My team of 6 people has been building a software to compete with an already established piece of software written by a major software corporation. I'm not saying we'll succeed, I'm not saying we'll be better nor that we will cover every corner case they do and that they learned over the past 30 years. But 6 senior devs are getting stuff done at an insane pace. And if we can _attempt_ to do this, which would have been unthinkable 2 years ago, I can only wonder what will happen next.

I agree with you, and share the experience. Something changed recently for me as well, where I found the mode to actually get value from these things. I find it refreshing that I don't have to write boilerplate myself or think about the exact syntax of the framework I use. I get to think about the part that adds value.

I also have the same experience where we rejected a SAP offering with the idea to build the same thing in-house.

But... aside from the obvious fact that building a thing is easier than using and maintaining the thing, the question arose if we even need what SAP offered, or if we get agents to do it.

In your example, do you actually need that simple CRM or maybe you can get agents to do the thing without any other additional software?

I don't know what this means for our jobs. I do know that, if making software becomes so trivial for everyone, companies will have to find another way to differentiate and compete. And hopefully that's where knowledge workers come in again.

Yeah I’m having a similar experience. I’ve been wanting a standard test suite for JMAP email servers, so we can make sure all created jmap servers implement the (somewhat complex) spec in a consistent manner. I spent a single day prompting Claude code on Friday, and walked away with about 9000 lines of code, containing 300 unit tests for jmap servers. And a web interface showing the results. It would have taken me at least a week or two to make something similar by hand.

There’s some quality issues - I think some of the tests are slightly wrong. We went back and forth on some ambiguities Claude found in the spec, and how we should actually interpret what the jmap spec is asking. But after just a day, it’s nearly there. And it’s already very useful to see where existing implementations diverge on their output, even if the tests are sometimes not correctly identifying which implementation is wrong. Some of the test failures are 100% correct - it found real bugs in production implementations.

Using an AI to do weeks of work in a single day is the biggest change in what software development looks like that I’ve seen in my 30+ year career. I don’t know why I would hire a junior developer to write code any more. (But I would hire someone who was smart enough to wrangle the AI). I just don’t know how long “ai prompter” will remain a valuable skill. The AIs are getting much better at operating independently. It won’t be long before us humans aren’t needed to babysit them.

> The problem might end up being that the value created by LLMs will have no customers when everyone is unemployed.

I'm not a professional programmer, but I am the I.T. department for my wife's small office. I used ChatGPT recently (as a search engine) to help create a web interface for some files on our intranet. I'm sure no one in the office has the time or skills to vibe code this in a reasonable amount of time. So I'm confident that my "job" is secure :)

It's not about the consumption of raw materials or repurposing of the raw materials used for chips. peterlk said:

> How many hospitals, roads, houses, machine shops, biomanufacturing facilities, parks, forests, laboratories, etc. could we build with the money we’re spending on pretraining models that we throw away next quarter?

It's about using the money for to build things that we actually need and that have more long term utility. No one expects someone with a 100M signing bonus at Meta to lay bricks, but that 100M could be used to buy a lot of bricks and pay a lot of brick layers to build hospitals.

Any serious LLM user will tell you that there's no way to get from LLM to AGI.

These models are vast and, in many ways, clearly superhuman. But they can't venture outside their training data, not even if you hold their hand and guide them.

Try getting Suno to write a song in a new genre. Even if you tell it EXACTLY what you want, and provide it with clear examples, it won't be able to do it.

This is also why there have been zero-to-very-few new scientific discoveries made by LLM.

Here's hoping you are chinese, then.