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Cake day: 2023年8月14日

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  • It’s cheaper to pay cash than use insurance.

    Yes, for most people, in most years. But the cost of health care tends to be very, very unevenly distributed. A person might see medical bills of less than $1000 per year for 20 years and then get a single $1,000,000 year. So at that point, it’s an annualized cost of $50,000 per year, even if most years it’s about $1,000. Some estimates are that 10-30% of all medical spending in the US is in the last year of life.

    Many believe that because of this distribution, health insurance should primarily be a catastrophic care model where most people pay a premium that doesn’t cover anything for the first few thousand, then covers a percentage of the cost up to the out of pocket maximum of like $15,000 or so for a family, but does cover everything after that. For a typical household, being able to predict annual healthcare expenses for the entire year is very useful.

    And personally, I’m pretty sympathetic to this catastrophic care model as a short term transition to an all payer model that looks like Switzerland’s system (private insurance, private providers, mandatory coverage, strict price controls, and subsidies for anyone who can’t afford the normal premiums).



  • MBA programs aren’t about the classes or any kind of academic rigor. They’re almost entirely networking plays: go get an MBA from a high ranking program, where you will drink with new friends you’ve made at different events, and then learn socially how to fit in with these MBA types, and then everyone gets their first post-MBA jobs at a big 3 consulting firm where they’ll do a bunch of stuff with executives of Fortune 500 companies, get to know execs who will vouch for them when the next VP position opens up. Then, 20 years after getting their degree, they still have an address book and text message threads with a lot of people who just happen to be the who’s who of senior management in different industries. All made possible by the MBA program, none of it coming from the coursework itself.


  • To be clear, women’s work before World War II was more than just the dishes. If you look at the guidebooks published for housewives back then, you’ll see that they were expected to have quite a few skills that most households now generally outsourc to external businesses:

    • Feeding the family. This was more than just cooking. They were expected to process foods from a much less processed state (much more butchery of meats and cleaning and processing of vegetable products, dairy products, baked goods), and then preserve foods for out-of-season consumption (pickling, preserving in jams/jellies, home canning, drying, and in some cultures smoking). Much of this work is now done by the industrial food processing industry so that we can buy cans or jars or boxes of the stuff that’s already processed or partially processed. Even our fresh foods have been cleaned and sorted and trimmed to mainly just the edible parts.
    • Making and maintaining textiles. We see bits of this surviving into knitting and crocheting as hobbies, but back before the rise of cheap apparel it was important to be able to clean and repair clothes that we’d now just take to our local dry cleaner.
    • Maintaining the house itself. Home improvement is masculine coded today, but a lot of the stuff that qualifies as home maintenance was traditionally the work of a homemaker. Plus things like heating the house required active involvement of keeping fires burning and fuel on hand.
    • Making household consumables. Homemakers were making their own soap, their own candles, and all sorts of little tools.

    The economic shifts that come from women leaving the home for the paid workforce are all over, and some of them are pretty pronounced. But it’s important to remember that women worked hard before they ever got paid for it. Life was toil.


  • It’s not actually a clear inverse relationship on the individual level, even if the data shows a correlation at the national level.

    There are a few things happening that complicate the analysis at the individual level, too:

    • Wealth/income are correlated with age, and 40 year olds tend to have both higher incomes and lower fertility rates than 25 year olds.
    • Wealth also correlates with race, for better or for worse, and there have always been persistent differences in birth rates by race.
    • The sample sizes aren’t big enough to show whether the very rich (95th+ percentile) actually reverse the trend, to where being richer is correlated with higher birth rates, where the curve ticks back upward at very high incomes.
    • The correlation is actually the other direction when looking at the individual incomes in certain countries (Netherlands, Sweden, Norway), and the effect is stronger when looking at men and their incomes.

    Other country level data also suggest that there are big cultural factors in birth rates as well.

    All in all, the relationship between income and fertility is complicated, with lots of other factors at play.


  • Ed Zitron publishes a lot of pretty biased reporting.

    The core thesis is sound, though:

    • Anthropic and OpenAI’s revenue comes in from customers.
    • The revenue does not translate into profits, because their capital expenditures investing in future capabilities is quite high, and because their operating expenses are also quite high, to where their revenue doesn’t even cover their ongoing compute cost.
    • The actual money Anthropic and OpenAI have to spend at a loss comes from their investors.
    • The customers are largely vulnerable to shocks and are themselves reliant on investor cash rather than a profitable business model of their own, and are essentially subsidizing a lot of the demand for the core services that Anthropic and OpenAI provide.

    Taken together, the whole ecosystem is currently relying on a continued influx of cash from investment: investors taking equity in these companies, lenders/bondholders charging interest on borrowed money, otherwise profitable businesses like Google and Meta steering their other profits into investment into AI.

    And so if there’s a shock to investment activity, such as if there’s a war in Iran causing an energy crisis and a global recession in real economic activity, that might translate into a cash crunch, as the investors pull back right at the time that the customers start defaulting on their payments. And if you remove the middleman startup businesses that pay Anthropic and OpenAI more than they receive from their own customers, the underlying “real customer” demand at the actual unsubsidized prices charged by Anthropic and OpenAI will plummet.

    I’m not a tech guy, but I am a business/finance guy, and I’m not seeing where the analysis is wrong. The argument is always that there’s a runway to profitability, and they just need to take off before they run out of runway. And we can argue about whether they will or they won’t get to takeoff, but if the business is relying on more runway being built because they know for sure they don’t currently have enough runway to take off, that’s a shaky situation to be in. Even if everyone is clamoring to be their runway-building partner today.






  • But lots of things can be pre ordered before they’re actually available.

    Services are an obvious example: I can buy tickets to a movie or a live event that will happen at some point in the future. Same with really any tickets or prepaid reservations, like plane tickets or hotel reservations or certain types of restaurant reservations.

    But it can happen with all sorts of consumer goods, too. I can put in orders for stuff to be made to order: handmade/custom jewelry or shirts or mugs or commissioned artwork, a pizza that won’t be made until I order it, etc.

    For businesses, their supply chains require advance planning and ordering. The people who make peanut butter generally have the peanuts ordered before the start of the growing season, so they’re buying peanuts that might not have been planted yet. The grocery store chain might be buying peanut butter before it’s made.

    When pork futures prices drop low enough, McDonald’s will snatch up those contracts and take delivery of a bunch of pork to make McRibs and make them available for a limited time. At the time they buy the contracts (that is, order the pork), the pigs might not even be alive yet, much less slaughtered and processed.

    None of this is defending the memory contracts, but the idea of buying things in the future is pretty common in the economy.


  • booly@sh.itjust.workstoScience Memes@mander.xyz*Permanently Deleted*
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    4 个月前

    Are you talking about battery storage itself being about $126/MWhr? Yeah, that incorporated into the solar+battery LCOE, because solar itself is $31, battery is $126, and the weighted average of how much energy is expected to come directly out of the solar panels onto the grid (at $31) and how much is expected to be stored for later ($31 plus $126) averages out to $53, presumably because most demand matches the daytime solar curve and doesn’t need to be stored for later.