Ten interesting things that we read this week

Some of the most interesting topics covered in this week's iteration are related to the end of cloud computing, India's money illusion and Amazon's business strategy

Published: Jun 24, 2017

mg_97433_bgshutterstock_279655406copy_280x210.jpgImage: Shutterstock.com (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’ note. Some of the most interesting topics covered in this week’s iteration are related to ‘The end of cloud computing, ‘India’s money illusion’ and ‘Amazon’s business strategy’.

Here are the ten most interesting pieces that we read this week, ended June 23, 2017.

India’s money illusion problem [Source: Livemint]
This article highlights how people think in nominal rather than real terms. The recent farmer protest offers a good cue to understand broader economic issues, especially about why the Indian economy does not “feel” like it is growing at 7%. The sharp fall in inflation in recent years has pulled down the growth in nominal gross domestic product (GDP). Many analytical muddles have followed. First, expectations continue to be set according to what people experienced during the years of high inflation after 2006. Employees who are used to large increases in nominal pay during episodes of high inflation feel cheated with the more modest pay hikes we see in these years of low inflation. They extrapolate from their past experience with inflation. That is also why inflation expectations in general have lagged the decline in actual inflation, a telling comment on the credibility (or the lack thereof) of the RBI.

Second, several analysts have tripped because of the unfortunate habit of comparing nominal values with real values. So, ratios such as bank credit growth to real GDP growth or corporate sales growth to real GDP growth can give extremely wrong signals. The numerators are nominal while the denominators are real. The issue of lags is another blind spot in many popular analyses. The problems may seem less dramatic when nominal GDP is considered as the base. Third, the tax base of the Government is the nominal GDP. People pay taxes on what they actually earn rather than on their real earnings after inflation. A sharp drop in nominal GDP growth creates budgeting problems for the Government. The UPA Government managed to inflate away public debt. In other words, the ratio of public debt to nominal GDP stayed manageable despite fiscal profligacy after 2009. The current Government does not have that option.

What will replace cloud computing? [Source: Andreessen Horowitz]
“I’m going to take you out to the edge to show you what the future looks like.” So begins Andreessen Horowitz partner Peter Levine as he takes us on a “crazy” tour of the history and future of cloud computing. He says that everything that’s popular in technology always gets replaced by something else. In this presentation he talks about the risks to cloud computing. Currently, the world functions basis a centralised or hub-and-spoke model wherein a central cloud server receives data processing request from multiple display devices (phones/laptops/tablets etc.) and send it back to them post processing. In the scenario, when cloud computing is dead one possibility is that devices on the “edge” become powerful themselves. This is a likely scenario especially as the devices under the internet of things” become more sophisticated.

A self-driving car then becomes a data centre on wheels. Moreover, as these objects need to process real-time information very quickly, there will be no time to send information to centralised cloud and wait for the response. This necessity of real-time processing of information by the devices on the “edge” is what will pave the path for obviation of cloud computing. He further explains how we are going back to the era of distributed computing and how machine learning will pave the way for cloud’s role reduced to just a store of information.

The new, subtle ways used by the rich to signal their wealth [Source: BBC]
In 1899, the economist Thorstein Veblen coined the term ‘conspicuous consumption’ to denote the way that material objects were paraded as indicators of social position and status. More than 100 years later, conspicuous consumption is still part of the contemporary capitalist landscape, and yet today, luxury goods are significantly more accessible than in Veblen’s time. This deluge of accessible luxury is a function of the mass-production economy of the 20th century, the outsourcing of production to China, and the cultivation of emerging markets where labour and materials are cheap. At the same time, we’ve seen the arrival of a middle-class consumer market that demands more material goods at cheaper price points. However, the democratisation of consumer goods has made them far less useful as a means of displaying status.

The new elite cements its status through prizing knowledge and building cultural capital, not to mention the spending habits that go with it – preferring to spend on services, education and human-capital investments over purely material goods. These new status behaviours are called as ‘inconspicuous consumption’ by the author. None of the consumer choices that the term covers are inherently obvious or ostensibly material but they are, without question, exclusionary.

Bans, do they work at all? [Source: Livemint]
From cows to alcohol, we are in the era of bans. Bans offer a clear display of “right intentions”. A chief minister who announces a total ban of alcohol is signalling genuine interest in eradicating the menace of alcoholism from the state. In politics, whether it is in the USA or in Bihar, being seen as having the right intentions is many times more important than the ultimate result of the act. History suggests that in the short term, some of the bans do show positive results. The vast majority of alcohol consumers are occasional drinkers. If any hurdles are placed in their path to consumption, many of them will curtail their drinking behaviour. In the initial months of prohibition in the USA, there was a 30% drop in alcohol consumption and a decline in arrests for drunkenness. However, the more important question is of behaviour—what impact do these bans have on the very “wrong” behaviours policymakers want to curtail? All bans generate psychological reactance.

According to psychologist Jack Brehm, humans hate to lose freedom. Whenever people believe that their freedom has been threatened, they enter into a reactance motivational state and act to regain their freedom. The individuals experience an increased motivation to indulge in the very behaviour that is forbidden. As soon as prohibition was announced, whether it was in the USA or in Gujarat or in Meghalaya, the liquor trade moved underground. When any social problem becomes more of an individual action in a private space, the ability of the Government to intervene to solve the problem reduces. From a behaviour change perspective, the most significant consequence of bans is that it absolves the individual of the responsibility of solving the social evils he indulges in and hands over that responsibility to law enforcement agencies.

Amazon’s new customer [Stratechery]
Amazon’s stated goal has revamped over the years from aiming to be “the leading online retailer of information-based products and services, with an initial focus on books” to “Our vision is to be earth’s most customer-centric company; to build a place where people can come to find and discover anything they might want to buy online”. From the looks of it, Amazon’s goal is to take a cut of all economic activity. To achieve this goal, Amazon has several different strategies. The key to the enterprise is AWS: if it is better to build an Internet-enabled business on the public cloud, and if all businesses will soon be Internet-enabled businesses, it follows that AWS is well-placed to take a cut of all business activity. On the consumer side the key is Prime. Thanks to its reliability and convenience (two days shipping, sometimes faster!), plus human fallibility when it comes to considering sunk costs (you’ve already paid $99!), consumers would hardly bother looking anywhere else.

With Prime, Amazon has created a powerful moat around consumer goods that does not depend on simply having the lowest price, because Prime customers don’t even bother to check. The “primitives” model conceptualised by Amazon at AWS modularised its infrastructure, effectively transforming raw data centre components into storage, computing, databases, etc. which could be used on an ad-hoc basis not only by Amazon’s internal teams but also by outside developers. This approach meant AWS could be sold as-is to developers beyond Amazon, increasing the returns to scale and, by extension, deepening AWS’ moat. This last point was a win-win: developers would have access to enterprise-level computing resources with zero up-front investment; Amazon, meanwhile, would get that much more scale for a set of products for which it would be the first and best customer. It applies similar approach to ecommerce and logistics businesses as well. This is the key to understanding the purchase of Whole Foods.

Amazon is not buying a retailer but a customer — the first-and-best customer that will instantly bring its grocery efforts to scale. Amazon is likely to transform the Whole Foods supply chain into a service architecture based on primitives: meat, fruit, vegetables, baked goods, non-perishables. What will make this massive investment worth it, though, is that there will be a guaranteed customer: Whole Foods Markets.

This is what slavery looks like, in the eyes of the slaveholders [Source: aeon.co]
Around half of the world’s slaves are held in debt bondage in India, Pakistan and Bangladesh. Debt bondage is a very old form of slavery in which radically marginalised members of society, often from India’s ‘untouchable’ caste, must trade all their labour for single small infusions of cash. Broader social and economic systems ensure that they do not understand the terms of such loans, and that the time required to repay them is interminable. Lack of other work, lack of credit, and the need to pay for schooling and marriages effectively guarantee that there is no single contractual debt between the landlord and labourer but rather a string of interconnected informal loans.

While slavery is illegal, the practice still festers in unreformed nests of feudalism, where threats and violence can suppress or eliminate pay for work. Where slavery is verboten, psychological control through deception and fear. In the case of debt bondage, it is the caste system – with Brahmin at the head and ‘untouchable’ beneath – that does the delicate work of stitching debts together into a seamless, infinite coercive system that leaves labourers trapped. This piece dives into the mindset of slaveholders and their limitations (both psychological and economic) which keep them hinged on this archaic practice.

Urbanization 2.0 [Project Syndicate]
We are now in the final days of the industrial age. Just as the second generation of steam engines propelled the Industrial Revolution forward, similarly, new technologies are advancing today’s digital revolution. As per the author, the future will be shaped by two key trends: digitization and urbanization. And the possibilities introduced by the former will likely help us overcome the problems associated with the latter. When the Industrial Revolution was first gaining momentum at the beginning of the nineteenth century, only a small percentage of the global population lived in cities. The world was still predominantly rural and agricultural, as it had been for thousands of years. But as industrialization accelerated, so did urbanization, as impoverished farmworkers flocked to factories. We are now in another period of epochal change, and urbanization is accelerating again. In 1950, approximately one-third of the planet’s 2.5 billion people lived in cities, whereas today, just over half of the world’s 7.5 billion people do. And by 2050, when the global population is expected to reach nine billion, an estimated two-thirds of all people will live in cities. Climate change is first of three major challenges that will confront us in this new period of hyper-urbanization.

The second challenge will be to address the effects of new digital technologies that are generally associated with the so-called sharing economy. Hardware and software applications that provide on-demand transportation, delivery, hospitality, and other services will revolutionize how cities operate and are organized; but adapting to these changes will require innovative new policies. The third challenge relates to migration and its attendant security concerns. Global migration will likely continue to increase in the coming decades, with the very rich and the very poor alike flocking to megacities. Without the policies and infrastructure in place to absorb these new arrivals, megacities could fail, and degenerate into urban jungles that pose a security threat to surrounding regions and the world beyond.

What makes Michael Lewis such a successful author? [Source: quora.com]
What exactly is it that makes Michael Lewis so consistently successful as a bestselling author. Michael Lewis’s books are the perfect combination of juicy, accessible and intellectual — a rare combination for non-fiction. Here is a summary of the possible reasons for his success: First, Liar’s Poker was something like a whistleblower’s account, and everybody loves a whistleblower. Lewis worked at Salomon Brothers before quitting to write Liar’s Poker, his first major book. Liar’s Poker is partially autobiographical and is critical of Wall Street culture and practice. The financial services industry overall has a bad reputation with the public — people love to hate it — so Lewis’s critical tone and salacious anecdotes are easy to lap up. It’s everything people imagined and more — validated and confirmed by an industry insider. Second, Michael Lewis makes his readers feel smart. Lewis is good at demystifying complex concepts — from mortgage trading to sabermetrics to the housing bubble. Importantly, he focuses on phenomena that people want to know more about — events and ideas that are frequently discussed in popular culture but that are commonly misunderstood. Third, Lewis’ works are highly character-driven, which is rare for his genre. Moneyball is the best example of this. It would have been natural for the book to be a presentation of facts in historical order, which would be boring for everyone barring a niche audience. But Lewis goes through the effort to develop the personalities in the book, which broadens its appeal. Finally, as an author, Michael Lewis is surprisingly likeable.

It would be easy for Lewis to come across as unlikable — he’s a Princeton grad who went off to work on Wall Street and then monetized that experience big time by writing a mass-market book about it. Lewis avoids this by steering clear of the high horse — he paints an unflattering picture of the financial services industry, but he doesn’t act like he was too good for it. He comes across as distinctly human, even though he’s clearly pretty spectacular and objectively not that relatable.

Where is the long-term price of oil headed? [Source: Bloomberg]
What's the right way to think about the long-term price of oil? Back in 2000, when mega-mergers formed giants like Exxon Mobil, it was typical to plug roughly $20 a barrel into valuation models. Only a few years ago, we were being told that "$100 a barrel is becoming the new $20". One crash later, no oil major's slide deck is complete without a pledge to fund itself at $55 or less. In fact, long-term oil futures have collapsed by almost half and are anchored around $55 a barrel. Why that level? The likeliest explanation is that it appears to be the trigger point for US shale producers to boost drilling and fracking, raising supply relatively quickly and thereby keeping a lid on prices. Meanwhile, the rest of the industry has had to squeeze costs to remain competitive with these Texas upstarts. And because shale's productivity owes more to inputs of capital, technology and innovation than traditional advantages of political access to territory, it represents a sea of change in oil's economics, upending the old paradigm centered on OPEC. With its members still supplying about 40% of the world's oil, their economic weaknesses represent a risk to seeing prices as being in inexorable decline from here. In an oil-dependent economy, governments must embark on radical and potentially destabilizing reform (like Saudi Arabia) or can, as in Venezuela's case, flirt with outright collapse. This inherent fragility around a large part of global oil supply raises the risk of a supply shock down the road, which could send prices higher again, just as in the 1970s and at several points over the past decade.

The twist this time, as opposed to previous shocks, is that if they occur, the resulting windfall for oil producers still standing would come at a big and lasting cost. Besides the peculiarities of oil-export economies, the other reason the cost-plus-return model of long-term prices may no longer work is that oil will increasingly have to compete for customers. Its core market -- transportation -- is now under threat at the margin from technologies like electric and autonomous vehicles and societal pressures around pollution and climate change. A jump in oil prices due to fears of scarcity arising from a failed state would have a predictable effect: spurring efforts to move away from an unreliable product.

Facebook built an AI system that learned to lie to get what it wants [Source: qz.com]
Humans are natural negotiators. We arrange dozens of tiny little details throughout our day to produce a desired outcome: What time a meeting should start, when you can take time off work, or how many cookies you can take from the cookie jar. Machines typically don’t share that affinity, but new research from Facebook’s AI research lab might offer a starting point to change that. The new system learned to negotiate from looking at each side of 5,808 human conversations, setting the groundwork for bots that could schedule meetings or get you the best deal online. Facebook researchers used a game to help the bot learn how to haggle over books, hats, and basketballs.

The pursuit of Facebook’s AI isn’t too different than other applications of AI, like the game Go. Each anticipates its opponent’s future actions and works to maximize its winnings. But unlike Google’s Go-playing AlphaGo, Facebook’s algorithm needs to make sense to humans while doing so. From the human conversations and testing its skills against itself, the AI system didn’t only learn how to state its demands, but negotiation tactics as well—specifically, lying. Instead of outright saying what it wanted, sometimes the AI would feign interest in a worthless object, only to later concede it for something that it really wanted.

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