This is the similar mindset in sport athletes. On a good day, a strong driver could beat Verstappen, or a chess player could beat Carlsen. Over the course of a tournament, or a season, then Verstappen and Carlsen are unbeatable. They get to the top, and go on great length to stay there.
That is true for the older generation (immigrating to US pre 1990-ish). The newer generation immigrating to US has a very different mindset now, and they embrace American values as much as the native born here.
I'd say it usually comes from the difficulty in hiring. Extremely difficult to convince someone to leave FAANG / quant firms to work in a no name startup with very high chance of failures, and therefore usually you need a good GTM strategy first (which often requires you have acquired customers / revenue). Often in startups, the priority is figuring GTM strategy > acquiring customers > ... > hiring.
Yes, it was lucky that he was tutored my a Fields medalist. But he did the hard work to get himself tutored: sitting in an algebraic geometry class (that saw attendances dropping from 200 to 5 within a few weeks), reaching out to the professor, traveling with him for 2 years, etc. And then he still needed to do all the work himself for his PhD, and so forth.
How can he make friends with other kids? Partying, dancing, playing baseball, going to date night, etc.?
It's clear that he's not interested in those, and forcing him to do so only causes him to withdraw further to his bubble. At least a job gets him to hang out with people closer to his ability.
MIT has an expectation that students would cover their programming skills via internships or outside jobs, and thus they focus on teaching CS which is quite hard to pick up on your own. Unlike frameworks or trends which change every 2 years, fundamentals are quite hard to change and can carry you quite far if you know how to apply it correctly.
Computer Science is not about learning to make software, although it is usually a byproduct, as strong understanding of fundamental allows you to design software correctly from the first principle. Programming; however, is usually done through training at work.
Computer Science is about understanding what computation can and cannot achieve, and more importantly, how to achieve it (that is where it differs from Mathematics, where mathematicians are usually not interested in the how part). Under this definition, we can put typical college subjects into consideration:
* Data Structures and Algorithms: about studying how to manipulate data to solve a task efficiently.
* Complexity Theory: about formal classification of hardness of problems. What makes a computational problem "hard" or "easy"?
* Computer System: about how to construct a software system to achieve certain purposes. What are the constraints in a system (performance, security, privacy, correctness, fault tolerance, etc), and how to design a system to address such constraints? What tradeoffs to be made when you cannot meet all the desired requirements?
* Distributed System: how to design system with a few to massive number of computers that are connected in a network? How do you reason about fault tolerance, consistency, sharding, and so on?
* Operating System: about how to create abstraction to the hardware, that allows for other softwares to interact with it without having to explicitly deal with the hardware?
The list could go on, but I just give a couple of examples.
As the old say, the devil lies in details. Writing a global control is no means an easy task, as
1. You need to recognize the opportunities exist in the first place.
2. You need a global controller that can aggregate and optimize for a global solution (and the global solution might not necessarily simply to maximize the aggregate throughput, but there might be other factors into account), which may involve some algorithmic design (in some cases, you need to design new algorithms).
3. You need to justify that global controller gives you a superior solution compared to locally greedy solution. As in this article, a global solution gives you about 3% improvement compared to the local controller, and the local controller algorithm is substantially easier to write.
Background: in my previous job at Meta, I wrote such a global control algorithm for controlling the rate of data going in and out each data center. It involved some really interesting algorithmic design.
For people looking for alternatives, Kalshi (YC W19) is a regulated exchange on event contracts. It offers some markets similar to PredictIt (and more).