Only Humans Need Apply: Winners and Losers in the Age of Smart Machines
Tags: #technology #ai #future of work #automation #society #economics #jobs #skills
Authors: Thomas H. Davenport, Julia Kirby
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Overview
In our book, “Only Humans Need Apply: Winners and Losers in the Age of Smart Machines”, we examine the dramatic impact of artificial intelligence (AI) and automation on knowledge work. While acknowledging the very real concerns about job displacement, our central argument is one of cautious optimism. We argue that the future belongs not to machines alone, but to those who can effectively collaborate with them. This is a book for knowledge workers who are worried that machines are coming for their jobs. It is a call to action, urging readers to understand the capabilities of smart machines, recognize their own strengths, and develop strategies to remain relevant and valuable. The book goes beyond the typical warnings about automation to offer a hopeful vision of the future, in which humans and machines work together in ways that amplify each other’s abilities. Throughout the book, we advocate for a framework we call “augmentation.” We argue that, instead of simply automating away human jobs, organizations should use technology to enhance and expand the work humans are able to do. We outline five specific “steps” for augmentation: “stepping up”, “stepping aside”, “stepping in”, “stepping narrowly”, and “stepping forward.” Each of these steps involves a different set of skills and a different relationship with machines. We offer practical guidance on how readers can identify the “step” that is best suited for them, and how they can develop the skills and knowledge they need to thrive in a machine-filled world. The book concludes with a discussion of how society can best adapt to the challenges and opportunities presented by advancing automation. We argue that governments, educational institutions, and businesses all have a role to play in ensuring that the benefits of these technologies are broadly shared and that humans remain in control of their destinies.
Book Outline
1. Are Computers Coming After Your Job?
We illustrate how automation is advancing rapidly, encroaching on knowledge work. We lay out the “Ten Reasons to Look over Your Shoulder”—signs that your job is on the path to automation. Most jobs today have some parts that can be effectively automated. The encroachment happens one task at a time.
Key concept: If work can be codified, it can be automated. And there’s also the corollary: If it can be automated in an economical fashion, it will be.
2. Just How Smart Are Smart Machines?
We show that while fears of job loss due to automation have traditionally been short-lived, the kind of work being displaced today is fundamentally different. Today’s machines can not only perform tasks that are manually exhausting and mentally enervating, but can now outperform humans at tasks requiring high-level cognition. We map the progress of smart machines, showing how they have advanced along two key dimensions: their ability to act and their ability to learn.
Key concept: Figure 3.1. Types of Cognitive Technology and Their Sophistication: A matrix plotting a machine’s ability to ‘act’ on one axis and ability to ‘learn’ on the other. We trace the history and likely future of smart machines as they progress from basic tasks, like analyzing numbers, to more complex activities, like performing physical tasks.
3. Don’t Automate, Augment
This chapter emphasizes the importance of ‘augmentation’—using machines to enhance human capabilities, rather than simply automating them away. We provide examples of how augmentation can be a win-win for both companies and employees.
Key concept: Don’t Automate, Augment: Rather than using machines to replace workers, we should use them to make human work more valuable. Instead of subtracting from what people do in a given job, we should use technology to enhance and deepen the work they are able to do.
4. Stepping Up
This chapter introduces our first ‘step’ for augmentation: ‘stepping up.’ It involves moving to a higher level of decision-making, focusing on broader strategic implications of automation, and managing the overall integration of human and machine capabilities. We offer examples of individuals who have successfully stepped up, such as a chief risk officer who steered his organization toward more intelligent use of risk models.
Key concept: Stepping Up: People who step up oversee automation projects, make high-level decisions about their implementation and direction, and are able to evaluate and modify them if necessary. It’s a role best suited to those with a ‘big picture’ perspective, and a passion for both technology and organizational change.
5. Stepping Aside
This chapter explores ‘stepping aside,’ which means focusing on activities that require a ‘human touch’ and are not easily automated—things like creativity, empathy, building relationships, and exercising good judgment in ambiguous situations. We discuss the rise of ‘artisanal’ jobs and the growing importance of multiple intelligences.
Key concept: Stepping Aside: Involves focusing on those aspects of knowledge work that are essentially human and not replicable by machines. It’s about letting machines do what they do best and building your career on uniquely human strengths, such as creativity, empathy, humor, or taste. It’s about making yourself irreplaceable by bringing more ‘art’ to your work.
6. Stepping In
This chapter delves into ‘stepping in,’ which involves collaborating directly with automated systems to enhance their effectiveness. We meet individuals who have become experts in this kind of work, such as a teacher who has mastered the use of adaptive learning software, and a lawyer who has become an e-discovery expert.
Key concept: Stepping In: This step involves working closely with automated systems, understanding how they work, monitoring their performance, identifying areas where human intervention is needed, and ultimately making them better over time. It’s about working in a complementary fashion with smart machines.
7. Stepping Narrowly
Here we explore ‘stepping narrowly,’ which means finding a niche area within a profession that is too small or specialized for anyone to be trying to automate, at least not yet. We provide examples of individuals who have succeeded by becoming experts in highly specialized fields, like a zoologist specializing in a particular type of rat.
Key concept: Stepping Narrowly: This means developing expertise in a field so specialized that it’s uneconomical for anyone to automate it. These narrow experts may have relatively few customers, but those customers value their unique knowledge enough to make them highly valuable. In an age of automation, specializing may be the best strategy for remaining gainfully employed.
8. Stepping Forward
This chapter focuses on ‘stepping forward,’ which involves creating new automated systems and technologies. We meet entrepreneurs, programmers, data scientists, and other professionals who are building the next generation of intelligent machines.
Key concept: Stepping Forward: This means developing the new systems and technologies that enable automation. It’s a fast-growing sector of the economy, and a path to good jobs for data scientists, engineers, software developers, and many other types of workers.
9. How You’ll Manage Augmentation
We shift our focus to how companies and organizations can best manage the implementation of augmentation strategies. We highlight the importance of understanding high-impact decisions and identifying knowledge bottlenecks, staying abreast of technology developments, and accounting for legal and regulatory constraints.
Key concept: No single right answer to this dilemma; what is best for your context probably depends on the appetite and expectations for such innovation.
10. Utopia or Dystopia? How Society Must Adapt to Smart Machines
We look at how society as a whole can prepare for the age of smart machines. We argue for policies that encourage augmentation, including education reform focused on developing critical thinking and collaboration skills, and government investment in job creation that emphasizes uniquely human strengths.
Key concept: The goal should be augmentation—in which humans and computers combine their strengths to achieve more favorable outcomes than either could alone.
11. Acknowledgments
This section consists of acknowledgments to individuals who provided the authors with valuable insights and assistance in writing the book.
Key concept: N/A
12. Notes
This section contains citations and sources for all references made throughout the book.
Key concept: N/A
13. Index
This section provides an index of key terms and concepts discussed in the book, allowing readers to easily locate specific information.
Key concept: N/A
Essential Questions
1. How is the nature of work changing due to the rise of smart machines, and who is most vulnerable to this shift?
The authors argue that the nature of work is fundamentally changing as machines become capable of not only performing routine tasks, but also making complex decisions that were once the exclusive domain of humans. This shift is driven by advances in artificial intelligence, machine learning, and related technologies. Knowledge workers are especially vulnerable to this new wave of automation. The authors lay out several signs that a job is at risk of being automated, including: if it involves little physical contact or manipulation of things; if it involves straightforward content analysis; if it involves answering data-dependent questions; and if it can be simulated or performed virtually. The authors emphasize that this is not a futuristic scenario but a reality that is already unfolding in many industries.
2. What is “augmentation,” and why is it a better approach than simply automating away jobs?
The authors present a framework called “augmentation,” which emphasizes the potential for human-machine collaboration. Instead of simply replacing humans with machines, they argue that we should focus on using technology to enhance and expand human capabilities. The core concept of augmentation is to find ways for humans and machines to work together to produce more valuable outcomes than either could alone. This approach is not only more beneficial for workers, who get to keep their jobs and do more fulfilling work, but also for companies, which can leverage the strengths of both humans and machines to gain a competitive advantage.
3. What are the five “steps” for augmentation, and how can knowledge workers use them to thrive in a machine-filled world?
The authors outline five specific steps that knowledge workers can take to leverage augmentation and thrive in a future of increasing automation. These five steps, “stepping up”, “stepping aside”, “stepping in”, “stepping narrowly”, and “stepping forward,” represent different strategies for individuals to adapt and create value in a machine-filled world. ‘Stepping up’ involves moving to a higher level of decision-making and overseeing automation efforts. ‘Stepping aside’ focuses on activities that require a ‘human touch’ and are less susceptible to automation, such as creativity or empathy. ‘Stepping in’ means working closely with automated systems, understanding their logic, and improving their performance. ‘Stepping narrowly’ involves specializing in a niche area that is too small or complex to automate. Finally, ‘stepping forward’ means building the next generation of automated systems and technologies. These steps, the authors argue, can help knowledge workers to not only keep their jobs, but to make them more fulfilling, valuable, and future-proof.
4. What are the social and economic implications of advancing automation, and how can we ensure a future where humans thrive alongside smart machines?
The authors argue that the future is not predetermined and that humans still have agency in shaping the role of AI in society. They acknowledge the potential downsides of automation, but they believe that with foresight and planning, we can create a future where machines work for us, not against us. They call for greater attention to be paid to the social and ethical implications of AI, and for policies that encourage augmentation and job creation. They also emphasize the need for individuals to take responsibility for their own careers, developing new skills and finding ways to add value in a machine-filled world.
Key Takeaways
1. Augmentation, combining human and machine intelligence, is a better strategy than pure automation.
By understanding the strengths and limitations of both humans and machines, organizations can create hybrid work systems where humans and technology complement each other. This leads to better outcomes than pure automation, which often fails to account for the nuances of human judgment and decision-making, or pure human effort, which is often too slow and expensive to be competitive.
Practical Application:
In product design, an AI engineer could ‘step up’ by leading a team that assesses the ethical implications of an AI product, rather than just focusing on its technical development. This would involve considering the potential impact of the product on society, and ensuring that it is designed and implemented in a way that benefits humanity.
2. Focusing on uniquely human skills will make you more valuable in the age of smart machines.
The authors argue that focusing on tasks that require uniquely human skills, like creativity, empathy, or complex communication, will make you more valuable and less likely to be replaced by machines. These are the areas where humans still hold a significant advantage, and where they can create value that machines cannot.
Practical Application:
An AI engineer who excels at coding but struggles with communication could ‘step aside’ by focusing on highly technical tasks like algorithm optimization, while collaborating with a product manager or technical writer who can communicate those advancements to a broader audience. This allows the engineer to leverage his strengths while benefiting from others’ strengths in areas where he may be less proficient.
3. Stepping into automated systems and becoming an expert in their workings is a valuable skill.
By understanding how automated systems work and where they fall short, workers can identify areas where they can add value and contribute to improving those systems. This involves learning about machine learning, data analysis, and other relevant technologies, and developing the ability to communicate effectively with both technical and non-technical stakeholders.
Practical Application:
An AI product manager could ‘step in’ to an automated system by becoming an expert in its underlying logic and decision-making processes. This would allow her to identify potential biases, monitor performance, and suggest improvements to make the system more effective and aligned with the company’s goals.
4. Stepping narrowly, developing expertise in a niche area, can be a successful strategy for avoiding automation.
By focusing your efforts on a narrow and challenging area of expertise, you can become a recognized expert and create a unique value proposition for yourself. This approach is especially relevant in the age of automation, as machines are less likely to encroach on highly specialized skills.
Practical Application:
An AI engineer who wants to “step narrowly” could focus on developing deep expertise in a specific type of AI algorithm, like reinforcement learning, becoming a recognized expert in that area. This deep knowledge would make him highly sought-after by companies working on applications that require that specific expertise.
5. Stepping forward and contributing to the development of new AI technologies offers significant opportunities.
As AI becomes increasingly integrated into various industries, there will be a growing demand for individuals who can build, design, implement, and improve these systems. This involves not only technical skills, but also business acumen, entrepreneurial spirit, and the ability to understand the needs of end-users.
Practical Application:
An aspiring AI entrepreneur could ‘step forward’ by founding a company that develops AI-powered solutions for a specific industry or niche. This could involve identifying a business problem that can be effectively addressed by AI, assembling a team of experts, and developing and marketing a product or service that leverages the latest advancements in cognitive technologies.
Suggested Deep Dive
Chapter: Chapter 3: Don’t Automate, Augment
This chapter provides the foundation for the authors’ argument for augmentation and introduces the concept of mutual empowerment between humans and machines. It lays out the fundamental philosophy that should guide AI development and implementation, focusing on enhancing human capabilities rather than simply replacing them.
Memorable Quotes
Introduction. 12
If work can be codified, it can be automated. And there’s also the corollary: If it can be automated in an economical fashion, it will be.
The Answer Is Augmentation. 65
The reason people hate automation is that it involves someone in a managerial position spotting a shortcoming or limitation in employees, or simply a weakness relative to machine performance, and then punishing them for that weakness.
Stepping Narrowly. 158
In an age of relentlessly encroaching automation, some human work will be granted a dispensation not because it is fundamentally emotional or otherwise unsuited to computerization by the nature of the tasks. Instead, this work will resist automation because no one can make a strong economic case for automating it.
How You’ll Manage Augmentation. 224
“This thinking will have to change, ironically, as we move more fully into the age of machines. Managers will increasingly understand that the key to their firms’ competitiveness is not the efficiency that automation provides but the distinctiveness that augmentation allows.
Is STEM Education the Only Answer?. 235
“More than anything, schooling needs to put more emphasis on teaching students how to augment their strengths with machines.”
Comparative Analysis
“Only Humans Need Apply” distinguishes itself from other books on automation by presenting a more optimistic and proactive view. While works like Martin Ford’s “Rise of the Robots” paint a stark picture of mass unemployment, Davenport and Kirby emphasize the potential for human-machine collaboration. They offer practical strategies for individuals to adapt and thrive, a perspective largely absent in other dystopian narratives. Their emphasis on “augmentation” aligns with concepts explored in books like “Human + Machine” by Paul Daugherty and H. James Wilson, but Davenport and Kirby delve deeper into specific steps individuals can take to leverage their unique human strengths. The book also echoes the call for responsible AI development, similar to the concerns raised by Max Tegmark in “Life 3.0”. However, “Only Humans Need Apply” focuses more on practical career advice than on existential threats. Its focus on individual agency and adaptation sets it apart, offering a roadmap for navigating the changing landscape of work.
Reflection
“Only Humans Need Apply” offers a timely and insightful perspective on the future of work in an age of accelerating automation. The authors’ focus on augmentation as a strategy for both individuals and organizations is a valuable contribution to the ongoing discussion about the impact of AI on society. However, the book may be overly optimistic in its assessment of the potential for widespread augmentation. It assumes that companies will prioritize human-machine collaboration over cost-cutting automation, which may not always be the case. The book also does not delve deeply into the potential social and economic challenges of a future where some individuals may be left behind by the rapid pace of technological change. Despite these limitations, “Only Humans Need Apply” provides a useful framework for thinking about the future of work and offers practical advice for knowledge workers who want to stay ahead of the automation curve. Its emphasis on individual agency and the importance of developing uniquely human skills is a refreshing counterpoint to the often-dystopian narratives surrounding AI and automation.
Flashcards
What is augmentation?
Using machines to enhance human capabilities rather than simply automating them away.
What is ‘stepping up’?
Moving up to a higher level of decision-making, overseeing automation projects, and focusing on broader strategic implications.
What is ‘stepping aside’?
Focusing on activities that require uniquely human strengths, such as creativity, empathy, and interpersonal skills.
What is ‘stepping in’?
Collaborating directly with automated systems, understanding their logic, monitoring their performance, and improving their effectiveness.
What is ‘stepping narrowly’?
Specializing in a niche area that is too small or complex for anyone to automate.
What is ‘stepping forward’?
Developing the new systems and technologies that enable automation.
What is situational awareness?
A state of being fully aware of one’s surroundings and the factors that can influence decisions and actions, including the potential impact of automation.
What is overconfidence bias?
The tendency for individuals to be overconfident in their own decision-making abilities, which can lead to resistance to automation even when machines can make better decisions.