The Dunning-Kruger of AI Disruption

The global AI revolution follows the classic Dunning-Kruger curve — overconfidence peaks, reality crashes in, and only then does true productivity emerge. Four disruption waves mapped against real-world events, from the 2022 earthquake through the predicted 2030 stabilization.

A prediction by Sparsh Gupta — Feb 2026

Market Confidence (DK Curve)

Scale: 0–100% · Composite Index

Aggregate market belief that AI can replace human developers. Combines VC investment sentiment, corporate hiring intentions, media narrative tone, and tech stock valuations. Follows the Dunning-Kruger cognitive bias pattern: overconfidence peaks, reality crashes in, then stable productivity emerges.

Shaded band in prediction zone shows ±15% uncertainty margin that widens over time.

Wave 1: ChatGPT Effect

Peak: ~45% · Center: Mid-2023 · Window: ~9 months

Immediate market shock from ChatGPT's launch. Measures layoff velocity, traffic displacement (Stack Overflow −50%, Chegg −48%), and panic-driven corporate restructuring across Big Tech.

Asymmetric Gaussian: fast rise (0.8σ), slower decay (1.3σ).

Wave 2: Agentic Builders

Peak: ~62% · Center: Early 2024 · Window: ~13 months

Impact of AI coding tools (Copilot 1.8M users, Cursor, Devin). Measures junior/mid-role displacement (−40%), bootcamp closures, and the $600B gap between AI infrastructure investment and actual revenue.

Wider distribution reflects gradual tool adoption across organizations.

Wave 3: Claude Code Era

Peak: ~82% · Center: Early 2026 · Window: ~10 months

The agentic AI revolution. Autonomous code agents (Claude Code, Codex, Gemini CLI) drive peak disruption. Mid-tier engineer displacement reaches −60%. Technical debt and security crises trigger the correction.

Highest amplitude wave. Coincides with the DK "Peak of Mount Stupid."

Wave 4: Super Teams

Peak: ~75% · Center: Late 2029 · Window: ~16 months

The equilibrium wave. Market recovery stabilizes into the ~6-person Super Team model (fullstack, security, ML, systems, maintainers + AI agents). Productivity gains become sustainable and repeatable.

Marks the DK "Plateau of Productivity." Wider sigma reflects gradual adoption.

Methodology & Prediction Boundary

Historical: pre-late 2025 · Predicted: late 2025 onwards

Dashed purple line marks where historical data ends and predictions begin. Wave shapes use asymmetric Gaussian distributions (fast rise, slower decay). The DK composite curve uses Catmull-Rom spline interpolation across 24 control points.

All events post-boundary are modeled projections. Diamond markers (◊) denote predicted events; circles denote historical.

Wave 1

The ChatGPT Earthquake

Nov 2022 — Mid 2023

Twitter/X cuts 50% overnight. ChatGPT launches to 100M users. Meta (11K), Google (12K), Microsoft (10K), Amazon (18K+) all slash headcounts. Stack Overflow and Chegg collapse. 60K+ tech jobs gone in 2023 alone.

-50%

SO Traffic

60K+

Tech Layoffs

Wave 2

The Agentic Builder Era

Mid 2023 — Early 2025

Copilot hits 1.8M users, Devin goes viral, bootcamps close. Junior devs displaced; seniors hired to review AI code. Goldman flags "$600B revenue gap."

-40%

Junior Roles

$600B

AI Rev. Gap

Wave 3

The Claude Code Revolution

Late 2025 — Early 2027

Claude Code, Codex, Gemini CLI go agentic. Salesforce cuts 1K+, IBM cuts thousands, Amazon slashes 16K corporate roles. Mid-tier engineers displaced. Cracks appear: AI code debt, security gaps. The bubble deflates.

-60%

Mid-Tier Jobs

2-3x

Top Eng Pay

Wave 4 — Recovery

The Super Team Equilibrium

2029 — 2030+

After the correction, the Super Team model matures: ~6 specialists (fullstack, security, ML, systems, maintainers) + AI agents. Sustainable productivity, stable market.

Team Size

10x

Sustainable

The Dunning-Kruger Journey of AI Adoption

How global market confidence overshoots, crashes, and stabilizes — the classic pattern playing out in real time

2022-2026

Rise of Hype

50+ → 12

Teams slashed aggressively. "AI replaces everyone" narrative peaks. Overconfidence drives reckless adoption.

→

Peak: 2026

Mount Stupid

4-8

Maximum overconfidence. Skeleton crews + AI. "We don't need engineers." Move fast, break everything.

Over-cuts AI slop Tech debt

→

2027-2028

Valley of Despair

Re-hire to 15+

Reality crashes in. AI-generated bugs, security breaches, outages. Companies scramble to re-hire. AI bubble deflates. Startups fail.

Re-hiring Bubble burst Quality crisis

→

2028-2029

Slope of Enlightenment

8-10

Understanding what AI does well vs. what needs humans. Quality frameworks emerge. AI-human collaboration matures.

→

2030+

Plateau of Productivity

Stable Super Team model. Fullstack, Security, ML, Systems, Maintainers + AI. Sustainable, proven, productive.

Fullstack Security ML Systems Maintainers

Prediction & Analysis by

Sparsh Gupta

Product Manager @Grant Thornton• AI Strategist • Co-Founder, EonLex Technologies

Feb 2026

Grant Thornton — PM, AI-driven Audit Transformation

EonLex Technologies — Co-Founder, GenAI + Legal Tech

Northvolt AB — Gen AI Procurement & Supply Chain

GreyB Services — Patent Analytics & IP Strategy

Kyushu University — AI/ML Research

5+ years across AI product management, generative AI implementation, and innovation strategy. This prediction model draws on firsthand experience building and deploying AI-driven products across audit, legal tech, procurement, and IP — observing how organizations adopt, over-correct, and eventually stabilize around new technology paradigms.