by Fred Fuld III
The “Net Gain” Argument: Historical Precedent
Historically, every major technological leap—from the steam engine to the internet—was met with “automation anxiety.” However, these technologies consistently created more jobs than they destroyed by lowering the cost of goods and services, which increased consumer demand.
Technology doesn’t just destroy jobs; it creates entirely new categories of work that were previously unimaginable (e.g., social media managers, cloud architects).
The World Economic Forum (WEF) Future of Jobs Report 2025 projects that while 92 million jobs may be displaced by 2030, 170 million new roles will be created. This results in a net gain of 78 million jobs globally. (Source: World Economic Forum, “Future of Jobs Report 2025.”)
Augmentation vs. Replacement
A critical distinction is that AI is automating tasks, not jobs. Most jobs consist of a “bundle” of tasks; AI handles the routine ones, allowing humans to focus on high-value, complex work.
AI acts as a “co-pilot,” making workers more productive. When workers are more productive, their labor becomes more valuable, often leading to higher wages and more hiring to handle the increased output.
A PwC 2025 Global AI Jobs Barometer found that industries most exposed to AI are seeing 3x higher growth in revenue per worker. (Source: PwC, “2025 Global AI Jobs Barometer.”)
The “Productivity Paradox” and Economic Growth
Higher productivity through AI leads to lower prices for consumers. This “saved” money doesn’t disappear; it is spent elsewhere in the economy, creating demand for jobs in sectors like healthcare, leisure, and personal services.
AI-driven efficiency boosts Global GDP, which inherently expands the labor market.
Goldman Sachs estimates that AI could eventually increase the total annual value of goods and services produced globally by 7% (roughly $7 trillion) and boost productivity growth by 1.5 percentage points over a 10-year period. (Source: Goldman Sachs Research, “The Potentially Large Effects of Artificial Intelligence on Economic Growth.”)
Addressing the “Labor Shortage”
In many developed nations, the bigger threat is not a lack of jobs, but a lack of workers due to aging populations. AI is a necessary tool to maintain economic output as the human workforce shrinks.
AI isn’t “taking” jobs; it’s filling the gap left by a global talent shortage and declining birth rates.
According to the Korn Ferry Institute, by 2030, there will be a global human talent shortage of more than 85 million people. AI is the only way to prevent economic stagnation. (Source: Korn Ferry, “The Talent Crunch.”)
The Resilience of the Labor Market
Since ChatGPT was released in November 2022, the U.S. economy hasn’t seen a wave of mass unemployment. In fact, the labor market remained historically tight through 2024 and 2025.
- Job Gains: While some tech firms saw layoffs, these were often corrections from pandemic-era over-hiring rather than “AI replacements.” Broadly, sectors like healthcare, construction, and hospitality continued to add hundreds of thousands of jobs monthly.
- The Unemployment Rate: In the years following the “GenAI explosion,” the U.S. unemployment rate hovered near 50-year lows (between 3.4% and 4.0%). If AI were truly a “job killer,” we would have seen a structural climb in these numbers.
The “Income Effect” and New Demand
When AI makes a company more efficient, that company becomes more profitable. Those profits are usually reinvested in one of three ways, all of which create jobs:
- Expansion: The company opens new branches or develops new products, requiring more staff.
- Lower Prices: Efficiency allows for cheaper products, leaving consumers with more disposable income to spend on other sectors (gyms, travel, entertainment), which creates jobs there.
- New Roles: Companies now need “AI Prompt Engineers,” “AI Ethics Officers,” and “Data Curators”—roles that didn’t exist in 2021.
Structural Adjustment vs. Mass Unemployment
The “adjustment” mentioned refers to the shift in skills. We are seeing a “hollowing out” of routine tasks, but a surge in demand for people who can manage the AI.
- Complementarity: For a lawyer, AI doesn’t replace the lawyer; it replaces the 10 hours they spent summarizing documents. This allows the lawyer to take on more clients or focus on high-level strategy.
- The Jevons Paradox: As a resource (in this case, data processing or content creation) becomes more efficient, the demand for that resource actually increases because it is now cheaper and more accessible.
Key Data
Even with AI integration, the BLS continues to project job growth in most professional categories through 2032.
If AI were a net job destroyer, we would see a rising unemployment rate alongside rising AI adoption. Instead, we see the opposite: businesses are using AI to bridge the gap in a labor-starved economy. We aren’t losing ‘jobs’; we are losing ‘drudgery,’ and humans are moving toward more creative, interpersonal, and strategic work.
Total US Nonfarm Employment (2022–2026)
| Period | Total Employed (Millions) | Context | |
| Nov 2022 | 154.16M | ChatGPT Released | |
| May 2023 | 155.61M | Rapid AI integration starts | |
| Dec 2023 | 156.93M | One year post-GenAI boom | |
| May 2024 | 158.01M | Tech “efficiency” layoffs peak | |
| Jan 2025 | 158.27M | Labor market remains resilient | |
| Jan 2026 | 158.63M | Sustained growth in services/care |
Source: Data compiled from U.S. Bureau of Labor Statistics (BLS) and FRED Economic Data.
As you can see from the table above, we have had three years of the most rapid AI adoption in history, yet we have 4 million more people working today than when ChatGPT was released.
The Skill-Biased Technological Change (SBTC)
AI is not deleting jobs; it is changing the composition of skills required for those jobs. While routine tasks (data entry, basic coding) are automated, the demand for “human-plus” skills—strategic oversight, emotional intelligence, and complex problem-solving—is increasing.
The Yale Budget Lab (2025) found that the “occupational mix” is shifting only slightly faster than it did during the 1990s internet boom. This is a standard evolutionary process, not a sudden collapse.
The Jevons Paradox in Action
Named after economist William Stanley Jevons, this paradox suggests that as a resource becomes more efficient to use, the total consumption of that resource actually increases.
The Software Engineering and Code Generation sector is a perfect example. This is particularly relevant because it is the industry most “threatened” by AI today, yet it perfectly illustrates how efficiency creates more work, not less.
Before Generative AI, writing a complex piece of software was expensive and slow. A company might have a backlog of 100 features they wanted to build but could only afford to hire enough developers to build 10.
AI coding assistants (like GitHub Copilot or Devin) make writing standard code 50% faster. An opponent would argue: “Now you only need half as many programmers!”
The Paradoxical Reality:
- Lower Costs = Higher Demand: Because it is now 50% cheaper and faster to build software, the “cost of entry” for new projects drops. Companies that previously couldn’t afford custom software now commission it.
- Expanding Scope: The company with 100 features in its backlog doesn’t fire its staff; it finally greenlights all 100 features because they are now economically viable.
- The Complexity Ceiling: As code becomes easier to generate, systems become more complex. We don’t need fewer people; we need more people to architect, secure, and integrate the massive influx of new code.
Since the release of ChatGPT in 2022, we haven’t seen the ‘job apocalypse’ predicted by alarmists. Instead, we have seen 4 million more Americans enter the workforce. We are not seeing the disappearance of work; we are seeing the evolutionof work.
AI is doing to the ‘mental cubicle’ what the steam engine did to the field. It is liberating us from the rote, the repetitive, and the mundane. By automating the ‘how,’ AI allows humans to focus on the ‘why’—the strategy, the empathy, and the creativity that no silicon chip can replicate.
The ‘Lump of Labor’ fallacy—the idea that there is a fixed amount of work—has been proven wrong in every century of human history. As long as humans have dreams, we will have work. We aren’t heading toward a future of unemployment; we are heading toward a future of unprecedented productivity and new industries that we are only just beginning to imagine.
Don’t bet against human ingenuity. Plan for a future where technology doesn’t replace us, but empowers us to do more than ever before.