1. The document discusses the need for new analytical frameworks to understand competition in the era of big data and digital convergence. Mainstream and neo-Brandeisian approaches do not adequately consider dynamic competition driven by innovation. 2. Data orchestration, or the collection, organization, and analysis of customer behavioral data across industries, is a key capability driving broad spectrum competition between big tech firms. Successful firms develop strong dynamic capabilities in data science. 3. Both new frameworks and a revised understanding of competitive dynamics focusing on firm capabilities are needed to guide competition policy regarding big tech companies and their data-driven business models. The status quo risks regulatory errors.

1. BIGTECH, BIG DATA,
AND DYNAMIC COMPETITION
David J. Teece
Institute for Business Innovation
Haas School, UC Berkeley
And
Executive Chairman
Berkeley Research Group
dteece@thinkbrg.com
January 20, 2021
Berkeley Research Group Seminar for CGSH
Copyright David J. Teece

2. 2
DYNAMIC COMPETITION IS WHAT MATTERS MOST
“competition from the new commodity, the new technology, the new source
of supply, the new type of organization— competition which commands a
decisive cost or quality advantage and which strikes not at the margins of
the profits and the output of existing firms, but at their foundations and their
very lives.” Joseph Schumpeter
1942
Implications
1. Static competition is “weak tea” compared to dynamic competition… innovation
is the turbocharger if not the engine of competition.
2. Innovation drives competition (perhaps more powerfully than competition
drives innovation).
3. The two way causation is absent from competition policy frameworks in the EU
and the US.
Copyright David J. Teece

3. 3
SOME COMPETITION EXPERTS AND POLICY MAKERS KNOW
THERE IS A LACUNA:
“Antitrust has historically focused on static (rather) than dynamic analysis…
for a number of reasons. First the antitrust community… both lawyer and economists… have
far greater familiarity and comfort with static analysis rather than dynamic analysis. Third
there’s a perception… that dynamic analysis is less well developed…”
Thomas Rosch
FTC Commissioner
2010
“Innovation over the longer run will deliver very large consumer welfare gains” yet
competition authorities “routinely struggle to account for dynamic effects”
Christine Wilson
FTC Commissioner
Sept 11, 2019
Copyright David J. Teece

4. MAINSTREAM AND NEO BRANDEISIAN APPROACHES TO COMPETITION
POLICY SIDESTEP THE LACUNA:
• Innovation is the driver of competition policy?
• Mainstream sees competition driving innovation; but does not recognize
that innovation drives competition
• Neo Brandeisians agree that innovation matters, but somehow only with
respect to new entrants, not Big Tech firms themselves
• Neither recognize the broad spectrum nature of Big Tech competition –
“Moligopoly?”
• Neither recognizes that “management matters,” and that firm level dynamic
capabilities are a driver of competition just as much as competition drives
innovation
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Copyright David J. Teece

5. 1. Neo Brandeisians don’t have one
2. Mainstream competition policy economists have at best an
extremely shallow claim to be masters of innovation economics
(writ large); the Neo Brandeisians don’t seem to care about
innovation… unless it comes from small firms and new
entrants.
3. Mainstream economists have frameworks; but they are
impaired by:
• Chicago and post-Chicago static equilibrium approaches
• what Nobel laureate George Akerlof calls the “hardness police”
who have too much sway. Silly but elegant static models, both
diagrammatic and mathematical, deflect attention from
innovation and are not only tolerated but admired.
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NEED NEW FIT-FOR-PURPOSE ANALYTIC FRAMEWORKS
Copyright David J. Teece

6. APPLYING NOBEL ECONOMIST GEORGE AKERLOF’S TRADE-OFF MODEL:
Source: Akerlof, “Sins of Omission in the Practice of Hardness,”
Journal of Economic Literature, 2020. Here hardness means
formal models, not difficulty.
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Copyright David J. Teece

7. WITHBIGDATAANDDIGITALCONVERGENCE,THEUNDERSTANDINGOFCOMPETITION
REQUIRESNEWANALYTICFRAMEWORKS
• Problem with mainstream:
• Favor “hardness” over importance (Akerlof)
• Favor “static” over “dynamic” competition frameworks
• Neither Neo Brandeisians (e.g., Kahn, Wu) or mainstream
economists (e.g., Shapiro) have analytical frameworks likely to
deliver good policy recommendations
• Akerlof points out that the “hardness police” stand in the way of
new ideas
INNOVATION GETS PUT IN THE BACK SEAT… ALTHOUGH EACH CAMP
PROTESTS OTHERWISE
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Copyright David J. Teece

8. BROAD SPECTRUM COMPETITION HAS ARRIVED
Digitally-based enterprises are different.
• Not just about network effects
• Not just about economies of scale
• The boundaries between different types of services are being demolished in
USA and EA… and even more so in China
Google and Facebook are different kinds of companies from IBM & Microsoft
• Not just about using/exploiting their data in a single domain
• Not just about the opportunity system or the algorithm
Competitive advantage is now about receiving and acting upon real time data on
customers and their behaviors that have relevance across multiple domains
Digital giants do not stick to their own swim lanes… Facebook is now promoting
shopping services on its social networks
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Copyright David J. Teece

9. BROAD SPECTRUM COMPETITION REQUIRES DATA ORCHESTRATION
a) N-sided markets literature indicates that firms need to
‘manage’/coordinate each side for success.
b) Modularization literature speaks to the protocols needed to manage
interfaces
c) Data orchestration requirements/literature speaks to how Big Tech
delivers and receives competition from each other… i.e., they are not
confined to their own swim lanes.
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Copyright David J. Teece

10. DATA ORCHESTRATION IS ABOUT INNOVATING/”INVENTING” BY
COLLECTING, STORING, AND USING DATA
• Sometimes data can be sold back to the very constituencies that helped
generate it (e.g. Toyota uses GPS data from drivers in Japan to send traffic
information back to municipal authorities).
• New data is being generated as a byproduct of using the IOT.
While Big Data isn’t new, new and powerful ways to collect and analyze it can
assist with product and service innovation.
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Copyright David J. Teece

11. RAW BEHAVIORAL DATA IS OF LIMITED VALUE
Customer Generated Behavioral data has low ex ante (“raw”) value; but if
manipulated and processed it can have high ex poste value.
• Whereas traditional demand theory sees references as fixed, with data
orchestration they may become variables.
• Customer data collected for one purpose can be reused for another
(e.g. Amazon)
BEHAVIORAL CUSTOMER DATA IS RELEVANT ACROSS MANY SECTORS. WE
NEED TO RETHINK THE FOUNDATION OF COMPETITIVE ADVANTAGE AND
THE NATURE OF COMPETITION ITSELF.
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Copyright David J. Teece

12. REAL TIME DATA ORCHESTRATION DRIVES INNOVATION
• Advanced machine learning (AI) allows the extraction of insights on
consumers… their habits, emotions, and needs.
• When this data is combined with complementary personal data
(e.g., age, geographic location, gender) new insights (sensing and
sensemaking) and new services become possible.
• These new insights mean very targeted sales are possible… and the
consumer gets the benefit of having much junk messaging screened out…
to the benefit of both customer and provider.
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Copyright David J. Teece

13. TECH FIRMS MUST HAVE STRONG DYNAMIC CAPABILITIES AND DATA
SCIENCE SKILLS TO COLLECT, ORGANIZE, AND ANALYZE DATA
• Machine learning and AI plays a role in classifying raw data.
• Understanding what data to store, and for how long, is a capability.
• Knowing how, when, and where to leverage data across markets is
capability that helps:
• improve the product
• create new products and services
• Leveraging is procompetitively employed in both B2B and B2C
situations (e.g. aircraft engines; Netflix movies)
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Copyright David J. Teece

14. • Data orchestration is a capability separate from platforms and complementary assets.
• The capabilities to orchestrate data at scale are not ubiquitous.
• Data orchestration across industry boundaries challenges traditional notions of
industries and markets.
• The arrival of IOT requires us to rethink the very notion of industry.
• Successful data orchestrators have an implicit semi-exclusive contract with their
customers.
• The customer is part of the production process, which is outside the standard
economic model.
SUMMARY OF DATA ORCHESTRATION ISSUES
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Copyright David J. Teece

15. • Excite and Lycos lost the search engine game to Yahoo. Then Yahoo lost out to
Google.
• Incumbency only gives you a seat at the table for the next round of innovation.
• Absent strong dynamic capabilities, incumbents will fail.
• Absent innovation and strong dynamic capabilities, new entrants will fail.
• There is not automatic “tipping point;” management matters
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Copyright David J. Teece
PLATFORMS THAT DO NOT INNOVATE WILL BE OVERTAKEN BY OTHERS
OFFERING SOMETHING BETTER

16.  View tech trusts like industrial age railroads and oil “trusts”
 Reckless focus on divesture… without an understanding of:
 (a) firm level competitive advantage
 (b) how big data matters for competition policy as well as competitiveness
 It’s not just about n-sided platforms… they are just one of many features of the
tech sector
THE ABSENCE OF A FULLY OPERATIONAL DYNAMIC COMPETITION FRAMEWORK INVITES
NEO BRANDEISIANS TO FILL THE VOID WITH SHIBBOLOTHS FROM THE PAST.
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Copyright David J. Teece

17. 1. Facebook acquisition of Instagram (type II error?)
2. US v General Motors and ZF Friedrichshafen (type I error?)
3. Alstrom – Siemens merger (type I error?)
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FIASCOS CAUSED BY ABSENCE OF DYNAMIC COMPETITION FRAMEWORK?
Copyright David J. Teece

18. 1. Moligopoly captures broad spectrum competition amongst and between
Big Tech players.
2. Broaden the (consumer) welfare standard and insist on long-term to embrace
innovation
3. Competitive outcomes can be shaped by firm-level dynamic capabilities (requiring
entrepreneurial management) as much as by market position. The latter is often
meaningless (only the paranoid and the dynamically capable survive)
4. Antitrust should allow innovators to capture Schumpeterian and Ricardian rents but
be skeptical of practices that generate naked monopoly rents
5. Need to develop a meaningful and operational theory of potential competition
based on capabilities… which will give merger enforcement agencies a better
chance of blocking anticompetitive transactions and approving good ones
6. The theory of complements needs to be developed further
SOME BUILDING BLOCKS FOR A THIRD WAY FORWARD
ABSENT AN UNDERSTANDING OF ORGANIZATION CAPABILITIES AND HOW THEY
EVOLVE, MISTAKES (BOTH TYPE I & II) WILL CONTINUE TO BE MADE
Copyright David J. Teece
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19. 19
WE MUST EMPLOY THE EXTENSIVE
RESEARCH IN TECHNOLOGY STRATEGY &
POLICY AND IN STRATEGIC MANAGEMENT
TO OPERATIONALIZE NEW FRAMEWORKS
THERE IS A NEED TO BRING ALL HANDS ON DECK TO MAKE THE
DYNAMIC COMPETITION FRAMEWORK MORE OPERATIONAL.
Copyright David J. Teece

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EUROPE NEEDS STRONGER DYNAMIC CAPABILITIES TO BECOME MORE
COMPETITIVE… BOTH THEN (1967) AND NOW!
“it is time for us to take stock and face the hard truth… what
threatens to crush us today is… a more intelligent use of skills”
What Europe needs is “the ability to transform an idea into
reality through… the talent for coordinating skills and making
rigid organizations flexible” i.e., dynamic capabilities!
Jean-Jacques Servan-Schreiber
Le Défi Américain
1967
Copyright David J. Teece

21. 1. Teece, David J., “Innovation, Governance, And Capabilities: Implications For Competition
Policy. A Tribute to Nobel Laureate Oliver Williamson by his Colleague and Mentee David J.
Teece,” Industrial and Corporate Change, forthcoming 2020.
2. Teece, David J., “Profiting from Innovation in the Digital Economy” Research Policy (2018).
3. Teece, David J., “Dynamic capabilities and entrepreneurial management in large
organizations: Toward a theory of the (entrepreneurial) firm” European Economic Review
(2016).
4. Teece, David J., “Next Generation Competition: New Concepts for Understanding How
Innovation Shapes Competition and Policy in the Digital Economy” Journal of Law, Economics
and Policy (Fall 2012).
5. Teece, David J., “Dynamic Competition in Antitrust Law” (with J. Gregory Sidak), Journal of
Competition Law & Economics 5:4 (December 2009), 581–631.
6. Teece, David J., “The Analysis of Market Definition and Market Power in the Context of Rapid
Innovation” (with Christopher Pleatsikas), International Journal of Industrial Organization 19:5
(April 2001), 665–693.
7. Teece, David J., “The Meaning of Monopoly: Antitrust Analysis in High-Technology Industries”
(with Mary Coleman), The Antitrust Bulletin 43:3/4 (Fall–Winter 1998), 801–857.
8. Teece, David J. with N. Petit “Big Tech, Big Data, and Competition Policy,” forthcoming.
SOME REFERENCES TO “THIRD WAY” WORK
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Copyright David J. Teece