Last week, Luiza Jarovsky, PhD, asked: Do you want AI to do your laundry so you can create, or do your creating so you can do laundry?
Three charts show why this question matters more than ever. For the first time in 100+ years, the historical pattern has broken.
CHART 1: Job Displacement vs. Job Creation (1900-2024)
For over a century, every major innovation wave followed the same pattern: initial job displacement was always exceeded by new job creation. The printing press, electricity, automobiles, computers – each disrupted existing work but ultimately created more opportunities than they destroyed. The net effect was consistently positive, sometimes dramatically so.
AI is different. For the first time since we began tracking in 1900, we’re seeing potential net NEGATIVE job impact. The blue bars (job creation) aren’t reliably exceeding the red bars (displacement). This isn’t speculation – it’s what the early data signals.
Key insight: The 100-year pattern of “innovation creates more than it destroys” may have ended.
CHART 2: Recovery Time Trend (1900-2024)
Here’s the silver lining from history: recovery times have been decreasing. When the printing press disrupted scribes, recovery took decades. The computer revolution? Just 5-7 years before net job growth resumed. Each wave recovered faster than the last.
But notice the question marks in the AI era. We don’t know if this pattern will hold. Will AI follow the trend toward faster recovery? Or has something fundamental changed? The decreasing recovery time trend offers hope – but only if we maintain the organizational and individual capabilities needed to adapt.
Key insight: Historical recovery acceleration offers hope, but AI’s impact remains unknown.
CHART 3: Innovation Impact Pattern Summary (1900-2024)
This chart tells the complete story: 100+ years of consistent positive outcomes, followed by our first uncertain future. Every previous innovation – from assembly lines to the internet – eventually showed net positive job creation. The pattern was so reliable we stopped questioning it.
AI breaks that reliability. Not because AI is inherently different, but because of HOW we’re adopting it. Undisciplined AI adoption (Daniel Lock’s “10 prompts replace consultants”) accelerates displacement without building capability for adaptation.
Key insight: The pattern broke not because of AI itself, but because of undisciplined adoption methodology.
UPDATED FORECAST: Three Scenarios
Since publishing these charts, I’ve seen evidence for all three scenarios playing out in real-time:
OPTIMISTIC (1-2 years recovery): Evidence: Professionals like Tahj developing systematic AI fluency through October Diaries methodology. They’re using AI to augment capability while maintaining critical judgment. Organizations assessing readiness (HFS) before deploying AI. Result: Faster adaptation, new hybrid roles emerge quickly.
MODERATE (3-5 years recovery): Evidence: Mixed adoption – some organizations using methodology, others following hype. Canda Rozier notes cognitive decline “has been going on for years” – AI accelerates existing trend. Laura Sellers emphasizes context-specific solutions over generic frameworks. Result: Recovery happens but unevenly, depending on methodology adoption.
PESSIMISTIC (7-10+ years recovery): Evidence: Daniel Lock’s approach getting 95 reactions, 41 comments – organizations embracing prompts without readiness assessment. Luiza’s warning: “hundreds of millions cannot brainstorm or write alone.” Organizations deploying AI without validation methodology. Result: Widespread cognitive atrophy, extended recovery period.
What determines which scenario we get?
Not AI capability—that’s advancing regardless.
Methodology adoption:
- Individual: Conversational AI fluency (October Diaries) that maintains judgment
- Organizational: Readiness assessment (HFS) before technology deployment
- Industry: Validation frameworks that prevent cognitive dependency
The recovery time depends on whether we adopt AI WITH methodology or surrender judgment TO AI outputs.
TODAY’S TAKEAWAY
The charts show the pattern broke. The forecast shows three possible futures.
Which one we get isn’t determined by AI capability – it’s determined by OUR methodology.
The choice is still ours. For now.
30
AIReadiness #FutureOfWork #ProcurementTransformation #DigitalTransformation #JobDisplacement
Procurement Insights
There Is No Fate But What We Make For Ourselves: Job Displacement In The AI Era
Posted on November 12, 2025
0
Last week, Luiza Jarovsky, PhD, asked: Do you want AI to do your laundry so you can create, or do your creating so you can do laundry?
Three charts show why this question matters more than ever. For the first time in 100+ years, the historical pattern has broken.
CHART 1: Job Displacement vs. Job Creation (1900-2024)
For over a century, every major innovation wave followed the same pattern: initial job displacement was always exceeded by new job creation. The printing press, electricity, automobiles, computers – each disrupted existing work but ultimately created more opportunities than they destroyed. The net effect was consistently positive, sometimes dramatically so.
AI is different. For the first time since we began tracking in 1900, we’re seeing potential net NEGATIVE job impact. The blue bars (job creation) aren’t reliably exceeding the red bars (displacement). This isn’t speculation – it’s what the early data signals.
Key insight: The 100-year pattern of “innovation creates more than it destroys” may have ended.
CHART 2: Recovery Time Trend (1900-2024)
Here’s the silver lining from history: recovery times have been decreasing. When the printing press disrupted scribes, recovery took decades. The computer revolution? Just 5-7 years before net job growth resumed. Each wave recovered faster than the last.
But notice the question marks in the AI era. We don’t know if this pattern will hold. Will AI follow the trend toward faster recovery? Or has something fundamental changed? The decreasing recovery time trend offers hope – but only if we maintain the organizational and individual capabilities needed to adapt.
Key insight: Historical recovery acceleration offers hope, but AI’s impact remains unknown.
CHART 3: Innovation Impact Pattern Summary (1900-2024)
This chart tells the complete story: 100+ years of consistent positive outcomes, followed by our first uncertain future. Every previous innovation – from assembly lines to the internet – eventually showed net positive job creation. The pattern was so reliable we stopped questioning it.
AI breaks that reliability. Not because AI is inherently different, but because of HOW we’re adopting it. Undisciplined AI adoption (Daniel Lock’s “10 prompts replace consultants”) accelerates displacement without building capability for adaptation.
Key insight: The pattern broke not because of AI itself, but because of undisciplined adoption methodology.
UPDATED FORECAST: Three Scenarios
Since publishing these charts, I’ve seen evidence for all three scenarios playing out in real-time:
OPTIMISTIC (1-2 years recovery): Evidence: Professionals like Tahj developing systematic AI fluency through October Diaries methodology. They’re using AI to augment capability while maintaining critical judgment. Organizations assessing readiness (HFS) before deploying AI. Result: Faster adaptation, new hybrid roles emerge quickly.
MODERATE (3-5 years recovery): Evidence: Mixed adoption – some organizations using methodology, others following hype. Canda Rozier notes cognitive decline “has been going on for years” – AI accelerates existing trend. Laura Sellers emphasizes context-specific solutions over generic frameworks. Result: Recovery happens but unevenly, depending on methodology adoption.
PESSIMISTIC (7-10+ years recovery): Evidence: Daniel Lock’s approach getting 95 reactions, 41 comments – organizations embracing prompts without readiness assessment. Luiza’s warning: “hundreds of millions cannot brainstorm or write alone.” Organizations deploying AI without validation methodology. Result: Widespread cognitive atrophy, extended recovery period.
What determines which scenario we get?
Not AI capability—that’s advancing regardless.
Methodology adoption:
The recovery time depends on whether we adopt AI WITH methodology or surrender judgment TO AI outputs.
TODAY’S TAKEAWAY
The charts show the pattern broke. The forecast shows three possible futures.
Which one we get isn’t determined by AI capability – it’s determined by OUR methodology.
The choice is still ours. For now.
30
AIReadiness #FutureOfWork #ProcurementTransformation #DigitalTransformation #JobDisplacement
Share this:
Related