Why Bill Gates is Right About Robots Paying Taxes

Bill_Gates_June_2015

In the recent past, there have been a spate of predictions about robots taking over human jobs.  One such prediction was made on October 3, 2016, by the World Bank President Jim Yong Kim, when he said in a speech: “Research based on World Bank data has predicted that the proportion of jobs threatened by automation in India is 69 percent, 77 percent in China and as high as 85 percent in Ethiopia.”[i]

As per predictions being made, it is not only jobs in the developing countries that are at risk. As Rutger Bergman writes in Utopia for Realists: “Scholars at Oxford University estimate that no less than 47 per cent of all American jobs and 54 per cent of those in Europe are at the high risk of being usurped by machines. And not in a hundred years or so, but in next twenty years.” He then quotes a New York university professor as saying: “The only real difference between enthusiasts and skeptics is a time frame.”[ii]

I have view which is different from robots destroying human jobs and I wrote about it in late January 2017. The fear of robots or automation destroying human jobs is not a new one and has been around for a while. So, what is it that makes this fear so believable this time around?

As Yuval Noah Harari writes in Homo Deus—A Brief History of Tomorrow: “This is not an entirely new question. Ever since the Industrial Revolution erupted, people feared that mechanisation [which is what robots are after all about] might cause mass unemployment. This never happened, because as old professions became obsolete, new professions evolved, and there was always something humans could do better than machines. Yet this is not a law of nature and nothing guarantees it will continue to be like that in the future.”[iii]

The question is: What has changed this time around?

Human beings essentially have two kinds of abilities: a) physical ability b) cognitive abilities i.e., the ability to think, understand, reason, analyse, remember, etc. As Harari writes: “As long as machines competed with us merely in physical abilities, you could always find cognitive tasks that humans do better. So machines took over purely manual jobs, while humans focussed on jobs requiring at least some cognitive skills. Yet what will happen once algorithms outperform us in remembering, analysing and recognising patterns?

And given this, robots will takeover human jobs is the conclusion being drawn. This conclusion has one basic problem—it assumes that human beings will sit around doing nothing and let robots take over their jobs. Now that is a very simplistic thing to believe in. Hence, if and when, it seems likely that robots are really look like taking over human jobs, it is stupid to assume that the governments will sit around doing nothing. There will be huge pressure on them to react and make it difficult for companies to replace human beings with robots.

Over and above this, governments will lose out on tax. When an individual works, he earns an income and then pays an income tax on it to the government. Income tax is a direct tax. Over and above this, in India, when he spends this money, he pays other kind of other taxes, which are largely indirect taxes, like excise duty, service tax, etc. A similar structure works all over the world.

Now let’s say this individual paying taxes gets replaced by a robot. So, the government does not get the income tax that it was getting in the past. Over and above this, the individual who has lost his job, will not spend as much as he was in the past. He will go slow on spending in order to ensure that his savings last for a longer period of time, or at least until he finds another job. If a substantial number of individuals lose their jobs to robots, in this scenario, the government will lose out on a portion of indirect taxes that it was earning earlier. It will also lose out on direct taxes.

So, what is the way out of this?

In a recent interview to Quartz.com, Bill Gates, the founder of Microsoft, said: “Certainly there will be taxes that relate to automation. Right now, the human worker who does, say, $50,000 worth of work in a factory, that income is taxed and you get income tax, social security tax, all those things. If a robot comes in to do the same thing, you’d think that we’d tax the robot at a similar level.

Basically, taxing the robot means, taxing the company which owns that robot. And how will this help? It will help the government to finance other jobs. As Gates put it: “And what the world wants is to take this opportunity to make all the goods and services we have today, and free up labor, let us do a better job of reaching out to the elderly, having smaller class sizes, helping kids with special needs. You know, all of those are things where human empathy and understanding are still very, very unique. And we still deal with an immense shortage of people to help out there… So if you can take the labor that used to do the thing automation replaces, and financially and training-wise and fulfillment-wise have that person go off and do these other things, then you’re net ahead.”

What Gates has explained is perhaps a solution to the problem that too much automation or too many robots are going to create. There is a basic law in economics which goes against the entire idea of robots destroying human jobs. It’s called the Say’s Law. One of my favourite books in economics is John Kenneth Galbraith’s A History of Economics—The Past as the Present. In A History of Economics, Galbraith writes about the Say’s Law.

This law was put forward by Jean-Baptise Say, a French businessman, who lived between 1767 and 1832. As Galbraith writes: “Say’s law held that out of the production of goods came an effective aggregate of demand sufficient to purchase the total supply of goods. Put in somewhat more modern terms, from the price of every product sold comes a return in wages, interest, profit or rent sufficient to buy that product. Somebody, somewhere, gets it all. And once it is gotten, there is spending up to the value of what is produced.”

Say’s Law essentially states that the production of goods ensures that the workers and suppliers of these goods are paid enough for them to be able to buy all the other goods that are being produced. A pithier version of this law is, “Supply creates its own demand.”

What does this mean in the context of robots destroying human jobs? If robots destroy too many human jobs, many people won’t have a regular income. If these people do not have a regular income, how are they going to buy all the products that robots are going to produce? And if they are not going to buy the products that robots are producing, how are these companies driven by robots going to survive?

Gates has offered a solution where he says that the government taxes the companies using robots, more. That money can then be used to retrain people and deploy them in areas where people are still needed.

This means that such people will be paid by the government. And when they are paid by the government they will pay income tax as well. Over and above this, these workers will also spend that money and pay several indirect taxes to the government. Hence, the government will use the money generated from taxing robots to generate more taxes for itself.

Gates suggestion is also in line with what I had said earlier about the fact that once automation becomes a real danger to human jobs, the governments will not just sit and wait it out, but will do something about it, given that there will be tremendous pressure on them from people who elected them. It will also work towards protecting its tax revenues. Hence, any government taxing the output of robots, will be in line with this.

Gates suggestion depends on several assumptions. First and foremost is that people who lose their jobs to robots will be trained for other professions. While, this sounds simple enough, it clearly isn’t. Second, it expects the governments to do the right thing. That as we all know, is easier said than done. Third, it assumes that companies will willingly pay tax on their robots and not look at loopholes to avoid making these payments.

Having said that, Gates’ suggestion still shows one way of getting through the economic mess that the robots are likely to create.

To conclude, if my job doesn’t get replaced by a robot as well, I hopefully will be around to keep writing about this trend in the months and the years to come.

Watch this space!

[i] Speech by World Bank President Jim Yong Kim: The World Bank Group’s Mission: To End Extreme Poverty, October 3, 2016

[ii] R.Bergman, Utopia for Realists—The Case for a Universal Basic Income, Open Borders and a 15-Hour Workweek, The Correspondent, 2016

[iii] Y.N.Harari, Homo Deus—A Brief History of Tomorrow, Harper, 2016

The column originally appeared in Equitymaster on February 23, 2017

What Happens When Bill Gates Walks Into a Bar

Bill_Gates_June_2015

The mathematician John Allen Paulos in his book Beyond Numeracy writes: “The fourth-grader notes that half the adults in the world are men and half are women and concludes therefrom that the average adult has one breast and one testicle.”

This is a rather extreme example of how the concept of mean or average is misused. An average of X numbers is obtained by adding those numbers and dividing it by X.

Here is another example of a situation where the concept of average is misused.  As Charles Wheelan writes in Naked Statistics—Stripping the Dread from the Data: “Imagine that ten guys are sitting on bar stools in a middle-class drinking establishment…each of these guys earns $35,000 a year, which makes the mean annual income for the group $35,000.”

The software billionaire, Bill Gates, walks into this bar. As Wheelan writes: “Let’s assume for the sake of the example that Bill Gates has an annual income of $1 billion. When Bill sits down on the eleventh bar stool, the mean annual income for the bar patrons rises to about $91 million. Obviously none of the original ten drinkers is any richer. If I were to describe the patrons of this bar as having an average annual income of $91 million, the statement would be both statistically correct and grossly misleading.”

The point being that the average or the mean of a given set of numbers can be very misleading. One thing that clearly comes out of this example is that the majority of the numbers that constitute an average can be lower than the average.

As was clear in this example, ten out of 11 men in the bar had a lower income than the average income of $91 million. Here is another interesting example. As Robert Matthews writes in Chancing It—The Laws of Chance and How They Can Work for You: “The world’s men provide an excellent example – in the shape of their penises. Or, to be more precise, size: according research, the average length is 13.24 centimetres, but the median value is 13.00 centimetres.”

And what is median value? As Paulos writes: “The median of a set of numbers is the middle number in the set.” Let’s go back to the bar example for a moment. Let’s say the eleven individuals in the bar are made to sit in the ascending order of their income. The individual setting on the sixth stool will represent the median income of the group.

Now let’s get back to the penis example. The average length of a man’s penis is 13.24 centimetres. But the median value is 13 centimetres. What does this mean? As Matthews writes: “First, it shows that the global distribution of penis sizes is skewed towards smaller values, and second that most men really do have below-average-sized penises.”

This becomes very important when we are discussing issues like per capita income of a country or the average income earned by a citizen of a country.
The economic health of a nation is also judged by the rise in its per capita income. But should that always be the case? Take the Indian case. A survey carried out by Gallup in December 2013, put India’s median income at $616. Data from the World Bank shows that India’s per capita income during the same year was $1455.

Hence, the median income was around 58% lower than the average income or the per capita income. And that is not a good sign at all. The difference is obviously because the rich (Bill Gates in the example) make substantially more than the poor and drive up the average income. Data from World Bank shows that the top 10% of India’s population makes 30% of the total income.

The point being that economic growth as measured by growth in per capita income is not always the correct way of going about things. Is this growth really trickling down? And that can only become clear if the median income is going up. The tragedy is that no regular data is available on this front.

(Vivek Kaul is the author of the Easy Money trilogy. He can be reached at [email protected])

The column originally appeared in the Bangalore Mirror on April 13, 2016

Bill Gates’ favourite business book tells us why Tata Nano “really” failed

TATA NANOVivek Kaul

In July this year Bill Gates wrote a blog. He titled it The Best Business Book I’ve Ever Read. As he wrote “Not long after I first met Warren Buffett back in 1991, I asked him to recommend his favorite book about business. He didn’t miss a beat: “It’s Business Adventures, by John Brooks,” he said. “I’ll send you my copy.” I was intrigued: I had never heard of Business Adventures or John Brooks.” Gates got a copy of the book from Buffett. “Today, more than two decades after Warren lent it to me—and more than four decades after it was first published—Business Adventures remains the best business book I’ve ever read. John Brooks is still my favorite business writer. (And Warren, if you’re reading this, I still have your copy),” Gates added. The book is essentially a collection of 12 long articles (I don’t know what else to call them) that Brooks wrote for the New Yorker magazine, where he used to work. A chapter that should be of interest to Indian readers is The Fate of Edsel. A reading of this chapter clearly tells us why Tata Nano, the most hyped Indian car ever, has failed to live up to its hype. But before we get to that, here is a brief summary of the chapter. In 1955, the Ford Motor Company decided to produce a new car, which would be priced in the medium range of $2,400 to $4,000. The car was designed more or less as was the fashion of the day. It was long, wide, lavishly decorated with chrome, had a lot of gadgets and was equipped with engines which could really rustle up some serious power. The car was called the Edsel. It was named after Edsel Ford, the only son of Henry Ford who started the Ford Motor Company. In 1943, Edsel Ford had died at a young age of 49, of stomach cancer. In fact, even before the Edsel car was launched there was a lot of hype around it. As Brooks writes “In September 1957, the Ford Company put its new car, the Edsel, on the market, to the accompaniment of more fanfare than had attended the arrival of any other new car since the same company’s Model A. A model brought out thirty years earlier.” The company had already spent $250 million on the car, before it was launched. The Business Week called it more costly than any other consumer product in history. Given this huge cost, Ford had to sell around 200,000 Edsels in the first year, if it had to get its investment back. Nevertheless, two years, two months and fifteen days later, it had only sold 109,446 Edsels. This included cars bought by Ford executives, dealers, salesman, workers etc. The number amounted to less than 1% of the cars sold in America during that period. On November 19, 1959, it pulled the plug on the car. Estimates suggested that Ford lost around $350 million on the car. So what went wrong? Some of the feedback from trade publications was negative. Over and above that, some of the cars that were sent out initially were badly made. As Brooks writes “Automotive News reported that in general the earliest Edsels suffered from poor paint, inferior sheet metal, and faulty accessories, and quoted the lament of a dealer about one of the first Edsel convertibles he received: “The top was badly set, doors cockeyed, springs sagged.”” Some individuals who worked on making and launching the car liked to believe in later that it was the because of the Sputnik, the first artificial space satellite launched by the Soviets that led to the car not selling. As Brooks puts it “October 4th[1957], the day the first Soviet Sputnik went into orbit, shattering the myth of American technical pre-eminence and precipitating a public revulsion against Detroit’s fancier baubles.” Detroit was the city were the biggest motor companies in the United States were head-quartered back then. While these could have been reasons for the car not selling, the real reason for the car not selling was the hype that accompanied it. As Brooks writes “It was agreed that the safest way to tread the tightrope between overplaying and underplaying the Edsel would be to say nothing about the car as a whole but to reveal its individual charms a little at a time—a sort of automotive strip tease…The Ford Company had built up an overwhelming head of public interest in the Edsel, causing its arrival to be anticipated and the car itself to be gawked at with more eagerness than had ever greeted any automobile before it.” C Gayle Warnock, director of public relations of the Edsel division of Ford, shares an interesting example, which provides the real reason behind the failure of the Edsel car. In 1956, a senior official working on the Edsel launch (in fact it wasn’t called the Edsel then, it was just the E-Car) gave a talk about it in Portland, Oregon. Warnock was aiming for some coverage regarding the event in the local press. But what he got was something he had not expected. The story got picked up by wire services and was splashed all across the country. As Warnock recounts in the chapter “Clippings [of the media coverage] came in by the bushel. Right then I realized the trouble we might be headed for. The public was getting to be hysterical to see our car, figuring it was going to be some kind of dream car—like nothing they’d ever seen. I said… “When they find out it’s got four wheels and one engine, just like the next car, they’re liable to be disappointed.”” And this is precisely the reason why the Edsel flopped. The hype was so much that the public expected something that was totally out of the world. But what Ford was basically giving them in the rephrased words of Larry Doyle, the head of sales at the Edsel division, “exactly the car that they had been buying for several years.” As Doyle put it “We gave it to them and they couldn’t take it.” Further, it did not help that the first lot of cars was not properly manufactured. “Within a few weeks after the Edsel was introduced, its pitfalls were the talk of the land,” writes Brooks. Now replace the word Edsel with Nano and the situation stays more or less the same. The hype around the car was huge. When the car was launched in 2009, the entire world media was in Delhi for the launch. In fact, before the car was launched the rating agency Crisil said that the car could expand the Indian car market by 65%. People who had cars were already worried about the traffic on the roads getting worse than it already was, because of the Nano. Before the car was launched in 2009, prices in the used car market fell by 25-30%, given Nano’s expected price point of Rs 1 lakh. Nonetheless, Nano could not live up to the hype. In a May 2014 newsreport, the Business Standard pointed out that Launched in 2009,Nano sales between 2010-11 and 2012-13 constituted 23-24 per cent of Tata Motors’ total sales. But Nano sales declined dramatically after peaking to 74,527 in 2011-12. The numbers came down by more than 70 per cent in two years to 21,129 in 2013-14. Tata Motors has set up a facility at Sanand in Gujarat to make 250,000 Nanos a year.” So, the car sold nowhere near the numbers it was expected to. What did not help was that when the car actually started hitting the market in 2010, some units caught fire. After all the hoopla around the Nano, this wasn’t what the public was ready to accept. Brooks’ sentence written for Edsel can be re-written for Nano as well: “After the Nano was introduced, its pitfalls were the talk of the land.” Further, the hype around the car was so huge that the people were expecting something totally out of this world. They did not know what they wanted, but they did not want, what they got. The question that remains is how much could you expect out of a car which was supposed to be sold at Rs 1 lakh? The article originally appeared on www.FirstBiz.com on Nov 18, 2014

(Vivek Kaul is the author of the Easy Money trilogy. He tweets @kaul_vivek)

Luck vs Pluck: The man who could have been Bill Gates

Michael J. Mauboussin is Chief Investment Strategist at Legg Mason Capital Management in the United States. He is also the author of bestselling books on investing like  Think Twice: Harnessing the Power of Counterintuition and More Than You Know: Finding Financial Wisdom in Unconventional Places. His latest book The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing (Harvard Business Review Press, Rs 995) has just come out.In this freewheeling interview with Vivek Kaul, he talks about the link between success and luck and how at times it is difficult to separate one from the other. The interview will appear in two parts. This is the first part.

 

The first line in your book goes “my career was launched by a trash can”. Can you take our readers through that story?
When I was a senior in college, I didn’t really know what I wanted to do with my career, but I knew I needed a job. Drexel Burnham Lambert, an investment bank that was very successful at the time, came on campus to interview and I did well enough to be invited to New York City for a final round. So I put on my best suit and made the trip.
The day of the interviews, we candidates were told that we would have six long interviews and just 10 minutes with the executive who ran the division. My interviews went fine, and then it was my turn to meet with the executive. Upon walking into his office, I noticed that he had a trash can that carried the emblem of the Washington Redskins, a professional American football team. Being a sports fan and having spent my last four years in Washington, D.C., I complimented him on the trash can. That comment hit in an emotional spot, and he launched into a discussion of his time in D.C., the virtues of sports, and the link between athletics and business. I sat and nodded, as 10 minutes stretched to 15.
You got the job?
I got the job, and accepted it. Indeed, my time at Drexel Burnham was extremely formative. After about six months into the program, one of the leaders pulled me aside. “You’re doing fine in the programme,” he started, “but I have to tell you something. The six interviewers voted against hiring you. But the top guy came down and insisted that we bring you in. I don’t know what you said, but it sure worked.” So I like to say that my career was launched by a trash can, and that was pure luck.
What was the broader point that you were trying to make through that example?
The broader point is that luck permeates many aspects of our lives and we’re frequently unaware of its role. So this book is about skill and luck, and includes the definition of each term, tools and methods to quantify the role of each, and what to do about it.
“Most of the successes and failures we see are a combination of skill and luck that can prove maddeningly difficult to tease apart,” you write. Can you explain that in detail?
I open a chapter with the story of an entrepreneur who was born near Seattle who was a brilliant programmer and wrote code that effectively launched the personal computer revolution. He started a company that by 1980 had a dominant market share in the software that ran on the Intel chip. But the company’s fate was sealed in 1981 when IBM came calling and sealed a deal.  Now if you know a little about Bill Gates, you can see how that series of facts fits him pretty well. But then I share the end of the story: this tech pioneer walked into a bar in California in 1994 and hit his head bluntly as a result of a fight or a fall—the details were never clear. He died three days later. His name was Gary Kildall, and he has a floppy disk etched on his tombstone. Chances are you’ve never heard of Gary Kildall but you have heard of Bill Gates.
That’s very interesting…
When IBM executives first approached Microsoft about supplying an operating system for company’s new PC, Gates actually referred them to Digital Research (Kildall’s company). There are conflicting accounts of what happened at the meeting, but it’s fairly clear that Kildall didn’t see the significance of the IBM deal in the way that Gates did.
And what happened then?
IBM struck a deal with Gates for a lookalike of Kildall’s product, CP/M-86, that Gates had acquired. Once it was tweaked for the IBM PC, Microsoft renamed it PC-DOS and shipped it. After some wrangling by Kildall, IBM did agree to ship CP/M-86 as an alternative operating system. IBM also set the prices for products. No operating system was included with the IBM PC, and everyone who bought a PC had to purchase an operating system. PC-DOS cost $40. CP/M-86 cost $240. Guess which won. But IBM wasn’t the direct source of Microsoft’s fortune. Gates did cut a deal with IBM. But he also kept the right to licence PC-DOS to other companies. When the market for IBM PC clones took off, Microsoft rocketed away from competition.
So what is the point?
The fact is, Kildall played his cards much differently than Gates did, and hence did well but enjoyed financial success vastly more modest than Gates. But it’s tantalizing to consider the possibility that with a few tweaks, Kildall could have been Gates.Now the book acknowledges that untangling skill and luck can be imperfect, but even some sense of the relative contributions of the two can really help you understand history and, more importantly, make better predictions of the future.
You write that “most people have a general sense that luck evens out over time. That may be true in the grand scheme of things. But the observation doesn’t old for any individual, and the timing of luck can have a large cumulative effect.” What do you mean by that? 
In some activities, the outcomes are largely independent: what happened before doesn’t affect what happens next. This is true, of course, in classic games of chance such as dice throwing or roulette wheels, but it also applies to relatively stable systems like sports. You can model the batting average of baseball players, for example, using a simple, independent model. In these cases, luck does tend to even out over time. In other activities, the outcomes are path dependent. What happens next is affected by what happened before. This is relevant in realms that are socially driven such as sales of books, music, and movies. That fact is if you ran the universe over again, it is very unlikely that the same products would be commercial smash hits. We know this through examining the results of some clever sociological experiments.
Can you explain it through an example? 
One example I give in the book is the income of college graduates. It turns out that men who graduate during times of relative prosperity earn more than those who graduate during more challenging conditions. That is not so surprising. What is more surprising is that that effect remains in place for 15 years (and perhaps more) following graduation. So two students of identical ability can have substantially different incomes over a long period by dint of when they graduated. So luck is not evening out in these cases. There is a large cumulative and apparently irreversible effect.
Why do we vastly underestimate the role of luck in what we see happening around us?
Especially when social processes are at play, it’s really hard to know how things are going to unfold. In other words, luck is a very large variable. You often hear executives in the entertainment industry lament how hard it is to manufacture hits. And that is true. When social processes are at play, there’s an inherent lack of predictability and generally high inequality.
Any examples?
One story that captures this is that of a young singer named Carly Hennessey. Music executives were looking for the next Brittney Spears, and Hennessey had everything they were looking for—a great voice, charisma, and drive. They spent millions on her first album, which ended up a commercial flop. In the first three months the album sold a grand total of 378 copies, earning less than $5000. There is simply no easy formula to predict success. The flipside is true as well. When Michael Eisner, then CEO of Disney, saw the pilot of the show “Lost,” he gave it a 2 on a scale from 1 to 10, with 10 being the highest. He later called it “terrible.” But “Lost” was a huge success and was very profitable for Disney. When the top executive at Disney has no idea what’s going on, it’s easier to accept that it’s really hard to anticipate hits.
Why are we so bad at distinguishing luck from skill?  
There’s a module in your left hemisphere that neuroscientists call “the interpreter.” Its job is to create a narrative that explains cause and effect. For most of mankind’s existence, cause and effect was a pretty straightforward affair: a rustle in the bushes likely signaled danger, for instance. This module has conferred extraordinary advantage, and some scientists argue it is at the core of what distinguishes humans from other species. Now, here’s the fascinating component. Studies of split-brain patients reveal that the interpreter will fabricate a cause whenever it sees an effect, even when the cause makes no sense. For example, researchers would feed information into the right hemisphere—largely absent of language—and ask the subject to explain what is going on. Since the hemispheres in these subjects are severed, there’s no way to communicate. The interpreter simply makes up a story.
So here’s the answer to your question. The interpreter doesn’t know anything about luck. When it sees an effect, it searches for a plausible cause. Once a cause is found, your mind puts the issue to rest. In fact, you start to believe your own story and dismiss any other possibility, a concept psychologists call “creeping determinism.” So once something has happened, we tend to grossly underestimate the role of luck.
Could you give us an example?
I’ll mention quickly a paper by Professor Andrew Lo at MIT. He studied about 20 accounts of the recent financial crisis—half of them by journalists and the other half by academics. He found that there was no common explanation for the crisis, and in fact some of the explanations contradicted one another.
 The interview originally appeared on www.firstpost.com on November 28, 2012.
(Vivek Kaul is a writer. He can be reached at [email protected])