We’re about to learn a painful lesson about delayed gratification in software engineering.
New data from China, 26,811 students tracked January 2023 through June 2025. Students using AI for homework saw their scores jump 20 percent. Completion time dropped nearly half. They aced the assignments.
Then exam season came. Those same students scored 20 to 40 percent worse when they couldn’t use the tool.
The homework phase is over. The exam phase is coming.
We’re doing this in software right now. Vibe coding feels incredible. Features ship fast. Nobody’s asking what happens in Month 18 when the original dev has left and nobody understands the codebase.
Commercial pilots fly with autopilot for most of every flight. They’re required to maintain manual flying proficiency regardless. If the system fails mid-air and the pilot can’t take over, people die.
Most teams using AI right now have forgotten how to fly manually. They’ve become passengers in their own systems. The autopilot flies, nobody checks instruments, and the first sign of trouble will be a breach notice or outage.
Three rules:
- Command the mission. Define architecture before prompting. Ambiguity kills in code and in flight. Delegate selectively. Offload mechanical work. Keep design and security reviews human. Verify everything. Audit before production.
- Never trust the automation without checking instruments.
- Quick wins feel good. Sustainable engineering feels boring. Boring keeps systems standing.
Organisations surviving the next two years won’t ship the fastest. They’ll be the ones who remember how to fly without the aids.
people insisting that you actually be skilled, independently of your tools, doesn’t make them Luddites. Rather, being unable to do so makes you a phony.
with local open weight models that is no longer an argument tbh
Who can afford the hardware to run an open weight model? We aren’t all earning enough to afford high end NVIDIA cards (when they’re even available).
If you consider what companies pay for the subscription you will see that the hardware prices aren’t really the issue here.
I’ve done it at my job.
lucky for you to have your boss paid for it. but it’s likely not yours, but your company owns it, and you have access as long as you work there. after that, you can say goodbye to your “professional expertise”
this is completely different from the calculator that can run off its puny solar panel
I run my models on a 1070. It’s slow but works.
You may be overestimating the hardware requirements for current open weights. I am running Qwen3.8-27b with 52.000 tokens context on a RX 7900 XTX from 2022 with 15 - 30 tokens / second. Sure, it was a high end card back then but for a graphics card today it’s quite affordable when you’re looking for a used one. Also it’s not blazingly fast, but it gets the job done.
“Just run it local, you can use cheap hardware”
is running it on a $1200 card and is still struggling to get it done quickly
Keep on sucking up that slop, I’m sure paying a company to do your thinking for you will pay off in the end, as we’ve been seeing.
Solarpunk scoffing at anything but maximum efficiency for minimum price is deeply funny.
They used a computer they already own and made up for the difference with simple patience… what a corporate whore, right? How dare they want their personal machine to do private magic unless the numbers say infinite growth forever.
Im not scoffing at efficiency, my issue is with the technology being paid for - the one that’s actively being used to ruin the planet, destroying old single edition books because they’re untainted by the slop bein pushed out, not to mention how it’s being used to spread misinformation, propaganda, mass surveillance, security threat level code, scams, and of course deepfakes of everyone from children to politicians.
When I bought that card this year I payed significantly less than you’re suggesting. That’s still a lot of money but it’s not more than many gamers where willing to pay for their gpus way, way before ramageddon. Of course you can’t run open weights on a potato but you also don’t need a $4000 gpu to do so.
As for the rest of your comment? What? 🤷
How much did you pay?
I spent $70 on a “new” Aisurix amd radeon 580 2048 SP graphics card earlier this year, but it was a sidegrade more than an upgrade to the Nvidia GTX 980 i had before (which had declining Linux support).
Modern graphics cards seem to be very expensive compared to how much it used to cost to be a few years behind the cutting edge.
I payed around 700 € used, but in turn I sold my previous GPU for ~ 400 € and I got a significant performance upgrade for that money.
Your graphics card cost more than my entire computer lmao
Sorry, I didn’t want to come across as detached. It’s true that my hardware budget increased in the last ~ 15 years but even then I would have considered 700 € (that’s what I paid for my card) a very tight budget for a moderate gaming pc in my country.
Okay, well you’re on a forum filled with nerds. The average AI user is asking ChatGPT or Claude or whatever the fuck. I garun-fucking-tee you they do not have the knowledge of local models or the capability to set one up. Like genuinely most Americans are fucking stupid.
The “open-weight models” argument just a fallacy IMHO, frontier models would require a pretty strong AI PC, not something that GTX1050 you put away as a backup could handle. The “just use smaller models” argument just gives leeway to just not using AI and using more classical methods of coding - use of dependencies, use of code generators, use of semantic search engines (can someone suggest me one?), etc. LLMs don’t do anything particularly new, they’re just mimic how humans talk.
Not every software company is a game developer, and even at home, not every programmer plays games on their PC that require a powerful GPU. If everyone working on, for example, business software all of a sudden needs a gaming PC that will be constantly running an AI model, that will greatly increase costs, both in terms of procurement of equipment and electricity. An increase in demand for powerful GPUs in fields that traditionally had no need for them isn’t exactly going to improve the hardware cost situation either.
Whether you run the model locally or you leave it to a company, someone is going to have to pay the cost.