Chasm Waxing: A Startup, Cyber-Thriller (6 page)

“
How far does the
Underground Railroad go? These tunnels are
huge
. You could drive two
semi-trucks in either
direction,
and still not hit
pedestrians.”

“
That, I can’t tell you,
Lin.
Your clearance only covers
the
Accelerator tunnel. But I could use
the word,
far
.”

“
General, before this
meeting, I was hoping you could tell me more about how the
Accelerator works? It’s still a little fuzzy to me.”

“
Sure,” he replied, as
they passed another guard station in the tunnel.

“
Defense Innovations
Accelerator is a combination of a VC firm and a startup
accelerator.
VC firms provide
money—capital—to promising early stage, high-growth potential
companies. Lots of famous companies you’ve heard of—Facebook,
Twitter, Tesla Motors, Uber—were started with VC capital. I wanted
the NSA to have its
own
VC firm, like the CIA. They have In-Q-Tel. I
desired a VC that the NSA Director could more easily
influence.

“
Now the entire IC has
benefited from In-Q-Tel.
For
example,
they were an early investor in
Palantir Technologies. We’ve used their software to track terrorist
money laundering.”

“
Don’t you have
DARPA?”

DARPA stood for the
Defense Advanced Research Projects Agency.
DARPA
was initiated
after the shock of the Russian’s launch of
Sputnik in 1957. DARPA used to be an agency that looked 10 to 20
years ahead of the curve. Now, their timeframe was much more
compact. DARPA’s programs helped create the Internet, stealth
aircraft, the global positioning systems (GPS), and Apple’s
Siri.

“
Not only do we have
DARPA, but we also have Intelligence Advanced Research Projects
Agency—
IARPA
. And the NSA has its
own
research and engineering labs. They
all have their
place,
but they can’t match the passion, creativity, and
innovation of startups.

“
I’m convinced that
America’s greatest strength is its innovators. God wired
entrepreneurs
differently. Most government leadership plays not to lose.
Entrepreneurs must play to
win—or
they starve.”

“
Ok, so I understand the
VC aspect. Why did you combine the VC with an accelerator? What
exactly does a startup accelerator do?”

“
The
fundamental
idea is to find
the best companies for your accelerator, provide them with a
curriculum, mentors, office space, and technology to help them to
succeed quickly. Accelerators give their portfolio companies a
template—a recipe—for success. And they facilitate access to
business leaders that have gone through similar startup
challenges.

“
A typical startup
accelerator runs two classes every year. The classes last for about
four months. During that four month period, business mentors
provide intensive instruction—like a mini MBA course—on all aspects
of startups; from
sales,
to product development, to finance.

“
The selection process is
intense and competitive. The accelerator typically receives a small
portion of equity in
exchange
for a small amount of cash,
usually around $25,000 to $50,000. After the four months, the
startups graduate from the accelerator.

“
Now, I wanted Defense
Innovations Accelerator to operate a bit differently than that
standard model. We
conduct
an
eight-week
introductory course for 12
competitively selected startups. After the introductory course, the
12 startups go through a down-selection process.

“
The down-selected
startups receive funding from the VC. The startups remain a part of
our Accelerator for at least one year. If needed, I can offer to
extend this period.
I can also
negotiate with the startups to provide additional funding.
They almost always
need
more money.

“
In our first Accelerator
class, we down-selected five companies.
This includes
Gamification
Systems, who you met yesterday; and CyberAI, who you’ll meet
shortly. A strength of our Accelerator is that we help our
portfolio companies with the intricacies of government contracts.
The NSA also works to expedite clearances for each startup.
Clearances are
a
huge benefit. TS/SCI clearances are difficult to
acquire
. And,
our Defense Innovations’ office complex contains two
SCIFs.”

SCIF—pronounced,
‘skiff’—stood for Sensitive Compartmented Information Facilities.
The SCIFs were secure rooms on the second and sixth floors of the
building. Both
SCIFs allowed for
TS
/SCI communications.

Lin asked, “So, who are the other
companies in the Accelerator?”

“
In addition to
Gamification Systems and CyberAI;
Flashcharge
and Prosthetic Thought went
through the introductory course.
Flashcharge
markets a sophisticated,
wireless charging system. Prosthetic Thought makes neural VR
controllers. The idea is to use thoughts to control video game
movement.

“
Defense Innovations
invested in another company, Swarmbot Corporation. They didn’t go
through the introductory course. I worked with the Co-Founders of
Swarmbot before the NSA, when I was Commander of the
24
^th
Air Force. So these five companies—Gamification,
CyberAI,
Flashcharge
, Prosthetic Thought, and
Swarmbot are the current portfolio companies of the
Accelerator. I’m confident that they’ll be able
to sell technology to the NSA in the next year.”

The
24
^th
Air Force
was located
in San Antonio, Texas. The
24
^th
provided defensive and offensive cyberwar capabilities to the
Air Force. In San Antonio, General Shields got very familiar with
the NSA because the 24
^th
contributed the
Airman
for CYBERCOM.

CYBERCOM was led by
the
NSA,
but comprised of servicemen and women from all the armed
forces. In other words, CYBERCOM was composed of cyber-warriors
from all four services: Air Force, Army, Marines, and
Navy.

“
Y
ou’re going
like
the kid who founded
CyberAI,
Josh
Adler. He’s a genius with AI, and he comes from good stock. His dad
is the billionaire hedge fund manager, Jared Adler.”

4:50 p.m. (EDT), Monday, July 27, 2020
- Columbia, MD

Suite 602, Conference
Room, Defense Innovations Accelerator

Josh Adler, the
Founder
and CEO
of CyberAI Defense, Inc., shot up from the conference room table.
Josh removed his navy blue, Hugo Boss blazer and draped it over the
chair. He wore a blue dress shirt with no tie.

Then he noticed his
armpits. Colossal sweat stains transformed his shirt into a
blue
Rorschach
test. The CEO recognized stress in the Rorschach blot. He quickly
reapplied his coat. Josh took a series of deep breaths, attempting
to reduce his heart rate. He didn’t bother to bring a laptop. All
the flat screens were black. He had no presentation. He had no
demo. This meeting was going to disappoint General
Shields.

Josh was an athletic
looking, five foot ten,
23-year-old
. He had an olive
complexion, with curly brown hair and hazel eyes. On any day except
today, Adler exuded a quiet confidence that naturally attracted
followers.
His enthusiasm for AI
was as contagious, as his dimpled smile. Josh won the gene-pool
lottery. He inherited his mother’s good looks and his father’s
analytical mind. The stereotypical bookish AI expert looked nothing
like Josh.

In order to
start CyberAI, Josh dropped out of MIT during his
junior year. In response, Jared Adler broke all contact with his
son. Josh’s greatest regret about
quitting
school
was leaving the
varsity MIT
Crew team.

The CEO recognized
opportunity when he saw it. He knew that automating
cybersecurity
with AI would lessen the need for human cybersecurity
administrators.
This would
save
money,
while at the same time improving
an enterprise’s security posture.

With the growth of
connected devices—phones, tablets, wearables, home automation
products, sensors, and the like—hooking up to the ‘Internet of
Things,’ the need for security administrators was
skyrocketing
.
There was so much more Internet traffic to watch. Reducing
companies’ need for human
cybersecurity
engineers was a game
changer.

Josh also felt that AI
applied to cyber was just the beginning. His grand strategy was to
use narrow AI for
cybersecurity as
a
beachhead. In time, he could expand to a
stronger form of AI. With this more general purpose AI, Josh could
pivot to other markets, especially
discovery
.

The genesis of CyberAI
Defense was a class assignment for a machine learning course at
MIT.
Some
Fortune 1000 companies and government agencies gave MIT
access to their large datasets. The goal was to see if the students
could derive innovative insights from the data.

Josh choose to work with
three petabytes of
cybersecurity
data supplied by the DHS
and FBI. The data consisted of text-based, log files from hacked
systems. Josh
tuned
his machine learning algorithms to perform
natural language processing—NLP—on
files
from servers, hosts, intrusion
detection systems, e-mail scanners, malware filters, and other
enterprise infrastructure. In this manner, he trained the
computer
to reliably
recognize
patterns left by
cyber-intruders.

Josh astutely perceived
that he could use his algorithm to perform a job that took many
human beings. Securing enterprise infrastructure was a labor
intensive task that caused burgeoning payrolls for IT departments.
And good security administrators were in short supply. They
commanded high salaries. Josh felt if he could improve
enterprise
cybersecurity
by
10
times, or
10X
, CyberAI would be an incredibly
valuable company.

Within the discipline of
computer science, AI had a long history. First coined as a term in
1956 by Dartmouth professor John McCarthy, the goal of AI was to
get computers to
think
like human beings.

To accomplish this task,
over the years,
AI
developed
a
number of
sub-disciplines that touched
upon what it meant to be human. How does a computer learn? How does
a computer process language? How does a computer see?

Due to the low processing
power of computers, progress in these areas was painstakingly
slow.
A noteworthy
milestone
occurred in 1997, when the Deep
Blue chess computer made by IBM, beat world champion chess player,
Garry Kasparov.

After that event, many
observers stated that it was
merely
an example of
weak AI
.
Weak
AI taught a
computer a given task, like chess. Naysayers rightly said that
there was
a significant
gap between playing chess and
truly
thinking like a human
being.
Strong AI
was defined as human
level thought.

The year 2010 was a
watershed in AI. IBM’s Watson
supercomputer
beat a group of former
champions on the TV
game
show
, Jeopardy. This feat was more
impressive than winning a game of
chess.
Watson had to listen to Alex
Trebek’s questions and then process the language to find the
answer. Watson married speech recognition with NLP, performed on
vast
libraries
of information, like Wikipedia.

At the age of 13, Josh
watched the Jeopardy contest.
Watson
completely mesmerized him. From
that day forward, Josh wanted to make machines think. Josh
envisioned AI algorithms that changed the world.