The conversations on consciousness though...oh man. I have to steel myself before diving into that mess.

And I completely agree with your point about touting film as "easier" than digital. That's a stretch.

Reducing people's interest to "social signalling" comes off as dismissive.

Even Sam Altman's "Ghiblified" twitter avatar looks nothing like him (at least to me).

Other models seem much more able to operate directly on the input image.

Unfortunately, the discussion around Mandela effects gets tainted by lots of people being so sure of their memory that the only explanation must be fantastical (the timeline has shifted!), giving the topic a valence of crazy that discourages engagement. I find these mass mis-rememberings fascinating from a psychological perspective, and lacking satisfying explanation (there probably isn't one).

So here we're seeing LLMs "experiencing" the same mandela effect that afflicts so many people, and I sincerely wonder why? The obvious answer is that the training data has lots of discussions about this particular mandela effect, ie people posting online "where is the seahorse emoji"? But those discussions are probably necessarily coupled with language that ascertains 'no, the seahorse emoji does not exist.' That's why the discussion is there in the first place! so why does the model take on the persona of someone that is sure it does exist? Why does it steer the models into such a weird feedback loop?

The department of HUD generates this ~771K figure from a "point-in-time" estimate, a single count from a single night performed in January. They literally have volunteers go out, count the number of homeless people they observe, and report their findings.

It's not hard to imagine why this is probably a significant undercount. There is likely a long tail of people that happened to be in a situation that night where they were not able to be counted (i.e. somewhere secluded, sleeping in a friend's private residence that night, etc).

Even if these numbers are correct, to my mind a "crisis" is still more characterized by the trend than the numbers in absolute. From the first link you provided, we saw a 39% increase in "people in families" experiencing homelessness, and 9% in individuals. A resource from the HUD itself suggests a 33% increase in homelessness from 2020-2024, 18% increase from 2023-2024. That is far apace of the population increase in general.

https://www.huduser.gov/portal/sites/default/files/pdf/2024-...

And even then, I would say many people would suggest that the change in visible homelessness they've experienced in the last 10 years would amount to "crisis" levels, at least relative to the past.

It's completely fair to argue that it is not in fact a crisis, but claiming that it is certainly not "baseless."

Is that the main thrust of it?

I don't think it is "bad" to be sincerely worried that the current trajectory of AI progress represents this trade.

If that's not what you meant my apologies. Reason I'm quick to point this out is I think some of the writing/headlines around are suggestive of this misconception.

There will be many such cases of engineers losing their edge.

There will be many cases of engineers skillfully wielding LLMs and growing as a result.

There will be many cases of hobbyists becoming empowered to build new things.

There will be many cases of SWEs getting lazy and building up huge, messy, intractable code bases.

I enjoy reading from all these perspectives. I am tired of sweeping statements like "AI is Making Developers Dumb."