You’re not wrong, unchecked inflation is bad for most people though. Stable currency is pretty important for trade and economic stability. Unless you prefer heating your home by burning stacks of cash
Oh, I agree. I never said unchecked inflation was at all desirable or even ok.
My point was that local and state governments do need your tax dollars, in the sense that that is literally their income. But for the federal government it's different. If federal tax revenue declines, they can just sell more treasury notes and continue to spend as much as before. In that sense, federal tax revenue has no direct effect on federal spending.
You know that’s not the entire budget right? You’re being an asshole by denying funding for disaster relief, schools, healthcare, roads, scientific research, all the public goods and services that don’t work on a profit driven model, but you still get a direct benefit from.
If you want to play concerned citizen get out and protest, vote with your dollars by not throwing them at big tech companies who kowtow to politicians and fund their campaigns. But if you think you’re sending kind of message by withholding your taxes, it’s really just that you’re a selfish asshole.
> vote with your dollars by not throwing them at big tech companies
Abstaining is not voting. If you want to vote with your dollar, spend it actively undermining big tech companies. Get out there and blind some cameras or something.
Fair if you’re already not giving them money. But if you manage a sizable chunk of cloud spend at AWS, GCP, Azure etc, you can send a meaningful signal by taking away that revenue and shifting it to a company that’s not aiming for neo-feudalism.
This just seems like panic M&A. They know they aren’t on track to ever meet their obligations to investors but they can’t actually find a way to move towards profitability. Hence going back to the VC well of gambling obscene amounts of money hoping for a 10x return… somehow
> In California people are very scared of poor people because they tend to commit more crime and the justice system refuses to prosecute and imprision them, especially if they are criminally insane.
Funny to read this when it’s common knowledge the rich commit so much tax evasion the IRS doesn’t bother investigating, and tech billionaires like Thiel are regularly abusing hard drugs and spewing unhinged theories about the end times and an AI god. You can just say you don’t like poor people. You don’t have to use some statistical fallacy that supports your confirmation bias.
The reality is that the visibility of criminal acts is inversely correlated with income. Why would a rich criminal spray paint graffiti on a building when they’re making so much money off white collar crime that they can just buy it and do whatever they want?
That’s not even getting into all of the things that should be crimes but aren’t, because the ultra wealthy and their megacorps can legally bribe politicians to their hearts content. Or the child sex trafficking. Epstein’s buddies weren’t living rough.
What you pointed out doesn’t change the argument. That IS a main driver for NIMBYism in wealthy areas, even if they’re wrong or misguided, even if it’s just false perception. Don’t really know how doing some whataboutism will change that. I think most people would likely choose to live next to a tax avoider over a violent criminal?
Performative ignorance is when you dispute something supported by tons of empirical evidence with a few anecdotes and whatever you just made up and expect me to spend time refuting it. It's the same technique flat earthers and young earth creationists use.
I’m sick of this idea that “free” services are beneficial to society. There is no such thing as a free lunch; users are essentially bartering their time, attention, IP (contributed content) and personal/behavioral data in exchange for access to the service.
By selling those services at a cost of “free”, hyperscalers eliminate competition by forcing market entrants to compete against a unit price of 0. They have to have a secondary business to subsidize the losses from servicing the “free” users, which of course is usually targeted advertising to capitalize on the resources paid by users for access. Or simply selling to data brokers.
With the importance of training data and network effects, “free” services even further concentrate market power. Everyone talks about how AI is going to take away jobs, but no one wants to confront how badly the anticompetitive practices in big tech are hurting the economy. Less competition means less opportunity for everyone else, regardless of consumer benefit.
The only way it works if the “free” service for tutoring or healthcare is through government subsidies or an actual non-profit. Otherwise it’s just going to concentrate market power with the megacorps.
This 1000x. "Free" is only a viable business model if the govt funds it. Otherwise, the $$ has to come from somewhere else in the company - how long will it take for the company to lose interest in a loss-leader when they're making $$ from other parts?
Look at all the deprecated Google products. What happens when Gemini-SaaS makes billions from licensing to other companies, and Gemini-Charity-for-the-poors starts losing money?
Sadly, the bigger the $$ in the tech pie, the more we have attracted robber barons, etc.
> I’m sick of this idea that “free” services are beneficial to society. There is no such thing as a free lunch; users are essentially bartering their time, attention, IP (contributed content) and personal/behavioral data in exchange for access to the service.
In aggregate, this is true, but there are many ways to game the system to one's advantage and get a true "free lunch." For example, people watching Youtube with an adblocker and logged out don't provide Google with any income or useful telemetry. Likewise you can get practically unlimited GPT/Claude/etc by using multiple accounts.
No, you are misunderstanding th economic principle. There is still a cost associated with serving that user, and the user is still paying for the cost of their internet connection and the opportunity cost of spending time on the service, or of setting up new accounts to get past usage limits. “No useful telemetry” I don’t really agree with in the YouTube example, as view counts are still vital for their recommendation algorithm.
TINSTAFL has two main implications. First that nothing is free, someone has to pay for it. Second is that money is not the only thing you pay with; every choice has an opportunity cost. Gaming the system costs someone something.
Hard disagree. How can it be “walled off” from the internet if it’s not connected? Despite the jokes, cutting access on its own is not the same as air gapping or a firewall. As soon as it’s plugged in there are zero controls.
Trading away your morals is definitely bad in a philosophical sense. Does selling your soul to the devil have a happy ending in any of the fairy tales?
It's either a a Career Limiting Event, or a Career Learning event.
In the case of a Learning event, you keep your job, and take the time to make the environment more resilient to this kind of issue.
In the case of a Limiting event, you lose your job, and get hired somewhere else for significantly better pay, and make the new environment more resilient to this kind of issue.
Realistically, there’s a third option which it would be glib to not consider: you lose your job, get hired somewhere else, and screw up in some novel and highly avoidable way because deep down you aren’t as diligent or detail-oriented as you think you are.
In the average real world, the staff engineer learns nothing, regardless of whether they get to lose or keep their job. Some time down the line, they make other careless mistakes. Eventually they retire, having learned nothing.
I was able to run some stats at scale on this and people who make mistakes are more likely to make more mistakes, not less. Essentially sampling from a distribution of a propensity for mistakes and this dominated any sign of learning from mistakes. Someone who repeatedly makes mistakes is not repeatedly learning, they are accident prone.
My impression of mistakes was that they were an indicator of someone who was doing a lot of work. They're not necessarily making mistakes at a higher rate per unit of work, they just do more of both per unit of time.
From that perspective, it makes sense that the people who made the most mistakes in the past will also make the most mistakes in the future, but it's only because the people who did the most work in the past will do the most work in the future.
If you fire everyone who makes mistakes you'll be left only with the people who never make anything at all.
In this case it was trivial to normalize for work done.
It’s very human to want to be forgiving of mistakes, after all who has not made any mistakes, but there are different classes of mistakes made by all different types of people. If you make a mistake you are the same type of person, but if you are pulling from a distribution by sampling by those who have made mistakes you are biasing your sample in favor of those prone to making such mistakes. In my experience any effect of learning is much smaller than this initial bias.
A decade of data from many hundreds of people, help desk type roll where all communication was kept, mostly chat logs and emails. Machine learning with manual validation. The goal was to put a dollar figure on mistakes made since the customers were much more likely to quit and never come back if it was our fault, but also many customers are nothing but a constant pain in the ass so it was important to distinguish who was right whenever there was a conflict.
Mistakes made per call, like many things, were on a Pareto distribution, so 90% of the mistakes are made by 10% of the people. Identifying and firing those 10% made a huge difference. Some of the ‘mistakes’ were actually a result of corruption and they had management backing as management was enriching themselves at the cost of the company (a pretty common problem) so the initiative was killed after the first round.
This sounds really interesting but possibly qualitatively different than programming/engineering where automated improvements/iterations are part of the job (and what's rewarded)
What if you define a hard rule from this statistics that « you must fire anyone on error one »? Won’t your company be empty in a rather short timeframe?
[or will be composed only of doingNothing people?]
Why would you do that? You’re sampling from a distribution, a single sample only carries a small amount of information, repeat samples compound though.
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