Personal debt is going down
Credit-card debt in the U.S. and other advanced economies has fallen. Fewer people are late on their credit-card payments. Consumer demand for new borrowing—through credit cards, personal loans and even pawnshops—is down sharply.
The main reason, according to economists and financial executives, is government stimulus programs launched in the U.S. and other advanced economies that have worked unexpectedly well. The flood of money, along with debt-relief measures such as deferred-mortgage and student-loan payments, has stabilized the finances of many households and even left some in better shape than before the pandemic—at least for now.
https://www.wsj.com/articles/consumers-flush-with-stimulus-money-shun-credit-card-debt-11596373201
How Robinhood is fleecing the very customers it was supposed to democratise
Welcome to the stock market, Robinhood-style. Since February, as the global economy collapsed under the weight of the coronavirus pandemic, millions of novices, armed with $1,200 stimulus checks and nothing much to do, have begun trading via Silicon Valley upstart Robinhood—the phone-friendly discount brokerage founded in 2013 by Vladimir Tenev, 33, and Baiju Bhatt, 35. The firm has added more than 3 million accounts since January, a 30% rise, and it expects revenue to hit $700 million this year, a 250% spike from 2019.
The problem is that Robinhood has sold the world a story of helping the little guy that is the opposite of its actual business model: selling the little guy to rich market operators with very sharp elbows.
Instead of taking fees on the front end in the form of commissions, Tenev and Bhatt would make money behind the scenes, selling their trades to so-called market makers—large, sophisticated quantitative-trading firms like Citadel Securities, Two Sigma Securities, Susquehanna International Group and Virtu Financial. The big firms would feed Robinhood customer orders into their algorithms and seek to profit executing the trades by shaving small fractions off bid and offer prices.
https://www.forbes.com/sites/jeffkauflin/2020/08/19/the-inside-story-of-robinhoods-billionaire-founders-option-kid-cowboys-and-the-wall-street-sharks-that-feed-on-them/
The cart full of mobile causing traffic jam on google maps teaches us a lot more
We shape our tools and thereafter our tools shape us. Google Maps provides a particularly illustrative example of that relationship. Not only is it a closed system, with little transparency around what data informs it and how it’s used, but Google Maps also uniquely shapes the physical world. If it picks up a traffic jam—real or fabricated—it might redirect vehicles to less-traveled streets, in turn putting strain on infrastructure that wasn’t built for the extra volume.
Systems people take for granted involve inputs and outputs, and that they themselves are sometimes both. It shows how simple it is to fool a product in which people put tremendous amounts of faith. And it illustrates how maps aren’t neutral, either in their creation or their interpretation.
Corporate espionage
For as long as there has been commerce, there has been espionage. The methods for spying on competitors have changed over time, but the desire to uncover a rival’s secrets has not. Here’s a sample of some notable cases of corporate espionage.
https://www.bloomberg.com/news/photo-essays/2011-09-20/famous-cases-of-corporate-espionage
Is the future of a car not a car??!!
Firstly, autonomous driving doesn’t actually seem to be ready for the reality of messy, complicated streets that are teeming with humans. Most experts now agree that full self-driving tech is far from ready — and in fact, may never be ready. That is a stark contrast from the claims from some companies that insist it’s already here.
Further, self-driving cars face another problem of their own making: Their various sensors and safety technology is actually making human-driven cars a safer than their autonomous brethren. As just one example, automatic emergency braking has already reduced rear-end collisions by 50 percent, and the National Transportation Safety Board believes this figure will eventually rise to 80 percent. It seems that predictive or avoidant technology, combined with the knowledge of a human driver, is a better solution to the problem of collisions and injury than cars that just drive themselves.
