This is the same thing. We need to reward things never going wrong as a society since this is pervasive.

In weight loss, many people take the approach of add and it doesn't work. Do what you are doing now and take this pill or run further or do more. But mostly the secret is eat less.

In design and engineering, it's often removing things instead of overcomplicating them.

In communication, it's often, remove lots of your side points and just focus on your core argument.

We may have removed a constraint that allows people to do more, but the skill has always been to choose, prioritize and remove the things that are making it worse

Thus to be ethical in your society, usually means you must follow the rules determined by a collective group of your nations ancestors or you will be shunned/jailed/harmed/etc. Which is essentially coercion. "Act this way or be punished."

For opinion pieces, I'd rather the human work through an opinion and read about that personal journey!

> It's also difficult to experiment or strike out on your own as an educator when the future of the students in front of you could be negatively impacted by any mistakes made (not to mention job/test scores/etc) so most just ride the rail all the way down.

It's pervasive and makes things move so slowly. The biggest issue I have with it is that the system is so protective of doing no harm, but doing no harm to a system that is failing students is a poor decision imo. We should be more experimental if things are generally bad. Be protective of the mechanisms when we have good results, not when we have bad ones.

That means either being the traveler and carrying things for others. Or be the demand and start shipping things this way and get some other people to carry packages for you.

The negatives are that AI clearly loves to add code, so I do need to coach it into making nice abstractions and keeping it on track.

And we have AI generated influencers now, ex. https://www.instagram.com/imma.gram, so why wouldn't people care about an AI the same way they do about people they never meet?

Especially when modeling acting with intent. The ability to measure against past results and think of new innovative approaches seems like it may come from a system that may model first and then use LLM output. Basically something that has a foundation of tools rather than an LLM using MCP. Perhaps using LLMs to generate a response that humans like to read, but not in them coming up with the answer.

Either way, yes, its possible for a thinking system to use LLMs (and potentially humans piece together sentences in a similar way), but its also possible LLMs will be cast aside and a new approach will be used to create an AGI.

So for me: even if you are an AI-yeasayer, you can still believe that they won't be a component in an AGI.

Except that those free credits will go away and you'll find yourself not wanting to do all the work to move it over when it would've been easier to do so when you just had that first monolith server up.

I think free credits and hyped up technology is to blame. So, basically a gamed onboarding process that gets people to over-engineer and spend more.