THE EXPONENTIAL REPLACEMENT CURVE
When Different Job Titles Could Be Fully Automated by AI
The Exponential Replacement Curve is a framework created by David Borish that maps when different job categories will be technically and economically replaceable by AI. Based on METR's research on AI task completion time horizons, David Borish's Open-Prem Inflection Point analysis, and David Sacks' projections on multiplicative AI growth, the paper establishes that AI capability time horizons are doubling approximately every 7 months. Three concurrent improvements in algorithms, chips, and compute multiply together for a potential 1,000,000x increase in AI capability over four years. The paper identifies three waves of job displacement: administrative and customer service roles in 2025, sales, finance, and software development in 2026, and research, strategic planning, and creative direction in 2027.
KEY FINDINGS
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AI task completion time horizons double approximately every 7 months, with leading systems at roughly 50 minutes as of early 2025.
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Open-source AI deployment costs are 4.2x cheaper than proprietary APIs in early 2025, growing to 22.4x cheaper by late 2027.
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Three exponential improvements (algorithms, chips, compute) multiply for 27-64x annual growth and a potential 1,000,000x improvement over four years.
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Entry-level roles face technical replacement by late 2025, mid-level professional roles by mid-2026, and senior specialized roles by early 2027.
ABOUT THE AUTHOR
David Borish is an Enterprise AI Strategist at Trace3 (an Apollo Management company) with 25 years of experience across technology (AI), CPG, sports tech, and finance. He created the Open-Prem Inflection Point framework and delivers the Open-Prem Strategy Accelerator workshop in partnership with IBM.​
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Last Updated: May, 2025
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Related: Link to Exponential Replacement Curve V2, Link to Open-Prem V1, Link to Open-Prem V2, Link to Open-Prem V2 Update, Open-Prem Workshop (open-prem.com), Speaking page