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!

On Attractive Early Stage Investments

Startup investments are weird and very unlike picking stocks. Startups in a successful VC’s portfolio tends to follow an incredibly skewed distribution called a Power law distribution. The following is a power law graph.

24 Power Law

Mark the X-Axis with the startup name and Y-Axis as returns from the startup, what you will get is a typical graph you get from successful VC funds. Some startups hit outsize returns relative to everyone else combined.

Here’s what Peter Thiel says about his Founders Fund portfolio,

“If you look at Founders Fund’s 2005 fund, the best investment ended up being worth about as much as all the rest combined. And the investment in the second best company was about as valuable as number three through the rest. This same dynamic generally held true throughout the fund. This is the power law distribution in practice. To a first approximation, a VC portfolio will only make money if your best company investment ends up being worth more than your whole fund. In practice, it’s quite hard to be profitable as a VC if you don’t get to those numbers. “

As Thiel says, in the best funds, one startup investment tends to return the value of the entire fund. Kind of hard to believe isn’t it?

Here’s a graph of the 100 US backed exits from 2009 to 2014.

25 Power Law Exits

Source: CB Insights

If that is the case, how do you approach VC investments? Peter Thiel tells how not to – don’t try to be safe:

“If you invest in 100 companies to try and cover your bases through volume, there’s probably sloppy thinking somewhere. There just aren’t that many businesses that you can have the requisite high degree of conviction about. A better model is to invest in maybe 7 or 8 promising companies from which you think you can get a 10x return. It’s true that in theory, the math works out the same if try investing in 100 different companies that you think will bring 100x returns. But in practice that starts looking less like investing and more like buying lottery tickets.

Chris Dixon of Andreessen Horowitz calls for a Babe Ruth kind of hitting it very big without fear of missing.

“How to hit home runs: I swing as hard as I can, and I try to swing right through the ball… The harder you grip the bat, the more you can swing it through the ball, and the farther the ball will go. I swing big, with everything I’ve got. I hit big or I miss big.”  –Babe Ruth

He presents an analysis of VC funds since 1985. Great funds have home runs of huge magnitude (of course).

26 Fund Home Runs

And great funds tend to have more money losing investments compared to good funds (surprising) as seen below.

27 Fund Losing Money

That most VCs don’t do well is a given. The Kauffman foundation published a report on VCs where for all the press coverage that a lot of VCs get, one simple statistic stands out:

The average VC fund fails to return investor capital after fees… A majority of funds failed to exceed returns available from public markets

Often times you need to invest in a startup early to get the kind of huge returns that will return your fund. For example consider the returns from the Box IPO of the different VC funds that invested in it:

28 Box Returns

The different returns invested at different timelines speak for themselves!

The invested startups must exit at a high valuation and the VC must be early in the startup. So how many VC funds have invested in billion dollar startups and have invested early? (Data from CB Insights 2004-2013)

29 At least one exit

Out of 479 active VCs (at the time) – only 68 or 14% ever invested in a billion dollar company. And only 5% invested in at least one billion dollar startup early. So it is very likely that the rest of the funds would likely have really poor or negative returns.

So what attributes of disruptive early stage startups to look for if you try to get home runs? In my opinion, the attractiveness of an investment goes up with each of the following:

1. Huge Potential Market or rather huge non-consuming market. The startup caters to potential customers who are not served by current incumbents. Startups cater to non-consumers by lowering one or more of the five barriers they face: wealth, time, access, skill or motivation.

2. The job-to-be-done of the customer is very important. Or to put it bluntly, it is a problem that the customer absolutely needs to a solution for.

3. The Founder(s) and as an extension his team. Are they competent and learn quickly (two separate attributes)? Does the team figure out quickly what works or does not work? If selling to companies rather than consumers, does the founder know the space very well? Is the founder competitive and attracts fast learners?

Does the founder have a history of experimenting with different things? Successful founders of very successful startups have often been very high on experimentation – breaking and fixing things. Also some side projects have been turned into very big companies – Twitter, Slack and Twitch.tv being some blowout successes that have emerged from the ashes of a previous startup.

4. Strong defensibility of the product. Does the product have IP? Does the team have unique domain competence? Are there network effects? Highly defensible characteristics like these prevents competition (at least in the short term) from other talented startups. Also businesses with highly defensible characteristics makes for a great exit later on.

This one characteristic might be very crucial. So much so that, the very successful venture capital firm USV had their earlier investing thesis where defensibility through network effects was a major point.

Large networks of engaged users, differentiated through user experience, and defensible through network effects.

5. A new business model that challenges some fundamental assumptions of how things are currently being done. New Technology enablers typically cause what I call fundamental distortions. These distortions enable startups to challenge assumptions. The more entrenched, basic and long held the assumptions, the more lucrative the investment.

6. The market is addressable or the team has a strong plan for addressability. Very successful startups find creative ways of addressing the market or building their initial mass. They don’t throw money around easily to acquire customers.

7. Startups compete in an area with Non-Tech Incumbents or with Incumbents not motivated to go after a market (often because it is too small initially).

8. Non-market forces don’t matter or are favorable. This is something that prevents sectors like education from producing multi-billion dollar startups.

9. The Timing. Why now? Why not earlier? Are there any technology, demographic, economic or culture trends contributing to the idea’s success?

10. High acquisition possibilities. Especially true in this age where the world is awash with capital. Startups with proprietary tech or new data sources or highly competent teams are going to be attractive acquisitions for the giants seeking to remain competitive.

The above characteristics in my opinion provides the best odds of an early stage startup becoming a home run investment.

Are there any other characteristics that you look for? Which are the top 3 characteristics from above that enable home run investments? Let me know in the comments.

The Business Opportunity in Underutilized Assets

Two of the highly valued startups currently, Uber and AirBnB are platforms that turn highly underutilized assets into monetization opportunities for ‘sellers’. For example, take the case of Uber. Morgan Stanley estimates that cars are utilized to the tune of only 4%. With Uber, sellers (car owners) can turn their highly underutilized assets (cars) into income generating opportunities.

The Internet helps in the ‘discovery’ of these seller assets by buyers. So the internet ensures sellers can find a market for their underutilized assets. Since theoretically, any positive cash flow with these otherwise non-monetized assets is welcome for sellers, buyers too get a better price over other solutions. And the 2008 financial crisis played no small role in the success of Uber and AirBnB where sellers wanted new income and buyers wanted a better price. (To be noted that Uber was more than a better price. It was a better product for customers in a market that was protected for long and where supply was artificially constrained).

Let’s stay with cars and vehicles for a minute. Can there be any other way this highly underutilized asset – the car, can be monetized?

Wrapify is a startup that turns cars into moving ads. The startup offers a two-sided platform between drivers/car owners and advertisers. The drivers can sign up and if they meet a set of minimum requirements, Wrapify will ‘wrap’ their car into ads like shown below.

23 Wrapify

Image Source: click here

Wrapify then using its algorithms calculates the amount of ‘impressions’ the advertiser got from the car moving around (for example higher the traffic, higher the impressions and hence higher the payout). The timing might be great at a time when wages are still stagnant and more and more drivers are complaining about Uber. Maybe a Uber+Wrapify will put more money into the pockets of drivers.

Veniam is another example. It incredibly turns the ‘underutilized asset’ of public transport into something remarkable. Co-founded by the founder of Zipcar, the startup claims to develop the networking fabric for ‘Internet of Moving Things’. The ambitious startup attempts to blanket entire cities in seamless Wi-Fi. Basically, the engineers tap into a city’s existing internet infrastructure and at many points place wireless routers. Then a fleet of public transportation – buses, garbage trucks, police cars and the like are fitted with Veniam’s NetRider routers which receive wireless signals from access points, creating hotspots on the move. It creates a mesh network that covers the entire city and the public needs one login and no hot-spot hopping.

Skills, Knowledge and Time are a commonly underutilized asset. Take a paper and jot down your top 3 skills. And how many of these skills do you use at your day job? The unused skills are underutilized and are potentially possible to monetize. Again the internet makes it easy for ‘discovery’ of people with different sets of skills and ‘discovery’ of demand for these skills. Many businesses and organizations have already been founded on this premise.

Quora unlocks the knowledge residing in individuals. Wikipedia survives on the knowledge of its volunteers. Innocentive, a platform that connects problem solvers with organizations is a typical example. Innocentive in 2007 sought solutions to help clean up remaining oil from the 1989 Exxon Valdez Disaster. A solver named John Davis successfully solved it and was awarded the prize of $20,000. The kicker? He didn’t have any background in the Oil Industry. He was working in the concrete industry. Players on FoldIt solved a scientific problem of deciphering the crystal structure of Mason-Pfizer monkey virus, an AIDS causing virus that was unsolved for 15 years. How long did these players take? 10 days! Are you working at some day job but you have a knack for advertising and can think up good ideas for advertisements? Then Tongal is the place for you. Top brands like Frito-Lay, Hasbro, Colgate, Gillette, Pantene post challenges on the platform for new creative ideas for advertisements. You can submit short ideas, pitches and even videos (they are in a staged process) and earn money for each stage if your ideas are selected. Kaggle is another interesting platform hosting challenges for Data Scientists. The company boasts more than 400,000 data scientists on its platform and has clients including GE, Mastercard, Amazon, Merck and Facebook.

There are still lots of skills that can be unlocked for different purposes.

  • If you have knowledge in law, can you be connected to someone who is ignorant about it but needs to get justice?
  • Lots of women in India cook food day in and day out for their families. And they are paid nothing for it. Can that be monetized by connecting them with willing buyers who want home cooked food?
  • Can people working in well paid jobs but looking for more meaning in life be connected to non-profits who would value your level of expertise on something but cannot afford to pay?

There is a flip side to the story though. People with skills that are available abundantly in the population may have a negative impact as their wages are driven down by technology. The great economist Ronald Coase in 1937 wrote an influential article called the ‘The Nature of the Firm’ where he focused on a simple question – why does a firm exist? His answer was ‘transaction costs’. If you are always contracting out every piece of your work, you have to search, evaluate and pay for every job that ends up costing more than hiring somebody full time. So companies hire full time. Enter the internet. The internet makes the ‘search’, i.e. finding employees easy. So for skills abundantly available, it becomes easier and easier to contract out. A study finds that in the United States, around 53 million people freelance which is a huge percentage of the population (35% of the workforce). It is difficult to make the case that their underutilized time is being ‘monetized’ in all cases.

Capital is another asset that may be underutilized. Bain Capital published an important research in 2012 titled ‘A world awash with money’. Here’s what they had to say about it.

“Our analysis leads us to conclude that for the balance of the decade, markets will generally continue to grapple with an environment of capital superabundance. Even with moderating financial growth in developed markets, the fundamental forces that inflated the global balance sheet since the 1980s—financial innovation, high-speed computing and reliance on leverage—are still in place. Moreover, as financial markets in China, India and other emerging economies continue to develop their own financial sectors, total global capital will expand by half again, to an estimated $900 trillion (from $600 billion in 2012) by 2020. More than any other factor on the horizon, the self-generating momentum for capital to expand—and the sheer size the financial sector has attained—will influence the shape and tempo of global economic growth going forward.”

“… Businesses and investors will struggle to find a sufficient supply of attractive, productive assets to absorb the amount of financial capital they have to invest, driving up asset prices and forcing them to lower their expectations for high rates of return on investments.”

In the era of low interest rates, can a new platform help divert capital to important and/or high return activity? Can some of this capital efficiently be diverted to high impact philanthropy or say social innovations?

According to me, Information is another asset that is heavily underutilized. Google and Facebook are some of the most valuable businesses because of the ‘information’ that can be unlocked in different ways. But new billion dollar companies will be built around underutilized information. This is primarily because of the development and advances in Machine Learning. Machine Learning to put in a simple way is used to find patterns in large amounts of data and give computers the ability to learn without being explicitly programmed. Kevin Kelly of Wired writes,

“In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI. This is a big deal, and not it’s here.”

Hyperbole or not, huge amounts of value will be unlocked from different types of data. It may disrupt many industries, especially professional services like Consulting and Finance in the process.

A startup Orbital Insight turns the ever improving Satellite images into valuable information for retailers, hedge funds and real estate companies. The startup uses its machine learning algorithms along with the power of hundreds of computers running on the cloud on millions of images to come up with interesting insights. The startup correctly predicted that Home Depot was going to have a blow-out quarter by looking at car counts during the past five years (the counting and analysis was automated of course).

14 Orbital Insights

Cognitive Logic helps organizations to pool data to get new insights without being anxious about privacy. In this case, ‘un-pooled’ data is an asset that is far less utilized.

Waldo Photos is attempting to build one of the very interesting business models I’ve seen in the recent times. It is a two-sided platform that connects customers with professional photographers. Sounds banal? Here’s the catch. All you need to do is take a selfie. And Waldo Photos will find your photos wherever they are in whichever professional photographer’s pockets. For example, say you go to a basketball game. And professional photographers capture multiple shots of the crowd. Waldo makes sure you find a picture you are in if it was taken by a photographer who have signed up as well. (Here the photographs with the photographers are the underutilized assets). Building a marketplace around it with image recognition is indeed an interesting idea.

Here are some more examples of underutilized information:

  • There is lots of value in the countless blogs, articles and journals lying around the internet underutilized. Can machine learning unlock its value and can you build a business model that disrupts consulting?
  • Can the crumbs left online by an individual like tweets, blog posts, Github submissions, Quora answers etc. be used to assess their skill and talent and can that disrupt recruiting?
  • Can lots of value be unlocked in electronic health records and digitized medical records?

Reimagining Businesses from the Ground up

When a new technology enabler arrives, entrepreneurs try to build businesses around it. And numbers of new startups really explode when the new technology enabler is sufficiently distributed to customers. For example, when more homes started getting the internet or when smartphone adoption explodes in a country, entrepreneurs and venture capitalists are in a mad rush to build businesses and deploy capital respectively.

Selling books around in a retail store? Then sell the same books online. Selling diapers in a retail store? Then sell the same online or as a subscription. Do you call a landline phone to book a cab? Use your smartphone to book a cab. Do you book movie tickets by standing in a queue? Then do the same online. Are you booking hotels calling a landline number? Do the same online.

Some successfully businesses have been built by doing the above. But what has changed is only the customer ‘interface’. The books remain the same, the diapers remain the same, the cabs remain the same, movies are produced in the same manner and the hotels remain the same.

What is truly transformational is when the underlying products themselves change. That is a true reimagination of business. Think about booking hotels. With AirBnB you can convert your underutilized living space into Hospitality. The underlying product that was transacted – the hotel room in itself is now different. AirBnB projects that during the Super Bowl weekend in Feb 2016, over 15000 guests will be staying at AirBnB listings.

Salesforce reimagined how software services will be used and paid for. AWS has reimagined how you can use computing infrastructure. Wikipedia has made it normal to pay 0$ for the biggest encyclopedia ever created.

With new technologies like Mobile, VR, 3D Printing, Big Data, Machine Learning AI, it is increasingly possible to reimagine businesses and how things are being done like never before. And these technologies are getting better and better. Take a look at the following image from Qualcomm.

21 Sensors
(Source: Qualcomm)

There are increasingly new sensors embedded in Mobiles. It is possible to think that Healthcare can be reimagined with all these new sensors in the Mobile. Some other sectors like Health Insurance too can be reimagined.

Many businesses can be reimagined if we remove any assumptions and start from scratch (the thinking called ‘First Principles’). Elon Musk talks about such ‘First Principles’ type of thinking:

“The normal way we conduct our lives is we reason by analogy… With analogy we are doing this because it’s like something else that was done, or it is like what other people are doing…  [With first principles] you boil things down to the most fundamental truths … and then reason up from there.”

Let us take the example of Consulting. It is possible to simple build a marketplace online connecting top consultants and clients improving the discovery and pricing. Some companies like Hourly Nerd and Expert360 are doing this. But that would just be replicating and not particularly reimagining Consulting as a business.

How can Consulting be reimagined? There are tons of free high quality content on the internet – blogs, articles, journals etc. Can you use technology to convert all of those into high quality advice? With Big Data, Machine Learning and AI it is increasingly possible. Startups like Orbital Insight are applying Machine Learning to satellite images to advice clients in Retail, Mining and Agriculture etc.

Note that startups like Orbital Insight use Big Data and Machine Learning while Hourly Nerd and Expert360 still use just the Internet without using any of the newer ones.

As a rule of thumb, businesses will be fundamentally reimagined as newer and newer technologies come about.

For example, the Internet sparked promises of a virtual workplace where people can login from their homes without having to show up for work (at least for the white collar ones). That clearly didn’t pan out well. I still have to go to office braving the Bangalore traffic.

But can Virtual Reality and Augmented Reality, two newer technologies deliver on that promise of a virtual workplace? There is a startup called AltspaceVR whose tagline is ‘Be together, in person’. It’s developing a virtual reality platform in which people can connect with one another in a kind of digital lobby. Represented by humanoid avatars, AltspaceVR users can speak with one another, communicate via body language and share experiences. It works whether users are wearing the cheaper Samsung Gear VR headset, or the high-end Oculus Rift or HTC Vive Pre.

VR might finally enable people to work remotely and with no loss of accountability and productivity. There will be profound implications of this in other areas as well. A significant number of vehicles on roads is about going to work. Congested cities are because a lot of jobs reside there. Real estate becomes costly in congested cities. Commutes are longer in such cities. If a good enough Virtual Workplace is rendered, there will be profound implications. People can spend time with their children more, move to a place with far lower rent, pollution will decrease etc.

When you think about the plans of AltspaceVR, Facebook’s acquisition of Oculus Rift two years back makes a lot of sense. Social will be huge in VR. Distant friends can connect with each other (the core service of Facebook) via Virtual Reality and can feel like being in the same physical space wherever they like to be.

Similarly VR will likely reimagine movies as we know them. Users might be able to be in movies or be able to direct the flow of a story based on their actions.

Think about News. The Internet has been a great democratizing medium for news. More people can write their opinions, people can amplify their voice through tweets and hashtags and people other than reporters can arguably break news.

22 Osama Tweet

The above was a tweet by a little known user Sohaib Athar unintentionally breaking the news of the Osama Bin Laden raid in Abbottabad.

There has been some successes in the Internet only news. Huffington Post and Buzzfeed (interestingly both co-founded by Jonah Peretti) are some of them. There has been some flirtations with different revenue models. New York Times makes money from monthly subscriptions of their digital content rather than relying on advertisements. Buzzfeed makes money from native advertising. They publish sponsored content that you wouldn’t even realize. For example, here’s one of its ‘lists’ – something that it regularly publishes: “15 bands that probably wouldn’t exist without Led Zeppelin”. Who wrote it? Spotify! “9 things that have changed in the last 20 years” by… Motorola!

The distribution of news has changed. But how the news is produced is yet to be fundamentally reimagined. Just like tens of thousands of volunteers have built Wikipedia, can hundreds of thousands of citizens be brought together to build the best unbiased investigative journalism platform? People consume news and pretty much don’t follow up on it. Also, some very important news doesn’t get much attention. Can you bring together journalism, the power of social media and the power of Crowdfunding to take immediate action following reading some news?

Food is another interesting example. Some very bold attempts are being made other than just connecting customers with restaurants. Hampton Creek is an ambitious food startup that wants to rethink food from the ground up. The company swaps meat based proteins for plant based ones while trying to retain the taste. The company currently has three products – Just Mayo, Just Cookie Dough and Just Cookies (all egg free products) and has distribution deals with Walmart, Safeway, Target and Costco. The company achieves better results because of its database of thousands of species of plants and uses machine learning algorithms to model likely ingredients in its products. The company is at the intersection of plant biology and machine learning (and good branding).

There’s a startup called Impossible Foods funded by Google Ventures which makes tasty burgers where the ‘meat’ and cheese is entirely made from plants. Here’s a video on the same. The meat-free burger bleeds and sizzles just like meat!

20 Impossible Foods
(Source: impossiblefoods.com)

How long would people recruit with resumes? If you were given the opportunity to reimagine recruitment, how would you do it? Would you just collect and parse resumes online? Would you filter candidates according to their GPA? Or would you use new technologies to quickly rate them from other exhibits like blogs, Quora answers, Github submissions etc.?

How would you reimagine education? Are lectures necessary? What should be the role of a human ‘teacher’? Can people learn from their homes? How would you change the curriculum? Would students solve tough real life problems while they are learning? What new technologies would be used?

There are so many opportunities with new technologies to reimagine businesses. Will investors fund such businesses? Will Government allow these business models to be deployed? But I’m sure bold entrepreneurs are not in short supply!

Five Barriers that customers face

When looking for new business opportunities or new investment opportunities in startups, it is useful to think about the customer and the barriers they face with existing products and services (let us call them solutions) in getting a job done. Customers face five common barriers while getting their job done:

  1. Wealth Barrier: Existing products and services may not be affordable. Healthcare that is not affordable, education that is not affordable, insurance premiums that are too high, consultants who charge high fee etc.
  2. Time Barrier: Not enough time to get the job done. No time to prepare food, no time to learn something, no time to shop around etc.
  3. Access Barrier: It is not possible to get access to something. For example, not able to access lectures of a Harvard Professor. Not able to access a talented programmer in India or a designer in Africa. Not able to access startup investments in emerging markets. Not able to access a collectible or a first edition of an obscure book.
  4. Skill Barrier: Customer does not have the skills to get the job done. For example, doesn’t have skill to create a website. Doesn’t have the skill to do financial valuations. Doesn’t have the skill to analyze big data. Doesn’t have the skill to manage accounts. Doesn’t have the skill to design marketing materials.
  5. Motivation Barrier: The customer knows how to get the job done but doesn’t have the motivation to get it done. Doesn’t have motivation to lose weight or doesn’t have motivation to study something that will help in career are obvious examples.

Startups have been using technology to lower barriers and bringing important solutions to customers. Some examples:

Barrier Company Description
Wealth Wikipedia Quality and Biggest Encyclopedia at zero cost
Khan Academy Thousands of lessons at zero cost
Rent the Runway Designer dresses and Jewelry at low cost
Oscar Reduced Insurance Premiums
McDonalds Reliable and Standardized low cost food
Walmart Low cost retailer
AirBnB Low cost hospitality
AWS Low capex computing infrastructure
Time Amazon Shopping a click away
Uber A cab appears in minutes
WordPress Get your blog in seconds
Glucose Meters Health monitoring at your convenience
Minute Clinic Quick resolution and treatment of common non-critical diseases
Access eBay Access to items difficult to find
Netflix Access to movies and television content
Charles Schwab Access to top funds
Lending Club Access to lenders
Kickstarter Access to donors and patrons
Google Access to information
Facebook Access to friends
Innocentive Access to solvers across the world
Coursera Access to top professors and universities
YouTube Access to a wide audience
Tinder Access to potential mates
Skill Intuit Easy tax preparation and accounting
WordPress Easy publishing of thoughts
AWS Easy access to infrastructure
Pregnancy Tests Easy checking for pregnancy
Spreadsheets Easy records and finance
Motivation Top Coder Play games and get recruited
FoldIt Play and fold proteins
HopeLab Play and fight cancer

A lot of opportunities exist in drastically lowering each of the above barriers. Consulting, Education, Healthcare, Recruitment, Law, Philanthropy, Pharma, Venture Capital etc. are likely to be transformed.

Wealth, Skill and Motivation Barriers fascinate me particularly. Will elaborate on these in future posts.

 

Data Strategy: Data Network Effects, New Data Sources and Gatekeepers

By now, the statement ‘Data is the new oil’ has become a cliché. Amin Vahdat of Google said this, “The amount of bandwidth that we have to deliver to our servers is outpacing even Moore’s Law. Over the past six years, it’s grown by a factor of 50.” Science Daily reported in 2013 that 90% of world’s data were generated over the previous two years. When there is sufficient data, new insights can be mined that businesses can take advantage of. You may have heard of the infamous case where retail giant Target sent a teenage girl promotions for diapers to her home after algorithms determined she was pregnant. The big problem was Target knew before her dad knew.

Venture Capitalists investing in networked markets or platforms look at network effects as an ‘unfair advantage’. In network effects, the more the users of your product, the more valuable the product is. Facebook, Uber, AirBnB, Quora, LinkedIn etc. operate on network effects. You use LinkedIn only because others use it and as a result, there is no scope for a second big player in the world of platforms – the Winner takes it all. So, the higher the network effects, the more lucrative the investment from a Venture Capitalist’s point of view.

One of the best performing venture capital firms – Union Square Ventures had for a long time, the following thesis to guide their investments (they modified it in December last year):

Large networks of engaged users, differentiated through user experience, and defensible through network effects.

Matt Turck of FirstMark Capital made an interesting argument called Data Network Effects. Data can be a ‘competitive moat’ – an entry barrier. The more data you have, the better you will be than your competition. Why? More customer data, more correlations, more insights and understanding about your customer, the better will be your product which will lead to more customers and which in turn will lead to more data and the cycle repeats. Startups and not only giants like Facebook and Google are able to benefit because of democratization of Big Data tools and machine learning/AI.

And new data is not only coming from the traditional point-of-sale machines or from your mobile phones. Some interesting startups are building their businesses around new data sources.

Orbital Insight hopes to create market intelligence for decision makers and investors in industries like Retail, Agriculture, Real Estate, Commodities etc. Sounds like yet another consulting company with suited MBAs? Wrong. Orbital Insight applies machine learning on satellite images to glean new insights. It sources images from DigitalGlobe – a provider of commercial high-resolution Earth imagery. The startup uses its machine learning algorithms along with the power of hundreds of computers running on the cloud on millions of images to come up with interesting insights.

Its CEO James Crawford explains one example:

For example, we’ve used convolutional neural networks to count cars. We had humans mark a few hundred parking lots, taking a mouse and putting a red dot on all the cars. That provides the triggering mechanism for the neural network, and then the neural network learns what a car is. Then we processed a million parking lots through the neural networks and counted millions and millions of cars in those images.

14 Orbital Insights

The startup correctly predicted that Home Depot was going to have a blow-out quarter by looking at car counts during the past five years (the counting and analysis was automated of course).

The startup is in Mr. Crawford’s words, intent on answering:

…big-picture questions such as “How has the United States recovered from the recession in the last five years?” or “How did it recover in one state versus another?” or “How did Target do versus Walmart?” In other applications we’re looking at how China is growing, how many new building are going up in cities across China and how different regions are doing. It’s all about automating the algorithms so we can process thousands and thousands of images in parallel and get a sense of what’s happening on Earth on a macroscopic scale.

Are you a property owner trying to sell your house or an agent trying to close real estate listings? You can book a peculiar kind of service with a startup called Drone Base. Drone Base ‘pilots’ will fly UAVs (unmanned aerial vehicles) or drones around your property and nearby areas and you can then download the images for listings as well as analysis.

The below video is a pilot flying a drone capturing the neighborhood and surrounding areas around the property.

Here again data will be the main course of a startup like Drone Base. Eventually it is going to use the high resolution videos and images to provide services that will be very different from just listing properties. A whole lot of high resolution images can be classified and analyzed.

Farmers Business Network (FBN) is sells membership of $500 to farmers in the US. Farmers of a region join the platform and contribute images and other data. With a lot of data aggregated and analyzed, FBN provides insights on the farm owner’s fields. No more just relying on advice from the seed company or fertilizer company.

15 FBN116 FBN2

Interestingly, FBN just collects $500 life time membership fee for now. Venture backed, it is going to have a way to use its data for additional use cases in the future.

Hampton Creek is an ambitious food startup that wants to rethink food from the ground up. The company swaps meat based proteins for plant based ones while trying to retain the taste. The company currently has three products – Just Mayo, Just Cookie Dough and Just Cookies (all egg free products) and has distribution deals with Walmart, Safeway, Target and Costco. The company achieves better results because of its database of thousands of species of plants and uses machine learning algorithms to model likely ingredients in its products. The company is at the intersection of plant biology and machine learning (and good branding).

17 Hampton Creek

Microfinance loans are expensive, the average interest rate in 2011 being a huge 35%. The small size of the loans, high risk of default and huge operational costs push up the interest rate for those who are most in need of credit. FirstAccess is a startup in different countries in Africa that wants to solve this problem. FirstAccess partners with Telecom companies and Microfinance institutions. The company uses mobile phone data which contain demographic, geographic, financial and social data and prepares fast and actionable credit scores.

18 FirstAccess

A typical recommendation is like the above and the credit is disbursed within minutes. No need for expensive loan officers.

Urban Engines wants to improve urban mobility with a very clever solution. The app uses the phone’s accelerometer and magnetometer sensors and recommends the routes one must take and the likely traffic through different routes. The company identifies traffic by improving accuracy on moving data – movements of buses, cars etc. The mapping service by the company uses Augmented Reality.

19 Urban Engines

The consumer app is free but the data collected can potentially gold for Urban Engines. The company has a solution for Businesses to help them plan their fleet and logistics. The company also has solutions for city administrations who have to grapple with overcrowded routes every day and allows them to make smart decisions.

Another startup that targets city administrations is Veniam. Co-founded by the founder of Zipcar, the startup claims to develop the networking fabric for ‘Internet of Moving Things’. The ambitious startup attempts to blanket entire cities in seamless Wi-Fi. Basically, the engineers tap into a city’s existing internet infrastructure and at many points place wireless routers. Then a fleet of public transportation – buses, garbage trucks, police cars and the like are fitted with Veniam’s NetRider routers which receive wireless signals from access points, creating hotspots on the move. It creates a mesh network that covers the entire city and the public needs one login and no hot-spot hopping.

Veniam claims its technology offers 10x the range of standard WiFi, 100x faster connection setup and 12 lower cost per GB than cellular. Interestingly, the data transfer is two-way and data like location of pot-holes, congestion points etc. are logged to the cloud for later analysis. Though now the service focuses on providing public Wi-Fi, this can be used in the future by cities for things like city planning, security or more controversially for surveillance.
With new data sources and the possibility of data network effects, tech companies will increasingly become gatekeepers of unique data sets.

Nobody knows more about your interests, preferences, concerns (and a whole lot of things) than Google. Nobody knows more about your social connections (also called the Social Graph) than Facebook. Nobody knows more about your professional connections and networks more than LinkedIn (the Professional Graph). In fact, LinkedIn conducted an experiment on Kurt Wagner of Re/code to predict where he would be professionally five years down the line. LinkedIn compared his profile with its database of 300 million users to find users with similar skillsets and career paths.

These gatekeepers in my opinion will be highly valuable hence justifying their current high valuations. Therefore, logically new startups tapping into new data sources will be a lucrative investment, if they are going to be gatekeepers of unique data sets. And this will push more business models to specifically include a data strategy.

The genetic testing startup 23andMe is a great example of this. The startup mails to your home a simple equipment to collect your saliva sample. The company is cleared for some tests like Bloom’s syndrome and it also helps track your ancestry. The sample is then mailed back to the company. Meanwhile you set up an account on their website. The company tests the DNA from the sample and you will get the results on your account. 23andMe will potentially be the gatekeeper for another unique data set – your DNA. Nobody will know more about your DNA than 23andMe. The company already has partnerships with Pfizer and Genentech which will access its data.

The ethical questions are obvious and must be tackled at some point. The companies know a lot about you and you in turn do not know how they are going to use it. Samsung Smart TV’s creepy privacy policy drew comparisons with the opening pages of Orwell’s 1984.

Meanwhile startups and tech giants will continue to fight for unique data sets – both to make the product better and be a gatekeeper. And investors will find them to be great investments.