Pervasive, intelligent cloud ecosystems, spectacular firms and frontier firms 021218

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Pervasive, Intelligent Cloud Ecosystems, Spectacular Firms and Frontier Firms: How Cloud Computing,
the Internet of Things, Artificial Intelligence and “intelligent Analytics” will spur Growth and Jobs
Robert B. Cohen, Senior Fellow, Economic Strategy Institute, February 5, 2018
How are Pervasive Cloud Ecosystems with the Internet of Things, Artificial Intelligence and Machine
Learning Different from IT as we have known it? In 1995, Frances C. Cairncross1
proposed the term the
“death of distance” to define how the Internet ties the world together so closely that the limitations of
distance are overcome. Cairncross also argued that the Internet would support new types of innovation
and be incorporated into many new products. She accurately described the early trajectory of the digital
revolution.
As we discuss below, the “death of time” is a suitable, though strong, term for the next phase in the digital
revolution. In this phase, several things change the relationship between software and data analysis and
time. For one, firms more rapidly create and revise software. They use continuous integration and
continuous delivery (CI/CD), microservices and other techniques that shorten these times. Target is a good
example of this change2
. Target was using “silos and processes that slowed innovation” and needed to
“keep up with the pace of change” especially during the holidays when it received over 7 million orders
from its stores. To address this, Target moved from a more traditional configuration management tool to
an agile model that accelerated software development between 2015 and 2016. Target shortened its time
to develop new software from 1440 minutes to 5 minutes, increased the number of applications it was
using from 3 to 42 and expanded the number of software deployments it did each day from 2 or 3, to 90.
In Appendix 1, we depict the enormous reduction in the time required to create new software in recent
years. This places Target’s effort in a more general context. The decline we illustrate there is a 10,000-
times reduction in the time to create new software over a 14-year period. Facebook, Google and Yahoo
are other firms that have risen to the top of their fields by adopting new processes and building up internal
skilled teams to speed software development.
Second, many firms move to employ “intelligent” data analysis where they apply artificial intelligence (AI)
and machine learning (ML) to data gathered from sensors on machines – the Internet of Things (IoT) --
from websites, from transactions and from service and manufacturing processes within the firm to take
actions to optimize operations. Firms that were early to deploy IoT and perform early machine analysis,
such as Boeing3
, Ford, Rolls-Royce, and John Deere have reported sizable efficiency gains from IoT alone.
We would expect the efficiency gains from joining IoT to AI and ML plus “intelligent” data analysis – where
machines adjust processes based upon analytics – might create even larger benefits.
1
Frances C. Cairncross, The Death of Distance: How the Communications Revolution Is Changing Our Lives, Harvard
Business School Press, 2001. Cairncross’s first publication using this term was "'The Death of Distance,' The
Economist, September 30, 1995.
2
Heather Mickman, “DevOps at Target: Year 3,” DevOps Enterprise Summit 2016, November 2016.
https://www.youtube.com/watch?v=1FMktLCYukQ
3
See my study, “Boeing and Digital Data Innovation: Changing Infrastructure to enhance Data Analytics and
Services offered to Airlines,” OECD “Enabling the Next Production Revolution” project, January 3, 2016.

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We argue that success in shortening the time to develop new software and deploy “intelligent” data
analysis transforms competitiveness. We employ the phrase, the “death of time,” because it focuses on
the chief benefit of pervasive, intelligent cloud ecosystems across a wide range of industries:
1. Creativity and innovation raises firms’ productivity and gives them important advantages that
derive from a new model for software development. They also learn how become unrivalled
in their ability to exploit analytics. This enhances efficiency throughout the firm. In software
development, firms4
turn to continuous delivery/continuous integration5
, DevOps and
microservices to replace proprietary intellectual property (IP) software for infrastructure and
applications. They create innovative solutions for releasing software that is often Open Source
and based on rapid development, promoting broader adoption of their innovations and
resulting in small costs. The innovations benefit from the acceleration of software
development and result in firms building large groups of software engineers. With Open
Source, firms also have a best in class level of software to build upon and recognize that when
they submit innovative software back to repositories such as GitHub, they will be vetted and
improved by other developers. With “intelligent” data analytics, firms master the ability to
employ the Internet of Things (IoT), artificial intelligence (AI) and machine learning (ML). Once
these capabilities are in place, they can not only manage a vast network of factories, but also
streamline a host of other enterprise operations from marketing to acquisitions to human
resources.
2. Once firms deploy not only large teams of IT engineers but also software and data analytics
teams that are knowledgeable about Open Source software, continuous integration and
continuous delivery (CI/CD) as well as DevOps, AI and ML they have distinct advantages over
their competitors. Major investments in knowledgeable software engineers and data
analysts/scientists who work with leading-edge technology processes and tools create a big
difference in productivity between “software-enriched” and “analytics based” firms and their
adversaries. These skills and capabilities can act as a barrier to competition. Unless the
competitors devote a similar sizable scale of resources to build expertise in rapid software
4
We developed these points based upon a summary of the benefits of Facebook’s software development, largely
using Open Source software. It is our belief that Facebook has adopted the additional techniques to speed
software development that we mention here. See Al Hilwa, “Analyst Watch: Ten reasons why open-source
software will eat the world,” Software Development Times, April 23rd, 2015.
https://sdtimes.com/communities/analyst-watch-ten-reasons-why-open-source-software-will-eat-the-world/
5
For a glossary defining the technical terms used here, please see Appendix 1.

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development6
, data analytics and related technologies, they will be less productive and
unable to compete7
. Catching up, once firms are behind, is a challenge.
3. Competitors with the ability to build similar large teams with these skills will do so and create
what have been called “superstar,” “frontier,” “front runner,”8
or “high performer”9
firms in
different industries. These high productivity firms will differentiate themselves further from
other firms by obtaining a greater market share as consumers and business customers
become more price sensitive10
.
4. Software creators and data analytics professionals will hone their skills and learn what the
usage patterns are for software they have created and be able to analyze patterns in data
quickly. They will do this before their competitors can. This reinforces the “lock-in” that occurs
through constant innovation that is required in environments that encompass the Internet of
Things, Open Source software, DevOps, CI/CD, microservices, etc. Fast innovation becomes a
lynchpin for business success.
5. As firms operate in a digital environment, the key elements in the economy become data and
information. Firms will win in this environment if they are highly-efficient users of “intelligent
analysis” employing “software-enhancing” technologies. Removing “friction points” will let
them automate many processes and shorten their time to completion. Automation11
will also
allow less-skilled employees to manage what had once been very professional tasks. They
accomplish this by deploying systems that we cited above, including the Internet of Things
(IoT), artificial intelligence (AI) and machine learning (ML). They also depend on intelligent or
cognitive analysis that implements decisions based upon the data that is analyzed.
6. Enterprises “increase the efficiency of engineering work” by standardizing on organizational
practices for working with Open Source software, “intelligent” data analytics and “software-
enhancing” technologies. This optimizes functions within an enterprise. It results in less waste
and speedier on-ramping of additional developers or skilled workers.
6
There is some evidence of a “catch up” in DevOps skills. In 2017, the gap between “high performers” and “low-
performers” narrowed for the first time during the Puppet Labs and DORA annual surveys DevOps. The difference
in deployment frequency dropped from 200x more frequent in 2016 to 46x more frequent in 2017. The lead time
for changes fell from 2555x to 440x. The authors report that although the gap “narrowed for throughput
(deployment frequency and change lead time), [it] … widened for stability (mean time to recover and change
failure rate). We speculate that this is due to low-performing teams working to increase speed, but not investing
enough in building quality into the process. The result is larger failures, and more time to restore service. High
performers understand that they don’t have to trade speed for stability or vice versa, because by building quality
in, they get both.” Dr. Nicole Forsgren, Jez Humble, Gene Kim, Alana Brown and Nigel Kirsten, 2017 State of
DevOps Report, Puppet Labs and DORA, p. 21. https://puppet.com/resources/whitepaper/state-of-devops-report
7
See the discussion below about “superstar” and “frontier” firms on pages 6 and 7.
8
PwC, "Sizing the prize: What’s the real value of AI for your business and how can you capitalise?" 2017. p. 8.
https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf
9
Dr. Nicole Forsgren and others, pp. 7 and 21.
10
We draw this point from David Autor, David Dorn, Lawrence F. Katz, Christina Patterson, and John van Reenen,
“The Fall of the Labor Share and the Rise of Superstar Firms,” NBER Working Paper No. 23396, May 1, 2017.
http://www.nber.org/papers/w23396 and https://economics.mit.edu/files/12979
11
This is also referred to as “augmentation software.” CB Insights, “Expert Automation and Augmentation
Software,” January 4, 2018. https://www.cbinsights.com/research/expert-automation-augmentation-software-
eaas/?utm_campaign=Social%20Market%20Maps&utm_content=66165316&utm_medium=social&utm_source=t
witter. This post argues that machine intelligence is being used to augment and automate many professional tasks.

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This new digital focus will have a far larger impact on the economy than any earlier stage of the use of
information technology (IT). Firms, operating as almost entirely “digital-based” businesses, will create
new industries and jobs. Innovations in the cloud-led group of technologies described here will spread
across a wide range of business operations from human resources to procurement and marketing12
.
These technologies will have important short- and long-term impacts on the U.S. economy. The charts
below describe our “working framework” to explore these impacts. Our proposed work differs from
previous studies because we plan to estimate upside benefits as well as the size of displacements due to
automation. We also plan to evaluate how software innovations and sophisticated data analysis result in
efficiencies and productivity gains that go beyond an initial phase of automation. The software
innovations include not only DevOps and containers, but also sophisticated data analytics.
From our viewpoint, this shift will make intelligent analytics more relevant to a business’ decision-making.
Intelligent analytics will evolve to a point where “smart” algorithms can reprogram machines. We also
expect, as noted above, that digital businesses will begin to tailor products and services to consumers’
preferences.13
This should provide them with real advantages in the marketplace, particularly when they
can offer consumers and business purchasers better prices and quality, enabling the digital businesses to
gain greater market share and raise their productivity.14
How These Technologies May Affect the Economy. The charts below offer an overview of how IoT, when
closely tied to AI and ML, might affect the economy. The first chart describes how IoT deployment, rather
than being deployed as an isolated technology, will very likely combine with AI and ML to provide rapid
insights into processes and services. This will improve productivity through greater profits and cost
savings. Automation will account for a major part of the early-stage, short-run cost savings and
productivity gains as it displaces manual jobs. We expect, as a few writers15
have suggested, that there
will be medium- and long-term adjustments by firms as they become more digital and expand their
expertise in data analysis and software, as well as key subject-matter areas, such as building cars. This
transformation of workforces will make digital skills extremely valuable. In fact, we believe that once firms
master these technologies, they will construct new business models including ways to exploit their digital
advantages to enter new fields before their competitors.
In the medium- and long-term, we expect changes in the workforce to be different than what other studies
have found. After an initial phase of automation tied to the deployment of robots, IoT and AI, firms will
very likely begin to redefine complex software and data analysis jobs. They will create software algorithms
that simplify the tasks for what were once highly technical occupations. This would open opportunities to
“upskill” middle- and lower-level data-oriented jobs to perform what had previously been complex
12
This is the conclusion of a recent SAP study about the benefits 100 leading firms achieved after they adopted the
wide range of technologies that are the focus of this paper. SAP Center for Business Insight, “4 Ways Leaders Set
Themselves Apart,” 2017. https://d.dam.sap.com/a/mxUAna
13
A good example of this is the case of Tesco, where the firm is creating a 360-degree view of its customers,
optimizing spend-and-save offers, and predicting sales. See Heath Terry et. al., “AI,” Goldman Sachs Equity
Research, November 16, 2016, p. 74. https://tech.fpt.com.vn/wp-content/uploads/2017/12/tai-lieu.pdf
14
See the case we discuss below. David Autor, David Dorn, Lawrence F. Katz, Christina Patterson, and John van
Reenen, “The Fall of the Labor Share and the Rise of Superstar Firms,” NBER Working Paper No. 23396, May 1,
2017. http://www.nber.org/papers/w23396 and https://economics.mit.edu/files/12979
15
PwC, “Sizing the Prize: What’s the real value of AI for your business and how can you capitalise?” 2017. Pp. 7-8.
https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf

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processes. This move to simplification16
has parallels in other fields such as the nuclear power industry;
there, medium-level employees now run complex nuclear energy-producing facilities. Simplification was
also an important feature of the PC revolution, where professions enabled by computers expanded. At
the same time, the rapid growth of computer software and services created large numbers of jobs; both
effects accounted for 18 percent17
of the net new jobs created after 1980.
As shown in the second chart, “Front Runner,” or early mover firms would exploit their ability to analyze
and act on the large amounts of data they possess to enter new business areas. This would, in the short-
and medium-run, expand their lead over their competitors.
16
A recent CB Insights study agrees with this argument. CB Insights, “Expert Automation and Augmentation
Software,” January 4, 2018. https://www.cbinsights.com/research/expert-automation-augmentation-software-
eaas/?utm_campaign=Social%20Market%20Maps&utm_content=66165316&utm_medium=social&utm_source=t
witter argues that machine intelligence is being used to augment and automate many professional tasks.
17
James Manyika et. al., “Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation,” McKinsey Global
Institute, December 2107. p. 40. https://www.mckinsey.com/global-themes/future-of-organizations-and-
work/what-the-future-of-work-will-mean-for-jobs-skills-and-wages
Impact of the Internet of Things, Artificial Intelligence and Machine Learning on Economic Growth
Internet of Things Productivity Gains Exploitation of Newly Developed Analytic Capabilities
Plus Artificial Intelligence → Cost Savings from Automation → in related Fields.
Plus Machine Learning Largely Manual Jobs Investment in New Opportunities.
↓ ↓ ↓
Rapid Insights into Data "New Business Models" Development of Additional Analytic Capabilities
Through Dynamic Analysis. Results from Building Expertise within the firm and in Cloud Service Providers
Potential for Tweaking Business Models, in Data, Data Analysis, Data Preparation
Greater Service Orientation, More and Operational Areas Related to ↓
Intimacy with Consumers. Firm-wide Analytic Capabilities. ↘ Simplifying New Jobs Related to Data Analytics and AI:
An Acceleration of Decisions. Automation of Some manual roles. "Upskilling" of Middle-Level and Lower-Level Jobs
Quick Modification of Processes. ↓ As a result of the automation of skilled processes and
↓ "New Consumer Related Jobs" → application of algorithms to complex tasks.
Supplemented with Blockchain Drawing upon Firms' Access to Lots of Data This simplifies the skills required for technical jobs
and other Transactional Tools and Ability to Tailor Products to Consumer and opens opportunities at Lower and Middle-Level Roles.
at points in its development Preferences.

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Analysis that is Relevant to the Present Project. Previous academic research has analyzed several themes
that are the focus of our efforts. One theme in this research is whether new services, such as cloud
services, have an impact on productivity and GDP growth. A second theme is what explains the significant
differences between some firms, called “superstar” or “frontier” firms, and other companies. These
“superstar” or “frontier” firms are high-productivity firms that historically increase their market shares.
They are also synonymous with many of the firms we are studying. The third stream is work on what
automation will mean for jobs. Here, many initial studies concluded that automation would cause large
dislocations of jobs. Later work examined whether augmentation software, which helps expand the range
of skills that workers who collaborate with intelligent systems might bring to a job, might deepen, rather
than diminish work that humans perform. In some cases, writers have argued that augmentation software
could expand jobs. In others, they believe that many professionals might find their jobs in jeopardy.
One paradox of research has been that while cloud computing has grown rapidly, official statistics indicate
that the productivity of information and communications technologies (ICT) contribute nothing to growth.
Carol Corrado and David Byrne18
analyzed whether the ICT sector might revive U.S. productivity and
economic growth if mismeasurement issues were addressed. They report well-known U.S. data that show
the relative productivity of ICT capital has been eroding for 10 years. This suggests that computer and
communications capital not contributing to productivity and growth. Corrado and Byrne find that
purchases of ICT services, such as public cloud services from Amazon, Microsoft and others, are identical
to the services firms produce when they own their ICT capital. They conclude that contrary to current U.S.
data, if the data on ICT include own-purchases of computers and software – from Amazon and others --
this contributes 1.4 percent per year to overall labor productivity growth. A quarter of this the
contribution of ICT to output per hour is due to the diffusion of ICT technology through purchases of cloud
18
David Byrne and Carol Corrado, “ICT Prices and ICT Services: What do they tell us about
Productivity and Technology?” Finance and Economics Discussion Series 2017-015. Washington: Board of
Governors of the Federal Reserve System, https://doi.org/10.17016/FEDS.2017.015.
Impact of the Internet of Things, Artificial Intelligence and Machine Learning on Consumer Demand for Improved Products and Economic Growth
2017-2030 2030 and after Benefits for AI "Front Runners"
Especially in early part of this period, New technologies linked to AI have been adopted Front Runners' advantage is Superior Consumer Insights.
Productivity Gains are Greatest. during the 2017-30 period. These result in better chances for Front Runners to tap
Labor productivity gains account for Consumers increase their demand for Improved into Consumer Preferences, tailor Output to Match
58% of all GDP gains from AI. Products. them, and Capture a Bigger Slice of the Market.
Many gains are from Automating Largely attributed to higher Quality
Routine Tasks and Augmenting and more personalized Products. Front Runners' ability to shape Product Development
Employees' Capabilities. around insights into customers makes it harder for
Competitors to keep up.
These gains diminish in the early The share of the GDP impact from GDP Impact from Product Innovation and Consumer
2020s, and the main benefits shift to Product Innovation increases over time. Uplift increases after 2030. Quality improvements and
Consumption Side Impacts Consumption uplift results in $9 trillion gain Personalization of Products drive these gains.
in GDP in 2030.
Capital-Intensive Industries are likely Increase in consumption creates a virtuous Already see these advantages in how Music, Video, and
to see the Greatest Productivity Gains Cycle where there are more Data Touchpoints Entertainment are Produced, Distributed and Consumed.
from AI since many Processes can be and more data, Better Insights, Better Products New business models may give rise to new Market Leaders.
Automated. and, as a consequence, More Consumption. Some traditional firms may be displaced.
Healthcare, automotive and financial services have the
greatest potential for AI-linked Product Enhancement
and Disruption.
On-Demand Manufacturing and Ai-related Entertainment
Content targeting can contribute to the advantages.
Note: Some of the estimates and the overall evolution of benefits is drawn from: PwC, "Sizing the prize: What’s the real value of AI for
your business and how can you capitalise?" 2017. pp. 5, 7-8. https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf

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and related services.19
This analysis suggests that consideration should be given to adjusting official
statistics to reflect spending by cloud service providers on own-purchases of ICT capital.
David Byrne, Carol Corrado, and Daniel Sichel20
use this earlier analysis to estimate how much spending
on ICT would change if they adjust for own-produced computers and software. Google, Amazon and
Microsoft build and develop most of the computers or servers and software they use “in-house.” If this
spending were included in the current statistics, it would increase spending on ICT capital. The authors
find that if this adjustment had been made for 2015, it would have increased nominal IT equipment and
software investment by $117 billion, or 0.65 percent of GDP. If investment in ICT capital were counted
this way, it would have increased “the average annual growth rate during 2005-2015 by a little over 1
percentage point compared with official estimates.”21
The authors express their belief that the move to
public cloud services or remotely accessible cloud services, i.e., using capital and software not owned by
private firms but by cloud service providers, will change the economy in extraordinary ways. It will also
have “important consequences for the structure of the economy, productivity growth, and economic
measurement”.22
A second theme we examine is the role of highly productive firms in the economy. These firms have
assembled large groups of sophisticated engineering or IT teams that are far ahead of the competition.
We review two papers that have analyzed these firms. The “superstar” firms are ones we incorporate in
our analysis, Google, Facebook, Apple, Amazon, Uber, Airbnb, Walmart, and Federal Express. “Frontier”
firms are also considered to be highly productive.
David Autor, David Dorn, Lawrence F. Katz, Christina Patterson, and John van Reenen 23
explored why
there appear to be superstar firms that have benefited from increased market concentration. These firms
also have a declining share of labor’s revenues in their own revenues, a factor linked to the long-term
erosion of labor’s share of GDP. Autor and his co-authors develop a new framework for firm behavior that
is based upon a “winner take most”24
model. If consumers become more sensitive to prices and quality
due to greater product market competition/globalization or new technologies, a small number of firms
end up gaining a large part of the market. The authors argue that this occurs because very productive
firms employ more inputs and have a higher market share. Thus, under monopolistic competition if
consumers become more price sensitive, the “superstar” firms will benefit more. Since they have fixed
overhead labor costs superstar firms will have higher revenue-based total factor productivity. So, as
consumers or corporate buyers become more price sensitive25
, they would shift more output to the
“superstars.” The result is that the reallocation of market share in an industry increases the concentration
of sales and further decreases the national share of labor in GDP.
19
Corrado and Byrne, p. 34.
20
David Byrne, Carol Corrado, and Daniel Sichel, “The Rise of Cloud Computing: Minding Your P’s and Q’s,” Bureau
of Economic Analysis Advisory Committee, March 12, 2017. https://bea.gov/about/pdf/acm/2017/bcs-cloud-bea-
2017-05-10.pdf
21
Byrne, Corrado and Sichel, “The Rise ….,” pp. 25-26.
22
Byrne, Corrado and Sichel, “The Rise ….,” p. 1.
23
David Autor, David Dorn, Lawrence F. Katz, Christina Patterson, and John van Reenen, “The Fall of the Labor
Share and the Rise of Superstar Firms,” NBER Working Paper No. 23396, May 1, 2017.
http://www.nber.org/papers/w23396 and https://economics.mit.edu/files/12979
24
Autor and others, p.2.
25
Autor and others, p.2.

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We suspect that the inputs and technologies employed by the “superstar” firms include many of the
technologies we have discussed above. This would create economic advantages that enable these
companies to become hyper-productive. This picture seems to corroborate the key points we enumerated
on pages 1 and 2. There, we define the role that software plays in “advantaging” firms that build the skills
and teams they need to accelerate software development. We believe that our method of evaluating the
level of software, data analytic and Internet of Things sophistication in firms across many industries shares
a common perspective with the work of Autor and his co-authors as well as with others including the
OECD paper we discuss next.
Dan Andrews, Chiara Criscuolo and Peter N. Gal26
of the OECD also examine why “superstar,” or what they
name, “frontier firms,” dominate the modern economy. This analysis, like the Autor, Dorn, Katz and others
study cited above, focuses on highly productive firms. The authors find that despite the global decline in
productivity, “frontier firms” demonstrated strong productivity growth. These firms are “at the global
productivity frontier – defined as the most productive firms in each two-digit industry across 23 countries
– are typically larger, more profitable, younger and more likely to patent and be part of a multinational
group than other firms.”27
The authors examine why “frontier firms” capture the largest share of highly
productive new technologies and find that these technologies take far longer to reach less productive
firms, as they do in the case discussed by Timothy Bresnahan and Pai‐Ling Yin.28
In conclusion, the paper
illuminates a highly uneven process of technological diffusion that is consistent with an early-adopter-
dominant model. Here, global frontier technologies only diffuse to laggards once they are adapted to
country-specific circumstances by the most productive firms within each country (i.e. national frontier
firms).”29
To explore this in greater depth, the authors examine differences in the productivity and size of
national frontier firms vis-à-vis the global frontier and explore how laggard firms might catch-up to the
national productivity frontier. They use econometric analysis to propose that “well-designed framework
policies can aid productivity diffusion by sharpening firms’ incentives for technological adoption and by
promoting a market environment that reallocates resources to the most productive firms.”30
The third theme we explore is whether AI will destroy or create jobs. The initial research in this area stirred
fears of massive unemployment from automation. The best-known analysis, a paper by Carl Benedikt Frey
and Michael A. Osborne31
, extends the task-oriented model for estimating computer replacement of jobs
– which assumes that manual jobs will be the main ones automated -- by considering activities where
bottlenecks might be addressed by engineering innovations and affect less manual positions. Most of the
26
Dan Andrews, Chiara Criscuolo and Peter N. Gal, “Frontier Firms, Technology Diffusion and Public Policy: Micro
Evidence from OECD Countries”, OECD Productivity Working Papers, 2015-02, OECD Publishing, Paris, p. 2.
http://www.oecd-ilibrary.org/docserver/download/5jrql2q2jj7b-
en.pdf?expires=1517544740&id=id&accname=guest&checksum=6034434EE9145A4DEC36D653C4A2C306
27
Andrews, Criscuolo and Gal, p.2.
28
Timothy Bresnahan and Pai‐Ling Yin, “Adoption of New Information and Communications Technologies in the
Workplace Today,” Stanford University, June 21, 2016. www.nber.org/chapters/c13762.pdf to appear in Shane
Greenstein, Josh Lerner, and Scott Stern, editors, Innovation Policy and the Economy, Volume 17.
29
Andrews, Criscuolo and Gal, p. 2.
30
Andrews, Criscuolo and Gal, p. 2.
31
Carl Benedikt Frey and Michael A. Osborne, “The Future of Employment: How Susceptible are Jobs to
Computerisation? Oxford Martin School, Programme on the Impacts of Future Technology, University of
Oxford, Oxford, UK, September 17, 2013.
https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf

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jobs that would not be automated would have “labor inputs into perception and manipulation tasks,
creative intelligence tasks, and social intelligence tasks.” By using these criteria and tasks identified in the
more recent U.S. O*Net labor statistics, Frey and Osborne forecast that “47 percent of total US
employment is in the high-risk category, meaning that associated occupations are potentially automatable
over some unspecified number of years, perhaps a decade or two.”32
Erik Brynjolfsson and Andrew
McAfee have taken a similar view of the dislocation of jobs due to automation. They assert that workers
with “special skills or the right education … can use technology to create and capture value.”33
Conversely,
workers without these skills will be displaced by computers, robots and other digital technologies that can
master these skills.
A more pessimistic view of AI and jobs also comes from CB Insights34
, an investment bank. It’s analysis of
AI and augmentation software, which adds new skills to jobs, is that the rise of this software will destroy
many professional jobs. The report’s main finding is that “the jobs of MBAs and their white-collar brethren
will also be impacted dramatically by AI.” This will include many highly-paid positions, such as wealth
managers, traders, human resource executives, journalists, software developers, teachers, accountants
and compliance officers. The study does suggest that there will be a new class of workers, “AI-enhanced
assistants.” They will do countless tasks that are “usually beyond human capacity” and continue to rely
upon experienced managers to handle subjective decisions they cannot make by themselves.
Thomas Davenport and Julia Kirby35
take a more optimistic view. They contend that augmentation
software used in intelligent machines will deepen what humans do today rather than diminish it. The
result will be to create jobs that draw on creativity and humans working with machines. They find that
intelligent machines will allow us to take on “tasks that are superior—more sophisticated, more fulfilling,
better suited to our strengths—to anything we have given up”. As a result, smart machines will become
“partners and collaborators in creative problem solving.” Davenport and Kirby opine that:
“employers must be convinced that the combination of humans and computers is better than
either working alone. That realization will dawn as it becomes increasingly clear that enterprise
success depends much more on constant innovation than on cost efficiency. Employers have
tended to see machines and people as substitute goods: If one is more expensive, it makes sense
to swap in the other. But that makes sense only under static conditions, when we can safely
assume that tomorrow’s tasks will be the same as today’s.”36
(my emphasis)
This view is congruent with our conceptual framework in pages 1 and 2, where we argue that competition
and productivity are driven by a constant need for innovation, not cost efficiency or savings. So, we would
agree with the emphasis Davenport and Kirby have placed on firms building on their ability to innovate.
32
Frey and Osborne, pp. 24 and 38.
33
Brynjolfsson, Erik, and Andrew McAfee. The Second Machine Age: Work, Progress, and Prosperity
in a Time of Brilliant Technologies. New York and London: W.W. Norton & Company, 2014, p. 11.
34
“Dear MBAs, AI is Coming for You: The Coming Wave of Expert Automation & Augmentation Software (EAAS),”
CB Insights, January 4, 2018. https://www.cbinsights.com/research/expert-automation-augmentation-software-
eaas/?utm_source=CB+Insights+Newsletter&utm_campaign=114d778fee-
ThursNL_01_04_2018&utm_medium=email&utm_term=0_9dc0513989-114d778fee-86324949
35
Thomas H. Davenport and Julia Kirby, “Beyond Automation,” Harvard Business Review, June 2015.
https://hbr.org/2015/06/beyond-automation
36
Davenport and Kirby, “Beyond Automation.”

10
Another more positive view of job creation and new technologies is the World Economic Forum’s37
“Towards a Reskilling Revolution: A Future of Jobs for All.” It explores job skills listed in job-posting data
to identify “what kind of jobs could affected workers actually reskill to?”38
This analysis classifies jobs into
their measurable component parts and compares them. By identifying the similarities between pairs of
jobs, it depicts the overlap between activities and tasks. From many the pairs of closely related jobs, the
report identifies jobs where reskilling might provide an opportunity. The study then eliminates from
consideration new jobs that would be unsustainable because they were unable to keep a worker at the
same financial level or improve their standard of living. If the new job is in a declining occupation, that
would also be unacceptable for a transition from a disrupted position.
The World Economic Forum study places a great deal of emphasis on transitions between existing
occupations. It does not attempt to focus on the emergence of new digital jobs. Our framework places
these emerging, digital jobs at the center of the new occupations that pervasive intelligent cloud
ecosystems will demand as they become more important to the U.S. economy.
Intelligent Analytics Results in a New Phase for IT. We contend that when intelligence is added to
analytics, it creates a new phase of information technology (IT). If software can learn, it, too, creates a
new world.39
Our examples draw upon deep learning40
, where software learns to make decisions like a
human would, employing data or visual perception to adjust a supply chain or safely guide a car along a
busy street. Today, intelligent software and analytics are already applied across the economy. When
driverless cars traverse streets filled with people they are analyzing tremendous amounts of visual images.
When software systems train robots to do tasks, using “virtual” environments that look exactly like where
the robot will operate, analytic software helps robots learn. In addition, when software improves the
performance of a complex supply chain in the aircraft or auto industry, it analyzes collections of complex
data.
Two changes illustrate how the proposed new stage of IT that we believe is emerging differs from previous
ones. One is the combination of the Internet of Things (IoT), artificial intelligence (AI) and machine learning
(ML); “live factories”, such as those at Caterpillar,41
employ these combined technologies to not only
37
Saadia Zahidi, Vesselina Ratcheva, and Till Alexander Leopold,” Towards a Reskilling Revolution: A Future of Jobs
for All,” World Economic Forum, January 2018.
http://www3.weforum.org/docs/WEF_FOW_Reskilling_Revolution.pdf?mkt_tok=eyJpIjoiWmpCbU1ETmhNek13Wl
RGbCIsInQiOiJ0ckpqQXllK2pYbjZucXd0YWNCaFM4cGY1XC9jYmZXcjRUaTUzYks3VWZBMlM4akZsNHN5MVZ3VVM1
WlpNQitkTmhlYjJna0o5QXNsMlZlVHBsM1JTTDYwV0tLaUhKY3ppMzZqaW9QWnlZXC9GZGQ0TGV0dEVlUkFtQ25uTl
BnS0lwIn0%3D
38
Zahidi, Ratcheva, and Leopold, p. 4.
39
Several consulting firms have employed CEO surveys to reach similar conclusions to the ones expressed here.
Tim Zanni, Kevin R. Bolen, Richard Hanley and Patricia Rios, “The changing landscape of disruptive technologies:
Part 2 Innovation convergence unlocks new paradigms,” KPMG 2016, pages 3-5.
https://info.kpmg.us/content/dam/info/tech-innovation/disruptive-tech-2017-part2.pdf
40
Gary Silberg, Tom Mayor, Jono Anderson, et. al., “I see. I think. I drive. (I learn.)” KPMG 2016.
https://advisory.kpmg.us/kpmg-strategy/thinking/industrial-manufacturing/i-see-i-think-i-drive-i-
learn.html?gclid=CjwKCAiAm7LSBRBBEiwAvL1-L1affFqv0WmXbkczny17--
wJ65ui1scHMoTpsIFNuul05QUNdLp__BoC5YIQAvD_BwE
41
Martin Groover, “Business Transformation with Live Factory,” SAP Leonardo event, Frankfurt, July 11-12, 2017.
http://assets.dm.ux.sap.com/de-leonardolive/pdfs/51479_caterpillar.pdf

11
manage machines in individual plants but also oversee global production networks. They accelerate firms’
ability to manage processes and oversee complex tasks.
To extend this discussion, cloud computing linked with IoT, AI and ML can be extended to oversee and
manage nearly all business functions, not just compute-intensive ones. Thanks to rapid data analysis, these
technologies’ intelligent systems offer managers vastly improved insights into the details of a wide range
of processes. Powerful analysis and rapid decision-making can be deployed to all corners of an enterprise’s
operations. This intelligence can manage supply chains, oversee manufacturing and service operations,
interact and deploy web-based information very effectively online, and perform essential functions, such
as managing investments, purchases and sales.
The second significant change is a dramatic reduction in the time for firms to create new software and
deploy it in data centers42
. New processes can streamline the development and deployment of software
--- DevOps43
, microservices, Docker/containers, and “serverless” computing. These software tools are also
tied to artificial intelligence and machine learning. They let businesses write and deploy software rapidly.
With such advances, businesses become more agile. They can respond to market changes with alacrity.
This makes them even more efficient as well as more profitable and productive.
The Emergence of a New Cloud Computing Ecosystem: Innovations in Rapid Software and Data Analysis
build on the Adoption of IoT, AI and ML
Improvements in software development and tools for data analysis have transformed the management
of business operations. Firms become very efficienct once they embrace these capabilities derived from
software tools and data analysis. CEOs, according to McKinsey McKinsey’s recent report, Embracing
Disruption Telecom CEO Outlook 2017, are focusing their efforts on “data and analytics, IoT and cognitive
technologies.”44
1. How Firms use Software Innovations to Improve their Performance
Firms respond to the data challenges they face by adopting new software and data analysis tools.
According to IBM, the Internet of Things plays a central role when combined with AI. IBM’s “Cognitive
Advantage”45
study finds that “six in ten early adopters report that AI is essential to tackling data
challenges that conventional analytics cannot, and 53 percent say it will unlock the hidden value of their
42
We quantify these changes in Appendix 1, “Changes in Time to Value for Software Development and the Time to
Deploy New Applications in Data Centers.”
43
These software innovations are described in Robert B. Cohen, “How Software is Transforming the U.S.
Economy,” report to the Ewing Marion Kauffman Foundation, July 5, 2017. As that essay notes, “In DevOps, the
focus of software development changes to focus on developers, but also brings them together in teams that
include quality assurance, testing and distribution.”
44
Paul Wissman, Embracing Disruption Telecom CEO Outlook 2017, KPMG Telecom, December 2016, p. 15.
https://assets.kpmg.com/content/dam/kpmg/us/pdf/2017/10/embracing-disruption.pdf
45
Cynthya Peranandam and Susanne Hupfer, “The Cognitive Advantage,” IBM Global Business Services, October
2016. https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=COW03020USEN&

12
organization’s dark data.”46
In addition, “Eighty-five percent of early adopters also say that IoT will play
an important role in their AI initiatives within two years.”47
Firms report that analyzing Big Data requires more efficient and powerful software. In Target’s case,
developers previously required four to six months’ time to create software to access the firm’s core data;
to do something as simple as listing the location of the firm’s stores. Added to this time was the need for
months of manual testing to ensure that the new software did not disrupt other parts of its business.
Recently, Target developed Application Protocol Interfaces (APIs)48
to expose, or provide access to, its
core data in seconds. Target now employs more than 100 APIs that connect to its retail and core data. It
is easy to look up the location of its stores. Target now supports over 7 billion monthly inquiries using
these APIs.
FamilySearch International, the largest genealogy organization in the world, also relied upon rapid
software changes to improve its operations. Previously, it needed to “analyze logs from all components
of its IT environment in real time, without additional developer effort.”49
It’s new, improved, reporting
framework captures data from the firm’s complete internal network, including “data from load balancers,
routers, APM tools and the applications themselves.” FamilySearch, using software from Splunk, captures
3.5 to 4TB of data per day, and “provides its developers access to all production data.” This lets developers
display the health of the FamilySearch systems visually on dashboards.
Using data analysis and rapid evaluation, FamilySearch increased the number of software releases it
performs from one a month to more than 900 a day.50
(the 2017 state of DevOps reports that Etsy, by
comparison, deploys 80 times a day and Netflix and Amazon deploy thousands of times per day51
). It was
able to move “from code check-in to deploy in under 20 minutes.” With this change, FamilySearch
monitors the performance of its customer-facing sites after every new software release; its system can
identify any issues that arise after a release.
Once it deployed Splunk’s analytic capabilities, FamilySearch implemented DevOps for rapid software
development. It made this change to accelerate the development of more powerful software. This
improved analysis of its websites’ performance and its software, something that would have been
impossible prior to the change. There was just too much data to analyze rapidly. By taking this new
46
Susanne Hupfer, “AI is the Future of IoT,” IBM Internet of Things Blog, December 16, 2016.
https://www.ibm.com/blogs/internet-of-things/ai-future-iot/ Dark data is data that has not been analyzed in a
conventional way.
47
Hupfer, “AI is the Future of IoT.”
48
Heather Mickman and Ross Clanton, “(Re)building an Engineering Culture: DevOps at Target,” DevOps Enterprise
Summit, November 5, 2015. https://www.youtube.com/watch?v=7s-VbB1fG5o A standard application
programming interface (API) can access databases without needing to “speak” the native language that the
database’s management systems (DBMS) uses. In computing, this is usually called Open Database Connectivity
(ODBC). “Open Database Connectivity,” Wikipedia, https://en.wikipedia.org/wiki/Open_Database_Connectivity
49
Splunk, “FamilySearch Moves to Continuous Delivery and Gains Real-Time Visibility for AWS Migration,”
Splunk Case studies, https://www.splunk.com/en_us/customers/success-stories/splunk-and-aws-at-
familysearch.html
50
Personal communication to the author by Andi Mann, Chief Technology Advocate at Splunk, December 11, 2017.
51
Dr. Nicole Forsgren and others, p. 24.

13
approach, FamilySearch transformed its culture and implemented economic changes that resulted from
a “high-speed, iterative product delivery lifecycle.”52
Cricket Wireless53
also illustrates the efficiencies tied to innovations in software development. In 2011,
Cricket was activating 50 new mobile lines a minute. It needed a way to automate its order processing
systems and workflows so that new accounts could be configured and activated without any need for
manual intervention.
With a software analytic system, Cricket was able to open and activate new accounts rapidly. In addition,
it obtained specific information about any problems that appeared. Although the case study of Cricket has
not been updated since 2011, we expect that new software development approaches have been adopted.
This would tie Cricket’s data analysis that employs Splunk to DevOps and continuous software delivery.
We expect that Cricket has benefitted from how this streamlines the updating and refining of software
used to manage its systems, particularly ones that interface with customers.
2. How Firms are Using Innovations in Data Analysis, AI and ML, as well as “intelligent analytics” to Improve
their Performance.
The plant-level, machine operations management system that Caterpillar54
deployed in a single U.S.
factory is an early effort to integrate IoT sensor networks, AI and ML. It offers Caterpillar a “360-degree
view” of its operations. This system simulates modifications of the plant floor before they are
implemented.
Using SAP’s Leonardo system, Caterpillar streamlined how it examines and manages business processes.
The new approach offers managers rapid insights into how machines are behaving. They can see things
better since the “physical and digital channels are aligned with business processes and tools, so customers
can go in and out of the different channels seamlessly.”55
By using machine intelligence, Caterpillar can operate as quickly as Amazon. The SAP Leonardo ecosystem
performs predictive analytics using data from sensors on machines. With this, plant managers can foresee
parts that are about to fail, replace them ahead of time and achieve nearly “zero-downtime,” or no
machine breakdowns. Caterpillar also gains performance metrics for the factory as well as other factories
that will be connected to it in the future.
Caterpillar has used this system improve its plant’s efficiency. As such facilities demonstrate their ability
to enhance productivity, we expect Caterpillar’s executives will explore other, more profitable operations
that can be linked into this system. For instance, connecting different locations that produce the same
product could result in a more efficient global network and new digital investments. Very probably, it will
increase Caterpillar’s need for digital jobs, such as data analysts, data preparation specialists, data
governance professionals and domain or area specialists – those familiar with building bulldozers or earth-
52
Personal communication from Andi Mann.
53
Splunk, “Cricket Communications Turns to Splunk for Assuring Automated Service Management,” Enterprise
Management Associates, Case study 4, 2011.
54
Martin Groover, “Business Transformation with Live Factory,” SAP Leonardo event, Frankfurt, July 11-12, 2017.
http://assets.dm.ux.sap.com/de-leonardolive/pdfs/51479_caterpillar.pdf
55
Groover, p. 6.

14
moving equipment. Rather than displacing many jobs, we expect improved efficiency or profitability at
these plants will increase jobs.
Other companies that use SAP’s Leonardo system include the mining firm Vale, BASF, Bayer and Bosch.
They have linked machines via the Internet of Things as well as artificial intelligence and machine learning.
This creates substantial efficiencies for the purchasers of new systems that SAP has helped its customers
develop. Stara, a Brazilian farm machinery and precision agriculture firm, has used Leonardo to develop
“precision agriculture.” It has increased crop yields by 51 percent. The result is helped by SAP’s system
that combines an analysis of soil quality, weather conditions, and fertilizer requirements, and evaluates
data collected by planting, watering and harvesting equipment. To succeed with “precision agriculture,”
Stara needs people to interpret the soil and weather results, the fertilizer requirements and the planting,
watering and harvesting needs and explain them to farmers.
After it decided not to purchase an SAP Leonardo system, Tesla built an enterprise management system
of its own, Wharp, or Tesla 3DX. It drew upon expertise from Dassault to integrate internal processes
including “selling cars online, servicing its own cars and frequent over-the-air updates.” The system
includes “a short feedback loop to engineers and quickly push[es] updates back to customers. It manages
everything from the ordering processing to the manufacturing workflow and supply chain
management.”56
The “Death of Time;” How Firms Adopt IoT, AI, ML and “Intelligent Analytics” to Become More
Productive and Competitive. The changes we expect to see in the economy are broader than the forecasts
reviewed above. As we have indicated, we expect that as firms adopt pervasive, intelligent cloud
ecosystems, or “smart software,” the economy will attain a new stage of technological sophistication.
Why do we expect these changes to be so vast? Innovations at “smart software” firms are likely to have a
tremendous impact on productivity.57
They should also help firms accelerate the use of data analytics so
that they can more effectively customize products and services. We want to explore the factors driving
these changes. Our proposed workplan will quantify the impacts of these changes among a range of
industries, including many early adopters.
We expect that sophisticated or “intelligent” data analytics will let firms harness new cloud ecosystems
with AI, ML and analytics to gain rapid insights into many corporate functions. This means that more
sophisticated software technologies will provide for a rapid evaluation of data. This change can make
many business functions more efficient. In our thinking, early adopter companies will employ cloud-based
computing linked to IoT and including AI and ML as well as dynamic analytics in nearly 20 to 30 percent of
all corporate functions over the next few years. Between 2025 and 2030, we expect firms will manage
more than 70 to 80 percent of all enterprise functions with these technologies.
When cloud computing combines with the Internet of Things, AI and ML, calculations occur in real-time
without a noticeable delay. Artificial intelligence takes the resulting data and makes immediate decisions
about how to adjust systems, including the ones we cited above.
56
Fred Lambert, Tesla is implementing a new custom end-to-end platform called ‘Tesla 3DX’ to ramp up for the
Model 3 and Tesla Energy,” Electrek, February 15, 2016, https://electrek.co/2016/02/15/tesla-3dx-model-3/
57
This conclusion agrees with the data presented in Zanni et. al., pages 5-9 and 21.

15
This is the “death of time.” Complex computing happens in a nanosecond. Computers make decisions
about how machines should operate and implement them without human intervention. Built upon the
shoulders of the Internet, the public cloud is serving as a handmaiden to deploy sophisticated functions
that cut the time to insights.
To make this change more understandable, let’s examine the way firms use these technologies. Through
the Internet of Things, firms deploy extensive sensor networks to capture machine data. Once it analyzes
this data, Caterpillar, can identify which parts of a machine are wearing out.58
Caterpillar applies artificial
intelligence via software to capture and interpret this data. Machine learning, another type of software
learns the conditions required for optimal performance. This software informs firms when system
performance is less than optimal. Rolls-Royce59
uses similar data to manage and oversee its engines’
operations. If there is a problem, machine learning software can notify the company to rectify it. In future
years, performance problems may be solved without human intervention.
Dynamic analytics lets firms access core business data. With a view of information, firms can adjust
inventories and track sales. In the Target case we discussed above, dynamic analytics remove “friction
points” from business processes and increase efficiency.60
The Centrality of the “Death of Time.” The “death of time” is crucial for understanding the new economy.
Until recently, innovations in computing and software affected only a small part of business operations,
largely compute-intensive modeling and management.
When enterprises can perform powerful data analysis in real-time, they can:
1) adjust machine performance and avoid downtime;
2) examine the performance of new software before it is deployed in a specific part of operations;
and
3) predict patterns of behavior that reduce costs or improve efficiencies.
When firms master powerful data analysis, they will eliminate or reduce costs that are dragging down
their efficiency; this will increase productivity.
Competitive Advantages Linked to Removing Friction Points. Time-savings illustrate how the new
technologies we discuss will reshape the business world. Firms make better decisions faster by including
intelligent software in their decision-making. With this edge, they can find new opportunities to expand
their operations into new businesses where they can exploit the technologies they have mastered. By
operating in this way, “leading edge” cloud and IoT adopters create a virtuous cycle. In this environment,
58
A case study of Caterpillar is described in Martin Groover, “Business Transformation with Live Factory,” SAP
Leonardo event, Frankfurt, July 11-12, 2017. http://assets.dm.ux.sap.com/de-
leonardolive/pdfs/51479_caterpillar.pdf
59
Robert Cohen, “Rolls Royce, “Power by the Hour” and the Internet of Things,” Paper prepared for the OECD
“Enabling the Next Production Revolution” project, May 31, 2016.
60
Seth G. Benzell, Guillermo LaGarda, and Marshall W. Van Alstyne, “The Impact of APIs in Firm Performance,”
Boston University Questrom School of Business Research Paper No. 2843326, 23 May 2017.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2843326

16
high-performing firms benefit even more from their experience with cloud computing and software
technologies, including “live factories”61
.
In addition, they can expand these advantages by increasing the size of their in-house talent that supports
such new technologies. This helps firms scale the in-house sophistication they have achieved in employing
cloud computing linked to IoT, AI and ML, as well as dynamic analytics. These firms also benefit from new
organizational forms and tools that let them create software rapidly.
Impact of Intelligent Software on Firms. Firms that are early to deploy the new technologies discussed
here:
1. Readily adopt the new cloud-related technologies to reduce costs, especially since firms can
intelligently manage complex corporate processes.
2. Use cost savings, i.e., improved profits, to expand into new business areas.
3. Behave in a more oligopolistic way because they possess the technical skills and can use the
latest software to manage operations, add intelligence to complex processes and build on
how they employ analytics.
4. Create a stage of technology-driven oligopolistic competition linked to technology mastery
and continuous changes in markets. ……………..
Economics: Can Artificial Intelligence Linked to IoT, ML and “Intelligent Analytics” Expand Growth and
Create New Jobs? We are not the first to argue that new IoT, AI and ML technologies will lead to economic
growth. A PwC study62
finds that “the titanic shift and ongoing disruption caused by AI is set to be like that
caused by the introduction of the personal computer in the 1980s. Like the PC, AI will lay the foundations
for an immense acceleration in innovation throughout and beyond the coming decade, creating a
significant boost for the global economy.”
Most of the studies reviewed here do not include a framework to forecast two distinct phases in the
deployment of IoT, AI and ML. Many studies have focused largely on the short-term efficiencies created
when robots automate plants. They have not evaluated what might happen when companies are more
digital and can handle volumes of data drawn from daily operations. With new technologies, firms will
interpret this data and act on it rapidly. This section explores how investment banks and consulting groups
have interpreted the impact of AI, ML, and industrial IoT.
Goldman Sachs63
analyzed the impact of greater adoption of AI and ML in global industries. It found that
through early labor savings – achieved through the widespread use of robots and the increased
productivity due to them – the AI/ML impact would raise productivity by 97 basis points. This would result
61
Not many firms have adopted the new SAP technology, Leonardo, the basis for this enhanced performance.
Caterpillar, Coca-Cola, Nvidia, Siemens, Stara, Vale, Cargill, Monsanto, McKesson, Lenovo, Johns Manville, Roche
and BASF have. We have also found that Tesla is using a very similar system that Dassault developed for them. In
addition, we expect that other firms, such as CA Technologies, IBM, and Dell are creating technologies that permit
dynamic analysis and add intelligence to IoT and ML technologies.
62
PwC, “Leveraging the upcoming disruptions from AI and IoT: How Artificial Intelligence will enable the full
promise of the Internet-of-Things,” 2017, p. 5.
63
Heath Terry et. al., “AI,” Goldman Sachs Equity Research, November 16, 2016, pp. 15-16.
https://tech.fpt.com.vn/wp-content/uploads/2017/12/tai-lieu.pdf

17
in a 2025 productivity growth IT contribution of 1.61 percent with the expectation of a U.S. GDP growth
rate of 3.0 percent or more (see the chart below). According to this analysis, AI will raise labor productivity
by 2 percent or more in the initial years it is deployed, 2017 and 2018. So, the Goldman Sachs study
expects an early increase in U.S. GDP growth that reaches 4 percent in the short run. This would decline
during the early 2020s. This 1.61 percent AI/ML productivity increment is like the gain IT sectors
contributed to productivity growth in the late 1990s. Attaining productivity growth of this level would
mean a measurable addition to growth as was the case in the late 1990s. At that time, a productivity
increase tied to the rise of PCs resulted in 3 percent GDP growth.64
Th Goldman Sachs study focuses on
labor saving due to automation. It doesn’t explore the medium-term opportunities for growth that may
be related to firms’ stronger data analysis skills. The Goldman analysis does not envision a positive, longer-
term impact of AI on the economy. It focuses on the short- and medium-term. We plan to explore whether
AI has longer-term impacts on the U.S. economy, largely by focusing on industry outcomes and firms that
we expect to be “Front Runners.”
Other studies have investigated on how AI will affect the global economy and China. UBS’s study65
of AI in
Asia finds that five industries will gain the most, financial services, healthcare, manufacturing, retail and
transportation. UBS forecasts that AI will add from $1.8 trillion to $3 trillion a year to the region’s economy
by 2030, or 6 to 10 percent of the combined revenues of the five Asian industries. During its initial
adoption, AI would put 30 million to 50 million jobs at risk in Asia, displacing lower-level, manual workers.
Later, AI would create many new jobs that almost offset the initial losses by 2030. AI would become a
common technology that is widely available after 2020. The UBS does not ascribe the turnabout in job
64
David M. Byrne, Stephen D. Oliner, and Daniel E. Sichel, “Is the Information Technology Revolution Over?”
Finance and Economics Discussion Series, Divisions of Research & Statistics and Monetary Affairs, Federal Reserve
Board, Washington, D.C. 2013. Page 11. https://www.federalreserve.gov/pubs/feds/2013/201336/201336pap.pdf
65
Sundeep Gantori, “How artificial intelligence will transform Asia,” UBS AG, Chief Investment Office WM, April
2017, pp. 4 and 15. https://m.ubs.com/global/en/chief-investment-office/features/artificial-intelligence-shifting-
asia/_jcr_content/par/linklist/link_678568297.1803584792.file/bGluay9wYXRoPS9jb250ZW50L2RhbS91YnMvbW9i
aWxlL2dsb2JhbC9jaW8vYXJ0aWZpY2lhbC1pbnRlbGxpZ2VuY2UtZ2xvYmFsLWV4LXVzLnBkZg==/artificial-
intelligence-global-ex-us.pdf. In the UBS study, Artificial intelligence includes cognitive computing, artificial
intelligence, machine learning, natural language processing, and neural networks.

18
growth to a specific factor, but it appears to assume to a large productivity increase would spillover to the
local economies and promote growth.
PwC66
takes a slightly different view. It assumes an early period of AI adoption when productivity is the
focus for healthcare, autos and financial services. After this stage, PwC expects to see a surge in firms’
abilities to tap into consumer preferences. This permits early adopters of AI, the “Front Runners” to shape
product development around the rich supply of consumer data they possess. PwC expects that this would
make it more difficult for the slower moving competitors to keep up with the “Front Runners.” PwC
modeled the impact of AI. It estimated that by 2030, it would contribute $15.7 trillion to the world
economy, $6.6 trillion from increased productivity and $9.1 trillion from consumption side effects. These
figures67
result in worldwide spending on IoT solutions that total $6 trillion when compounded between
2015 and 2020. Combined business and consumer spending on these solutions will be $1.6 trillion in 2020
alone. The results also suggest that there will be a return on investment of $13 trillion by 2025 on the $6
trillion investment that is expected between 2015 and 2025.
The McKinsey Global Institute68
(MGI) analyzes how AI will affect occupations by examining changes in
hours worked for 800 occupations. It forecasts a median-range scenario where 15 percent of jobs in 60
percent of occupations are displaced by 2030. Viewed in a global perspective, 0 percent to 30 percent of
the hours worked worldwide would be automated by 2030. From this, MGI concludes that 375 million
people in the workforce may need to change occupations by 2030; in 2012, there were nearly 3 billion
people69
in the global workforce, so without assuming any job growth, more than 12 percent of the global
workforce would need to shift occupations. MGI’s analysis forecasts that the benefits of automation will
be greatest in the oil and gas, retailing, healthcare, aircraft maintenance, and mortgage origination
industries. It will also have big impacts on auto redesign and new development, pharmaceutical R&D; and
marketing to consumers.
MGI’s analysis forecasts that middle-wage jobs will decline in advanced economies but expand in
emerging ones. This forecast depends upon assumptions about the pace of automation and future labor
demand for jobs. It anticipates that “occupations that require application of expertise, interaction with
stakeholders, management and coaching of others, or a high degree of social and emotional response will
be less susceptible to automation.”70
The study employs a general equilibrium macroeconomic model with
66
PwC, “Sizing the Prize: What’s the real value of AI for your business and how can you capitalise?” 2017. Pp. 7-8.
https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf This study uses a
large model of the global economy that focuses on 57 different economic sectors and includes information about
how trade through supply chains affects the results.
67
John Greenough, “The Internet of Things 2015: Examining How the IoT Will Affect the World,” BI Intelligence,
November 2015 as cited in Raman Chitkara and Rob Mesirow, “The Industrial Internet of Things: Why it demands
not only new technology—but also a new operational blueprint for your business,” PWC, 2016.
https://www.pwc.com/gx/en/technology/pdf/industrial-internet-of-things.pdf
68
James Manyika et. al., “Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation,”
https://www.mckinsey.com/global-themes/future-of-organizations-and-work/what-the-future-of-work-will-mean-
for-jobs-skills-and-wages
69
Torres, Raymond, ed., “World of work report 2013: Repairing the economic and social fabric, Geneva:
International Labor Organization, International Institute for Labor Studies, 2013.
http://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---
publ/documents/publication/wcms_202326.pdf
70
Manyika and others, “Jobs Lost,” p. 30.

19
details for each country. MGI’s analysis focuses on job automation. In contrast to some of the other
studies, it does not consider how firms might react if they become more digital. MGI does not find, as PwC
does, that firms might be more focused on collecting and analyzing data. It does not consider that this
might help them improve their position in existing and new markets.
Studies of IoT foresee that it will reshape manufacturing, not only displacing jobs but also creating big
gains in productivity. A Boston Consulting Group (BCG) analysis finds that deploying only IoT and investing
250 billion euros in Germany’s manufacturing sectors over the 2015 to 2025 period would result in 90 to
150 billion euros in productivity gains as well as annual revenue growth of 30 billion euros in these
industries. This energizes overall productivity gains of 5 to 8 percent for German manufacturing. Sectors
such as mechanical engineering would see 4 to 15 percent productivity gains over the same period.
BCG envisions structural changes to the economy where “sensors, machines, workpieces and IT systems
will be connected along the value chain beyond a single enterprise.”71
These connected systems would be
able to “analyze data to predict failure, configure themselves, and adapt to changes.” The result would be
faster, more efficient and more flexible processes that create higher quality goods at reduced costs. This
increases productivity and changes the competitiveness of companies and regions. The BCG analysis
provides a vision of how productivity gains might restructure the value chain beyond the single enterprise.
This would require using sensors (IoT) and IT systems (AI and ML). The result would be a likely expansion
of jobs with modest job gains. The BCG study may be optimistic since it assumes that many displaced
manufacturing employees find jobs that are as productive as the ones they lost.
Creating Large Numbers of New Jobs. There are several ways that large numbers of new jobs could be
created through pervasive cloud ecosystems and the Internet of Things linked to AI, ML and sophisticated
data analysis.
71
Rüssmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P. and Harnisch, M. (2015), “Industry 4.0:
The Future of Productivity and Growth in Manufacturing Industries, Boston Consulting Group, Boston, MA. Page 4.
https://www.zvw.de/media.media.72e472fb-1698-4a15-8858-344351c8902f.original.pdf
The Impact of Industry 4.0 on the German Manufacturing Industry, 2015-2025 Employees in Thousands
2015 Addition 2015-2025 Productivity Gains
including Materials Costs
Employees 6,060 390 5 to 8%
Mechanical Engineering 95 4 to 15%
Food and Beverage 50 5 to 10%
Autos 15 6 to 9%
Other 230 4 to 7%
Source: Rüssmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P. and Harnisch, M.,
“Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries,
Boston Consulting Group, Boston, MA., 2015. pp. 8 and 9.
https://www.zvw.de/media.media.72e472fb-1698-4a15-8858-344351c8902f.original.pdf

20
Our view is that two trends will create many of these jobs. These are 72
: 1) rapid software innovation that
has shortened the time to create new applications; and 2) the convergence of sensor networks (the
Internet of Things), artificial intelligence and machine learning to solve complex problems very rapidly.
These two phenomena are changing how businesses use cloud technologies.
Our view is that innovations in software and data analysis will create a series of “digital jobs.” We have
seen this occur in recent data that show that software developer jobs began to decline but similar
positions, such as jobs for DevOps professionals emerged to exploit the creation of new tools and
processes. We consider digital jobs to include:
1. Data Analysis –
a. Data analysts and data scientists
b. Process management
c. Predictive analytics and predictive modeling
d. Data governance
e. Data center infrastructure and mobile device management
2. Software innovation related
a. Artificial Intelligence, Machine Learning, Computer Vision and Deep Learning.
b. Software Engineering and development
c. DevOps, Continuous Integration and Continuous Delivery
d. Docker/Containers
e. Mini-services
f. Serverless Computing
3. Business Intelligence and Security
a. Business intelligence and data visualization
b. Cybersecurity and computer security
c. Network virtualization
We expect job creation to accelerate after an initial phase of job displacement. The expansion will be
driven by firms’ expanding their use of the Internet of Things, artificial intelligence, machine learning, and
intelligent analytics. We forecast that this later phase would spur the emergence of a wide range of new
occupations over the next decade or two. Many jobs will support data analysis and software development.
We also expect that as these roles become more widespread, new algorithms would simplify more
complex tasks that previously only a skilled professional could perform. This would open opportunities for
less-skilled workers.
One view that agrees with our approach is Davenport and Kirby’s hypothesis that “expertise automation
and augmentation software73
can drive the growth of new jobs. This interpretation of new jobs posits that
72
We present evidence of these broad trends in Appendix 1. For a different point of view, see Carl Benedikt Frey
and Michael A. Osborne, “The Future of Employment: How Susceptible Are Jobs to Computerisation?” Oxford
University Martin School, September 17, 2013.
https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf
73
Thomas H. Davenport and Julia Kirby, “Beyond Automation,” Harvard Business Review, June 2015.
https://hbr.org/2015/06/beyond-automation. https://hbr.org/2015/05/how-people-will-use-ai-to-do-
their-jobs-better

21
jobs will grow where humans can bring higher levels of intellectual skills to jobs and fulfill tasks that are
superior and more sophisticated. It also expects that some jobs will be based on intuition rather than
strengths that are codifiable or rational. In our view, this argument assumes a far larger role for judgement
and intuition in digital jobs, rather than entire new occupations, such as the ones we list above.
A Workplan to Analyze the Pervasive, Intelligent Cloud Ecosystem. We think that an analysis of this new
ecosystem should begin by examining the industries at the forefront of the adoption of “intelligent
systems.” These sectors will obtain the greatest benefits from innovative software and data analysis. We
expect that they will use these new capabilities to improve their competitiveness.
We expect that the industries at the forefront of using “linked processes” will include:
o manufacturing sectors with complex processes, i.e., autos, aircraft, computing, processor
chips and farming equipment; we expect “intelligent software” to change business
models for many of the firms in these sectors, from marketing vehicles to offering
“vehicles as a service;” from facilities-oriented to customer and service-focused.
o Financial services firms and government users;
o logistics firms with mixed product (e.g., 3-D printing) and service offerings;
o service industries that have not been affected by digital analysis, such as healthcare and
retailing; and
o online services, such as Netflix.
To describe the new cloud computing ecosystem and its likely impacts we plan to:
1. Create a series of case studies to pinpoint the main benefits of the new ecosystem.
2. Define industries that are moving to this new cloud ecosystem by:
a. The pace by which they are implementing changes related to IoT, AI and ML.
1. The milestones that are significant in adding efficiencies for certain firms and
industries.
2. Examining whether these milestones are related to innovative software of data
analysis. (see points b and c)
b. The intensity of their use of innovative software development, such as DevOps and
containers during different periods over the next five years.
c. The intensity of their use of data analytics and new tools to support the latest type of
analysis during different periods over the next five years.
We plan to analyze this data using cluster and factor analysis to identify groups of similar industries, such
as “Front Runners” and others. We will rank industries by their implementation of emerging technologies
related to IoT, AI and ML. We will also quantify their implementation of innovative software tools and
sophisticated data analytics.

22
Based upon this classification we will use a survey of intelligent software to identify:
1. The industries that will spur the creation of this new ecosystem
2. Whether these industries will largely displace jobs due to automation (in the short run) or
create new jobs with more digital skills in the medium- and long-term. For slower adopters of
innovative software and data analytics, we expect that the gains from tailoring products to
consumer tastes will take longer to achieve. If our assumptions about software, analytics and
the management of processes is not closely related to improved economic benefits, we will
rework the framework for measuring impacts that we describe above. In sum, we very much
expect that software innovations and sophisticated analytics will be extremely important to
business operations in the next decade.
3. The main new jobs these changes create.
4. The skills employers demand from employees to win these jobs.
When we develop the case studies mentioned above, we would attempt to:
1. Identify the ways that cloud computing adoption will be influenced by software innovations
and enhanced data analytics.
2. Determine the triggering factors that prompt firms to become “digital enterprises.”
3. Quantify the range of impacts the adoption of new “intelligent systems” is likely to have on
the firm.
4. Define changes at the industry level over the next five to ten years using the University of
Maryland’s INFORUM Input/Output model of the U.S. economy. For the industries we study,
this will let us estimate new levels of investment, cost changes, productivity gains, and
employment changes. It will also provide estimates of the impact of the new ecosystem on
cloud service providers and telcos. In addition, we would be able to estimate how cloud
services, IoT, AI and ML will affect the U.S. economy.
5. Identify how trade related changes connected to the adoption and use of AI might affect U.S.
economic performance. For instance, if China and India become centers for AI development
and use, how will the prices of AI-related products and quantities of goods exported by these
countries affect the growth of U.S. industries as centers for “intelligent systems.”

23
Appendix 1. Glossary of Technical Terms
Defining New Software Development and Data Analysis Processes
Continuous Delivery/Continuous Integration (CD/CI) -- In software engineering, continuous integration
is the practice of merging all developer working copies to a shared mainline several times a day.
Continuous delivery is a software engineering approach in which teams produce software
in short cycles, ensuring that the software can be reliably released at any time.
It aims at building, testing, and releasing software faster and more frequently. (Wikipedia)
DevOps -- DevOps is the combination of cultural philosophies, practices, and tools that increases
an organization's ability to deliver applications and services at high velocity: evolving and
improving products at a faster pace than organizations using traditional software development
and infrastructure management processes. (Amazon)
Open Source -- Open-source software is computer software with its source code made available
with a license in which the copyright holder provides the rights to study, change, and distribute
the software to anyone and for any purpose. (Wikipedia)
Microservices -- microservice architecture is a method of developing software applications as a suite of
independently deployable, small, modular services in which each service runs a unique process and
communicates through a well-defined, lightweight mechanism to serve a business goal. (SmartBear)
Containers/Docker -- A container image is a lightweight, stand-alone, executable package of a piece of
software that includes everything needed to run it: code, runtime, system tools, system libraries,
settings. Available for both Linux and Windows based apps, containerized software will always run
the same, regardless of the environment. Containers isolate software from its surroundings,
for example differences between development and staging environments and help reduce conflicts
between teams running different software on the same infrastructure. (Docker)
Internet of Things (IoT) -- The Internet of things is the network of physical devices, vehicles, home appliances
and other items embedded with electronics, software, sensors, actuators, and network connectivity
which enables these objects to connect and exchange data. (Wikipedia)
Artificial Intelligence -- Artificial intelligence makes it possible for machines to learn from
experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about
today – from chess‐playing computers to self‐driving cars – rely heavily on deep learning and
natural language processing. (SAS)
Machine Learning (ML) -- Machine learning is a method of data analysis that automates analytical
model building. It is a branch of artificial intelligence based on the idea that machines should be able
to learn and adapt through experience without being explicitly programmed. (SAS, Wikpedia)
Intelligent Analysis or Cognitive Analysis -- Cognitive analytics can refer to a range of different
analytical strategies that are used to learn about certain types of business related functions, such as
customer outreach. Certain types of cognitive analytics also may be known as predictive analytics,
where data mining and other cognitive uses of data can lead to predictions for business intelligence (BI).
(Technopedia)

24
Appendix 2. Changes in Time to Value for Software Development and the Time to Deploy New Applications
in Data Centers.
The following graph depicts the rapid reduction in the time required to develop new software over the
past decade and a forecast for the continued reduction in time over the next two years. This includes the
progress firms have made as they adopted DevOps, microservices, Docker/containers and serverless
computing. Using this data, we compare the expected pace of software innovation to Moore’s Law. The
orange line depicts the pace of innovation assuming software innovation took place as fast as Moore’s
Law did.
We did the same with data describing the time to value for deploying applications in data centers. We
also compared this data to Moore’s Law.
Data source: Adrian Cockcroft, "Evolution of Business Logic from Monoliths through
Microservices to Functions," https://read.acloud.guru/evolution-of-business-logic-from-
monoliths-through-microservices-to-functions-ff464b95a44d#.r8eel3vze

25
Data source: VMware Accelerate Advisory Services, "Delivering on the Promise of the
Software-Defined Data Center," 2013.
https://blogs.vmware.com/vmtn/author/aluciani/page/3
These charts indicate that there has been an extremely rapid decline in the “time to value” for both
software innovation and the time required to deploy applications in data centers. We would expect that
these gains would indicate a big fall in prices over time, the same behavior as the prices associated with
Moore’s Law for semiconductors.
There is only limited information about prices that would permit us to compare this rapid shortening in
“time to value” to price changes. We suggest that if the pace of software innovation is more rapid than
Moore’s Law, one would expect to see price changes that might be faster than those estimated by Moore’s
Law in the data describing the cost of creating new software.
We have not found any reports to illustrate how rapidly software prices have declined over time. We can
cite a few studies that indicate the benefits of software innovation. A 2015 study by Freeform Dynamics
and CA Technologies found that, for just the 20 percent of large firms actively adopting DevOps -- one of
the main approaches to shorten the time to software coding and deployment – these 20 percent were 2.4
times more likely to have increased profits, twice as likely to have increased revenues and about 3.4 times
more likely to have increased market share, as compared to the firms not actively adopting DevOps.74
This
report, however, only describes increases for the active DevOps adopters. It only estimates their
performance compared to the 80 percent of firms that did not adopter it.
74
Assembling the DevOps Jigsaw,” Freeform Dynamics, 2015: http://rewrite.ca.com/us/articles/devops/assembling-the-
devops-jigsaw.html. Cited in Aruna Ravichandran, Kieran Taylor and Peter Waterhouse, DevOps for Digital Leaders, CA
Technologies and CA Press, 2016, p. 7.

26
An alternative might be found in an examination of prices for cloud computing services. If we make a
heroic assumption that these prices should reflect improvements in “time to value” for software
development, this might be reasonable. A recent Wellesley B.A. honors thesis, “Price trends for cloud
computing services,”75
estimates price changes over time for Amazon’s EC2 web services run on the Linux
operating system. The models used for this study find that Amazon implemented one large price change
in 2014 – when it first offered its Lambda service -- but did not cut prices much after that, adjusting for
quality improvements. The following chart illustrates the large, 36% price drop that occurred in 2014 and
the lack of price declines after that point.76
75
Liang Zhang, “Price trends for cloud computing services,” (2016). Wellesley College Honors Thesis Collection 386.
https://repository.wellesley.edu/cgi/viewcontent.cgi?article=1472&context=thesiscollection
76
Based on the analysis, Zhang concludes that it is unlikely that Amazon will make many price changes in the
future. She asserts that “as an innovative new IT product…, Cloud Computing Services have been taking advantage
of the price reduction trend of Computing Products in previous periods but will eventually get caught up by the
diminishing rate of cost reduction. In other words, the drastic price drop of Cloud Computing Services in recent
years is unlikely to be observed again in the future.”76
In fact, Amazon has made recent adjustments to its pricing,
shifting from by the hour pricing to by the second pricing. (see Matt Weinberger, “Amazon just made a huge
change to its $12 billion cloud computing juggernaut,” Business Insider, Sep. 18, 2017.
http://www.businessinsider.com/amazon-web-services-ec2-cloud-goes-to-per-second-pricing-2017-9 ).
The dramatic price drop Zhang records in 2014, however, was probably related to Amazon’s introduction of its new
Lambda service (Zhang does not attribute the large price declines she identifies to this event). The new Lambda
service provided customers with "serverless" computing. From our point of view, Amazon’s {as well as Microsoft’s
or Google’s behavior as a cloud service provider) would lead one to suggest that important product innovations
might provide opportunities for cloud service providers to adjust prices.
The new Lambda service, “serverless computing,” represented a way to give programmers even more flexibility in
completing jobs. With the Lambda service, they did not need to set up the servers to do the computing they
needed; Amazon automated this step as part of its new service. Thus, when a customer uses “a translator app …
built using Lambda, the servers to do the translation might not even exist until you hit the okay button, and they
disappear again after the process is finished and the result is displayed” on a phone or computer.”
In November, Google, one of Amazon’s competitors, announced a 36 percent price reduction for using Nvidia’s
Tesla GPUs in its Compute Platform. Google made this change to offer developers a way to analyze very large
amounts of data using machine learning on the Google cloud. (see “Google Cloud Lowers the Price of NVIDIA Tesla
GPUs,” NVIDIA Developer, November 21, 2017. https://news.developer.nvidia.com/google-cloud-lowers-the-price-
of-nvidia-tesla-gpus/ ).h

27
Source: Liang Zhang, ““Price trends for cloud computing services,” (2016). Wellesley College
Honors Thesis Collection 386, p. 28.
https://repository.wellesley.edu/cgi/viewcontent.cgi?article=1472&context=thesiscollection
The 36 percent price reduction that Zhang identifies is much less of a price change than our earlier charts
suggest might be possible. We believe that not only the time to develop new software but also the time
to deploy it in data centers could have had a greater impact on end-user performance. Urs Hoelzle77
,
Google’s senior vice president for technical infrastructure, has noted that dramatic changes to the Google
Cloud Platform’s management software reduced internal costs by about 80 percent in just one instance.
Perhaps we might mention a case where gains conform more closely to the scale of benefits we would
expect. That might be identified were it possible to consider customers’ efficiency gains – essentially
second-order benefits – for customers from Google’s cloud services. When Google made changes to
attract high volume data users as customers by switching to Nvidia’s higher performance Tesla GPUs,
Google Cloud Platform’s (GCP’s) customers noticed a few major changes. They found that their “engineers
[we]re 10 times more productive than they were before, and our costs are 10 times less than they would
77
Urs Hoelzle, “Open Flow @ Google,” Open Networking Summit 2012.
https://www.youtube.com/watch?v=VLHJUfgxEO4

28
have been because GCP handles our infrastructure, so we don’t have to.”78
These changes, which are very
recent, suggest a 100-fold improvement in productivity per engineer over an extremely short period of
time.
78
Mickey Hsieh, Viant, quoted in Google Cloud Platform, “Price Leader.” https://cloud.google.com/pricing/price-
leader v

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