Maybe it’s not a given, but it is part of the sales pitch for CEOs. A few others have announced layoffs due to AI being better and more efficient than humans.
How much truth there is to it we don’t know for sure. But it’s not something to be ignored.
> It was comparing to a hypothetical world where everything is perfectly organized, everyone is perfectly behaved, everything is perfectly ordered, and therefore we don't have to have certain jobs that only exist to counter other imperfect things in society.
> Jobs that don't provide value for a company are cut, eventually.
Uhm, seems like Greaber is not the only one drawing conclusions from a hypothetical perfect world
People here seem to be conflating thinking hard and thinking a lot.
Most examples mentioned of “thinking hard” in the comments sound like they think about a lot of stuff superficially instead one particular problem deeply, which is what OP is referring to.
It seems more like a non experienced guy asked the LLM to implement something and the LLM just output what and experienced guy did before, and it even gave him the credit
> then you're doing the opposite of what the author proposes
No, it’s exactly what the author is writing about. Just check his example, it’s pretty clear what he means by “thinking in math”
> Scientific conensus in math is Occam's Razor, or the principle of parsimony. In algebra, topology, logic and many other domains, this means that rather than having many computational steps (or a "simple mental model") to arrive to an answer, you introduce a concept that captures a class of problems and use that.
If you think the ads are working and have 10k potential customers then you start thinking about how to increase your conversion rate thinking you could get a chunk of those 10k, you might think distribution is solved.
But if it turns out only 2.5k are real humans then your conversion rate might not even be an issue and it’s just the marketing strategy that needs tweaking.
The whole point is that they are giving you fraudulent traffic which you use as real data to figure out the next steps. If you don’t know it’s fraudulent or how much of the clicks are fraudulent then you are taking decisions under the wrong assumptions.
> You can’t stop fraudulent clicks just like you can’t stop your SuperBowl ad from playing while your viewers are in the bathroom
That’s not even a good analogy, we are taking clicks, not impressions.