It is rather puzzling how this major contributing factor is going overlooked. It seems obvious from ground-level. Economists often talk about how expensive labor drives price increases, but don't talk about how price increases cause expensive labor.
The paper is about temporary workers, specifically for seasonal jobs. They’re not buying houses or renting long-term. In fact some of these jobs include on-site accommodation for workers.
Did you get a chance to read the paper? Did you read Section VII and footnote 51? If so, do you still believe that the article is "clamoring for more 'cheap' labor?"
And what other issues would you like to see besides housing and employment in the article?
Uh, in the housing that they build? The amount of housing we can build with cheap immigrant labor is so much more than what we can do without them.
Housing gets more expensive when immigrants are pushed out, because the cost to build rises more than the reduced demand reduces prices.
Partly true, though to a first approximation, the reason we don't build more houses isn't an issue of construction labor costs. Many places with jobs don't allow building homes.
(That's not inherently a universal problem, especially if you're talking about the entire labor market. But it's a problem in many places.)
It's very unlikely anyone with the intelligence to contribute to math research can't already find a non-hectic job that provides for their basic survival. People choose hectic jobs because they want to do better than basic survival.
This is such an insane take I don’t even know where to start. Even in the western world people increasingly need to work more than one job / have multiple incomes to live a life where basic necessities are given, and some luxuries are attainable (read: vacation, not rolex). That’s without children, in rich countries. That isn’t even touching on the problems someone e.g. from a lower caste in India might face. Asserting that „everyone with the intelligence to contribute to math“ would be able to just “find a job” that would them also contribute to the field is completely detached from reality.
People working multiple jobs in the western world have some reason beyond "basic survival". Supporting other people, living in a desirable location, or like the vast majority of people they want to make enough money to go beyond basic survival because basic survival is low status. Which is why the idea that all we have to do is give people enough money for a basic survival lifestyle and they'll be happy and spend the rest of their time on things with perceived aesthetic value like math research is the insane take.
You’re leaving out the niceties like living in a neighborhood where you don’t wake up and see if your child is still alive every time you hear gunshots in the night. Or working a job that doesn’t risk your limbs or even your life if you make a mistake. Or working for someone who will not just fire you the instant you stop being able to work due to an on-the-job injury.
Most of these aren’t even new inventions; read about the working conditions in the 19th century.
The researcher in this case was doing a security review for their company who was a potential customer. Sending potential customers more than a token amount of cash is usually prohibited by corporate ethics rules for obvious reasons.
That's incorrect. It's not only perfectly acceptable, but absolutely vital, to pay someone for their services (incl a customer) for assisting with an existential threat against the corporation.
Any counsel or HR who would draft a corporate ethics rule that wouldn't allow for a bug bounty to be paid out on a massive vulnerability, merely because the person was "a potential customer", should be immediately replaced.
Like the mathematicians working on famous problems in private until they could claim full credit for something interesting wasn't also a marketing exercise for their own careers. The commercial value (or lack thereof) of a proof doesn't depend on whether it was done by a human or a machine.
These mathematicians dedicated their life to math and were working for a long time to achieve the pinnacle of their careers.
OpenAI just burned millions of dollars over a weekend after hearing that someone else was close to solving the problems. Their interest was in their AI system more than the actual math problems.
I see how it can be devastating to their ego, but no, I don't see a particular difference in a company spending money for clout vs. a person spending time for clout. The underlying motivation is the same.
for many fairly strong mathematicians, the career calculus is fame and status (mild though it may be) through mathematics or anonymity but financial reward in tech or finance. Yes, clout by becoming a lifelong academic is in fact rational for some and part of their motivation. Of course they really like what they do as well, but earning the respect of the peers they know are also respected by a large swathe of society is very important.
I guess that type of “clout” feels different to me.
Wanting to be validated by peers for your talents in a niche field vs. using millions to try to solve a math problem that you don’t really care about with AI to market the gigantic company you work for.
That fairly insular community pretty much gave up on commercial success in order to be insular. But the commercial forces were not happy being commercially successful, so they decided to disturb even their insular and commercially-unviable activities.
The point is that nearly every white collar worker is in the same boat right now, and the majority of them probably have already had much more profound impacts to their fields. So yeah, we can certainly imagine what it’s like for mathematicians.
> Like the mathematicians working on famous problems in private
This is an extremely rare situation that practically never happens. Most mathematical research is done in the open, with partial results being published, conferences where approaches are discussed, collaborations... The Navier-Stokes solution is a great example: the approach that OpenAI ended up using was something that two mathematicians had proposed previously and was being studied and followed by several others, with different sub-paths within the same approach.
It could be as simple as Apple committed to a certain volume with Qualcomm before they were certain they could get mmWave working so they have to keep using Qualcomm modems somewhere while they burn down the commitment.
Not sure about that. Labor costs are lower in China and the CCP is strongly incentivized to keep enough jobs around for humans to maintain their own hold on power.
Labor being cheap is more of a locale dependent condition in China now, their well developed cities and industries have had rising wages for 2+ decades now and the labor discount is small enough to be more like a side-benefit to already manufacturing in China, rather than a driving force to outsource to China. China has itself started outsourcing certain productions to chase cheaper labor prices.
That labor costs are rising and are now significantly above poorer countries like Bangladesh or Myanmar doesn't mean they're anywhere near high-income countries or above the cost of automation for low-volume tasks like last-mile delivery, which drivers do for under a dollar per trip: https://archive.ph/SizjZ
Automation can make sense even with low labor costs if it enables higher volume. Like nobody is paying humans to chisel individual screws by hand, because machines can do it quicker and better, and therefore cheaper.
But you can't substantially increase the volume of last-mile delivery by replacing the human with a robot, because the robot would have to move faster, and moving faster would be dangerous. So robotic automation of low-volume tasks can be expected to happen in high-income countries first.
Labor costs are lower, but rising, and already high enough that automation can be appealing.
The Chinese government is also pretty good at finding things for people to do, so I doubt that's a factor. Go to any big park in a city and see how many people are sweeping up leaves, or guarding a building/area that isn't particularly secure.
What frontier model did GitHub train? All of the frontier labs scraped GitHub like they scraped everything else. While GitHub has certainly tried to profit from AI, directing ire at GitHub for frontier labs training on public code seems misplaced.
> not found
Yet another chamber of commerce article clamoring for more cheap labor without addressing where all of these people are supposed to live.
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