Ten interesting things that we read this week

Some of the most interesting topics covered in this week's iteration are related to 'behaviour of stock market manias', 'the need to be a morning person?', and the 'Code of Hammurabi'

Published: Nov 25, 2017

Image: Shutterstock (For illustrative purposes only)

At Ambit, we spend a lot of time reading articles that cover a wide gamut of topics, including investment analysis, psychology, science, technology, philosophy, etc. We have been sharing our favourite reads with clients under our weekly ‘Ten Interesting Things’ product. Some of the most interesting topics covered in this week’s iteration are related to ‘behaviour of stock market manias’, ‘the need to be a morning person?’, and the ‘Code of Hammurabi’.

Here are the ten most interesting pieces that we read this week, ended November 24, 2017.

1) The behaviour of stock market manias [Source: Base Hit Investing]
John Huber of Saber Capital in this piece says that while he agrees that the US markets today are not cheap, he doesn’t quite believe they are in the ‘bubble’ territory yet. To provide context he looks at the late 1990s which saw unbridled enthusiasm among individual investors, reckless behaviour by corporate managers, and significant complicity by lawmakers and regulators. He cites an example from the book ‘Bull’ by Maggie Mahar to illustrate the speculative fervor that existed at the time. One of the perks about being CEO of a publicly traded company of the 1990s was that you could pay yourself with huge amounts of stock options, but yet not count those stock options as an expense on your company’s financial statements. It was creating money out of thin air. Top executives could mint multi-million dollar pay packages each year that seemingly had no strings attached. This was obviously a complete fallacy. The options were an expense—a significant one. While the stock options did not show up in the income statement, they did so in the shareholder’s equity statement in the form of increased shares outstanding. This in effect diluted EPS for the shareholders.

But as it turned out, granting options wasn’t a free lunch. Apart from lowering EPS, there was another second derivative behaviour, and one that is still practiced today – companies began buying back their own stock to “offset dilution” that occurred from giving out options to management.  It’s common knowledge that buying back shares only makes sense when they are priced below deemed intrinsic value. However, managers do not seem to take this into account. The act of buying back shares of stock is a separate capital allocation decision, regardless of the reason why you’re buying those shares. Doing so without any regard for value (simply to “offset” the dilution that you created with a previous decision) is completely illogical. Yet it occurs all the time. And in the late 1990s, it was running rampant. John says this practice has a bit of irony attached to it, because the corporate managers who fought tooth and nail to prevent these options from being called what they were (an expense) began paying for these options by draining the company’s cash to hide the true impact and cost that the options were creating in the first place.

The book ‘Bull’ in particular highlights Dell as a company that was an excessive practitioner of this idea. Michael Dell paid himself huge amounts of options (in 1998 he gave himself 12.8 million options to buy Dell’s stock, while at the same time unloading 8 million shares in the open market). To finance these generous option grants, Dell began ramping up his company’s buyback programme. In 1998, Dell Computer spent a whopping $1.5 billion buying back its own stock—which by the end of the year was trading hands at 70 times earnings. The company management’s viewpoint on buyback was highlighted in its 2000 10-K:  “During fiscal year 2000, the company repurchased 56 million shares of common stock for an aggregate cost of $1.1 billion, primarily to manage dilution resulting from shares issued under the company’s employee stock plans.” $1.1 billion equaled about half of Dell’s pretax earnings for the year.

These rampant stock buybacks led to yet another behaviour (now a third derivative of the decision to grant excessive options). This next level is what separates bad corporate behaviour from the bubble behaviour that typically is only found in manias. Companies not only began using cash to buy back common stock of their own company, they began using cash to buy call options of the common stock of their own company! These are not options given to managers but the options that traders use to make short-term bets on the price of the stock. Dell was again a proponent of this practice and amazingly, it not only bought calls on its own stock, it also sold puts to finance the cost. Dell built up a truly massive position in options on its own stock. At the end of 1999, the company owned call options on 118 million shares and had sold put options on 69 million shares, an amount that equaled billions of dollars of notional value of Dell common stock—real cash that Dell would have to come up with if things went bad. And things went bad. Dell lost billions of dollars over the next few years on the puts—they had to shell out over $1.2 billion in one year alone to buy back millions of shares of stock that had lost half their value in the previous year—this was more than they made selling computers that year.

The mania of the 1990s was extreme, and John says, while we will most likely see similar excesses at some point, we are nowhere close right now. There are almost always isolated areas of excess, fraud, or aggressive accounting, but it gets widespread during manias.

2) Catalonia and the problem with separatism [Source: Financial Times]
Catalonia’s freedom movement has slogans like “Spain steals from us!” or “Catalonia is not Spain”. Simon Kuper says that rhetoric like this divides people into opposite groups, each with a single identity: us (Catalans) and them (Spaniards). The Government in Madrid unintentionally sharpens this divide by imposing direct rule on Catalonia. According to him, someone else who thinks in terms of single identity is Donald Trump. As he tells it, you’re American or Muslim; you’re a real American or a liberal elitist. There’s an uncomplicated joy to single identity: find your essence, then taunt an enemy who doesn’t share it. And along with your identity comes a free set of opinions that you never need to test against reality. But thinking in single-identity terms breeds conflict. JH Elliott, the British historian and expert on Spanish history, describes Catalonia as “a very unhappy society over the last year or two”, in which “families have not been talking to each other”.

Amartya Sen, the philosopher who won the Nobel Prize for economics in 1998, believes that we need to ditch single identity and understand that every person has multiple identities. Sen began thinking about these issues aged 11. One day in 1944, a Muslim labourer named Kader Mia staggered bleeding into the Sen family garden in Dhaka (then in British India). Mia had just been knifed during Hindu-Muslim street riots. Sen recalls in his book ‘Identity and Violence’, “I could not do much for Kader Mia as he lay bleeding with his head on my lap.” Mia was rushed to hospital by Sen’s father but died there. Sen could never forget him. Mia would have lived if his Hindu assailant had recognised him as a fellow Indian, Bengali or poor male, instead of seeing him as a Muslim. Like Mia we all have multiple identities. Nobody is just one thing. No person is “only” Muslim, or only Catalan. It’s ludicrous to file millions of people - of different life experiences, genders, ages, classes and passions — under just one category. Worse, dividing people into single identities automatically sets them against each other.

Sen says: “The main hope of harmony in our troubled world lies in the plurality of our identities, which cut across each other and work against sharp divisions.” Nobody is a caricature made of one or two demographic factors. We’re not all the same but we do have commonalities. But talking in single identities is infectious. Each time Trump says something racist, some liberals dismiss all his supporters as racist halfwits. When Trump slurs women, or black people, he encourages those groups to cluster around a single identity. The Black Lives Matter movement and last January’s Women’s Marchers have excellent goals. But as Mark Lilla argues in his recent book “The Once and Future Liberal: After Identity Politics”, these groups will probably fail if they become exclusively single-identity movements. If you want to persuade people who don’t share your one particular identity, you need to appeal to some of our shared identities. Lilla writes: “I am not a black male motorist . . . All the more reason, then, that I need some way to identify with one if I am going to be affected by his experience . . . The more the differences between us are emphasised, the less likely I will be to feel outrage at his mistreatment.”

3) Why you shouldn’t try to be a morning person [Source: BBC]
We’ve all heard it before: to be successful, get out of bed early. After all, Apple CEO Tim Cook gets up at 3:45am, Fiat CEO Sergio Marchionne at 3:30am and Richard Branson at 5:45am – and, as we know, “the early bird catches the worm.” But just because some successful people wake up early, does that mean it’s a trait most of them share? And if the idea of having exercised, planned your day, eaten breakfast, visualised and done one task before 8am makes you want to roll over and hit snooze, are you really doomed to a less successful life? For about half of us, this isn’t really an issue. It’s estimated that some 50% of the population isn’t really morning or evening-oriented, but somewhere in the middle. Roughly one in four of us, though, tend more toward bright-eyed early risers, and another one in four is a night owl. For them, the effects can go beyond falling asleep in front of the TV at 10pm or being chronically late for work.

Research shows that morning versus evening types show a classic left-brain versus right-brain division: more analytical and cooperative versus more imaginative and individualistic. Numerous studies have found that morning people are more persistent, self-directed and agreeable. They set higher goals for themselves, plan for the future more and have a better sense of well-being. And compared to night owls, they’re less likely to be depressed, drink or smoke. Although morning types may achieve more academically, night owls tend to perform better on measures of memory, processing speed and cognitive ability, even when they have to perform those tasks in the morning. Night-time people are also more open to new experiences and seek them out more. They may be more creative (although not always). And contrary to the maxim (‘healthy, wealthy and wise’), one study showed that night owls are as healthy and wise as morning types – and a little bit wealthier.

“If people are left to their naturally preferred times, they feel much better. They say that they are much more productive. The mental capacity they have is much broader,” says Oxford University biologist Katharina Wulff, who studies chronobiology and sleep. On the other hand, she says, pushing people too far out of their natural preference can be harmful. When they wake early, for example, night owls are still producing melatonin. “Then you disrupt it and push the body to be in the daytime mode. That can have lots of negative physiological consequences,” Wulff says, like a different sensitivity to insulin and glucose – which can cause weight gain. In many ways, that makes sense, since research shows that our chronotype, or internal clock, is mainly biological.

In our rush to figure out the ‘secrets’ of success, we tend to forget a couple of things. First, not all high achievers are early risers, and not all early risers are successful. But more importantly, in a phrase beloved by academics everywhere, correlation isn’t causation. In other words, it’s not clear that waking up early itself provides the benefit. Instead, it may be that most of us are expected to start work or school by 8 or 9am. If you’re a morning person, a combination of biological changes, from your hormones to body temperature, will get you up and at 'em way ahead of your night owl peers. That means people who enjoy rising early will be more aligned with their workday and likely to achieve more. For a night owl waking at 7am, her body still thinks she’s asleep and is acting accordingly, so she’s groggy for much longer than a morning person who wakes up at the same time. One recent study found, even as people tried to become ‘morning’ people, it didn’t make them have a better mood or life satisfaction, suggesting these traits are “intrinsic components of the late chronotype.”

Other research also has hinted that your sleep preference may be biologically ‘bundled’ with other characteristics. In one recent study, for example, the University of Haifa’s Neta Ram-Vlasov found that more visually-creative people had more sleep disturbances, such as waking several times at night or insomnia. Again, correlation isn’t causation, she says. But there may be a connection to genetics. “There is a dopamine receptor gene that has been previously associated with both increased creativity and also with insomnia and sleep disturbance,” she says. That said, in case you want to become a morning person, morning exposure to bright (or natural) light, avoiding artificial light at night and carefully-timed melatonin intake can help. But because you’re effectively overriding your biology, any changes take discipline and must be consistent to last. And because night owls tend to have a longer circadian cycle, putting them even more at odds with a 24-hour schedule that can be tougher for them to achieve.

4) Blockchain and Big Data: When two super technologies meet, what happens? [Source: bigdataanalyticsnews.com]
Bitcoins, or digital currencies like it, come with a certain level of anonymity that is, suffice it to say, the point of the whole technology. Typically, all credit charges and related details pass through a third party, creating a practical, traceable trail. During a bitcoin transaction, however, the general transaction info creates a block in the underlying technology called blockchain. That block alone is publicly accessible and readable, so you could see all the transactions happening over time. However, the identity of the seller and the buyer remain anonymous, visible only to those two parties, with no middleman between them. It’s not really an issue with over-the-table and legal transactions. But there’s always a chance you could be dealing with criminals or unscrupulous parties on the other end, and be none the wiser. Therein lays the big obstacle with digital currencies. A business that unknowingly enters a transaction or agreement with a criminal party may be unaware but is still in the wrong. So why would this currency even be an option, then? Why consider the risk?

Big data will help solve this problem in many ways, or at least help lower the risk of damaging transactions for big business involving digital currencies. To understand the how and why, you first need to understand the major technology behind this new form of currency, the aforementioned blockchain. The best way to describe a blockchain is to call it a publicly accessible electronic or digital ledger. It is shared between a community of users, creating or adding a finite and unchangeable record of all transactions taking place. Each transaction is time and date stamped, and then linked to a previous transaction, creating a long chain or history of exchanges, hence the name. Each individual record or transaction is referred to as a block, which is where the “block” part of the title comes from. What makes the blockchain particularly special is the information or blocks can only be updated and altered by participants’ actions, such as making a purchase: Also, this information or new data is always updated and added, but it can never be erased or altered again. Therefore, the blockchain becomes a true, current and verifiable record of any and all transactions made through the system.

While technically nothing is unhackable, you’d have to be insane to alter with a blockchain and its transactions. Why? Because it’s essentially a peer-to-peer network. Instead of hacking into a single access point, folder or directory to change information, you’d have to hack every system or computer on the network, which is just unfeasible. Furthermore, even if it was possible, it would take an incredibly long time to change all records, so surely someone would notice. Therefore the blockchain is an incredibly reliable digital ledger, set to disrupt many industries and party transactions.

So now where does Big Data Factor in? Blockchain is, in and of itself, a more complex and public form of big data virtualization. All transaction and exchange information is included with each passing of a currency. Over time, that’s a huge swath of data and information carried on from party to party. More importantly, big data and data analytics make it possible to identify patterns and trends, thus allowing businesses or parties to pinpoint nefarious individuals along the network. This would allow a brand to blacklist a particular user, to prevent being wrapped up in shady or illegal dealings. It works a lot like credit checks on a credit or payment system. You can make sure most — if not all — actions or exchanges along a user’s history are legal, genuine and on the up-and-up. That will become increasingly more important as this technology evolves and gets adopted on a much grander scale in the business world. It can also provide social data on cryptocurrency trends and buying patterns. Data analysts have so much information in their hands, they can uncover and build powerful profiles and consumer demographics, which organizations and businesses can put to use. That is part of the allure of the modern blockchain as it pertains to big data. It will enable companies to build new forms of data monetisation based on transaction history, trends and more.

5) Taming the masters of the tech universe [Source: Financial Times]
Eight of the world’s most highly-valued companies are technology businesses. The combined market capitalisation of these companies is $4.7tn. That is 30% of the combined market capitalisation of the other 92 companies in the world’s 100 most valuable firms. Of these eight companies, five (Apple, Alphabet, Microsoft, Amazon and Facebook) are from the USA, two are Chinese (Alibaba and Tencent) and one is South Korean (Samsung). Moreover, the businesses in which these companies are engaged are all different in important respects. Nevertheless, the rise to prominence of these technology groups has to be telling us something important. The author of this piece, Martin Wolf belies these numbers tell us something important about the extent to which these firms can reshape the wider economy and society and talks about seven challenges therein.

First, he talks about the implications of the remarkable US dominance. While five of the 10 most valuable US companies are technology companies, not one of the most valuable European companies is. This he thinks suggests that unlike US or China, Europe is at a risk of not being in the economic games of the future at all. Second, he talks about the reason behind stratospheric valuations of such companies – monopoly. According to him, current valuations reflect the expectation of enduring “super-normal” profits. This may not be the product of malign behaviour, but of innovation and economies of scale and scope, including the network externalities that lock in customers. Yet, he says only monopoly could deliver such super-normal profits. Third, he discusses how one should think about competition policy for businesses that benefit from such powerful monopoly positions. It’s important to figure out whether such (monopoly) position is temporary or lasting. He says new entries are a necessary condition for eroding such temporary monopolies and so the technology giants should be strongly deterred from buying up their potential competitors.

Fourth, he discusses the macroeconomic impact of such companies. He cites Apple’s example which has total assets of $375bn with fixed assets at a meagre $34bn. This company evidently has no profitable way to invest its huge profits in its business. It is now an investment fund attached to an innovation machine and so a black hole for aggregate demand. Mr. Wolf believes that the idea that a lower corporate tax rate would raise investment in such businesses is ludicrous. Fifth, he discusses the point on taxation. He thinks that a well-designed corporate tax would fall on monopoly rent. The way to execute will be by expensing of investment, together with a higher, not lower, corporation tax than at present. He adds that territorial taxes are inescapably defective in taxing global technology companies, since the location of their production is so hard to define. The inability to tax technology companies in a way that matches taxation of territorial competitors creates a huge economic distortion. Sixth, he discusses impact of such tech companies on media. Here Google and Facebook are currently the main players. In 2017, these two businesses are expected to receive 63 per cent of all US digital advertising revenue, itself a rising share of the total. Yet these enormously profitable businesses are parasitic on the investments in collecting information made by others. He further cautions against the possibility of these firms becoming highly efficient disseminators of non-information.

Finally, he believes the activities in which the technology industry is now engaged — what Andrew McAfee and Erik Brynjolfsson call “machine, platform, crowd” — are going to have a huge impact on our labour markets and, if artificial intelligence continues to advance, on our very place in the world. While he believes technology will continue to play an ever important role in our future, policymakers must get an intellectual grip on what is happening.

6) The code of Hammurabi: The best rule to manage risk? [Source: farnamstreetblog.com]
King Hammurabi of Babylon, Mesopotamia became ruler in 1792 BC and held the position for 43 years. He was a fair leader and concerned with the well-being of his people. He transformed the area, ordering the construction of irrigation ditches to improve agricultural productivity, as well as supplying cities with protective walls and fortresses. Hammurabi also renovated temples and religious sites. However, by today’s standards, Hammurabi was a dictator. Far from abusing his power, however, he considered himself the “shepherd” of his people. Although the Babylonians kept slaves, they too had rights. Slaves could marry other people of any status, start businesses, and purchase their freedom, and they were protected from mistreatment. Our modern beliefs are not separate from those of people in Hammurabi’s time; they are a continuation of them. Early legal codes are the ancestors of the ones we now put our faith in. Whether a country is a dictatorship or democracy, one of the keys to any effective legal system is the ability for anyone to understand its laws. There’s a lot to learn from the simplicity of Hammurabi’s Code, which concerned itself with practical justice and not lofty principles. Three important concepts are implicit in Hammurabi’s Code: reciprocity, accountability, and incentives.

Construction laws within the code made builders liable (amounting to death sentence in some cases) for any damage to the house and/or to its occupants. While we do not know the impact of these laws on construction quality, we do know that human self-preservation instincts are strong. Wanting to avoid death is the most powerful incentive we have. If we assume that people felt and thought the same way 4000 years ago, we can guess at the impact of the Code. Imagine yourself as a Babylonian builder. Each time you construct a house, there is a risk it will collapse if you make any mistakes. So, what do you do? You allow for the widest possible margin of safety. You plan for any potential risks. You don’t cut corners or try to save a little bit of money. You want to walk away certain that the house is solid. Now contrast that with modern engineers or builders. They don’t have much skin in the game. The worst they face if they cause a death is a fine. During Hurricane Katrina, 1600 people died due to flooding caused in part by the poor design of hurricane protection systems in New Orleans. Hindsight analysis showed that the city’s floodwalls, levees, pumps, and gates were ill designed and maintained.

The portions of Hammurabi’s Code that deal with construction laws, as brutal as they are illustrate an important concept: margins of safety. When we construct a system, ensuring that it can handle the expected pressures is insufficient. A single Black Swan event — such as abnormal weather — could cause its collapse and in turn the builder’s own death, so builders during Hammurabi’s rule had to allow for a generous margin of safety. But our current financial systems do not incentivize people to create wide margins of safety. Instead, they do the opposite - they encourage dangerous risk-taking. Nassim Taleb referred to Hammurabi’s Code in a New York Times opinion piece. He wrote:” …it’s time for a fundamental reform: Any person who works for a company that, regardless of its current financial health, would require a taxpayer-financed bailout if it failed should not get a bonus, ever. In fact, all pay at systemically important financial institutions — big banks, but also some insurance companies and even huge hedge funds — should be strictly regulated.” The issue, in Taleb’s opinion, is not the usual complaint of income inequality or overpay. Instead, he views bonuses as asymmetric incentives. They reward risks but do not punish the subsequent mistakes that cause “hidden risks to accumulate in the financial system and become a catalyst for disaster.”

Some career fields have a strict system of incentives and disincentives, both official and unofficial. Doctors get promotions and respect if they do their jobs well, and risk heavy penalties for medical malpractice. With the exception of experiments in which patients are fully informed of and consent to the risks, doctors don’t get a free pass for taking risks that cause harm to patients. The same goes for military and security personnel. As Taleb wrote, “we trust the military and homeland security personnel with our lives, yet we don’t give them lavish bonuses. They get promotions and the honor of a job well done if they succeed, and the severe disincentive of shame if they fail.” When you align incentives of everyone in both positive and negative ways, you create a system that takes care of itself. Contrarily, when we lack an incentive to protect ourselves, we are far more likely to risk the safety of other people. This is why bankers are willing to harm their customers if it means the bankers get substantial bonuses. This is why companies that market harmful products, such as fast food and tobacco, are content to play down the risks.

7) Leonardo’s principles
[Source: LinkedIn]
Ray Dalio of Bridgewater in this LinkedIn post gives us a sneak peek into Leonardo da Vinci's mind. He says that Da Vinci was a prophet—i.e., he could see what others could not see and brought those visions to mankind so we could all see more clearly. His thinking and creations have stood the tests of time like few others' have. While the art he produced is expensive, his principles are free, and in Dalio’s opinion much more valuable. Having published a book recently on similar topic, Dalio says that principles are ways of dealing with reality effectively, so it is common for different people who practice principled thinking to learn the same ones. Principled thinking, he says, is thinking at a higher level and in a more simplified way that captures timeless truths than more common thinking which observes things in more of a one-off way, without regard to patterns and or a deep understanding of how things really work. He shares some of Leonardo’s principles in the article. We’ve showcased some of the best ones below.

"There are three classes of people: those who see. Those who see when they are shown. Those who do not see."

"It had long since come to my attention that people of accomplishment rarely sat back and let things happen to them. They went out and happened to things."

"I have been impressed with the urgency of doing. Knowing is not enough; we must apply. Being willing is not enough; we must do."

"The greatest deception men suffer is from their own opinions."

"Where there is shouting, there is no true knowledge."

"Beyond a doubt, truth bears the same relation to falsehood as light to darkness."

"Human subtlety will never devise an invention more beautiful, more simple or more direct than does nature because in her inventions nothing is lacking, and nothing is superfluous."

"He who does not punish evil commands that it be done."

"It is easier to contend with evil at the first than at the last."

"As a well-spent day brings happy sleep, so a life well spent brings happy death."

8) Little room for error as investors chase leveraged loan boom [Source: Financial Times]
Adam Cohen, founder and chief executive of Covenant Review, a New York-based research house, says standards have been seriously slipping in the $940bn market for leveraged loans, where debt-laden companies take on more debt. But over the past six months terms have got looser still, he says, as still-low interest rates combine with strong demand from specialist credit vehicles to encourage the riskiest of companies to lever up. Lenders still get the basic promise of their money back in a few years, with “x” amount of interest. But the traditional assurances on top of that — such as covenants that restrict further borrowing, open lines of communication with the company or maintaining a lender’s position in the capital structure — have been steadily worn away.  Riskier “covenant-lite” loans now account for about 70% of new leveraged loans, up from 30% before the Lehman Brothers crisis.

Take a recent deal from Eating Recovery Center, a Denver-based specialist in treating anorexia and bulimia, among other disorders. Less than a decade old, the company has 25 clinics across America and last year did about $22m in earnings before interest, tax, depreciation and amortisation. Even so, when the new private-equity owner CCMP Capital sold a couple of hundred million dollars of loans last month to back a $550m buyout, it sought the kind of terms that are normally reserved for much bigger shops, such as Apollo or Bain, according to Mark Xiong, an analyst at Covenant Review.

One example of riskier practice is a so-called “trapdoor” structure, whereby collateral can be moved to subsidiaries that are out of reach of creditors, effectively adding leverage. TPG, the big private equity group, pulled off a similar stunt last year, when it plucked out $250m of assets from a collateral pool backing loans issued by J Crew, the clothing retailer it bought for $2.8bn in 2010.

It is possible that buyers of these loans just do not realise what they are giving up. Many are vehicles known as collateralised loan obligations, or CLOs, where loans are pooled together and passed on to different classes of owners in various tranches. Managers of CLOs may lack the resources to plough through a 500-page credit agreement or to track a quarterly covenant that limits debt to, say, cash flow. But it probably pays to study the fine print. On the ERC deal, for example, leverage was about six times EBITDA on an adjusted basis. But if you take out all those adjustments — including assumptions that CCMP will do much better than the previous private-equity owner, Lee Equity Partners, in managing the company’s supply chain and inventories — then the leverage was more like 10 times. That is well beyond the six-times level that regulators have flagged as a threshold for extra caution. Debt burdens, such as these leave any company with little room for error. And if things do go wrong, lenders have signed away protections that could have given them a seat at the table.

9) Chess novice challenges Magnus Carlsen [Source: WSJ]
Speed learner Max Deutsch is testing the limits of self-improvement. He went through a month of training before he travelled across the ocean, sat down in a regal hotel suite at the appointed hour and waited for the arrival of the world’s greatest chess player. Max was not very good at chess himself. He’s a self-diagnosed obsessive learner. He knew from the beginning of his peculiar year that the hardest challenge would come in October: defeating Magnus Carlsen in a game of chess. Magnus Carlsen is a 26-year-old world champion from Norway who has become a global celebrity because of chess. He belongs alongside Garry Kasparov and Bobby Fischer in any conversation about the most talented players ever. Max’s original idea had been to beat a computerised simulation of Magnus. But when The Wall Street Journal stumbled across his “Month to Master” project while reporting another story, it offered to put him in touch with the real-life version. Max was game. So was Magnus. At the heart of their chess match was a question about success: Can we hack our brains in a way that radically accelerates the traditional learning curve?

Max had played Magnus on his Play Magnus app, which is powered by an engine that simulates the Norwegian’s skill and style at different ages from the time he was five years old. But he didn’t expect to play Magnus in person. Not even Max imagined that Magnus would agree to play a novice he’d never met. Magnus Carlsen has always been a bit of a showman. Magnus agreed to play Bill Gates and limit himself to a severe time handicap. He crushed the billionaire in nine moves. Just out of genuine curiosity Magnus agreed to play Max. He wanted to know whether someone could become good enough in one month to beat him. Malcolm Gladwell popularised the idea that world-class success can be earned through a certain amount of serious practice, which became known as the 10,000-hour rule. There has been a contentious debate over how widely it should be applied.

It was the reigning world champion’s right to set the rules of the match. His camp decided it would be rapid-format chess in which each player had 20 minutes to make all his moves. The date was set for November 9 in Hamburg, where Magnus was already scheduled to host a promotional event. Max figured he could only improve by playing better competition—that he would have to lose as much as possible to learn as much as possible. He took advantage when Magnus offered access to his own youth coach, Norwegian grandmaster Torbjørn Ringdal Hansen, and they discussed chess principles before settling on two potential styles of play: conservative or aggressive. “Both are low-probability, but I think I’m going with the second option,” Max said. “There’s no reason to play this safe.” Max realised that he would have to be more inventive in his approach to learning chess. “If I can’t play like a human,” he said, “then how can I play?” Max figured he would have to play like a computer. But he calculated that would take approximately one trillion trillion trillion years. He was relying on his own brain to process the information.

When the day came, and the board was set, all the bets were on Magnus. But, after eight moves, using his own limited chess ability, the unthinkable was occurring: Max was winning. Magnus had reason to believe his opponent was better than he actually was. He was aware of Max’s algorithm, but Max hadn’t informed the enemy it wasn’t done. Max had his full attention because Magnus didn’t know he was bluffing. At one point, Magnus’s hands were shaking, not unlike his first world championship, when he was so nervous that he dropped his pencil. Max knew the probability of him winning. But even while being highly rational, he’d allowed himself some irrational thoughts. A small part of him believed he could win. It was on the ninth move—the same point in the game that Magnus checkmated Bill Gates—that Max showed vulnerability.

Every move he’d made until then had been the right one. And yet he knew immediately that he’d done something wrong, even if he didn’t know what it was. He could see it on Magnus’s face. It was an opportunity for Magnus to attack. Magnus’s body language shifted. He barely thought about his moves anymore. Max deliberated for minutes; Magnus swiped his pieces in seconds. He felt the board shrinking. Max was beginning to see he couldn’t escape. At one point, Max accidentally toppled his king. Not long afterward, he was officially checkmated. The match had lasted 39 moves each over 22 minutes and 21 seconds. Max’s year of monthly challenges was over. But he refused to take his loss as anything but a victory. He said in a postgame interview that attempting to beat the most unbeatable chess player had introduced him to new lines of thinking.

10) Why is it so difficult to cure cancer [Source: TED]
We’ve harnessed electricity, sequenced the human genome, and eradicated smallpox. But after billions of dollars in research, we haven’t found a solution for a disease that affects more than 14 million people and their families at any given time. Cancer arises as normal cells accumulate mutations. Most of the times, cells can detect mutation or DNA damage and either fix them or self-destruct. However, some cells allow cancerous cells to grow untracked and invade nearby tissues or even metastasize to distant organs. Cancer becomes almost incurable once they metastasize. And cancer is incredibly complex. It’s not just one disease, there are more than 100 different types and we don’t have a magic bullet which can cure all of them. For most cancers, treatments usually include a combination of surgery to remove tumors, and radiation and chemotherapy to kill any cancerous cells left behind. Hormone therapy, immunotherapy and targeted treatments tailored for a specific type of cancer are sometimes used too. In many cases, these treatments are effective and the patient becomes cancer-free. But they are very far from 100% effective, 100% of the time.

So what we have to do to find cures for all the different kinds of cancers. We’re beginning to understand a few of the problems scientists would have to solve. First of all, we need new, better ways of study and cancer. Most cancer treatments are developed using cell lines grown in labs from samples of human tumors. These lab grown cells have given us critical insights about cancer genetics and biology. But they lack much of the complexity of the tumor in actual living organisms. It’s frequently the case, that the new drugs which work on these lab-grown cells will fail in clinical trials with real patients. One of the complexities of aggressive tumors is that they have multiple populations of slightly different cells. Over time, distinct genetic mutation accumulates in different parts of the tumor giving rise to unique sub-clones. For example, aggressive brain tumor called Glioblastoma can have as many as six different sub-clones in a single patient. This is called the clonal heterogeneity and it makes treatment difficult because the drug that works on one sub-clone may have no effect on another.

Another challenge is that a tumor is a dynamic, interconnected eco-system where cancer cells communicate with each other and healthy cells nearby. They can induce normal cells to form blood vessels that feed the tumor and remove the waste products. The can also interact with the immune system to actually suppress its functions, keeping it from recognising or destroying the cancer. If we could learn how to shut down these lines of communications, we’d had a better shot at vanquishing the tumor permanently. Additionally, mounting evidence suggests, we’ll need to figure out how to eradicate cancer stem cells. These are rare but seem to have special properties that make them resistant to chemotherapy and radiation.

In theory, even if the rest of the tumor shrinks beyond detection during treatment, a single residual stem cell could seed the growth of a new tumor. Figuring out how to target these stubborn cells might help prevent cancers from coming back. However, even if we solve those problems, we might face new ones. Cancer cells are masters of adaptations. Adjusting their molecular and cellular characteristics to survive under stress, when they are bombarded with radiation or chemotherapy, some cancer cells can effectively switch on the protective shields against whatever is attacking them by changing gene expression. Malignant cancers are complex systems that constantly evolve and adapt. To defeat them, we need to find experimental systems that match their complexity, and monitoring and treatment options that can adjust as the cancer changes. The good news is we are making progress. The average mortality rate, for most kinds of cancers, has dropped significantly since 1970s and is still falling. We are learning more every day and each new piece of information gives us one more tool to add to our arsenal.

- Saurabh Mukherjea is CEO, and Prashant Mittal is Strategist, at Ambit Capital. Views expressed are personal.

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