brianchu

Also the domain was registered 3 days ago (August 6th). An earlier version, doctorseb.com (which redirects to the website) was registered July 20th.

Your example is hogswash. Absolutely, the perceived long term value dropped 30% as people feared a million deaths (with lockdown) and dead bodies piling up outside hospitals across the country (with lockdown, and not just New York City). The stock market is rising now that people realize the pandemic, while still bad, isn't going to be as bad as those predictions. Our perception/understanding of the pandemic has rapidly changed.

Some models were predicting multiple hundreds of thousands of deaths, with lockdown. The Imperial College model was predicting 1 million deaths, with lockdown. I completely agree cases will rise as places begin reopening. But whatever the outcome it will be with reopening - still better than what the market expected in March.

Yes, NYC was the only place in America where the system was close to overrun and some hospitals actually were overrun, I'm not disputing that.

The explanation is very simple. The pandemic is not nearly as bad as people thought it would be in March.

Models were predicting hundreds of thousands of deaths in the USA over the next few months, with lockdown. Many people were predicting hospitals would be widely overrun in New York City, parts of California, etc (again, with lockdown). These models and predictions, of course, were wrong.

Printing money and stimulus should have been expected (given the government's response in 2008) and therefore priced in, at least in theory. If we actually had massive numbers of bodies piling up outside hospitals in all major US cities, no amount of money printing would have propped up the markets.

With the disclaimer that I'm only using Google Translate, it appears this document is from one hospital and is not a requirement, merely a guideline / informational document.

Yep. Obviously this is anecdotal and limited to my own social circles, but I live in the Bay Area, and almost everyone (90%+) I know in San Francisco does not own a car. The ones that do own cars only use them if they need to get out of the city (within the city they bike/scooter/walk/Uber/Lyft/transit).

When I did the math, if I lived in the city it would be cheaper to use rideshare everywhere and rent cars when needed, than to own a car.

Shorting a stock is not a basic feature, it's an advanced feature for power users.

You can buy put options on Robinhood already.

They are not sealed from the ambient air (not usually), which is what this study is saying is where the bacteria comes from.

Regardless of what common sense might say, the point is that the study didn't actually do a direct comparison.

An interesting study, but I wouldn't read too much into it. Who's to say that paper towels don't collect and deposit just as much bacteria onto your hands?

I'm coming to the view that gig workers are neither employees nor independent contractors. Employees don't get to unilaterally set their own hours, and contractors don't get prices unilaterally dictated to them or barred from their profession if their rating falls too low.

All this regulatory squabbling is arguing over whether a square peg fits a round hole or fits a triangular hole.

We need a third classification for gig workers that affords them some protections while preserving the economic viability of ridesharing companies. Disregarding any problems we might have with specific companies, I think ridesharing companies are a benefit to consumers.

This is really interesting and thought-provoking, but I'm skeptical.

1. In my experience, each data source and each data format requires a lot of custom work. Each kind of prediction task requires additional custom work. I don't see this going away. Even if a company develops a solid core of reusable engineering infrastructure, it will always need to be adapted to the problem at hand. At this point, this company would seem more like a consultancy, with non-trivial marginal/variable costs. This reminds me of Palantir, which operates this way - core set of tools and infrastructure, consultants implement and apply these tools/infra at each client company with a lot of custom integration work.

2. Assuming this is not a problem and the shared infrastructure is able to generalize enough of the custom work to be feasible, this thesis actually seems like an argument for the big tech companies dominating all data companies. Google, Microsoft, and Amazon have the engineering talent and resouces to develop this hypothetical infrastructure. They also have the internal political will because they can then expose this as cloud APIs. Indeed, it appears they are already attempting this in certain domains.

3. Superior engineering infrastructure is indeed a competitive advantage, but isn't enough of a moat for a single company to dominate this space. Yes, great engineers are hard to find, but there are enough of them for more than one company to feasibly develop this infrastructure, with a lot of money. You can't say the same about trying to buy the social network of Facebook or Instagram.

The interesting thing about the $3000 "upgrade" is that if you actually want to get it, I think you're better off taking the $3000 and investing it in Tesla.

Assuming autonomy is make or break for Tesla in the long term: if Tesla fails at it your stock will still be worth something (versus a useless upgrade). If Tesla succeeds you'll make more than enough to cover a retrofit.