I was thinking the same thing. Humans push terrible code to production all the time that slips through code reviews. You spot it, you fix it, and move on.

I do not know the future, every line of code is a plausible-looking liability.

Good code is explicit about its assumptions and enforces them; good companies set hiring bars so as to filter out developers that can't write good code.

There's no such thing as bulletproof, but there is definitely such a thing as knowing where your vital organs are and how to tell when they've been hit.

They set up a GitHub action that has AI do an immediate first pass (hallucinates high on drugs and not the good kind) and leave a review.

Considering 80% of team mates are usually dead weight or mid at best (every team is carried by that 1 or 2 guys who do 2-3x), they will do the bare minimum review. Let’s be real.. PIP is real. Job hopping because bad is real.

It’s a problem. I have dealt with this and had to fire.

The G in AGI is a big deal and it’s missing from LLMs.

Anything coded by an LLM risks being under-generalised.

Asking an LLM to think in a generalised way does not make it an AGI. The critical ability to generalised beyond learned patterns and to not only come up with arbitrary patterns but to use correct logic to derive them is missing from LLMs because LLMs do not have a logical layer, only a probabilistic one with learned constraints. The defect is the lack of internal logical constraints. It’s a big subject.

I say more about it here:

https://www.forbes.com/sites/hessiejones/2025/09/30/llms-are...

Aka

“layered system”

A lot of people seem to equate using AI tools and deploying code that you don’t understand. All code should be fully understood by the person using the AI tool, then again by the reviewer. The productivity benefit of these tools is still massive, and there is benefit to doing the research and investigation to understand what the LLM is doing if it was not clear up front.

i'm not an llm superfan but i do find them useful for coming up with one-off scripts to process datafiles or other small tasks that would have taken me a couple hours to get right. The llm produces something 85% of the way there including command line argument processing and all that stuff i hate to type out. I just fix the broken bits and make some adjustments and i have what i need in about 20 min. For that kind of task they are very useful IMO.

you could localhost ollama, vLLM, or something like that; Open Models are good enough for simple task, With a bit of extra effort and learning, this is usually just works for most case.

But in that situation, there may be no further updates, the future remains uncertain.

I heard that photonic chip stuff might make running this stuff cheaper in data center environments.

That is literally the exact promise of CASE tools in the 80s and the early 90s; UML code generation tools in the 2000s, and "low-code/no-code" platforms in the 2010s. It turned out to be a disaster every time, especially when the Idea Persons chucked their creations over the wall to SWEs to bash them into actual products because the Idea Persons had Far More Important Things To Do than maintain their coalesced brain farts.

We're repeating history but with more energy consumption.

There better be new bootcamps on how to maintain these systems because without CRUD jobs how would someone new get the experience

Member of Golgafrinchian Ark Fleet Ship C here.

I like to make stuff, hack on projects. I code, I woodwork, I solder, I build. I love AI in the same way I love a router and dovetail jig in the workshop.

My son and I were playing minecraft, and we wanted to build a massive egg, just for fun. (My son is 5).

We try, we fail, we try again. We start learning about spheres and how to draw circles, this is peak project-based-learning. But we still can't build an egg that looks good, and now it's becoming less fun and there's no drive to keep trying.

So I spin up claude after hours and in ~30 mins I have an parametric egg-generator in 3d space, mapped to voxels. There is no universe in which my child would be interested in the many years of training to get to the point of building this himself. I also don't have 20 hours free to learn and implement my own 3d voxel rendering systems, just to build an egg in minecraft as a silly teaching exercise.

That weekend we try to use this tool, and we see it's really hard to just see an egg model and build it, so 15 minutes later it can now show us a sliced layer-by-layer view.

The weekend, my son built a massive fucking egg in Minecraft and he's been talking about circles and radiuses and eggs and coding software ever since. He was SO excited to see that we could take a running program, something "real" in the world, and then directly change it. (And now he's trying to learn about code and graphics and stuff. Again, he's 5 - this is the passing interest of curiosity in a child, he's not studying 50-hour courses to learn low level skills)

Are you saying that's not a massive win for everyone involved?

I feel like you've never actually tried to make tool with Claude Code or similar because BASIC is not it - that's viewing the past with rose coloured glasses. However I understand your central thesis that we could have actually put effort into making something that average folk could use to effectively leverage computers in a way that requires 'code'. But you know we have tried - we have Scratch - we have all of the node graph spaghetti in Unreal Engine and others - I am a programmer but I finally sat down and went through the process of making a working finished tool in a language I'm unfamliar with and using Claude Code and it went really well. And if folks like Ben Krasnow of Applied Science channel are using AI coding tools that they would formally take them 3 to 5 x longer to struggle through unfamiliarity then practically it's working - although I also take a nod to your 'at what cost'. But the idea that we could have been living in some Utopian BASIC derived alternate uinverse seems a little bit optimistic to me. I like AI coding (if I don't have to think of the costs)

From what I can tell, some professionals seem to perceive it as a way to not write the easy stuff and only deal with the harder, more specific stuff that llms don't get (because they are incapable of new ideas). I don't know all the facts, but it seems as though this "home cooked software" boom will be really dull because of llm limitations. It always seems like I am actually learning something when copying code from books, maybe that is one reason why the 80s was interesting in terms of software, but what do I know, I wasn't alive.

In my experience, the people using AI to make programs are still programmers. As in, they were trained in programming pre-AI. Managers are using AI to output manager stuff - documents, spreadsheets, etc. Similarly, marketers are using AI to write ad copy, not the marketing manager.

This may change as tools become better known or adopted. But for know the same people who did the job before are now using AI in that job.

FWIW, in my IRL experience, all the work output of those using AI for whatever task has been of poorer quality than without.