I deleted my Facebook account a couple of years ago and my Twitter one yesterday.
It's not just LLMs, it's how the algorithms promote engagement. i.e. rage bait, videos with obvious inaccuracies etc. Who gets rewarded, the content creators and the platform. Engaging with it just seems to accentuate the problem.
There needs to be algorithms that promote cohorts and individuals preferences.
Just because I said to someone 'Brexit was dumb', I don't expect to get fed 1000 accounts talking about it 24/7. It's tedious and unproductive.
That's the same algorithm Youtube has and is more blatant. Phone mics and your coworker's proximity does a great job at picking up things you've said even after disabling mic access plus airplane mode just by process of elimination.
I'll only use an LLM for projects and building tools, like a junior dev in their 20s.
The key dynamic: X were Y while A was merely B. While C needed to be built, there was enormous overbuilding that D ...
Why Forecasting Is Nearly Impossible
Here's where I think the comparison to telecoms becomes both interesting and concerning.
[lists exactly three difficulties with forecasting, the first two of which consist of exactly three bullet points]
...
What About a Short-Term Correction?
Could there still be a short-term crash? Absolutely.
Scenarios that could trigger a correction:
1. Agent adoption hits a wall ...
[continues to list exactly three "scenarios"]
The Key Difference From S:
Even if there's a correction, the underlying dynamics are different. E did F, then watched G. The result: H.
If we do I and only get J, that's not K - that's just L.
A correction might mean M, N, and O as P. But that's fundamentally different from Q while R. ...
The key insight people miss ...
If it's not AI slop, it's a human who doesn't know what they're talking about: "enormous strides were made on the optical transceivers, allowing the same fibre to carry 100,000x more traffic over the following decade. Just one example is WDM multiplexing..." when in fact wavelength division multiplexing multiplexing is the entirety of those enormous strides.
Although it constantly uses the "rule of three" and the "negative parallelisms" I've quoted above, it completely avoids most of the overused AI words (other than "key", which occurs six times in only 2257 words, all six times as adjectival puffery), and it substitutes single hyphens for em dashes even when em dashes were obviously meant (in 20 separate places—more often than even I use em dashes), so I think it's been run through a simple filter to conceal its origin.
Other than that I'd rather choose a comprehensive article than a summary.