Notes from Transform.AI - Part 1: The Productivity Paradox
By Kathryn Hume, Sep 07, 2017
I spent the a few days in June at Transform.AI in Paris, a European conference designed for c-level executives managed and moderated by my dear friend Joanna Gordon. This type of high-quality conference approaching artificial intelligence (AI) at the executive level is sorely needed. While there’s no lack of high-quality technical discussion at research conferences like ICML and NIPS, or even part-technical, part-application, part-venture conferences like O’Reilly AI, ReWork, or the Future Labs AI Summit (which my friends at ffVC did a wonderful job producing), most c-level executives still actively seek to cut through the hype and understand AI deeply and clearly enough to invest in tools, people, and process changes with confidence. Confidence, of course, is not certainty. And with technology changing at an ever faster clip, the task of running the show while transforming the show to keep pace with the near future is not for the faint of heart.
Transform.AI brought together enterprise and startup CEOs, economists, technologists, venture capitalists, and journalists. We discussed the myths and realities of the economic impact of AI, enterprise applications of AI, the ethical questions surrounding AI, and the state of what’s possible in the field. Here are some highlights.1
The Productivity Paradox: New Measures for Economic Value
The productivity paradox is the term Ryan Avent of the Economist uses to describe the fact that, while we worry about a near-future society where robots automate away both blue-collar and white-collar work, the present economy “does not feel like one undergoing a technology-driven productivity boom.” Indeed, as economists noted at Transform.AI, in developed countries like the US, job growth is up and “productivity has slowed to a crawl.” In his Medium post, Avent shows how economic progress is not a linear substitution equation: automation doesn’t impact growth and GDP by simply substituting the cost of labor with the cost of capital (i.e., replacing a full-time equivalent employee with an intelligent robot) despite our — likely fear-inspired — proclivities to reduce automation to simple swaps of robot for human. Instead, Avent argues that “the digital revolution is partly responsible for low labor costs” (by opening supply for cheap labor via outsourcing or just communication), that “low labour costs discourage investments in labour-saving technology, potentially reducing productivity growth,” and that benefiting from the potential of automation from new technologies like AI costs far more than just capital equipment, as it takes a lot of investment to get people, processes, and underlying technological infrastructure in place to actually use new tools effectively. There are reasons why IBM, McKinsey, Accenture, Salesforce, and Oracle make a lot of money off of “digital transformation” consulting practices.
The takeaway is that innovation and the economic impact of innovation move in syncopation, not tandem. The consequence of this syncopation is the plight of shortsightedness, the “I’ll believe it when I see it” logic that we also see from skeptics of climate change who refuse to open their imagination to any consequences beyond their local experience. The second consequence is the overly simplistic rhetoric of technocratic Futurism, which is also hard to swallow because it does not adequately account for the subtleties of human and corporate psychology that are the cornerstones of adoption. One conference attendee, the CEO of a computer vision startup automating radiology, commented that his firm can produce feature advances in their product 50 times faster than the market will be ready to use them. And this lag results not only from the time and money required for hospitals to modify their processes to accommodate machine learning tools, but also the ethical and psychological hurdles that need to be overcome to both accommodate less-than-certain results and accept a system that cannot explain why it arrived at its results.
In addition, everyone seemed to agree that the metrics used to account for growth, GDP, and other macroeconomic factors in the 20th-century may not be apt for the networked, platform-driven, AI-enabled economy of the 21st. For example, the value search tools like Google have on the economy far supersedes the advertising spends accounted for by company revenues. Years ago, when I was just beginning my career, my friend and mentor Geoffrey Moore advised me that traditional information-based consulting firms were effectively obsolete in the age of ready-at-hand information (the new problem being the need to erect virtual dams – using natural language processing, recommendation, and fact-checking algorithms – that can channel and curb the flood of available information). Many AI tools effectively concatenate past human capital – the expertise and value of a skilled-services work – into a present-day super-human laborer, a laborer who is the emergent whole (so more than the sum of its parts) of all past human work (well, just about all – let’s say normalized across some distribution). This fusion of man and machine2, of man’s past actions distilled into a machine, a machine that then works together with present and future employees to ever improve its capabilities, forces us to revisit what were once clean delineations between people, IP, assets, and information systems, the engines of corporations.
Accenture calls the category of new job opportunities AI will unlock The Missing Middle. Chief Technology and Innovation Officer Paul Daugherty and others have recently published an MIT Sloan article that classifies workers in the new AI economy as “trainers” (who train AI systems, curating input data and giving them their personality), “explainers” (who speak math and speak human, and serve as liaisons between the business and technology teams), and “sustainers” (who maintain algorithmic performance and ensure systems are deployed ethically). Those categories are sound. Time will tell how many new jobs they create.
About the author
Kathryn Hume is VP Product & Strategy at integrate.ai, a SaaS startup applying AI to a unique combination of social, behavioral, and enterprise transaction data to help large B2C businesses optimize customer engagement. Alongside her work at integrate.ai, she is a Venture Partner at ffVC, a seed- and early-stage technology venture capital firm, where she advises early-stage artificial intelligence companies and sources deal flow. While at Fast Forward Labs, Kathryn helped Fortune 500 companies accelerate their machine learning and data science capabilities. Prior to that, she was a leader in Intapp’s Risk Practice, focused on data privacy, security, and compliance. A widely respected speaker and writer on AI, Kathryn excels at communicating how AI and machine learning technologies work in plain language. Kathryn has given lectures and taught courses on the intersections of technology, ethics, law, society at Harvard Business School, Stanford, the MIT Media Lab, and the University of Calgary Faculty of Law. She speaks seven languages, and holds a PhD in comparative literature from Stanford University and a BA in mathematics from the University of Chicago.
Accelerate your organization’s journey to analytics maturity
Get the data sheet to learn how the Research & Advisory Network advances analytics capabilities and improves performance.