I thought the same thing. But, should we be surprised about what people believe in these days?
I think that the issue is in part due to the variables. Plotting the mean request time is less intuitive than plotting throughput.
If you plot throughput vs number of servers, it'll be a straight line. And asking people that, I think most would agree on a straight line. But who knows!
If the producers see a long term demand for memory they will meet the challenge and produce enough to bring prices down.
Economics will invariably alleviate the memory crunch. It just takes long and requires a huge upfront CapEx.
They have been burned in the past and are hesitant to over invest, worried that the bubble might burst.
I expect high prices to stick around for a while, but I would be surprised if this was permanent.
Which means to me, that price pressure probably won't be the driving force for writing more memory efficient software.
For those who want, I expect AI to make it easier to do that, assuming it's done right, i.e. not vibe coding it.
If you have a subscription to The Economist, I recommend listening to this Money Talks podcast. They talk about the shortage and the economics behind it.
The bike/motorbike/car analogy is good. In each you can find enjoyment that suits your character.
I have been drawn to coding from the first day I took a course at college learning Pascal and assembly on an IBM PC. Hell I even wrote an IEEE/488 driver for an Osborne 1.
As an experimental physicist, coding was always central to my work. I always felt guilty because I had more pleasure coding than tackling the physics itself.
This is a new age we are entering. AI changes how we do things, but I believe that the human passion to be creative and do intellectual work will always be part of it. It's in our nature.
So I'm not worried that we'll all dry up and turn into zombies.
For me personally, holding a dialogue with several agentic dev streams in parallel keeps me very much on my toes mentally. It's quite exhausting, and certainly not letting my brain atrophy.
I'm probably losing some coding skills, but replacing them with different ones, and honing some others.
I used to manage dev teams of 20+ people inside high pressure, high stakes projects.
I've been coding all my adult life, on big things and small.
To me, agentic engineering is a deja vue of managing teams, except in real time.
Nobody uses Amex for payments, so the system isn't ever under high load.
Just kidding!
I find the idea quite good, and have to assume that the amount of payment fails they experience due to partitions/outages isn't very high and that the post-payment reconciliation and reclamation process gives them the liberty to rank availability a bit higher than correctness.
One thing that looked a bit shaky was the interplay between the global transaction router's state of knowing which cells can handle a particular payment and the asynchronous distribution of the "failover data", which I presume it needs to know to route correctly. To me that seems to create a window where it might route to the wrong cell due to an outdated routing state.
It also doesn't go into the HA setup of the global transaction router itself.
Consumer-grade subscriptions of the frontier models give you superb capabilities per dollar, them being heavily subsidized. But if you're working in an enterprise setting, that won't work. You need to upgrade, and that gets significantly more expensive.
Furthermore, basing the SDLC on leveraging the bargain subscriptions risks falling apart in the future, both from a cost perspective as well as the question of availability (e.g. Mythos).
So from a strategic perspective, going local on the LLM and still achieving great results with the right approach is very relevant.
> The New York Fed blog noted that the modest impact on jobs so far may not hold in the future. "Looking ahead, firms anticipate more significant layoffs and scaled-back hiring as they continue to integrate AI into their operations," New York Fed researchers wrote.
That may hold to a certain extent for relational databases where your business model doesn't align well with physical model (tables). Although you might wonder why stored procedures and triggers were invented.
In databases where your domain is also your physical data model, coupling business logic to the database can work quite well, if the DBMS supports that.
But, I think the underlying problem is that we don't understand how this sh*t works. So, it's just an empirical, iterative mess.
Like physics in the the years shortly before relativity and quantum mechanics.