Like so many inventions, the scooter was a child of necessity: Specifically, the need to get a bratwurst without looking like an idiot.
One night in 1990, Wim Ouboter, a Dutch-Swiss banker and amateur craftsman, was “in the mood for a St. Gallen bratwurst at the Sternengrill in Zurich,” or so the story goes. He wanted to get from his house to the brat place and then to a bar, stat, but the stops seemed too far apart to walk, and too close to drive. What he really needed, Ouboter decided, was a mode of transportation that would let him swiftly cover that micro-distance. A bike seemed like too much trouble to take out of the garage. What he wanted was a kick scooter.
General Motors Co. and Google couldn’t be more different. GM musters an army of people and machines to produce the 10 million cars it sells each year. What Google makes doesn’t really exist: You type on a laptop or click play on a YouTube video, and Google zips back bits of digital information.
But Google parent Alphabet Inc. and the other four dominant U.S. technology companies—Apple, Amazon.com, Microsoft, and Facebook—are fast becoming industrial giants. They spent a combined $80 billion in the last year on big-ticket physical assets, including manufacturing equipment and specialized tools for assembling iPhones and the powerful computers and undersea internet cables Facebook needs to fire up Instagram videos in a flash. Thanks to this surge in spending—up from $40 billion in 2015—they’ve joined the ranks of automakers, telephone companies, and oil drillers as the country’s biggest spenders on capital goods, items including factories, heavy equipment, and real estate that are considered long-term investments. Their combined outlay is about 10 times what GM spends annually on its plants, vehicle-assembly robots, and other materials.
The splurge by tech companies is behind an upswing in capital-goods spending among big U.S. companies, which is seeing its fastest growth in years, according to a Credit Suisse analysis. The $80 billion tab also is a snapshot of why it’s tough to unseat the tech giants. How can a company hope to compete with Google’s driverless cars when it spends $20 billion a year to ensure it has the best laser-guided sensors and computer chips? There are a lot of physical assets behind all those internet clouds.
A year ago the world was beset by a bicycle rental craze that resulted in piles of discarded equipment and mountains of claims about the size of each fleet’s business.
I even fielded complaints from one outfit’s PR representatives that I underreported its figures. (They didn’t get back to me with their official numbers.)
Now, thanks to securities law and Meituan Dianping’s Hong Kong IPO today, I can shed some light on these claims.
Meituan bought one of these bike-sharing startups, Mobike, on April 4, and gives details of its operations in the IPO prospectus. Because it’s generally considered unwise to lie in an offer document, it’s probably safe to assume it’s a reasonably truthful account of what’s happening at the bike rental business.
Compare the details in the prospectus with statements made in press releases and the divergence is striking.
The convergence of new technologies including artificial intelligence, the internet of things, electric cars, and drone delivery systems suggests an unlikely solution to the growing housing crisis. In the next few years, we may use an app on our smartphones to notify our houses to pick us up or drop us off.
Honda recently announced the IeMobi Concept. It is an autonomous mobile living room that attaches and detaches from your home. When parked, the vehicle becomes a 50-square-foot living or workspace. Mercedes-Benz Vans rolled out an all-electric digitally-connected van with fully integrated cargo space and drone delivery capability, and Volvo just unveiled its 360c concept vehicle that serves as either a living room or mobile office. In other cases, some folks are simply retrofitting existing vehicles. One couple in Oxford England successfully converted a Mercedes Sprinter van into a micro-home that includes 153 square feet of living space, a complete kitchen, a sink, a fridge, a four-person dining area, and hidden storage spaces.
For those who are either unwilling or unable to own a home, self-driving van houses could become a convenient and affordable solution. Soon, our mobile driverless vehicles may allow us to work from our cars and have our laundry and a hot meal delivered at the same time. In Los Angeles alone, it is estimated that 15,000 people are already living in their cars and in most countries it is perfectly legal to live in your vehicle.
Car makers are collecting massive amounts of data from the latest cars on the road. Now, they’re figuring out how to make money off it.
With millions of cars rolling off dealer lots with built-in connectivity, auto companies are gaining access to unprecedented amounts of real-time data that allow them to track everything from where a car is located to how hard it is braking and whether or not the windshield wipers are on.
The data is generated by the car’s onboard sensors and computers, and then stored by the auto maker in cloud-based servers. Some new cars have as many as 100 built-in processors that generate data.
On a recent afternoon at a bus stop in the business district of Nanshan in Shenzhen, China, the air was filled with the sound of chirping birds in a nearby park. The street was quiet, with the exception of the occasional diesel truck chugging past—holdouts against a future that glided in with barely a sound: an electric bus.
A woman who’d been browsing on her smartphone while she waited hadn’t noticed the bus creeping up to the stop. Not until the doors opened with a beep, beep, beep and a man barking boisterously into his phone stepped out did she spring into action and hop aboard. Passengers scanned in with their smartphones, paying through WeChat, the app developed by Tencent Holdings Ltd., the Chinese social media giant whose flashy 50-story headquarters could be seen from the bus stop.
For decades, Canadians interested in post-secondary education (PSE) have decried the lack of easily available, easily digestible data on the post-secondary sector. In part, this lacuna results from some very large gaps in our PSE data system, especially with respect to colleges, staff, and student assistance (in contrast, statistics on institutional finances are among the best in the world). There are also some types of statistics which take an inordinately long time to appear (data on international students, for instance, routinely take three to four times as long to appear in Canada as they do in the US, the UK, or Australia). Our decentralized, federal system is partly to blame, but mainly, Canadian governments and statistical agencies just seem not to care about good education data the way some other countries do.
That said, there actually is a considerable amount of data on Canadian post-secondary education available, but it is just not usually put in a narrative form which is easily accessible. The Canadian Association of University Teachers (CAUT), for instance, puts out an invaluable annual “almanac”, but the data has a profound university skew and tends to be presented in tabular form rather than through more intuitive graphics. Universities Canada occasionally puts together some good publications on the state of the system, but these have become rarer as of late and in any case largely miss the colleges. The Council of Ministers of Education, Canada (CMEC) has an irregularly published system of “Education Indicators” but these are more focused on education as a whole rather than on post-secondary and fall prey to the same preference for tables over graphs. Statistics Canada produces a great deal of data (if not always very promptly), but does very little to help people interpret it.
As a result of all this, Higher Education Strategy Associates has decided to produce an annual publication called “The State of Post-Secondary Education in Canada”. We took as our model a similar set of publications produced by Andrew Norton and his colleagues at the Grattan Institute in Melbourne entitled “Mapping Australian Higher Education”. Like the Australian exercise, we expect we will take on slightly different issues in each future edition, depending on what new data come available. For the inaugural year, we chose to stick to the basics: describing the Canadian system (trickier than it sounds), detailing trends in student and staff numbers, and looking at how the system is financed, both from an institutional and a student perspective. We hope that by putting all of this information in a handy and convenient format, and providing some accompanying narrative, that we can help improve the quality of public dialogue on post-secondary education policy issues. Any and all comments or suggestions about how to improve the publication for future years will be gratefully received.