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Cake day: June 12th, 2023

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  • Fizz@lemmy.nztoMicroblog Memes@lemmy.worldScience
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    14 hours ago

    I can tell you have no idea what im talking about you’re literally the person in the tweet who thinks its a simple issue that is easily solved by giving people food and housing. I dont know what planet you live on but people have food and housing and still commit crime. Giving people a good quality of life reduces crime but there are other factors at play.


  • Fizz@lemmy.nztoMicroblog Memes@lemmy.worldScience
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    16 hours ago

    Yes I agree with what you’re saying but the tweet ignores that you’re likely already doing both giving money to cops and giving money to poor people so the nuance of effectiveness of where to allocate the extra funds is not as simple as the tweet puts. I can think of a ton of times where increasing welfare does not impact crime statistics and increasing cop spending does and vice versa. I really do wish it was as simple as the tweet makes it out to be because throwing money at a problem is actually pretty easy.










  • They’re kinda past that phase and now need to show that they have sustainable revenue and user growth. From all the numbers I’ve seen they(open ai, Gemini, anthropic) have crazy numbers. Hundreds of millions of users paying $50 a month. It’s not enough to cover training but it covers inference very nicely.

    Then with agent bullshit they’ve managed to turn 1 prompt into 12 and bill the user for that extra so it’s even more profitable than the monthly subscriptions.


  • The economics for daily use of inference seem to make sense. The cost of inference is highly profitable. The margins on inference around 80%. The lost money from power users is made up but the average user who doesn’t user their tokens. They lose money on the free inference given away but that’s marketing and getting used to people having the product there as a crutch. It’s not the best business model but they can change it at any time and have vc cash to burn.

    What doesn’t make sense is recouping the investment cost of model training and building new data centers. Because the moat on a new model doesn’t last long enough to recover its training cost.