Deep Tech Projects, Uncertainties, and Entrepreneurial Strategies: An Explanatory Typology
Francis D. Kim
Assistant Professor
Chulalongkorn School of Integrated Innovation, Chulalongkorn University
Email: francis.ki@chula.ac.th
Rajendra Srivastava
Novartis Professor of Marketing Strategy and Innovation
India School of Business
Email: rks@isb.edu
Deep tech projects, based on breakthrough scientific or engineering innovations and
new ways of problem-solving, are riddled with uncertainties as both opportunity and
challenge. When technological innovations occur and are well-received by customers
and investors, deep tech projects can be highly profitable, as seen in the rise of the so-
called “Magnificent Seven” deep tech companies during recent years. On the other
hand, enormous R&D spending does not always lead to technological breakthroughs,
and deep tech projects are often met with skepticism and slow recognition on the
market.
Given technological and market uncertainties with deep tech projects, how can
investors differentiate among varieties of deep tech projects and select potentially more
successful and investment-worthy ones? Furthermore, how can project leaders calibrate
strategies of communicating their innovations with customers and investors according to deep tech project types? For instance, how can you tell the differences among various
projects of Apple, Tesla, Meta, and OpenAI, beyond lumping them together under the
generic rubric “deep tech”? Also, how can you systematically assess why, say, Elon
Musk was more successful in managing uncertainties with electric vehicle (EV)
technology than Mark Zuckerberg with the metaverse or Tim Cook with the self-driving
car?
4 Types of Tech Innovations
This article proposes a novel typology of deep tech projects that offers insights into
these questions. Building on the literature’s emphasis on uncertainty in
entrepreneurship,1,2,3 we map out 4 types of the entire tech innovations (of which deep
tech projects are a part) based on two sources of uncertainty: Is technological
uncertainty high (or low) because a given deep tech project uses discontinuous (or
continuous) technology? and Is market uncertainty high (or low) because the deep tech
project targets a potential (or present) market?
4 Types of Tech Innovations
Pivoting Innovation. Tech innovations that couple existing technology with a potential
market discovery fall on this type. Pivoters repurpose and incrementally improve
existing technologies without relying on resource-intensive technological breakthroughs,
thereby minimizing technological uncertainty. Instead, they create values by discovering
a potential market of many customers and seeking feedback and learning from the
market to cope with high market uncertainty.4 Here, project leaders need to focus on a
product-market fit and to show evidence that their product meets a need in the
potentially ever-increasing market. This is also the information that investors are looking
for.
Facebook is a good example of pivoting innovation. Launching it from a college dorm
room in 2004, Facebook’s founders capitalized on the extant widespread use of
computers and the internet (that is, continuous technology that college students could
comfortably tinker with). Its success mainly came from a clever marketing strategy that
satisfied a great pool of potential users, by, for example, simplifying the registration and
ID verification processes.
Forecasting Innovation. This innovation type is characterized by low technological and
low market uncertainties because it utilizes existing or incrementally-updated
technologies for a present (that is, existing) market.5 Once startups have become
successfully established and transitioned to familiar stock names, they often
concentrate on forecasting innovation. Here, project leaders emphasize forecasts of
their earnings momentum relative to that of peers within the established sector in
communicating with investors.
Projecting Innovation. In our typology, projecting and backcasting innovations capture
deep tech projects because both face high technological uncertainty arising from
enormous R&D spending and the use of breakthrough or discontinuous technology that
can disrupt the socioeconomic and cultural status-quo.5 Yet, they differ from each other
in terms of the level of market uncertainty they need to cope with.
Projectors are characterized by low market uncertainty because, while they develop
breakthrough technologies, their innovations, upon available, will likely resonate with the present market of customers who will find their immediate utility, such as a cure for
Alzheimer’s disease. SpaceX’s reusable rocket technology and OpenAI’s ChatGPT are
real-life examples of projecting innovation. Both shocked the public with their radical
innovations and problem solutions, yet were soon to become commercialized and
profitable. Here, project leaders rely on R&D breakthroughs to communicate with
investors because these breakthroughs help defuse high technological uncertainty
associated with projecting innovation.
Backcasting Innovation. Backcasting innovation is the second type of deep tech
projects that features high market uncertainty as well as high technological uncertainty.
This innovation type requires project leaders to imagine the future regarding how
potential customers would embrace and use an entirely new, radical product, and to
work backward to enable such applications.
In our view, backcasting innovation is the least recognized type in the literature, often
conflated with projecting or pivoting innovation. However, it should be regarded as a
distinct type of deep tech projects. To begin with, backcasters are similar to projectors in
that both need R&D breakthroughs to mitigate high technological uncertainty associated with use of discontinuous technology. However, unlike the latter, backcasters face high market uncertainty because their technology, even if developed, may not find a clear fit within the present market, as seen in customers’ confusion about what to do with bitcoin and blockchain technology when it was released for the first time in 2009. Also, while both backcasters and pivoters face high market uncertainty, backcasters’ reliance on discontinuous technology makes their project outcome and market success far more open-ended and unpredictable.
In our recent work, we have demonstrated that Tesla since 2004 under Elon Musk’s
leadership exemplifies how backcasting innovation could be successful in managing
both high technological and high market uncertainties. Specifically, Musk succeeded in
mitigating market uncertainty associated with Tesla’s backcasting innovation, by
convincing the public and the American government about a climate change-impacted
future world and EV technology as a viable solution. Also, Tesla effectively addressed
technological uncertainty by continuously improving product capabilities (such as driving range and battery life) while pursuing experience-based cost reductions. Here, project leaders need to gain investors’ confidence by effectively visualizing expectations of the future and their capability to meet challenges and opportunities in the present through radical innovations.
Challenge of Backcasting Innovation
We further demonstrate our typology’s utility by focusing on the heretofore understudied backacasting innovation, and by analyzing the Meta debacle in 2021 and the Apple car demise in 2024.
The Meta Debacle in 2021 as a Tale of a Backcaster Who Acted Like a Projector.
As explained above, Facebook, founded by Mark Zuckerberg and colleagues in 2004,
started as a typical pivoter because it needed no breakthrough technology. However,
when Zuckerberg attempted to rebrand Facebook into Meta in 2021, it was criticized for
lacking substance in the company’s name change. Although we do not wish to minimize
the unavailability of metaverse technology as a source of Facebook’s problem, we argue that the Meta debacle was a consequence of Facebook project leader Zuckerberg’s misidentifying his company’s essentially backcasting innovation as a projecting innovation and hence failing to mitigate high market uncertainty in communicating with investors.
In 2021, Zuckerberg introduced the metaverse as “a logical evolution” and “the next
generation of the internet,” emphasizing its relevance to the present market of
customers as if the metaverse were a projecting innovation. Of course, the pitch was
undermined by the lack of sufficient R&D evidence, leading investors to lose confidence
in Meta’s proposal. In our view, however, the Meta debacle could not be solely
attributed to metaverse technology’s underdevelopment, that is, high technological
uncertainty. Equally important was Meta CEO Zuckerberg’s inability to convince
customers to imagine themselves as beneficiaries of the metaverse’s more immersive,
3D experience in everyday life through use cases that would facilitate both product and
market development. In consequence, while Meta lost $50 billion in R&D, its loss in
market value amounted to two-thirds of $1 trillion throughout 2022. The Meta meltdown continued until Zuckerberg abandoned the metaverse and jumped on the artificial intelligence bandwagon in early 2023.
The Apple Car Demise in 2024 as a Tale of a Backcaster Who Acted Like a
Forecaster. In 2014, Apple CEO Tim Cook launched a new project to develop and
design a self-driving electric car, codenamed “Project Titan.” However, in February
2024, after a decade-long R&D effort, Cook abandoned the project completely. We
claim that the Apple car demise was a result of Cook’s approaching this intrinsically
backcasting innovation as if Apple were dealing with a forecasting innovation and thus
failing to resolve high technological uncertainty.
Apple’s “Titanic disaster” was driven by both high market and high technological
uncertainties. Although Apple conceived of its new car as basically a luxury living room
on wheels with no steering wheel and no windshield, it was unclear whether customers
would actually want such “a product that didn’t look and feel like a car” without resistance. Yet, while the challenge of a future market discovery was of certain
importance, how Apple approached high technological uncertainty was crucial. By the
time Project Titan took off, Apple had already established itself as a tech giant focusing
on earnings momentum through product launches and updates. In our view, this
forecaster mindset appeared to shape the way Cook and executives tackled
uncertainties. They were “concerned about the vehicle being able to provide the profit
margins that Apple typically enjoys on its products,” especially given the car industry’s
low margins and no guaranteed return from enormous R&D spending.8 More generally,
historically Apple has been a “design” and “branding” company and, compared with
other tech giants, been very conservative on R&D spending, committing only 4-5% of its
revenue to R&D in comparison with its peers’ 10-15%. As such, Apple has tended to
concentrate on developing market acceptance rather than technological prowess and
breakthroughs.
Also, Apple’s commitment to the self-driving electric car as a backcasting innovation
was weakened by the worry that Project Titan might negatively affect Apple’s earnings
momentum by draining engineers from Apple’s other projects like Apple Watch and
iPhone and interfering with their scheduled release.8 Ultimately, although Apple was
spending $10 billion and 2,000 employees of its Special Projects Group were working
on the project for 10 years, the company ended up failing to develop a fully autonomous
driving system required of the Apple car. That the EV industry became far less
compelling and attractive by early 2024 delivered a coup de grace to the faltering Apple
car project.
Both the Meta debacle and the Apple car demise underscore the real risk of
backcasting innovation characterized by the double layers of high technological and
high market uncertainties. They are in contrast with Tesla that, as we have explained,
showcases successful management of both technological and market uncertainties,
with its market capitalization during recent years surpassing that of top rival carmakers
combined. Even so, pioneering backcasters’ success like Tesla’s poses its own
challenge because followers such as BYD can emulate and outperform the first movers
through more efficient and effective entrepreneurial strategies.
If you as a project leader or an investor are involved in deep tech projects, you should
understand their inner differences and associated risks in a systematic and nuanced
way. This article offers you an explanatory typology of tech innovations that can serve
as a useful guideline and assessment tool.
Endnotes
1. F. H. Knight, Risk Uncertainty and Profit (Newburyport: Dover Publications, 1920).
2. M. D. Packard, B. B. Clark and P. G. Klein, “Uncertainty Types and Transitions in the
Entrepreneurial Process,” Organization Science (August 2017): 840-856.
3. T. Grohsjean, L. Dahlander, A. J. Salter, and P. Criscuolo, “Better Ways to Green-
Light New Projects: Organizations Can Make Better Choices About Which R&D
Projects Gain Funding by Managing Bias and Involving More People, MIT Sloan
Management Review (December 2021).
4. E. Ries, The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to
Create Radically Successful Businesses (Crown Business, 2011).
5. D. J. Knight, “Three trips around the innovation track: an interview with Clayton
Christensen,” Strategy & Leadership (2005): 13-19.
6. F. D. Kim and R. Srivastava, “Backcasting from the Future Strategies for Accelerating
and De-Risking Discontinuous Innovations,” California Management Review (2024).
7. D. L. Yohn, “Facebook’s Rebrand Has a Fundamental Problem,” Harvard Business
Review (2021).
8. M. Gurman and D. Bennett, “We Were Promised Apple Cars,” Bloomberg
Businessweek (2024): 17-21.
