PHP (Meta). I don't see that changing any time soon.
Same as with the "good internet". It's still out there, only under layers and layers of mediocre and/or terrible stuff nobody has the time to go through.
What I've found is that LLMs are excellent tools for:
- Spitting out white collar corporate speak that sounds plausible to other corporate speakers
- Hugely accelerating the process of going through academia which, except for a few specific cases, is essentially years of copy/pasting
- Writing code as long as such code as been written many times before
- Figuring out which one of the 10 ads and 12 misleading links in a web search is the one you're looking for
- Summarizing a book in ten paragraphs so you don't have to read it
...which turns out a lot of people defined as "thinking". I don't agree with that. But in either case, not "too much" is being offloaded: it's all of it.