So, regarding the productivity argument: I don't get it. It doesn't really matter (for regular employees) that you can do now in 2h what before it took 2 days. Why? Because it's not that you have the rest of the day for yourself. You still have to work 8h/day as usual. But now the pattern is different: instead of enjoying the craft digging deeper into problems in the span of 2 days, now you are rushing into some slot machine with the hope of it giving you the right answer with the right prompt.

So, if any, I would say it's worse for us. Obviously, it's the completely opposite situation for corporations and executives: they are loving the AI situation so much!

I was saying that AI is going to make software development cheaper as in the salaries of software engineers will go down because some of that salary will now be redirected to AI companies and the fact that the world will need to absorb twice-(x10?) the amount of the development power.

In which world do you live where employees work 8 hours per day ? They clock 8 hours per day maybe, but they don't work that time

Employees who get paid a flat rate per hour don't have the incentive to do more than their job

Equity / profit sharing should be commonplace in the age of AI.

You can dig deeper into problems with AI. For me, it supplements my knowledge in domains I don’t fully understand. It also helps me learn. So I can tackle problems I wouldn’t otherwise.

I’m excited for ultrafast AI. It likely means less temptation to multi-thread and deeper flow in single sessions.

I dig into problems way, way deeper with AI than without. I can also add a lot more polish to features, add more test coverage, write more documentation, explore multiple approaches rather than go with gut-feel, and so on.

It's making things less fun, for me at least.

You have to think LLM as the genie that tries to trick you.

First make it write a contract (REQ/ARCH/IMPL documents). Skim through those for any mistakes.

Then based on those ask it to write tests. Again skim through them.

Now you have a context full of guardrails. It’s less likely to surprise you.

That's the fundamental trade off of a job where someone else gives you stuff to do and you get money. We may pride ourselves on software development being a job 'above' flipping burgers, but you're getting paid to have your butt in a chair for 40 hours a week. In exchange, you don't have to worry about the business shit. How much a burger or SaaS license costs the user isn't your problem. You take Jira tickets and implement them. You trade time for money. If, instead, you work for yourself; contracting, writing your own apps, buying lottery tickets, then you're trading results for money. If you're a freelance web developer with a stable of clients, it's a great time! What used to take a week takes hours, and you can charge your clients the same amount to build an even better website with you using AI, which means you get the choice of building a new website for additional clients, or you can take the time off and not build additional websites. But you have to hustle to continually get new clients, before AI and after AI. So it's a different life.

I think of it as a genetic algorithm loop. The LLM is basically a mutator function within the loop. If you can define the end shape you're looking for using tests and specification then you can throw the LLM at the problem and have it converge on the solution. It generate some code, it gets run, the LLM is fed the result back, and it iterates. If you can run the LLM at a really high throughput, then you can iterate on the solution faster. This can largely compensate for the overall capability of the model. Instead of hoping it gets the right solution in a few shots, you can just have it try a whole bunch of things until you get a useful result.

>instead of enjoying the craft digging deeper into problems in the span of 2 days, now you are rushing into some slot machine with the hope of it giving you the right answer with the right prompt.

If you're treating it like a slot machine you're doing it wrong. It will give you exactly what you ask for if you ask clearly, i.e. write a clear, detailed specification, not just "do X!". The nondeterminism comes from vagueness in specification.

Generally, I agree because what happens is the messaging around AI is doing more, faster. Not using AI to deliver at a higher quality level, etc. But I think it boils down to incentives and discipline. So given the incentives we have today at most workplaces faster AI will just be used to produce more slop.