In the meantime keep learning and practicing cs fundamentals, ignore hype and build something interesting.
Anyone who tells you they know what the future looks like five years from now is lying.
On a codebase of 10,000 lines any action will cost 100,000,000 AI units. One with 1,000,000 it will cost 1,000,000,000,000 AI units.
I work on these things for a living and no one else seems to ever think two steps ahead on what the mathematical limitations of the transformer architecture mean for transformer based applications.
Humans also keep struggling with context, so while large contexts may limit AI performance, they won't necessarily prevent them from being strongly superhuman.
> 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.
> 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.
that each of these things, being logically consistent, have equal chances of being the case 5 years from now?
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.
Not necessarily a bad approach but feels like something is missing for it to be “intelligent”.
Should really be called “artificial knowledge” instead.
No, it's more like asking a 4th-grader college math questions, and then desperately looking for ways to not be impressed when they get it right.
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.
What would have been impressive is if the model had replied, "WTF, do I look like Google? Look it up there, dumbass."
>There’s a significant difference between predicting what it will specifically look like, and predicting sets of possibilities it won’t look like
which I took to mean there are probability distributions around what things will happen, and it seemed to be your assertion that there wasn't, that a number of things only one of which seemed especially probable, were equally probable. I'm glad to learn you don't think this as it seems totally crazy, especially for someone praising LLMs which after all spend their time making millions of little choices based on probability.
What's the point of this anecdote? That it's not omniscient? Nobody is should be thinking that it is.
I can ask it how many coins I have in my pocket and I bet you it won't know that either.
It’s not that it knows grammar, it just was trained on a dataset that applied proper capitalization.
Humans learn from seeing patterns. I suspect AI only repeats them, more like a parrot.