The Case For Digital Moats

Over the last few decades, an interesting phenomenon has been puzzling top economists and analysts – the profit margins of a few large and successful companies seem to be defying gravity, soaring above the rest. One would expect the powerful forces of reversion to mean and competition to have restored this anomaly within a decade or so; after all, that is how capitalism is supposed to work. Yet, year after year of super-normal margins for the elite few demands a new explanation of what might be going on in the economy.

The excellent Philosophical Economics blog, in an intriguing post Profit Margins in a “Winner Take All” Economy, comments on this phenomenon. He links to an interesting post by Patrick O’Shaughnessy that presents the following graph showing all US stocks with market capitalization that is larger than $200 million sorted into five bins by their initial profit margins. A clear trend is visible over more than fifty years – from 1963 to 2015 – the top bin of companies remains at the top; the bottom quintile remains at the bottom. This means that the margins of already high-margin businesses are expanding whereas the margins for low-margin businesses seem to be falling even more.


Furthermore, O’Shaughnessy shows that this profit margin gap has been increasing over the last two decades in some sectors – technology and finance (see the following graph), but also in the health care and consumer staples sectors.


Coming at this anomaly from a different perspective, a recent labor productivity study conducted by OECD found that productivity of the top manufacturers – those at the “global productivity frontier” — grew at double the speed of the average manufacturing firm over the decade after 2000. This gap was even more extreme in services: firms on the productivity frontier grew productivity at a 5% rate, 16 times the 0.3% average rate. And, once again, the gap between the most productive firms and the rest has been increasing over time (see the graph below).


So what kind of companies might be occupying the productivity frontier?

I think it is highly likely that the gap between those on the frontier and the rest is about their strategic positioning vis-à-vis the digital disruption that is ongoing. In a previous post I have written about the investing implications of this phenomenon, dubbed “software is eating the world” by venture capitalist Marc Andreessen. Companies that are doing the “eating” tend to have increasing profit margins. Moreover, the very nature of digital technology tends to create a “winner-take-all” dynamic, where moats from network effects, patents, regulation, and scale economies allows a concentrated group at the top to earn increasingly higher profit margins. We have already pointed out that the profit margins of the various bins moved roughly in parallel until the late 1990s, after which the profit margins of the top bin proceeded to rise rapidly. This time-frame aligns nicely with the rise of the internet-based economy.

Conversely, it is notable that companies in the older sectors of the economy – such as the energy and materials – do not show this rising margins effect. A recent study by the chairman of the president’s Council of Economic Advisers also found that the biggest gains in profits have been among technology and drug manufacturers; their profits don’t come from tangible assets like factories and land, but from intangible assets such as software, standards, patents, and network effects. This also might explain the otherwise puzzling absence of strong capital expenditures even as the economy continues recovering steadily from the great recession – the modern digital economy just does not require that much spending on heavy equipment as the industrial economy of previous recessions.

The following diagram is my attempt to depict the on-going battle of companies in the age of digital disruption. The companies in the inner-most ring are former giants that are already dead – killed by the software-advantaged company with the arrow pointing at them. In their heyday we bought physical books at Borders, music records at Tower Records, rented movie DVDs at Blockbuster, and bought photography film from Kodak. These were the large companies of the time that failed to anticipate and adapt to the digital tsunami that has led our reading e-books, stream music and videos, and take digital pictures with our smartphone. The middle ring depicts companies we think are not yet dead but whose profit margins are under severe pressure from their software-armed foes. Again, these are giants of our times such as the venerable New York Times (and all physical newspapers), Time (and all other magazines), the big-four TV networks, medallion-based taxi companies, hotels, travel agencies, and physical retailers like Macy’s and Walmart.


I was particularly struck by fact that, even as they were in the process of losing to their digital competition, the companies in the inner ring (Borders, Blockbuster, etc.) actually looked quite attractive on traditional value investing metrics like the Price/Earnings ratio; right up to the very end!


  • There is an elite set of global companies with high and increasing labor productivity as well as high and increasing profit margins.
  • It is vitally important or a company to be strategically aligned with the powerful forces of digital disruption to prevent it from being “eaten”; ideally, it should be the one doing the eating.
  • Value-investing grandmasters like Buffett and Munger have virtually proven the vital importance of investing in companies with long-term-sustainable competitive advantage – moats – since such companies can maintain and compound their earnings over long periods of time.

“Digital moats” are precisely those companies that at the intersection of these concepts: they are at the productivity frontier and moated in a way that benefits from the disruption caused by powerful digital trends. Examples are shown as the outer ring of the previous diagram (Amazon, Google, Facebook, Uber, Airbnb, Pandora, Netflix, etc.).

Market capitalization indexes as such the S&P 500 must, by their very definition, contain all companies that qualify by their capitalization – whether they moated or not, and companies doing the disrupting as well as those that are being disrupted. I think one can get an edge over the market by concentrating the portfolio around the elite few – the crème-de-la-crème – of companies at the productivity frontier. Of course, a great company does not automatically equate to a great investment – valuation always matters, and a myriad of company-specific issues must to be carefully taken into account before including a stock in the portfolio. Having said that, as more and more people from all over the globe get a smartphone connected to the cloud of digital services, I do think it reasonably likely that digital moats will continue outperforming the market over the next few years.


Full Disclosure: As of the time of writing this post, I am long many stocks mentioned in this post, including Amazon, Google, Facebook, Twitter. This post is not meant to be and should not be construed as investment advice of any sort. Investing is extremely difficult, the risk of permanent loss is high, and past results are meaningless in the future. 

The Outsiders: Seven Other CEOs With Buffett-class Returns
















































I want to end with some caveats:






It is Changes in Abundance and Scarcity That Drives Disruption

scarcity loop

A key question in business (and hence in investing) is: what drives change? Why do dominant businesses get disrupted so frequently by challengers? I posit in this post that most of this disruption is a consequence of a shift in economic scarcity, mainly caused by technological advances.

Most businesses can be conceptualized as offering a product or service bundle of value to their customers. The bundle is made up of various modules that combine together to provide the customer a valuable offering. I suggest that advances in technology cause changes in the relative scarcity or abundance in the underlying economics of these modules, and it is these changes in economics that create an opening for a challenger to topple a dominant business.

Consider an example given recently by Marc Andreessen:




But, when you have the ability online to download or stream individual tracks, then all of a sudden that bundle just doesn’t make sense. So it [got unbundled] into individual MP3s.


In general, once the deck of economic value has been shuffled by the shift in scarcity, it can create an opening to an entrepreneur to start from scratch by targeting a key module of the old bundle that is now relatively scarce — hence valuable — and leverage the newly created abundance. The Moore’s law driven plunge in the price of communications, for instance, is enabling a lot of startups to rethink existing business bundles by exploiting the “free” distribution available on the internet, just like iTunes did to unbundle CDs and Pandora is doing to iTunes in the example.

Once the challenger has won, it is fairly easy for the winner to bundle more and more features around the core module, to increase its value and to capture incremental marketshare. Of course, this process eventually sets up the bloated bundle to become a target for the next new challenger on the block, as technology changes the point of scarcity again!

The former CEO of Netscape, Jim Barksdale’s observation: ( “there’s only two ways I know of to make money: bundling and unbundling” captures this cycle of unbundling followed by bundling; but he does not really explain why should this be so? Andreessen, in a recent Tweetstorm, has provided a detailed example of this phenomenon of bundling and unbundling (

Thus the key driver of all the disruption and unbundling is technology driven changes in economic scarcity. A particularly powerful example of such a technology driver is the virtuous cycle of semiconductors and software advances feeding into each other, diagrammed below (I have previously written about this loop here:

I think it is vital for a disruptor to succeed that it be better aligned with this loop than the product it is challenging!

This is why I think the Christensen model of disruption, while insightful, is not complete. It comes in two flavors: low-end disruption and new-market disruption. Neither is fully satisfying as an adequate model of disruption – a counter-example to Christensen’s framework is the fact that the expensive, richly-featured iPhone manage to completely disrupt the cheaper, less functional feature-phone business (e.g., Nokia) — the exact opposite of what his model predicts! In my framework, by contrast, the key driver (driven by Moore’s law) was the relative abundance, and hence cheapness, of the internet. This allowed the iPhone to feature internet-enabled apps as the main attraction, rather than phone calls (in fact the initial iPhone was not all that great at making calls!). Thus the shift in scarcity/abundance created an opening for Apple to target internet connectivity as the core offering. This, I claim, is a better framework to explaining why they succeeded in disrupting the plain old cellphones despite being much more expensive; it was clearly not an attack from the bottom. (To be sure, smartphones also disrupted PCs, and that fact can be explained as an attack from the bottom as well as an unbundling of PCs due to a shift in scaricity/abundance; I chose the disruption of dumb-phones in this example since that cannot be adequately explained by one of the two frameworks).

To be sure, there can be other kinds of technology changes that are not related to semiconductors. But it is really hard to find other examples of something that can grow 40% per year for nearly 50 years! Moore’s law in likely unique in this aspect, which is why I think it has plays such a crucial part in the persistence of the disruption phenomenon, of the kind we have been experiencing in the last few decades.

Why Blockchain should be unchained from Bitcoins – two analogies

Like many, I see great value in the de-centralized blockchain protocol that enables the trusted transfer of a digital token. (Briefly: since the token can represent the ownership rights to just about anything in the digital world, or for that matter, in the physical world, the blockchain technology can make transactions much lighter weight and much cheaper, creating potentially enormous economic value.)

However, I do not share that optimism about the fate of Bitcoin as a global currency; in fact, I argue in this post that Bitcoin represents the Achilles heel of blockchain technology.

Of course, the reason why Bitcoins and blockchains are so tightly coupled together is that the blockchain protocol needs incentives for all those decentralized computers busily engaged in the reliable transfer of trust. And the current solution is to get this task done as a side-effect of the distributed systems trying to “mine” Bitcoins. In other words, the mining of Bitcoins incentivizes the distributed computing needed to implement any blockchain’s trusted ledger of transactions.

But unlike the obvious value in decentralized blockchain transactions, I think there are fundamental economic problems with the very notion of a currency that is not supported by a central bank of some kind.

To see why, consider the following analogy to build up some intuition.

Circulating blood is the medium of exchange of energy in our bodies, just like circulating currency (whether digital or physical) is the medium of exchange of goods and services in an economy. As the body grows larger, the amount of total blood needed to fulfill its exchange-of-energy role is proportional to the body size. If there is too little blood, the energy exchange will be deficient; we need to transfuse blood in extreme cases of blood loss. By analogy, as the global GDP level grows, as it does every year, the amount of currency in circulation must also rise, roughly in proportion!

The Bitcoin algorithm, however, has no relation to the level or growth of the global GDP. This may well prove to be a significant fatal flaw in its design.

To further deepen intuition, I recommend the brilliant essay Paul Krugman wrote for Slate magazine way back when he was a professor at MIT. In it, he explains the irreplaceable role played by the central bank in maintaining a stable currency — one that is neither inflationary not deflationary —  using an insightful baby-sitting analogy based on a real world case study. I suggest pausing here to read that brief (one-page) essay, mentally replacing Bitcoins for the baby-sitting scrips in his example: “Baby-Sitting the Economy.”

A strong implication of thecurrency experiment described in that essay is that any form of currency —  including Bitcoin  — just cannot become a stable, mainstream currency without a central bank function that acts to stabilizes their value from time to time, and keeps the circulating base of currency proportional to the size of the economy. One could, perhaps, imagine an algorthmic replacement for a human central banker —  say a Taylor-type rule —  implemented in a decentralized manner, although it would still be a central policy decision, in essence, since it needs to control the aggregate amount of the currency in circulation. However, all that is besides the point since the current algorithm behind Bitcoins is completely decoupled from the size of the economy, since there are going to be exactly 21 millions Bitcoins, a fixed parameter in its very design. This is a major problem that I think will prevent Bitcoins from becoming a global currency of any major standing (as envisioned by its enthusiasts).

Perhaps, however, it can survive as a limited sort of transient exchange medium, its value always secured at both ends by currencies that are stably backed by central banking. Indeed, this is exactly how it seems to be working these days. But I am dubious of the long term fate of this as well, since the point about the need to be proportional to the size of the economic “body” still stands, even for transient exchanges; after all, the number of simultaneous exchanges will surely rise in line with the growth of the global economy, thus raising the need for more Bitcoins in circulation.

More importantly, if all Bitcoins do is transmit money, then their role as a store of value becomes problematical. Every succesful currency must perform both functions (store of value and medium of exchange) simultaneously. If all Bitcoins do is transmit money, then Warren Buffett has this to say about its ability to hold any intrinsic value:


In response to this, Marc Andreessen, the venture capitalist behind many Bitcoin related investments has tweeted:

“Warren has gone out of his way for decades to avoid understanding new technology. Not a surprising result.”

Much as I admire Andreessen, I think this completely misses Buffett’s point, which is not about technology at all. Buffett understands moats, and he is saying that there is no moat in any technology in the role of a transmitter of money. As he says, there have been many technologies to transmit money in the past but that this carrier function has never managed to have much value, primarily due to competition. (Incidentally, Buffett is a student of business moats and has written in the past about the lack of moats in many innovative technological breakthroughs — airplanes, and cars being two examples that immediately jump to mind.)

Buffett’s point about money transmitters not being moated implies that the motivation of miners will be surely affected if the value of what they are mining is subject to competitive erosion over time. Note that Buffett is carefully not saying anything about the value of the blockchain protocol itself. Most articles on Bitcoin end up laying out the future possibilities of blockchains, rather than Bitcoins. I actually agree that the blockchain protocol has great potential; but I find the conceptual foundation behind Bitcoin not quite up to the task.

At least for now the two are tightly joined at the hip, and that is a bug, not a feature, in my opinion!

My argument so far is about the long-term issues with Bitcoins. But even over the short term, the recent plunge in Bitcoin prices is troublesome — in fact, it is now worse than the notable crash of both the ruble and oil prices:

This matters, since it will affect the motivation of Bitcoin miners. As explained in NYT’s DealBook blog:

Bitcoin miners are computers that run Bitcoin’s open-source program and perform complex algorithms. If they find the solution before other miners, they are rewarded with a block of 25 Bitcoins — essentially “unearthing” new Bitcoins from the digital currency’s decentralized network. Such mining operations, though potentially lucrative, are also expensive, requiring huge amounts of equipment and electricity.

It is vital for the integrity of blockchain protocol that Bitcoin miners continue to mine, since blockchain maintenance is a side-effect of Bitcoin mining! Any plunge in the motivation level, and hence the capital investment level, of Bitcoin miners clearly slows things down. If I am right about this (I am not a professional economist), the conceptual deficiency behind Bitcoin could really undermine the long-term prospects of blockchains.

The key question: Can the blockchain protocol be decoupled from Bitcoins in some way?

If only there was some other way to incentivize all those miners performing the distributed computations needed to maintain the integrity of blockchains …

Disclosure: I have no Bitcoin related investments at the time this post was written.

The Strange Economics Of Information

I have argued in my last post, Value Investing As Software Eats The World, that value investors these days can no longer avoid coming to grips with subtle differences between information-based and physical goods that might affect their moat and valuations. As Hal Varian (now chief economist at Google) and Carl Shapiro explain in their excellent (albeit slightly dated) book, Information Rules: A Strategic Guide to the Network Economy, while the classic principles of economics remain valid in concept, they do have some interesting implications due to the unusual cost structure of information-based goods. In this post I would like to highlight some of these implications that seem to me to be particularly relevant from an investor’s point of view.

Perhaps the most important of these differences is that information goods usually have high fixed costs of initial production but practically zero costs of reproduction. Think of the creation and production of a new song, a book, a movie, or a new software program. All the costs have to incurred upfront, before the first item is sold. But after that initial item is shipped, reproducing it is just a matter of copying the digital bits and transmitting them over the internet, for hardy a few cents per copy these days; and even that low cost is exponentially approaching zero!  Since the marginal cost of an information good is nearly zero, age-old economics principles confidently predict that its price will also head towards zero, in the presence of any significant competition. This implies that information goods cannot really be priced on the usual “cost of production plus profit” basis of physical goods — their cost of production is practically zero.

Another unusual aspect is that information goods often have to be experienced in order to be valued. Consider, for instance, how much would you pay for a news article? If it turns out that you already knew the news in it, it no value to you; but you cannot know this until you have actually read it!  Similarly, a piece of music has to be heard before one can properly put a value to it. Producers of information goods have to solve this conundrum in pricing; this often requires giving away some version of it for free and investing heavily in building a reputation for quality. The high quality of past editions of, say, The New York Times is the reason we agree to pay for today’s newspaper, even before knowing whether it contains anything of value.

A more consequential implication of near-zero variable costs is that an information-based products can be reproduced in billions of units with very little additional investment: a successful product can scale to massive volumes with surprising speed and efficiency. Value investors of the old school, used to the slow and steady expansion needed for scaling up physical goods, are often blindsided and tend to “miss” moated companies such as Google and Facebook, as they go from huge losses (due to high fixed costs of producing the first item) to billions in profits within a matter of just a few years (due to minimal costs of scaling up).

Zero incremental costs also explains the differential pricing often found in information-based products, where the same core product is sold at vastly different prices in multiple versions with superficial variations, including free versions. This abundance of digital copies must, however, deal with the harsh reality of that ultimate scarcity — human attention. Metrics related to the consequent “battle of eyeballs” are often used as a proxy for competitive advantage at the early stages of a new digital product. Such metrics are often scorned by traditional value investors, who prefer to measure actual cash earnings, but I think they are missing the point; at their early stages of production, metrics of attention capture are perfectly rational measures of value for information-based goods, due to the unusual economics of networks that applies to them.

The economics of networks

Notice that information-based goods have to interconnect with many complementary products in order to function: a browser has to work with an operating system; a smartphone with a mobile carrier; an app with an app store; a TV show with a cable network, etc. Such interconnections eventually lead to entire networks; and once a product is entrenched into the fabric of a customer’s network, it is very difficult to persuade that customer to switch to a newer product, even if it is quite a bit better or cheaper than the older one. These high costs of switching due to networks creates a strong moat against competition, once an information product has achieved a critical mass of acceptance.

The implication is clear: to really understand the information economy, it is necessary to master the economics of networks.

Think of buying the very first fax machine – it was useless by itself because it could talk to no other fax machine, but as soon as other businesses also purchased fax machines, it became more and more valuable. Similarly, the first user of Facebook had nobody to communicate with, but the value of the Facebook network goes up with every additional Facebook user that joins — the benefit to the nth user rises in proportion to (n-1), the number of previous users. The inventor of the Ethernet, Robert Metcalf was one of the first people to point out a mathematical implication of this: the total value of a network is proportional to the square of the number of connected users in the network [n*(n-1)]. This suggests that, if all other things are equal, as Facebook grew its user base 10 times, its value grew not 10 but 100 times! (Of course all things are not really equal – a user in a poor economy is not as valuable as a user from a rich one; nevertheless, the value of a network increases to very large numbers surprisingly quickly.)

The term “network” does not mean that wires or wireless connections have to exist between the nodes. The term is used metaphorically whenever there are interdependencies between nodes, which is almost always for information-based goods. For example, as more and more office workers adopted Microsoft Word in the 1990s as their editor of choice, the more valuable it became for a new user to adopt it, and the value of the “Word network” kept rising faster than the size of the installed base. Network effects from office workers dependent on Word, Excel, and PowerPoint explains why even a fading Microsoft still commands over 300 billion in market value. Any challenger product for processing a document or spreadsheet immediately runs into the problem that no one else in the company can work with it; it is extremely difficult to persuade everyone to move simultaneously to a new kind of document or spreadsheet editor, even if it is somewhat better than Word or Excel – a classic example of the powerful moat created by network effects.

Traditional value investors repeatedly underestimate this powerful arithmetic of networks. The market value of such companies can grow exponentially even as their P/E ratio shrinks arithmetically; they can change from apparently insanely expensive multiples to quite reasonable valuations all too quickly.

Tipping points and positive feedback loops – the dynamics of networks

Producers of physical goods usually face diminishing returns from the physical and bureaucratic costs of being too big. The demand for such a product usually is not too influenced by the number of previously sold products. Information-based goods, on the other hand, usually have decreasing supply costs as their fixed costs are amortized over larger volumes; in addition they benefit from increasing demand for every incremental user, due to the network effects noted earlier.  These double-barreled positive feedback loops can create runaway winners due to the extreme economies of scale involved. As noted by Geoffrey Moore in his prescient book, Inside The Tornado, such companies have an unusual growth pattern: they grow slowly at first before reaching a “tipping point” of acceptance; products that cross this point can show explosive growth afterwards. The demand for such products is also subject to self-fulfilling loops: a product that is expected to win often obtains more and more followers, following a herd-like dynamic of adoption.

This economics of such increasing returns to scale, have long been discussed but only recently have elegant models developed by economists such as Paul Krugman, Paul Romer, and Brian Arthur, enabled them to be tackled mathematically. Taken to extreme, increasing returns can lead to a winner-takes-all monopoly for some products, famously Microsoft’s Windows and Intel’s x86 chips; Facebook’s social network is showing similar adoption dynamics these days. A generative model for how such networks grow has been described by the physicist Albert-Laszlo Barabasi in his insightful book, Linked: The New Science of Networks. In Barabasi’s “preferential attachment” model, even a slight preference of a new node to join one of the bigger hubs of an existing network causes the big hubs to get even bigger. Such a growth process can explain many of the “power laws” often found in a variety of network-like phenomenon, from the size of cities to the popularity of blogs. Barabasi also found that a second factor, intrinsic quality, can explain the occasional exception to this “big get bigger” pattern, provided that the challenging product’s quality is an order of magnitude better than the existing product. A similar notion was first posited by Intel’s legendary CEO, Andy Grove, who observed that a new technology that is, say, only 10% better often fails to make a dent to a well established incumbent, but one that is 10 times better stands a good chance.

In light of such non-linear growth dynamics, it is easy to see why information-based companies spend so heavily in their early stages, sometimes even giving away their initial product in order to establish the impression that they have already won. Growing to critical mass quickly is often crucial to success, monetization can wait; an established network of users is much easier to monetize later on. Clear examples of this strategy are Google and Facebook: both patiently waited for many years before monetizing their growing base of users, and were quickly and hugely profitable once they turned on their monetization engines. Similarly, new products such as Twitter, Instagram, and Whatsapp are still growing their user networks, deliberately delaying monetization for now. While many value investors roll their eyes at the absence of current profits, the delay strategies are rational due to the strange economics of networks.

Invading the network moat

If there are such strong positive feedback loops as I have described above, how could Facebook topple Myspace, the earlier social network? In general, how can a challenger win in the face of the tremendous moat created by the high switching costs of networks?

The co-founder of PayPal and an early investor in Facebook, Peter Thiel, has shared some fascinating insights on this topic in his recent book, Zero to One. He thinks it is crucial for the new network to start by targeting a small universe of users that it can hope to dominate. Facebook, for example, initially targeted only Harvard university students, and quickly came to be perceived as the social network on the campus. Growth then involves expanding this concentric circle of users, all the while remaining dominant within it. Facebook expanded by growing into the adjacent markets of other universities; only much later did it expand to the general set of users outside its initial university focus. By then its tremendous momentum had a sense of inevitability that helped it topple Myspace, and seize the lead to become the dominant network of today.

Similarly, Amazon initially targeted just selling books online, and quickly came to dominate this niche. It then expanded into other media; only much later did it take on the much bigger eCommerce market. Consequently, it has enjoyed the benefits of self-fulfilling expectations — being perceived as the inevitable winner throughout.

It seems to me that another way a challenger can win the network battle against a well-established incumbent is by linking to an even larger network. Microsoft successfully toppled Netscape’s web browser by tightly linking their Internet Explorer to Windows, thus leveraging the massive network of the Windows installed base against Netscape’s then dominant browser network.

This appears to be happening these days to eBay’s dominant network of third party sellers. Amazon is trying to leverage its massive 250 million first party customers into its smaller but rapidly growing third party marketplace. It has opened up its existing (first party) product catalog and its massive shipping infrastructure (“Fulfillment by Amazon”) to third party sellers. I predict that it is only a matter of time before this battle tips decisively towards Amazon and against eBay.

A series of monopolies

Perhaps due to diminishing returns on scaling production of physical goods, the older industrial economy has been characterized by a set of stable oligopolies (a few large companies dominate the production of cars, airlines, chemicals, miners, oil, paper, etc.). In contrast, the presence of positive feedback loops in demand as well as supply often leads to the winner establishing a winner-take-all monopoly-like position in the economy of information-based goods. However, such monopolies do not necessarily last forever. The demise of MySpace and Netscape shows that a determined challenger with a 10x better product, or a clever linkage to an even bigger installed base, can overcome an existing network and establish a new monopoly.

I expect the information age will likely be governed by a series of monopolies, each successive disruptor toppling the older incumbent. This is an important pattern to note, since Buffett has shown during the past fifty years how monopolies can lead to amazing compounded investment returns. I think, however, that digital monopolies will be a lot more volatile — positive feedback can lead to vicious cycles as well, if a challenger succeeds in unbundling a monopoly incumbent. Value investors need to be very cognizant of the powerful forces of disruption that can destroy an existing monopoly and create a new one in its stead.


Value Investing As Software Eats the World



Warren Buffett

This question is of vital interest to me since:



Value Investing: a very brief introduction





— Thomas J. Watson Sr. legendary CEO of IBM








A personal dilemma



— Charlie Munger




— Yogi Berra



The great disruptor





Moore-Andreesen Loop


  • Yellow Pages got “eaten” by Google software.
  • Bank clerks got “eaten” by ATM machines.
  • Human brokers were “eaten” by online brokerage sites.
  • … the list grows every day.





Embracing my spots





Invert, Always Invert!          

— Carl Jacobi, 19th century mathematician, on solving difficult problems




— Charlie Munger


The strange economics of information



Information is the new oil





— Charlie Munger

Information economy is more than the technology sector



Rediscovering Value






The Moore-Andreessen Disruptive Creation Loop

Moore-Andreesen Loop

This post combines two famous but separate observations, made 46 years apart, about technology advances into a single integrated feedback loop that I think has been the major source of disruptive creation in the world over the last fifty years; it still continues, unabated, today.

  1. Moore’s Law: This is not really a law in the sense of scientific laws of nature; rather, it is a prescient observation made by in 1965 Intel’s co-founder Gordon Moore on the exponential pace of change in the semiconductor industry. He noticed that the transistor count in semiconductors seems to double every couple of years. Surprisingly, his observation continues to be valid more than 49 years after he first made it. This relentless advance has been like a giant clock that drives the entire technology industry forward every year.
  2. Venture capitalist Marc Andreessen’s observation (first made, I think, on the pages of WSJ in 2011) that “software is eating the world” trenchantly captures the fact that software replaces more and more functions that were formerly performed by humans or physical machines with every passing year. This is also a relentless advance that feels like a force of nature in its power to disrupt.

Moore’s Law is the result of human ingenuity and hard work fueled by ever rising capital expenditure and R&D on semiconductor fabrication processes. Each advance has required major breakthroughs at the level of material science, industrial processes, and applied quantum physics. Amazingly, such advances have indeed been made just in time to preserve the cadence of the “law” for over four decades. The following graphic captures the fact that the number of transistors found on a chip has been doubling every two years — i.e., growing at more than 40% per year!


But the “ante” in terms of capex and R&D dollars needed for such complex advanced rises every year. Intel, Samsung, Taiwan Semiconductor, et al are nowadays spending ten-plus billion dollars every year to keep the semiconductor manufacturing process advances coming. So where are the returns needed to fund these investments coming from?

This is where the crucial role played by software comes in. As it becomes more and more capable, it can do more and more (a tautology!) thus expanding the market for semiconductors. And the expanding semiconductor market, in turn, justifies an increase in the investments needed for the capex and R&D required for driving Moore’s law forward.

There are two parallel channels that allow software to become more and more capable as a result of better semiconductors: better and cheaper computing and better and cheaper communications. Think of the speed and cost of today’s internet and speed and cost of today’s smartphones as compared to the slow modem lines and slow and clunky computers of even a decade ago. Both these advances are synergistic and are critical enablers of the advances in programming techniques and tools needed for software to eat more of the world.

As software captures more functions it expands the market for computers and communications. Nearly 2 billion people already have the powerful supercomputers in their pockets known as smartphones, and serious projections are suggesting the possibility of 4 to 5 billion people having smartphones communicating with each other and running powerful apps in the next few years.

Every part of the loop sketched at the top of this post is important, and mutually supportive, to all the other parts:

  • Software, by itself, cannot advance at 40% per year type rates without improved hardware to run on and improved networking speeds. The major advances in software tools — from assembly coding to higher level languages to object-oriented languages, from waterfall to Agile programming techniques — are all fundamentally enabled by faster computing speeds provided by the underlying hardware.
  • Computers by themselves will completely starve of data-to-compute if the speed and reach of communication pipes linking them together did not advance along with them. Imagine connecting your super-powerful, instant-on, tablet to any of the slow dial-up modems of the old days!
  • The massive capex in semiconductors needed for improving both computers and communications cannot be justified without the expanded market provided by software eating more of the world.
  • And so on; all components of this positive feedback loop have to advance together, mutually re-enforcing each other, to keep it going.

Scaling the Power of the Loop

The scale of this phenomenon is not that easy to grasp. In personal conversations, people nod knowingly, thinking that all I am saying is that technology progresses every year, and of course it does. However, that is not my point at all. There is probably no other phenomenon, natural or artificial, that has shown this kind of exponentially fast growth such an extended period of time. I can safely claim this because of the unusual power of compounding: if anything has been compounding at this high a rate for so many decades, we would see it nearly everywhere already today!

To appreciate the stupefying scale of the phenomenon, let me contrast it to another technological revolution.

During the Industrial Revolution, that supernova of all changes in the human condition, growth in Western Europe accelerated by a puny 1% per year! Even this tiny amount though, when steadily compounded for a hundred years (most historians date the Industrial Revolution roughly between 1750 to 1850), resulted in a massive improvement in living standards from the complete lack of growth for the thousands of years before it (see the zero net growth in income per person for thousands of years on the left of the Industrial Revolution, during the so-called “Mathusian Trap” period, in the graphic below; source: figure 1.1 from a recent book on this subject by economic historian Gregory Clark).

great-divergence-graph Clark

So if the Industrial Revolution changed the world so dramatically with a mere 1% growth compounded for a hundred years, consider the relative impact on the world affected by Moore’s Law, which has compounded at over 40% per year for nearly 50!

Anything growing at this blistering pace should become millions of times better (faster/cheaper) in only a few decades, and indeed our computers do show exactly such improvement – that smartphone in our pocket really is more than a million times more powerful than the most powerful computer on the planet from fifty years ago. It is hard to think of many other examples that show such an astounding growth pattern; and the examples that do come to mind seem to be, in one way or another, intersected and transformed by the power of the Moore-Andreessen loop. Try thinking of a counter-example!

Shift in Scarcity

This sort of massive change can cause a fundamental shift in what is scarce and what is abundant every few years. In my opinion, it is this frequent shift in the point of scarcity that has been the real driver of the constant disruption that characterizes our information age. As if an avatar of the ancient god Shiva of Hindu mythology, who is said to destroy in order to create anew, this loop is a powerful force of disruptive-creation: companies that are better aligned with this loop can — and often do — replace incumbent companies that are slower in adapting to its consequences.

A corollary: SoCs are eating the surrounding chips

A phenomenon that is strikingly similar to “software eating the world” seems to be happening right within the motherboard of all the smartphones and tablets and laptops. A “System on a Chip” (abbreviated SOC or SoC), as the name implies, contains a whole system of modules on one silicon chip by absorbing more and more functions that were formerly performed by independent chips. Functions such as graphics, modems, DSP, and various kinds of memory have already been absorbed.

I think the abandonment of the baseband chip market by Texas Instrument and Broadcom is the latest example of this corollary phenomenon. The baseband function now forms just a piece of a larger SoC chip produced by companies like Qualcomm, Mediatek, and Intel. The economics of producing them as part of an integrated SoC became increasingly tilted in their favor compared to the baseband function chips made by TI and Broadcom until the latter finally got eaten up.

I see this as a trend powered by essentially the same kind of loop as diagrammed above, with Moore’s law shrinking the size of semiconductors and interconnection technologies with every new generation when combined with the ever-growing power of chip design software. The expanded market (e.g., billions of smartphones and perhaps tens of billions of “internet of things” devices) enabled by the resulting SoCs fuels the necessary investments in capex and R&D, completing the loop.

Disclosure: I am long Qualcomm and Intel in various portfolios at the time this post was written.