Interesting research, looking forward to the follow ups to see how it evolves over time. For sure the number of issues is way to high still to make trustworthy systems around search and news.

This might be accidental but this highlights the lack of transparency on how those models are produced. It also means we should get ready for future generation of such models to turn into very subtle propaganda machines. Indeed even if for now it's accidental I doubt it'll be the case much longer.

People really need to be careful about the short term productivity boost... If it kills maintainability in the process you're trading that short term productivity for a crashing long term productivity.

This is definitely a problem. It's doomed to influence how tech are chosen on software projects.

The security implications of using LLMs are real. With the high complexity and low explainability of such models it opens the door to hiding attacks in plain sight.

This is an interesting way to frame the problem. We can't rely too much on LLMs for computer science problems without loosing important skills and hindering learning. This is to be kept in mind.

Of course it would be less of a problem if explainability was better with such models. It's not the case though, so it means they can spew very subtle propaganda. This is bound to become even more of a political power tool.

This is clearly pointing in the direction of UX challenges around LLM uses. For some tasks the user's critical thinking must be fostered otherwise bad decisions will ensue.

Again it's definitely not useful for everyone... it might even be dangerous for learning.

Be wary of the unproven claims that using LLMs necessarily leads to productivity gains. The impacts might be negative.

When you put the marketing claims aside, the limitations of those models become obvious. This is important, only finding the root cause of those limitations can give a chance to find a solution to then.

This will definitely push even more conservatism around the existing platforms. More articles mean more training data... The underdogs will then suffer.

Looks like the monopolists are vexed and are looking for arguments to discredit the competition... of all the arguments, this one is likely the most ridiculous seeing their own behavior.

I mostly agree with this piece. There's lots of room for optimization still so we might see a temporary drop in the energy consumption of those systems. That said, longer term energy consumption is indeed the main leverage to improve performance of those systems. It can only get us so far, so new techniques will be needed. Hence why my position is that we'll come back to symbolic approaches at some point, there's a clear challenge at interfacing both worlds.

Excellent satire, it summaries the situation quite well.

Maybe at some point the big providers will get the message and their scrapers will finally respect robots.txt? Let's hope so.

I guess this was just a matter of time, the obsession of "just make it bigger" was making most player myopic. Now this obviously collides with geopolitics since this time it's about a Chinese company being ahead.

Yet another attempt at protecting content from AI scrapers. A very different approach for this one.

There was a time when scraping bots were well behaved... Now apparently we have to add software to actively defend against AI scrapers.

Nice reminder that the tasks necessary to robotics are clearly much harder to develop through machine learning than language.