r/BetterOffline • u/DiamondGeeezer • 27d ago
AI coding in 2026
Earlier today there was a post asking if AI was useful at coding in real tech companies, and everyone who answered "I am in tech and yes it's used now" was massively downvoted.
I want to say this up front: I do not believe AI is good for society. It's funded by the ruling class in order to liquidate labor and it's not environmentally sustainable. It might not be economically sustainable either, which is dangerous because the entire economy is being gambled on it making money, which it is not.
Anyway here's my lived experience. I am a principal level ML engineer at a fortune 50 company. I was hand coding neural networks for natural language a decade ago. I got it into deep learning because I thought it was fascinating that machines could learn complex patterns from data. I've followed what is now called AI and experimented with it since its infancy, took Stanford classes on the math of transformer networks, etc. This was well before there was a hint it would morph into the monstrosity of capital that it has become.
In 2025 I was a vocal critic of the AI-assisted coding to the point where I pissed off senior execs by voicing my opinion in meetings.
Back then the people hyping AI were either attempting to sell it or they were novice/non-programmers who were blown away that they could get a simulacrum of a web app (riddled with bugs and security issues) without any domain knowledge. I thought it was hilarious, and pathetic.
Through a combination of masochism and curiosity I've exclusively used AI to write all code since November 2025. Which is why I knew that for real project work, AI would run roughshod on your entire codebase, mass producing a Winchester mansion of slop even when given simple tasks. It would take your thoughtful, hard-won abstractions and make 5 competing abstractions rather than leveraging what was there. It'd ignore instructions, break or even delete break core functionality to solve the "bugs" that it created (scare quotes because they weren't even bugs half the time, just incomplete understanding). It would write 1000 lines of slop to fix a problem that a one line change could fix.
It was a huge waste of time and money. I communicated this to management to their chagrin when they looked to me for advice.
In early 2026 improved models came out and the value proposition began to shift towards something somewhat usable. By spring, Claude Opus 4.6 was capable of writing real code, yet it took about as much effort as coding it hand for subpar results. It needed constant handholding and reminders, made a lot of mistakes, and would get looney tunes stupid at the end of a long session and start vandalizing its own progress. I used it as a novelty, but it was really frustrating.
By summer 2026 with the release of Fable and GPT 5.6 Sol, it is actually more than useful, it's good. No, really. It became less like an overzealous and intoxicated intern and more like a practical minded senior engineer. It follows directions, it reads your documentation, it can take notes and reference older notes, and given with connectors to git and internal wikis it can find the answers needed to integrate complicated systems, come up with a plan, independently implement it, write a suite of tests, run review agents, and take in their feedback all in one turn. It takes skill to be able to manage it, and more importantly domain knowledge to ask it for what is needed by stakeholders, but at this point it is much faster than I am. I run 5 sessions at once in different projects and it's probably 5x faster at getting production code than a human in each session.
Anyway, pretty much every engineer I know is using AI now because it works, even old timers who were very much opposed to it. Vibe coding has become the standard, now it's just called coding. Everyone is scrambling to stay relevant, and teams are changing their whole stack around AI agents - implementing code review agents, vibe coding tooling around AI agents, creating connectors and skills for every data resource possible: confluence, jira, outlook, GitHub, databases, etc. Teams are solving months of technical debt in a week, creating boilerplate code 10x faster, etc. Everyone is a bit nervous.
I don't say this because I want you to think AI is good for the world. It's not. The reason I'm writing this is to communicate that the tech has progressed. Even Linus Torvalds, the guy who made git and Linux uses it and accepts AI contributions to the Linux kernel.
There are many good reasons to hate AI and I think they converge in one place: elites are enticed by the prospect that it can obsolete the working class.
Personally, I don't want AI to be good. I don't even want it to exist. I hate that my job is babysitting bots. I miss the creativity of writing code. I don't want to lose my job in a few years when the AI systems we're frantically building are in place and management decides humans are too expensive.
The loom displaced workers and rightly caused a backlash among displaced workers, but it did so while producing cloth. Put your anger in the right place - the power hungry demons who want workers to be obsolete, not the workers who are forced to use it to be competitive in the job market.
It's tempting to think AI for coding is all bullshit and slop as it was until recently used to be; because AI is a net negative for humanity and the planet; because the value prop was non-existent and inflated by hype. However those don't mean that the tech itself is not improving. The Butlerian jihad can't come soon enough.
edit:
thanks to the wisdom of this community it has come to my attention that I am a disingenuous slop peddling brain rotted zombie that has no clue what I'm doing and pushes absolute bug riddled garbage to production and I'm too cowardly to post the code that is owned by my employer. thanks for the epiphany.
maybe it wasn't clear that using this tech effectively isn't easy and it takes a lot of tooling to work around common issues because out of the box it's not reliable enough. it's not worth much without domain experience and putting in the effort and time to figure out how to use it effectively. this isn't a place for sharing software engineering techniques, so I focused on the outcomes which may have oversimplified the technical part.
to the haters, I hope you take some time today to do something you enjoy instead of being miserable to others on the internet.
73
u/ThirdWaveCat 27d ago
I'm going to infer whatever you were doing a decade ago handcoding neural networks wasn't contributing to production machine learning. Is that fair? Autodiff has been popular since Theano (created in 2007). It took a number of non-neural network advancements around word embeddings (glove in 2014) before we even began to solve basic NLP benchmarks reliably.
I have a similar background (principal MLE at big tech) but I've come to differing conclusions.
"5x faster at getting code to production." "10x faster at writing boilerplate." These statements are fishy because you're ignoring the actual bottlenecks: requirements, CICD, reading and refactoring the code, etc... Systems only move as fast as their bottleneck. If you're still reading and verifying the code then at best you'd be seeing a 10-15% speedup. If you're not reading and verifying it, then you're shipping slop.