Elsewhere

Since taking a break from my daily notes in this space about a year ago, I’ve published a handful of longer pieces on Medium; and for some of you who used to follow me here, I thought you might be interested in these. The most recent one is copied below and the link for all the others is this. Maybe one day I will return to the daily exercise, and until then I hope you will stay well and check in on occasion.

Ten network value lessons from the digitized economy

Networks are having a moment, or rather, they’ve been having it for quite some time, long before the big contagion and the flattening of curves, but the moment is expanding. Stephen Wolfram is working on a new physics model that would explain the universe in terms of network evolution and lead to the long-sought Unified Theory in the field. Niall Ferguson published a book that presents modern history as a series of network events, governed by network behavior and attributes. It is generally recognized in financial markets (themselves dense network structures) that the modern economy was borne of the InterNET (emphasis added). Most currently, cryptocurrencies — a revolutionary but natural extension from all that — are inherently network structures.

And yet there has been very little in the areas of finance and economics — in the mainstream at least, really nothing — to seek to bridge the analysis of value and strategy with what would seem an important counterpart in network science. If science is in this case too strong a word, network theory would be a good enough place to start. While the footprint of commercial networks has been growing to the point where regulators are even taking note, the resulting arguments don’t seem rooted in any network concepts or their context, which renders the debate — at a minimum — incomplete.

Perhaps the financial conventions that we still resort to, predicated on old-economy metrics and trends, are adequate enough. It is nevertheless non-trivial and now also opportune to try to understand the network asset on its terms. What follows are high-level notes, almost a preface, in support of an investment thesis that may be backtested with promising results. This is only a summary, purposefully light on supporting detail and examples that could one day turn it into someone’s massive book. But the interested reader should be able to consider cases, circumstances and criteria from one’s own observations that will fit the mold.

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1. In an economy dominated by technology advancement and the digitization of virtually all things, there is a diminishing distinction between companies in the so-called “technology” segment and all the others. On the supposition that a time will soon arrive when the distinction will have altogether vanished, the more important difference is between companies that offer product or service solutions with and those without network effects.

2. The latter are in a state of constant need to upgrade, update, reinvent, ride cycles up and down, and reduce pricing on their offering, or else become disrupted or commoditized in a competitive environment that is moving ever faster. The former — businesses with network value — are better able to withstand attack and well positioned to drive down the costs of growth, because the network mesh is both a base and self-perpetuating driver.

3. But not all networks are the same, in fact they’re mostly very different from each other. At the highest level, these include marketplaces, exchanges, communication systems, platforms, artificial intelligence and other connected data tools — to name some of the principal categories — which may also combine several of the listed fields. And by the same token, network effects — defined as the improvement of network experience or value with the addition of new sources of engagement — are also different among the varied types. These can be broadly classified as user or data network effects — the first a matter of popularity and the second a matter of depth — which may also for some networks work in combination.

4. At levels below the very high, there are more nuanced distinctions that define the nature of the asset, often relative along a continuum, rather than binary or absolute. Examples of such qualities include the centralized, decentralized, and distributed topologies; the single-, bi-, and multi-directional data flows; and the single and manyfold layers of the network, which often co-exist and may in fact be linked, internally or externally to the individual business unit. There are also differences of strength between the ties that link the nodes together, and there are differences in size and numbers of the clusters that ensue.

5. These things and others shape the network profile, which in turn shapes its value and potential. Distributed networks, for instance, all other things being equal, may be more valuable than the centralized variety at the opposite extreme, because the single center-point of the former is a vulnerability to its whole. But all other things are not equal, and thus the network whole is best to understand without such oversimplification. Perhaps the one true constant in the general assessment of all sorts is the value of engagement, an attribute that’s always worthy, regardless of the other qualities described.

6. The so-called FAANG contingent — a less than ideal grouping as each of the five constituents is a different type of network from the others — is a highly visible sampling of the digital network asset class. Because they’re big and public and have evolved in more or less transparent ways, they make good subjects for more general analysis that can be carried over to the less developed cases. The fastest growing (if not already dominant) forces in many if not all the major industry segments — transport, finance, commerce, education, health, security, most recently biotech and manufacture — are additional examples.

7. In all the branches and the realms, it is difficult to the extreme to build a network from ground up. Unlike a product or a service that is designed, produced, and introduced into the market — successfully or not — a network is a complicated being, almost biological in nature, and subject to improbable conditions to take root. And when it does — a miracle in ways, which follows an inflection point that’s hard to manufacture — the network must be nurtured like an organism. The influences and the outcomes (which is to say, the behavior of nodes and clusters) aren’t always easy to anticipate, and there further comes a time at which the growth will asymptotically stall. When this occurs, new use cases or network offerings may provide a lift, but it isn’t always known during such times if the desired effect will materialize. Conversely, a robust and growing network can create great optionality, which may among other things enable an expansion into contiguous network areas.

8. Despite the frequent overlaps of categories — for instance, in finance and commerce, in information and entertainment, messaging and transactions — it has been observed that where user network effects are a core network driver, the resultant entities tend to be unique. Think, Instagram, LinkedIn, Twitter, YouTube, in the social realm, despite the basic similarity ingrained in messaging. It’s also been observed that as new categories form, the landscape tends to winner-take-most or even winner-take-all (power law) competitive effects. The same is true of the behavior within the network, as big clusters tend to become bigger and even dominant at times. (The influencer economy is a result of such network patterns.) What all of this describes is a tendency to centralization, even among the decentralized, that the subject networks may seek to mitigate.

9. Financially, the network’s most attractive features are profitability (after the inflection point) and predictability (as network effects take form). In combination with the winner-take-most advantage previously described and the high-gross-margin nature of the software business in the digital economy, the cash flow of the operation can be superior to almost any other business type. In cases where this is not so, it is advisable to wonder why. Perhaps the subject isn’t quite the network that we might assume it is, while, on the other hand, it’s also true that there are companies that unsuspectedly reveal themselves as such.

10. As certain network platforms have grown and reinvested cash over the years, the concern has recently arisen that these have taken on monopolistic forms which have to be controlled. Should there be a breakup of some kind (which wouldn’t be a first, e.g., Ma Bell), it may be worth remembering the nature of the living organism. Plants can continue to grow after a branch or two have been sliced off, and even branches may evolve into a tree or two once they’re replanted. After a while, we may be back to where we started, or someplace very different and strange, because the consequence of change in complex systems is often unintended.

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One way perhaps to summarize all the above and draw a financial conclusion in terms that match traditions and accepted standards, is this:

In the digitized economy, networks and network effects are capital, and seeking to develop these is a capital investment; technology advancements and disruptions are important, but on their own are an expense.

In the long term, however, network effects may also turn negative and spiral down, while the technology expense can in the short term create value.

Related reading:

Clusters of decentralized influence (2021)

Linear perception, exponential change, and the new value (2021)

Reinterpreting the networks (2020)

Markets and the year(s) ahead: Post-digital edition (2018)

Interpreting the networks (2017)

Networks 3.0: Defined by digital dimensions (2016)

300 posts

I started to jot down my morning notes 300 days ago. It was right after Labor Day, when the world is energized and set to blossom. I didn’t know what I would write about, although I set myself some ground rules in the masthead, to limit the endlessly potential subject matter with enough diversity to keep it interesting. The interest was mainly for my own part, to be honest, which is to say, I used the vehicle to take notes, to learn, to force myself to think about the meaning(s) of events and patterns that I saw or lived or read about.

It started brief and barely richer than a tweet in the initial weeks. Like this one here, for instance, The miracle of blocks, from a Boulder hotel room; and this other the next day, The bridge, hanging at the Denver airport. As time went on, and somewhat to my own chagrin, the posts got longer and more verbose; in part, I guess, because I felt encouraged by a growing readership, in part because I got more comfortable with my posting voice, but also, to be fair, because the world became more complicated and amazing into the new year and after.

So, lately, what started as a series of brief morning notes has turned into a daily blog of standard form and length (e.g., Convergence, platforms and new market color, The consequence and its intentions, New verticals and horizontals, The truth, which isn’t linear, The market standard-bearers), examples from just the past ten days or so. And I don’t think I can keep this up.

The time it takes each morning is far longer now than it once was, and, what is much more problematic, I’m falling behind as a result in reading that I used to do during these same early hours. The writing itself, I think, suffers, as the subject of these notes and of my learning starts to get repetitive and stagnant.

In other words, I think, the time has come. 300 posts, including this, is a handy milestone number on which to end. I’ll probably return (I say this to myself) to speculate and write about some favorite subjects (if I had to guess), but perhaps it won’t be daily and it may not be for a while. On the other hand, when it does happen, I will likely have some new material, having caught up by that time with so much reading I would like to do. By then as well, perhaps, the world will also settle into a new normal, which hopefully will be a good thing and provide a new and interesting area of study.

Until then, the posts are all here for the browsing, categorized by subject and pull-down menu reference, each with links to others at the bottom, suggested by the platform based on some tagging reference, I imagine, which makes the bounce-around even more of an adventure. As well, there is a drop-down menu organized by month, which may at some point be another reference, a document of sorts as we progressively evolved from one world to another.

I guess, in ways, this may have been a book. Maybe sometime in the future, and maybe we’re already there, such interactive and dynamic books will be a whole new category. Like fiction, history, biography, business, mystery and etc., but converging all of these and more, like markets do, and other networks.

The market standard-bearers

There is much self-inflicted pain in markets caused by stubborn, antiquated measures, definitions, guideposts, dating back to who-knows-when and still the basis of the segmentation and the analytics, the theories and allocations, which have not much to do with what in actuality goes on out there, in the world where it most matters.

The More Markets Change, the More They Stay the Same, according to The Wall Street Journal, in a profile article that includes the following chart in support of the observation.

I’m sure there are clear demarcation lines somewhere that separate and organize the items in each of the listed pairs, but if the average investor were to take a minute and really think through what if anything these designations mean and what would cause a given stock to be in one and not another, it might take more than a minute to arrive at a dubious non-answer.

To keep things simple by focusing on the biggest of the subjects for this type of exercise, we can look at the grand five public companies that we sort of know as FAA[M]G, or Big Tech, or the Big 5: Apple, Amazon, Facebook, Google, Microsoft, listed alphabetically to avoid subjective bias. Eyeballing the categories again, I think these five could comfortably fit in every single one of every listed pair, except for small – which is itself a clue, I think, that we should dig a little further.

These were at one time small but they no longer are. And the trajectory has come about in record time, and hasn’t budged since the five’s grand arrival.

And now they keep on growing and expanding the area of their shadows, where all the others in this new market move around. In the chart below, the dark blue line down at the bottom stands for the 505 companies in the S&P 500 Index, which includes the five of the superior lines above. Were these to be stripped out, the index obviously would look worse, much worse…

Yahoo Finance

How much worse exactly would depend on the percentage composition of the total that the Big 5 represent. As of June 30, according to Slick Charts, that figure stood at roughly 22%. So, of 505 stocks in the widely accepted benchmark index, five constitute more than one fifth.

It pays, I think, given this circumstance, to look to these Big 5 (which straddle all the standard categories, as mentioned) for particular analysis and profiling. What makes them special? What drives their largeness, value, growth, tech and non-tech, U.S. and all the world, and all of that?

A few years back I shared my views on what I believed (and still do) was the answer, and even though there’s been a lot that’s changed since then, the principle remains: Networks 3.0 – defined by digital dimensions. As I extrapolate from there to have a look at all the others in the referenced big list (i.e., look to the networks and their deep effects), here are some results:

All five of the top 5 comprise 22%, as has been said. Of the remaining 15 in the top 20, eight are similarly characterized (including two global finance institutions) and in the aggregate make up another 8% of the S&P 500. And because it pains me to leave out #21 and #22 on the list, as they’re such obvious examples, this adds another 2% to the total.

Thus, roughly 32% (rounding error excused) or almost one-third of the S&P 500 Index – that which is the standard of all performance measurement in markets – is supported by 15 companies (out of the top 22).

Each of these is different from the others in the grouping, judged by product, service, customer base, technology solution, location, and so on, but each of these is a very large and growing network. It may be about time to recognize this as a new market fact and standard, and draw some new conclusions, research some new metrics, publish new reports, maybe even build a whole new index. It helps, as a start, to be merely cognizant, as apparently the investors and traders implicitly already are.

Related reading: Interpreting the networks (2017).

Convergence, platforms and new market color (cont’d)

The new Stratechery essay on the evolution of Apple computing is a great history lesson in the evolution of digital networks. The same principles and patterns that we associate with other network systems – the forms and value of engagement, the clusters that grow or sometimes dissolve, the community behavior that shapes the network system overall and leads to outcomes that lead to other outcomes as the network profile and its set of links and possibilities perpetually change their shape – are seen in the evolving architecture and resulting marketplace of Apple’s ecosystem.

The End of OS X

An interesting aside in Ben Thompson’s essay is a hat-tip to Paul Graham, commenting on hackers and their interests of the period, whatever these may be, as a leading indicator of the network’s future patterns:

The context of Graham’s comment was his perspective on Apple’s stock value, shaped by network engagement as he saw it. Wittingly or not, the observation was way ahead of its time. I posted something yesterday about this same subject more or less: Convergence, platforms and new market color.

The fate of the big data banks

I remember “disintermediation.” In finance that used to be a thing. The vision and the trend line was predicated on the dismantling of banking institutions that stood between depositor/investor/consumer and borrower/issuer/vendor into various component parts to give each more direct access to the other.

And the result was many funds – hedge, venture, growth equity, assorted debt, and so on – and funds-of-funds, and other forms of go-between through which sources and uses passed. And banks of course were still involved in all of that, plus countless new advisory (gatekeeping) operations to help sort through the tangled mess. And later on the ETFs and index funds that would in theory disintermediate the mutual fund segment. (And who even knows what dark pools really are, apparently these also are disintermediating something.) And as the funds-flow architecture evolved to also disintermediate the system, there is by now a so-called payments category made up of layers like a jigsaw puzzle, where finance and technology converge.

So anyway, that’s how “disintermediation” seems to have unfolded, while the disintermediated banks are now many times larger than they were when this whole thing began. And every layer in the illustrated market network takes a cut for services provided. And every layer grows the shrinking distance between depositor/investor/consumer and borrower/issuer/vendor, the space where there is interference of some kind, for payment that is rendered.

With that by way of history, we enter now a time of break-up and anti-trust motif, directed at new data banks that have emerged. These, too, it seems, are ripe now for their fated disentanglement.

The cheapening of market presence

The winner-take-most statistical power law effect we’ve seen in many emerging business categories is a phenomenon that network scientists have noticed in their natural investigations for some time. It is a trait in certain business models where network effects can reinforce a market presence, sometimes exponentially and to the marginalized exclusion of competitors who are relegated to a long (and narrow) tail, which is dreaded. The Big 5 techs, so-called, are more truly the Big 5 networks (in the broad sense), and it apparently will take some kind of intervention to keep their dominance in check. There are other examples.

An overlooked example in this way of seeing the network power law phenomenon in market presence, is on the funding side of the equation. That is to say, there is a winner-take-most parallel in financial markets, which has as much to do with the nature of the underlying business target as it does with the network nature of the markets themselves. Bubbles form sometimes, concentrations gather, attention focuses or fades, and thus the masses of financial capital shape similar leaderboard formations at all their many levels. The portfolio positions of fund managers tend to overlap, the structure of securities go in and out of fashion, certain institutions amass growing troves, and so on etc. (The wealth gap that is growing, perhaps, is also part of the event and its network effect drivers.)

And just as the individual products, messages or links that pass through the Big 5 networks (and others) tend to commoditization by sheer undifferentiated volume in these network concentrations, it’s possible to see financial flows and products the same way. That is to say, the financial category and its varied funding elements that accumulate, are contributing to the cheapening of these, if you will. And the return opportunity fades.

In this context, some news items from the financial press the other day, which, like everything described herein, is a participant in the gatherings.

Progress and the clusters

The formations and movements of the clusters is the pattern recognition that ultimately matters. It’s referred to as momentum by some, strategy by others, depending on one’s technical or fundamental bias. In the first case, the criss-cross lines of history are extended out, subjectively, until it’s noticed that the line is broken. In the second case, the exercise is in principle the same, but with a more attentive look at underlying drivers and a proactive push. No matter, the clusters form and move, and thus the markets.

The “markets” in the aggregate are the network graph manifestation. Equities are just one part, with debt and all the other asset classes, and all the non-financial forms, such as the shopping mall and Amazon dot com, which in turn tie to supply and value chains back to financial categories.

The economy is reflected in markets, they say, and the reverse is also true. Underneath it all, the clusters grow or shrink or change their shape and nature of their links to other clusters that are very rarely static. The change has its effect in ways that aren’t easy to predict, because the system is multivariate and dynamic.

Nevertheless, it is inherent in our nature (and important) that we try. A bubble only happens if and when it pops. And sometimes even then, it bounces back. It isn’t final, though possibly transformed to something else. That is the pattern, that is progress.

Morgan Housel

Security, conservation, defense

It’s in a way symbolic that the proposed Uber acquisition of Grubhub has left its target to Just Eat Takeaway instead. If Uber stands for the conveniently mobile and spontaneous ways of an outgoing urban environment, the meal delivery businesses of both Grubhub and its new strategic partner stand for something more reluctant and withdrawn.

WSJ

The segment’s other dominant competitor, DoorDash, is in the meanwhile securing a market vote of confidence of its own… which is also symbolic.

The Information

While much of the crystal ball viewership is homing in on remoteness as the new wave (including the symbolic market moves described)… remote learning, remote work, remote health, and so on…

BI

… perhaps there is a bigger and more interesting theme in the making, something to do with security, conservation, and defense at a more general level. It’s in any case a theme worth watching.

TechCrunch

The pocket and the tech

Some of the best finance reading of 2019 was the S-1 filings for the year’s class of IPOs. It’s nice to see the 2020 editions start to surface, and just as there seemed to be a theme across the 2019 publications, so also 2020 seems to have a subject of its own, thus far.

If 2019 was the year of consumer marketplaces – Uber, Lyft, Peloton, Pinterest – this year’s product seems to go in the direction of tech-enabled resellers. (I am thinking about Lemonade and Vroom in particular, which have attracted some attention.)

There are some common elements and there are distinctions between the two categories. Stripping the business models down to their basic elements – data network effects and user network effects, which go together – I feel like where the marketplace scenario depends on the former in support of the latter, the reseller model does it in reverse. If the edge in the marketplace model is the community of buyers and sellers that is formed and the efficiency of the transaction that results, the edge in the reseller model is the efficiency of customer acquisition and the economic offload of the product to an incumbent vendor. If the marketplace model and its network effects result in a winner-takes-most competitive landscape determined by a naturally growing presence on both sides, the reseller model is perpetually competitive and dependent on continuous efficiency improvements.

Perhaps a good analogy to illustrate the difference is the telecom segment of the ’90s. The network infrastructure, which had taken decades and many dollars to build, was increasingly overlaid by certain CLECs, DSLs, ISPs, and other forms of resale where the underlying lines were used (with owner’s approval) to market to end-users more efficiently than the incumbent did, or to enhance the incumbent’s reach through what was effectively outsourced marketing. The economics were split based on contracts that defined the term and financial responsibilities of each, among other things, and in many of these cases the telecom was essentially rented out by the marketing organization. In a sense.

There is a risk that comes with that, for either side. For the deep-pocket incumbent, it’s that the marketer becomes the magnet and consumer-facing brand that over time may take over. For the marketer, it’s that the deep-pocket incumbent may at any time pull the plug. It’s a delicate balance in what is almost a frenemy equation, each side dependent on the other and thus distrustful with a warm smile on its face; each motivated to increase its self-reliance, as the efficiency of the incumbent and the capital access of the upstart both improve.

I’m thinking about these things as I read about the reinsurance model and its financial risks and benefits to the data-driven tech-enabled insurance startup…

Form S-1

… and the “asset-light” strategy of the used car operation.

Form S-1

It’s a race, just like before, to see which of the sides prevails, the deep and capital intensive pocket that might catch up with the tech, or the deep and data intensive tech that might start pocketing the capital.

Fear and missing

The fear of missing out prevails. In an era of accelerating change and visibility – that is to say, of quick and massive opportunities (and sometimes gains), widely and immediately publicized (and oftentimes exaggerated) – it isn’t an improbable outcome. Among many.

The phenomenon has been so common in the past decade, more or less, to even get acronym’d by popular culture – FOMO – a mark of status and abundant use, when printed characters must keep up with breathless and persistent messaging, so no one misses out.

The power law manifestations in competitive dynamics where some winners take the most, are rooted somewhere not too far from FOMO, on a certain level. The market bubbles that emerge may be more obvious examples, although the network graphs and clusters are not so different from the first case, I don’t think.

Sometimes these bubbles burst, sometimes they don’t, and then it’s arguable that they were even bubbles. The speed however is the thing that is important. It signals to the next big wave, which starts out small, that missing out could be an issue. A missed gain is almost like a loss, and maybe even worse, if others made it.

The underlying basics aren’t of particular importance. It may even be that there aren’t any, or that the ones that are, are not thought through for lasting impact. Perhaps that doesn’t matter much, because the impact is inherent in the value or the bubble that gets formed. By fear and by the missing.

When the economy reopens, this may ignite the spark that leads to a recovery, which may be faster, sharper than if purely based on need.