The Paradox of Applying AI. AI, Martec’s Law, and the Management… | by Tristan Post | Medium.pdf

Search Write The Paradox of Applying AI Tristan Post · Follow 8 min read · Nov 1, 2022 139 AI, Martec’s Law, and the Management Challenge of the 21st Century An analysis of the history of technology shows that technological change is exponential, contrary to the common-sense “intuitive linear”...

Search Write The Paradox of Applying AI Tristan Post · Follow 8 min read · Nov 1, 2022 139 AI, Martec’s Law, and the Management Challenge of the 21st Century An analysis of the history of technology shows that technological change is exponential, contrary to the common-sense “intuitive linear” view. So we won’t experience 100 years of progress in the 21st century — it will be more like 20,000 years of progress (at today’s rate). The “returns,” such as chip speed and costeffectiveness, also increase exponentially. There’s even exponential growth in the rate of exponential growth. Within a few decades, machine intelligence will surpass human intelligence… Ray Kurzweil (2001) “The Law of Accelerating Returns” Technology progresses at an exponential rate. One of the first people to denote this trend was Gordon Moore, co-founder and chairman emeritus of Intel. Moore predicted in 1965 that the number of transistors that fit on a computing chip doubles approximately every two years. Moore’s law not only inspired futurist’s Ray Kurzweil’s Law of Accelerating Returns, but would set the pace for our modern digital revolution, as his observation had widespread impact in many areas of technological progress, including artificial intelligence (AI). Danny Hernandez and Tom Brown from OpenAI observed that AI models are becoming cheaper to train at an exponential rate faster than Moore’s Law, as algorithmic efficiency and hardware efficiency — two key driving factors for the advance of AI — are growing exponentially. AI models becoming more efficient Compute usage for AI models has increased exponentially. Looking ahead, we can only speculate as to what decades of exponential improvement in the efficiency of AI algorithms will bring to the future. We have already observed major breakthroughs in the last years, when we witnessed AI algorithms coming at par — and even surpassing — human capabilities. Algorithms such as AlphaGo, GPT-3 or AlphaFold are impressive breakthroughs that pushed the boundaries of what seemed possible and have defined the history of AI. However, we must emphasize that the idea that AI is progressing exponentially is slightly misleading, as anyone who has used a conversational assistant might tell you. My experience repeating impatiently my commands to Alexa, who has been my companion for a couple of years now, sometimes leaves me wondering if there is any progress at all. Similarly, if the AI applications driving self-driving cars would have progressed exponentially, I would not always have to ask my brother to pick me up with his car from the train station whenever I visit home. When I say AI is growing at an exponential rate, I thus refer to the growing range of applications and capabilities that we see in the field of AI. As developments in AI speed up, organizations must react in order to keep up and prosper. However, many organizations are not adept at changing, especially as they grow in scale. If a company increases in size it also increases in complexity and bureaucracy. We human beings are limited in our ability to organize and handle complexity and scale. According to Charlie Munger, investor and vice chairman of Berkshire Hathaway, the greater the number of people working together in an organization, the more bureaucratic, self-interested and dysfunctional they tend to become: “They also tend to become somewhat corrupt. In other words, if I’ve got a department and you’ve got a department and we kind of share power running this thing, there’s sort of an unwritten rule: “If you won’t bother me, I won’t bother you and we’re both happy.” So you get layers of management and associated costs that nobody needs. Then, while people are justifying all these layers, it takes forever to get anything done. They’re too slow to make decisions and nimbler people run circles around them.” Entrenched interests slow organizations down and create resilience. Compared to the exponential rate of change of AI technology, organizations change at a logarithmic rate. The idea that technology is changing faster than organizations is at the center of Martec’s Law formulated by Scott Brinker, founder and chair of the MarTech Conference. Brinker sees the gap between the rate of change at which technology progresses and the ability of organizations to adapt as the quintessential management challenge of the 21st century Martec’s Law: Technology changes exponentially (fast), yet organizations change logarithmically (slow) The same challenge can be observed when we look at the struggles of organizations starting to adopt AI: Many still struggle to embrace AI or fail to extract value. One study found that only 6% of companies have adopted AI. A survey from BCG from 2019 found that “many AI initiatives fail. Seven out of 10 companies surveyed report minimal or no impact from AI so far. Among the 90% of companies that have made at least some investment in AI, fewer than 2 out of 5 report obtaining any business gains from AI in the past three years.” Recent research from 2020 found that even though “organizations are using AI as a tool to generate value […] the majority of companies [are] still struggling to capitalize on the technology.” Teslas, who has adopted an AI first approach, saw its market value skyrocket compared to traditional car makers Even though many still struggle to turn AI into value, they understand the opportunities that AI can bring for their organization. The benefits of adopting AI can be seen from the handful of (primarily tech) companies that have been able to transform their organizations to embrace and adopt AI at scale. These so-called AI-factories have shifted their strategies to put AI in the center of their organizations’ business and operating models. Google became one of the first companies to start this transformation when its CEO Sundar Pichai announced in 2017 that Google would shift from a mobile first world to an AI first world. An AI-first approach allows organizations to automate decision-making, thereby transforming the way they create and capture value. Not only have these companies been able to increase their competitive advantage, but they have been rewarded by skyrocketing stock prices. As of writing this article, all companies valued over 1 trillion have adopted an AI first approach. One of these AI first companies is Tesla, whose market value reached 1.01 trillion USD in October 2021, making Tesla worth as much as the next 10 most valuable global automakers combined, even though it sells only a fraction of the cars of the others. The paradoy of applying AI The faster the field of AI progresses and the slower the rate of adoption the greater the gap between expectations and practice will become. The challenge is that AI requires fundamental organizational change. Most companies, however, are far away from adopting an AI first approach, as this requires a clear vision and a holistic approach to transform the organization. This, in turn, requires rethinking and commitment over all divisions and hierarchies. Historically, there have been especially many large and successful companies that seem to be more likely to struggle when new innovations require them to change their organizational structures and reinvent themselves. For instance, despite inventing the digital camera, Kodak struggled to transform themselves and eventually filed for bankruptcy in 2012. Blockbuster did not manage to adapt their business model to the internet and so lost out to Netflix, one of the big companies applying AI at scale. So, what should companies do? First of all, there are no quick wins. A holistic transformational change is not an easy task and requires substantial resources. However, doing nothing is not an option. McKinsey predicts that AI technologies could lead to a performance gap between front-runners and nonadopters. As with Tesla, front-runners are likely to benefit disproportionately, while late adopters might see their cash flow decrease as they become less competitive and eventually might face similar consequences as Kodak or Blockbuster. One way to reduce the gap between technological change of AI and organizational change is to become more agile and start setting up the company in a way that enables the adoption of AI. Organizations must make sure they have the right structures in place to adapt, innovate, and take AI to scale. This process starts with acknowledging the value that AI can bring to the organization, defining a vision for AI, working on a strategy for building an AI-powered organization, and enabling data driven decisions. As AI can help to automate decision making it also empowers the whole organization to become more agile and responsive, increasing the rate at which an organization can adapt to change and incorporate technological progress. Another solution could be an organizational “reset”. Because of their small size and their entrepreneurial can-do attitude, startups often show a high degree of flexibility and innovation. This agility allows them to adapt to change to a greater degree than the more complicated bureaucracies found in large organizations. These startups do not have to rely on old legacy systems and processes that slow them down. At the heart of the organizational reset lies the question: “If we were to build the organization from scratch, what would it look like?” Organizations can achieve a reset through an internal reorganization or by spinning off a new group that can operate with a fresh start, without the inertia of the existing organization. Digital transformation initiatives typically take one of those two approaches to leapfrog to a new baseline on the technology curve. As organizational resets can be extremely disrupting, some larger organizations have created incubator-like structures to systematically spin-up small, intrapreneurial startup teams. Looking back, we see that most organizations will not be able to adapt. Ultimately, a reset will happen when a company fails and the resources are reallocated to the market. This happens much more often than we do realize. From 1955 to 2014 88% of Fortune 500 companies failed , having undergone ‘creative destruction’. Even Jeff Bezos believes that one day Amazon will face inevitable death: “[…] I predict one day Amazon will fail. Amazon will go bankrupt. If you look at large companies, their lifespans tend to be 30-plus years, not a hundred-plus years.” AI brings a lot of opportunities. For many existing organizations its adoption will be a challenge. However, it is not too late to start the necessary transformation. Many organizations are still at the beginning of this process. As the technology progresses they will face increasing competition from other players more capable of leveraging AI. 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