Which one's do you see as the top ones?

That sounds like it's relatively easy to game by the company who chooses the investigators.

Exploitation is using a superior negotiating position to inflict great costs on someone else, at small cost to yourself.

I think the word exploitation as it's generally used, is about one party getting a benefit at the expense of another party. It's not about one party getting nothing/pays a small cost while the other party suffers a lot.

Promoting an alternative definition of what it means to exploit is likely going to make reasoning harder. Google suggests as definition for exploit "make use of (a situation) in a way considered unfair or underhand". 

Wage theft is a clear example of exploitation. For many jobs, there's information asymmetry where the person seeking the job does not get informed fully about how his job will be before they accept the job, that's also clearly exploitation. Multiple-level marketing companies like Amway are exploitative because they mislead people about the likely results of working for them.

In general, there's value created through trade. If one party captures nearly all of the surplus value of the trade, many people consider that unfair and thus exploitative. 

A key aspect of your examples is further that total utility might not be maximized and because one party has little power, utility maximizing trades don't happen. That's a different issue from how the trade surplus is distributed.

If people complain about Amazon, to my knowledge most of the people complain that while Amazon runs very efficient and is run to maximize total utility, they capture most of the generated value and don´t pay their employees very much. 

Maybe, economists do have a term for the case where one party being powerless leads to utility not being maximized?

I think it would be good to automate the moderation process. Current LLM should be able to make the decision about whether a post is containing the kind of profanity that would lead to account bans.

Annoying civilisational inadequacy:

USB-C cables differ a lot. Some only allow power delivery and no data, while others support different levels of data transfer. Power delivery capabilities also differ.

Most cables do have an E-Marker chips that contain the relevant information. However, Android does not provide that information to the user when they plug into an USB-C cable. 

Is answer assumes that you either have a fully chat based version or one that operates fully autonomous.

You could build something in the middle where every step of the agent gets presented to a human who can press next or correct the agent. An agent might even propose multiple ways forward and let the human decide. That then produces the training data for the agent to get better in the future.

You could say that Wikipedia falls into the category but given the way it's discourse goes right now it tries to represent the mainstream view. 

For specific claims, https://skeptics.stackexchange.com/ is great. 

https://www.rootclaim.com/ is another project worth checking out.

You see that each of the project has their own governing philosophy, that gives the investigation a structure. 

Yet discourse about these topics more than anything else fundamentally combats propaganda and misinformation. 

The phrase "combat" is interesting here. Julia Galef speaks about the soldier mindset and the scout mindset. Combating anything is essentially about the soldier mindset. On the other hand you need the scout mindset to think well and come to correct conclusions.

By in large the movement that bills itself as "combating misinformation" is about defending the hegemonic Western elite discourse. It's not about truthseeking. 

When reading posts about AI development I get the impression that many people follow a model where the important variables are the data that, out there in the world, the available compute for model training and the available training algorithm.

I think this underrated the importance of synthetic training data generation.

AlphaStar trained entirely on synthetic data to become much better than humans.

There's an observation that you can't improve a standard LLM much by retraining it by just feeding it random pieces of it's own output.

I think there's a good chance that training on the output on models that can reason like o1 and o3 does allow for improvement.

Just like AlphaStar could make up the necessary training data to become superhuman on its own, it's possible that this is true for the kind of models like o3 simply by throwing compute at them.

Why do you care about how effectively the iron in iron supplements gets absorbed? The iron that's not absorbed just gets flashed out. Can't you just supplement more to get what you need?

It's worth noting that the Californian choice isn't free. Californian like residential solar to allow homeowners to feel good about themselves and use net metering to incentives residential solar. Grid electricity in California are double of what residential customers in Texas pay.