ChatGPT is the VisiCalc of the AI Era

VisiCalc, a spreadsheet program, was the catalyst that enabled enterprises to adopt personal computers. providing the practical use case for the machines. Technology adoption often is triggered by one specific use case or capability.

So many could point to ChatGPT as the breakthrough app enabling widespread use of artificial intelligence by people as a retail application, rather than as a background feature. It is a common occurrence in computing echnology. 

VisiCalc, released in 1979, was a watershed moment for personal computing. As the first electronic spreadsheet program, it provided a compelling reason for businesses to invest in personal computers. It paved the way for future spreadsheet programs like Lotus 1-2-3 and Microsoft Excel.

Microsoft Office, particularly Excel and Word, has dominated knowledge work for decades, and is a development based on early spreadsheet and word processing use cases. 

In similar ways the BASIC Programming Language (Beginner's All-purpose Symbolic Instruction Code), developed in 1964, played a crucial role in democratizing programming, making coding accessible to a wider audience, including non-specialists.

The Unix operating system, developed in the late 1960s and early 1970s, laid the groundwork for open-source development models.

Some would say the same for the Oracle database management system, considered a standard for enterprise relational database systems. 

The World Wide Web, HTML and Netscape Navigator enabled the multimedia Web, making the internet experience useful to the general public. 

The SABRE computerized reservation systems enabled real-time transaction processing systems for airlines, and was a forerunner of online e-commerce. Likewise, the IBM Customer Information Control System enabled real-time transaction processing. 

The Java Programming Language, released by Sun Microsystems in 1995, introduced the concept of "write once, run anywhere," enabling cross-platform development and reducing the need for platform-specific code. 

The Linux Kernel further enabled open source. Likewise, the Apache web server democratized web hosting, allowing more individuals and organizations to establish an online presence.

It's hard to disagree with Jensen Huang, Nvidia CEO, about OpenAI's artificial intelligence influence or role. And its strategy--"insane" levels of capital investment--seem calculated to destroy the ability of would-be competitors to keep up. The analogy of an armaments race is apt. 

Immediate or near-term payback is not likely, and perhaps not possible, using that strategy. The possible outcome is that most of the other would-be competitors have to concede some level of defeat as their investors punish firms for investing without reward. 

Warning Labels for GenAI are Really Important

Liability is by definition a contentious matter, and will have to be updated with the rise of generative artificial intelligence content. Who is responsible for hallucinated or incorrect material, for example? Some might argue it is the language model provider, but that seems unlikely to happen, as a rule. Instead, users will likely still be held liable. 

LinkedIn, for example, is updating its user agreements to make clear that the site might show some artificial intelligence generated content that is inaccurate. 

Some might argue that product liability frameworks apply while others might see service contract frameworks as a possible model. In the former case, suppliers could be held liable for product defects or “failure to warn” of misuse. The “product defect” defense might be hard to prove, as it requires some proof of faulty design or production. 

The latter should be easy to defend: just make sure warnings about possible inaccuracies are prominent.

In that sense, the "warning labels" are really important, as they offer liability protection for providers of large language models.

Will Alphabet Antitrust Even Matter, By the Time is is Finally Resolved?

Potential antitrust action against Alphabet could include asset divestitures, though some believe  more-likely outcomes are behavioral measures that might temporarily slow Alphabet growth, but could  have fewer longer-term negative consequences--with one glaring exception.

If Alphabet leaders are consumed with defending themselves against antitrust, it is possible they will be constrained from moving forward in some key new area (possibly related to artificial intelligence), much as some believe Microsoft fell behind in mobility because of its antitrust efforts. 

The precedent there is the Microsoft antitrust action of 2001, which caused some initial changes in business practices intended to benefit competitors, but which arguably did not slow down Microsoft growth in other areas, with the notable exception of mobility and smartphones. 

Indeed, some might argue that Microsoft sought growth in other areas precisely because of the behavioral remedies. In other cases, even asset divestitures have had complex outcomes. 

The breakup of the AT&T system in the early 1980s was intended to promote competition, and did so. But consolidation followed and AT&T essentially was reassembled. Competition--the intended outcome of the breakup--did increase. 

But AT&T arguably was more affected by the emergence of the internet and the mobile communications revolution than by the antitrust actions. AT&T’s legacy businesses are a much-smaller part of overall revenue compared to mobility, which generates more than half of total revenue. 

Indeed, the long-term impacts on industry structure and dominant firm performance are complex. Standard Oil was broken up into 34 different competing firms. But consolidation followed and the surviving firms arguably were not harmed. 

Exxon; Mobil (eventually combined to form ExxonMobil; Chevron; Amoco (later acquired by BP) and Marathon Oil were formed by state-level Standard Oil divisions, for example. 

IBM’s antitrust suit eventually was dropped, but that firm’s fortunes were arguably shaped more by the emergence first of the minicomputer and then by personal computing than regulatory action. 

In fact, one possible outcome is that the case drags on long enough that market dynamics already have shifted, lessening the importance of any proposed remedies. It is possible that Google’s search dominance already will have declined because of generative AI alternatives, long before the antitrust action is applied. 

Much product search and other forms of discovery already have shifted to Amazon or social media, while AI-powered “answers” are poised to disrupt other forms of search as well. In other words, the Department of Justice antitrust action might be coming just at the point where it becomes almost irrelevant, by the time it is settled, if any action occurs at all. 

The Microsoft antitrust action lasted a decade before being resolved, and some might argue that shifted the playing field to mobility and phones, making the original reasons for antitrust actions around personal computers and browsers somewhat moot. 

Also, Alphabet also faces antitrust action in other countries, which could lead Alphabet to take voluntary actions that alleviate those concerns in ways that lessen Alphabet’s market dominance, but without huge structural changes such as breaking Alphabet up into smaller “bets.” 

Some have suggested Android and Chrome, or perhaps YouTube wind up becoming products owned by separate companies. What remains unclear are possible changes--or continuity--of consumer behavior. Users might not switch from using Google for search, Android for the operating system of their mobile devices or Chrome for their preferred browser, anymore than they’d switch from using YouTube to some other app. 

Rapid technology change can upend even well-intentioned reform. The Telecommunications Act of 1996, for example, aimed to increase competition in the communications market largely around voice services. 

Though arguably succeeding in that sense, the Act missed the arrival of the internet, and exempted mobile services, both of which had an even more profound impact on connectivity, content and communications than the effort to promote competition in fixed-network voice services. 

To some genuine extent, the Telecom Act focused on the wrong problem, or perhaps on an unnecessary problem, given the disruptive changes the internet and mobility were unleashing. 

One might have a disquieting--and similar--feeling about the antitrust action against Google. By the time it is resolved, it might not matter so much.

DT Revenue Growth: Scale or Scope?

Deutsche Telekom says it plans to boost revenue growth by increasing economies of scale and using artificial intelligence. The promise of AI to reduce costs is likely understood by all observers. The “economies of scale” might be more complicated, as that term implies wringing cost out of existing operations by selling more, or to more customers, using the same assets. 

Strictly speaking, the latter phrase (“scale”) refers to selling at higher volumes (to more customers). But some of DT’s stated plans might involve selling new or different products to the same customer base, which, strictly speaking, is “economy of scope.” 

In other words, “scale” means selling a product to more customers. “Scope” means selling additional things to existing customers. As a practical matter it might not matter whether what DT intends are examples of scale or scope. It is likely both will be at work.

  

DT expects to sell “additional products and services ranging from payment services for cell phone insurance services and platforms for payment services through to AI solutions for consumers” in its mobile business, which is a clear example of scope economics. 

In the global business markets, DT seems to suggest gains will come from higher sales to more customers, which is a “scale” economy. 

In the telecom industry, “economies of scale” can be operationalized as instances where the average cost per user decreases as the volume of services provided increases. That generally arises from spreading large fixed infrastructure costs over a growing number of subscribers; increasing sales to those customers or otherwise optimizing network usage to reduce cost per unit.

So, compared to some other industries, scale economies are more difficult, as the physical network footprint generally has to be increased to reach more potential customers (acquisitions of other telcos, for example; or building out new networks outside the present geographic footprint). 

Industry	Economies of Scale Potential	Fixed Costs	Marginal Costs	Scalability	Barriers to Scaling
Virtual Products (e.g., SaaS, streaming)	Extremely High	High (development, initial infrastructure)	Near zero (reproducing digital products)	Unlimited (global reach)	Low (mainly infrastructure scaling, user acquisition)
Telecom Networks (e.g., Fiber, Cellular)	Moderate	Very High (infrastructure: cables, towers)	Significant (capacity upgrades, maintenance)	Limited (capacity constraints, physical coverage)	High (geography, regulation, infrastructure costs)
Manufacturing (e.g., Electronics)	High	High (factories, machinery)	Low (once economies of scale are achieved)	High (limited by supply chain and logistics)	Moderate (supply chain constraints, capital investment in machinery)
Automobile Production	Moderate to High	High (factories, R&D, supply chains)	Moderate (labor, raw materials, logistics)	High (dependent on supply chain, market demand)	Moderate (complex supply chain, regulation, capital intensive)
Retail (e.g., E-commerce)	Moderate	Moderate (warehousing, logistics)	Low (online distribution, logistics costs decrease with scale)	High (digital platforms scale easily)	Moderate (logistics, competition, last-mile delivery costs)
Healthcare (e.g., Hospitals)	Low to Moderate	Very High (equipment, staff, real estate)	High (labor, equipment usage, pharmaceuticals)	Limited (physical capacity, staffing limitations)	High (regulation, physical constraints, capital-intensive infrastructure)
Energy (e.g., Renewable energy production)	Moderate	Very High (plant construction, grid integration)	Low to Moderate (depending on energy source)	Moderate (limited by physical infrastructure)	High (regulatory barriers, physical infrastructure expansion)
Education (e.g., Online platforms)	High	Moderate (platform development, content creation)	Near zero (digital content distribution)	Very High (global reach, online scalability)	Low (content development, digital infrastructure scaling)
Logistics (e.g., Delivery services)	Moderate	High (transportation, warehousing)	Moderate (fuel, labor, vehicle maintenance)	Moderate (dependent on infrastructure and efficiency)	Moderate (geography, labor, fleet expansion)
Financial Services (e.g., Banking, FinTech)	High	Moderate (technology, regulatory compliance)	Low (digital transactions, account maintenance)	High (digital services can scale globally)	Moderate (regulation, cybersecurity, trust building)

Still, some might argue that telco potential for economies of scale is less than might be expected. When a new geography is to be served, additional capital investment is required. So, by definition, the additional customers and revenue are not generated by the “same” assets, which would imply lower cost per customer. 

To be sure, there are possible economies in other areas (back office, overhead), but telco geographic expansion on a facilities basis requires additional investment in plant. 

So DT’s possible upside is more likely to come from “scope” in its consumer business, but possibly “scale” in its global business customer segment.

How Much Might Generative AI Boost Productivity Across Industries?

According to Bank of America equity analysts, AI impact on productivity is going to vary. Most industries--but not all--should see productivity gains of two percent or less over the next five years, with a handful of industries supplying infrastructure expected to outperform.

As you might expect, software and semiconductor industries will lead the list of winners, with software profit margins gaining as much as five percent and semiconductors gaining nearly five percent. 

source: Bank of America Institute 

Healthcare and telecom were laggards, despite some claims that telcos are deploying generative AI faster than the other industries, at least according to telco firm survey respondents who were technology C suite or IT heads at about 1600 global organizations. 

Ignoring the obvious self interest technology leaders have in claiming they are moving rapidly to adopt generative AI and AI, the point made by the Bank of America analysis, which was produced by industry-specific financial analysts, is that actual outcomes related to productivity might be relatively modest in most instances, at least over the next five years. 

One problem is that some industries are likely positioned to improve productivity at faster rates than others, with or without GenAI, perhaps because they already are better positioned to deploy new technology to boost outcomes. 

 

source: SAS

The other caveat is that since knowledge worker productivity is notoriously difficult to measure, such surveys might be looking at other matters, such as firm agility, industry adaptiveness to new technology or industry growth rates in general, which are higher in some industries than others. 

Industry	Revenue Growth Rate
Technology	8-12%
Healthcare	5-10%
E-commerce	10-15%
Financial Services	4-8%
Renewable Energy	12-20%
Real Estate	3-6%
Consumer Goods	2-5%
Telecommunications	1-4%
Automotive	2-3%
Travel and Hospitality	6-10%

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.