DAZE Melbourne 2015: Obey the Power Law – The Importance of Hit-Driven Thinking in Venture Capital and Technology

1. OBEY
THE
POWER
LAW
The
importance
of
hit-­‐driven
thinking
in
venture
capital
and
technology

2. Babe
Ruth:
Home
runs
define
success.

3. In
VC,
a
fund’s
best
investment
returns
more
$
than
all
other
investments
combined.

4. As
expected,
great
venture
capital
funds
have
more
“home
run”
investments.

5. And
great
funds’
home
runs
are
of
greater
magnitude.

6. But
great
funds
also
lose
money
more
oOen
than
good
ones
do.

7. To
be
hit-­‐driven,
need
to
trade-­‐off
probability
of
success
with
magnitude
of
impact.
100%
0%
High
Low
Probability
of
success
Magnitude
of
impact

8. Consensus
Non-­‐
consensus
Right
Wrong
Nature
of
belief
Outcome
In
VC,
this
translates
to
being
“non-­‐consensus
right.”
“Vulture”
Money
already
in,
race
to
remaining
profit
opportunity
“Thunderlizard”
Big
breakthroughs
not
obvious
at
:me
of
investment
“Lemming”
Lowest-­‐risk
failure
via
safety
in
numbers
“Babe
Ruth”
Big
swing
and
a
miss

9. How
valuable
is
“consensus”?
Expert
predicUons
oOen
have
liVle
predicUve
value.

10. Most
of
us
think
of
the
world
as
Normally
Distributed.

11. But
power
laws
govern
most
of
what
happens
in
technology.

12. Here
are
some
reasons
why.
Zero
marginal
cost
of
so=ware
reproduc@on
makes
hit
products
dispropor@onately
profitable
Consumer
demand
for
new
technologies
is
usually
very
difficult
to
predict
Mutually
reinforcing,
mul@-­‐step,
posi@ve
feedback
loop
between
plaIorms
and
applica@ons

13. “Companies
spend
more
than
$2
trillion
on
acquisi@ons
every
year.
Study
a=er
study
puts
the
failure
rate
of
mergers
and
acquisi@ons
somewhere
between
70%
and
90%.”
Example
#1:
Most
tech
M&A
transacUons
are
complete
failures.

14. But
the
few
that
succeed
can
be
completely
transformaUve.

15. Example
#2:
Winner-­‐take-­‐most
markets
(applicaUons)

16. Example
#2:
Winner-­‐take-­‐most
markets
(infrastructure)

17. Example
#2:
Winner-­‐take-­‐most
markets
(search)

18. Example
#3:
“10x
engineers”
Task
Best
engineer
Worst
engineer
Coding
@me
1
20
Debugging
@me
1
25
Program
size
1
5
Program
execu@on
speed
10
1

19. Example
#4:
InsUtuUonalised
Hit-­‐Making.

20. Example
#5:
Technology
Udal
waves
and
incumbent
failure.

21. Embrace
power
curve
thinking
in
your
organisaUon.
Incen@vise
outcome
value,
not
frequency
of
success
Make
dispropor@onately
big
investments
in
a
few
products
designed
to
appeal
to
large
audiences
Make
failure
allowable
(not
desirable):
Quan@ty
+
Learning
=
Quality
Find
secrets
(“non-­‐consensus
rights”)
Don’t
overweight
expert
predic@ons