OOP was (at least partly) an attempt to create a mechanical system for modeling any area of knowledge with only partial understanding. As a result, its default result is usually extremely defensive and verbose code (see screenshot).
Techniques like abstract base classes and inheritance hierarchies are precisely for guarding against future changes caused by ignorance or lack of planning. The problem is this level of generality/flexibility is almost always unnecessary and has non-trivial compile time, run time, and complexity costs.
Since people seem to be confused about this, here is a more complete explanation:
I thought the point of the OP was that it is important (for any reason) whether or not something can be determined reliably to be a Monet painting or an AI derivative of a Monet painting - as in, if given an A/B test, can an average (or even well-trained) human tell the difference between something painted by AI and something painted by Monet? Will they misidentify a Monet as AI? Will they misidentify an AI as Monet? Etc.
My point in saying Monet is a bad example (or any famous painter, I might say) is because that criteria has not historically been important even before AI, as far as I can tell. Humans seem to care whether a painting was painted by Monet, not whether anyone can tell by looking at it that it was painted by Monet (including themselves). They will happily invest in a painting if they are assured it is Monet, whether or not they themselves could ever tell the difference between that painting and a forgery, and they will be very upset if it later turns out it was not painted by Monet - even though they clearly could not tell the difference in the interim, etc.
So to me, they were trying to prove one point, but accidentally proved the other point. Can most humans figure out if something is a genuine Monet or not? No. Do most humans care if something is a genuine Monet or not anyway? Absolutely.
And I would argue this is a very important thing to understand about "AI art". Humans care about the origins or things - they don't just care about the things, even if they cannot themselves determine that origin definitively.
I strongly believe there are entire companies right now under heavy AI psychosis and its impossible to have rational conversations about it with them. I can't name any specific people because they include personal friends I deeply respect, but I worry about how this plays out.
I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now its... the whole software development industry (maybe the whole world, really).
It's frightening, because the psychosis folks operate under an almost absolute "MTTR is all you need" mentality: "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!" We learned in infrastructure that MTTR is great but you can't yeet resilient systems entirely.
The main issue is I don't even know how to bring this up to people I know personally, because bringing this topic up leads to immediately dismissals like "no no, it has full test coverage" or "bug reports are going down" or something, which just don't paint the whole picture.
We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happens so fast that nobody notices the underlying architecture decaying.
I worry.
Also, if you claim AI is useless, that is a claim about the current state of AI. If it subsequently improved to the point where it was clearly not useless (from your perspective), then if you are honest you have to revisit your objection. It's not a very sensible position to hold about an emerging technology which changes rapidly.
There are plenty of reasons not to use AI, or not to like AI - and there are many, many, many reasons not to like AI companies. But AI itself being "useless" now and forever doesn't sound like a well-considered reason to me.
All effort to make my words seem full
Falls forward once I read them more
I toss away my scrawl
Now only ever said in my head at all
I wish I had known you more
My empty self to think
What song could I have sang
Whose voice could I have rang
I wish I had made you my friend today
Professional Programmers:
Stop being cheap and starting paying for your tools.
$99 for a Text Editor which you'll spend 7+ hours a day in—how is that even an issue?
Same with loads of tools like Superluminal at €289. If you're a developer, you're making that back in days.
“Force begets force, hate begets hate, toughness begets toughness…ultimately ending in destruction for all and everybody. Somebody must have sense enough and morality enough to cut off the chain of hate and the chain of evil in the universe. And you do that by love.”
—MLK Jr.
How to become a better programmer?
The only way is to attempt a project that's above your pay grade, above your current skill level.
This is it, no way around it.
The only Tech I'm excited about in 2026 is probably "Jai", which is quite sad that it the amount of Tech I'm excited about narrowed down this much at this point...
@cmuratori If you love running, you should LOVE cars. You can complete so many more marathons than you could otherwise without them, and you won't even break a sweat. Incredible.