1.
AI, Automation, and Economic
Impact - National Security
Implications
Daniel Faggella, CEO at Emerj

2.
Presentation Overview
● Critical trends and commonalities for automate-able work
● Job types and sectors with highest automation risk
● Basic frameworks for thinking through automation impact in major sectors
(including sectors critical for national security), and policy considerations
● Considering artificial intelligence and automation as a competitive advantage
on a national scale

3.
Emerj AI Research
● Emerj is a market research firm focused the implications of
AI for business / government leaders
● Organizations planning very expensive technology initiatives
use us to support critical decisions
● We work with clients as large as the World Bank, and
startups who’ve only raised $10MM

4.
Emerj AI Research
● We examine AI across three dimensions:
○ Applications (Possibilities) “What’s Possible?”
○ Implications (Probabilities) “What’s Working?”
○ Strategy (Plans) “What Action Should We Take?”
● Our work focuses on extrapolating themes
and trends from thousands of interviews,
surveys, and case-study assessments across
critical AI sectors
● Our largest areas of focus: Healthcare,
Finance, Defense

5.
3 C’s of Job Automation
Context, Connection, Coordination

6.
1 - Context
● “Context” refers to the number of competencies required to
perform a job. Automate-able work has predictable inputs,
work steps, and outputs, tends to involve a very limited
number of competencies (low context).
● Blue-collar examples:
○ LOW Context: Welding on an assembly line
○ HIGH Context: Plumbing
● White-collar examples:
○ LOW Context: Insurance underwriting
○ HIGH Context: Managing a procurement department

7.
2 - Connection
● “Connection” refers to the ability to relate to other people on
an interpersonal level, to empathize and attune to the
emotions of others.
● Examples:
○ LOW Connection: Welder on an assembly line
○ LOW Connection: Software engineer
○ HIGH Connection: Elementary school teacher
○ HIGH Connection: High-ticket salesperson
○ HIGH Connection: Hospice nurse

8.
3 - Coordination
● “Coordination” refers to the ability to marshall teams and
resources to reach an orgazation’s goals.
● Examples
○ LOW Coordination: Nurse
○ LOW Coordination: Programmer / software engineer
○ HIGH Coordination: Sales manager
○ HIGH Coordination: Chief technology officer

9.
3 C’s in Depth
Full Presentation: https://www.youtube.com/watch?v=4NEIwKooOoI

10.
People Skills and Automation
● Jobs that might involve people skills are not always safe
from automation.
● Checkout clerks and telephone customer support reps
might use “people skills” on a daily basis, but their jobs are
potentially automate-able.
● Jobs that are predicated on coordination and connection are
relatively safe from automation, but jobs where connection
or coordination are “nice to have”, but not necessary for the
job, are less safe from automation.

11.
Sector-Specific Impact
● The impact of AI on specific sectors (agriculture, banking,
retail, etc.) should be expected to vary from country to
country based on a variety of variables:
○ Processes / management
○ End product
○ Trade agreements with other countries
○ Access to materials
○ Timeline to AI / tech adoption
○ Labor costs (high cost = high automation incentive)
● Deep-dives into an individual country (Romania example
with World Bank) allow for more detailed sector assessment

12.
McKinsey’s Research*
● McKinsey sees the following sectors as ripe for automation in the near term:

13.
Bloomberg’s Research*
● Highest risk: Cashiers, waiters, retail salespeople, sales reps

14.
Emerj Research
● Our interviews and surveys in large enterprises yield a
relatively predictable pattern of the “types” of jobs that are
likely to be automated in major sectors like healthcare,
finance, and heavy industry:
○ Data entry and basic data manipulation roles
○ Low-context customer service and sales enablement roles
○ Extremely wrote processing jobs (insurance underwriters)
○ Search and discovery lackeys (paralegals)
● These low-context jobs (and jobs like them) have a
reasonably high likelihood of near-term automation.

15.
Planning and Preparation for
Automation

16.
Determining Sectors of Relevance
● Assess the industries that matter most for national security,
or international economic competitiveness.
● Considerations:
○ What sectors do we suspect to grow most in the coming ten
years?
○ What sectors represent the greatest opportunity to strengthen
our national security position?
○ What sectors involve technologies that we might repurpose for
defense / security objectives?

17.
Determine Critical Processes
● Determine the processes / aspects of this work that is the
most automate-able (lacking the 3 C’s):
○ Agriculture:
■ Seed planting (for certain crops), weeding (for certain crops)
○ Services:
■ Customer service, rote white-collar paperwork
○ Manufacturing:
■ Machine maintenance, manual assembly
○ Etc.

18.
Determine Policy Levers
● Determine the policies that might help to:
○ Plan for with potential job loss scenarios
■ Examples: Determine plans to dead with losses in
transportation jobs, customer services jobs, etc
○ Retrain or upskill workers in critical fields
■ Examples: Expand the skillsets of data entry and data
management employees, possibly by giving them more
department-specific contextual skills
○ Enhance international competitiveness
■ Examples: Relieve restrictions to data access, or provide
tax benefits to incentivize certain kinds of innovation

19.
Defense / Security Levers
● Almost every major industry sector has AI-related
applications that can be repurposed for defense objectives:
○ Heavy industry: IoT data and predictive maintenance
○ Online media: Behavior attribution and user profiling /
predictions
○ Finance: Enterprise search and document understanding
○ Etc.
● Nations with a knowledge of their sectors of strength will
likely want to turn economic power into defense advantage.

20.
Defense / Security Levers
● The United States seems to be at least bringing these
questions to bear.
● My most recent presentation at National Defense University
(for generals / colonels) was less about AI innovation, and
more about broad-repurposing and private sector
partnerships.
● I suspect that a large part of our research in the defense
sector in the coming years will focus on translating economic
strength into defensive strength.

21.
Artificial Intelligence and
Economic / Defense
Competition

22.
“Proprietary Data Plume”
● In 2017 we conducted a lengthy series of interviews with
venture capital investors in Silicon Valley, asking them to
explain their methods for determining the potential of an
artificial intelligence company.
● The “Proprietary Data Plume” was the greatest theme that
we encountered in this series of interviews.
● The idea is that a company has the most monopoly power
and sustained defensibility when it collects proprietary, high-
value data, and leverages it continuously. The same basic
concept can be applied to countries.

23.
“Proprietary Data Plume”
● In order to create a “Proprietary Data Plume”, companies (or
nations) must involve the following three traits:
● High value: Data must have direct relevance for the
objectives that the organization is aiming for.
● Perpetual, building with momentum: Data continuously is
collected, and encourages more and more collection.
● Proprietary: Data collected is unique and not accessible by
other parties.

24.
“Proprietary Data Plume”
● No other companies have access to Google’s search data,
or Amazon’s purchase data.
● Outside of China, Google is untouchable for when it comes
to search, because Google continuously gets more searches
(interaction from users) than anyone else, allowing it to
continue to present better and better results to users, further
solidifying it’s position from any potential competitor, and
gathering more user engagement (data) in the process.
● The same could be said of Facebook and Amazon.

25.
Example: India

26.
Example: India
● India is currently the back office of the world (IT services,
customer services, outsourced IT).
● IT services represents arguably the most important growth
sector in the Indian economy, and it’s one of the relatively
most profitable sectors as well.
● Value proposition: Cheap, fast, less risk than hiring full-time
staff or hiring domestic (US, EU) consultants / teams.

27.
Example: India
● Three possible scenarios with India IT services in the AI era:
● 1 - Get automated and lost most of the business in that
sector. (Bad)
● 2 - Switch from low-level IT work to low-level data
management work (labeling data, cleaning data). (Still bad)
● 3 - Encourage these companies to build robust products and
BE THE ONES who automated and capture the value of
these currently outsourced processes. (GOOD!)

28.
Application
● Question:
● How might these same dynamics be managed in your
country?

29.
A Place for Regulation?

30.
What Next?

31.
That’s All, Folks
I’ve included a list of some of the resources
from this presentation on my final “References”
slide.
Feel free to send along an email with questions,
or for a copy of the slides:
dan@emerj.com
Twitter - @danfaggella
emerj.com

32.
Resource List
Emerj resources:
● https://emerj.com/ethics-and-regulatory/job-security-in-the-age-of-artificial-intelligence/
● https://emerj.com/ai-podcast-interviews/surviving-the-machine-age-kevin-lagrandeur/
● https://emerj.com/ai-market-research/artificial-intelligence-risk/
● https://emerj.com/ai-market-research/artificial-intelligence-in-india/
● https://www.huffingtonpost.com/entry/investors-speak-where-ai-drives-real-value-in-
business_us_58f6e1eee4b0f5cf16c7baaf
● TEDx on automation: https://www.youtube.com/watch?v=4NEIwKooOoI
Outside resources:
● https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/where-machines-could-
replace-humans-and-where-they-cant-yet
● https://www.bloomberg.com/graphics/2017-job-risk/