Totally agree. When this current wave of GenAI really started heating up, I guess 2020-2021, my analysis was very straightforward. What are the high level inputs to long-term success? I basically came up with a couple of criteria:
1. Data. Lots of data.
2. Money. Lots of money.
3. Access to necessary hardware.
4. Business alignment/will to do it.
5. Access to talent, current and future.
This is certainly incomplete/naive. In my mind, though, Google was the clear answer.
On a more personal level, I've been deep into the Google ecosystem since I got diederich@gmail.com in 2005. (I actually paid 50 cents on ebay to get a very early invite.) There was no question in my mind that Google's AI work would deeply integrate into their whole ecosystem in very powerful and productive ways. (Yes, I can join you to discuss, at length, the various ways that Google's dominance is problematic/scary.)
Having said all that, I'm quite happy that there is, at the moment, a very rich competitive landscape. Indeed, not too long ago, with Gemini Pro 3.1 languishing, I moved most of my deeper thinking work to ChatGPT, which was, for me at least, clearly outperforming Gemini.
While I certainly didn't anticipate it, Google's strategy of making their fast/relatively inexpensive models surprisingly powerful has been a welcomed surprise.
I think you forgot to mention energy efficiency. If you build AI hardware in-house, your only other expense is energy and the producer surplus is greatest for companies producing below the equilibrium market price.
It's the difference between billions in revenue and billions in profits.
LWN is one of the cornerstones of my very successful career.
Starting from the beginning, I have (at least) read through every story and every article, every week, since 1998. Especially in the beginning, there was a lot in each article I didn't understand. I would try to do some digging into those topics.
One way or another, just the weekly exposure to absolutely top notch technical writing was foundational to me.
When newer folk would ask me how to really get good with Linux, I would always point them to LWN.net.
I've been a subscriber since the beginning. That small investment is the least I can do given the incalculable value LWN has brought to me.
Sorry to be that guy, but... Please consider donating a significant amount to them, given that they have been a cornerstone of your very successful career.
I did something similar in the past, in relation to Ubuntu, and I never regretted it, even if that money would have been very useful if spent in other ways.
I couldn't find anything about donation on the site. Just in case I'm blind, is there one or is subscribing or getting a gift certificate the only way? I should also definitely put lwn on my "charity list".
"It simply does not add up that someone who lacks purely technical skills in an area can make good decisions."
I've been thinking about this and your related points, and I've been (with growing concern) instinctively in agreement.
Another analogy has been coming up. I started programming (as a kid) in the late 1970s, first with BASIC (of course) but pretty quickly moved to machine language for obvious performance reasons, and then to C, followed by many other languages. In the late 1980s, at university, I learned proper assembly language, and used it quite a bit.
Back then, I was pretty good with assembly/machine language. Not super star or expert level, but comfortably solid. Since then though I've spent very little time at that low level.
Granted, such early exposure and experience has been a pretty big fairly indirect benefit. But over the years, I've met and worked with many really smart, productive people who have never had any experience at that low level, and that lack was rarely noticeable.
For any given 'layer of abstraction', there is some use in being familiar with the lower layers. I think, in general, the closer the lower layer is, the more useful the knowledge is. For example, how useful is deep knowledge of CPU microcode to a Java programmer? Perhaps some, but not a lot.
In the tech world, GenAI is likely to push most everyone quite a ways up the tower of abstractions. We're in the middle of that right now, and it certainly feels pretty alarming to me.
The biggest uncertainty that comes into my mind is how insanely fast this change is coming upon us. My crawl up the abstraction tower happened over the course of about 12 or so years, from machine language/assembly to C then to Perl then to many other things. In retrospect, that felt pretty fast.
GenAI is seemingly pushing us in that direction many times more quickly.
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