If AI Emerges as a General-Purpose Technology, Watch for Both Disruption and Creation

Any general-purpose technology might be envisioned as a set of layers of other technologies that build on it. Many could agree that GPTs are characterized by pervasiveness, flexibility, spillover effects and transformative impact. 

So the internet might underpin layers of core infrastructure and industries and businesses built around protocols such as TCP/IP and physical networks and industries (mobile and fixed networks, terrestrial and satellite wireless networks). 

Then there might be layers of roles and businesses supplying web technologies such as HTML, CSS, JavaScript, and related web development tools that enable building websites and web applications.

Networking technology including routers, switches, firewalls would be another layer. 

So would databases, cloud storage, and content delivery networks.

Then there would be many application and service layers for communication (e-mail, instant messaging, video conferencing, and social media platforms) e-commerce and online marketplaces, content and entertainment, social media, video and audio streaming or online gaming. 

Internet of Things businesses built around smart devices, sensors, and connected appliances, as well as many types of business software could be listed.

Some might include artificial intelligence as among the layers built on the internet. But some of us would say AI is a new general purpose technology that will create its own pyramid of technologies, businesses, industries and applications. 

Era	General Purpose Technology	Impact
Pre-Industrial	The Wheel	Revolutionized transportation, agriculture, and warfare. Led to the development of roads, carts, and other wheeled vehicles.
18th Century	The Steam Engine	Powered the Industrial Revolution, driving mechanization and mass production in factories, transportation (trains, ships), and agriculture.
19th Century	Electricity	Transformed daily life with lighting, appliances, communication (telegraph, telephone), and industrial processes.
20th Century	Internal Combustion Engine	Propelled transportation revolutions with automobiles, airplanes, and ships. Changed industries, warfare, and leisure activities.
20th Century	Electronics & Semiconductors	Enabled miniaturization of devices, leading to computers, radio, television, and countless electronic gadgets.
20th Century	The Internet	Connected the world, democratized information access, facilitated communication, and fueled e-commerce, digital services, and the knowledge economy.

And some of those roles or industries might presently be viewed as built on “internet” foundations. 

Intelligent infrastructure such as smart cities, autonomous vehicles, adaptable robotics, “personalized” healthcare, neurotechnology (brain-computer interfaces),  bionic limbs and prosthetics and much “metaverse” style immersive experiences, plus much of virtual and augmented reality, hyper-personalized content creation, AI-powered companions, precision agriculture and other use cases that might today be attributed to the  “internet” GPT might eventually be properly seen as built on AI as a GPT. 

Perhaps analogies can be seen in the Apple iPhone and Google search. Apple did not invent the smartphone or the mobile phone. But it completely reshaped the business, destroying Nokia and BlackBerry in the process as former market leaders. 

Google was not the first search engine, but it destroyed Altavista and other existing search engines in the market. The point is that many existing industries might be fundamentally reshaped if AI emerges as a GPT. 

And as has been the case before, AI might reshape and disrupt existing industries, functions and roles, in addition to spawning entirely-new industries, as all prior GPTs have done.

Will IoT Boost Productivity? How Long Will it Take?

The lag time between first deployment of a general-purpose technology (steam engine, railroad,, electricity, electronics, automation, automobile, the computer, the internet) and quantifiable productivity increases is not immediate, not clearly and unmistakably causal, and sometimes impossible to isolate from the impact of other general-purpose technologies.

That is important because we cannot determine whether important new technologies actually increase productivity--although people mostly assume it does--or not. Nor can we see with precision how long it will take: gains often take decades to appear in quantifiable form.

That is worth keeping in mind in assessing the return from internet of things, artificial intelligence, connected vehicles and so forth.

Consider the impact of electricity on agricultural productivity.

“While initial adoption offered direct benefits from 1915 to 1930, productivity grew at a faster rate beginning in 1935, as electricity, along with other inputs in the economy such as the personal automobile, enabled new, more efficient and effective ways of working,” the National Bureau of Economic Research says.  

There are at least two big problems with the “electricity caused productivity to rise” argument. The first is that other inputs also changed, so we cannot isolate any specific driver. Note that the automobile, also generally considered a general-purpose technology, also was introduced at the same time.

That is not to say correlations between important new technology and process efficiency are undetectable.

Looking only at use of machine learning, error rates in labeling the content of photos on ImageNet, a dataset of over 10 million images, have fallen from over 30 percent in 2010 to less than five percent in 2016 and most recently as low as 2.2 percent, say researchers working for NBER.

Likewise, error rates in voice recognition have decreased to 5.5 percent from 8.5 percent in 2017, for example.

At the same time, “there is little sign that they have yet affected aggregate productivity statistics,” the researchers note.  Labor productivity growth rates in a broad swath of developed economies fell in the mid-2000s and have stayed low since then.

“For example, aggregate labor productivity growth in the U.S. averaged only 1.3 percent per year from 2005 to 2016, less than half of the 2.8 percent annual growth rate sustained from 1995 to 2004,” NBER researchers say.

source: National Bureau of Economic Research

“Fully 28 of the 29 other countries for which the OECD has compiled productivity growth data saw similar decelerations,” they say. “The unweighted average annual labor productivity growth rates across these countries was 2.3 percent from 1995 to 2004 but only 1.1 percent from 2005 to 2015.”

So how do observers explain the apparent failure of big applications of technology to produce productivity gains? “False hope” is one explanation.

“The simplest possibility is that the optimism about the potential technologies is misplaced and unfounded,” NBER researchers say. Perhaps new technologies won’t be as transformative as many expect.

More compelling, perhaps, is our inability to measure the productivity gains. Many new technologies, like smartphones, online social networks, and downloadable media involve little monetary cost.

That poses an obvious challenge when only quantifiable price metrics can be used. A personal computer that costs 10 percent less, but supplies double the computing power or memory actually might be deemed a decrease in economic activity, for example.  

Technology improvements that boost qualitative power or potential utility might not show up in price metrics in a fully-capturable way, as imputed value is higher, but price lower. But we cannot measure higher possible value; only price changes.

Another argument is that the impact of potentially-transformative technologies is limited by limited diffusion (not all firms and industries use them equally well). In other words, the gains are not equally distributed. Some industries and firms seem to capture most of the benefits.

Perhaps the most-persuasive opinion is that it takes a considerable time to sufficiently harness the power of a new general-purpose technology, since whole business processed need to be created before the advantages can be reaped.

The bottom line: we assume IoT improves productivity, as we assume electricity and broadband also contribute. But we need to invest in a measured way, as the actual benefits might not show up for a decade or two.

That might be the case for new 5G-based enterprise and consumer use cases as well.

AI Will Eliminate Whole Industries, Not Just Some Jobs

Virtually all observers believe artificial intelligence is going to eliminate some jobs, in line with the ability AI might have to automate key job functions. The attrition could come because higher output can be achieved using fewer people, perhaps more so than because AI completely eliminates a particular job role. 

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But that is not even the most important "threat." Whole industries can disappear when a general-purpose technology appears, and AI is likely to be a GPT.

Industry	Disrupted by	New Industries/Roles
Horse-drawn carriage manufacturing	Internal combustion engine	Automobile manufacturing, transportation services
Typewriter manufacturing	Personal computer	Computer hardware and software manufacturing, word processing services
Film photography	Digital photography	Digital camera manufacturing, digital imaging services
Record stores	Digital music distribution (MP3, streaming)	Music streaming services, digital music production
Travel agents	Online travel booking websites	Travel technology companies, online travel booking services
Traditional retail	E-commerce	Online retail, logistics and delivery services

If AI does prove to be a general-purpose technology on the pattern of agriculture, steam power, the internal combustion engine, computing or electricity, that is inevitable. A look at the impact of various computing technologies, from personal computers to AI, illustrate the point, but huge changes in labor forces have always accompanied the emergence of a new GPT. 

Agriculture allowed humans to settle, rather than living as hunters and gatherers, creating the underpinnings for economic surplus that in turn enabled population growth, job specialization and settlements that enabled  the development of art, writing, legal systems, mathematics, new tools,  medicine and more-complex social structures. 

Study Title	Date	Publisher	Key Findings
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies	2014	W. W. Norton & Company	Discusses how technologies like AI, robotics, and personal computers have automated routine jobs in clerical, manufacturing, and administrative sectors, while creating new roles in software development, IT management, and creative industries.
The Race between Education and Technology	2014	National Bureau of Economic Research (NBER)	Shows how general-purpose technologies, particularly computers, have reduced demand for middle-skill jobs (clerks, machine operators) and increased demand for high-skill (software engineers, data analysts) and low-skill jobs (service workers).
Technological Change and the Labor Market: A Survey	2017	Journal of Economic Surveys	Examines how the introduction of GPTs (such as AI, automation, and IT) has led to job polarization, with job losses in low-to-middle skill categories and gains in high-skill, technology-oriented positions, such as data scientists, AI researchers, and cybersecurity experts.
Automation, Jobs, and the Future of Work	2019	International Monetary Fund (IMF)	Reviews global trends in automation and AI, showing how GPTs have reduced the need for routine manual and cognitive jobs (e.g., typists, cashiers, machine operators), while increasing demand for jobs in tech, management, and highly skilled service sectors.
AI, Robotics, and the Future of Work	2018	Brookings Institution	Analyzes how AI and robotics, as general-purpose technologies, have transformed sectors like manufacturing, retail, and logistics by displacing low-skill jobs, but creating high-skill roles in programming, system analysis, and tech support.
Digital Transformation and the Future of Jobs	2020	World Economic Forum	Finds that GPTs like AI, blockchain, and the Internet of Things (IoT) have contributed to job displacement in traditional sectors like agriculture and manufacturing, while creating job opportunities in digital and tech sectors, such as cloud computing and cybersecurity.
The Impact of Information Technology on Labor Demand	2019	Journal of Economic Surveys	Examines the role of IT and general-purpose technologies in reshaping labor demand. It finds that routine jobs in finance, clerical work, and manual labor have declined, while demand for IT professionals, software developers, and project managers has risen.
How AI and Robotics are Transforming Labor Markets	2020	European Central Bank (ECB)	Demonstrates that AI and robotics have led to the disappearance of low-wage, routine manual jobs, and the growth of tech jobs in AI systems, machine learning, data processing, and AI ethics. Also highlights growing demand for high-skilled healthcare roles enabled by AI.
The Effects of Automation on Jobs and Wages: A Global Perspective	2023	OECD	Highlights that GPTs like AI and robotics have not only automated routine jobs in manufacturing and services but also created new job categories in software development, healthcare technology, and digital infrastructure management.
Automation and the Shift in Labor Markets: Evidence from AI and Robotics	2021	Review of Economics and Statistics	Provides evidence that AI and robotics have led to the shrinking of low-skilled jobs (e.g., assembly line workers) while new opportunities have emerged in tech-heavy fields like AI programming, cybersecurity, and data analysis, where skill requirements are much higher.
Technology, Jobs, and Inequality: A Survey of the Evidence	2018	Economic Policy Institute	Analyzes how the adoption of GPTs, including AI and automation, leads to job polarization, creating a split between low-wage, low-skill jobs in the service sector and high-wage, high-skill jobs in tech fields like cloud computing, data science, and AI.
Work in the Age of AI: The Impact of Automation and AI on Job Categories	2022	MIT Technology Review	Examines the effects of AI-driven GPTs on job categories, showing a decline in traditional roles such as truck drivers and retail workers, while new categories like AI ethics officers, machine learning trainers, and drone operators emerge.
The Future of Jobs in the Age of Automation	2017	McKinsey Global Institute	Explores how general-purpose technologies like automation and AI displace routine work (e.g., cashiers, clerks), but open up new career categories, particularly in fields such as AI system design, data analytics, and machine maintenance.
The Economic Impact of Automation and AI	2021	Stanford Center for Digital Economic Studies	Shows that GPT adoption has led to a shift in employment patterns: many low-skill roles have been automated (e.g., assembly line workers, clerks), while there is an increased demand for data scientists, digital marketers, and machine learning engineers.

So concerns about changes in job composition of the labor force are realistic, if quite possibly inevitable. U.S. dockworkers recently conducted a strike among which key demands included a complete ban on automation of dock work. Discussions about that portion of new contracts remain active, but the larger point is that demands by workers to ban the use of machinery of all types has arguably slowed, but never stopped, the deployment of new technology based on a GPT that automated formerly-human labor. 

Personal computers, for example, did not so much eliminate whole jobs as make possible the ability of each worker to produce some output that formerly might have been created by others (people write their own emails and documents, where in the past stenographers would have done so. 

PCs democratized access to tools that allowed workers at all levels to produce output that once would have been handled by others, such as document production, data entry, analysis, and design.

Before PCs became widespread, tasks like document production, data processing, and basic design were often handled by specialized staff such as secretaries, typists, clerks, and graphic designers. “Desktop publishing,” for example, was an early use case for Apple computers. 

As always, new jobs arose, as well. 

Sector/Occupation	Job Function Pre-PC	Job Function Post-PC	Change in Demand
Clerical/Administrative	Typist, Data Entry Clerk, Secretary	Office Manager, Executive Assistant, Office Coordinator	Decreased
Accounting/Finance	Bookkeeper, Ledger Clerk, Accounts Clerk	Financial Analyst, Accounting Software Specialist	Decreased
Manufacturing	Manual Laborer, Assembly Line Worker, Machine Operator	CNC Operator, Maintenance Technician, Robotics Specialist	Decreased
Retail	Inventory Clerk, Cashier, Stock Clerk	E-commerce Manager, Inventory System Analyst	Decreased
IT and Technology	None	Software Developer, Systems Administrator, IT Support	Increased
Customer Service	Telephone Operator, Customer Service Representative	Help Desk Technician, Customer Support via Online Platforms	Increased
Marketing and Sales	Sales Clerk, Telemarketer, Market Research Assistant	Digital Marketing Specialist, Social Media Manager	Increased
Education	School Secretary, Paper-Based Research Assistant	EdTech Specialist, E-learning Coordinator, Curriculum Developer	Increased
Health Services	Medical Records Clerk, Billing and Coding Clerk	Medical Data Analyst, Health IT Specialist, Telemedicine Support	Increased
Logistics/Transportation	Shipping Clerk, Inventory Handler, Dispatcher	Supply Chain Analyst, Logistics Software Manager	Increased

At a high level, most observers might agree that AI poses similar sorts of upside and downside, from the standpoint of jobholders. New jobs are going to be created, but some existing jobs will likely decrease in number. In that sense, we can expect continued opposition to the use of AI in many industries, though such opposition will fail, over time, as obvious productivity gains often compel all contestants in a market to adopt the new technologies. 

Study Title	Date	Publisher	Key Findings
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies	2014	W. W. Norton & Company	The adoption of digital technologies leads to a polarization of the job market, creating high-skill, high-pay jobs while diminishing middle-skill jobs.
The Impact of Digital Technologies on Employment	2016	International Labour Organization (ILO)	Found that automation through digital technologies has the potential to displace a significant number of jobs, especially in manufacturing, but also create new job categories in tech and service sectors.
Automation, Skills, and the Future of Work	2019	McKinsey Global Institute	Predicts that up to 375 million workers may need to switch occupational categories due to the adoption of automation and AI, necessitating retraining and new skill development.
The Future of Jobs Report 2020	2020	World Economic Forum	Identified the net job creation potential from GPTs, predicting that 85 million jobs may be displaced while 97 million new roles could emerge, emphasizing the need for upskilling.
Artificial Intelligence and the Future of Work	2021	Brookings Institution	Discusses how AI adoption can create job growth in sectors requiring complex human interactions and creativity, but also warns of significant job displacement in routine tasks.
The Economic Impact of Artificial Intelligence on Work	2022	MIT Technology Review	Highlights that GPTs like AI can lead to job transformation rather than outright replacement, with new roles in data management, AI oversight, and ethics emerging as key areas of growth.
Industry 4.0 and Its Impact on the Labor Market	2023	Journal of Business Research	Analyzes the impact of Industry 4.0 technologies on job dynamics, indicating a shift towards more specialized roles and increased demand for technical and soft skills.
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies	2014	W. W. Norton & Company	Examines how advances in automation and artificial intelligence (AI), including GPTs, lead to job displacement in routine tasks, but also creates opportunities for higher-skill jobs in sectors like healthcare and education.
The Impact of Information Technology on Labor Demand: A Review of the Literature	2019	Journal of Economic Surveys	Analyzes the role of IT and GPT adoption in reshaping labor demand across industries, revealing a shift toward higher-level cognitive tasks, while routine manual and clerical jobs decline.
Artificial Intelligence and the Economy	2020	Federal Reserve Bank of Dallas	Highlights that AI and GPTs automate a wide range of tasks, particularly in manufacturing and services, leading to job displacement but also the creation of new roles in data analysis, AI training, and tech support.
Technology, Jobs, and Inequality: A Survey of the Evidence	2018	Economic Policy Institute	Reviews evidence that while GPTs like AI and robotics reduce demand for low-skill jobs, they lead to greater inequality, with job growth concentrated in high-skill and managerial sectors.
Labor Market Polarization and Technological Change: A Historical Perspective	2016	National Bureau of Economic Research (NBER)	Finds that technological advancements, including GPTs, result in labor market polarization: growth in high-skill jobs and decline in middle-skill jobs, with a hollowing out of mid-level occupations.
Automation, Skills, and the Future of Work	2021	Brookings Institution	Concludes that GPT adoption accelerates demand for highly skilled labor (in areas like data science, programming), while jobs in routine sectors such as manufacturing and transportation face displacement.
The Effects of AI and Automation on Jobs and Wages: A Global Perspective	2023	OECD	Examines global trends in AI and automation adoption, indicating that the adoption of GPTs displaces low-wage jobs but creates more skilled positions in technology management, software development, and AI maintenance.
How Automation Affects Occupations: Assessing the Task Content of Occupations	2019	Quarterly Journal of Economics	Analyzes how automation via GPTs impacts occupations by reducing routine, repetitive tasks, but increasing demand for creative, problem-solving, and technical roles.
Digital Transformation and the Future of Jobs	2020	World Economic Forum	Provides evidence that GPTs spur job growth in technology-driven sectors (e.g., software development, cybersecurity) but reduce demand for jobs in routine, manual, and clerical functions.
Technological Change and the Labor Market	2017	Journal of Labor Economics	Identifies that technological progress, including GPTs, leads to increased job turnover and retraining requirements, with displaced workers often finding new roles in technology and service sectors.
Technological Shocks and Labor Markets: Evidence from GPTs and AI	2022	Review of Economics and Statistics	Examines the effect of GPTs on labor markets and finds a clear correlation between GPT adoption and shifts in employment toward knowledge-based industries, with a decline in jobs that involve manual labor.
The Impact of AI and Robotics on Labor Markets: A Review of Empirical Evidence	2018	International Journal of Robotics Research	Reviews empirical evidence on the impacts of AI and robotics (a form of GPTs) on job displacement in manufacturing and logistics, while showing job growth in healthcare, software, and robotics management.