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Cake day: March 10th, 2025

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  • Three points. Firstly, in the 1950s, CEOs earned around 20 times what the lowest-paid employee did (including things like bonuses, shares, etc). Now the average is around 400, but can be as high as 2,000.

    Secondly, in the US in the 1950s the highest tax band was 91%. Today it’s 37%.

    Both these things are perfectly sustainable. And all that’s working under the false premise that there aren’t numerous tax loopholes available to the rich but not the poor.

    Thirdly, there’s a tonne of research into what best stimulates economies, but it’s often dismissed because it doesn’t favour the rich. If you give money to the poor, they will spend it in their local communities. Then that money gets spent again, and again, and again, getting taxed each time. IIRC, for every dollar given to someone poor the government itself gets something like a dollar fifty back. Because the money just keeps circulating.

    Give money to the rich, though, and what happens? They hoard it, or they spend it abroad. It drains money from the country, either by taking it out of circulation, or by taking it out of the country entirely.


  • I’m not saying they don’t have applications. But the idea of them being a one size fits all solution to everything is something being sold to VC investors and shareholders.

    As you say - the issue is accuracy. And, as you also say - that’s not what these things do, and instead they make predictions about what comes next and present that confidently. Hallucinations aren’t errors, they’re what they were built to do.

    If you want something which can set an alarm for you or find search results then something that responds to set inputs correctly 100% of the time is better than something more natural-seeming which is right 99%of the time.

    Maybe along the line there will be a new approach, but what is currently branded as AI is never going to be what it’s being sold as.


  • If you follow AI news you should know that it’s basically out of training data, that extra training is inversely exponential and so extra training data would only have limited impact anyway, that companies are starting to train AI on AI generated data -both intentionally and unintentionally, and that hallucinations and unreliability are baked-in to the technology.

    You also shouldn’t take improvements at face value. The latest chatGPT is better than the previous version, for sure. But its achievements are exaggerated (for example, it already knew the answers ahead of time for the specific maths questions that it was denoted answering, and isn’t better than before or other LLMs at solving maths problems that it doesn’t have the answers already hardcoded), and the way it operates is to have a second LLM check its outputs. Which means it takes,IIRC, 4-5 times the energy (and therefore cost) for each answer, for a marginal improvement of functionality.

    The idea that “they’ve come on in leaps and bounds over the Last 3 years therefore they will continue to improve at that rate isn’t really supported by the evidence.





  • Someone has died due to a touchscreen. A woman had a Tesla which you put in park forwards or reverse with a touchscreen. She’d always had trouble with it and got it wrong and reversed into a pond. That meant the power went out so she couldn’t open that door. To get to the emergency escape handle you have to remove the speakers in the doors. So she drowned.

    The kicker? Her husband was a millionaire and he immediately put out a statement absolving Tesla and musk from any wrongdoing.