There are a few interesting things that stand out to me:
The line about amortizing cost of production across a base of viewers as a differentiator business model (often compared against Hulu) made me consider what the other options are:
(1) Apple TV dropped considerable investment into becoming an HBO of streaming as well (what feels like to make Apple TV the preferred hardware choice, but now they’re mostly just another channel… not sure what the long term strategy is here)… and used that huge investment to build a user base… aka production as customer acquisition.
(2) Disney already had high-value IP and so their customer acquisition was low based on an established brand, but they had hard limits to the volume of content they could pump out without ruining their brand and oversaturating their IP… so they used their considerable heft to purchase new IP and squeeze extra production out of it (Star Wars, Marvel, ESPN, Discovery), and then expand that to other branded content. (Places, things).
(3) Amazon followed Netflix’s licensing model first, but gave away their video content free as part of Prime. It was a value play for Prime, but also uses amazon video as a portal to sell more video (I think only Apple TV also sells video on demand of the big players). So it’s both a streamer and a sales channel in itself.
Like @peter , I’ve been thinking about that recent Stratechery post, but I’ll try to take it a step further. There are a lot of interesting scale-related things, but the thing that really jumps out to me is that video production (shows and movies) is in itself something that requires a certain kind of scale. Look at the credits of almost any modern movie. Even the more indie productions nowadays (horror movies excluded) have a budget of 10-20million at the low end and require hundreds if not thousands of people.
So Netflix investing in movie production is a huge investment. It’s cheaper to build out the software and technology than it is to build out marquee movie/show production capabilities. But once the coat of technology is low enough, IP holders can have their own channels (isn’t that what these are nowadays?). Hence Peacock, Showtime, even the doomed CNN+ pulling their IP and trying to launch their own channel. Way fewer companies are moving in the other direction - AppleTV, Netflix, and Hulu are the only ones that stand out - we understand Netflix, Hulu was absorbed into a mostly flexible business catch all for Disney, and AppleTV makes no sense to me.
Anyway, thinking about the scale of movie production against something like the production and distribution of books and music (video games will be a different can of worms). For Music, this Substack (Record Labels Dig Their Own Grave. And the Shovel is Called TikTok.) kind of lodged itself on my head:
Record labels have lost their ability to launch new careers. 2. Like Bartleby the scrivener, they really prefer not to deal with this whole issue because career development is such a hassle . 3. So they demand that musicians build their own audience via TikTok and other social media platforms. 4. But the moment musicians become capable of doing this, they don’t need record labels anymore.
Meanwhile on the Farnham Street Knowledge Project podcast, author Hugh Howey (Hugh Howey: Winning at the Self-publishing Game) makes a similar case for how publishing and book distribution is changing and why self publishing and avoiding the distributors makes better financial sense because what do distributors actually do for you nowadays?
Which brings me back to Stratechery (different post, can’t find it right now) where Ben Thompson talks about it really being a unique content and distribution game - that these are just the same old content games as before, but it took forced innovation to come full circle.
So then, what is the interesting thing that Netflix did? I think it was on being ahead of the curve, multiple times, and leveraging the gains from one cycle to feed the next cycle.
(1) They built a huge audience, direct relationship with that audience, and consumption profile with the DVDs business at a time when Blockbuster and Best Buy (or movie theaters) were the only options.
(2) They leveraged that audience ownership to launch a novel new business model of streaming, way ahead of the curve. (I remember reading an interview with Reed Hastings where he described their constant fear that Blockbuster would do it first or jump in… and they never did).
(3) Then they leveraged that heretofore unproven streaming business model to license IP at low prices because studios didn’t think that would ever be a meaningful business.
(4) They then started producing their own media, knowing that others would catch up and were able to amortize the costs of production against a large paying base, while also creating differentiable content that others didn’t have, and also leveraging their knowledge of real viewing habits to make each production investment work better, because they had a much tighter feedback loop on what content worked and what didn’t work… something that I think gets lost in the talk just about costs amortization. (They also gave considerable freedom to creators to try things out in the beginning, which itself drew a lot of unique creative production into Netflix rather than to other channels.
(5) … which brings us to where we are today, where Netflix is one fish amongst many, and we are seeing them struggle (subscriber loss in recent earnings call) against a lot of old timers that caught up.
I’m not quite sure the scale-related lesson to learn from this, but there’s definitely a lot here that I haven’t had time to really think about.
The one thing that really don’t understand though, is why debt financing at such large sums? Debt financing tends to be more expensive. Was this after they went public? Was this because it had no strings attached, business-model wise?
I suppose it might not be as clear from the case alone, but we actually decided to look into Netflix after Hamilton Helmer referenced it in 7 Powers (link goes to the relevant section; it was the marquee example in his chapter on Scale Economies).
I realise now that it’s not so clear how the Netflix case is related to scale. I’m so sorry. We’ll have to rewrite. cc @guanjief
The point Helmer made in his book was mostly that Netflix took an expensive variable cost (licensing fees to content producers) and turned it into a fixed cost, which could then be amortised across their entire subscriber base. Otherwise, they’d have to continue paying licensing fees to content producers, giving the producers leverage.
I’m not entirely sure if it’s clear what’s behind Netflix’s recent subscriber loss. Certainly, there’s a narrative that has emerged today about how ‘oh their content sucks’ — but is this really true, or a case of narrative chasing the price?
One thing that I’ve drawn from Netflix’s more recent adventures, though, is just how difficult it is to ‘turn the ship’ when you’ve got massive fixed cost spending justified by your scale — though perhaps for a different reason from your ‘Sword-of-Damocles’ point about Ford’s sunk costs earlier in this thread.
This case seems a lot more narrowly focused than the others, and the economies of scale strike me as more of an emergent afterthought than something they planned for - especially when it comes to increasing costs for their competition.
The case reminds me of the situation Mailchimp found itself in when they launched their freemium product about 13 years ago. They started getting clobbered with fraud, but they kept up with it and got good enough at detecting it that they were able to command the low end email market for the better part of a decade. And this had some interesting downstream benefits, including being able to do low cost experimentation on low value free customers.
I think AppleTV is Apple’s android. It’s less of an offensive move into the streaming space, and more of low-variable-cost barrier to being displaced by other competitors…perhaps similar to what they’re doing with Safari or Apple Maps. Not quite to the extent as Ford with bringing everything in house, but retaining the option to do so if they ever need to.
I really enjoyed the PayPal case study. I think the benefit of scale here is the exact opposite of Ford — Ford built a rigid production line that was really good until the moment it wasn’t, and then it was too inflexible to adapt (though Ford did eventually adapt and they’re still around as a very successful company). One thing I started to question when thinking about that case was, “how do you build resilience into your scale model?” (I thought Toyota’s Just-in-Time manufacturing as a process was an interesting solution, but it felt incomplete… 3d printing as a technology feels like an interesting solution from the other angle, but its still early there).
Meanwhile, PayPal was forced to build resilience into their scale economy. @peter, you write that it seems like “an emergent afterthought […] especially when it comes to increasing costs for their competition”, but when I was reading it, I framed it really differently. As the Levchin said, “PayPal [the consumer interface] was more or less a commodity business” (brackets mine). The front-end commodity business was a Trojan horse, IMO. On one hand, banks — by nature conservative — didn’t really want to jump into that early and unproven space, so they left the consumer side open for someone to jump in, and PayPal was it. On the other hand, it was a hard problem to solve which is why there was opportunity here.
The commodity consumer interface was a Trojan horse/forcing function because the defensible business moat was the hidden complexity beneath the surface, which didn’t stay static — the moat/complexity continued to grow as PayPal addressed it. [1] By having a large consumer base, they increased their attack surface and were forced to confront more and more risks. And because they had lead time with no one else in the market, their head start kept getting bigger and bigger — hence Cedric and Guan’s “Poisoning the Competition”/increasing costs of entry to the competition. Not only that, but that also builds up a trustworthy brand and it allows them to keep pricing low for consumers (revenue comes from having less “leak” via fraud, rather than extracting more from consumers).
So the lesson I am taking away is that in high-risk and dynamic market sectors, scale and resilience/risk management go hand in hand.@peter your note on MailChimp is a great example of that as well. [2]
So if I were to reflect on the three case studies so far, I’d say:
Ford is an example of an initially low complexity, low dynamic environment with commodity products where scale could be built up in a fixed fashion — doing more of the same, but using scale/growth to drive unit costs down. This model fails when new consumer trends mean that your upfront investment actually prevents you from adapting due to sunk costs. By the time you realize you need to change and implement that change, the competition has already eaten significant market share away from you. I think of Tesla as a modern day analogue — from horses to the model T, from internal combustion to electric motors.
PayPal is an example of high complexity, high dynamic environment, where scale builds up resilience. Both PayPal and Ford were able to build temporary moats in terms of high-cost to enter (building a factory to produce new cars is a huge investment, limits number of people who can enter a market in a way that, say, production of cereal or shoes doesn’t have the same sunk investment costs), but Ford’s was rigid while PayPal was flexible. This grew PayPal’s moat. Because the complexity is the the backend and the consumer interface is a commodity, I think PayPal could have pivoted to off be doing what Stripe and remittance companies are doing nowadays as well. I actually think that Stripe is a similarly great case study here as well — it’s what PayPal potentially could have been if it wasn’t bought by eBay. So I think that this is where PayPal’s scale model went “wrong” — they made their backend robust, but let the consumer commodity portion stagnate, which allowed new competitors to enter and eat what should have easily been their market share.
So what then of Netflix?
My point wasn’t in trying to say that content sucks — just the opposite, I think that content differentiation is the only thing [4] they could have done to remain a defensible business (and they’ve gotten smarter and better and content production due to having tight feedback loops between production and consumption). My questioning was around that if you poke at it, there were what felt like more than a few scale-related lessons beyond the financial one, as well as operational curiosities. Being able to turn variable costs into fixed costs as a function of scale was definitely interesting and unique — I guess my point was more so that even though they managed to do that, they just recreated the studio production model. Meanwhile, studios realized what Netflix was doing, and recreated the streaming model Netflix has pioneered.
The end result is that Netflix and major studios converged on the same strategic location: you need to own great IP and you can improve the value-generation of your IP by having a tighter feedback loop to consumption. That allows you to amortize costs across an established and committed market segment and also to diffuse risk from by knowing consumer preferences better — what people actually do, vs what they say they do, or previously relying on critics. To paraphrase Ben Thompson for a moment, ‘At this point, Netflix’s chief problems are traditional media problems, not technology or financing problems.’ [4]
So while I agree that Scale allows you to do interesting things that others can’t, I was thinking that a competitor like Disney also used their scale in an interesting way. Their scale was a huge backlog of IP and Brand reputation, which they were able to use to launch a wildly successful streaming program that grew tens of millions of users almost overnight. Netflix used scale of audience to decrease costs of production, Disney used scale of existing production/backlog to generate an audience with low explicit acquisition costs. Or to say it slightly differently: Disney was able to also use their scale to move away from variable costs (revenue from when their stuff was shown on a channel or purchased somewhere) to fixed costs vis a vis a streaming subscription, a similar amortization Netflix pulled off.
[Edit 1: Still trying to figure out the best way to simplify the idea. I think Disney is an interesting counterpoint because they already had the scale of IP and suddenly made the IP much more valuable by pivoting to recurring revenue from Disney +. Scale of existing investment into IP, and scale of that IP and brand recognition to make their user acquisition cost near zero. Of the other streamers, only HBO was able to pull off something similar. ]
The other thing that’s interesting to me to unpack further about Netflix is in what they produce. Compared to Ford’s Model T, marquee content tends to have a power-curve of value. The most impactful content will continue to generate value for long periods of time, for both new customers and repeat customers. So when done right, marque content has a one-time upfront cost and a long tail of return over time (see: Disney). This is somewhat unlike the Ford model, where the upfront investment locks you in — what worked for the Model T production wasn’t generalized into flexibility to produce many other things.
If Ford sunk a significant portion of earnings into a fixed production line and hit gold, there is a one-time value generated from selling that product. If the product tanked, they’d be sold for scrap metal. If Netflix sunk significant portions into production and hit gold, that IP continues to pay dividends over a long period of time and builds up a moat. If it doesn’t, that IP still has value and the production process they’ve invested in is a one-time cost… they can just produce something different using the same production studio investment… said inversely, Netflix’s production process produces many different products while Ford produced only one.
That’s why for Netflix I keep going back to being able to own the audience and understand their real consumption patterns to amortize both costs of production and risks/certainty of production value.
(Hmm — as I think about it, one could describe the scales relationship between production process and output as One:One for Ford, One:Many for Netflix, and Many:One for PayPal. Is there a many to many case?)
Edit 2: What could Ford have done? Rather than scale as overfitting for a single product, they could have used scale to build out modularization. Which is why car companies today do: most car models across a single company share a base platform and design system and have interchangeable pieces. This allows for flexibility across trim levels and also across models. However, this wouldn’t have been obvious in Model T times because Ford’s challenge was marketing-making: by offering only one model and using scale to decrease costs, he made it easy to buy a car and built out the market.l away from horse carriages. Once people understood the value of cars, then flexibility to cater to customer desires became a thing.
——
[1] - I think of this similar to Uber in some ways, the way people ask “why do you need thousands of engineers to build what is essentially an app with 5 screens?” — it’s the complexity and edge cases that make things difficult.
[2] - PayPal’s execution of risk management actually makes me want to compare them to insurance companies vis-a-vis’s Warren Buffett explanation of how they have low upfront differentiation but leverage risk management to drive down costs and how re-insurers manage existential risks. Buffet also mentioned insurance is highly dependent on sales people and marketing for customer acquisition though, so it’s not an ideal comparison.
[3] - If anyone is interested in diving really deep into the complexities of payments and fraud infrastructure, Stripe’s Patrick McKenzie runs a fantastic deep-dive blog about those topics: Bits about Money (Page 1) … the payments infrastructure of Japan was eye-opening (for me as an American), as an example.
[4] - I worked in healthcare technology for nearly a decade and one thing that was really interesting to me was Oscar Health Insurance. For those who aren’t familiar, they launched a consumer-friendly insurance plan way ahead of the curve, and tried to grow competitive differentiation through a consumer-friendly interface, subsidizing wearable technology to learn more about human behaviors to lower costs, and deeply invested in the tech stack (including early virtual care experiments) to make ‘healthcare a better experience’. Where they didn’t manage to compete strongly enough was in provider networks (which traditional plans have a strong hold on) and getting into Employer healthcare options (which is where the majority of healthcare comes from in the US… or said inversely, they were only able to reach otherwise-uninsured people). They’re still a player in some markets and unambiguously proved their point about technology/data being able to drive costs down and care results up, but because of barriers to entry and how health insurance operates in the US, they had a hard time growing. More interestingly, they’ve started to use their success to “Oscar-fy” traditional insurers. I used to joke that my employer should have been positioned as “We help you Oscar-fy your health plan”, until Oscar actually started doing that. Whelp! Anyway, the point of this story — other than being an interesting case study on scale barriers to look into — is to look at Netflix and think, “ok — we will all converge on the same consumer solution vis-a-vis streaming… can I sell my tech to other providers and leverage learnings therein to grow more competitive?” (As opposed to pivoting to ads and degrading the consumer experience, as they seem to be moving towards now.)
I want to quote from Kris Abdelmessih, who directly inspired the PayPal case:
Competitive equilibrium will mean that the casinos who can bid the highest for the “customer” is the house that can:
a) source the most uncorrelated offsets to the wager
and
b) has the biggest bankroll
In the trading business, condition A is satisfied by the market makers with the best data/analytics and “see the most flow”. A firm entrenched in both equity markets and futures markets with licenses from both the SEC and CFTC is going to be more efficient at laying off the risks it acquires from serving tourists regardless of the venue they choose to play in.
A and B will create a virtuous loop. The best players will build larger bankrolls which allow them to outbid competitors further which earns them first look at the flow which improves their models and so forth.
Just thinking aloud here, rather than presenting a polished thought: Generally speaking, massive scaling is often connected to establishing a new product/category/industry.
That makes it dependent on discovering new problems in these emerging categories. To pick up on Roman’s iterative approach, it’s about discovering new bottlenecks and solve one by one with the application of additional resources and focus.
For example, based on their volume, Netflix and Paypal had both more data available than their competitors. With the resources available to invest in these areas.
For one, this generated an additional moat in form of customer insights that weren’t available to anyone else at that scale of pattern recognition. Which would allow them to tweak their product closer to what the customer wants.
But the data also brought previously unknown problems to the surface. For example, Paypal’s understanding that fraud is one of the critical bottlenecks that need to be addressed, while everyone else still thinks it’s the interface/technology to send money digitally from email to email.
Once declaring fraud a major problem, adding focus and actively developing technology dedicated to just that problem.
Then, in addition to understanding the problem, being targeted by the fraudsters forced them to apply themselves even more, as it turned into a swim-or-sink scenario. Which turned into a cold-war-like, nuclear-capabilities competition, with both sides heavily investing, iterating, and progressing at an accelerated pace. Due to the short feedback look based on the volume, development cycles (or OODA loops) were shorter than for anybody else, resulting in most other competitors vanishing.
That makes scaling not only a function of efficiently administrating more resources but utilizing them in a way that builds additional moats. It’s ultimately scale/volume + focus on the right problems that accelerated growth for these companies.
Good observation on reorienting on fraud. Certainly not every competitor was savvy enough to focus on fraud while growing; those that did cracked down so hard and so onerously that they crippled their growth.
Texas Instruments
Discussion for the Texas Instruments case goes below. What are the similarities or differences to the cases that came before?
Building off @roman’s thought on PayPal…
For Ford’s vision, scale was a feature
For PayPal’s plans, it was inevitable
For TI’s industry, it was a prerequisite.
But what makes sense for infrastructural manufacturing doesn’t always translate into scaling for mass consumer goods.
I have a TI data math calculator and a TI 99/4A computer - about which Bill Cosby famously joked that it’s easy to get people to buy a computer when you pay them $100 to do it. The situation with Commodore and TI reminds me of an anecdote from this book about two railroad builders who were competing over a route. One started subsidizing cattle transport to drive the other out of business; the other started investing in cattle and then transported them on his competitors line.
It strikes me that TI’s turmoil occurs at about the same time (just a few years before) Intel chose to drop their DRAM business.
Finally, it seems that TI’s approach subtly shifted over time. In the beginning, they recognized that scale was the only thing holding back certain types of development. (You can, I think, make a similar case for solar panels and lithium batteries over the past decade). But by the 70s they were looking on scale as a way to conquer markets, period.
One thing that perhaps we should have added to the TI case — they fell behind Intel when IBM went with Intel for their IBM PC, and then the Intel processor rode the PC wave to huge success.
We’ll add this going forward.
Also, did anyone notice any similarities between TI and Ford?
Catching up on these - there’s a few so I’m going to try to brief. Apologies if I repeat myself, I’m working through the ideas out loud.
This framing makes a lot of sense to me. When I read it, I was thinking of it in financial terms first:
For Ford, pricing was a byproduct of scale bringing unit economics down.
For TI, pricing was a way to build scale.
I’m going to assume (hopefully not incorrectly) that Ford never really underpriced themselves. They maintained the margin by using scale and operational efficiency. Meanwhile, TI/Chang underpriced to reach scale because their business could only exist at scale.
Chang calls it “learning curve pricing”, which is an important distinction from what TI had later, aka the "race to the bottom”. Learning curve pricing only works while there is a learning curve… when an industry matures and yield is high [1], there is no learning curve - it becomes a war of attrition for who can bring prices down. That tends to not be entirely within a company’s control: international competition can benefit from lower costs labor, proximity to vendors/suppliers, government subsidies in key developing industries, or tariff imbalances.
“Learning curve pricing”/underpricing to build scale is also a pattern I think we see with modern venture-funded startups that pursue growth at all costs. I.e., food/grocery delivery and on-demand transportation are the poster-children of what it looks like to try to build scale while also have competition flush with cheap cash, driving pricing down. [2] It becomes a race to see who can survive longer, though instead of overseas competition, they compete on access to cheap capital.Many of these companies don’t have the same problem of a “learning curve”, but they do have similar problems vis-a-vis needing to build two-sided marketplaces large enough to support each other. [3]
Within the TI case study itself, there’s another comparison/difference to draw out. As Peter pointed out, sales DTC and sales to enterprise buyers are really different beasts.
Enterprise sales tend to be made in bulk precommitments and are driven by a sales team. A single sale might be for a million + units and an enterprise-focused company can thrive even with relatively few clients. (Arguably, enterprise sales is itself a form of scale).
Individual DTC product sales are made per-unit and are mostly driven by marketing team. Even if you wholesell to accounts, many have provisions where they can return unsold inventory. (Another way to think of it might be as supplier vs retailer)
Change moved from high revenue bulk sales (chips) to low revenue individual sales (watches). With chips, you could presell them in bulk at a lower cost knowing that you have some lead time to iron out the kinks and make the low price point work (that’s learning curve pricing in a nutshell – the optimization catches up to the price. Said another way, optimization follows price rather than price following optimization). The commitment reduces uncertainty and risk – you can forecast what pricing should be if yeild was at X%. Whereas for consumer goods, you need to have the product to sell it. You enter a market with uncertainty as to current and future consumer preferences and that uncertainty/risk grows as others bring products into the marketplace. If you’re competing on price, can you hold out long enough to put competitors out of business before consumer preferences change? I’d hate to be in that business. [4]
Another thing I am noticing is that Ford and TI were individual product sales, while PayPal and Netflix were more akin to marketplaces (if you squint at it).
Ford and TI sold individual products and optimized for the production of those individual products. Their revenue model was directly related to individual products they produced, and they were optimized for producing the same thing, over and over. When consumer preferences changed, they had a hard time changing with that.
PayPal and Netflix tackled the whole marketplace problem. (again, you have to kind of squint at PayPal’s underlying business). PayPal wasn’t concerned with any individual fraud-fighting feature – they skim off the top of any and all transactions. Likewise, Netflix isn’t concerned with the relative success of any individual show/movie – get paid for access to a variety of movies/shows. You can be into Horror or SciFi or K-dramas, you can watch one show or one hundred and they’ll get the same value from you, and the same amortization of production across the whole consumer base. They were designed for producing lots of different one-off things rather than the same thing over and over. And whereas Ford/TI used scale to bring pricing down, PayPal/Netflix used scale to drive market resilience up.
Also, reflecting further on CFT - in addition to teasing an idea apart and finding differences/similarities between cases, I’m also noticing the inverse effect: as you look at different case that might be unrelated, you can build up an ill-structured domain.
Particularly, I’m noticing the disintegration of a first-mover advantage across all of the cases (which extends to the next case as well).
[2] - Mark Stoller has a really in-the-weeds newsletter about monopoly power (https://mattstoller.substack.com/) and one of the recurring themes is using underpricing to drive competition out of business, then raising prices back up beyond their original levels. I’m starting to notice that same sort of behavior in VC-funded markets as well, especially as businesses are being pushed to show actual profit.
[3] - Or to use Andrew Chen’s framing, most network-effect businesses are like this. If your two-sided network has an imbalance (too much or too little supply, too much or too little demand) then the network can collapse on itself, and there’s a critical point to hit for the network to be useful.
[4] - As a personal aside, pricing psychology is super interesting. For example, at one of my businesses we could not compete on price, and the lower price positioned us for completely different buyers and market segments. We found that price-conscious buyers were often more critical and more selective and were looking for different tradeoffs than what we were bringing to the market. By raising our prices (we currently sell at probably 500% more than what our former competition was, we actually entered into a new market with different consumers. Our sales grew because of the price increase and what consumers positioned us against – we didn’t change anything but the price.
The learning curve and economies of scale are two separate ideas. The learning curve sees the reduction of cost over time–as learning takes place. Economies of scale deals with cost as a function of scale for a given period.
So you’re getting at something really interesting @kharling. I think one common way of talking about concepts and reality is that reality is ‘continuous’ and concepts are ‘discrete’ — that is, we lift certain clumps up from continuous reality to an abstract level, so that we may discuss them with each other or manipulate them in books, etc.
(I’m not saying anything particularly profound here; @DRMacIver makes this point much better than I can in his Learning to Walk Through Walls piece — arguably, so much of human cognition are mental moves like these).
And so one of the things that’s come up while writing this series of cases is ‘do we call out all the discretised concepts?’ For instance, we could have written a concluding piece explaining:
Scale economies deals with cost as a function of scale (as you’ve noted)
If we want to be pedantic, learning economies are separate from scale economies.
The Nike case study is actually an example of growth economies … which Wikipedia defines as ‘growth economies occur when a company acquires an advantage by increasing its size. These economies are due to the presence of some resource or competence that is not fully utilized, or to the existence of specific market positions that create a differential advantage in expanding the size of the firms.’
The reason I’ve refrained from going down this path is because I’m not sure that calling out exact definitions are that useful to the business practitioner.
(Feel free to convince me otherwise, by the way — I’m ambivalent about this).
What do I mean by this? Well, let’s take the learning economies as an example. To cite Wikipedia:
Learning and growth economies are at the base of dynamic economies of scale, associated with the process of growth of the scale dimension and not to the dimension of scale per se. Learning by doing implies improvements in the ability to perform and promotes the introduction of incremental innovations with a progressive lowering of average costs. Learning economies are directly proportional to the cumulative production.
That last sentence is key. Let’s say that a 5% reduction in costs occurs every 100k units produced. That means that in theory, anyone can drive their costs down so long as they cumulatively produce many 100k units. Of course the scale player will reach there first, but in theory everyone could do it!
But in practice, the scale player hits 100k units first, enjoys the 5% cost reduction, slashes its prices to screw with the non-scale players (while guaranteeing yet more scale for itself), and then continues to push its advantage (see: the Texas Instruments case).
Is there a modern instantiation of this? Yes — Samsung’s 5nm node process reportedly has a measly 35% yield (circa March this year), while the scale player — TSMC — has yields in the 80% range.
So can you really separate the learning economies effect from scale economies? I’m not so sure; I’d want to go hunting for real world cases where that is true.
Ok but here’s the interesting thing. The reason I brought up David MacIver’s “discretised concepts vs continuous reality” is because the Texas Instruments case is the first time learning economies were weaponised in this way. It is literally an example of someone innovating in continuous reality, before his hired consultants came in to concretise the concept for their own benefit.
While researching that case, @guanjief and I learnt that:
Henry Ford experienced learning curve effects intuitively. He never calls out the concept, but you see him living it by constantly slashing prices as a way to increase volume; he then forces his company to drive costs downwards. The interesting thing is that he doesn’t seem to realise there is a relationship between volume and learning opportunities. He just thought the two things were good ideas.
As such, Wright’s Law turns out to be the first articulation of the concept, in 1936 — Theodore Paul Wright observed that every time total aircraft production doubled, costs dropped by 20%.
But the strategic implications of the learning curve effect was really thanks to Morris Chang, who used BCG to crunch the numbers for this intuition he had about scale and pricing.
In other words, Chang the practitioner was operating in continuous reality, while BCG — then a small handful of consultants — came in later to discretise the concept and claim it for themselves. BCG’s early growth was really due to the learning curve pricing — they took Chang’s insight, firmed it up, renamed it ‘experience curve’ and turned it into a thing for their firm.
Right now what we’ve decided to do is to write out our cases to include as many related concepts as possible, without necessarily calling out every discretised concept.
I’m a bit torn up about this:
On one hand, ideally we should highlight every concept that shows up in the text.
On the other hand, calling out discretised concepts isn’t very helpful to the business practitioner? If you’re an exec faced with a scale incumbent, calling out the discretised concepts won’t give you ideas on how to beat the player — but giving you stories of many players who won through scale and then lost it might.
Sorry for the messy reply — I got very very excited about the history of learning economies and went into that history in the middle of my reply. What do you think though? Should we try as much as possible to call out discretised concepts?
One idea I’ve been toying with is just telling the stories, and letting the community call out the concepts on their own. That way we can move quickly on case creation, while the community gets the bulk of the pedagogical benefit for calling out the discretised concepts.
This speaks to something I was trying to figure out how to say around cash strapped hiring - that the opposite of cash strapped hiring is to be overpaying for people because you’re hiring without a sense of what you actually need. But another way to look at this is “learning curve hiring” - hiring when you’re not sure yet what you need.
I would suggest that there’s an opportunity here…to think of this case study project as a learning economy. The inherent problem in ill structured domains is that we don’t know what’s important - by learning to highlight it, we’re bringing structure to the domain, and we’re refocusing our attention on the known knowns and known unknowns. A good starting point could be something like a collection of “further reading” links similar to what you send out in your weekly email update, and to actively augment the case study content with the sorts of references called out in this post. That would create an interesting opportunity for lateral reading through the cases - where someone engages with the study series because it’s adjacent to something they’re interested in, but as they go through it they recognize that there’s a more precise way to discuss the thing they’re interested in and can pivot off naturally from there.
I like this, provided it can be tied in closely to the case text. I have found it more difficult to cross-reference ideas through this forum conversation.
Noted. I said earlier in this thread that I wasn’t sure if having a separate thread for each case was a better idea, but I’ve received enough feedback now to know that it likely is. Will make that change going forward.
I think having separate threads is a win - but I’m also attracted to the idea of being able to engage with the case more as hypertext, where comments about a part of the text can be more closely mapped to their context in the source document. (I’m thinking something along the lines of where Medium or my Kindle will show me sections that others have highlighted, but then allow me to dig in more deeply to their comments.)
I’m a late starter in the series, so still parsing the ford & netflix cases that landed in my mailbox.
I’m not sure I agree with the scale description of the netflix case. Their original scale came from the amount of customers. The way streaming video content works over the internet you are likely to experience both scale and network effects based on your audience size. And Netflix surely did outrank it’s closest competitors. As is clear from the case, it was 4x the size of Amazon in 2010, while Amazon was giving the service away for free
Pushing large volumes of data over the internet becomes cheaper as you scale due to the nature of pricing network capacity on bandwidth instead of bits sent and peering arrangements. In addition you enjoy network effects as a large player, once you have established a local cache with a local-loop network provider for your customers, any additional customer is “free” or much much cheaper…
You can still see that back in Netflix’ GTM strategy on bundling with local-loop providers and later mobile providers
There is a sentence in the case on 2008 changes that is undervalued from a strategic perspective: " (At the time, Netflix paid studios based on content utilisation. It has since switched to fixed fees)"
Had they not managed to make this shift in cost model this early in the game they would never have been able to start their second tier of scale advantage in content and (I think) would have had to start much earlier and under less favorable financial conditions with their original content bet. I think this is worth highlighting clearer in the case description.
There is an additional aspect to netflix scaling that I’ve always found interesting, curious what other folks think about this aspect /flywheel.
Because of the huge costs in content acquisition / content distribution, your R&D costs become almost invisible in your overall COGS. Combined with a fixed cost content model instead of rev share model, you have basically incentivised your product/R&D group to run as many experiments on demand generation and/or customer engagement as possible because the potential payoff is large. Even small increases in customer content engagement yield large benefits especially when combined with demand generation capabilities that steer additional engagement to high profit parts of your content catalogue.
A digital streaming service is uniquely suited for these kinds of experiments as opposed to the other earlier content business models. But it puts the recent subscription woes in a different light IMO.
Looking forward to the rest of the cases, really like the experiment
I’m going to add my vote to individual threads per business case
My late entry after reading netflix case ended up after a whole discussion on TI, PayPal e.a that I was trying to avoid to read because spoilers
Hi @ramon! You’re right — the Netflix case needs to be completely rewritten to focus on the switch to content creation as a scale advantage (i.e. turning variable costs into a fixed cost, amortised across the entire subscriber base). We were so in the weeds that we didn’t realise that it was not clear. A clearer, shorter version may be found in the 7 Powers summary (in the scale economies section)
I’m so sorry. We’ll have to rewrite. cc 
What are the similarities or differences to the cases that came before?
