Genius Myopia: Why Smarter Models Aren't Enough

December 22, 2025: The AI industry spent 2025 betting that stronger engines would drive mass adoption. The data proves that bet wrong. Eight breakthrough model releases in seven months, including GPT-5 and Gemini 3, produced just four percentage points of daily usage growth. 75% of Americans have tried AI, but only 25% use it daily. The bottleneck is not model intelligence. It is Trust and Context.


Executive Summary

December 22, 2025: The AI industry spent 2025 betting that stronger engines would automatically drive mass adoption, but the data proves that bet wrong. Despite eight breakthrough model releases in seven months, including GPT-5 and Gemini 3, daily usage rates grew by a meager four percentage points. Adoption remains stuck: 75% of Americans have tried AI, but only 25% have found sufficient utility to make it a daily habit. The bottleneck is not model intelligence; it is a crisis of Trust and Context.

Privacy concerns are the number one barrier keeping 45% of Americans on the sidelines, with privacy scoring a dismal Net Promoter Score (NPS) of -29.7—the lowest of any AI product attribute by 28 points. However, when trust is established and data is connected, the economics of AI transform. Recon Analytics' dataset of over 120,000 respondents reveals a massive conversion divide: users who successfully connect AI to their first-party data convert to paid subscriptions at 3.4x the rate of those performing basic tasks. The report concludes that growth will not come from 'ask me anything' models but from trusted data infrastructure that enables 'help me with my data' utility.

Table of Contents

  1. Executive Summary 2
  2. Chapter 1: The Adoption Gap 3
  3. Chapter 2: The Reluctant 45% 6
  4. Chapter 3: Platform Landscape 9
  5. Chapter 4: The Conversion Divide 12
  6. Chapter 5: The Infrastructure Effect 15
  7. Chapter 6: The Privacy Problem 17
  8. Chapter 7: The Second Inning Playbook 20
  9. Conclusion 22
  10. Appendix 23