[godelski]

A constant reminder: you can't have wizards without having noobs.

Every wizard was once a noob. No one is born that way, they were forged. It's in everybody's interest to train them. If they leave, you still benefit from the other companies who trained them, making the cost equal. Though if they leave, there's probably better ways to make them stay that you haven't considered (e.g. have you considered not paying new juniors more than your current junior that has been with the company for a few years? They should be able to get a pay bump without leaving)

[lunarboy]

I'm sure people (esp engineers) know this. But imagine you're starting a company: would you try to deploy N agents (even if shitty), or take a financial/time/legal/social risk with a new hire. When you consider short-term costs, the math just never works out in favor of real humans.

[godelski]

Every single time I post my comment I get this response...

1) There is no universal rule for anything. It doesn't have to apply to every single case. No one is saying a startup needs to hire juniors. No one is saying you have to hire only juniors. We haven't even talked about the distribution tbh. That's very open to interpretation because it is implicit that you will have to modify that based on your context.

2) Lots of big companies still act like they're startups. You're right, that short term "the math" doesn't work out. But it does on the medium and long term. So basically as long as you aren't working at the bootstrapping stage of a startup, you want to start considering this. Different distributions for different stages, of course.

But you shouldn't sacrifice long term rewards for short term ones. You are giving up larger rewards...

[geraneum]

Well, in the beginning, the math doesn’t work out in favor of building the software (or the thing you want to sell) either.

[QuercusMax]

What about the financial / legal / social risk of your AI agent doing something bad? You're only looking at cost savings, without seeing the potentially major downsides.

[shinycode]

To follow up my previous comment, I worked on a project where someone fixed an old bug. This bug became a feature for clients who build their systems around this api endpoint. The consequence is hundreds of thousands of user duplicates with automations attaching new ressources and actions randomly on the duplicates. Massive consequences for the customers. If it were an AI doing the fixing with no human intervention, good luck understanding, cleaning the mess and holding accountable. People seem lightly think that if the agent is doing something bad it’s just a risk to take. But when a codebase with massive amounts of loc and logic is build and no human knows it, how to deal with the consequences on people’s business ? Can’t help but think it’s crappy software with a « Google closed your Gmail account, no one knows why and we can’t do anything about it, sorry ». But instead of a mail account it’s part of your business

[tonyhart7]

"What about the financial / legal / social risk of your AI agent doing something bad?"

the same way we treat it like human making mistake??? AI cant code themselves, someone command them to create something

[shinycode]

I can’t stop thinking that this way of thinking is either plain wrong and misses completely what software development is really about. Or very true and in X years people will just ask the trending AI « I need a billing/CRM/X system with those constraints ». Then the AI will ask questions and refine the need. Work for 30mn the time to use libs and code the whole thing, pass into systems to test and deploy and voila. Custom feature on demand. No CEO, no sales, nobody. You just deploy your own SaaS feature. Then good luck to scale properly and migrate data and add features and complexity. If agents hold onto their promise, then the future is custom based, you deploy what you need, SaaS platform is dead with everyone in between useless.

[QuantumGood]

I think too many see it more as "every stem cell has the potential to be any [something]", but it's generally better to let them self differentiate until survivors with more potential exist.

[godelski]

Be careful there... There are destructive steady state solutions. For example, all your cells can become cancerous. The stem cells are shaped by their environments, just like people. Don't just approach things with a laissez faire attitude. Flexibility is good, and an overly heavy hand is bad, but that doesn't mean a subtle hand is bad

[TuringNYC]

>> A constant reminder: you can't have wizards without having noobs.

Try telling that to companies with quarterly earnings. Very few resist the urge to optimize for the short term.

[godelski]

  > Try telling that to companies with quarterly earnings. 

Who do you think I'm saying it to?