Will AI Substitution Really Cut Jobs? Maybe Not

Artificial intelligence cost savings and job cuts are a tricky matter. Consider claims that AI allowed IBM to cut between 3500 and 8000 jobs by using AI. In fact, job losses might have been in the “hundreds,” not thousands.  

It is argued that IBM then redeployed its resources from AI automation to hire new talent for high-growth areas like software engineering, sales, and marketing. Headcount might have actually grown, as a result. 

The other issue is that some job “losses” were related to the prior company spinoff of Kendryl, the managed services business with 90,000 employees, about 25 percent of IBM’s total workforce. 

That led to later job cuts at both Kendryl and IBM. 

The point is that it is hard to quantify how AI affects jobs. There could be a combination of cuts as well as new jobs created. There will be some displacement of existing human jobs as automation creates substitutes.

Also, there are other effects. New technology gets used because it boosts productivity. If that lowers the cost of producing goods and services, lower prices happen, increasing consumer demand. 

The increased demand often requires more production, which can offset initial job losses by increasing demand for labor in other parts of the value chain or other growing sectors of the economy.

For example, the introduction of the Model T made cars cheaper, increasing demand for cars, which ultimately required more, not fewer, auto workers.

In addition, technology doesn't just automate old tasks. It creates entirely new ones that only humans can perform, particularly those involving non-routine cognitive, creative, and social skills (managing the new technology, developing software, data analysis, ethical oversight, or personalized services). 

That can lead to the formation of whole new industries and occupations that did not exist before.

Historically, this pattern has played out repeatedly, from the Industrial Revolution (replacement of artisans with factory workers and creating engineering/maintenance roles) to the information technology revolution (eliminating typists and switchboard operators while creating software developers, network administrators, and digital content creators). 

Artificial Intelligence automation should have the same net positive impact on jobs. That said, the immediate focus often will be on "lost jobs."

Over the past six years, IBM has continuously refined their internal virtual agent, AskHR, to automate more than 80 HR tasks and handle over 2.1 million employee conversations annually. Most recently, in 2025, the team integrated IBM watsonx Orchestrate.  

The AI agent helped contribute to a 40-percent reduction in the HR team’s operational costs over the past four years, IBM says. AskHR also achieved a 94 percent containment rate of common questions, has led to a 75 percent reduction in support tickets raised since 2016, and has created more than 10 million employee interactions each year since 2023.

That might suggest that AI will only work in one direction: substituting itself for existing jobs. That will happen, but is only part of the story.

The point is that AI might often not be a zero-sum game, where AI only destroys jobs. It should also create the need for new jobs.

Fourth Industrial Revolution Now is More About AI than IoT

Until artificial intelligence seemingly swept nearly everything before it, the “Fourth Industrial Revolution” or “Industry 4.0” was mostly about applying sensors and the internet of things to machinery used in factories. Recently, we are more likely to hear it said about applied AI. 

When the term “Fourth Industrial Revolution”  was popularized (notably by Klaus Schwab of the World Economic Forum starting around 2016), its initial core was the fusion of physical and digital realms. 

The Internet of Things was viewed as the key enabler, referring to the network of physical devices, sensors, machines and software that allows machines and computers to collect and exchange data. In a factory setting, this meant equipping machines with sensors to gather real-time data on their performance, environment, and output.

Of course, the issue, as always, is not “data” as such but the ability to wring useful insights from data. For that reason, we are starting to hear AI mentioned as driving the Fourth Industrial Revolution. 

Revolution	Key Characteristics	Approximate Time Period
First Industrial Revolution	Mechanization of production using water and steam power. The transition from agrarian and handicraft economies to industry and machine manufacturing.	Late 18th to mid-19th Century (c. 1760s - 1840s)
Second Industrial Revolution	Mass production driven by the widespread use of electricity and the advent of the assembly line (e.g., in steel, oil, and automobile industries).	Late 19th to early 20th Century (c. 1870s - World War I)
Third Industrial Revolution	The Digital Revolution, involving the use of electronics, IT, and automated production. The rise of computers, the internet, and early automation.	Mid-to-late 20th Century (c. 1950s - 1970s onwards)
Fourth Industrial Revolution?	Fusion of the physical, digital, and biological spheres, use of artificial intelligence. `	21st Century (2024 and forward)

Wikipedia Pageviews Drop 8%; Generative AI Believed the Reason

The artificial intelligence disruption of existing functions, jobs and businesses continues, with a new bit of evidence coming from Wikipedia. 

“We are seeing declines in human pageviews on Wikipedia over the past few months, amounting to a decrease of roughly eight percent as compared to the same months in 2024,” Wikipedia says. “We believe that these declines reflect the impact of generative AI and social media on how people seek information, especially with search engines providing answers directly to searchers, often based on Wikipedia content.”

“These declines are not unexpected,” Wikipedia says. “Search engines are increasingly using generative AI to provide answers directly to searchers rather than linking to sites like ours.”

“Many other publishers and content platforms are reporting similar shifts as users spend more time on search engines, AI chatbots, and social media to find information.”

By some estimates, website traffic declines of 15 percent to 70 percent might happen because of AI chatbot substitution. Anecdotally, my own use of Wikipedia has dropped to virtually zero, as I find AI overviews in Google search are an absolutely satisfactory alternative. 

Sector	Possible Change/Disruption	Quantitative Data on Shifts (Where Available)	Notes/Source
Journalism & Media	Automation of article/video generation; reduced human roles; content scraping without payment	Proportion of people believing journalists often use AI up by at least 3 percentage points in 2025; existential threat intensifying financial crisis	https://reutersinstitute.politics.ox.ac.uk/generative-ai-and-news-report-2025-how-people-think-about-ais-role-journalism-and-society
Creative Industries (e.g., Writing, Design)	Job losses and diminished worker agency; shift to reviewing AI output	300 million global jobs (9.1% of workforce) at risk; 77,999 tech job losses linked to AI in early 2025; 58% decline in tech hiring from 2024 to 2025	https://www.nu.edu/blog/ai-job-statistics/
Website/Content Publishers	Traffic diversion via AI search summaries	15-64% decline in organic traffic from AI Overviews; up to 70% drop for some sites; educational platforms saw ~50% decline	https://www.forbes.com/sites/torconstantino/2025/04/14/the-60-problem---how-ai-search-is-draining-your-traffic/
Stock Photography	Erosion of licensing revenues; AI generators replacing stock libraries	Stock photo market at $6.1B in 2025 (projected to $11.3B by 2032); AI image gen market from $350M (2023) to $1.1B by 2030	https://colinritman.medium.com/comprehensive-analysis-of-stock-photography-revenue-potential-a-2025-market-assessment-0b881c4d1703
Music Industry	AI tracks flooding streams; reduced royalties for human artists	AI-generated music could take 20% of streaming revenues by 2028; platforms using AI to lower royalty costs	https://info.xposuremusic.com/article/how-ai-generated-music-could-impact-music-catalog-valuations
Education (Textbooks, Online Courses)	Shift to AI-personalized materials; reduced demand for static content	No direct loss stats yet; concerns on plagiarism/accuracy prominent; AI reimagining textbooks for adaptive learning	https://www.sciencedirect.com/science/article/pii/S2666920X24000225
Overall Online Content	AI dominance in creation; commoditization of human work	Estimates suggest 90% of online content AI-generated; 750M apps integrating AI models	https://seedblink.com/blog/2025-04-23-generative-ai-and-the-future-of-content-creation-at-scale

"My Truth" Versus "The Truth"

The concept of "objectivity" in U.S. journalism education has undergone a significant transformation since the 1970s, and in a more subjective direction, perhaps mirroring the more-subjective intellectual tendencies of post-modern thinking in general. 

Here’s the problem: it has become harder to separate “what a thing is” from “what I say a thing is.” Subjectivism inevitably means there is no external, objective truth independent of individual or cultural perspective. And that is a major problem.

Subjectivism leads to the notion that there is “no such thing as truth.” Instead, there is only a linguistic and cultural creation: “my truth,” as the saying goes. But, by definition, one person’s “truth” (“what is true for me”) cannot be a universal truth shared by all or most other people. 

Such “truth” is essentially “my opinion,” and while that is fine in many situations, it is destructive of broader culture and social cohesiveness. In a sense, it is the logical conclusion when all we focus on is “the individual” and not “society” or “the culture” or the “nation” or the “group.” 

So here’s the problem: When the concept of shared, verifiable facts is undermined, it encourages the belief that all perspectives are equally valid. This makes reasoned compromise and a unified approach to problems virtually impossible, as each side operates from its own constructed, in-group reality or "narrative."

In a nutshell, the problem is that we then cannot commonly agree on “what a thing is,” but only upon what each of us says a thing is. 

This shift toward subjectivism is one of the most defining and consequential trends of post-modern thought, a profound philosophical departure from the Enlightenment-era belief in objectivity, universal truth, and the capacity of reason and science to arrive at a single, verifiable reality. This arguably is most significant in the area of ethics and morality, since there are no “universal truths.”

Field	Modernist Stance (Objective)	Post-Modernist Stance (Subjective/Relative)
Epistemology (Theory of Knowledge)	Knowledge is a process of objective discovery; certain knowledge is possible.	Knowledge is always mediated by individual or cultural context, making it subjective and relative.
Ethics/Morality	Universal moral laws exist (e.g., natural rights, categorical imperatives).	Moral claims are expressions of individual or cultural attitudes (Ethical Subjectivism or Moral Relativism).
History	History is a linear, factual record of events to be objectively uncovered.	History is a narrative or literary form, with no single, objective account; focus is on whose voices (perspectives) have been excluded.
Art/Aesthetics	Art should strive for universal beauty or a coherent, original meaning.	Art is open to infinite, individual interpretation; the meaning resides in the subjective experience of the viewer, not the intention of the artist.

Postmodernism generally views the concept of objective reality as a "social construct" or a product of language and power structures, leading to a focus on the subjective experience.

In journalism, we see a comparable shift from "neutrality" to "truth-seeking," "transparency," and "context," all of which is subjective. 

Since the 1970s, journalism education has increasingly moved away from solely event-centered reporting ("just the facts") to providing analysis, explanation, and context ("what does it mean?"). That might sound like an improvement, but it necessarily is explicitly more subjective. 

And since “absolute impartiality is impossible,” the focus shifts to journalists (in principle) being transparent about their sources, methods, and inherent biases. In principle, journalists are supposed to actively correct for biases. In practice, this often simply means imposing biases. 

Aspect	Prior to 1970s (Traditional "Objectivity")	Post-1970s (Evolving Conceptions)
Guiding Ideal	Detachment and Neutrality (Journalist as a dispassionate recorder)	Truth-Seeking, Context, and Transparency (Journalist as an active interpreter)
Core Practice	"Both-Sides-Ism": Giving equal time and weight to all sides of a controversy.	Verification and Context: Prioritizing factual accuracy, evidence, and scientific consensus over false equivalence.
Journalist's Role	Invisible, impersonal, non-judgmental conduit of "facts."	Self-aware, transparent, providing analysis and interpretation; may involve "moral clarity."
Tone and Style	Simple, straight-laced "inverted pyramid" style; emphasis on the visible facts.	Interpretive, analytical, and narrative-driven; emphasis on the meaning and causes of events.
Critique Addressed	Minimal focus on systemic bias or impact of reporting routines.	Addressing inherent biases, the reinforcement of the status quo, and the silencing effect of traditional neutrality on diverse voices.

The problem is that without a firm commitment to the concept of shared truth, social cohesiveness becomes very hard, and perhaps impossible. Whether in the realm of philosophy, culture, economics or politics, subjectivism is a key problem. 

We cannot fix problems we cannot agree exist. We also can create problems if we cannot agree on some shared understanding of the nature of reality.

In the "post-truth" era, feelings and personal beliefs seemingly matter more than facts.

Individuals Working with an AI "Team Mate" Sometimes Outperform Two-Person Teams

A study by a team of researchers finds that AI “significantly enhances performance” perhaps in part by breaking down functional silos. “Without AI, R&D professionals tended to suggest more technical solutions, while commercial professionals leaned towards commercially-oriented proposals,” the authors say. “Professionals using AI produced balanced solutions, regardless of their professional background.”

source: The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise

Individuals using AI matched the performance of teams without AI, suggesting, at the very least, that AI usage does not harm output. “Our findings show that AI replicates many of the benefits of human collaboration, acting as a “cybernetic teammate,” the team says.

“Individuals with AI produce solutions at a quality level comparable to two-person teams,” they add. “The adoption of AI also broadens the user’s reach in areas outside their core expertise.”

“Overall, our findings indicate that adopting AI in knowledge work involves more than simply adding another tool. By enhancing performance, bridging functional expertise, and reshaping collaboration patterns, GenAI prompts a rethinking of how organizations structure teams and individual roles.”