If anything - it seems that AI is _attacking_ the things the elite have. Elite people have private tutors/expert networks, now people do too for $20/month. What if the cost of education collapses to zero? It already sort of was - I learned to program on a ~$1,000 computer while Google engineers had access to $100s of millions of hardware, and it was enough.
Also, open-weight models are powerful and relatively cheap to run. So it's not like OpenAI can just close the doors (short of extremely strict regulation which seems to be a lost cause).
I second that question. Several times already I wished I had such a setup because the system was so congested that jobs would just time out.
Of course you can increase the duration, but if something is supposed to take less than 10 minutes but takes over half an hour, increasing limits won't really address the problem at hand.
Many medical practices (and other businesses) are poorly operated and administered. I think of my dentist: terrible website (even by 1995 standards), awful at follow up, weird insurance coverage (since she doesn't have time to follow up with new plans it seems) and almost no appointment reminders. There are obvious things to do that could drive business for her.
It makes sense to me that someone could come in and say "hey, let me run the business + finance side of the house while you practice medicine" and at least on paper I can see a real world where that works out for everybody.
Of course, soon you end up with dentists pushing unnecessary procedures and more, so it doesn't always works out that way.
In human med, it's a pretty standard practice to offer a management company 10% ownership for them to handle the business shit.
I'm not arguing with you; I'm legitimately curious what happened to that model and why PE has swooped in as more attractive to doctors. Maybe it's the payout and/or the fact that they don't have to handle business owner decisions at _all_ anymore?
Those are two different things, typically.
A business manager taking 10% means they're still running the practice day to day and seeing patients.
PE will come in to buy the entire practice from the doctor/dentist/vet that owns the practice, buying it from them entirely.
Oh, definitely. But what my parent comment was suggesting that PE do for their dentist is what 10% business management companies were already doing.
An acquaintance doctor in vet med was pitched 10% ownership in a practice buyout; PE would do business management. The doctor countered with the PE company getting 10%, since they won't be doing any medicine. The PE company declined.
Improving efficiency and optimizing profit are two overlapping segments on the road to crap. It’s not easy to stop the car once you put the bean counters in the driver's seat. If a company is able to do so, it’s usually because there’s a strong leader or culture to resist the slide. But otherwise it’s a thousand small decisions that all seem reasonable on their own.
Maybe a tangent, but I've gotten a lot of the joy back with 3D printing. You can just describe something, and Astra can model it in CAD extremely well. It usually takes a few iterations, and then you can just print it. I found that before, it would take days to build even simple models in Tinkercad - which while fun, was high enough friction that a lot of things would stay in the "ideas" folder - now I can have something real in an hour or two.
But I think it's really about finding the right abstraction. As a staff or principle engineer in the beforetimes, I also didn't understand most of the code being written by the org. I dove in where needed, but more importantly I found the right layer to understand what was going on - dependencies between teams and services, data flows and data models, etc. And where I found myself most effective was when I was shaping and understanding those effectively.
I don't know. We've managed to avoid most of this, I think. We have tech debt, but it feels more like regular tech debt than sloppy engineering practices. What's worked for us:
- Very strict linting, type checking, and so on.
- An opinionated flow of data and clear layers in the codebase.
- Good code review workflows with multiple AI code reviewers, along with a pretty opinionated SDLC.
- Agents that look for duplication and opportunities to simplify.
- Strong opinions about our core data model.
I really think that the companies that are struggling with slop probably struggled with slop before. It just moved slow enough that things could be fixed.
I don't understand this slop debt either. I make lots of small changes, as I did before, alongside self-reviews. This time, the AI puts together the PR and helps with reviews.
> Slop debt comes from a lack of decision. Nobody chose the variable names, the error handling pattern, or the layer of indirection that does nothing.
Ofc people agreed on these things (and avoided the last one) because our teams do proper objective reviews.
> You open a file to fix a bug and find three different retry strategies, two different logging conventions, and a helper function that duplicates a standard library method for no discoverable reason.
Our team literally used AI to remove this duplication. It's very good at it.
> Slop debt resists this because it is everywhere at once, at low intensity. There is no single module you can point to and say, this is where the debt lives. It is smeared evenly across the whole codebase, a little bit of incoherence in every file.
This is pure FUD. Automated consistency will out perform manual consistency at scale and over a long period of time. This is statistically relevant and one of the many things AI is helpful with.
I still don't understand what the "debt" here is supposed to mean and this seems like the same anti-AI hand waving I've been seeing all year. Probably aimed at people who are vibe coding entire projects or modules, I assume.
It's my own ignorance speaking, but is this as true in math? I could see this being a problem for programs where you have few, if any, real axioms and the axioms themselves change. But if we're talking math, the axioms should be fixed.
Most math does proofs somewhat informally (as in the proofs are written in a conversational style, and they're considered valid when they convince the majority of mathemeticians), but those proofs can be traced back to ZFC (a specific formulation of set theory and first order logic).
Type theory, especially dependant types, are a pretty recent development, and aren't really used in practice in math. Even in Mathlib (what most people mean when they say they've used lean), they encode first order logic. The proofs are dependently typed, but the actual logic of the proof is all in ZFC.
For what it's worth, I really enjoy superpowers. In particular, it does a great job with TDD that stops the model from jumping to conclusions, and I've been able to get it, even with Opus, to execute on much longer specs quite well.
In addition to what other people have said, I've taken some time to do leetcode questions lately - both architecture ones and coding ones. I'm not looking for a job by any stretch, but the practice and forcing a detailed zoom in has been really cathartic, and leetcode gives a nice structure/feeling of progress to it.
If anything - it seems that AI is _attacking_ the things the elite have. Elite people have private tutors/expert networks, now people do too for $20/month. What if the cost of education collapses to zero? It already sort of was - I learned to program on a ~$1,000 computer while Google engineers had access to $100s of millions of hardware, and it was enough.
Also, open-weight models are powerful and relatively cheap to run. So it's not like OpenAI can just close the doors (short of extremely strict regulation which seems to be a lost cause).
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