Gig work, future of AI, service science, T-shapes, open source

1. Online Platform Economy and Gig Workers: A
USA Perspective
Jim Spohrer
Director, IBM Cognitive OpenTech
Questions:
Twitter: @JimSpohrer
LinkedIn: https://www.linkedin.com/in/spohrer/
Slack: https://slack.lfai.foundation
Presentations on line at: https://slideshare.net/spohrer
For Expert Workshop:
The Platform Economy as an Opportunity
for SMEs and Start-ups (December 9, 2020)
Skills for Industry
Fostering New Services and Jobs Creation
Expert Workshop
Promoting: Service Science & T-Shaped Skills

2. Today’s Talk:
• Title: Online Platform Economy and Gig Workers: A
USA Perspective
Abstract: My goal in ten minutes would be (1)
to summarize a few experts POV (Kenney &
Zysman, Owyang) that do a good job of showing
a balanced perspective of the challenges,
opportunities, and ongoing evolution, as well as
(2) describe their connection to the service
science community - speaking at previous
conferences, contributing papers to books - and
then (3) provide a few thoughts about what
platform related best practice actions (policy
directions) are aligned with creation of T-shaped
adaptive innovator with broad awareness of
advanced technologies, work practices, and
growth and entrepreneurial mindsets.
12/3/2020 (c) IBM MAP COG .| 2
Consumers and society at large are expecting
more from business. Embracing those
responsibilities can be good for shareholders, too.
the urban escalator seems to be faltering ….
regional programs to improve job quality …
Reframing employer responsibilities…

3. Gig work is
growing…
Platforms help.
Torpey E, Hogan A (2016)
Working in a gig economy.
Pros and cons of gig work
Pros: Flexibility, Passion
Cons: Inconsistency,
Scheduling, Lack of benefits

4. Reskilling…
Limited….
Silo to silo.
ILO (2017) Helping the gig
economy work better for
gig workers
WEF (2017) Towards a
reskilling revolution - a
future of jobs for all. URL:

5. Definitions – getting beyond silos
Farrrel D, Grieg F (2014)
Online Platform
Economy.

7. The future of employment (Sapjic 2019)
The Size of the Gig Economy
1. About 36% of US workers are now involved in the gig economy. (Gallup)
2. 40% of US-based workers generate a large part of their income via the gig economy. (PYMNTS)
3. The yearly increase in the number of freelance workers keeps getting higher. (Wonolo)
4. In 2018, US independent workers spent a billion hours per week freelancing. (Upwork)
5. The gig economy is expanding three times faster than the US workforce as a whole. (Forbes)
6. US freelancers contributed $1.28 trillion to the American economy in 2018. (MBO Partners)
7. In 2013 alone, micro-businesses owned by freelancers generated $2.4 trillion. (AEO)
8. If the gig economy keeps growing at its current rate, more than 50%
of the US workforce will participate in it by 2027. (Wonolo)

8. The future of employment (Sapjic 2019)
Job Security and Satisfaction in the Gig Economy
9. More than 50% of full-time independent workers feel more financially secure
than those in traditional employment. (MBO Partners)
10. 51% of freelancers would not go back to traditional work for any amount of money. (Upwork)
11. 26% of millennials think the gig economy offers more security than traditional work. (T. Rowe Price Group)
12. 84% of freelancers are living their preferred lifestyle compared to just 54% of those working in traditional jobs. (Upwork)
13. 78% of gig workers say they’re happier than those working traditional jobs, while 68% say they’re healthier. (McKinsey)
14. One in six traditional job workers would like to become an independent earner. (McKinsey)
15. Those who work in the freelance economy by choice are the most satisfied group within the workforce. (McKinsey)
16. 64% of independent workers intend to stay independent. (Small Biz Labs)
17. 61% of independent workers freelance by choice. (Upwork)
18. The average freelance hourly rate ranges from $11 to $28 across the globe. (Payoneer)
19. 81% of full-time freelancers work as independents in order to be their own boss. (Statista)

9. The future of employment (Sapjic 2019)
The Gig Economy and Tech
20. More than 70% of freelancers find jobs through online markets and gig economy websites. (Payoneer)
21. More than three in four freelancers say technology makes it easier to find work. (Upwork)
22. 53% of young adults in the US use a smartphone to search for a job. (Federal Reserve Bank St. Louis)
23. 54% of freelancers use Facebook for self-promotion. (Payoneer)
24. 20-30% of the workforce in the US and EU-15 countries is involved in the gig economy. (McKinsey)
25. The gig economy in the UK doubled in size between 2016 and 2019. It now comprises 4.7 million workers. (The Guardian)
26. The number of freelancers has increased by 24% in the period from 2008 to 2015. (IPSE)
The Future of the Gig Economy
27. 90% of freelancers think the industry has an even brighter future ahead of it. (Upwork)
28. 42% of young people freelance. (Upwork)
29. Millenials will form 75% of the global workforce by 2025. (Forbes)
30. 80% of large US companies plan to switch to a flexible workforce. (Intuit)
How big is the US gig economy? 2019, 57 million gig workers in the US economy, amounting to 36% of all US workers.

10. Upskilling…
T-shapes …
Beyond Silos.
Rodgers S (2016) Jeremiah
Owyang on the Collaborative
Economy.
Kenny M, Zysman J (2016) The
Rise of the Platform Economy.

11. Upskilling…
T-shapes
Gardner P, Maietta HN
(2020) Advancing Talent
Development: Steps
Toward a T-Model
Infused Undergraduate
Education.
Moghaddam Y, Demirkan
H, Spohrer J (2018) T-
Shaped Professionals:
Adaptive Innovators.

12. Concluding Remarks
1. Early days – more data needed to inform policy around platform economy and gig work
2. Clear benefits to realize– universal healthcare and basic income would “solve” two key drawbacks in USA
3. Clear challenges to tackle - platform super-powers versus government institutions – predictable outcome
1. See (Van Dijck J, Poell T 2018) – re “moderation” concept in book ”The Platform Society”
4. Needed: Reskilling is a bandaid (silo to silo) – Upskilling will be key (make a job, not take a job) to platform society
5. Needed: T-shaped adaptive innovators – depth (problem-solving with tech) and breadth (collaboration-communications)
1. Broad knowledge of advanced technologies (AI, Data Science, IoT, Quantum, Cloud, GreenTech,Regtech, etc.)
2. Work practices skills (agile, service design, open technologies, etc.)
3. Mindset (growth mindset, entrepreneurial mindset)
6. Needed: Service Science – Competing for collaborators in value co-creation is a constant while tech/org changes.
1. From a service science perspective balancing winner-take-all and improve-weakest-link important
2. Also, T-shaped adaptive innovators is best for capacity building in industry, academia, government, and regional small enterprises
7. Best practice examples: MIT-NSF, IBM, USA-Cities, Databricks, Seldon.

13. References
• Autor D, Mindell D, Reynolds E (2020). The Work of the Future: Building Better Jobs in an Age of Intelligent Machines. MIT Work of the Future Task Force. URL:
https://workofthefuture.mit.edu/wp-content/uploads/2020/11/2020-Final-Report.pdf
• Farrel D, Grieg F (2014) Online Platform Economy. JP Morgan Chase. URL: https://www.jpmorganchase.com/institute/research/labor-markets/jpmc-institute-
online-platform-econ-brief
• Gardner P, Maietta HN (2020) Advancing Talent Development: Steps Toward a T-Model Infused Undergraduate Education. Business Expert Press. URL:
https://www.amazon.com/Advancing-Talent-Development-Undergraduate-Education/dp/1951527062
• Hunt V, Simpson B, Yamada Y (2020) The case for stakeholder capitalism. McKinsey Report. URL: https://www.mckinsey.com/business-functions/strategy-and-
corporate-finance/our-insights/the-case-for-stakeholder-capitalism
• ILO (2017) Helping the gig economy work better for gig workers. URL: https://www.ilo.org/washington/WCMS_642303/lang--en/index.htm
• Kenny M, Zysman J (2016) The Rise of the Platform Economy. Issues in Science and Technology. Vol. XXXII, No. 3, Spring 2016. URL: https://issues.org/the-rise-of-
the-platform-economy
• Moghaddam Y, Demirkan H, Spohrer J (2018) T-Shaped Professionals: Adaptive Innovators. Business Expert Press. URL: https://www.amazon.com/T-Shaped-
Professionals-Innovators-Yassi-Moghaddam/dp/194784315X
• Rodgers S (2016) Jeremiah Owyang on the Collaborative Economy. Dassault Systemes – Navigate the Future. URL: https://blogs.3ds.com/northamerica/jeremiah-
owyang-on-the-collaborative-economy/
• Sapjic DJ (2019) The Future of Employment –30 Telling Gig Economy Statistics. Small Business by the Numbers. URL: https://www.smallbizgenius.net/by-the-
numbers/gig-economy-statistics/#gref
• Spohrer JC (2011) On looking into Vargo and Lusch's concept of generic actors in markets, or “It's all B2B… and beyond!”. Industrial Marketing Management.
2011;2(40):199-201.
• Spohrer J (2017) IBM's service journey: A summary sketch. Industrial Marketing Management. 2017 Jan 1;60:167-72.
• Spohrer J, Kwan SK, Fisk RP. (2014) ”Marketing: A Service Science and Arts Perspective”. In Roland T. Rust and Ming-Hui Huang Handbook of Service Marketing
Research (489-526). [Competing for collaborators is the constant across time]
• Torpey E, Hogan A (2016) Working in a gig economy. USA Bureau of Labor Statistics. URL: https://www.bls.gov/careeroutlook/2016/article/mobile/what-is-the-
gig-economy.htm
• Van Dijck J, Poell T, De Waal M (2018) The platform society: Public values in a connective world. Oxford University Press. [book review]
• WEF (2017) Towards a reskilling revolution - a future of jobs for all. URL: http://www3.weforum.org/docs/WEF_FOW_Reskilling_Revolution.pdf

14. Jim Spohrer, IBM Director, Cognitive OpenTech
Jim Spohrer directs IBM’s open-source Artificial Intelligence
developer ecosystem effort. After his MIT BS in Physics, he
developed speech recognition systems at Verbex (Exxon) before
receiving his Yale PhD in Computer Science/AI. In the 1990’s, he
attained Apple Computers’ Distinguished Engineer Scientist and
Technologist role for next generation learning platforms. He was
CTO IBM Venture Capital Group, co-founded IBM Almaden Service
Research, and led IBM Global University Programs. With over ninety
publications and nine patents, he received the Gummesson Service
Research award, Vargo and Lusch Service-Dominant Logic award,
Daniel Berg Service Systems award, and a PICMET Fellow for
advancing service science. Jim was elected as LF AI Technical
Advisory Board Chairperson and ONNX Steering Committee
Member (2020-2021). Jim is ISSIP.org founding board member.
12/3/2020 (c) IBM 2020, Cognitive Opentech Group 14

15. Roundtable:
Inclusive Prosperity in a Digital World
Jim Spohrer
Director, IBM Cognitive OpenTech
Questions: spohrer@gmail.com
Twitter: @JimSpohrer
LinkedIn: https://www.linkedin.com/in/spohrer/
Slack: https://slack.lfaidata.foundation
ISSIP Workshop: Future of Work and Learning (Thur Dec 10, 2-5pm PT) –
http://www.issip.org/issip-discovery-summit-future-of-work-learning2/
EU Sponsored Workshop on Gig Workers: (Wed Dec 9, 8-11am ET) –
https://eskills2030.eu/workshop-3-expert-webinar.html.
Presentations on line at: https://slideshare.net/spohrer
For Kauffman Foundation Workshop:
The Future of Work, Entrepreneurship, and
Access to Opportunity (December 3, 2020)
Roundtable: Inclusive Prosperity in a Digital
World
Host: Derek Ozkal, Senior Program Officer,
Entrepreneurship, Ewing Marion Kauffman
Foundation

16. 12/3/2020 16
1955 1975 1995 2015 2035 2055
Better Building Blocks

17. Timeline: Every 20 years,
compute costs are down by 1000x
• Cost of Digital Workers
• Moore’s Law can be thought of as
lowering costs by a factor of a…
• Thousand times lower
in 20 years
• Million times lower
in 40 years
• Billion times lower
in 60 years
• Smarter Tools (Terascale)
• Terascale (2017) = $3K
• Terascale (2020) = ~$1K
• Narrow Worker (Petascale)
• Recognition (Fast)
• Petascale (2040) = ~$1K
• Broad Worker (Exascale)
• Reasoning (Slow)
• Exascale (2060) = ~$1K
1712/3/2020 (c) IBM 2017, Cognitive Opentech Group
2080204020001960
$1K
$1M
$1B
$1T
206020201980
+/- 10 years
$1
Person Average
Annual Salary
(Living Income)
Super Computer
Cost
Mainframe Cost
Smartphone Cost
T
P
E
T P E
AI Progress on Open Leaderboards
Benchmark Roadmap to solve AI/IA

18. Timeline: GDP/Employee
12/3/2020 (c) IBM 2017, Cognitive Opentech Group 18
(Source)
Lower compute costs translate into increasing productivity and GDP/employees for nations
Increasing productivity and GDP/employees should translate into wealthier citizens
AI Progress on Open Leaderboards
Benchmark Roadmap to solve AI/IA

19. 12/3/2020 (c) IBM MAP COG .| 19
T-shaped Adaptive Innovator: Deep Problem-Solving and Broad Communication/Collaboration
Advanced Tech: AI to IoT to Quantum, Green RegTech, etc.
Work Practices: Agile, Service Design, Open Source
Mindset: Growth Mindset, Positive Mindset, Entrepreneurial
Many disciplines
Many sectors
Many regions/cultures
(understanding & communications)
Deepinonesector
Deepinoneregion/culture
Deepinonediscipline

20. “The best way to predict the future is to inspire the
next generation of students to build it better”
Digital Natives Transportation Water Manufacturing
Energy Construction ICT Retail
Finance Healthcare Education Government

21. Narrow AI
Emerging
Broad AI
Disruptive and
Pervasive
General AI
Revolutionary
▼ We are here 2050 and beyond 21IBM Research AI © 2018 IBM Corporation
The evolution of AI
Borrowed from David Cox, IBM-MIT Lead

22. 22September 2018 / © 2018 IBM Corporation
Petaflops = 1,000,000,000,000,000 or a
million billion = 10 ** 15
Megaflops = 1,000,000 = million = 10 ** 6
Gigaflops = 1,000,000,000 = billion = 10 ** 9
Larges Super Computer in the World,
= 13 MegaWatts of Power (HOT!)

23. 23September 2018 / © 2018 IBM Corporation
Exascale = 1,000,000,000,000,000,000 or a
billion billion = 10 ** 18
Megaflops = 1,000,000 = million = 10 ** 6
Gigaflops = 1,000,000,000 = billion = 10 ** 9
Human Brain
= 20 Watts (COOL!)

24. Timeline: Leaderboards FrameworkAI Progress on Open Leaderboards - Benchmark Roadmap
Perceive World Develop Cognition Build Relationships Fill Roles
Pattern
recognition
Video
understanding
Memory Reasoning Social
interactions
Fluent
conversation
Assistant &
Collaborator
Coach &
Mediator
Speech Actions Declarative Deduction Scripts Speech Acts Tasks Institutions
Chime Thumos SQuAD SAT ROC Story ConvAI
Images Context Episodic Induction Plans Intentions Summarization Values
ImageNet VQA DSTC RALI General-AI
Translation Narration Dynamic Abductive Goals Cultures Debate Negotiation
WMT DeepVideo Alexa Prize ICCMA AT
Learning from Labeled Training Data and Searching (Optimization)
Learning by Watching and Reading (Education)
Learning by Doing and being Responsible (Exploration)
2018 2021 2024 2027 2030 2033 2036 2039
12/3/2020 (c) IBM 2017, Cognitive Opentech Group 24
Which experts would be really surprised if it takes less time… and which experts really surprised if it takes longer?
Approx.
Year
Human
Level ->
+3
See: https://paperswithcode.com/sota

25. Two disciplines: Two approaches to the future
Artificial Intelligence is almost seventy-years-old discipline in computer
science that studies automation and builds more capable technological
systems. AI tries to understand the intelligent things that people can do
and then does those things with technology. (https://deepmind.com/about “...
we aim to build advanced AI - sometimes known as Artificial General Intelligence (AGI) - to
expand our knowledge and find new answers. By solving this, we believe we could help
people solve thousands of problems.”)
Service science is an emerging transdiscipline not yet twenty-years- old
that studies transformation and builds smarter and wiser socoi-
technical systems – families, businesses, nations, and other special
types of responsible entities and their win-win interactions that
transform value co-creation and capability co-elevation mechanisms
that build more resilient future versions of themselves – what we call
service systems entities. Service science tries to understand the
evolving ecology of service system entities, their capabilities,
constraints, rights, and responsibilities, and then then seeks to improve
the quality of life of people (present/smarter and future/wiser) in those
service systems.
26-30 July 2015 3rd International Conference on The Human Side of Service Engineering25
Automation
Transformation
Service systems are dynamic configurations of people,
technology, organizations, and information, connected
internally and externally by value propositions, to other
service system entities. (Maglio et al 2009)

26. Service Science: Conceptual Framework
12/3/2020 (c) IBM MAP COG .| 26
Service Science

27. Smarter and Wiser Service Systems:
Entities working to transform to
better future versions of themselves
Past Present Future
Organizational
Units
Family
Clan
Family
Business/Nation
Family
Business/Nation
“CyberGlobal
AR/VR Platforms”
Change Generalist
(Breadth)
Specialist
(Depth)
T-shaped
(Adaptive
Innovator Depth
and Breadth)
Constant Competing for
collaborators:
win-win games
Competing for
collaborators:
win-win games
Competing for
collaborators:
win-win games
12/3/2020 (c) IBM MAP COG .| 27

28. Trust: Two Communities
12/3/2020 IBM Code #OpenTechAI 28
Service
Science
OpenTech
AI
Trust:
Value Co-Creation,
Transdisciplinary
Trust:
Ethical, Safe, Explainable,
Open Communities
Special Issue
AI Magazine?
Handbook of
OpenTech AI?

29. Timeline: Short History
12/3/2020
© IBM Cognitive Opentech Group (COG)
29
Dota 2
“Deep Learning” for
“AI Pattern Recognition”
depends on massive
amounts of “labeled data”
and computing power
available since ~2012;
Labeled data is simply
input and output pairs,
such as a sound and word,
or image and word, or
English sentence and French
sentence, or road scene
and car control settings –
labeled data means having
both input and output data
in massive quantities.
For example, 100K images
of skin, half with skin
cancer and half without to
learn to recognize presence
of skin cancer.