The meager difference is that, in theory, you can eventually sue people in the US.

In theory.

Also, this is a feature for people who live in America, and mostly irrelevant for everyone in the global south.

As a European, I honestly don't see a difference between the US and China from this perspective. They are both equally untrustworthy in my book.

As a Western European, I see the same untrustworthiness in Europe.

We just have our personal privacy security theater in the form of GDPR and a feeling of moral supremacy that's been drilled into our heads from primary school on.

GDPR isn’t theatre in many organisations. Yes large tech firms (mostly US) probably ignore or circumvent. But most businesses I’ve worked for have taken concrete steps to reduce the data they hold and consider how it’s being used asa direct consequence of gdpr.

Don't understand why you are downvoted.

The article + linked study also point out that performance of solar is higher around water bodies.

Not mention that this changes how information is found and how it is created.

The sometimes convenience comes alongside ejecting society from the previous system of figuring out what is true and false.

That was a system that was already failing, but we may have gotten on top of. Now we have one that is simply alien and inimical to any human scale solutions to staying ahead of it.

Generation capacity simply outstrips verification capacity, and with search summaries taking over links to the actual sites, the funding/incentives to run sites and publish content is removed.

The curtain call of the era of the open internet, and the opening act of the LLM mediated web, isn’t a show I am excited to have front row seats to.

> If we sorted through

If.

The labour of finding better goods has become harder with more content showing up.

I’ve seen the problem crop up in several places.

At a broad level, the issue isn’t generated content, it is the ratio of verification capacity to generation capacity (V/G). Your pain is because generation capacity has increased significantly, while verification is laborious and capacity has not (and can not) catch up.

Unlike spam which is from external sources and can be ignored, messages from other employees have to be responded to. I guarantee this is creating bottlenecks all across the firm, outside of the individuals who are feeling productive.

For fixes, theres theoretical approaches that might work?

If you need leadership to help you, then this issue has to become something that is on their radar, which means that something needs to go wrong or costs need to be registered.

The shortest conversation for that is to make people aware that generation has improved individual productivity, while moving the costs of that production to the rest of the firm.

If leadership is not at the stage to listen, then you need to move the costs you are incurring to the people who are sending them to you. Maybe set time aside to sit down with whoever sent a PR and then read what they sent together, to understand it.

It also makes a difference if tokens are being subsidized or not. If the firm doesn’t care how many tokens are being used, then you are naturally going to have over production.

A poll of estimates would be quite helpful in figuring out what the heck is actually going on.

How many are you seeing / estimating?

Drive by code dumps from people who have "democratized Natural Language code" which pushes all the work of verifying whether it works on the few active maintainers is hell.

Any collective group with open contribution will end up hating it. Why is this a surprise?

I did not expect to see a Leamington Spa beer fest poster being used as a reference image today.

What results though. The people seeing measurable improvements to their core work with LLMs are coders.

Everyone else is taking over intern level work from someone else’s team. They are reducing the friction costs of talking to someone else, for about a 30% productivity gain.

Firms are trying desperately to automate their white collar workers, and that is following the same trend as all other automation projects, and ML/deep learning efforts in history.

Process vs outcomes.

If your work has little liability then you can afford to not care beyond “does it work”.

If you have to worry about quality and ensuring you don’t get sued, you make sure the process works.

If it has to maintainable, you you need the mental model to be present in someone’s head.