A Pessimistic Economy and The Great Misallocation

The Great Pessimism

“Unless developed countries learn how to increase the productivity of knowledge workers and service workers, they will face economic stagnation and severe social tension” – Peter Drucker in ‘The Post-Capitalist Society’

The year 2008 saw one of the worst recessions after the Great Depression in the 1930s. Hundreds of millions of people even in the so called ‘Developed World’ were affected. Fast forward to 2016, the case can be made that there has been a recovery but an insufficient recovery.

The return to pre-recession employment was inordinately long (See Figure 1).

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Figure 1

There are signs of a dangerous pessimism about the economy and the future. Interest rates in the United States have been rock-bottom near-zero for six years now. In fact, we may have the lowest interest rates in history! (See Figure 2)

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Figure 2

Despite having the lowest interest rates in history, every time there is a signal that the Fed is going to increase interest rates, the market reacts wildly, implying very low confidence in the economy.

As of July 2016, there are more than 13 trillion in Negative Yield Bonds. That number was only a few tens of billions as recently as 2014. Investors will be essentially paying Governments for the ‘privilege’ of holding these bonds. In other words, investors feel they will be losing far greater money otherwise.

One thing is clear – there is Great Pessimism about the future.

Low confidence in the Government is at record breaking levels. Only 14% of citizens in United States approve of Congress (See Figure 3).

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Figure 3

The good electoral performance of Donald Trump and Bernie Sanders (fringe candidates in any other era); Britain leaving the EU; Right-wing and Left-wing fringe parties (both having the commonality of being protectionist) performing very well against Centre-Right and Centre-Left parties in Europe; All of these illustrate this Great Pessimism. This Great Pessimism is a direct consequence of low growth for many years and the fact that the middle class has been struggling for decades (median wages that are stagnant since 1973) (See Figure 4).

Figure 4

There have been some very good attempts at explaining why we are stuck and frozen in slow growth. Larry Summers has proposed that we are in an Age of Secular Stagnation. He thinks that there has been an increasing propensity to save resulting in a drag in demand.

Robert Gordon seeks to explain this from the supply side – Innovation has slowed down. In his fantastic book, “The Rise and Fall of American Growth”, he argues that the years 1870-1970 were some sort of a special ‘century’ where we luckily ended up with miraculous Innovations. Compared to that period, Gordon is not impressed by the innovations post 1970 (most of which were in the area of computing). The visual argument is if you time-travel to 1940 from 2015 you wouldn’t be really shocked by the standard of living. But if you time travel from 1940 to 1870, you will be truly shocked at the quality of life even in a relatively rich country like the United States. There was no indoor plumbing, no leisure, men were expected to work in the fields since the age of 15, women had their entire time consumed with household chores, food was expensive, horses ate one fourth of the food, no one travelled outside the city for vacations, high infant mortality rates and so on.
Gordon explains with the help of Total Factor Productivity which has taken a dive, the slowdown in Innovation and in turn Growth. (See Figure 5)

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Figure 5

Gordon points out that unless there is a major technological revolution, growth is doomed and he leans towards the possibility that the chances of such a major revolution happening is minuscule. And Innovation is desperately needed to offset the headwinds like demographics, debt and inequality which further drag down growth.

This is how growth looks like today in the advanced economies (See Figure 6):

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Figure 6

An even more alarming statistic would be that Growth would have fallen short of projections even without the financial crisis (See Figure 7).

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Figure 7

Rules of the Game and The Great Misallocation

But both the Secular Stagnation theory and the End of Growth Theory have some uncomfortable assumptions. Larry Summers argues that the cure for Secular Stagnation is a massive expansionary fiscal policy by the Government and growth would be restored. But what if it does not address the root cause of stagnation whatever it might be and you end up with high inflation and not much to show for it? And Gordon’s End of Growth theory assumes that new technological revolutions are not possible.

Bain Capital notes in its 2012 report, “A world awash in money” that the total size of financial assets will grow from $600 Trillion in 2010 to $900 Trillion in 2020. Meanwhile the total GDP in 2020 would be only $90 Trillion – fewer opportunities than money available for investment (See Figure 8). Yet, growth is languishing.

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Figure 8

What if Gordon is right about the part that Innovation has slowed? Peter Thiel, legendary investor and co-founder of Paypal laments, “We were promised flying cars and instead we got 140 characters”.

I strongly believe, we will indeed not have massive tech innovation – with the current ‘rules of the game’. I believe Innovation can be supercharged if we change the rules in the system and incentives are redesigned.

William Baumol notes that entrepreneurs, highly talented individuals and innovators can be involved in productive or unproductive entrepreneurship activities.

What are productive activities and unproductive activities? Productive activities can be thought of activities that are highly beneficial to the society while also simultaneously enriching the entrepreneur/innovator. Unproductive activities enrich the entrepreneur without being beneficial to the society.

For example, finding a low cost drug for a certain type of cancer will be a productive activity. The society benefits and the entrepreneur with a patent on the drug can also be enriched by solely manufacturing it. On the other hand, an unproductive activity will be if an entrepreneur decides to buy a company having patents on life-saving drugs and increase the price of the drugs unilaterally. The entrepreneur becomes rich but the society is worse off than before. This is not a fictional example. Turing Pharmaceuticals bought an important 62 year old drug called Daraprim and raised the price from $13.50 to $750 a tablet. The efficacy of the drug remains same though the price went up by 5000%. This is not an isolated example and not an isolated price gouging experience. New York Times called it a new business strategy in pharma – “acquiring old, neglected drugs, often for rare diseases, and turning them into costly “specialty” drugs.

Entrepreneurs and innovators allocate their time, effort and ingenuity to productive or unproductive activities depending on the rules and incentives in the system.
A simple way to illustrate this is by the following matrix (See Figure 9):

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Figure 9

Highly Talented Individuals engaging in Productive Entrepreneurship Efforts will have a positive impact (A). And Highly Talented Individuals engaging in Unproductive Entrepreneurship Efforts (or Destructive Efforts) will have a negative impact (B). When Capital and Talent flow towards unproductive economic activities rather than towards productive ones, I call it the Great Misallocation. The Great Misallocation happens when payoffs in unproductive activities is greater than productive activities.

In my opinion, in most other systems other than capitalism, B will be far greater than A. Talented individuals opt to participate in unproductive activities rather than productive activities. Before 200 years, that would mean, becoming a bureaucrat at best or waging wars. Capitalism and free markets provide the opportunity for A to be far greater than B. Joseph Schumpeter noted in his famous theory of Creative Destruction that capitalism, “…incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one” and this innovative element of capitalism is what made it the best system according to Schumpeter.

But here is the key thing. Capitalism provides opportunity for A to be far greater than B. But it does not guarantee it and sadly often does not self-correct. The rules of the game are important and under perverse rules, this Great Misallocation of talent will take place and may end up undermining capitalism itself. Schumpeter in his chapter, ‘Can capitalism survive?’ in his seminal book “Capitalism, Socialism and Democracy” begins with an ominous answer – “No. I do not think it can”.

To secure the future, we must ensure that Capital and Talent:
1. Must flow to productive activities
2. Must not flee from productive activities
3. Must not flow to unproductive and destructive activities

Sadly, however, increasingly the reverse is happening. A lot of capital and high quality talent is flowing towards unproductive (and potentially destructive) activities. Why? Because the payoffs of engaging in unproductive activities have become extremely attractive. And we have prioritized short term rewards over long term miracles.

Peter Drucker sounded the alarm on short-termism and bad incentives way back in 1993 in ‘The Post-Capitalist Society’:

“Instead of being managed “in the best balanced interests of stakeholders,” corporations are now being managed exclusively to “maximize shareholder’s value”. This forces the corporation to be managed for the shortest term and damaging the wealth-producing capacity of the business. It means decline, and fairly swift decline. Long-term results cannot be achieved by piling short-term results on short-term and long term needs and objectives.”

Only 15% of capital coming from financial services today is used to fund business investments, whereas that would have been most of what banks did in the 20th century. In other words, the role of banks and capital markets in making new investments is decreasing. When most of the capital is not going to new business investments, new jobs are not going to be created as well. And that is exactly what has happened. Most of the new jobs have been low-paying jobs. (See Figure 10)

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Figure 10

S&P 500 firms are spending $1 trillion each year on share buybacks and dividends. That represents 95% of their net earnings and a paltry amount goes into research and development and towards the long term? Why? Because the rules and incentives of the game direct this behavior. CEO compensation is tied to yearly performance of the stock and in the short term the best way to increase the stock price is through buybacks.

IBM and Pfizer have spent $157 and $139 billion respectively in buybacks and dividends since 2005 and only $111 billion and $100 billion respectively in capital spending and R&D. Let us digest that for a second. A tech icon and pharmaceutical giant basically acknowledge that they will succeed in short term value creation than in exciting long term possibilities of innovation. Or they are being incentivized to ‘forget’ the future.

A Stanford study finds out that innovation slowed down by 40% at tech companies after they went public – mostly because of expectations from the public market. Which is shocking because going public allows companies to raise cash that they can plow into more R&D. And they are incentivized not to do it!

There are signs that the misallocation of capital (and talent) might be increasing. Legendary investor Stanley Druckenmiller in the Sohn Conference this year presented a couple of charts that showed this great misallocation. (See Figure 11)

Figure 11

And if this wasn’t disturbing enough, take a look at the use of that debt in this cycle. While the debt in the 1990’s financed the construction of the internet, most of the debt today has been used for financial engineering, not productive investments. This is very clear in this slide. The purple in the graph represents buybacks and M/A vs. the green which represents capital expenditure. Notice how the green dominates in the 1990’s and is totally dominated by the purple in the current cycle.”

 

Unless we solve the Great Misallocation problem, capital and talent will be stuck in unproductive and destructive activities and the world in turn will be trapped in low growth and in a zero-sum economy where someone has to lose for someone to gain.

How do you solve this Great Misallocation problem? Hope to write about it in another post soon.

 

Non-Consensus and Right – An Essay on Investing

A. Introduction – Investing is not easy. You have to be Non-Consensus and Right
Charlie Munger once said:

“Investing’s not supposed to be easy. Anyone who finds it easy is stupid”

John Kenneth Galbraith made an equally profound statement:

There is nothing reliable to be learned (about making money). If there were, study would be intense and everyone with a positive IQ would be rich.

Making money on the market is hard. If I think something is underpriced, so does the chance that everyone else on the market does and the price is bid up. My opportunity to earn a profit greatly reduces in an efficient market. In fact, the market can be seen as a system that brings down excess profits to zero!
Like the legendary investor Howard Marks (someone I follow keenly) says, it is not enough to be right. You have to be contrarian and right.
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You have to be right, and the your opinion should be non-consensus relative to the market. If you think Apple is going to sell more iPhones this quarter and everyone else thinks too, then the price is already factored in and it doesn’t make any sense to buy the stock (assuming you want to make money in the short term).
Similarly, Goldman Sachs reported its first quarter earnings on April 19 2016. By every objective measure, it was a completely bad quarter, but the stock was up by 2%! That was because the bad news was already factored in and the stock had already taken a beating over the past 3 months. Consensus and Right would have again made no money here.
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Recently, there was an interesting money making opportunity on May 18 2016. Exit polls on May 16 predicted a DMK win in the Tamil Nadu Elections. The stock shot up on May 17 opening. On May 19, the market opened to the news that ADMK has taken an early lead. The stock price tanked. Shorting the stock on May 18 would have brought handsome profits. What if DMK had won? There would have been limited downside to shorting the stock because the good news that DMK won had already been factored in! So it was good odds to take the bet.
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B. Where to find Non-Consensus
But markets are not perfect. Markets offer opportunities to make money when ‘it’ miscalculates and the pricing is incorrect. Some common areas markets tend to miscalculate (and hence where you can find non-consensus and be right):
1. Total Available Market – Markets sometimes underestimate the market size that a company is going after. For example, when Apple announced the launch of the iPhone in the now famous keynote on June 29 2007, what would be the total market size that smartphones would capture? Would it capture the entire luxury Blackberry market? Or would it be disruptive to the computer itself? Betting on the latter would mean stockpiling Apple stock yielding enormous profits.
2. Innovation Premium – Markets miscalculate the ability of some companies (especially tech companies) to come up with new blockbuster products. Let us call Innovation premium is the amount of premium in the market cap that is not explained by the NPV of their cash flows. Tesla for example, currently holds a high innovation premium as does Facebook. But Apple currently has a low innovation premium. Its price/sales ratio is 2.28. Is the market wrong in assuming that Apple wouldn’t innovate much in the future?
Similarly, in my opinion Google is uniquely positioned because of its unique assets of datasets (derived from their Search and other products), they can attack sometime in the future a large market in health and medicine. A lot might happen in the intersection between Biotech and AI. But my sense is that market is highly underestimating Google’s play in these areas (and hence probably undervaluing them).
3. Rate of Adoption – This happens more on the venture capital side. Analysts look at the current adoption numbers of a new innovation or product and vastly underestimate at the rate of adoption. For example, Automated Investment Advisors (or called in the press as Robo Advisors) collectively held only $19 billion AUM by end of 2014 – a drop in the bucket compared to total assets under management. But the growth might be tremendous.
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4. Extreme Market Movements – Howard Marks says in one of his thought provoking memos –
“…the market does not have above average insight but it is often above average in emotionality. Thus we shouldn’t follow its dictates”.
Extreme market movements have always proven to be one driven by psychology, herd behaviour and extreme irrationality and have been happy hunting periods for really savvy investors.
Cornwall Capital (whose founders were featured in the book the Big Short) made a killing on Capital One at the beginning of the 21st century when it was under investigation for accounting fraud. A simple due diligence meant Capital One would not be convicted and Cornwall bought a bunch of stock at rock bottom prices.
More recently, after Brexit, the stock market in UK (and around the world) tanked. Was it an overreaction? Will it greatly affect the prospect of UK companies whether or not it was in the EU (other than say financial services)? Interestingly, the stock market regained all its losses in a few days (see the FTSE 100 below for example), suggesting it would have been a good bet to buy in that scenario.
ftse 100
5. Incentives – Markets often tend to ignore incentives and especially bad ones. One example of bad incentives is the fee that banks got paid for originating loans. Another related example is the incentive for credit rating agencies to generally give high ratings for bonds. Government policies can create bad incentives which the markets might underestimate (and even willfully ignore!).
IBM spent at least four times more money on share buybacks vs. CapEx. What is the incentive of the management to do so? Will the short term focus hurt IBM in the long term? When Marissa Mayer was brought in as Yahoo CEO, her compensation was tied to Yahoo stock (including that of Alibaba by virtue of Yahoo’s stake in it). Does this incentivize the manager to maximize the value of Alibaba’s stake which was an easier thing to do rather than doing the hard work of turning around Yahoo.
6. Non-Market Forces – Markets underestimate or overestimate the Non-Market Forces involved. For example, there have been some honest attempts to create Education related startups but the success rate of these startups has been phenomenally low in India. Unless the Government and politicians loosen the grip on Education, very little value is going to be created by the hungry and passionate entrepreneur.
Solarcity, a company founded by Elon Musk could be selling at a premium precisely because of him – Elon Musk. But the non-market forces might be a far bigger factor in the success of the company than Elon Musk himself.
A company like Reliance can be expected to have a stable cash flow based on its Non-Market activities in India.
7. Unique Assets – Some companies have unique assets that maybe highly underestimated by the market. These unique assets maybe used to launch future blockbuster products/services or might be highly valuable to other companies and may be an acquisition target.
Unique datasets can be an example of a unique asset. Google with its enormous data will reap blockbuster rewards if AI quickly advances. Uber’s datasets might be far more valuable than is currently imputed. Facebook has a lockdown on the ‘Social Graph’ of a person. Nextdoor has the ‘Local Graph’ of the person. A startup Orbital insight converts Satellite images into actionable information to be used by entities from the Government to Hedge Funds to Retailers.
This is Twitter’s stock price over the past year.
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Twitter might be the most ‘mentioned’ company in history. Every day the news media mentions tweets. All the most famous people are present there to connect directly with the audience from Heads of State to sports and movie stars. Yet it doesn’t make enough money ‘yet’. Seems like a pretty good bet that the company or future activist shareholders will eventually figure out how to kickstart growth and a good case can be made that it is undervalued.
8. Shifts and Trigger Events – Shifts are trends that are going to last for the foreseeable future and may cause consumers to make decisions far different from the status quo. For example, American retailers are suspecting that there has been a big change in consumer behaviour from baby boomers to millennials. It has also been documented that millennials are far less keen on ownership – be it cars or homes. What would that mean for certain stocks? Have we reached peak car ownership in the west?
Another shift would be the trend towards the Gig Economy or the rise of Freelance work. Will open new opportunities for new products in Insurance, Financial Services etc. Backing companies that capitalize on these shifts would be a great opportunity.
Trigger events are those which will quickly lead to widespread adoption of a business model. For example, the quick adoption of AirBnB (both from the supply and demand side) was helped in no small part by the 2008 recession. Similarly there will likely be some trigger events that will unleash new business models in areas like Healthcare and Education.
In my opinion, the trigger event for education would be the increased automation of entry level jobs that students get post-college. Once the cost-benefit of college (and college is often seen as an insurance policy) looks bad, there will be huge pressure to allow other business models. Student Loan debt will no more be sustainable and changes will be forced.
Similarly, healthcare costs are on track to becoming a huge burden which will naturally cause massive deregulation to spur innovation and reduce costs.
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There are some other examples of Trigger Events like Privacy. For example, traffic to the privacy oriented search engine Duck-Duck Go increased tremendously post the Snowden revelations.
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Another example would be the low level of confidence of people they place on News Media. Which would mean new business models like Crowdfunded News is ripe for take off.
C. Great Companies
The above points can be used to find out opportunities where there can arise non-consensus and the market might miscalculate. Then you can use financial theories and formulae to get a ball park on what might be the right valuation/price for a company.
However, companies with certain characteristics tend to outperform the market and can be considered for investment if they are undervalued:
1. High Defensibility – Companies with one or more of the following – IP, Scale, Network Effects, Brand etc.
2. Zero Marginal Costs – The marginal cost to acquire the nth customer is zero (Windows OS, Microsoft Office etc.). In such cases, when Revenue increases, profit margins also tends to increase.
3. Customer Lock-in/High Switching Costs
4. High Gross Margin Levels – The same revenue but with higher gross margins would trade at a much higher multiple
5. Low Bargaining Power of Customers and Partners – The lower the bargaining power of the customers, the better will be the price. SpaceX and Palantir (if it goes public) would have high unpredictability since the US Government will be their biggest customer. On the other hand, Google Ad words has much lower customer concentration. Similarly some Partners may have a huge bargaining power that may be outside of company management. The high dependency of Zynga on Facebook for example.
6. Organic Demand – Low Customer Acquisition Costs and Marketing Spend are also good indicators of a terrific business
7. Unique Assets – Have covered in the previous section. Such companies are increasing in number and are highly valuable
8. High Optionality companies – Some companies are very good at new product development and/or in acquisitions. These initiatives are examples of the ‘optionality’ thinking of the company. For example, Facebook’s acquisition of Instagram seems to paid off handsomely. Google’s acquisition of Android, YouTube and DoubleClick have turned out to be tremendous successes. Amazon seems to do very well in their in-house development – Kindle, AWS, Amazon Echo etc.
9. Monopolies – Do companies control a huge share of a (even a small) market? Much better than businesses that control a small share of a big market.
10. Greater Cash Flow than Earnings
11. Growth!

Trigger Events and Business Models

Bill Gross of IdeaLab gave a small interesting TED talk titled ‘The single reason why businesses succeed’. He analyzed 200 successes and failures. The following was his answer to ‘the single reason why businesses succeed’.

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Very interesting results. But wait. Can there be some possible overlap between the above factors? Let us consider this combination possibility – can timing help a business model succeed?

I believe that some scenarios or events that I call ‘trigger events’ greatly help in the success of a business model. At the very least such trigger events lead to quicker widespread adoption of a business model.

For example, I think the 2008 recession in the United States led to faster adoption of the cheaper AirBnB and Uber business models. In the case of AirBnB, it would have helped on the demand side. Customers and travelers would want to spend much lesser than in traditional hotels and willing to compromise on the unreliable environment of AirBnB residences (at least when it initially started). In the case of Uber it would have helped on the supply side. Stagnant and lost wages would mean more drivers taking up the opportunity for part time employment via Uber.

Let us consider college education. When will new business models arrive in college education to relieve students of the unbelievably high prices? The technology is here. Far better business models can be constructed right now. But it won’t be possible in the highly protected and regulated environment. I believe only a trigger event will allow creation and quick adoption of new business models.

My bet is that the trigger event will be automation of entry level services jobs post college education. Advances in Big Data, Machine Learning and AI have allowed computers to start eating into services jobs. Symantec Clearwell’s eDiscovery Platform analyzes and sorts more than 570,000 legal documents in just two days. It uses language analysis to identify general concepts in documents. It is clear that the software can be used for pre-trial research, save months of para-legal work and replace hundreds of them and can augment the capabilities of a lawyer. A reminder that this ‘discovery’ from 570,000 documents can be done by just one lawyer.

IBM’s famed Watson is being deployed in healthcare, finance, retail and other sectors to ‘simplify’ service jobs. If you are a consultant or an analyst and want to be scared about losing your job, watch this video on Watson’s next generation project called CELIA.

Machine grading of essays have become at par with expert human grading of essays.

Check out the following article that appeared in Forbes.

Wall Street is high on Moody’s, expecting it to report earnings that are up 6% from a year ago when it reports its second-quarter earnings on Friday, July 24, 2015. The consensus estimate is $1.19 per share, up from earnings of $1.12 per share a year ago.

The consensus estimate has dipped over the past three months from $1.23. Analysts are expecting earnings of $4.61 per share for the fiscal year. Revenue is expected to be $899.4 million for the quarter, 3% higher than the year-earlier total of $873.5 million. For the year, revenue is projected to roll in at $3.54 billion.

A year-over-year dip in revenue in the first quarter broke a three-quarter streak of revenue growth.

Profit slid in the first quarter, breaking a two-quarter streak of rising net income. Net income fell 28% in the first quarter while the figure rose 14% in the fourth quarter and 17% in the third quarter.

The majority of analysts (56%) rate Moody’s as a buy. This compares favorably to the analyst ratings of two similar companies, which average 43% buys.

That was written by an algorithm not a human being. I rest my case on automation of entry level service jobs for now. For a thorough argument read Martin Ford’s Rise of the Robots.

When a lot of entry level ‘intuitive’ jobs are automated, the tuition fee though very eagerly backed by student loans would become highly unreasonable. This will catalyze new business models in college education. Students will get an equivalent education at far less cost. I expect the change in MBA education to be drastic.

I would give some more examples of possible trigger events and their effects on new business models without going into great detail.

The below is the approval ratings of US Congress. People cannot put off getting things done indefinitely with a stagnant Government. People would use the internet and mobile in new ways to overhaul politics or bring in more peer-to-peer citizen solutions circumventing the Government.

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The hangover from the Iraq war and the costs of the great recession of 2008 would have a big reluctance to put boots on the ground and will ensure that UAVs or drones will become better and better as Government investment flows in.

Growing worldwide consensus on the threats of climate change would push new investment in new energy sources. But the whims and fancies of the Government would favor certain politically safe technologies like Solar and Wind over other technologies like Nuclear Power.

Privacy will be a greater concern in the future than it is now. A sizeable portion of people will look for alternatives to less privacy friendly alternatives. The search engine DuckDuckGo has privacy as its USP. This is its growth.

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The cultural differences between Millenials and previous generations in the United States will have huge effects on ownership of assets like home and cars. Millenials in the US are preferring a more nomadic lifestyle. Add to this fact that about half of United States are in zero savings or net debt.

Healthcare spending in United States has increased from 7% of GDP in 1970 to over 17.5% of GDP now. The US Healthcare spending is now $3 trillion or $9523 per person! 75% of those costs were related to chronic diseases like Diabetes.

Diabetes Increase

This will catalyze cost effective business models that try to prevent such diseases rather than cure them after the fact. A startup called Omada Health tries to solve this problem using Software and very clever design. The design includes a social element with peers having similar diabetes problems, peer pressure, rewards, habit reinforcement methods etc. Due to the high healthcare spending on Diabetes and related diseases, it will be easy for companies like Omada to sell to not only Individuals but also to companies as well as they will benefit from lower health spending per Individual.

Will be updating this post on other possible trigger events.

Startups and Addressability

Venture Capitalists looking to invest in a startup look for the TAM – Total Addressable Market. But not only is Total Addressable Market important but how the startup plans to address that market. In Hunter Walk’s post, “Your Total Addressable Market Stat is probably a lie” he writes:

If your pitch includes “And if you think of all the people who currently have a pet, that’s $80 GAZILLION DOLLARS of TAM,” you don’t even have to say “if we get only 1% of that we’re a unicorn,” before I start rolling my eyes.

He ends the post with an advice: “…focus on the A in TAM”.

Turns out great products often don’t sell themselves. Great startups don’t raise money from VCs and splash it on TV ads and billboards (as MBAs are prone (taught?) to do). The following great startups have been highly innovative in how they address their market.

Facebook reached college students first and built a huge market share (within a short time) in whichever college campus they launched.

Twitch.tv, a live streaming video platform focused on video gaming tapped into existing offline gaming communities first to build their first set of users. Once the power users were on the platform, the momentum was very high. Twitch has 45 million monthly unique viewers and is considered the fourth largest source of peak internet traffic in United States. Amazon acquired Twitch for $970 million.

Flipkart.com, the e-commerce leader in India followed the footsteps of Amazon and launched books as its first category. The founders back in 2007 used to hand over free bookmarks with the name flipkart.com to people outside bookstores. When Flipkart was getting traction, they hit another road block – Indians’ great mistrust of using credit cards to transact online. They rolled out ‘Cash on Delivery’ where customers would pay cash after their orders were delivered.

PayPal focused on eBay users (with Elon Musk and Peter Thiel shutting down other services). More than 70% of eBay auctions accepted PayPal payments and 1 in 4 transactions were via PayPal. The story is well known – PayPal competed with eBay’s own subsidiary Billpoint and eventually eBay bought PayPal for $1.5 billion. PayPal addressed its market with a simple referral structure. In Peter Thiel’s words:

PayPal’s big challenge was to get new customers. They tried advertising. It was too expensive. They tried BD deals with big banks. Bureaucratic hilarity ensued. The turning point was when Luke Nosek got a meeting with the chairman and top brass at HSBC in London. Several old school bankers crowded into a large wood paneled conference room. They had no idea what to make of these California startup guys talking about the Internet. They looked so dazed and confused that they very well could have been extras who knew nothing about payments and tech at all. Luke, despite being on a life-extension calorie restriction diet, found a Häagen-Dazs. And over ice cream, the PayPal team reached an important conclusion: BD didn’t work. They needed organic, viral growth. They needed to give people money.

So that’s what they did. New customers got $10 for signing up, and existing ones got $10 for referrals. Growth went exponential, and PayPal wound up paying $20 for each new customer. It felt like things were working and not working at the same time; 7 to 10% daily growth and 100 million users was good. No revenues and an exponentially growing cost structure were not. Things felt a little unstable. PayPal needed buzz so it could raise more capital and continue on. (Ultimately, this worked out. That does not mean it’s the best way to run a company. Indeed, it probably isn’t.)

If you are one of the several million Dropbox users, you would be familiar with its referral program. The unique referral program where you get more storage space for referring someone took Dropbox from 100,000 registered users to four million in just four months (35% of its daily signups were because of the referral program).

AirBnB pulled off one of the most impressive hacks with a Craigslist integration. To tap into the huge Craigslist market (the supple side), AirBnB let users who listed properties on AirBnB the opportunity to post them on Craigslist as well. Andrew Chen calls the hack ‘anything but simple’.

Qualtrics, which lets users create and publish good looking surveys tapped into users at universities to grow its user base. It is now valued at more than a billion dollars.

One of the impressive old stories on addressability would be about Tupperware. Even today Tupperware is sold mostly through its fabled Tupperware parties.

One of the most underestimated points about very successful startups is how they successfully addressed their target market. The eventual winners are highly creative and innovative and highly frugal in this regard. So, as Hunter Walk said, time to think about your ‘A’ in TAM.

 

Challenging Basic Assumptions: Fundamental Distortions of Technology

 

Let us look at some assumptions that customers have lived with at some point of time:

  • We have to go to a bank to avail loans
  • To access thousands of books you have to go to the library
  • To listen to Prof. Clayton Christensen or Andrew Ng you have to be enrolled at Harvard or Stanford
  • You have to pay hundreds of dollars to get the best Encyclopedia series
  • You have to stay in a hotel when you go to some other city that is not your place of residence
  • You can hail a cab that only passes you by
  • If you want to make a documentary but have zero reputation that precedes you, it is unlikely you won’t get the funds to make it
  • If you are building Oculus Rift, you have to pitch first to Sand Hill Road VCs
  • You cannot customize and manufacture a single toy. It is going to cost a lot of money

Of course all the above have been challenged today and most of them very successfully.

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Of course, all of those assumptions have been challenged with arrival of new technology enablers. Some of the enabling technologies can be Internet, Mobile, Cloud Computing, 3D Printing, Virtual Reality, Big Data etc.

Why does a new technology so successfully enable challenging entrenched assumptions of how a business is done?

This is because new technology enablers bring with them what I call ‘fundamental distortions’. Let us look at some technologies and the fundamental distortions they have brought about:

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These fundamental distortions of technology are nothing but the fundamental distortions in the assumptions embedded in business activities before the arrival of the technology.

A brief history of Encyclopedias from 1980s to 2005 clearly illustrate fundamental distortions of technology. Britannica’s marginal cost of production per copy was $250 and the salesperson’s commission was rumored to be around $500 to $600. Enter digitization and CD-ROM. The marginal cost for a CD based Encarta was just $1.50. Encarta got a taste of its own medicine with Wikipedia with fundamental distortions of internet where you could strangely get people who owe you nothing to write, edit and update articles for free and keep it as accurate as Britannica or Encarta.

The fundamental distortions caused by technology give rise to the possibility of new business models by altering one of the nine elements in a business model.

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Alexander Osterwalder’s Business Model Canvas

It is these fundamental distortions that place the incumbent business models on the path to sub-optimality and eventually to obsolescence. An incumbent manager and a new entrepreneur can identify threats and smack her lips respectively by looking at the fundamental distortions introduced by a new technology and the relevance of these distortions to the incumbent business model.

I will leave you with some basic assumptions. Contemplate if they can be challenged with fundamental distortions of new technologies!

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Do these assumptions look too basic to be challenged? My guess (prediction) is that these will be challenged very effectively in the very near future.

High impact businesses are built by challenging such very basic assumptions! Who gain and who in society will be affected by these fundamental distortions is a post for another day.

Thinking by Association

“Good artists copy but Great artists steal” – Pablo Picasso

Try googling ‘Uber for X’ and you will find startups solving a totally different problem than Uber but are similar to Uber in the way they solve the problem: on-demand and connect customers with freelancers who are available at the push of the button. Product Hunt has curated a list of such ‘Uber for X’ startups. There are Ubers for everything imaginable: Uber for dog walking, Uber for groceries, Uber for home cleaning, Uber for pizza, Uber for haircuts, Uber for security, Uber for chocolate chip cookies etc.

Taking inspiration from an entity, scenario, feature or business model and applying it in a totally different context is what we can call Thinking by Association or Associating Thinking. You look at a something and a light bulb moment hits you that something similar can be applied in a totally different context.

Some interesting businesses (some of them very successful) have had their fair share of Associating Thinking.

Research students would be familiar with Citation Analysis – examination of frequency, patterns and graphs of citations in articles and books. Basically, it uses citations in journals and other works to link to other works or researchers. It is said to have highly influenced Larry Page and Sergey Brin (PHD students themselves) in the development of the PageRank algorithm.

You would be associating McDonalds with Obesity. Can you imagine associating McDonalds with Eye Surgeries and in a positive way? Arvind Eye Care is a fabled pioneer of low cost eye operations. It was founded by a retired doctor Dr. Venkataswamy (fondly known to his admirers as Dr. V). Restless to solve the problem of avoidable blindness and faced with the constraints of severe shortage of eye doctors in the country, he hit upon a brilliant idea that revolutionized eye care in India and saved millions of people from losing their sight. He was inspired by the McDonalds process of standardization. Most of the work in an eye surgery would be done not by expensive (and very scarce in India) doctors but by highly trained, far less educated and expensive paramedics and nurses. Only the critical part of the surgery would be carried out by doctors.

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Eye operations at Arvind Eye Care inspired by McDonalds

Mark Benioff is said to have got his inspiration for Salesforce from Amazon – “why not sell software over the Internet”? Slideshare, acquired by LinkedIn for $119 million calls itself the Youtube for powerpoint presentations. Scribd bills itself the Netflix for documents.

Jim Yurchenco was the IDEO engineer who helped build iconic products like Palm V and the Apple mouse. In this fascinating article in Wired post his retirement, he talks a little about having been tasked with the Apple mouse. The Xerox mouse in those days cost $400 to make! Yurchenco’s job was to transform it into something made on the cheap by the tens of thousands. He made two important ‘associations’ that transformed the mouse.

One was the inspiration from the Atari Trackball found in Atari Arcade machines. The track ball just floats unlike the ball used in the Xerox Mouse. Just let gravity do the work. And the result was less friction and fewer parts. The Atari machine also used optics instead of mechanical switches.

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Atari Machine that inspired the Apple Mouse

Yurchenco’s second great association was the steering wheel of the car. You don’t pay attention to what you are doing with the steering wheel. The steering wheel need not be accurate – you just adjust the wheel until the car goes where you want. The same thinking went into the computer mouse. So, he designed the mouse with less stringent demands on accuracy which meant fewer parts and lower costs.

Returning to the Picasso quote, when an artist just copies she would be having trouble in living up to the original. But stealing some inspirations carefully while adding your own can result in extraordinary work.

When thinking by association, I think if the following is the route your brain takes, then it is deeply flawed:
I want to start a business à Uber works in this way à Why can’t I do this for food or cleaning or dog walking à What is the problem I am solving with food or cleaning or dog walking?

A high profile startup Homejoy (Uber for Cleaning) shut down a few months back. Though plagued by non-market factors like regulations, the service was flawed on unit economics.

Instead the best associations happens when you clearly understand the problem in a business (often as a result of having thought about the problem for a very long time) and understand the constraints and you look for inspirations somewhere else.

Sometimes associations can be included in some features in your product. Khan Academy was inspired by games to motivate its young users to learn more and added gamified features. Topcoder was inspired by baseball cards to find a way to rate the top programmers who compete on its platform.

Rob Morris, a trained Psychologist earlier this year launched Koko, a social media network that calms your mind and is very positive.

Rob Morris was inspired by Stack Overflow where he got help from strangers (who owed him nothing) on Stack Overflow whenever he was stuck in his programming problems.

Wired writes about it,

“It was as if Whisper or Secret had repopulated its trolling avatars with actual humans who, for some inconceivable reason, gave a shit about my shit. It was weird and strangely helpful.”

“Koko is a mobile social media platform focused on mental health. It’s what you’d get if you were to combine the swiping gesture of Tinder, the anonymity of Whisper, the upvoting of Reddit, and the earnestness of old-fashioned forums. It is, in other words, an online social experience unlike anything else out there.”

Keep an eye out for associations!

Would like to leave you with a few thought provoking associations:

  1. How can you apply inspirations from the popular Ice Bucket Challenge (people challenging each other for example) to healthcare or fitness?
  2. Can the Netflix model be applied to Toys?
  3. Can Kickstarter be used to collect income taxes?
  4. AirBnB for food to provide employment to millions of unemployed women home makers in India?

 

‘Patternization’, Machines and Experts

A scene from the movie ‘Back to the Future – II’
Marty McFly: [Reading the newspaper from 2015] “Within two hours of his arrest, Martin McFly Jr. was tried, convicted and sentenced to fifteen years in the state penitentiary.”? Within two hours?
Doc: The justice system works swiftly in the future now that they’ve abolished all lawyers.

Bill Gates made this prediction in 2014 about low-skilled jobs, “Software substitution, whether it’s for drivers or waiters or nurses … it’s progressing. … Technology over time will reduce demand for jobs, particularly at the lower end of skill set. … 20 years from now, labor demand for lots of skill sets will be substantially lower. I don’t think people have that in their mental model.”

Many people have predicted the eventual taking over of low-skilled jobs by machines in the not-so-distant future. But what about the experts? How safe are they?

The Case of Loan Officers

Loan officers have been an integral part of the lending process of banks. They earlier used their intuition to assess whether a borrower will repay the loan. Then they were guided by tools like the FICO score (credit score) and other metrics like debt-income ratio to make choices. Now they are in danger of being replaced. Completely. Peer-to-Peer marketplaces like Lending Club and Prosper ask applicants to fill information online and then check credit reports and assign a grade with a fixed interest rate. It crunches numbers to quickly make lending decisions. Because it cuts out expensive employees (yes, these loan officers), it can pass those savings to both lenders and borrowers. Apparently, many believe in this vision since LendingClub has/had investments by Google, BlackRock, T. Rowe Price Group etc.

Darin Inc. another online peer-to-peer lender has zero loan officers. A startup called Upstart, founded by ex-Googlers facilitates loans based on your education, area of study and job history and not just based on your FICO score and years of credit. The company positions it as an attractive alternative to millennials. Here again, the loan officer is conspicuous by his absence. Carl Benedict Frey and Michael Osborne predict in a research paper that loan officers have a 98% probability of being completely replaced in the next decade or two.

So, can machines replace other experts like it is replacing loan officers?

There are two main major factors impacting the jobs of experts:
1. Development of Patterns or what I call ‘Patternization’
2. Technology Improvements

‘Patternization’

In any field, when problems are loosely defined, it takes expertise to solve a problem. For example, some doctors treating cancers are very expensive because they have very high expertise. They formulate hypotheses and test them. A mediocre doctor will fail more often than a highly trained doctor. But over time, invariably, patterns emerge in solving these problems. When these patterns emerge, a mediocre doctor for example, is often as good as the best doctor in the world. Who would perform better in curing strep throat? The performance of a mediocre doctor would be almost the same as that of the best doctor in curing strep throats for which clear medications are established. Similar for any disease for which medicine exists that has been empirically proven to cure most of the time. When this happens, the best doctors move ‘upmarket’ focusing on complex diseases and the small diseases can be treated by nurses and other staff whose wages are lower. This in turn, causes the prices of the treatment to crash.

MinuteClinic has built a thriving business on the above logic. It established the first walk-in-clinic and treats common diseases which can be easily diagnosed. Patients do not wait in long queues. The business enjoys a customer satisfaction rating of about 95%.

The above is an example of ‘Patternization’. Whenever entrepreneurs have found patterns in solutions and have understood them, they can devise solutions that can be implemented at a far lesser cost than when done by experts. To reiterate, at this point, experts move upmarket and focus on more complex problems. For example, the first chip designers were pushed upmarket when software could emulate the patterns of chip designing. The first ‘experts’ who assembled PCs were no longer needed when patterns emerged on how to assemble them reliably every time and how the different parts fit with each other. Many accountants are not needed as before because software from Intuit takes care of it. A lot of companies that helped build websites no longer exist, as WordPress and companies like Wix and Weebly make it easier for people to build good enough websites by themselves.

When patternization occurs, experts move to places where their intuition and expertise is rewarded.

Abhijit Bannerjee and Esther Duflo recount an experiment in their remarkable book, ‘Poor Economics’. A NGO called Pratham ran a program called Balasakhi (children’s friend). The program took the twenty children (from very poor neighborhoods) in each classroom who needed the most help. They then worked with a young woman from the community who is the ‘balasakhi’. These ‘balasakhis’ with barely ten years of schooling have produced far greater results in learning outcomes compared to Government schools with highly paid teachers. Perhaps there is a pattern that produces results in teaching that does not need very expensive teachers to produce certain minimum outcomes.

Technology Improvements – When Technology Patternizes Jobs

Technology improvements often also work on the aforementioned point – develop patterns. But with the huge amount of data being generated, they can develop far more reliable patterns and can do so at a faster rate.

IBM’s Deep Blue could analyze 200 million positions per second and beat World Chess Champion Garry Kasparov. IBM computer Watson beat the top Jeopardy (a TV quiz show) champions Ken Jennings and Brad Rutter. Typical questions in the round were like: “And anytime you feel the pain, hey” this guy “refrain, don’t carry the world upon your shoulders.” Ken Jennings’ response after Watson’s win: “I, for one welcome our new computer overlords”.

Technology improvements do NOT immediately replace experts. They usually augment their outcomes. And they do so by developing better patterns.

Watson for example, has moved on from Jeopardy to working in Industries solving complex challenges along with human beings. It is being deployed in Healthcare, Retail, Finance and the Public Sector. Oncologists at the Memorial Sloan-Kettering Cancer Center are using Watson to provide chronic care and cancer treatment diagnostics. Watson accesses knowledge from 600,000 medical evidence reports, two million pages of text from 42 medical journals and 1.5 million patient records and clinical trials in the field of oncology. Watson then compares each patient’s individual symptoms, family history, genetic makeup, diet, vital signs, exercise routine etc. to diagnose and recommend a patient. In a nutshell, technology augments the work of experts.

Palantir builds software that do not replace experts but instead augments them. The company’s philosophy is that computers using artificial intelligence alone cannot defeat an intelligent adversary. They instead believe that a combination of human experts and computers make a much better team and build breakthrough software with that mental model. They call this ‘Augmented Intelligence’. This argument seems to have gained some support from results in chess. Garry Kasparov who was beaten by a machine introduced a new format called Advanced Chess or Centaur Chess, where teams consisting of both man and computer play against each other. Centaur Teams or ‘Man plus Machine’ teams are said to perform consistently better than just pure chess AI engines. Wired made a pertinent argument from this result: “If AI can help humans become better chess players, it stands to reason that it can help us become better pilots, better doctors, better judges, better teachers”.

Merck in association with Kaggle, organized the ‘Merck Molecular Activity Challenge’. A team from university of Toronto won the challenge. The team worked on data sets describing the chemical structure of thousands of different molecules. They have proved that a deep learning computer could develop its own rules to narrow down the thousands of unique molecules to those with the greatest potential to be effective. This could save thousands of hours of valuable ‘expert’ hours and ensure that their work yields faster results. Drug discoveries can occur faster than it happens now. Government subsidies in drug research can be more efficient and yield world changing outcomes.

Now let your imagination go wild to think about how the same ‘man + machine’ combinations as in Centaur Chess and Palantir can industries like Consulting, Finance, Law, Software Development, Marketing, General Management, Venture Capital etc. The results can be far more impressive and desirable.

Symantec Clearwell’s eDiscovery Platform analyzes and sorts more than 570,000 legal documents in just two days. It uses language analysis to identify general concepts in documents. It is clear that the software can be used for pre-trial research, save months of para-legal work (and replace hundreds of them?) and can augment the capabilities of a lawyer. A reminder that this ‘discovery’ from 570,000 documents can be done by just one lawyer.

In all the above examples, machines have developed better patterns and have produced far better outcomes in ‘collaboration’ with experts.

So, which is it? Are experts going to be replaced or are they simply going to become better and more valuable with the help of machines?

Patterns in Non-Routine Jobs

David Autor, in his paper, ‘The Polarization of Job Opportunities in the US Labor Market’ suggests that routine jobs, be it cognitive or manual are being automated. So experts, by definition since they are doing non-routine jobs, which often involve intuition are safe from being displaced?

In my opinion the ‘replace’ or ‘augment’ question about routine largely depends again on one important factor – have patterns already been identified on how the non-routine work is done. Once patterns emerge and are identified, machines will eventually replace experts in those jobs.

Loan officers are at such a high risk of being displaced simply because their jobs have largely become ‘patternized’. We saw in previous examples on how machines have made deep inroads into typically what you would consider non-routine jobs. Measurement Incorporated which won a Hewlett Prize in 2012 has developed technology which could grade essays (a non-routine job clearly) as good as a human grader.

As more and more non-routine parts of an expert’s job becomes ‘patternized’, the experts will be forced to move ‘upmarket’ and work on other parts of his job where patterns are yet to be discovered. But make no mistake. Machines are gunning for those other non-routine parts too faster than ever.

Machines will start ‘patternizing’ expert jobs faster than ever.

If you are a successful teacher, can the way you teach and motivate students be patternized? If you are a headhunter, can the way good candidates are found be patternized – for example by looking at the online crumbs left by the candidate? If you are a marketer, can the way you allocate resources across brands be patternized?

To summarize and make some inferences/predictions:
1. Experts are needed as long as their jobs are not ‘patternized’. Their work can be done at a lower cost if their jobs are ‘patternized’.
2. Machines are ‘patternizing’ parts of the jobs which experts used to do.
3. In the near future, machines will augment experts’ capabilities that will result in far better outcomes for consumers.
4. But all the while, machines will find patterns to perform a part of an expert’s job making it viable to let him ‘move up’ to focus on more intuitive jobs.
5. Fewer ‘Superstar’ experts will be needed in the future.
6. If you are an entrepreneur, there exists real opportunity in ‘patternization’.
7. If you are an employee, it makes sense to analyze if machines would soon find patterns to do your job good enough. Is there a pattern in playing chess? Is there a pattern in the way you do financial analysis? Is there a pattern in how you arrive at good consulting recommendations? Is there a pattern in how you make good venture capital investments?
8. If you are an employee, it is probably a good idea to join a startup that attacks these ‘experts’ and make them irrelevant.
9. I leave the implications about inequality as an exercise to the reader!