THE IMPACT OF ARTIFICIAL INTELLIGENCE ON INNOVATION
Iain M. Cockburn
Rebecca Henderson
Scott Stern
Working Paper 24449
http://www.nber.org/papers/w24449
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
March 2018
The authors would like to thank the organizers and participants at the first NBER conference on
the Economics of Artificial Intelligence, and in particular our discussant Matthew Mitchell for
many helpful suggestions and ideas. Michael Kearney provided extraordinary research assistance.
The views expressed herein are those of the authors and do not necessarily reflect the views of the
National Bureau of Economic Research. Funding for this paper was provided by the MIT Sloan
School of Management, by the HBS Division of Research and by the Questrom School of
Management.
At least one co-author has disclosed a financial relationship of potential relevance for this
research. Further information is available online at http://www.nber.org/papers/w24449.ack
NBER working papers are circulated for discussion and comment purposes. They have not been
peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies
official NBER publications.
ABSTRACT
Artificial intelligence may greatly increase the efficiency of the existing economy. But it may
have an even larger impact by serving as a new general-purpose “method of invention” that can
reshape the nature of the innovation process and the organization of R&D. We distinguish
between automation-oriented applications such as robotics and the potential for recent
developments in “deep learning” to serve as a general-purpose method of invention, finding
strong evidence of a “shift” in the importance of application-oriented learning research since
We suggest that this is likely to lead to a significant substitution away from more routinized
labor-intensive research towards research that takes advantage of the interplay between passively
generated large datasets and enhanced prediction algorithms. At the same time, the potential
commercial rewards from mastering this mode of research are likely to usher in a period of
racing, driven by powerful incentives for individual companies to acquire and control critical
large datasets and application-specific algorithms. We suggest that policies which encourage
transparency and sharing of core datasets across both public and private actors may be critical
tools for stimulating research productivity and innovation-oriented competition going forward.
Introduction
Rapid advances in the field of artificial intelligence have profound implications for the
economy as well as society at large. These innovations have the potential to directly influence
both the production and the characteristics of a wide range of products and services, with
important implications for productivity, employment, and competition. But, as important as
these effects are likely to be, artificial intelligence also has the potential to change the innovation
process itself, with consequences that may be equally profound, and which may, over time, come
to dominate the direct effect.
Consider the case of Atomwise, a startup firm which is developing novel technology for
identifying potential drug candidates (and insecticides) by using neural networks to predict the
bioactivity of candidate molecules. The company reports that its deep convolutional neural
networks “far surpass” the performance of conventional “docking” algorithms. After appropriate
training on vast quantities of data, the company’s AtomNet product is described as being able to
“recognize” foundational building blocks of organic chemistry, and is capable of generating
highly accurate predictions of the outcomes of real-world physical experiments (Wallach et al.,
2015).
Such breakthroughs hold out the prospect of substantial improvements in the productivity
of early stage drug screening. Of course, Atomwise’s technology (and that of other companies
leveraging artificial intelligence to advance drug discovery or medical diagnosis) is still at an
early stage: though their initial results seem to be promising, no new drugs have actually come
to market using these new approaches. But whether or not Atomwise delivers fully on its
promise, its technology is representative of the ongoing attempt to develop a new innovation
“playbook”, one that leverages large datasets and learning algorithms to engage in precise
prediction of biological phenomena in order to guide design effective interventions. Atomwise,
for example, is now deploying this approach to the discovery and development of new pesticides
and agents for controlling crop diseases.
Atomwise’s example illustrates two of the ways in which advances in artificial intelligence
have the potential to impact innovation. First, though the origins of artificial intelligence are
broadly in the field of computer science, and its early commercial applications have been in
relatively narrow domains such as robotics, the learning algorithms that are now being developed
suggest that artificial intelligence may ultimately have applications across a very wide range.
From the perspective of the economics of innovation (among others, Bresnahan and Trajtenberg
(1995)), there is an important distinction between the problem of providing innovation incentives
to develop technologies with a relatively narrow domain of application, such robots purposebuilt for narrow tasks, versus technologies with a wide—advocates might say almost limitless—
domain of application, as may be true of the advances in neural networks and machine learning
often referred to as “deep learning.” As such, a first question to be asked is the degree to which
developments in artificial intelligence are not simply examples of new technologies, but rather
may be the kinds of “general purpose technologies” (hereafter GPTs) that have historically been
such influential drivers of long-term technological progress.
Second, while some applications of artificial intelligence will surely constitute lower-cost or
higher-quality inputs into many existing production processes (spurring concerns about the
potential for large job displacements), others, such as deep learning, hold out the prospect of not
only productivity gains across a wide variety of sectors but also changes in the very nature of the
innovation process within those domains. As articulated famously by Griliches (1957), by
enabling innovation across many applications, the “invention of a method of invention” has the
potential to have much larger economic impact than development of any single new product.
Here we argue that recent advances in machine learning and neural networks, through their
ability to improve both the performance of end use technologies and the nature of the innovation
process, are likely to have a particularly large impact on innovation and growth. Thus the
incentives and obstacles that may shape the development and diffusion of these technologies are
an important topic for economic research, and building an understanding of the conditions under
which different potential innovators are able to gain access to these tools and to use them in a
pro-competitive way is a central concern for policy.
This essay begins to unpack the potential impact of advances in artificial intelligence on
innovation, and to identify the role that policy and institutions might play in providing effective
incentives for innovation, diffusion, and competition in this area. We begin in Section II by
highlighting the distinctive economics of research tools, of which deep learning applied to R&D
problems is such an intriguing example. We focus on the interplay between the degree of
generality of application of a new research tool and the role of research tools not simply in
enhancing the efficiency of research activity but in creating a new “playbook” for innovation
itself. We then turn in Section III to briefly contrasting three key technological trajectories
within AI—robotics, symbolic systems, and deep learning. We propose that these often
conflated fields will likely play very different roles in the future of innovation and technical
change. Work in symbolic systems appears to have stalled and is likely to have relatively little
impact going forwards. And while developments in robotics have the potential to further displace
human labor in the production of many goods and services, innovation in robotics technologies
per se has relatively low potential to change the nature of innovation itself. By contrast, deep
learning seems to be an area of research that is highly general-purpose and that has the potential
to change the innovation process itself.
We explore whether this might indeed be the case through an examination of some
quantitative empirical evidence on the evolution of different areas artificial intelligence in terms
of scientific and technical outputs of AI researchers as measured (imperfectly) by the publication
of papers and patents from 1990 through 2015. In particular, we develop what we believe is the
first systematic database that captures the corpus of scientific paper and patenting activity in
artificial intelligence, broadly defined, and divides these outputs into those associated with
robotics, symbolic systems, and deep learning. Though preliminary in nature (and inherently
imperfect given that key elements of research activity in artificial intelligence may not be
observable using these traditional innovation metrics), we find striking evidence for a rapid and
meaningful shift in the application orientation of learning-oriented publications, particularly after
The timing of this shift is informative, since it accords with qualitative evidence about the
surprisingly strong performance of so-called “deep learning” multi-layered neural networks in a
range of tasks including computer vision and other prediction tasks. Supplementary evidence
(not reported here) based on the citation patterns to authors such as Geoffrey Hinton who are
leading figures in deep learning suggests a striking acceleration of work in just the last few years
that builds on a small number of algorithmic breakthroughs related to multi-layered neural
networks.
Developments in
neural networks and machine learning thus raise the question of, even if the underlying scientific
approaches (i.e., the basic multi-layered neural networks algorithms) are open, prospects for
continued progress in this field—and commercial applications thereof—are likely to be
significantly impacted by terms of access to complementary data. Specifically, if there are
increasing returns to scale or scope in data acquisition (there is more learning to be had from the
“larger” dataset), it is possible that early or aggressive entrants into a particular application area
may be able to create a substantial and long-lasting competitive advantage over potential rivals
merely through the control over data rather than through formal intellectual property or demandside network effects.
Strong incentives to maintain data privately has the additional potential
downside that data is not being shared across researchers, thus reducing the ability of all
researchers to access an even larger set of data that would arise from public aggregation. As the
competitive advantage of incumbents is reinforced, the power of new entrants to drive
technological change may be weakened. Though this is an important possibility, it is also the
case that, at least so far, there seems to be a significant amount of entry and experimentation
across most key application sectors.
In contrast to technological
progress in relatively narrow domains, such as traditional automation and industrial robots, we
argue that those areas of artificial intelligence evolving most rapidly—such as deep learning—
are likely to raise serious challenges in both dimensions.
First, consider the challenge in providing appropriate innovation incentives when an
innovation has potential to drive technological and organizational change across a wide number
of distinct applications. Such “general purpose technologies” (David, 1990; Bresnahan and
Trajtenberg, 1995) often take the form of core inventions that have the potential to significantly
enhance productivity or quality across a wide number of fields or sectors. David’s (1990)
foundational study of the electric motor showed that this invention brought about enormous
technological and organizational change across sectors as diverse as manufacturing, agriculture,
retail, and residential construction.
As emphasized by Bresnahan and Trajtenberg (1995), the presence of a general-purpose
technology gives rise to both vertical and horizontal externalities in the innovation process that
can lead not just to underinvestment but also to distortions in the direction of investment,
depending on the degree to which private and social returns diverge across different application
sectors. Most notably, if there are “innovation complementarities” between the general purpose
technology and each of the application sectors, lack of incentives in one sector can create an
indirect externality that results in a system-wide reduction in innovative investment itself. While
the private incentives for innovative investment in each application sector depend on its the
market structure and appropriability conditions, that sector’s innovation enhances innovation in
the GPT itself, which then induces subsequent demand (and further innovation) in other
downstream application sectors.
Various aspects of artificial intelligence can certainly be understood as a GPT, and learning from
examples such as the microprocessor are likely to be a useful foundation for thinking about both
the magnitude of their impact on the economy, and associated policy challenges.
A second conceptual framework for thinking about AI is the economics of research tools.
Within the research sectors, some innovations open up new avenues of inquiry, or simply
improve productivity “within the lab”. Some of these advances appear to have great potential
across a broad set of domains, beyond their initial application: as highlighted by Griliches (1957)
in his classic studies of hybrid corn, some new research tools are inventions that do not just
create or improve a specific product—instead they constitute a new way of creating new
products, with much broader application. In Griliches’ famous construction, the discovery of
double-cross hybridization “was the invention of a method of inventing.” (Hereinafter, “IMI”.)
Rather than being a means of creating a single a new corn variety, hybrid corn represented a
widely applicable method for breeding many different new varieties. When applied to the
challenge of creating new varieties optimized for many different localities (and even more
broadly, to other crops) the invention of double-cross hybridization had a huge impact on
agricultural productivity.
Advances in machine learning and neural networks appear to have great potential as a
research tool in problems of classification and prediction. These are both important limiting
factors in a variety of research tasks, and, as exemplified by the Atomwise example, application
of “learning” approaches to AI hold out the prospect of dramatically lower costs and improved
performance in R&D projects where these are significant challenges. But as with hybrid corn,
AI based learning may be more usefully understood as an IMI than as a narrowly limited solution
to a specific problem. One the one hand, AI based learning may be able to substantially
“automate discovery” across many domains where classification and prediction tasks play an
important role.
On the other, they may also “expand the playbook” is the sense of opening up
the set of problems that can be feasibly addressed, and radically altering scientific and technical
communities’ conceptual approaches and framing of problems. The invention of optical lenses
in the 17th century had important direct economic impact in applications such as spectacles. But
optical lenses in the form of microscopes and telescopes also had enormous and long-lasting
indirect effects on the progress of science, technological change, growth, and welfare: by making
very small or very distant objects visible for the first time, lenses opened up entirely new
domains of inquiry and technological opportunity. Leung et al. (2016), for example, evocatively
characterize machine learning as an opportunity to “learn to read the genome” in ways that
human cognition and perception cannot.
Of course, many research tools are neither IMIs nor GPTs, and their primary impact is to
reduce the cost or enhance the quality of an existing innovation process. For example, in the
pharmaceutical industry, new kinds of materials promise to enhance the efficiency of specific
research processes. Other research tools can indeed be thought of as IMIs but are nonetheless
relatively limited in application. For example, the development of genetically engineered
research mice (such as the Oncomouse) is an IMI that has had a profound impact on the conduct
and “playbook” of biomedical research, but has no obvious relevance to innovation in areas such
as information technology, energy, or aerospace. The challenge presented by advances in AI is
that they appear to be research tools that not only have the potential to change the method of
innovation itself but also have implications across an extraordinarily wide range of fields.
From a policy perspective, a further important feature of research tools is that it may be
particularly difficult to appropriate their benefits. As emphasized by Scotchmer (1990),
providing appropriate incentives for an upstream innovator that develops only the first “stage” of
an innovation (such as a research tool) can be particularly problematic when contracting is
imperfect and the ultimate application of the new products whose development is enabled by the
upstream innovation is uncertain. Scotchmer and her co-authors emphasized a key point about a
multi-stage research process:
when the ultimate innovation that creates value requires multiple
steps, providing appropriate innovation incentives are not only a question of whether and how to
provide property rights in general, but also of how best to distribute property rights and
incentives across the multiple stages of the innovation process. Lack of incentives for earlystage innovation can therefore mean that the tools required for subsequent innovation do not
even get invented; strong early-stage property rights without adequate contracting opportunities
may result in “hold-up” for later-stage innovators and so reduce the ultimate impact of the tool in
terms of commercial application.
The vertical research spillovers created by new research tools (or IMIs) are not just a
challenge for designing appropriate intellectual property policy.1 They are also exemplars of the
core innovation externality highlighted by endogenous growth theory (Romer, 1990; Aghion and
Howitt, 1992); a central source of underinvestment in innovation is the fact that the intertemporal
spillovers from innovators today to innovators tomorrow cannot be easily captured. While
tomorrow’s innovators benefit from “standing on the shoulders of giants,” their gains are not
easily shared with their predecessors. This is not simply a theoretical idea: an increasing body of
evidence suggests that research tools and the institutions that support their development and
Challenges presented by AI-enabled invention for legal doctrine and the patent process are beyond the scope of
this essay.
diffusion play an important role in generating intertemporal spillovers (among others, Furman
and Stern, 2011; Williams, 2014). A central insight of this work is that control—both in the
form of physical exclusivity as well as in the form of formal intellectual property rights—over
tools and data can shape both the level and direction of innovative activity, and that rules and
institutions governing control over these areas has a powerful influence on the realized amount
and nature of innovation.
Of course, these frameworks cover only a subset of the key informational and
competitive distortions that might arise when considering whether and how to provide optimal
incentives for the type of technological change represented by some areas of AI. But these two
areas in particular seem likely to be important for understanding the implications of the current
dramatic advances in AI supported learning. We therefore turn in the next section to a brief
outline of the ways in which AI is changing, with an eye towards bringing the framework here to
bear on how we might outline a research agenda exploring the innovation policy challenges that
they create.
III.
The Evolution of Artificial Intelligence: Robotics, Symbolic Systems, and Neural
Networks
In his omnibus historical account of AI research, Nilsson (2010) defines AI as “that
activity devoted to making machines intelligent, and intelligence is that quality that enables an
entity to function appropriately and with foresight in its environment.” His account details the
contributions of multiple fields to achievements in AI, including but not limited to biology,
linguistics, psychology and cognitive sciences, neuroscience, mathematics, philosophy and logic,
engineering and computer science. And, of course, regardless of their particular approach,
artificial intelligence research has been united by from the beginning by its engagement with
Turing (1950), and his discussion of the possibility of mechanizing intelligence.
Although early pioneers such as Turing had emphasized the importance of teaching a
machine as one might a child (i.e., emphasizing AI as a learning process), the “symbol
processing hypothesis” (Newell, Shaw, and Simon, 1958; Newell and Simon, 1976) was
premised on the attempt to replicate the logical flow of human decision making through
processing symbols. Early attempts to instantiate this approach yielded striking success in
demonstration projects, such as the ability of a computer to navigate elements of a chess game
(or other board games) or engage in relatively simple conversations with humans by following
specific heuristics and rules embedded into a program. However, while research based on the
concept of a “general problem solver” has continued to be an area of significant academic
interest, and there have been periodic explosions of interest in the use of such approaches to
assist human decision-making (e.g., in the context of early-stage expert systems to guide medical
diagnosis), the symbolic systems approach has been heavily criticized for its inability to
meaningfully impact real-world processes in a scalable way. It is of course possible that this
field will see breakthroughs in the future, but it is fair to say that, while symbolic systems
continues to be an area of academic research, it has not been central to the commercial
application of AI. Nor is it at the heart of the recent reported advances in AI that are associated
with the area of machine learning and prediction.
A second influential trajectory in AI has been broadly in the area of robotics. While the
concepts of “robots” as machines that can perform human tasks dates back at least to the 1940s,
the field of robotics began to meaningfully flourish from the 1980s onwards through a
combination of the advances in numerically controlled machine tools and the development of
more adaptive but still rules-based robotics that rely on the active sensing of a known
environment. Perhaps the most economically consequential application of AI to date has been in
this area, with large scale deployment of “industrial robots” in manufacturing applications.
These machines are precisely programmed to undertake a given task in a highly controlled
environment.
Often located in “cages” within highly specialized industrial processes (most
notably automobile manufacturing), these purpose-built tools are perhaps more aptly described
as highly sophisticated numerically controlled machines rather than as robots with significant AI
content. Over the past twenty years, innovation in robotics has had an important impact on
manufacturing and automation, most notably through the introduction of more responsive robots
that rely on programmed response algorithms that can respond to a variety of stimuli. This
approach, famously pioneered by Rod Brooks (1990), focused the commercial and innovation
orientation of AI away from the modeling of human-like intelligence towards providing feedback
mechanisms that would allow for practical and effective robotics for specified applications. This
insight led, among other applications, to the Roomba and to other adaptable industrial robots that
could interact with humans such as Rethink Robotics’ Baxter). Continued innovation in robotics
technologies (particularly in the ability of robotic devices to sense and interact with their
environment) may lead to wider application and adoption outside industrial automation.
These advances are important, and the most advanced robots continue to capture public
imagination when the term AI is invoked. But innovations in robotics are not, generally
speaking, IMIs.
The increasing automation of laboratory equipment certainly improves research
productivity, but advances in robotics are not (yet) centrally connected to the underlying ways in
which researchers themselves might develop approaches to undertake innovation itself across
multiple domains. There are of course counterexamples to this proposition: robotic space
probes have been a very important research tool in planetary science, and the ability of
automated remote sensing devices to collect data at very large scale or in challenging
environments may transform some fields of research. But robots continue to be used principally
in specialized end-use “production” applications.
Finally, a third stream of research that has been a central element of AI since its founding
can be broadly characterized as a “learning” approach. Rather than being focused on symbolic
logic, or precise sense-and-react systems, the learning approach attempts to create reliable and
accurate methods for the prediction of particular events (either physical or logical) in the
presence of particular inputs. The concept of a neural network has been particularly important
in this area. A neural network is a program that uses a combination of weights and thresholds to
translate a set of inputs into a set of outputs, measures the “closeness” of these outputs to reality,
and then adjusts the weights it uses to narrow the distance between outputs and reality. In this
way, neural networks can learn as they are fed more inputs (Rosenblatt, 1958; 1963).
Over the
course of the 1980s, Hinton and his co-authors further advanced the conceptual framework on
which neural networks are based through the development of “back-propagating multi-layer”
techniques that further enhance their potential for supervised learning.
After being initially heralded as having significant promise, the field of neural networks
has come in and out of fashion, particularly within the United States. From the 1980s through
the mid-2000s, their challenge seemed to be that there were significant limitations to the
technology that could not be easily fixed by using larger training datasets or through the
introduction of additional layers of “neurons.” However, in the mid-2000s, a small number of
new algorithmic approaches demonstrated the potential to enhance prediction through back
propagation through multiple layers. These neural networks increased their predictive power as
they were applied to larger and larger datasets, and were able to scale to an arbitrary level
(among others, a key reference here is Hinton and Salakhutdinov (2006)).
These advances
exhibited a “surprising” level of performance improvement, notably in the context of the
ImageNet visual recognition project competition pioneered by Fei-Fei Li at Stanford
(Krizhevsky, Sutskever and Hinton, 2012).
How Might Different Fields within Artificial Intelligence Impact Innovation?
Distinguishing between these three streams of AI is a critical first step towards
developing a better understanding of how AI is likely to influence the innovation process going
forward, since the three differ significantly in their potential to be either GPTs or IMIs—or both.
First, though a significant amount of public discussion of AI focuses on the potential for
AI to achieve super-human performance over a wide range of human cognitive capabilities, it is
important to note that, at least so far, the significant advances in AI have not been in the form of
the “general problem solver” approaches that were at the core of early work in symbolic systems
(and that were the motivation for considerations of human reasoning such as the Turing test).
Instead, recent advances in both robotics and in deep learning are by and large innovations that
require a significant level of human planning and that apply to a relatively narrow domain of
problem-solving (e.g., face recognition, playing Go, picking up a particular object, etc.) While it
is of course possible that further breakthroughs will lead to a technology that can meaningfully
mimic the nature of human subjective intelligence and emotion, the recent advances that have
attracted scientific and commercial attention are well removed from these domains.
Second, though most economic and policy analysis of AI draws out consequences from
the last two decades of automation to consider the future economic impact of AI (e.g., in job
displacement for an ever-increasing number of tasks), it is important to emphasize that there is a
sharp difference between the advances in robotics that were a primary focus of applications of AI
research during the 2000s and the potential applications of deep learning which have come to the
fore over the last few years.
As we suggested above, current advances in robotics are by and large associated with
applications that are highly specialized and that are focused on end-user applications rather than
on the innovation process itself and these advances do not seem as of yet to have translated to a
more generally applicable IMI. Robotics is therefore an area where we might focus on the
impact of innovation (improved performance) and diffusion (more widespread application) in
terms of job displacement versus job enhancement. We see limited evidence as yet of
widespread applications of robotics outside industrial automation, or of the scale of
improvements in the ability to sense, react to, and manipulate the physically environment that the
use of robotics outside manufacturing probably requires.
But there are exceptions: developments
in the capabilities of “pick and place” robots and rapid progress in autonomous vehicles point to
the possibility for robotics to escape manufacturing and become much more broadly used.
Advances in robotics may well reveal this area of AI be a GPT, as defined by the classic criteria.
Some research tools/IMIs based on algorithms have transformed the nature of research in
some fields, but have lacked generality. These types of algorithmic research tools, based on a
static set of program instructions, are a valuable IMI, but do not appear to have wide
applicability outside a specific domain and do not qualify as GPTs. For example, while far from
perfect, powerful algorithms to scan brain images (so-called functional MRI imaging) have
transformed our understanding of the human brain, not only through the knowledge they have
generated but also by establishing an entirely new paradigm and protocol for brain research.
However, despite its role as a powerful IMI, fMRI lacks the type of general-purpose applicability
that has been associated with the most important GPTs. In contrast, the latest advances in deep
learning have the potential to be both a general-purpose IMI and a classic GPT.
Rather than focusing on small well-characterized datasets or testing settings, it is now
possible to proceed by identifying large pools of unstructured data which can be used to
dynamically develop highly accurate predictions of technical and behavioral phenomena. In
pioneering an unstructured approach to predictive drug candidate selection that brings together a
vast array of previously disparate clinical and biophysical data, for example, Atomwise may
fundamentally reshape the “ideas production function” in drug discovery.
If advances in deep learning do represent the arrival of a general-purpose IMI, it is clear
that there are likely to be very significant long-run economic, social, and technological
consequence.
First, as this new IMI diffuses across many application sectors, the resulting
explosion in technological opportunities and increased productivity of R&D seem likely to
generate economic growth that can eclipse any near-term impact of AI on jobs, organizations,
and productivity. A more subtle implication of this point is that “past is not prologue”: even if
automation over the recent past has resulted in job displacement (e.g., Acemoglu and Restrepo,
2017a), AI is likely to have at least as important an impact through its ability to enhance the
potential for “new tasks” (as in Acemoglu and Restrepo, 2017b).
Finally, if deep learning does indeed prove to be a general-purpose IMI, it will be
important to develop institutions and a policy environment that is conductive to enhancing
innovation through this approach, and to do so in a way that promotes competition and social
welfare. A central concern here may be the interplay between a key input required for deep
learning—large unstructured databases that provide information about physical or logical
events—and the nature of competition. While the underlying algorithms for deep learning are in
the public domain (and can and are being improved on rapidly), the data pools that are essential
to generate predictions may be public or private, and access to them will depend on
organizational boundaries, policy and institutions.