The investment “moat”
I look for economic castles protected by unbreachable ‘moats’ -Warren Buffett
The quote above from Warren Buffet, a statement he first made in a 1996 investor letter, is one of his most famous. It neatly encapsulates his investment approach: invest in giant companies that can achieve a “moat” by operating at a scale that others can’t reach.
By spreading the fixed costs of expensive, non-transferable assets like machinery or a banking licence, as well as highly-geared operating expenses like brand marketing and regulatory compliance, over a larger revenue base than competitors, these companies could be better known and cheaper. And, if you look at Buffet’s portfolio, it’s full of companies operating in industries with high fixed costs and high operational gearing: capital goods companies like BYD, consumer goods like Coca Cola and, above all, financial services companies like Wells Fargo, Amex and Bank of America.
The investment approach was massively successful — until it wasn’t. In the period 1979 to 2008, Warren Buffet outperformed the S&P 500 by 12.6% a year on average, cementing his reputation as the Wizard of Omaha, the most successful investor of all time. But — a less known fact — since the financial crisis, Warren Buffett has underperformed the S&P. One might be tempted to attribute this relative under-performance to the heavy financial services weighting in the Berkshire Hathaway portfolio. However, while a factor, deeper structural changes are at play.
Scale is no longer a moat
Problem number one with the Buffett investment philosophy is that, in the digital age, critical mass is within most companies’ reach. Critical mass — or minimum efficient level of scale — is the scale of production a company needs to reach where it won’t have a major unit cost disadvantage compared to its competitors. After this point, diminishing returns to scale kick in, which means that even if a competitor has greater volume it won’t translate into the same order of magnitude differential in unit costs.
However, as we’ve written before, companies can now plug into the scale economies of third-parties like AWS, which spread fixed costs over the volumes of all customers, to get to scale faster. In banking, you see the emergence of banking-as-a-servce providers, like Railsbank or SolarisBank, levelling the field for new entrants. All in all, this means that scale does not represent the barrier to entry it used to.
Scale can become a hindrance
The second problem with Buffet’s investment philosophy is that diseconomies of scale, or negative returns to scale, manifest themselves more frequently and earlier.
In the industrial age, the trick to achieving an unbreachable moat was to produce standardized goods at mass scale and then invest in marketing to create sufficient demand to sell all of these goods. The challenge now is two-fold. Firstly, the broadcast channels that companies used to advertise are being eroded at the same time as there are many more demands on the consumer’s attention, making it harder to engage in the same type of mass-marketing.
The second issue is that, since consumers are now online, we can know much more about them, as well as have a direct relationship with them. This means that at the same time as it’s become possible to operate profitably at smaller scales of production, it’s become possible to produce goods which cater to smaller customer demographics, and to reach these customers directly — which explains the rise of artisanal goods and direct-to-consumer brands.
But, for digital goods, it goes further, artificial intelligence increasingly allows platforms to match services to customers as well as personalize services to each customer.
To put it another way, in the digital age, the mass consumer is dead.
The new moat
This begs the question, is it still possible to create a moat in the digital age? One answer could be that the idea of a moat is obsolete, a relic of the industrial age, sort of what Elon Musk said when he challenged Warren Buffett recently. But, the reality is a new moat is possible and it’s the diametric opposite of what came before.
Scale isn’t the barrier to keep out new entrants, scale is what attracts new entrants to work with you. Scale doesn’t allow you to push a mass produced product to the mass consumer, scale is what enables you to tailor an individualized product to every consumer.
“I think moats are lame. If your only defense against invading armies is a moat, you will not last long” - Elon Musk
This definition of scale is one that accepts and capitalizes on the new realities of the digital age. Maximizing production scale by itself is less of a competitive advantage and, increasingly, a competitive disadvantage. But the fact that consumers and business are connected means that a new competitive advantage can be achieved by maximizing network size.
Where a network has strong social engagement, like Facebook, adding more users increases the value of the network for everyone. Where a network matches buyers and sellers, like Amazon, increasing the network size increases choice and, by extension, value. Where a platform analyzes data to serve up the best results, like Google, the more data that comes from adding users, the better the results become. And most platforms are a combination of these social, two-sided and data network effects.
What is more, the new moat is a superior moat. Supply-side economies of scale, while a formidable barrier to entry in the industrial age, always suffered from diminishing returns.
Demand-side economies of scale, however, are subject to increasing returns to scale since more users create more value for other users in a self-reinforcing positive cycle. This is why in markets where network effects are strongest, there are winner-takes-all dynamics.
Does this mean that supply-side economies of scale are irrelevant? Not at all, as we wrote a few years ago, these platforms based on demand-side economies of scale (network effects) often become asset heavy as a way to reinforce the strength of these network effects and maximize profitability. But the difference is that maximizing scale economies was not the goal in itself. Instead, these companies found a route to mass adoption and, from there, put in place the assets to sustain the network. In other words, a business grows its assets top down like the roots of a tree.
Achieving network effects in banking
If the new moat is to achieve network effects, how can these be achieved in banking? In our mind, this is probably asking the wrong question. Banking is inherently a transaction-based activity. This makes it unsuitable to most types of network effects.
For example, most companies that have tried to build social network effects into banking, either as part or whole of their USP, have failed. We don’t want to chat with our friends specifically about money, we don’t want to share all of the information on our assets and liabilities. Which means that, although the new banks sprouting up might be cheaper and more convenient than what came before, they aren’t able to arrive at meaningfully and sustainably lower costs of customer acquisition numbers once they’ve gone beyond the early adopter audience.
It is possible to create marketplaces for financial services, but because banking is transaction-based (and fundamentally not a social activity), the surface area around which to create a marketplace is limited. Basically, we don’t spend much time on banking apps, which makes it difficult to introduce us to other products and services, which we then don’t purchase frequently anyway.
Some banks and fintech providers get round this by targeting specific demographics and then giving them the tools they need to run their business/life, such as Tide, which understands that freelancers and small businesses will send invoices and submit expenses more frequently than they’ll apply for a loan. But, these business are niche.
When this is attempted on a bigger scale, it comes back to the same problem of unit economies: high CAC in the absence of social network effects and low lifetime value in the absence of the engagement.
The mistake we think many people make when they think about banking and network effects is to apply the following logic: banking is a massive market, therefore we must target it and find a way to generate network effects. We believe it is smarter to turn the logic on its head and think about how to put banking into channels and services that have high engagement and strong network effects, what Anthemis calls “Embedded Finance”.
There is already a strong move underway to do this. Because payments is the most frequent of the transactions we make (as well as an extremely rich data source), it makes sense that payments has been the beachhead for most non-banks from Uber to Apple to start their push into banking or, better put, into embedding banking. But it won’t stop there.
As Amazon is showing, the goal isn’t picking off a few high value revenue lines, but making value flow ever more easily within the Amazon ecosystem, removing friction and making it easier for buyers and sellers to trade. Similarly, the Alibaba and WeChat models both serve a higher purpose: to embed financial services into people’s lifestyles.
The direction of travel can go in the other direction too: that is, starting with banking and seeking to embed it in other services with higher engagement. This is what Moneo is trying to do and what TinkOff Bank in Russia has done so successfully. Through partnerships as well as launching its own products, Tinkoff has created a super app akin to WeChat in China where consumer can do everything from booking theatre tickets to giving their kids chores.
But, in general, it seems more probable that banking will get embedded into other services than vice versa for the reasons already stated: it’s a high CAC and low engagement starting point from which to build out an ecosystem or Super App.
That doesn’t mean that there won’t be plenty of opportunities to build big businesses in banking, that enjoy strong network effects. But, to our mind, these are unlikely to be directly client-facing.
What’s the fintech opportunity?
Earlier this year, we wrote a piece about systems of intelligence in finance. The piece looked at these systems mostly from a supply-side and architectural standpoint, arguing that solution architecture needed to change in response to the split of distribution and manufacturing and to capitalize on open banking. It concluded that systems of intelligence would emerge as the most valuable parts of the Enterprise IT value chain.
Here we make the same argument, but from more of a market standpoint. If we accept that banking will become increasingly embedded in third-party services and channels, it doesn’t necessarily follow that, as many people argue, banking will become completely commoditized.
As markets digitize, two types of intermediaries tend to emerge: those that seek to internalize network effects by commoditizing supply, aggregators like Amazon or Facebook, and platforms that externalize network effects by empowering suppliers, like the Apple AppStore or Shopify (Ben Thompson sets out this distinction very well in this much recommended post).
In financial services, then, the same pattern will play out: aggregators like Amazon will commoditize financial services suppliers, while platforms will emerge to intermediate between suppliers and distributors in a non-zero-sum, value accretive way. These platforms will be systems of intelligence.
Systems of intelligence are evolving. Today, most systems of intelligence are deployed for individual clients and with the end of digitizing services. But this is just the first step. Digitizing services makes them consumable through non-proprietary bank channels, but it also generates a new stream of data that can be used to make the services better fit consumer needs. So the next step will be that systems of intelligence will then use that data to help providers more intelligently price and package financial services.
But once that has been achieved, the opportunity will exist to then serve up the right service to the consumer at the moment of need, which mean systems of intelligence become systems of network intelligence, matching the needs of consumers with the inventory of suppliers in the smartest way.
This is the evolution we observe happening at companies like additiv, Assure Hedge and Trade Ledger. Trade Ledger is digitizing the origination of credit services so that lenders can supply credit at the right price and with the speed needed by fast-growing SMEs. But beyond that, it is able to use data to give lenders a real-time picture of asset quality, even for intangibles assets, allowing lenders to offer new types of services better matched to changing customer needs. But, ultimately, the opportunity exists to then link lenders with the different players in the ecosystem, helping embed banking into whatever is the right channel to serve the customer at the point of need. Martin McCann, Trade Ledger CEO, puts it well in this excellent blog:
“Within business finance, the opportunity exists not just to connect banks with their customers, but banks with banks, corporates with corporates, corporates with complementary third-party services providers and so on.”
Don’t just believe us
If Warren Buffett has missed the shift from supply- to demand-side economies of scale, there is one investor who most certainly hasn’t. That is Peter Thiel. His investment in Facebook, a business underpinned by massive network effects, made him a billionaire. Conversely, Buffett passed on Facebook, like Google and Amazon, because he couldn’t get comfortable with the valuation, saying “I didn’t understand the power of the model as I went along.”
And the performance of the two investors also couldn’t be more divergent. Whereas Buffett has underperformed the S&P since 2009, Thiel’s Founders Fund has more than two-fold outperformed the VC fund industry since 2011 (the only figures we could find in the public domain). Since 2011, the Founders Fund is up by $4.6 for every $1 invested.
And where is Peter Thiel investing now? If you look at his holdings, there are many B2C companies there for sure. But, more than anything, there are systems of intelligence — across many industries, but especially in financial services. This leaves Peter Thiel well-placed to capitalize on what Matthew Harris, another venture capitalist, sees as the fourth major wave of digitization after internet, cloud and mobile; one that, in his view, will create more value— $3.6 trillion — that its three predecessors combined.
So, you don’t need to believe us that systems of intelligence are the next big thing. Just look to Peter Thiel, the new investment wizard.