STATE OF THE PLATFORM REVOLUTION 2021 - by Sangeet Paul Choudary
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Report
Technology
This 90-page report lays out the key themes in the platform economy for the year 2020-21. Themes span platform regulation, inequality in the gig economy, platform strategy for incumbents, bigtech movements into new industries etc.
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NINE THEMES
The post-pandemic world will witness many
important shifts. Executives who anticipate
these value shifts early and reorganize their
business model portfolio around platforms
will be best positioned to gain advantage.
Reliance Jio demonstrates that incumbents may
leverage their deep infrastructural and
regulatory moats and partner with BigTechs to
dominate the ecosystem together.
Public and private actors in China are
working in close cooperation—in a country-
level platform strategy—to create digital
infrastructure, to promote standards, and to
strengthen China’s control points in the
digital economy.
MASSIVE VALUE MIGRATION THROUGH A
YEAR IN CRISIS1
THE RISE OF PLATFORM CARTELS6
COVID ACCELERATES BIGTECH
TAKEOVER OF HEALTHCARE4
CHINA'S COUNTRY-AS-A-PLATFORM
STRATEGY
3
THE POST-PANDEMIC DIARIES: BIG
GOVERNMENT TAKES ON BIG TECH8
THE GIG ECONOMY AND THE
K-SHAPED RECOVERY
9
REWRITING THE INCUMBENT PLAYBOOK
2
EMBEDDED FINANCE IS REBUNDLED
FINANCE5
THE PANDEMIC DIARIES: BIGTECH
TAKES ON BIG GOVERNMENT7
THE STATE OF THE PLATFORM REVOLUTION 2021
In the platform economy, cartels coordinate
on two specific aspects - data usage and
ecosystem governance - to exert new forms of
influence over ecosystem-wide activity.
BigTech platforms have positioned themselves as
powerful global actors. Tech companies are now
gaining greater leverage by showcasing
themselves in roles usually occupied by
governments.
As platforms set up competitive bottlenecks,
they often act against the interests of their
ecosystem. As a result, ecosystem
participants, and society and markets at
large, may get adversely affected because of
the platform’s dominance.
The BigTech platforms have made strategic moves
into healthcare over the past decade. Covid-19
has further accelerated their efforts as they
leverage their resources to respond to the
global health crisis.
The last 10 years were largely about
unbundling finance, the winners in the next 10
will be the ones who successfully rebundle
finance.
The K-shaped recovery of 2020 was most visible
in the increasing divergence in the platform
economy. While platform businesses soared to
new valuation heights, the workers in many of
these platforms’ ‘ecosystems’ were
increasingly commoditized and disenfranchised.
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ABOUT THE AUTHOR
Sangeet Paul Choudary is the best-selling co-author of Platform
Revolution and the author of Platform Scale and the founder of
Platformation Labs. He has advised the leadership of more than 35 of
the Fortune 500 firms and has been selected as a Young Global Leader
by the World Economic Forum.
Sangeet's work on platforms has been ranked by the Harvard Business
Review as one of its top 10 strategy articles ever, alongside the works of
Michael Porter, Clayton Christensen and others, and has been featured
on four occasions in the HBR Top 10 Must Reads compilations,
including the HBR Top 10 ideas 2017.
Sangeet serves on several boards and committees, including the Global
Innovation Council of the ING Group, the advisory committee to the
Ministry of Housing and Urban Affairs, Govt of India, the WEF's Global
Future Council on Platforms and Systems, and the board of the ASEAN
Financial Innovation Network. He has also served as an Entrepreneur-
in-Residence at INSEAD Business School and as the co-chair of the MIT
Platform Strategy Summit, and is the youngest ever recipient of the
IIMB Distinguished Alumnus Award. He is a frequent keynote speaker
at leading global forums including the G20 Summit, the World50
Summit, the United Nations, and the World Economic Forum.
LEARN MORE ABOUT OUR WORK
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Migration
VALUE MIGRATION
The economic shock of the coronavirus pandemic has accelerated several pre-
existing trends while also giving rise to entirely new ones. In the face of such rapid
change, executives are piecing together the future landscape of value and the new
rules of competitive advantage. New value shifts are being driven by shifting
customer needs and behaviours on the demand side, increased value chain
uncertainty on the supply side, and a reversal of many of the trends that have
defined pre-pandemic globalisation. Competitive positions, likewise, are more
vulnerable during such shifts, spelling out both promise and peril for executives.
Variously referred to as 'the new normal', 'the big reset', and with other such
elaborate monikers, the emerging landscape will be characterised by the
emergence of new value pools and the erosion of existing ones.
There is hope in the midst of such uncertainty. Migration of value to these new
value pools can be predicted, prepared for, and harnessed. Indeed, executives who
'take the tide at the flood' and anticipate these value shifts will be best positioned
to seize tomorrow. This essay provides a framework for executives to identify
potential new value pools and realign their business portfolio to these new
positions.
NEW VALUE POOLS, SHIFTING CONTROL POINTS
The keys to transformation beyond a crisis may often lie in new value pools created
through the crisis. Times of crisis are accompanied by sudden changes in supply-side and
demand-side dynamics. These changes lead to the migration of value from established
business positions to new ones, enabling new players to emerge and new business models to be
created. Such migration of value is also accompanied by a shift in value network control points.
This value migration commoditizes some business positions and empowers others.
To understand this migration of value, we need to start by identifying key trends impacting a
value space. These trends may be classified into demand-side and supply-side effects.A
combination of demand-side and supply-side effects helps us determine the emergence of new
value pools.
Next, we need to determine whether shifts in value will be accompanied by shifts in control
points. Control points refer to control of key assets, relationships, and data flows in a value
network. Firms that emerge stronger from a crisis are those that can respond swiftly to a shift
in control points.
MASSIVE VALUE MIGRATION THROUGH A YEAR IN CRISIS
“Thereisatideintheaffairsofmen,Whichtakenattheflood,leadsontofortune...Onsucha
fullseaarewenowafloat.Andwemusttakethecurrentwhenitserves,orloseourventures.”
William Shakespeare
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THE FOOD RETAIL
INDUSTRY
To illustrate this further with an example, consider the food retail industry. Compared to other
retail categories, food retail has been a relative laggard in moving to ecommerce. In emerging
markets, in particular, the online food retail model never took off pre-pandemic. Grocery
deliveries typically involve many more items per order than other ecommerce, leading to high
costs of fulfilment. These models become profitable only at scale when fulfilment can move from
the store to automated fulfilment centers. However, the convenience of informal neighbourhood
grocery stores, especially in emerging markets, never drove demand scale in online grocery.
However, the pandemic has driven massive value migration, through a combination of supply
side and demand side effects, which is unlikely to be merely transitory.
Consider demand-side shifts in behavior. With many countries moving into lockdown during the
pandemic, there's been a significant shift towards ecommerce in food retail. In the US, more
than 40% of grocery deliveries in the week ending March 13 were made to first-time customers.
This is further reinforced by the disruption of food supply chains during the pandemic, a
supply-side effect.
A combination of these demand-side and supply-side effects is leading to value migration
towards online grocery retail.
In emerging markets, the situation is no different. Online grocery has accelerated at an
unprecedented pace since the start of the pandemic. Neighbourhood grocers are feeling the
squeeze as they struggle with a lack of technology to take online orders and an over-reliance on
informal supply chains that have been disrupted because of the pandemic. Many of these stores
are going out of business owing to poor cash flow and high rents.
Control points over demand shift significantly in the midst of a crisis, leading to an
aggregation of demand with a few large players. Aggregated demand, when combined with
a shift in supply, allows these players to reconfigure the value network around their
business. In this new value network, neighbourhood stores may be relegated to serving as
logistics providers to the larger players with relatively resilient supply chains. With
centralized demand as a control point, large online grocery firms will best orchestrate the
entire value network and might even leverage third party warehouses, delivery agents, and
fulfilment centers to create strong network effects that further strengthens their position.
As we note with further examples below, firms that orchestrate such ecosystems using
digital platforms are the ones best positioned to harness value in these new value pools.
Meanwhile, demand is increasingly getting centralized with a few large online grocery
providers who can use centralized demand data to better predict demand patterns, improve
stocking of fulfilment centers and better inform their supply chain. Greater centralization
of demand data also creates a machine learning advantage for large online grocery firms,
further moving value away from smaller stores. In addition, a post-Covid 19 world is likely
to feature supply chain inspections and quality control requirements that will also favor
larger players. This combination of demand and supply side effects will strengthen large
grocery players.
As we note with this example, a combination of demand-side and supply-side effects
reinforce each other to move value away from small stores to large grocery retailers. This
value migration is further reinforced through a combination of machine learning on
demand-side data and scale advantages in a consolidated food supply chain. Smaller
players, meanwhile, are increasingly commoditized.
MASSIVE VALUE MIGRATION THROUGH A YEAR IN CRISIS
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BUT… THIS TIME IS
DIFFERENT?
Value migration has happened during other periods of crisis in the past. The SARS
crisis of 2003 forced consumption behavior to move online across China, enabling
Alibaba move into B2C ecommerce. Alibaba launched the Taobao website in May 2003,
in response to a self-imposed quarantine prompted by SARS. Consumers across China
were staying home from work out of fear of contracting SARS, not very different from
the lockdowns during Covid19. Chinese consumers, stuck at home, took to ecommerce.
Duncan Clark, the author of “Alibaba: The House That Jack Ma Built” notes that the
SARS outbreak “came to represent the turning point when the Internet emerged as a
truly mass medium in China.”
Firms like Alibaba benefited from this period of intense change, building demand-side
control points by aggregating online consumer demand, and strengthening this with a
network effect by opening out the supply side to third party merchants. As more
merchants came on board, the demand-side control point became even stronger.
As evidenced by the grocery example above, we're seeing similar shifts in value pools
during the coronavirus pandemic. However, these shifts are not specific to pandemics.
In fact, they are not even specific only to times of crises. Shifts in control points and the
creation of new value pools result from any combination of technological, market, or
regulatory shifts.
MASSIVE VALUE MIGRATION THROUGH A YEAR IN CRISIS
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How much value is created in these new value pools depends on two factors. The first
is the duration of value migration – the longer these shifts persist, the greater the value
creation. The second, often less visible, factor is the shift of control points. In fact,
seemingly transient shifts may result in permanent effects, when accompanied by
the shift of an important control point.
Since the start of the coronavirus pandemic, we're seeing such a phenomenon play out
in the movie distribution industry. Streaming platforms like Netflix and Amazon Prime
have witnessed a surge in engagement during lockdown. However, this seemingly
transient shift in demand-side behavior is reinforcing a much more permanent
supply-side shift.
The closure of major theatre chains, owing to the pandemic, is driving studios to break
what's known in the industry as the "window" – the three-month period between when
a movie hits the big screen, and when it's offered for video on demand purchase or
rental, and then on streaming devices. This “window” that movie theatres have to
launch movies exclusively, is a moat protecting theatre revenues.
Since the start of the pandemic, studios have been launching directly on streaming
channels, thereby eroding the “window”. Universal was the first studio to take these
steps, announcing that it will make movies available at home on the same day as their
global theatrical release, starting with "Trolls World Tour," which had been scheduled
to open April 10, 2020 in the U.S. In India, Amazon Prime secured rights to premiere
several Bollywood movies on Prime Video. These movies were originally scheduled for
a theatrical release.
TRANSIENT SHIFTS, PERMANENT EFFECTS
We’re likely going to see new revenue models emerge as well. In China, Huanxi Media has
partnered with Douyin, a streaming platform, to launch direct to streaming on a new
business model. Under this agreement, Douyin’s parent ByteDance would pay Huanxi at
least 630 million yuan (US$90.8 million) for new content to stream first on its streaming
video platforms. With this deal, ByteDance gets exclusive access to a portfolio of Huanxi
movies and TV shows. Huanxi gets a licensing deal and a share of the advertising
revenues. Within two days of the deal, Lost in Russia - one of Huanxi's movies - was
released on ByteDance and gathered 600M views.
Studios will get to test the success of movie releases on streaming platforms and use that
to negotiate post-lockdown. Though theatres won’t go away, their negotiating power may
decrease, leading to a shift in value. The longer the lockdown, and the more the hits
released away from the theatres, the more likely we are to see this shift.
Within the first three months of the WHO declaring Covid19 a global pandemic, we have
already seen several permanent shifts. For one, AMC Studios blocked off Universal movies
from ever launching a movie in their theatres after the studio went direct-to-streaming.
With AMC Theaters struggling post-pandemic, Amazon is looking to acquire its assets,
further driving the consolidation we see during such periods of value migration. In India,
Amazon was already increasing its negotiating power against movie theaters pre-
pandemic by getting into online sales of movie tickets. If Amazon gains enough control
over ticket sales, it can negotiate windowing with movie theaters, leading to more fresh
content launching first on Amazon Prime. With the pandemic, this balance has further
tilted in Amazon's favor.
We note with this example that a relatively transient demand-side shift can reconfigure
bargaining power in the value network because of a massive shift in control points. A
transient demand-side shift may lead to a permanent supply-side shift by changing
bargaining power of players in the value network.
MASSIVE VALUE MIGRATION THROUGH A YEAR IN CRISIS
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CONSOLIDATE
YOUR POSITION
REARCHITECT
YOUR VALUE
NETWORK
In traditional value chains, value migration largely manifests as a shift in bargaining power between the supply side and
demand side. In platform-mediated ecosystems, value shifts can rearchitect the entire value network. Migration of a
demand-side control point can lead to a reframing of the value network on the supply side. In certain instances, as with
the rise of Taobao during the SARS crisis of 2003, this migration may result in a more open value network, mediated by a
platform, and allowing for participation of third parties. In other instances, players in an open and distributed ecosystem
may get rapidly commoditized leading to greater consolidation of value around a few players.
We're seeing such consolidation play out in the restaurant and food delivery space in the aftermath of the pandemic.
During the first three months of the pandemic, China's Meituan hit a $10Bn valuation. In the West, we're seeing increasing
consolidation among players with Amazon buying Deliveroo and Uber making an offer to Grubhub, which eventually
merged with JustEat Takeaway. While these valuation and acquisition plays are important, the pandemic has accelerated
deeper value shifts in this industry.
Again, let's start by looking for demand-side and supply-side effects. On the demand-side, social distancing regulations and lockdowns have led to falling demand for in-
restaurant dining and a corresponding increase in demand for in-home food delivery. A combination of rising delivery fees paid to aggregators, high rents, and falling demand
for dine-in is squeezing restaurant profits, leading to important supply-side shifts. As restaurants go out of business, delivery businesses are integrating into cooking and food
processing operations, and setting up 'dark kitchens' exclusively for delivery, without a restaurant dine-in experience.
Food delivery platforms have the advantage of a demand-side network effect where more consumers attract more restaurants onto the platform and vice versa. This demand-
side network effect reinforces supply-side scale in kitchen operations as more dark kitchens are set up.
‘Dark kitchens' benefit from a more profitable operating model. They are typically located closer to residential areas, leading to lower real estate costs and lower delivery costs
as kitchens and delivery hubs can be located closer to demand, enabling superior route optimization. Using superior demand data from their delivery business, delivery
platforms can also apportion cooking and sourcing across different kitchen locations based on demand patterns near those locations. These dark kitchens also leverage
market-wide demand patterns to better predict sourcing and preparation, leading to lower food waste costs.
'Dark kitchens' consolidate value away from restaurants towards a few central food delivery platforms. These platforms, now, own the demand-side control point with their
delivery business and the supply-side control point with their dark kitchen business.
MASSIVE VALUE MIGRATION THROUGH A YEAR IN CRISIS
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Future
A BLUEPRINT FOR THE FUTURE
We’ve talked extensively about shifts resulting from the pandemic but there are
many other equally important macro shifts that present opportunities for new
value pools.
Technological shifts continue to accelerate and artificial intelligence is rapidly
advancing across sectors. More importantly, traditional industry boundaries are
disappearing, often leading to creation of new value pools and business models
where none existed in the past. Geopolitically, the rise of China is reshaping the
global economic order and the rise of stakeholder capitalism will further create
new value pools as business priorities shift to embrace larger societal and
environmental value. Finally, investor activism and regulatory shifts will also
play an important role in determining future value pools.
The ability of executives to benefit from such shifts is dependent on three
factors: how early they spot the shift, their ability to innovate and reconfigure
their business model portfolio to position themselves in the new value pool, and
the strength of the control point that consolidates their position in this new value
pool.
Firms that develop a strong sense of market shifts and an ability to harness their value network
towards these shifts will be best positioned to win. The importance of harnessing your value
network makes agility and digitization ever-more important priorities for executives. More
importantly, firms that compete through ecosystems will be best positioned to rearchitect their
value network. Ecosystems are fluid and dynamic, and the most successful orchestrators
govern them through digital platforms. Businesses that orchestrate ecosystems can leverage
external assets and benefit from demand-side economies of scale. They may also selectively
capture supply-side control points by owning important assets of their own.
The post-pandemic world will witness many important shifts. Executives who 'take the tide at
the flood' and anticipate these value shifts early, reorganize their business model portfolio, and
consolidate their control points will be best positioned to seize tomorrow.
MASSIVE VALUE MIGRATION THROUGH A YEAR IN CRISIS
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Future
BIGTECH RELIES ON RELIANCE
During the pandemic-ravaged summer of 2020, many of Silicon Valley's top tech
giants (and leading investment firms) invested more than 20 billion dollars in
Reliance Jio, a seemingly traditional telecom company in India. Over the course
of 14 weeks, the company raised capital from Facebook, Google, Intel,
Qualcomm, and a host of investment firms.
This was all the more confounding because Reliance was essentially playing a
traditional telecom game and building an asset-intensive business. Since the
mid-2000s, the telecom industry has been impacted by two waves of disruption,
first when Apple and Google built their app platforms and next when Skype,
WhatsApp, and other providers of free communication services eroded
traditional telco revenue streams. Many have hailed this as the rise of the
platform business, the shift from asset-intensive to asset-light businesses, and
the rise of over the top (OTT) players.
The results had been declared. Telcos had lost the game and had gotten
relegated to commoditised, asset-intensive businesses while the bigtech firms
had won with asset-light platform business models.
Why then were the same BigTech firms scrambling to invest in a traditional
telecom business, no less one that had invested more than $30 Bn in building
out traditional telecom infrastructure and had more than $20 Bn of debt on its
books?
Industry observers tried hard to explain this. Some heralded this as the triumph of ‘free’
but couldn’t quite explain why that made any business sense. Others pointed to Jio’s
investments in new digital services but struggled to explain its larger investment in 4G
infrastructure. Some called this a new form of vertical integration but were further
confounded when Jio opened up its most capital intensive asset - its 4G network - to its
competitors, where vertical integration would have protected it.
None of these adequately explain Jio's strategy nor the tremendous upheaval it has brought
about in India's telecom industry. Since Jio’s arrival, 4G has become the default network for
most of India, with Jio accounting for ~70% of the country's 4G traffic. In less than 5 years,
the company has amassed more than 400M customers and propelled India from #155 to
#1 in the world in mobile data consumption.
To understand Jio’s strategy, we need to understand a deeper shift in industry structure
that’s playing out across the economy.
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From Vertical To Horizontal
Through most of the twentieth century, businesses scaled through vertical integration,
integrating multiple activities across supply, production, and distribution. This offered
greater control and greater capture of profits, and was a natural solution to the
problem of transaction costs - costs incurred in coordinating activities across the value
chain.
Transaction costs determine an industry’s structure - the manner in which firms
organize themselves and interact with other players. To minimize transaction costs,
most firms engaged in vertical integration.
Most industries, accordingly, took on a vertical architecture with a few large vertically
integrated firms competing with each other.
As digital technologies proliferate across industries, we’re seeing a fundamental shift
in this architecture. Digital technologies enable cheaper inter-firm communication,
greater interoperability, and higher standardization. These factors together reduce
transaction costs and enable firms to more effectively coordinate without requiring
vertical integration or bilateral contracting.
As a result, the links in the vertically integrated value chain start to break up and new
specialized competitors emerge that are more agile and innovative in delivering a
specific task in the value chain. The vertical industry architecture is increasingly
giving way to a more horizontal ‘layered’ architecture where firms at every layer
specialize in a particular value creating activity, and where firms, across layers, are
The mobile communications industry has itself weathered this shift. The
vertical structure of the telecommunications industry was first dismantled by
Apple and Google, which dominated the horizontal operating system layer, and
subsequently by WhatsApp and Skype which dominated the communications
services layer at the top. These shifts relegated telecom operators and handset
manufacturers to commoditized asset-intensive business models.
The response of telecom operators to these shifts have been predictable, with
some trying to directly copy the new business models, while others resort to
competing among themselves, launching aggressive price wars. None of these
responses acknowledged the new competitive landscape, as telcos grew
increasingly commoditized. That is, until Jio came along.
WhatsApp/Skype
Device
iOS/Android
Data traffic
Infrastructure
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Future
THE NEW INCUMBENT PLAYBOOK - PART 1
Jio invested more than $30 Bn in setting up cell-phone towers and laying out a
country-wide fibre-optic network. It launched the world’s cheapest 4G phone
and set up deals with 20 smartphone manufacturers to bundle its SIM card. It
then made a series of investments in digital services, investing more than $3
Bn in upcoming startups.
Jio was pursuing a strategic selection of business positions and a calculated
choice of competitive moves that acknowledged the new value stack and
positioned Jio for domination across multiple layers.
At the infrastructure layer, Jio’s aggressive investment monopolized 4G
infrastructure in India, unleashing data consumption across the country,
which now is the second largest internet market in the world after China and
also the one with the lowest data tariffs. At the device layer, Jio built out the
world’s cheapest feature phone on KaiOS, a Hong Kong-based operating
system, deliberately avoiding the Android operating system to minimize
dependence on a third party. At the communications layer, Jio massively
subsidized voice calls as well as data tariffs, triggering a price war that
consolidated the industry and accorded Jio the leadership position at that
layer.
Services
Device
Operating System
Data Traffic
Infrastructure
JioPhone (4G Featurephone)
Partnership with KaiOS
Subsidized data traffic
Jio's 4G infrastructure
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Future
Having gained 400M users across India with a monopoly at the infrastructure layer
and a dominant position at the communications layer, Jio reached out to investors
painting a full picture of the ecosystem with the specific positions each investor would
occupy within it. Intel and Qualcomm would further strengthen Jio’s bid to not just
expand to 5G but also become a global exporter of the technology and an alternative
to Huawei. Google, now with a seat at the table, would help build out a low-cost
smartphone, enabling domination of the OS and device layers. Facebook, most
importantly, would provide user-facing services and position WhatsApp as a ‘super
app’ within which all apps would sit.
Reliance Jio demonstrates that incumbents may leverage their deep
infrastructural and regulatory moats and partner with BigTechs to dominate the
ecosystem together. It also demonstrates the sobering reality that such large
ecosystem plays require a bold vision, an appetite for high-risk investment, and
the execution chops to scale and dominate rapidly.
In this new landscape, power concentrates with players that dominate a specific
layer. The strongest players leverage their dominance at one layer to occupy
multiple positions across different layers without requiring traditional vertical
integration. Firms can effectively excel at both innovation and efficiency by
choosing a combination of such positions.
Services
Device
Operating
System
Data Traffic
Infrastructure
WhatsApp
Jio Smartphone
Android
Commoditize telcos
Qualcomm, Intel:
5G Infrastructure
Jio's example demonstrates how comprehensive ecosystem strategies will
increasingly define the next generation of industry winners.
Economic value is increasingly created not within individual firms but through the
interactions among firms across the ecosystem. Understanding value creation at the
level of the ecosystem is more important than merely understanding it at the level of a
single firm. Competitive advantage, in turn, is no longer determined merely by a firm’s
activities but by the layers those activities occupy in the value stack.
THE NEW INCUMBENT PLAYBOOK - PART 2
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In the digital economy, platforms require us to rethink the economics of exchange.
Platforms such as Uber and Airbnb and the app stores run by Apple and Google don’t
provide their customers with any tangible good. Rather, they create marketplaces for
consumers and businesses to exchange goods. In building hugely successful platforms,
these companies have built massive communities in which apps are bought and sold,
rides are hailed, and apartments are rented out.
Successful platforms also create points of control. Take Google’s Android operating
system, which has allowed the company to dominate the smartphone software industry.
On the one hand, the software is entirely open source: Anyone can review it and write
apps for it. But to achieve effective distribution to the full ecosystem of phones running
Android, apps have to go through Google’s app store review. By controlling Android and
the app store, Google sets the standards for how the ecosystem works and what apps
appear in it.
As platforms continue to grow, control over the trade in goods and services is shifting
from countries to digital platforms. And as trade, labor, and money grow increasingly
digitized and are exchanged on platforms, countries need to rethink their positions in
the global flow of these goods. If they are to gain a competitive advantage, countries need
to increasingly pursue a platform strategy.
No country is doing this as effectively as China, which in recent years has set up a
concerted country-as-a-platform strategy, aggressively exporting its digital
infrastructure, playing a critical role in the development of technical standards, and
developing unique points of control in the digital economy. Much like Google established
itself as a dominant player in the smartphone ecosystem, China is attempting to do the
same in an increasingly digital geopolitical landscape. Understanding this dynamic will
be key to a future Biden administration getting the U.S. relationship with China right.
China’s National Informatization Strategy calls upon China’s internet companies to go out
into the world and support the creation of a “Digital Silk Road”—which refers to the export of
Chinese technology alongside the Belt and Road Initiative (BRI), China’s massive global
infrastructure investment project. The “Digital Silk Road” is China’s bet on a country-as-a-
platform strategy.
Public and private actors in China are working in close cooperation—in a country-level
platform strategy—to create digital infrastructure that aligns with the BRI, to promote
standards that drive the adoption of such infrastructure, and to strengthen China’s points of
control in the digital economy. This strategy extends across four key themes: trade,
payments, smart cities, and social credit. If successful, this strategy could fundamentally
shift trade and financial flows toward a China-centric economic order and could even
reshape political systems in participating countries.
CHINA'S COUNTRY-AS-A-PLATFORM STRATEGY
INTRODUCTION
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Linking up global economies through trade is the first step in China’s efforts to
export its digital infrastructure. To do so, Chinese tech companies are building the
digital tools to facilitate trade—and to do so using Chinese technology.
In 2016, Alibaba announced it would launch the Electronic World Trade Platform
(eWTP), which enables small- and medium-sized business to easily participate in
global commerce by creating a global trade network between China and
participating countries. First, the eWTP established two central hubs in China: an
imports hub in Hangzhou and an exports hub in Yiwu. Next, it set up eHubs with
local partners in each participating country, allowing small businesses in those
countries to trade internationally. Each eHub provides a centralized logistics and
fulfilment facility, as well as digital payment and financing capabilities, supported
by local banks. These eHubs are connected back to the central hub in China
through Alibaba OneTouch, the company’s global cross-border supply chain
management platform. The eWTP worked with governments to create Digital Free
Trade Zones (DFTZs), which enable cross-border trade with low or no import duties
and faster customs clearance.
These eHubs and DFTZs, when connected globally to each other and to the central
hubs, aim to create a global digital trade network. Coupled with the DFTZs, China is
also setting up international courts in Beijing, Xi’an, and Shenzhen to arbitrate
trade disputes between countries that use this trade infrastructure.
In March 2017, Alibaba helped launch a digital free trade zone in Malaysia, consisting of
a regional logistics center serving Southeast Asia, an e-commerce platform, and a digital
payments and financing service. Since its launch, three more countries—Belgium,
Rwanda, and Ethiopia—have joined. Two more hubs in China complete the eWTP
network of six hubs, as it stands today. The creation of these hubs is expected to
accelerate cross-border digital trade for small businesses. At the Yiwu eHub in China,
small businesses recorded more than $41 billion in international trade, across three
million active buyers in 2019-20, with an 82% year-on-year growth in online
transactions. The Malaysian eHub expects to manage a total trade volume of $65 billion
by 2025. Belgium has invested nearly 100 million euros in hopes of a similar boost in
digital cross-border trade.
Growing international ecommerce allows companies like Alibaba and Ant Group to
create points of control in the international economy. As countries sign up with the
eWTP, Alibaba and Ant are winning customers and growing their ecommerce and
financial platforms. And as their platforms grow, Alibaba and Ant are helping to write the
rules for how small businesses engage in international trade. Between them, Alibaba and
Ant provide key logistics, payments, and ecommerce capabilities to the eHubs,
positioning them as matchmakers between the global market and small businesses.
TRADE
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China’s platform strategy includes an ambition to create an alternative payments
infrastructure. China’s leading payment systems—Ant’s Alipay and Tencent’s WeChat Pay—
serve 28 million and 50 million merchants, respectively, in China, covering more than 90%
of the mobile payments market in the country. These payment systems provide a digital
wallet service, an offline payment app enabling instant payments by scanning barcodes or
QR codes on smartphones, and an escrow service, among other functionalities.
Both payments players have rapidly expanded internationally to enable merchants to serve
Chinese tourists. Prior to the pandemic, nearly 150 million Chinese travelers traveled
abroad every year, 32% of them paying for transactions overseas with their mobile phone.
By early 2019, WeChat Pay was accepted in 49 countries outside China, while AliPay was
accepted in 42. The payment systems have also demonstrated rapid growth, with the
number of Europe-based merchants on WeChat Pay growing 350% year-over-year in 2019.
Ant’s ambitions extend beyond just proliferating its payment system to creating a new
infrastructure and standard for global payments. To do so, it must solve major challenges
related to global payments interoperability. Payments interoperability typically plays out at
three levels. First, scheme interoperability involves two or more financial institutions
agreeing to work on the same payments system, or scheme, enabling payments to readily
flow between them. Check settlements and electronic fund transfers between two financial
institutions are examples of scheme interoperability. Second, to enable cross-border
payments, network interoperability is required so that payment schemes across countries
may negotiate exchange agreements with each other. For example, when a domestically
issued credit card is used for international payments, it benefits from network
interoperability. Finally, parallel system interoperability allows a seamless experience
across different payment schemes. For instance, a store that accepts both Visa and AmEx
delivers the same user experience to the customer because of parallel system
interoperability.
In order to create an alternative across all three layers of payments interoperability, Ant is
providing a common technology backend to financial institutions around the world. By using
common backend technology, participating financial institutions are effectively part of the
same “scheme” and benefit from scheme interoperability. With one standardized backend, the
need for parallel system interoperability is obviated. Finally, Ant manages cross-border
settlements within its infrastructure, enabling network interoperability.
Ant also provides traditional lenders with cloud computing and open banking solutions,
backed by its AI-powered risk engine and credit scoring models. As more banks around the
world get on board, Ant seeks to create a common financial infrastructure powering small to
mid-size banks across countries.
One of Ant’s key value propositions to banks is the ability to lend to merchants in the cash
economy. Ant’s credit-scoring models have been trained on loans made to merchants in the
Alibaba ecosystem, with rich data profiles. These AI capabilities allow banks to extend their
loans to cash economy merchants on whom they may have limited data and to whom they
would otherwise be reluctant to lend.
Alibaba and Ant have also invested in leading e-commerce and payments companies in
countries that have signed up with the Belt and Road Initiative, including EasyPaisa in
Pakistan, Ascend in Thailand, GCash in the Philippines and PayTM in India. As part of these
investments, Alibaba and Ant are providing backend technology to their investee companies.
The investment terms typically require investee firms to move their technology stack to the
Alibaba Cloud. By moving diverse payment firms to the Alibaba cloud, Alibaba is
homogenizing the backend infrastructure and the AI capabilities that power these different
payment systems, which could create greater interoperability across payment systems and
grant a stronger control point to Ant, whose credit-scoring and anti-fraud capabilities power
these diverse payment systems.
PAYMENTS
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By exporting its infrastructure but not directly participating in lending and deposits
businesses, Ant can expand globally without being hindered by local regulation or
needing to acquire local banking licenses. By providing a common infrastructure to
banks and payment wallets, Ant is also better set up to define standards for payments
between participating countries.
Chinese regulators are closely watching Ant’s transformation into a global financial
giant, and earlier this November forced the company to suspend its highly anticipated
IPO after regulators rolled out new rules for the financial sector. Draft regulations
would require companies like Ant to underwrite loans with their own funds,
demonstrating the leverage that Chinese authorities continue to exert over the
country’s largest platforms and the regulatory tools at their disposal to wield control.
The intense regulatory scrutiny on Ant may stem from the fact that control of
payments infrastructure and standards represents an important foreign-policy tool.
American credit card firms process a large portion of global payments, global
infrastructure such as SWIFT and CHIPS support the U.S. dollar payments system, and
the U.S. dollar remains the international reserve currency. The dollar’s position as the
reserve currency allows the United States to impose unilateral sanctions against
transactions between other countries, and moving away from a dollar-based
international financial system would be a major boon to China by diminishing U.S.
control over Chinese transactions. American officials understand this risk. In 2018,
the U.S. government blocked Ant’s acquisition of Moneygram, a U.S.-based money
transfer company. And the U.S. government is currently considering imposing a ban
on Tencent’s communications and payments app WeChat.
China’s ambitions to set up an alternative payments infrastructure are perhaps best
manifested in its creation of a state-backed cryptocurrency known as Digital Currency
Electronic Payment (DCEP), a centrally managed Chinese digital currency that acts as a
digital bearer instrument and obviates the need for settlement through an intermediary. As
of July 2020, Didi—China’s largest ride-hailing and transportation platform—is piloting the
DCEP with its customer base. The DCEP can be implemented to enable digital devices to
directly exchange information and money. Consider, for example, a self-driving truck
automatically paying a toll to an autonomous highway tollbooth without any human
intervention. The DCEP could eventually be offered for other such autonomous machine-to-
machine payments along the Belt and Road Initiative. It could also serve as a low-risk
currency for international trade between countries with highly volatile currencies. A
decentralized escrow system could let foreign businesses engage in trade while hedging
their exchange rate risk.
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The third theme in China’s platform strategy is smart cities. China made smart city
development a priority starting 2012 and was home to 749 pilot smart city projects
by 2019. Leading Chinese firms—Alibaba, Huawei, ZTE, and others—provide
technology infrastructure for managing smart cities. These include cloud-hosted
services to integrate city management databases as well as central AI capabilities to
manage city operations. Huawei smart city systems are active in more than 200
cities around the world. Alibaba provides a similar smart city management system
and is currently piloting its “City Brain” project in Kuala Lumpur, Malaysia, where it
uses traffic light information and traffic camera feeds, as well as data from the ride-
hailing service Grab, to predict traffic patterns and reduce traffic congestion.
Smart city infrastructure needs 5G technology, and Chinese firms have been at the
forefront of the 5G race. ZTE and Huawei are key partners to major telecom
operators globally and are deploying 5G and related technologies that are core to
autonomous driving, industrial automation, and smart cities. In countries where
ZTE and Huawei deploy 5G networks, these companies will have greater leverage to
promote their smart city infrastructure.
Along with China Mobile, these firms have increased their participation in
international standard-setting bodies for 5G. As of April 2019, Chinese companies
were involved in 52 5G initiatives across 34 countries, according to the Australian
Strategic Policy Institute, which may position them to advance proposals in line
with Chinese industrial policy. By setting the standards and providing the
infrastructure, China establishes important control points over the equipment, the
technical services, and the shape of future technology across participating
countries.
As providers of a smart city’s data operations, Huawei’s “Intelligent Operation
Center” and Alibaba’s “City Brain” project gain visibility into patterns of citizen
behavior and city infrastructure utilization. By aggregating data flows across these
global smart city implementations—particularly those along the Belt and Road—
Chinese companies like Huawei are well positioned to centralize smart city AI and
create control points over cities along the Belt and Road. By creating the best
trained and centralized AI brain for city operations, these companies could directly
influence city-level governance and decision-making, including by controlling the
profiling of citizens, citizen access to city services, and city infrastructure
management.
The combination of smart city infrastructure exports, investment in 5G standards,
and smart city management AI sets up China’s platform ambitions in urban
infrastructure.
CHINA'S COUNTRY-AS-A-PLATFORM STRATEGY
URBAN INFRASTRUCTURE
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Social credit and national identity constitute the final component of China’s platform
strategy. These social credit programs create a national-level reputation system for both
individuals and businesses, built on data captured from government programs as well as
data-sharing agreements with private actors like Alibaba and Tencent. These scores are
used to determine citizen rights and qualify their access to services, including the ability
to buy property, access loans, and even book a flight.
China is home to many social credit programs and is now exporting them to other
countries after having trained the social credit systems on domestic data flows. China’s
ZTE launched the “Fatherland Card” program in Venezuela, a social credit and mobile
payments system, which centralizes data about citizen activity and gives the government
greater visibility into individuals’ activities. Beyond its borders, China has exported facial
recognition software to Zimbabwe to train its AI on a wider range of facial image data. ZTE
is working with several other governments on similar projects. These projects raise
concerns around the development of surveillance states, especially when such technology
is exported to countries with authoritarian regimes and fragile democratic systems.
Chinese firms have trained their surveillance systems and scoring algorithms on millions
of data points in China, providing them a unique control point to deliver such
infrastructure to other countries. Alibaba’s Sesame Credit system, for instance, is trained
on data from commercial and financial activity in the Alibaba and Ant ecosystems. The
export of social credit systems allows China to export not just its technology but also its
preferred governance model to partner countries. It also enables the export of various AI
surveillance technologies that feed data into these social credit systems. By building these
systems abroad, China’s AI benefits from a wider range of data gathered across these
countries, strengthening it further as a control point.
CHINA'S COUNTRY-AS-A-PLATFORM STRATEGY
SOCIAL CREDIT
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PLATFORM STRATEGY FOR THE POST-PANDEMIC WORLD
The COVID-19 pandemic has created a global public health crisis and an economic
slowdown that is likely to further drive the adoption of country-as-a-platform
strategies. As the world recovers from supply chain disruption, countries will likely
adopt alternate digital trade infrastructure to empower small businesses to digitize
and easily participate in global trade. As countries turn to surveillance and contact-
tracing to manage COVID-19, countries will need to import digital urban
infrastructure from cities that have demonstrated the ability to trace and stop the
disease’s spread. With its platform strategy, China is well-positioned to increase
export of its digital infrastructure and standards in a post-pandemic world.
But the export of China’s platforms and standards come with risks to partner
countries. For one, Chinese government interests in private actors like Huawei raise
geopolitical and security concerns. 5G technology itself is fraught with security
vulnerabilities and could enable backdoor access into critical infrastructure,
putting participating cities and supply chains at risk. The export of social credit
systems, backed by surveillance tech, raise significant privacy and human rights
concerns, as evidenced by Chinese repression, monitoring, and internment of
ethnic minorities and political dissidents using biometric and other surveillance
data. In Uganda, the government has used surveillance systems to take action
against the opposition, while Venezuelans remain concerned that the Fatherland
Card will eventually be used to track members of the political opposition. The
export of surveillance systems threaten to undermine opposition movements and
democratic systems in partner countries.
More broadly, AI-based systems continue to be plagued with errors, which could
have unintended consequences when rolled out as large scale digital infrastructure.
In trade and financial services, this may result in incorrect sanctions. Implemented
in social credit systems, these systems could erroneously strip individuals of their
rights.
CHINA'S COUNTRY-AS-A-PLATFORM STRATEGY
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BIGTECH ENTERS
HEALTHCARE
The BigTech platforms - Google, Apple, Amazon, Alibaba, and others - have made
strategic moves into healthcare over the past decade. The Covid-19 pandemic has
further accelerated their existing efforts, while also enabling them to leverage their
resources to respond to the global health crisis.
In response to the pandemic, Google and Apple - which together dominate the global
smartphone operating system industry - partnered to create a global contact-tracing
infrastructure, which aims to help public health agencies track and manage the spread
of COVID-19. Amazon meanwhile launched a global initiative to accelerate COVID‑19
diagnostics, research, and testing.
The pandemic has also changed consumer behaviour, accelerating demand for remote
healthcare, even as regulations like the Digital Healthcare Act (DVG) in Germany have
emerged to facilitate reimbursement of digital medical solutions by payers. Through
the course of the year, Google launched its Healthcare API, Microsoft launched its
Cloud for Healthcare, Apple integrated an electrocardiogram and sleep monitor in its
Watch, and Amazon launched the health tracker Halo.
These moves are not merely opportunistic moves into a new industry driven by the
onset of the pandemic. They are part of a larger strategy that these platforms have been
putting in place over the last 5 years.
As BigTech platforms advance from one industry to the next, incumbents need to
better understand the effects BigTech entry and expansion can have on an industry's
structure.
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The healthcare industry architecture is becoming more modular, unbundling
healthcare delivery from traditional care facilities.
Technological shifts coupled with deregulation have enabled more diverse
models of healthcare delivery. Simultaneously, the nature of healthcare
demand has also shifted away from a need for episodic interventions only to an
increasing demand for wellness, preventative healthcare, and management of
chronic conditions. Together, these shifts in healthcare needs and healthcare
delivery models are restructuring healthcare consumption.
First, the proliferation of sensor-enabled wearables has led to an increase in
self-assessment by consumers as well as remote monitoring of patients by
providers. 40%+ consumers use personal technologies to measure fitness in
2020, compared to only 17% in 2013. Wearables and other forms of remote
monitoring devices also generate health data, which may be used by physicians
to better design the care regimen.
Next, urgent care clinics and retail medicine have also increasingly unbundled
care from traditional institutions. As patients move to alternate channels,
hospitals are increasingly shutting down, further driving specialisation of
actors.
The use of telehealth has further been accelerated by the pandemic as patients
prefer avoiding visits to care facilities and payers extend their reimbursement
policies to cover telemedicine. For instance, Epic Systems had less than 50,000
appointments on its telehealth services in February 2020, but that number had
risen to 2.5 million by April 2020.
Parts of primary and specialized medicine are also unbundling from large hospital
networks and operating as specialized independent entities, driving further
modularity in the healthcare delivery landscape. For instance, the East River Medical
Imaging Center in New York specialises as an independent center for MRI and CT
scans, and X-rays, while Pure Cardiology specialises in cardiology-related services
and operates as a user-centric membership plan, and the Surgery Center of Oklahoma
specializes in various forms of surgeries.
On the other hand, remote monitoring solutions with integrated messaging and alert
systems enable care providers to monitor patients remotely. Remote monitoring is
particularly relevant for chronic disease management and for eldercare, where risk
indicators like glucose, blood pressure, respiratory strength may be monitored on an
ongoing basis without requiring clinic visits. For these patients, care extends across
interventions by providers as well as ongoing care, which may be self-administered or
delivered by a care team, comprising informal caregivers.
THE UNBUNDLING OF HEALTHCARE
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Despite the unbundling, the lack of data interoperability - e.g. Electronic Health Records (EHRs)
interoperability - has created a fragmented patient journey, as patients cannot easily port their
data from one provider to another, or integrate data from wearables with their EHR data. Despite
greater consumer choice, the coordination costs to drive end-to-end patient care increase.
However, two key shifts - increasing data interoperability and improvements in AI and machine
learning - are driving down coordination costs, leading to the rise of platforms in healthcare.
The first shift - increasing data interoperability - is being driven through the adoption of FHIRs
or (Faster Healthcare Interoperability Resources), which create standards for data exchange,
allowing developers to build APIs to access datasets across different systems.
The second shift - improvements in artificial intelligence (AI) and machine learning (ML) -
changes the economics of healthcare production and delivery.
Incorporating AI into healthcare platforms takes on two broad forms. First, machine learning
(ML) models that analyse structured data - imaging, genetic, EMR data etc - may be employed to
study patient groups or perform diagnosis for specific patients. ML models can also be used to
improve quality of medical data. Second, natural language processing (NLP) techniques process
unstructured data - clinical notes, voice recordings etc - to create, enhance and enrich machine-
readable, structured data. The NLP procedures turn texts to machine-readable structured data,
which can then be analysed by ML techniques.
Let's first consider the impact of AI and ML on diagnosis. Advances in AI and ML have
commoditized prediction - the ability to anticipate future outcomes based on available data.
Improvements in computational speed (using superior GPUs and TPUs) as well as data
processing and analysis techniques over the past decade have worked together to bring down
the cost of predictions based on machine learning, thereby commoditising them.
At the same time, greater investment and regulatory push towards data interoperability increases
the availability and accessibility of data, making predictions more accurate as well as more
applicable across a wider scope of diseases.
The commoditization of predictions reduces the cost of medical diagnosis, which can now be
increasingly performed by machines. This, in turn, makes it feasible to perform diagnosis more
frequently and easily. For chronic diseases, in particular, cheaper diagnosis, allowing for wider
testing, may enable early detection and effective treatments. An increasing number of diagnoses
may be performed as ongoing assessments to aid disease management outside the care facility,
either self-administered by the patient or by an in-home care provider. Further, doctors and
radiologists can now spend less time diagnosing, allowing their team to be freed up for other
activities, in particular making more granular judgments on the appropriate intervention on the
basis of these diagnoses.
Next, consider the impact of NLP techniques on extracting information from unstructured data.
This, again, has important implications. For instance, a home-based voice assistant can better
capture patient data without requiring the patient to visit a care facility. By commoditising data
capture, the frequency of data capture may be increased, allowing for superior disease monitoring.
Extraction of data from voice also plays an important role in the effectiveness of telehealth, where
data may now be extracted from remote patient consultations more easily, making telehealth
interventions more effective. Finally, the ability to extract data from unstructured notes and voice
records reduces operational overhead for doctors, allowing clinical staff to serve a larger number of
patients.
Effectively, improvements in AI and machine learning, coupled with increasing data
interoperability, further reduce coordination costs. These two shifts are driving the rise of platforms
in healthcare.
HOW AI AND DATA INTEROPERABILITY DRIVE THE RISE OF HEALTHCARE PLATFORMS
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Against the above shifts, let’s look at how BigTech platforms - Apple and Google,
in particular - are now rapidly moving into healthcare.
APPLE - MANAGING THE PATIENT HEALTH RECORD
Apple is pursuing a platform strategy centred around the Apple Health Record.
Apple’s Health Record aims to be the central health record for users, combining
data from acute care - currently stored in EHRs - with data from a variety of
wellness and disease management devices and services, using FHIR-based
integration. Apple’s partnerships with health systems and EHR vendors enable
it to integrate EHR data with the Health Record. Apple also partners with Health
Gorilla, a clinical data API exchange, to integrate diagnostic data.
Apple’s Health Record acts as a key control point attracting five diverse
communities of producers looking to access these consumers - developers,
device manufacturers, healthcare providers, pharma companies and medical
researchers.
First, Apple provides access to its health record API to third parties through a
software development kit called HealthKit. Every app connecting to HealthKit
may access data from the Personal Health Record, aggregated across other third
party data providers, while also contributing its own data to make the Personal
Health Record richer. Nike, Jawbone, Withings, and other prominent device
manufacturers use the HealthKit API to integrate their devices as complements
to the Personal Health Record.
Next, Apple's CareKit provides a central platform for caregivers, physicians, patients, etc.
to monitor a patient across the care pathway, by enabling them to develop apps to
monitor patients in real-time. CareKit is specifically focused on chronic disease
management and surgical episodes (pre- and post-operative) and enables the creation of
complementary devices and services to assess, diagnose, and manage these conditions.
Apple's large scale aggregation of its user base can also enable population health
interventions focused on driving large-scale behavioural changes, including improving
medication adherence.
Finally, Apple's ResearchKit enables medical researchers to conduct studies leveraging
the iPhone's user base. As an aggregator platform, Apple makes it easier to identify,
target, and recruit eligible candidates for a research study, based on data in their health
record. Leveraging Apple's consumer-facing services, these studies can be managed at
much larger scale than traditional research studies. For instance, the Apple Heart Study
has recruited nearly 420,000 people in less than a year. ResearchKit's customisable
templates also make it easier to manage informed consent and obtain data access
permissions from participants.
Beyond academic researchers, pharma manufacturers can also use ResearchKit for
conducting remote clinical trials. Traditional site-based recruitment and testing is
restrictive and expensive. Utilizing sensors, ResearchKit participants can more easily
participate in the study virtually, enabling Pharma manufacturers to capture real-world
data about the performance of their drugs. Moreover, the continuous assessment enabled
by sensors enables data capture during episodes of relevance, such as during an asthma
attack. Leading pharma manufacturers, including GlaxoSmithKline, Novartis, and Pfizer
have partnered with Apple to conduct such studies.
BIGTECH MOVES INTO HEALTHCARE
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BIGTECH MOVES INTO HEALTHCARE
GOOGLE - THE DATA INFRASTRUCTURE OF HEALTHCARE
Google's platform strategy involves provisioning clinical and operational
infrastructure that underpins production across healthcare operations,
diagnostics, drug R&D, surgery, and claims management. This combines a
HIPAA-compliant Google Cloud with the Google Healthcare API, enabling
healthcare providers to store and aggregate data across multiple sources.
Further, Google’s DeepMind enables access to diverse, siloed data in a
standardized format, enabling a wider scope of data elements to be analyzed for
clinical decision making. Google's infrastructure platform also includes
capabilities like DeepVariant, which provides an open-source deep learning tool
for genomic analysis, aimed at the life sciences industry.
Leveraging its capabilities in data storage and processing, information
management/indexing, and AI, Google is setting up a platform to provide core
capabilities in healthcare production. Google aims to provide data storage,
processing, and management infrastructure to a wide range of actors in the
healthcare production ecosystem. As mentioned earlier, data silos and poor
data interoperability pose one of the key challenges to effective coordination
across healthcare ecosystems.
1. Data storage and management: To execute on its healthcare ambitions,
Google has invested heavily in making its existing infrastructure - including its
data warehousing, machine learning, and G Suite tools - HIPAA-compliant. For
instance, the STRIDES Initiative, a partnership between Google and NIH,
provides Google Cloud's storage, computing, and machine learning capabilities
to 2,500+ research institutions. Google's G Suite for healthcare is a HIPAA-
compliant cloud service which is positioned as an alternative way for
healthcare firms to share files (X-rays, CT scans etc.) without being restricted by
the constraints of EMRs.
2. Data logistics and interoperability: Beyond data storage and management, Google is
also looking to enable data logistics and interoperability with its infrastructure platform
play. As noted earlier, the healthcare industry's shift towards FHIRs (Faster Healthcare
Interoperability Resources) enables greater interoperability and innovation around
healthcare datasets. Apigee, an API management company acquired by Google, was one of
the early movers in building FHIR-based APIs, and has worked with healthcare firms like
Walgreens, McKesson, Cleveland Clinic, and others . For instance, Walgreens uses Apigee's
API management capabilities to enable customers to order prescription refills online and
gets these refills electronically approved by a doctor's office connected to Walgreens by
APIs. This end-to-end connectivity eliminates delays and increases patient adherence to
medication programs. Google Healthcare API enables health care providers to store and
access healthcare data in Google Cloud. The API also enables connectivity between
traditional care systems and applications hosted on Google Cloud.
3. Data standardization and innovation: Beyond interoperability, Google's infrastructure
platform aims to encourage innovation by enabling data normalization across a wide range
of data sources and formats. Google’s DeepMind enables access to diverse, siloed data from
EMRs, devices, and clinic software to be accessed in a standardized format. This
standardization enables new innovation by allowing analysts and app developers to analyse
different data elements in one standard format.
4. Information indexing and querying: As the leading search engine, Google has
developed deep expertise in information indexing and retrieval, and is looking to extend
those technologies to its healthcare data infrastructure. BigQuery, its HIPAA-compliant data
warehouse enables healthcare providers to develop personalized therapies for patients by
combining and analyzing multiple datasets - genomics data, insurance claims, public
health data, and environmental data - with EMR/EHR records.
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5. Google's infrastructure platform also includes capabilities like DeepVariant,
which provides an open-source deep learning tool for genomic analysis, aimed
at the life sciences industry. Google benefits from infrastructure lock-in. For
instance, as more researchers build on top of Google Cloud, Google becomes a
more integral part of the healthcare infrastructure.
Google's expansion into the healthcare industry further involves bundling key
components to its data management infrastructure. These complements
include complementary datasets that may be accessed by companies using the
infrastructure, diagnostic services that aid clinical decision making, robotic
surgical tools, data capture tools that capture unstructured data from across the
clinical and operational workflows, as well as clinical and operational services
that assist healthcare producers in delivering care, leveraging the data
infrastructure platform.
Healthcare datasets serve as complements that increase the attractiveness of
Google's platform. Research programs run by Google collect health data from
participants and store these datasets in Google Cloud. These datasets may
eventually be accessible to researchers using Google's infrastructure. As Google
expands, it is likely to further open up the platform to other dataset providers,
particularly organizations that are already using Google Cloud to store their
research datasets.
Google's Deepmind is also building and bundling its own applications to
demonstrate the value of its infrastructure platform. For instance, DeepMind's
Streams app detects acute kidney injuries by analyzing information across
diverse sources, managed by Google's infrastructure platform. Eventually,
DeepMind will likely open this to a larger number of third party developers.
Surgical capabilities, enabled by robotics, also act as complements to Google's
infrastructure platform. Verb Surgical - created by Google's Verily and Johnson & Johnson -
is building out robotic surgery complements that aim to use machine learning capabilities
in Google's platform. These robotic surgery tools are aimed at augmenting surgeons'
abilities to perform surgery.
Next, Google bundles data capture tools, which act as important complements to the data
infrastructure platform. Google's MedicalDigitalAssist, a project in partnership with
Stanford Medicine uses speech recognition to transcribe conversations and extract relevant
notes by recognising medical terminology and other relevant keywords in the discussion,
with the goal of helping doctors with note-taking and paperwork. Suki, another
complement to Google Cloud - is a voice assistant capability to help physicians with
administrative tasks like documentation or EHR information retrieval.
Eventually, Google's infrastructure play may open up to integrate other third party tools that
similarly extract unstructured data and store it in structured form in its data infrastructure.
Google's Study Watch, by Verily, is another important complement to its infrastructure and
may be deployed in clinical studies to collect physiological data from volunteers, eventually
stored in Google Cloud.
Google's venture fund investments include Klara, a system to automate workflows across
the patient's care journey, including scheduling, pre-visit instructions, reminders and no-
show engagement. It integrates with hospital EHR and practice management systems and
may, in future, be bundled with Google's infrastructure proposition to healthcare providers.
BIGTECH MOVES INTO HEALTHCARE
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GOOGLE AND AMAZON -
IN-HOME PLATFORM PLAYS
Google's platform play also focuses on in-home care and aggregates interactions
across connected devices, telehealth services and on-demand, in-home care
services. Google Home, Google’s voice assistant, could be used to ensure medication
adherence, assist with disease-related lifestyle management, and ask questions to
assess a patient’s health risk.
Google has been running several projects on AI-enabled diagnostics. While many of
them may translate to devices and services for providers, Google is also likely to
build connected devices in the home that enable in-home testing, assessment, and
diagnostics. Alphabet’s subsidiary Verily is working on a range of data capture and
diagnosis mechanisms to enable a superior condition assessment, diagnosis, and
disease management experience. Verily's Study Watch - a sensor-based wearable
device for non-invasive, continuous monitoring - plays a strategic role in data
capture and monitoring of several health conditions, by collecting "biometric health
information." Verily‘s Personalized Parkinson’s Project combines data from the
Study Watch with clinical data to identify the onset of Parkinson's Disease.
Google's investment in Ready provides another key component of Google's platform
play targeted at in-home care. Ready provides access to an on-demand network of
“Ready Responders” — trained as paramedics and nurses — to patients’ homes,
with relevant testing and monitoring equipment.
Google Cloud's $100 million investment in Amwell, a telehealth operator indicates that
Google is also looking to engage in patient-facing services to connect in-home care to the
hospital. Telehealth services, integrated with Google Cloud and its AI assets, could play an
important role connecting the consumption ecosystem with the production ecosystem.
Telehealth software, in combination with doctor assistance capabilities like
MedicalDigitalAsssist could seamlessly enable data capture. Doctors using the telehealth
service could also benefit from Google's AI-enabled diagnostics capabilities.
Also on the connected devices front, Nest - Google's home automation subsidiary - is
targeting assisted living facilities. and nursing homes in the eldercare market. For
instance, Nest has indicated using its motion sensor to track movements and
automatically turn on the lights when seniors get up in the middle of the night.
Amazon is following a similar strategy. Amazon Alexa developed software that would meet
HIPAA regulations. By becoming HIPAA-compliant, Alexa is allowed to receive and
transmit information that is protected under the U.S. HIPAA (1996). After gaining HIPAA
compliance, Amazon also onboarded six business partners to bundle complementary
Alexa Skills for the healthcare industry. These skills allow consumers to make
appointments, access medical instructions, or track a prescription, among other things.
Amazon’s wearable Halo captures a variety of healthcare indicators using 3D body scans
and voice tone analysis and will also play an important role in in-home care.
COVID ACCELERATES BIGTECH TAKEOVER OF HEALTHCARE
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By acquiring mail-order pharmacy PillPack, Amazon has established a platform
position in the pharmaceutical distribution value chain. It may further complement
Pillpack by setting up a retail pharmacy network across Whole Foods locations to
provide a way to fulfil same-day prescriptions.
Amazon’s Whole Foods acquisition also provides it a storage and distribution
network with the physical infrastructure (including cold storage) required for
perishable pharmaceuticals. In addition to the licenses gained from the PillPack
acquisition, Amazon has also filed for wholesale pharmacy licenses. With the
additional acquisition of a manufacturing license, Amazon would be well placed to
set up a logistics infrastructure platform to manage pharmaceutical logistics end-
to-end.
AMAZON - HEALTHCARE LOGISTICS
STANDARDS DEVELOPMENT
In addition to building proprietary platforms, BigTech firms also engage in open
standards development when entering a new industry. Open standards
development may help change the competitive dynamics in an industry by
commoditizing incumbent advantages. While traditional firms, particularly EHR
vendors, resist interoperability in healthcare, the BigTech firms are working
together to promote open standards. Google and Amazon have joined efforts to
support FHIRs through Project Blue Button, which aims to make it easier for
patients to view and download their health records. They are also implementing the
standard in their cloud infrastructure and consumer-facing applications.
Google’s“Cloud Healthcare API” provides a solution for storing and accessing
healthcare data in FHIR format, while Apple has implemented FHIRs in its
consumer-facing Health Records.
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The last 10 years were largely about unbundling finance, the winners in the next 10 will be the
ones who successfully rebundle finance.
Fintechs began by “unbundling” financial services. They identified one traditional banking
function, and executed it with finesse. However, unbundling creates a fragmented customer
journey and increases search costs for consumers. The next decade will be dominated by
financial services players that can successfully “rebundle” services to cater to the evolving
financial needs of its customers.
Unbundling economics
Several factors drove fintech unbundling. A few are outlined below:
• Niche market targeting - Clearbanc catered to entrepreneurs while SoFi focused
on students. These players targeted an underserved customer, underwriting customized
loans, to develop primacy of user relationship.
• Automation - Mutual funds and ETFs had already been using algorithms. Client
advisory, however, continued to be manual. Robo-Advisors such as Wealthfront and
Betterment did away with this by automating routine functions of financial advisors. As a
result, they were successful in providing balanced, diversified portfolios at lower
management fees.
• Margin arbitrage - Fintechs, with lower cost operating structure, can offer more
credit at better interest rates than incumbents.
• Saving platforms - Some fintechs ‘gamified’ savings, using rewards systems and
social benchmarking, again promising a higher return on their savings accounts.
THE GREAT REBUNDLING OF FINANCE
THE FIRST PHASE OF FINTECH DISRUPTION UNBUNDLED THE BANK
Banks have primarily served to collect deposits and pay interest on savings.
Payments, lending, and asset management were other key functions that banks
performed. Across the 90s and the early 2000s, banks emerged as “one-stop shops”
or financial supermarkets that essentially offered bundled financial services to
customers.
However, these bundles were not centered around customer needs. They were
structured around the capabilities of a bank’s delivery channel.
By the late 2000’s, fintechs disrupted this traditional banking model offering niche
banking services and executing it better than incumbents that were struggling with
legacy issues. Venmo and Square specialized in P2P payments, Mint specialized in
budgeting and Lending Club helped consumers identify P2P loans at best interest
rates.
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In the music industry, file-sharing services like Kazaa and Napster unbundled the
music album (the original bundle) but Spotify’s playlists rebundled them. News
media provides a more powerful example. The traditional newspaper bundle was
unbundled by digital distribution on the web. Eventually, Facebook rebundled it as a
news feed and arguably Google did the same through its search engine. Both
‘rebundlers’ then centralized advertising power and moved it away from the old
bundles.
Previous instances of unbundling and rebundling show a template for the future of
financial services. The first phase of unbundled financial services is unlikely to
sustain unless some of those unbundled services serve as beachheads to attract
customer engagement and then rebundle services to serve those customers.
HOWEVER, UNBUNDLING IN ITSELF DOESN’T
CREATE SUSTAINABLE BUSINESS MODELS.
Creating value through rebundling
Square expanded from an easy-to-use payments terminal for on-demand workers to
a financial services bundle centered around a business’s needs. Ant Financial started
out as Alipay and has rebundled lending and other financial products around it.
Stripe, similarly, started as a payments API and now combines small business
solutions and treasury-as-a-service. Paypal’s prepaid debit card that includes bank
transfers, deposits, and cashing checks.
Both incumbents and startups are driving this rebundling of financial services. PSD2
and other open banking directives make banking services more fluid, further driving
this unbundling.
THE GREAT REBUNDLING OF FINANCE