Enrollment at Trade Schools Is Expected to Grow

Are you so sure?

Almost every animal has solved locomotion, some even with incredibly primitive brains. Evolution knocked this out of the park hundreds of millions of years ago.

Drosophila can do it, and we've mapped their brains.

Only a few animals have solved reasoning.

I'm sure the robotics videos I've seen lately have been cherry picked, but the results are nothing short of astounding. And there are now hundreds of billions of dollars being poured into solving it.

I'd wager humans stumble across something evolution had a cake walk with before they stumble across the thing that's only happened once in the known universe.

Your exponential problems have exponential problems. Scaling this system is factorially hard.

> Five years from now AI might still break down at even a small bit of complexity, or it might be installing air conditioners, or it might be colonizing Mercury and putting humans in zoos.

do all these seem logically consistent possibilities to you?

OK, I will bite.

So "Sparsely-gated MoE" isn’t some new intelligence, it's a sharding trick. You trade parameter count for FLOPs/latency with a router. And MoE predates transformers anyway.

RLHF is packaging. Supervised finetune on instructions, learn a reward model, then nudge the policy. That’s a training objective swap plus preference data. It's useful, but not breakthrough.

CoT is a prompting hack to force the same model to externalize intermediate tokens. The capability was there, you’re just sampling a longer trajectory. It’s UX for sampling.

Scaling laws are an empirical fit telling you "buy more compute and data" That’s a budgeting guideline, not new math or architecture. https://www.reddit.com/r/ProgrammerHumor/comments/8c1i45/sta...

LoRA is linear algebra 101, low rank adapters to cut training cost and avoid touching the full weights. The base capability still comes from the giant pretrained transformer.

AlphaFold 2’s magic is mostly attention + A LOT of domain data/priors (MSAs, structures, evolutionary signal). Again attention core + data engineering.

"DeepSeek’s cost breakthrough" is systems engineering.

Agentic software dev/MCP is orchestration, that’s middleware and protocols, it helps use the model, it doesn’t make the model smarter.

Video generation? Diffusion with temporal conditioning and better consistency losses. It’s DALL-E style tech stretched across time with tons of data curation and filtering.

Most headline "wins" are compiler and kernel wins: FlashAttention, paged KV-cache, speculative decoding, distillation, quantization (8/4 bit), ZeRO/FSDP/TP/PP... These only move the cost curve, not the intelligence.

The biggest single driver the last few years has been the data so de dup, document quality scores, aggressive filtration, mixture balancing (web/code/math), synthetic bootstrapping, eval driven rewrites etc etc. You can swap half a dozen training "tricks" and get similar results if your data mix and scale are right.

For me a real post attention "breakthrough", would be something like: training that learns abstractions with sample efficiency far beyond scaling laws, reliable formal reasoning, causal/world-model learning that transfers out of distribution. None of the things you listed do that.

Almost everything since attention is optimization, ops, and data curation. I mean give me exact pretrain mix, filtering heuristics, and finetuning datasets for Claude/GPT-5 and without peeking at the secret sauce architecture I can get close just by matching tokens, quality filters and training schedule. The "breakthroughs" are mostly better ways to spend compute and clean data, not new ways to think.

It’s like asking a college student 4th grade math questions and then being impressed they knew the answer.

I’ve use copilot a lot. Faster then google, gives great results.

Today I asked it for the name of a French restaurant that closed in my area a few years ago. The first answer was a Chinese fusion place… all the others were off too.

Sure, keep questions confined to something it was heavily trained on, answers will be great.

But yeah, AI going to get rid of a lot of low skilled labor.

All it took was an internet connection and a decent laptop.

See the many software and other computing people who successfully run under a consultant / contractor model. You can absolutely be self employed. Good service at a proper rate (and pretty high too, usually.) Self employed and high percentage remote if you want it.

This strikes me as underselling the hurdles here. Ignoring the whole "just start a self-sufficient business" thing, what happens when you get sick? What about medical costs as you age? Retirement plans?

Typically you are sponsored by your employer.