"Guaranteed income helps people leaving jail and prison, and that helps everyone"
https://www.prisonpolicy.org/b...
      "Upon coming home from prison, people face the same â" and rising â" costs of living as the rest of us. But they have to bear additional costs imposed by the criminal legal system as well, all while navigating additional and unique barriers to employment. The resulting financial insecurity makes it harder to succeed at reentry. Cash assistance (often called âoeguaranteed incomeâ) makes reentry easier by providing people with a monetary safety net, helping them get jobs, housing, and food, and fulfill any remaining court or parole obligations.
      In this piece, we explain how guaranteed income reduces recidivism and results in taxpayer savings. We highlight the work of the Just Income program in Alachua County (Gainesville), Florida as a concrete example that demonstrates cash assistance with no strings attached is a smart policy choice for supporting people in reentry. ..."

"Omega-3 and vitamin D supplementation to reduce recidivism: a pilot study"
https://link.springer.com/arti...
"These pilot data suggest that omega-3 and vitamin D supplementation, a simple and relatively cheap health intervention, could reduce 3-year recidivism by 16.6%."

You wrote: "Isn't it funny how the Republican Party always gets very concerned about spending and the reach of government when the Republican Party doesn't control government; but just as soon as they do have control they start spending like crypto bros and use government to interfere in literally everything that doesn't fit their questionable narratives?"

See also: "The GOP used a Two Santa Clauses tactic to con America for nearly 40 years; This scam has been killing wages and enriching billionaires for decades"
https://www.salon.com/2018/02/...
        "The Republican Party has been running a long con on America since Reagan's inauguration, and somehow our nation's media has missed it - even though it was announced in The Wall Street Journal in the 1970s and the GOP has clung tenaciously to it ever since.
        In fact, Republican strategist Jude Wanniski's 1974 "Two Santa Clauses Theory" has been the main reason why the GOP has succeeded in producing our last two Republican presidents, Bush and Trump (despite losing the popular vote both times). It's also why Reagan's economy seemed to be "good."
        Here's how it works, laid it out in simple summary:
        First, when Republicans control the federal government, and particularly the White House, spend money like a drunken sailor and run up the US debt as far and as fast as possible. This produces three results - it stimulates the economy thus making people think that the GOP can produce a good economy, it raises the debt dramatically, and it makes people think that Republicans are the "tax-cut Santa Claus."
        Second, when a Democrat is in the White House, scream about the national debt as loudly and frantically as possible, freaking out about how "our children will have to pay for it!" and "we have to cut spending to solve the crisis!" This will force the Democrats in power to cut their own social safety net programs, thus shooting their welfare-of-the-American-people Santa Claus. ..."

So it is not hypocrisy so much as a precisely-thought-out effective political strategy. Whether the majority of voters in the USA like the results or realize where those results come from is a different issue.

I just wanted to add that whatever the truth there, this idea that LLMs are not (by themselves) the way forward is increasingly appearing in various places. One recent example on Slashdot:
https://slashdot.org/story/25/...
"Project Prometheus is building AI systems that learn from physical experiments rather than just analyzing digital text."

Humans learn to speak usefully with just a few years of immersion in a social world and without reading the entire internet. My college advisor back in the 1980s (George A. Miller) though this suggested language had a partially genetically-wired component in the brain even as much was also learned.

Beyond reading Asimov robot stories as a kid, I first learned more formally about AI taking an independent study course in High School in the late 1970s based around Patrick Winston's first edition Artificial Intelligence textbook.
https://en.wikipedia.org/wiki/...

Then in the 1980s, some of my college work was also related to AI as cognitive science and exploring triplestores and so on (which very indirectly helped inspire George to create WordNet as I was graduating, where WordNet lead to Simpli and Google AdSense). I spent about a year hanging around the CMU Robotics Institute after graduation (where I got to ride in the first "Autonomous Land Vehicle" or "ALVAN"). And then I was a research assistant co-managing a robotics and expert system lab for a time. I also made one of the first simulations on a Symbolics of kinematic self-replicating robots (presenting that work at a conference on AI and simulation, where I commented on the total surprise to me when I saw emergent behavior of unexpected cannibalism of offspring in it until I kludged in a virtual sense of smell to avoid eating creatures that smelled the same). As a grad student later I learned a bit about neural networks related to self-driving vehicles.

I later worked for a time in IBM's speech research group in the late 1990s (mainly using existing tools to build implementations, aspects of which were forerunner to Apple's Siri as IBM's "Personal Speech Assistant" and also an interactive speech-operated display wall I built mostly for fun which was intended to in-theory eventually support advanced design and also patent writing).

Anyway, with that for context, I think LLMs are pretty amazing, but they just don't seem like how humans learn to think and speak. Not saying they can't be useful as part of a larger system though. But fundamentally, even if neural networks are involved, humans think in concepts (or word senses, as in WordNet) which they mostly learn by inference from just a relatively few examples. And that learning tends to have a precise aspect to it related to the actual experience and some notion of "truth" (as in actual experience even if the experience is hearing or reading about what someone else experienced or said they experiences).

So the idea proposed here by "Cringely" makes some sense (as part of this trend to seeing the limits of LLMs) -- although whether or not he can pull it off is a different issues.

But there remains a concern of whether or not such a thing (making powerful self-taught AIs) is worth doing right now given a competitive economic system and also the existential risk of creating essentially a new intelligent species (one without all the evolved safeguards humans have as a social species, limited as they may be as demonstrated by various tech-bro behavior). Anyway, such concerns is why I mostly left the AI research field in the 1980s (other than to kibitz about it from the outside).

This YouTube comment was not posted by me but it almost could have been in some ways:
https://www.youtube.com/watch?...
      "@Jenkkimie 2 weeks ago
      Former AI developer here. Hear Mo Gawdat's message to heart. I regret my past, regret that I helped companies to build AI's at all. I can't undo history but I left the AI industry when I saw companies were starting to plan on using AI in unethical ways that I could not stand by. I've lost a lot of money over the years but as far as I am concerned that is the sacrifice I made because I don't want to be part of the destruction of humanity and the world.
      There has got to be better ways to use AI than pure greed, and we need to do better than this. To remember ethics, not just our bank accounts. So I've joined among many other former and current AI developers in advocating for regulations, change of how we think about economies and the role of money in our world and what is our place in it. Maybe we are fighting a losing battle but all of us should do what we can to steer and orient this world to a better tomorrow rather than submit to the will of the oligarchs evil desires. The fight is not over yet, we can still change the direction of it all."

Mo Gawdat (interviewed in the video that comment is posted on) is the only major AI executive who so far I see seems to get the main idea my sig in relation to AI: "The biggest challenge of the 21st century is the irony of technologies of abundance in the hands of those still thinking in terms of scarcity."

Whatever AIs we build, unless we (or they) understand that irony, it seems unlikely that there will be a happy result for humanity of such work.

Wow. Thanks for posting this, A.C.. In trying to verify any of what you posted (which was all news to me), I found this:
"The cost of lies: A Mineserver story" by Jeremy Reimer
https://jeremyreimer.com/rocke...
      "Creating and shipping a brand new product is insanely difficult. It takes a ton of money, sweat, and time. Even people with tons of experience can underestimate timelines and encounter unexpected difficulties. So telling the story of a failed Kickstarter is not especially interesting.
      This is not that story.
      This is a story about what happens when someone builds up a reputation over decades of work and then destroys it in a couple of years. Not because they failed, but because they lied about it. Over and over again. Until the lies got too much to handle, and they had to create newer, even larger lies to cover them up.
      Why would anyone do this? We'll get into that at the end. But first, the story. ..."

I can still wonder on the use of the word "lie" in that article by Jeremy Reimer versus, say, "irrational exuberance" especially if his kids were involved in making the Minecraft server project happen? But the article does make it sounds like a larger pattern. Ironically, the behavior even sounds a bit like an overly-people-pleasing LLM hallucination?

Having read many Robert X. Cringely articles in InfoWorld and so on way back when, I would be sad if this was all true. Kind of like losing faith in a celebrity of computing from my younger days.

Related (although in general I have not found it that true about most computing people):
"Never Meet Your Heroes: What It Means & If You Should Meet Them"
https://www.wikihow.com/Never-...
        "Itâ(TM)s a proverb that suggests meeting your idols can lead to disappointment. âoeNever meet your heroesâ is a piece of advice that means people shouldnâ(TM)t meet their heroes because they may be disappointed by the heroâ(TM)s true personality. This happens because people tend to idealize people they look up to instead of viewing them as multifaceted humans with flaws, and they may have unrealistic expectations about what will happen when they meet their hero.
        The hero might not have the time, energy, or interest in meeting their expectations, destroying the perfect image that person has built in their head.
        The logic behind this proverb is that many celebrities craft public personas, and the image they portray online or on camera may be vastly different from how they act in real life.
        With that being said, some people say that meeting your heroes can be a positive experience and serve as a reminder that heroes are no different than normal people. ..."