AI's Real Value Lies in Practical Applications by People Solving Real Problems, Not Tech Company Hype
Source: Josh Tyrangiel. "The Secret to Understanding AI - The Atlantic." May 7, 2026. www.theatlantic.com
The Gist
The author argues that AI's real value comes from regular people using it to solve actual problems in their fields, not from tech companies making wild promises about the future. Instead of listening to hype about AI either saving or destroying the world, we should look at teachers, doctors, and government workers who are quietly using AI to do their jobs better.
Conclusion
The most meaningful and effective use of AI comes from practitioners outside major tech companies who apply it to solve specific, important problems in areas like healthcare, education, and government services
Premises
- Tech companies and their leaders have created confusion by making either grandiose claims about AI saving civilization or apocalyptic warnings about extinction
- There exists an 'AI counterculture' of people working outside tech megalopolises who are using AI for practical improvements in education, healthcare, government, and human connection
- These practitioners focus on fixing existing problems rather than following the 'move fast and break things' philosophy because they don't want to break important systems
- Defensive responses to transformative technology are ineffective - the only way to preserve what you care about is to engage with the technology and shape its application
- Government agencies like the IRS are successfully implementing AI incrementally to improve services while maintaining necessary safeguards and privacy protections
- Real AI applications that matter involve people who encountered problems that defied conventional solutions and were motivated enough to learn technology to solve them
Assumptions
- Tech company motivations are primarily profit-driven rather than focused on genuine human benefit
- Practical, incremental applications of AI are more valuable than revolutionary breakthroughs
- People closest to real-world problems are best positioned to develop meaningful AI solutions
- Technology adoption should be guided by domain expertise rather than technological capability alone
- Bureaucratic constraints and careful implementation can actually lead to better AI outcomes than rapid deployment