The Deep Tech Investment Paradox: a call to redesign the investor model - Hello Tomorrow
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The Deep Tech Investment Paradox: a call to redesign the investor model
Contents
4 Executive Summary
6 1. Introduction: the great wave of deep tech innovation is coming, but the current
investment model is broken
8 2. Despite growing funding, deep tech suffers from a capital gap with insufficient
and imbalanced investment
12 3. Frictions appear along every link of the deep tech investment chain, while
uncovering four paradoxes
12 a) Venture Capital funds
14 b) Private Equity funds
14 c) Limited Partners
15 d) Corporates
15 e) Governments and Institutions
18 4. Create and spread an articulated narrative for deep tech investment
20 a) Deep tech market and technology risks are high, but they can be mitigated
21 I. Problem-oriented mindset and problem/market-fit
21 II. Design-Build-Test-Learn (DBTL)
21 III. Design to value and cost
21 IV. Deep tech IP
21 b) Deep tech equity needs can be controlled
23 c) Deep tech investment track record is growing but it’s just the beginning
30 5. The deep tech investment model requires a new approach and new principles
The Deep Tech T
his paper is the third of a series of reports 31 a) Adopt a new approach
on deep tech. It focuses on the investment
31 I. Grow in-house deep tech knowledge and build an ecosystem
dynamics of deep tech.
Investment
31 II. Become problem-oriented
In this third report, we outline the different friction
32 III. Rethink the portfolio strategy and the value of distributed returns
sources along the investment chain as well as the
opportunities of investing in deep tech. We conclude 34 b) Embrace new investment models
Paradox: a call with a proposal on how to improve and rethink the
investor model and create new investor archetypes.
34 I. Adapt financing tools to future needs
35 II. Invest for longer
to redesign the
We will address the “why invest now” question
35 III. Adopt new investment structures
and the strategic imperatives that investors must
understand in order to seize the full potential of 37 c) Emphasize the profound and societal impact of deep tech
investor model
deep tech.
38 6. New investment archetypes required in an ecosystem of dynamized players
38 a) Deep Tech Venture Capital funds
42 b) Deep Tech Adaptive Capital
43 c) Deep Tech Venture Building Capital
43 d) Deep Tech Private Equity funds and institutional investors
43 e) Deep Tech-Savvy Corporates
44 f) Governments and Institutions
46 7. Now is the time for investors to seize the deep tech investing advantage
2 THE DEEP TECH INVESTMENT PARADOX: A CALL TO REDESIGN THE INVESTOR MODEL HELLO TOMORROW | BOSTON CONSULTING GROUP 3
•G
overnment & Institutions power research in when SDG and climate concerns are becoming
universities but lack (as a state-mission) sup- ever more central and become mission driven
port for deep tech ventures, to move them from for the coming existential challenges ahead for
grant to venture funding and scaling. humankind.
Despite frictions, four paradoxes arise and raise These principles shape investor archetypes in
hopes that we can rethink the investor model an ecosystem that is shifting from few players
• Deep tech offers an opportunity to rediscover and assumptions trapped in a static equilibrium,
that early venturing mindset, just when VC has to players engaged in the evolution of both the
shifted away from its pioneering roots, relying boundaries and rules of the game in a dynamically
on the power of distributed returns adaptive equilibrium:
• While investors categorize deep tech as risky, • Deep tech VCs are better suited to support
the reality is that not being exposed to deep ventures across investment stages, empowered
tech investment is riskier, as it is poised to dis- by approximate 10-15-year lifetimes, $150-300
rupt incumbents and PE portfolios million fund size, multi-disciplinary teams, a re-
• Barriers to raise deep tech funds are increasing, search engine and a wide network
consolidating capital towards the largest funds, • Deep tech adaptive capital offers a wider array
Executive
Despite investment growing to more than $60 bil-
whereas barriers to innovation and deep tech of financing tools to ventures and a new value
lion in 2020 and its massive disruption potential as
venture building are falling along with the re- proposition to LPs willing to diversify their risk
the Fourth Wave of Innovation, deep tech is hin-
combination of scientific breakthroughs profile and maximize deep tech impact
Summary
dered by the current investment model:
• Investment dry powder has never been so high; • Deep tech venture building capital (e.g., studios,
• Difficulty in shifting from laboratory (grant/
bond returns are expected to be depressed accelerators) broadens investment opportuni-
subsidy-based) to venture funding
while deep tech offers the next wave of invest- ties for the creation and acceleration of deep
• Insufficient and unequally-spread VC funding,
ment returns tech ventures and moves them through key mo-
mostly directed to Synthetic Biology, Artificial
ments of truth, growing deep tech deal flow and
Intelligence and Advanced Materials, and domi-
To solve these paradoxes, it is a prerequisite to signaling new opportunity niches
nated by US ventures
reframe and articulate the narrative for deep tech • Deep tech PE funds have a higher value propo-
• Paradoxically, investment “dry powder” is reach-
investment, and share it widely across ventures, sition on growth of ventures or diversified pro
ing record levels at $1.9 trillion across PE, VC and
direct investors and their LPs: ject financing, and can benefit from vertical in-
Growth money and is at risk of the depressed
• Deep tech market and technology risks are high, tegration; Sovereign Wealth Funds can enrich
returns of bonds and safe investments, pushing
but, once the early science risks have been elim- their portfolio as trusted investors in deep tech
investors towards higher risk-adjusted invest-
inated in the laboratory, they can be mitigated and contribute to societal transformation
ments
by shifting to a problem-market orientation, ac- • Deep tech-savvy corporates act as go-to-mar-
celeration of DBTL cycles, design to value and ket accelerators to catalyze their industry’s eco-
Both the deep tech-based battle against climate
cost and defensible IP systems while validating deep tech business
change and Sustainable Development Goal (SDG)-
• While deep tech ventures require higher early models through a venture client model
supporting progress are being impeded due to
dilutive equity compared to digital, it remains • Governments and institutions provide strate-
frictions along the investment chain, fueled by
controlled on average over time, as revenues gic stimuli, impacting on R&D funding, seed-
mindset paradoxes and investment model biases:
from the first commercialized product enable ing provocative grand challenges, establishing
• VC funds are structurally unfit (lifetime, size, in-
ventures to switch to non-dilutive instruments deep tech hubs and clusters to build the future
centives) to invest in deep tech, relying on the
• Deep tech investment activity is already grow- knowledge workforce needed to scale the mar-
traditional blueprints of ICT (high market risk,
ing with billions invested, unicorn valuations, ket, provide blended finance, emerging talent
low technology risk) and Pharma / biotech
corporate M&A, and is maturing, with sovereign production and matching, and signaling drum-
(high technology risk, low market risk) and they
wealth funds investing directly, most tradition- beat investors
often lack the expertise needed to understand
al funders see the swells but misdiagnose the
advanced science, engineering risks and to sup-
coming wave deep tech represents Deep tech investing presents a unique opportunity
port ventures
for investors as well as a moral imperative
• Part of the VC landscape has lost its original
The deep tech investor model is emerging along • Deep tech addresses massive untapped mar-
“venture” mindset and has ended up relying in-
three design principles: kets (e.g., quantum, nature co-design)
stead on the power of distributed investments
• Adopt a new approach: growing in-house know • The deep tech “tax” is lower than ever (e.g., low-
• Deep tech remains outside the risk profile and
ledge and building a large ecosystem to support er tech costs, descaling infrastructure)
deal flow of most PE funds, despite the high risk
ventures, acquiring a problem-market orienta- • Now is the time to seize a first-investor advan-
of disruption to their portfolio companies by
tion mindset favoring risk mitigation over risk tage, to avoid missing the exponential wave
these new technologies
minimization, and rethinking the portfolio strat- • We estimate that deep tech investments could
• LPs remain risk-averse towards deep tech, pre-
egy thus reshaping the distribution of returns exceed $200 billion by 2025 if this new investor
ferring to invest in “big names” and the largest
• Embrace new investment models with adapted model (and ecosystem) is mobilized into action
funds
financing tools, larger funds with possibly lon- • Investors have a critical part to play in support-
• While at the pointbreak of the innovation wave,
ger timelines, and new investment structures to ing in parallel all the breakthrough solutions that
most corporates are not as well-equipped as ICT
support it. alone can meet the world and society’s most in-
/ PharmaCos to be deep tech-savvy and digest
• Emphasize the profound SDG and societal im- tractable problems
external innovation
pact deep tech ventures aspire to have at a time
4 THE DEEP TECH INVESTMENT PARADOX: A CALL TO REDESIGN THE INVESTOR MODEL HELLO TOMORROW | BOSTON CONSULTING GROUP 5
While there is no such thing as a “deep” technology, Imagine it is the early 1980s and the PC and biotech
successful deep tech ventures all share a unique ap- revolutions are starting to get traction… At that
proach and differentiate themselves with four main time VCs provided the steppingstones to activate
attributes1 (see our report Deep Tech: The Great the disruption. Venture capital pioneers from the
Wave of Innovation) 1960s-1980s invested in science and technology
• Successful deep tech ventures are problem-ori- companies: Georges Doriot (Digital Equipment
ented. Very often they work on solving large Corporation - DEC) and Arthur Rock (Arthur
and fundamental problems: 97% of deep tech Rock & Co) funded the rise of minicomputers
ventures contribute to at least one of the UN’s and microelectronics. Kleiner Perkins, then KPCB,
Sustainable Development Goals. participated in the emergence of semiconductors
• They look at using the best existing or emerging and microprocessors (Sun Microsystems) and was
technologies to solve the problem at hand. As deeply involved in the rise of the biotech industry
a result, they play at the convergence of tech with the creation of Genentech. These pioneers
nologies: 96% of deep tech ventures use at least were the founders of the venture capital industry
two technologies and 66% use more than one and created its forward-looking mindset.
advanced technology. They generate defensive
IP: 70% of deep tech ventures own patents in However, most VCs have found it difficult to explore
their technologies. new horizons beyond biotech and ICT/digital since
• They are shifting the innovation equation from then, and some of them have further constrained
bits alone (digital) to “bits and atoms” (physi- their investment strategy opting instead for the
cal). They build on the ongoing digital transfor- power of distributed returns. They started to de-
mation, the power of data and computation, to pend on the rearview mirror for their investment
develop mostly physical products, rather than strategy rather than looking through the windshield
software. About 83% of deep tech ventures are at what lies ahead.
building a physical product.
• They are at the center of a deep interconnect- While deep tech ventures face both high market
ed ecosystem: because of the complexity of and technological risks (mainly engineering and
the task at hand and the deep scientific back- science risks), these risks are often misunderstood.
ground needed, it is impossible for two people Deep tech ventures are often only seen as requir-
in a garage to come up with a meaningful deep ing bottomless equity funding compared to today’s
tech innovation. Some 1,500 universities and re- scalable Software-as-a-Service (SaaS) and digital
search labs are involved in deep tech, and deep ventures, paired with uncontrollable development
tech ventures received some 1,500 grants from timelines. However, these risks can be methodically
1. Introduction:
governments in 2018 alone. and systematically mitigated leading to controlled
W
hile digital transformation is accelerating development timelines and funding in the long run.
across world economies, catalyzed by the Deep tech has the potential to impact the world In addition to the approach embraced by deep tech
as fundamentally as the Internet did and is leading ventures, a new investment model should be es-
the great wave
Covid pandemic and led by the GAFAMs,
BATXs (tech giants including Google, Apple, Face- the fourth wave of innovation. The first wave tablished that is a better fit with the unique charac-
book, Amazon, Microsoft, Baidu, Alibaba, Tencent, gave birth to the first two industrial revolutions teristics of the field.
Xiaomi) as well as data-savvy startups, a deeper especially through chemical inventions such as
of deep tech revolution is on the way. What we call deep tech
ventures are at the forefront of this wave of techno-
logical innovation. One of the largest constellations
the Haber Bosch process for ammonia or the
Bessemer process for steel production. The second
wave post-WWII, the information revolution, was
Investors need to grow deep tech know-how to ad-
vise and understand the landscape, adopt a prob-
lem-focused and DBTL-based approach to de-risk
innovation is
of satellites in orbit is launched by a startup (Planet driven mainly by corporate labs such as IBM, Xerox investment portfolios and offer appropriate sup-
Labs); another startup is working on building su- Parc, with high-caliber multi-disciplinary teams port (funding and timeline) to their ventures. This
personic airplanes (Boom Supersonic); others lead strongly involved in the scientific community, doing new breed of deep tech investors should bridge the
basic research, among which came the revolution capital gap and help bring deep tech ventures more
coming, but
the synthetic biology revolution (Ginkgo Bioworks,
Zymergen); more of them are revolutionizing food of semiconductors. The third wave, the digital easily through the funnel, while exploring different
by cultivating cell-based meat (e.g., Memphis Meat) revolution, saw the decline of corporate research, exit options including M&A by deep-pocketed and
or through precision fermentation (e.g., Impossi- and the emergence of small disruptive firms, backed ideally “deep tech-savvy” corporates. In parallel,
the current ble Foods), just to mention a few. Some even have
ambitions to unlock the power of atoms: Common-
wealth Fusion Systems and Seaborg Technologies
by venture capital, defining a Silicon Valley model,
focusing on Internet-based ICT/digital giving birth
to Apple, Google, Alibaba, and in biotechnology to
20% of the 2050 target for reducing greenhouse
gas emissions (to bring global warming to a 2°C
target –not even 1.5°C) cannot be achieved with
investment
are planning to build the next small-size nuclear Genentech. US governmental agencies like DARPA, conventional solutions. After a decade of frenzied
(fusion and fission respectively) reactors by 2025, NSF and NIH were no strangers to the last two VC activities in digital, venture capital needs to
D-wave is developing quantum computers and Sila waves. While the innovation engine is seizing and confront head-on the deep tech opportunity stand-
crystallizing over ICT and biotech, the fourth wave is ing in front of it, much needed for our battle against
model is broken
Nanotechnologies uses nanoparticles to improve
Lithium-ion battery capacity. now building with deep tech and nature co-design. climate change and for a sustainable future.
1. N
ote: deep tech is still a nascent terminology, there are still
multiple definitions for deep tech and no single consensus
6 THE DEEP TECH INVESTMENT PARADOX: A CALL TO REDESIGN THE INVESTOR MODEL HELLO TOMORROW | BOSTON CONSULTING GROUP 7Exhibit 1
Exhibit 1: deep tech investments quadrupled between 2016 and 2020
Deep tech total investments
Deep tech total investments in start-ups and scale-ups ($B)
in start-ups and scale-ups ($B)
x4
62
51 56
30
15
Exhibit 2
2016 2017 2018 2019 2020
Exhibit 2
Note: investments include private investments, minority stakes, initial public offerings, and M&A; ~25-30% of undisclosed transactions
Source: Capital IQ, Crunchbase, Quid, BCG Center for Growth and Innovation Analytics, BCG and Hello Tomorrow analysis
100 40
Exhibit 2:Average
average transaction
amount per amounts of deep tech private investments
Medium amount per are rising
private investment ($M) private investment ($M)
75 30
100
Average amount per private 40
Medium amount per private
Average amount per Medium amount per
investment ($M) investment ($M)
private investment ($M) private investment ($M)
50
75 20
30
25
50 10
20
2. Despite D
eep tech investment is on the rise: disclosed 0
25
2016 2017 2018 2019 2020
0
10
2016 2017 2018 2019 2020
funding amounts increased from about $15
billion in 2016 to more than $60 billion in 2020 0 0
growing funding,
(Exhibit 1). Similarly, when looking at private invest- 2016 2017 2018 2019 2020 2016 2017 2018 2019 2020
ments, transaction amounts rose from $13 million to Exhibit 3
$44 million on average, fueled by the acceleration Advanced Materials Artificial Intelligence Synthetic Biology All technologies
of synthetic biology (Exhibit 2), and transactions
deep tech
Note: ~25-30% of undisclosed transactions
involving corporates among investors rose from $5
Sources: Capital IQ;Advanced
Crunchbase; Quid; BCG Center
Materials for Growth
Artificial & Innovation Analytics;
Intelligence Synthetic BCG and Hello Tomorrow
Biology Analysis
All technologies
billion in 2016 to $18 billion in 2020 (Exhibit 3). Private investments with corporates
Exhibit
among the3: private investments
investors ($B) in deep tech involving corporates are on the rise
suffers from a 18.3
capital gap with
Private investments with corporates
among the investors ($B) 14.0 7.1
12.9
insufficient and
3.0 1.7
9.4
imbalanced
Scale-ups
2.1
Start-ups
investment
5.1 11.0 11.2 11.2
0.7
7.3
4.5
2016 2017 2018 2019 2020
Note: ~25% of undisclosed transactions
Sources: Capital IQ; Crunchbase; Quid; BCG Center for Growth & Innovation Analytics; BCG and Hello Tomorrow analysis.
8 THE DEEP TECH INVESTMENT PARADOX: A CALL TO REDESIGN THE INVESTOR MODEL HELLO TOMORROW | BOSTON CONSULTING GROUP 9Long-term interest rates (%)
Nevertheless, deep tech ventures experience issues In addition to this funding gap, deep tech invest- Exhibit 5: long-term interest rates decreased to record-low levels
moving from grant funding to equity. As shown by ment is unevenly distributed across sectors. Fol- 8
Different Funds, almost 50% of grant-funded deep lowing the previous innovation focus on biotech
tech ventures require several rounds of grants be- and ICT / Digital, deep tech ventures in Artificial 7 Long-term interest rates (%) France
fore failing or succeeding at attracting VC funding. Intelligence and Synthetic Biology collected two- 6
This is confirmed in our latest BCG and Hello To- thirds of deep tech investment in 2020 (Exhibit 4), Germany
morrow survey, 41% of deep tech ventures state thus leaving only one-third to the remaining hetero- 5
that “there is more security with grant funding than geneous and vast population of deep tech startups. Italy
4
with equity funding” (Exhibit 7). Synthetic Biology itself has been the fastest grow-
ing technology segment with a CAGR 2016-20 of 3 Japan
61% (after Quantum Computing).
2
United Kingdom
1
United States
0
Exhibit 4: deep tech investment is unequally
Exhibit 4spread with around 80% accounting for
-1
Synthetic Biology, Artificial Intelligence and Advanced Materials
Deep tech total investments by 0,2 61,8
2008 2010 2012 2014 2016 2018 2020 2021
Deep tech totalin
investments by 0,5
technology 2020 ($B) 4,5
Source: OECD
technology in 2020 ($B) 5,2 Exhibit 6 Exhibit 6
8,2
9,6
Exhibit 6: capital raised through SPACs boomed in 2020, mainly driven by the US
33,6 IPOs (20-YTD) by stock exchange
IPOs (20-YTD) by stock exchange
Capital raised throughraised
SPACsthrough
by regionSPACs
in B$ by region in B$ 3x Euronext, 1x DB
~80% of total
Capital
Capital raised through SPACs by region in B$ 3x Euronext, 1x DB
484 NSYE + NASDAQ
484 NSYE + NASDAQ
investments
Europe RoW US
Europe RoW US
83
~2/3 of total 83
investments
74
74
2 2 2 2
Synthetic Biology Artificial Advanced Drones & Photonics & Quantum Blockchain Total
intelligence Materials Robotics Electronics Computing
CAGR
61% 27% 48% 23% 17% 115% -10% Various SPACs with EU
2016-20 79 Various SPACs
70 79 listed in US
focus
focus listed in U
70
Note: investments include private investments, minority stakes, initial public offerings, and M&A; transactions mapped on several
technologies were split equally between these technologies; ~25-30% of transactions remain undisclosed 12
Source: Capital IQ, Crunchbase, Quid, BCG Center for Growth & Innovation Analytics, BCG and Hello Tomorrow analysis 12
8 9
8 9
2
2
Deep tech investment is also uneven at the regional This dry powder is at risk of depressed returns. Low 2016 2017
2016 2018
2017 2019
2018 2020
2019 YTD
2020 YTD
level where the US comprises almost 75% of total (or even negative) interest rates are driving inves-
Note: YTD, year to date is March 23rd, 2021
investments. However, when looking at private in- tors away from bonds and safe placements (Exhib- Source: S&P Capital IQ, BCG analysis
vestments only, Europe and China have grown fast- it 5), towards higher risk-adjusted return pockets
er than the US with respective CAGRs 2016-20 of (equity and stocks). As a matter of fact, the num- While part of this dry powder is actively reserved able to promote a business that could be several
49%, 34% and 28%. ber of active PE investors grew by CAGR 11% over for follow-up rounds, investors have not yet been years out from today, a significant share of them
the past 10 years2 and the S&P 500 annual return able to fully match this excess of available capital first fail because they ran out of money and less
But it’s not because there is a dearth of available over 2009-2019 reached 13.6% according to Berk- with the funding needed by deep tech. Lux Capital because of market risks. Hopefully, this has been
capital. Paradoxically, investment “dry powder” is shire Hathaway (including earnings from dividends managing partner, Peter Hebert analyzes the cap- improving over the past years thanks to deep tech
at record levels (totaling $1.9 trillion2 in Decem- paid by stocks). The most recent symptom of large ital market situation as follows: “near-zero inter- ventures proving their successes to investors”. Fail-
ber 2020, of which $1.1 trillion is in Private Equity pools of available capital is the boom in SPACs est rates have moved trillions of dollars to equities ure to consider a significant capital reallocation not
and $331 billion Venture Capital and $250 billion is (Exhibit 6). on the risk curve looking for better performance, only risks capital missing the rewards of the next
Growth Capital). These record sums are driven by and venture as an asset class has been among the wave of innovation: it also risks slowing down the
PE & VC funds raising capital from LPs more easily greatest beneficiaries. But unless deep tech ven- progress of humankind and our race against time to
than ever before. tures have charismatic founders like Elon Musk combat climate change.
2. Preqin
10 THE DEEP TECH INVESTMENT PARADOX: A CALL TO REDESIGN THE INVESTOR MODEL HELLO TOMORROW | BOSTON CONSULTING GROUP 11•T heir mindset crystallized along the two arche- funds versus $148 million for non-deep tech funds.
types of the previous innovation wave: biotech They lack the size to provide relevant financial
(high technology risk, low market risk) and ICT support and their partnership ecosystem may be
(low technology risk, high market risk) limited. The deep tech investment landscape would
• Deep tech teams inevitably comprise academic benefit from more partners who are capable of both
scientists and too few funds have suitably qual- understanding and funding high-potential projects.
ified experts in-house or a network of advisors,
who can both understand the science and com- Since traditional references do not apply (e.g.,
municate well with the team. According to our clinical trials gates in biotech, customer base /
latest survey, 81% of deep tech ventures con- revenue model / burn-rates in SaaS) or have not
firm that “investors on average lack scientific / yet been properly defined by funds, ventures may
engineering expertise to assess deep tech po- miss milestones and KPI targets because time-
tential” (Exhibit 7). Those issues are especially to-market expectations and business models are
important in the early stages when there is no different, often based on physical products and
or limited commercial traction to compensate. B2B channels. This lack of framework also limits
Because the commercial dynamics of deep tech investors in correctly assessing the deep tech
are not the same as, for example, digital plays, ventures valuations.
VCs struggle to see the true value of a venture’s
IP, technological (i.e., scientific and engineering) But the issue isn’t just that the investment blueprint
risks and opportunities. Investors have issues needs to change: it’s also a matter of finding a
scoping deep tech. Both nascent and complex, different mindset.
deep tech lacks an articulated narrative and, as
a result, suffers from a void of understanding or Historically, first business angels and then VCs were
inaccurate reputation. investment entrepreneurs focusing on breakthrough
science, joining efforts to mitigate its risks and
Exhibit 7: 81% of deep tech ventures build innovative businesses. Influenced by digital /
SaaS success stories making the headlines, the VC
indicate that “investors on average lack
industry has seen a progressive mindset change.
scientific / engineering expertise to
3. Frictions
O
Exhibit 7
bstacles exist at every point in the invest- assess deep tech potential” Short fund lifetime may force managers to invest
ment chain, involving all players in the in- Which of the following statements about too quickly and exit too early in order to meet LP
vestment ecosystem: Venture Capital and fundraising do you agree about
with fundraising
as a deep do tech expectations, sometimes before an investee’s full
appear along
Which of the following statements you agree
Private Equity funds, Limited Partners (LPs), Cor- entrepreneur
with as a deep tech?entrepreneur
(% of deep tech
? (% ventures)
of deep tech ventures) potential is realized. Since the 2000s, the much
porates, Governments and Institutions. lower initial capital needed for new digital ventures
Investors on average lack scientific / engineering
81% made it much cheaper, and faster, to test their
every link of
expertise to assess deep tech potential.
a) Venture Capital funds potential. An exponential digital wave flooded
deal flows. Funds were left with limited time to dig
There is a limited interest from
There are obvious reasons why frictions exist investors regarding deep tech.
48%
into the value proposition of each venture. Some
among standard, generalist funds but deep tech turned from “active-seeker” mode to passive “deal-
the deep tech funds have their own issues too. Generalist funds
which have not yet invested in deep tech can be
There is more security with (non-dilutive)
grant funding than with equity funding.
41%
receiver” mode, as funds with successful deal
reputations often attracted deal-flow automatically.
reluctant to do so for several reasons: Trusted and copy-pasted models of the digital era
investment
Early-stage deep tech entrepreneurs
•Most Venture Capital (VC) General Partners have limited exposure to investors.
33% (e.g., “the Amazon of”, “the Deliveroo of”, “the
(GPs) are used to the structure of large and Instagram of”) became shortcuts to assess the
“safer” funds, comforted by fixed management Pitching to investors is a more difficult
23%
potential of a venture.
chain, while
task than asking for grants.
fees (of one or two percent) based on total As-
sets Under Management (AUM). The larger the Consequently, two opposing VC views started to
It is difficult for deep tech ventures
fund, the bigger the fees for GPs, with associat- to find suitable applications / markets.
16% prevail. Some, like Founders Fund (c.60 ventures
ed economies of scale. Moving away from their in-portfolio for a c.$5 billion fund size), stood for
uncovering four traditional investments could limit their ability
to attract capital from Limited Partners.
Source: BCG and Hello Tomorrow survey across 116 ventures and
investors, March 2021
selective investments in promising companies.
Others, like 500Startups (c.2,500 ventures for
•Unfortunately, most of these funds catego- c.$600 million), turned to the power of distributed
paradoxes rize deep tech as high-risk and uninvestable. If
a fund’s cycle, at ten years, is shorter than the
runway from laboratory to exit, some deep tech
“Deep tech specialized” funds have emerged over
the past years as deep tech became more in vogue.
However, they are on average relatively small: over
returns by betting on large numbers of promising
pitches and teams, hoping that at least one in ten
succeeds to compensate for the nine that don’t.
ventures can look uncommercial. According to 2010-2020, deep tech VC funds raised on average
our latest survey, 48% of deep tech ventures $96 million compared to $106 million for non-deep While performing well in SaaS / digital as a risk
agree that “there is limited interest from inves- tech funds, when including growth funds, the gap minimization approach (see chapter 5 on the prin
tors regarding deep tech” (Exhibit 7). widens with $105 million on average for deep tech ciples for a new investor model), the “spraygun”
12 THE DEEP TECH INVESTMENT PARADOX: A CALL TO REDESIGN THE INVESTOR MODEL HELLO TOMORROW | BOSTON CONSULTING GROUP 13investment strategy doesn’t work for deep tech, are reinventing manufacturing processes from con- therefore blocking deep tech funds growth, which arms need to be equipped to perform deep tech
where time and expert analysis are required to sumer products to industrial goods. Quantum tech- then have to rely on numerous smaller investors. due diligence and support ventures as a VC investor
complete proper due diligence and select the best nology will accelerate drug and protein discoveries (not just provide funding). Next, cooperation can
ventures based on evidence, science, technology, to treat and heal people, unlock complex network Nevertheless, not all LPs have the same approach fail if corporates do not have the appropriate talent
market potential and team composition. In addition, optimization problems such as those in mobility. towards deep tech investment: and structure to work with them and leverage
the standard mindset of maximizing quick returns What companies are ready for it? • Pension Funds, and more specially closed ones, their technologies. Successful integration can be
raises risks of constraining the venture towards are committed to paying benefits every month. difficult to achieve, due to cultural differences.
short-term potential thereby missing out on high Ultimately, there is only one thing riskier than With such responsibilities, they need to focus Incumbents need to overcome the R&D “Not
return opportunities that lie in the long term. investing in deep tech and that is, not being on selected assets classes (a few hundred Invented Here” syndrome, which isolates and
exposed to deep tech investment. million minimum ticket), with a majority of rejects disruptive acquisitions that challenge the
The second order risk of spread-betting and hoping low-risk liquid assets, and few higher-risk less- status quo. Corporate R&D activities are often
that unicorns will compensate for losses, is to fall
into the “too big to fail” spiral. As demonstrated
c) Limited Partners liquid assets (2-5 years), often with a thematic
investing angle (e.g., energy, autonomous
focused on incremental development rather than
major disruption. Incumbents are at the breakpoint
by cases such as Theranos or WeWork, the stakes Similarly, LPs are still reluctant to invest in deep vehicles) of the disruption wave. And finally, by waiting until
are so high that investors may be blind to endemic tech funds due to a perceived mismatch with their • Sovereign Wealth Funds, if not responsible for a venture is market-proven, corporates often pay a
weaknesses (especially uncontrolled cash burn- expected risk/reward profile. They are often neither pensions, balance state strategic priorities (e.g., hefty valuation premium.
rates or technology challenges) or adopt lax sufficiently qualified to understand the science innovation funding, ESG, strategic industries),
governance. behind deep tech nor, as a result, exposed to it.
In some cases, their network includes risk-averse
long-term capital support and liquidity (e.g.,
stock trading, private equity)
e) Governments and
b) Private Equity funds intermediaries such as banks that will dissuade LPs • Family Offices would be good candidates for Institutions
from deep tech investment, or just don’t have the patient capital (10-20 years) as long as they are
On the Private Equity (PE) side, deep tech often right narrative to convince them. guaranteed exit opportunities. Family Offices, Often underestimated as players in the funding
remains outside their investment profile, perceived especially in Europe, first think in terms of landscape, Governments and Institutions form the
as early-stage only and incompatible with their LPs tend to invest in the largest and best-known future generations and legacy, instead of a 10 + backbone of deep tech investment (but not only
skillset. funds and are conservative in their choices. 2-year timeline. However, each family office has deep tech). As conceptualized by Bill Janeway in his
According to Mountain Ventures, only 20% of LPs a different investment philosophy, not always book Doing Capitalism in the Innovation Economy,
At the risk of sounding like a prophet of doom, surveyed invested in a fund they had known for matching deep tech. innovation sits in the middle of a three-player game
history tells us to take heed. PE funds need to invest less than a year. The bias is backed by the fact between markets, speculators and the state. But
in deep tech to anticipate the inevitable disruption
in-play and to diversify their portfolio risk, by either
that the largest funds have proven to be safer: the
spread between top-quartile and median net IRRs
d) Corporates the state plays a specific two-sided role which
should not be forgotten: it facilitates innovation and
divesting condemned assets or investing in deep has steadily risen over the past decade3, explaining At the end of the investment chain sit corporates, it must cope with the consequences of innovation.
tech ventures. The option to simply “buy” this as a why less well-known funds have been chronically whose importance in the investment ecosystem More specifically on the facilitation side, it is
service on the market has two major disadvantages: undersubscribed. There is also a strong network has grown over the last five years. Post-WWII, the public capital that disburses grants to early-stage
first, the capabilities needed to understand and component where LPs tend to invest and reinvest in corporate labs of IBM, Bell or even Dupont, played a ventures, making government and institutions
apply deep tech are far from plentiful, and second, investment managers whom they are close to and crucial role in driving innovation and funding it. But, the highest risk-takers. Leading-edge research
such an approach would fail to capitalize on they trust. today few corporates have the necessary internal at the early stage is fraught with uncertainty, and
important knowledge by combining it with the R&D capabilities and agility to apply the deep tech off-putting to traditional VCs looking for a more
internal investment process. The dominance of the biggest players is reinforced approach. According to our latest survey, 47% of advantageous and efficient risk/reward profile. Bill
as LPs first look at a fund’s track record and founders’ deep tech ventures recognize that “corporates lack Janeway summarizes it as follows: “efficiency is the
PE funds would do well to remember the stress names, instead of its approach: according to agility to work with deep tech ventures”. There are enemy of innovation”.
caused by digital. Many of them saw their assets Mountain Ventures, 60% of LPs say that track record exceptions: for example IBM, Honeywell or Atos on
threatened by digital attackers who exploited their is the number one criterion. Harvard Business School quantum computers and hardware, Microsoft on That is not where it ends: public bodies often
hidden weaknesses, by reinventing customer jour- (HBS) has analyzed the impact of this on venture data storage and computing leveraging DNA and subsidize specific industry segments to provide
neys, improving performance with data analytics, capital as a whole: 5% of venture capital firms raised holographic technologies, Bayer launching Joyn benign market conditions, reducing price and cost;
and leveraging asset-light business models. The half of the total capital between 2014 and 2018. Bio, a joint venture with Ginkgo Bioworks aiming they provide university laboratories and other assets
dominating question was: “is my asset an Uber or at replacing fertilizers with genetically engineered to help researchers; they act both as regulators and
a taxi company? Will it be able to seize the bene- This trend is reinforced by the growing buyout fund microbes. Others compensate by targeted political facilitators for infrastructure and project
fits of digital?” Funds need to ask similar questions size: the average buyout fund size4 rose from $700 acquisitions (e.g., Amazon’s acquisition of Zoox in finance, bringing together stakeholders such as
about deep tech and its power to rewrite the rules. million in 2015 to $1.6 billion in 2019. These top funds 2020, Hyundai’s acquisition of Boston Dynamics for banks, companies, municipalities, associations and
An additional factor underscoring the de-risking are gatekeepers and market makers, relegating deep $920 million in 2020) or investments (e.g., BASF in private investors.
potential of deep tech is the breadth of its impact tech to smaller funds, less addressed by LPs. A vicious Zapata Computing in 2019, Tyson Foods in Memphis
– most deep tech ventures solve large and funda- circle occurs when deep tech funds raise capital but Meats in 2018, Danone in Nature’s Fynd in 2019, On the one hand, governments can ignite deep
mental issues which have applications across mul- lack critical scale for follow-ons, therefore failing Volkswagen in Quantumscape in 2018, Siemens tech ventures through grants and subsidies. On
tiple industries, therefore increasing its de-risking to build a critical positive track record. A second in Lanzatech in 2014). These examples show how the other hand, it can be hard to quit the grants
potential. Synthetic biology is revolutionizing the vicious circle emerges as the largest LPs will not companies can gain a leapfrog advantage by and subsidies world and deal with the VC world:
food we eat with cultivated meat, the clothes we take a significant share in a fund (typically not more investing in market-proven ventures. more than 50% of grant-funded deep tech ventures
wear with bio-produced silk, our petrochemical in- than 10%) due to regulation or risk management, require several grant rounds before they reach a
dustry with engineered microorganisms to produce Such strategies can work but only under specific success proof point and are ready to apply for VC
3. From 3.8 pts for vintage 2006 funds to 11 for vintage 2016
biofuels, and even our medicine with mRNA vac- according to Preqin conditions. First, corporate venture capital (CVC) funding. Governments and Institutions, too, lack
cines. Advanced materials and nanotechnologies 4. Preqin
14 THE DEEP TECH INVESTMENT PARADOX: A CALL TO REDESIGN THE INVESTOR MODEL HELLO TOMORROW | BOSTON CONSULTING GROUP 15Exhibit 8
an efficient network and vital bridges between the investor model for deep tech: a mindset paradox, Exhibit 8: four paradoxes emerge from the current deep tech investment model
academic world and the investment world, both in a risk paradox, a barrier paradox and a funding
terms of visibility and mutual understanding. This paradox (Exhibit 8). Hidden within each paradox is How to adopt a deep tech investment orientation
How to adopt a deep tech investment orientation
means grant-funding alone can be a dead end. The a way forward that helps us rethink the deep tech
Mindset paradox VC investors have a heritage that is aligned VCs have drifted away from that heritage,
Engine, a venture fund spun of the Massachusetts investor model: with deep tech, because of their to an ICT or biotech model of distributed
Institute of Technology observed that most US • Deep tech ideally matches the very origins of longstanding interest in advanced science returns: less aligned with deep tech and its
and breakthrough technology mindset
grant funding plans fail because governments the venture capital mindset focusing on science
do not have the same to privileged access to and breakthrough problem-solving with long-
HowHow to mitigate
to mitigate therisks
the risks in
in deep
deep tech
techand seize
and its opportunities
seize its opportunities
entrepreneurs as VCs and involve them with com term vision (risk mitigation approach) just when
Risk paradox Investors associate deep tech with high risk Investors and incumbents are at greatest
mercial opportunities. VCs have progressively shifted away from their because of a lack of experience in assessing risk if they ignore deep tech, miss the
roots mainly relying on the power of distributed its risk and reward accurately opportunity and thus become vulnerable to
disruption
Although a number of initiatives have been returns and well-established, narrow paths of
laun
ched (the European Innovation Fund; the ICT and biotech (risk minimization approach)
How to establish channels for funding deep tech
Intellectual Property Financing Scheme in Singa • Investors perceive deep tech as risky with both How to establish channels for funding deep tech
pore, France’s Quantum National Plan, the $1 billion technology and market risks colliding with long- Barrier paradox Barriers to fundraising are expanding, with Barriers to innovation are falling, which
National Quantum Initiative Act in the US), most term and high investments and yet it is riskier large legacy funds positioned as the default will enable more deep tech ventures and
governments have not yet developed a broader not to be exposed to deep tech investment option, drawing capital away from new deep thus more investment opportunities
tech funds
policy framework for deep tech. Such policies might at all. Rather deep tech threatens to disrupt
include tax incentives, prefential loan conditions incumbents and PE portfolios, destroying value How to prioritize investment in deep tech
How to prioritize investment in deep tech
and guarantees, investment in tech hubs, and IP • The barriers to raise deep tech funds are
licensing, for example. increasing, consolidating most capital towards Funding paradox “Dry powder” has never been so high ($1.9T) Deep tech is increasingly recognized as the
and safe investment returns are declining, future of innovation, but has not yet been fully
the largest traditional funds and few deep leading investors to accept higher risk accepted as such by investors
As Steve Blank describes in the Secret History of tech funds which grow their unfair advantage
Silicon Valley, many breakthrough technologies that whereas the barriers to innovation and deep
Source: BCG and Hello Tomorrow analysis
have been the foundations of successful ventures tech venture building are falling (e.g., DBTL
– radar, Internet, nuclear technology, GPS – were cycle times decreasing, cost of prototyping and
launched in order to serve the state- (and world-) testing are falling)
missions of beating the Germans during WWII • The investment dry powder has never been
and later the Soviets and Koreans during the Cold so high with depressed returns from cautious
War. The US government harnessed its universities investments in safe havens and bonds shifting
and their brightest minds to win the war; in the to higher risk-adjusted investments, while deep
UK, Alan Turing developed the first computer to tech ventures lack funding and are the next
break German codes. Today, deep tech is a unique wave of investment returns, with valuations not
opportunity for governments to address the UN’s yet sky-rocketing.
Sustainable Development Goals, especially the
climate change challenge. These paradoxes have persisted due to misunder-
standings and a lack of knowledge of how deep
While all these numerous frictions clearly penalize tech ventures succeed and how to fund them. It is
deep tech investment, four core paradoxes surface time to set the record straight.
from analyzing the inadequacy of the current
16 THE DEEP TECH INVESTMENT PARADOX: A CALL TO REDESIGN THE INVESTOR MODEL HELLO TOMORROW | BOSTON CONSULTING GROUP 17Third, deep tech stories should cascade over the •M
arket and science risks are overrated by
three investment levels: ventures, direct investors investors on average, while deep tech investors
(e.g., VCs), and LPs. The narrative is nurtured at disagree that “market risks are too high” at 69%
the venture level. Founders build a story to VCs (deep tech ventures typically offer a 10x better
highlighting the targeted problem and how their solution) and that “science / technology risks
technologies enable a breakthrough solution to it. are too high” also at 69% (deep tech investors
VCs also build their pitch to LPs, bringing together typically invest once the science risk has been
an investment thesis around the problems they left behind in the lab).
are willing to invest in, how they will assess the •
Interestingly, deep tech investors are 47%
potential of ventures, through which mechanisms concerned by too high engineering risks and
money could be invested (see chapter 5). LPs are 48% by equity amount risks. Indeed, and as
the source of all funding to be unlocked for deep articulated below, investors should care about
tech ventures. LPs should also educate their peers how to mitigate these risks. Average investors
to activate funding and grow the deep tech network. are only 22% likely to anticipate these risks to be
too high, disregarded compared to market and
Our latest BCG and Hello Tomorrow survey of deep science risks.
tech investors and deep tech ventures highlights •
Deep tech investors still confirm that “time
an asymmetry of perceptions between deep tech to market is too long” (59%) and that “there
investors and all investors on average (based on the is a lack of exit track record” (54%), despite
average investor feedback received by deep tech accelerating development cycle times and
ventures) (Exhibit 9). a growing investment track record detailed
hereafter
Exhibit 9: different risk perception between deep tech investors and the feedback
Exhibit 9
deep tech ventures receive from investors on average
D
Question to deep tech investors: what are your current perceptions about deep tech investment?
4. Create and eep tech was born in laboratories reserved
for privileged researchers operating within
a small community of experts. This breed of
I completely agree I mostly agree I mostly disagree I completely disagree
Question to deep tech ventures: based on your experience, what is the main feedback you received from investors on average?
spread an
Feedback received
deep tech pioneer is very different from Silicon Val-
ley’s entrepreneur kings. Deep tech entrepreneurs
are often scientists passionate about their technol- Market risks are Science / technology Engineering / scaling Overall equity needs Time to market There is a lack of
articulated
too high risks are too high risks are too high are too high is too long exit track record
ogy but sometimes less able to build a supporting
narrative. Among the many testimonies from sur- 100% 6% 2% 2%
12%
veyed deep tech ventures, it was acknowledged 14% 16%
that “the biggest challenge we faced was being
narrative for
38%
able to tell a story about what the tech means”. 48%
50%
30%
55% 57% 53%
First, deep tech faces a vocabulary problem, groun 45%
deep tech
39% 39%
ded in technicality. A pitch is very different from 44%
a thesis presentation and needs to excite even an 37% 22% 40% 22% 48%
uneducated audience. Pitching genetically-modi- 29% 25%
investment
fied nitrogen-fixing microorganisms may sound ab- 15%
6% 10% 8% 6%
struse, if not like wizardry, to investors. It becomes 0% 2%
even less engaging if it misses the end-applications Investors Ventures Investors Ventures Investors Ventures Investors Ventures Investors Ventures Investors Ventures
or the commercial opportunity and terminology.
Source: BCG and Hello Tomorrow survey across 116 ventures and investors, March 2021
Second, investors may need to read between
the lines of deep tech pitches, either beyond the
technology presentation or dig into the unsaid.
Indeed, scientists and engineers are usually very
conservative about data proofs and evidence:
they may keep additional opportunities which are
only 90% backed by evidence and experiment to
themselves. Investors need to adjust their evaluation
strategies accordingly.
18 THE DEEP TECH INVESTMENT PARADOX: A CALL TO REDESIGN THE INVESTOR MODEL HELLO TOMORROW | BOSTON CONSULTING GROUP 19Most investors know very little about deep tech by David Grimm, Investment Director for the II. Design-Build-Test-Learn (DBTL) III. Design to value and cost
and what they do know can be fraught with UCL Technology Fund. In deep tech, market and Despite the high risks, one deep tech entrepreneur Market and engineering risks are further mitigated
biases or clichés, putting them off. According to technology risks are often integrated, but so are stated that he and his colleagues were “not risk when manufacturing beyond the prototype is
Prime Movers Lab founder, Dakin Sloss, “There are the ways to mitigate them. tolerant but rather risk averse”. They navigate approached with a design to value and cost strategy.
three big myths about [...] deep tech: that it takes through uncertainty in a methodical way. Although It frontloads the cost analysis into the design
longer, that it’s more capital intensive, and that it’s I. P
roblem-oriented mindset and deep tech investors fund breakthrough scientific phase, while making sure that the value (better
higher risk”. On top of the problem solved and the discoveries, they are unlikely to take science risks and possibly cheaper product) is delivered, rather
technologies leveraged in solutions, the narrative
problem/market-fit which are mainly mitigated during the laboratory than addressing them later. Practically, this means
to investors should clarify the de-risking approach Successful venture-backed, deep tech teams discovery phase, funded by governments and mapping the projected cost curve decrease to the
of deep tech, reassure on the control of its equity must address a real problem – a need, a market. philanthropists, and are IP-protected. CEO and specific applications or situations of the problem
needs and emphasize the existing track record This focus on the problem acts like a compass to Managing Partner at The Engine, Katie Rae clarifies where the highest value can be delivered. This
showing that deep tech investment is dynamic and guide the entrepreneur through the valley of death, further that “the frontier between science and way deep tech ventures can minimize the market
that exit opportunities are real. ensuring that there is market-fit at every stage engineering risks is blurry especially in the early adoption risk. SILA nanotechnologies, for example,
of development. To borrow from Seth Bannon, stages, so that deep tech (Tough Tech in the words was able to develop new battery technology using
founding partner at Fifty Years, every deep tech of The Engine) investors have to believe that they globally available components for piloting and bulk
a) D
eep tech market and outcome should pass the “Mr Burns Test” – to are substantially only taking engineering risks and synthesis reactors that scale efficiently. Adoption
technology risks are high, “build a product that Mr Burns (the prototypic self-
absorbed, egoistic, greedy capitalist) would buy not
not pure scientific risks. Whenever science risks
inadvertently resurface, there are still opportunities
risk needs to be further anticipated when dealing
with corporate clients who could be slower to move
but they can be mitigated because it’s sustainable but because it’s the best/ for another grant funding". and adopt a new solution.
cheapest/most convenient.” Similarly, the example
Deep tech lives at the intersection of science and of climate change is too broad to be treated as a Then, the DBTL frames and accelerates the miti “Early win” applications can be identified upfront,
engineering: it usually involves several advanced problem; successful ventures drill down to specific gation of the engineering and scaling risks. The reaching first commercial revenues faster, proving
technologies and has a physical product as its problem roots with enough clients willing to pay DBTL approach in a deep tech context is the the value and financing the cost reduction.
outcome. It’s not an app. Successful deep tech for it, thus identifying the closest problem/market- adaptation of Lean startup methodology to deep Successful deep tech ventures improve the
ventures are not sitting in labs creating a hammer fit to tackle. As highlighted by Russell Tham, Joint tech, and brings together multi-disciplinary teams trajectory (shrinking the time) to profitability if
looking for nails, but rather focusing on the world’s Head, Enterprise Development Group & Strategic from science, engineering and design to maximize design to value and cost is embedded in the first
most intractable problems in domains such as Development at Temasek, investors need to problem solving and de-risking. commercial pilot.
hunger, climate change, pollution, sustainable prioritize ventures with a “strong focus on the go-
energy. Investors may well feel that, given the to-market stakes and the business model, not just Having targeted a problem, the team uses DBTL IV. Deep tech IP
complexity of the problems many deep tech the technology alone”. cycles to iterate and experiment fast. More
ventures address, and the immaturity of their importantly, deep tech teams prioritize in the cycle Because the barriers to entry for deep tech are set
emerging technologies, they are inherently risk- One key differentiator of deep tech ventures is their the most critical risks to secure MVP delivery. As much higher than for digital ventures (while barri-
laden, but these fears are overstated (Exhibit 10). ability to propose a ten-times-better product, not the DBTL cycle rejects sub-optimal pilots, activity ers to run DBTL cycles are falling), they offer a high
Yes, “deep tech is hard” as confirmed in Sifted just a 10% improvement. It is a strong de-risking and capital is directed constantly at mitigating the measure of protection from competition risk, lim-
lever for many ventures once the problem is well- most significant risks upfront, building an all-in-one iting the costs to outcompete rivals. Besides pat-
scoped, but scoping it requires a major effort. A “full-stack” solution, as Eclipse VC describes it. ent protection, scientific complexity and engineer-
Exhibit 10: market and technology risks 2020 HBS survey estimates that problem orientation ing difficulty together offer the investor insurance
Exhibit 10
of deep tech investment can be mitigated and market research are a top contributory factor On the one hand, DBTL cycles lead to continuous against a proliferation of me-too lookalikes that can
to high valuation ventures: “38% of low valuation learning and design adaptation to improve the steal the market. Closing the door behind patented
Market risk
Market risk startups completed at least six months of customer technology, de-risk the solutions, and accelerate deep tech and technology advantage is relatively
research before launching their products, compared time-to-market (and therefore earlier revenues) easy.
High
Prospective to 53% of high valuation counterparts.” Similarly, thanks to falling technology barriers and costs. On
deep tech
venture
(high risks)
“no market need” is the main reason why start-ups
fail5.
the other hand, they will improve the product to fit
customer needs.
b) Deep tech equity needs
can be controlled
Typical ICT
ventures Problem-orientation • Biofoundries like Ginkgo Bioworks or Doulix re-
Design to value and cost duce the time to synthetic biology DBTL cycles
Acceleration of DBTL1 cycles from months to weeks Some investors will argue that even if the market
Intellectual property protection • Commonwealth Fusion Systems focused on and technology risks are mitigated, deep tech still
Deep tech the fastest and least expensive part to improve requires high initial investment. This is generally
ventures reactors, i.e. the magnets instead of the plasma true: compared to SaaS, for example, deep tech has
physics, and shortened the DBTL cycle from a higher capital needs at the early stages. However,
one-year average to one month. it is also true that the lifetime capital needs of a
Traditional • It is not only learning from failure that helped deep tech investment may be no higher than its
investor focus
Typical Biotech SpaceX but also failing early: the first SpaceX counterparts in other fields.
ventures launch failed in 2006. As a result of lessons
learned, SpaceX realized its first successful On the one hand, SaaS ventures typically have low
Low Technology risk
(science & engineering) launch in 2008, only six years after the startup’s early-stage equity needs but for some of them it
Low High
founding. can blitzscale due to high cash burn rate as they
go to market, acquire and retain customers below
1. Design Build Test Learn; Source: BCG and Hello Tomorrow analysis 5. according to CB Insights (2019)
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