[theplumber]

Indeed that’s the case but most of the programming problems are solved problems and unless you care how things are done someone with less/no knowledge can still make great use of programming to do what it needs to do.

Let’s not pretend you need to be programmer to use AI for programming like mathematicians need to solve math problems. Programming (most of the time)solves real problems rather than abstract constructs.

AI is already good enough to create the next “Facebook”(v1 and maybe v2 as well) without any real programmer. This makes the AI a big enabler and reduces the need for programmers vastly in the early stages of any business.

That being said as more online businesses will flourish these will require actual programmers after they get enough traction so the debate is still on if it will lead to massive layoffs in our industry.

[lanstin]

That seems only partially true. I have no doubt it could crank out a UI like Facebook, but having it work for high volume is less well represented in the training data. Things that seems obvious to us, like sharding, using asynchronous messaging systems to provide more durability at the cost of instant consistency, traffic shedding, intelligent clients, conscious decisions on CAP properties in the light of user experience. etc., are not going to be requested by that non-technical founder.

Facebooks early years were dominated by a concern for users per engineer ratio; the rather florid style of LLMs suggest that they will generate so many systems of such a high complexity you will get Hadoop levels of non-application support needed - forget self healing, it’ll need constant LLM spend just to keep running at scale.

[theplumber]

I call it BS. When you are hitting real scalability issues that the AI can no longer help you are already a successful business. The non technical founder will not know about async message passing but will ask the AI why the site is loading so slowly and sure enough the AI is able to implement caching and various mechanism to improve the performance and observability. You are hitting a wall when you do novel stuff (I.e a special search engine) but something like Facebook which is more or less a CRUD app is not a problem at all. And this is the case with Today’s AI systems(GPT Sol, Kimi K3 etc).

I can’t imagine what models will be able to do next year, especially the open weights ones that are not nerfed for economic or other reasons(I.e Fable saga)

[RugnirViking]

Have you ever tried to get an LLM agent to improve performance in a complex system? It's not good at it at all, it doesn't have a very scientific mindset about it, loves to do premature optimisation and "fancy tricks" while ignoring/missing glaring issues/gains - I'm sure it could be taught to be more methodical (verify what is actually slow before attempting a fix) but it's anything but right now.

Even if you walk it through the process, it also has terrible intuition about what time certain things "should" take - dismissing the possibility of massive speedups because it thinks some number is normal etc

[lanstin]

I thought the model was the one person company with enough usage to be worth 1 billion USD. Badly designed systems can hit bottle necks at like a few thousand DB calls per second which a well designed system can handle on one container. Getting those new features adding to table width or SQL length (or document nesting if you are going that route) with each new feature, I feel the company with a few experts will be at an advantage. Like the old beating the averages essay on using lisp. More smart flexibility wins in the marketplace.

[bdcravens]

True, but I think it's the difference between doing it for a single $200 subscription vs spending $200 a day in tokens.

[theplumber]

I think 5 x max subscriptions would suffice.