At our G100 Spring Meeting earlier this month, entrepreneur Barney Pell explained the importance of incrementality in integrating machine learning processes in the workplace. Andreessen Horowitz partner Benedict Evans expands on where machine learning succeeds today, offering a starting point for companies deciding what questions to ask of existing data:
We’re not automating experts. Rather, we’re asking ‘listen to all the phone calls and find the angry ones’. ‘Read all the emails and find the anxious ones’. … In a sense, this is what automation always does; Excel didn’t give us artificial accountants, Photoshop and InDesign didn’t give us artificial graphic designers and indeed steam engines didn’t give us artificial horses. Rather, we automated one discrete task, at massive scale.
This concept of automating simple tasks at scale defuses the immediate threat of mass job displacement, say Jeff Weiner, CEO of LinkedIn, and John Donahoe, CEO of ServiceNow, in a joint interview on McKinsey’s New World of Work podcast. What this looks like in practice:
About 10 percent of [ServiceNow’s 400 customer support] engineers’ time is spent trying to figure out—categorizing—what the problem is and getting it to the right person to fix it. So, we turn the machine learning on in our platform. And within a week, the machine was more accurately categorizing what the inbound customer problem was and getting it to the right person so it could be solved more quickly and solved the right way the first time. So, you could say, “Oh my God, that took away 10 percent of the jobs of the 400 engineers.” Of course, that’s not what they felt. They felt like it was the bottom 10 percent of what they hated to do.
Stock buybacks topped a record $189 billion from January to March of this year, with firms including Apple, Microsoft, and Amgen announcing repurchase campaigns – and drawing not-insignificant consumer pushback. The Economist outlines six misconceptions about buybacks, including connection to CEO compensation:
The third mix-up is that firms’ main motivation is to manipulate either their stock prices or their earnings-per-share (EPS), which can be cosmetically boosted as the number of shares falls. The charge is hard to sustain, in aggregate, because buy-backs are small relative to the stock market, worth 2% of its value and 1% of shares traded for S&P 500 firms each year. True, if executive-pay schemes are poorly designed around EPS, they can artificially encourage buy-backs. But of the 20 largest repurchasers today, three-quarters do not have EPS as a main element of their pay plans.
New census data confirms that US population is dispersing more broadly to suburbs, exurbs, and Sun Belt cities than anticipated at the outset of the decade. From Brookings, indicators of growth shifts:
Turn to China – and its Western city of Xi’an – to see this trend in reverse. The Economist looks at the Chinese urban centers competing to become focal points of a “digital Silk Road”, wooing programmers, researchers, and large companies:
Xi’an’s efforts to attract tech firms, such as offering housing subsidies to their staff, are paying off. Huawei, a maker of telecoms equipment, has built its largest programming research-facility there. HSBC, a bank, uses developers in Xi’an to produce a variety of software, including versions for cyber-security and market analysis. What is happening in Xi’an, says Frank Tong, HSBC’s head of innovation, “is for real.” … The city has considerable pull for young techies. It is only a bit less cosmopolitan than Beijing or Shanghai. Yet its housing is far cheaper.
At our G100 Spring Meeting earlier this month, we discussed the future of American education and whether we will see the collapse of a higher education bubble, as predicted by professor and futurist Brian Alexander. Private college closures, coupled with budget cuts squeezing public universities, lend credence to Alexander’s argument. New data reveals enrollment is down too:
In the spring of 2013, there were 19,105,651 students enrolled in higher ed; this spring, there were 17,839,330, according to recently released data from the National Center for Education Statistics. That represents a roughly 7-percent decrease—and is driven largely by declining enrollments in the for-profit and community-college sectors, as well as stagnant enrollments among four-year non-profit public and private institutions. And the trend of declining enrollment in higher education is likely to continue, [Alexander] argues, for a couple of reasons, but most notably, a declining birth rate.
As institutional priorities shift, how should companies think about educating entry-level and adult workforces? This is a top of mind question for German industrials; half a million employees enter the German workforce through vocational programs today. To better prepare employees for technological disruption, Siemens is adding a dual-studies track, with a degree component, to its apprenticeship program:
The young people in the program are benefiting from the best of both traditions. They also have the advantage of landing placements with a company like Siemens, which can afford to update its training programs frequently [the programs cost around 18,000 euros a year per student]; by the end of this year, it intends to introduce a new curriculum that will include AI.
Harvard Business School’s Michael Porter and Nitin Nohria released findings from a decade-long study of how the CEOs of the world’s largest companies manage their time and set their priorities. Salient insights from the study, which is worth reading in full: