Every major technology shift I've lived through - broadband, mobile, social media, Web 2.0 - followed the same pattern. The teams that thrived weren't the ones who adopted the technology first. They were the ones who figured out how to combine the new capabilities with human judgment. AI is no different, except the stakes are higher and the timeline is compressed.
Why I Jumped In
When I saw early OpenAI demonstrations, I knew this was different. I remember watching demos of LLMs that could create books, write computer programs, even develop entirely new religious systems from simple prompts. The potential went far beyond typical technology shifts.
One of my most recent jobs made this personal. I watched as a thriving 17-year old business was nearly wiped out within a year by AI disruption. This experience led to a career-defining decision. I left corporate product management and dedicated myself full-time to building AI-powered businesses. I knew that even if I didn't create the next breakthrough application, I would spend my time mastering this new medium while it was still emerging.
This journey has been full of ups and downs, false starts and false hope, but the truth is it has been a lot of fun and it has renewed my enthusiasm for what I do. Most importantly, it taught me that AI mastery isn't about the technology itself. It's about building teams confident enough to experiment with it.
When AI Resistance Nearly Killed an Opportunity
I saw the confidence problem firsthand at a company with a decade-old content corpus. When AI emerged as a potential threat to their core value proposition, the initial response was resistance. The data science team refused to allow LLM technologies into their solution, citing accuracy concerns and hallucination risks that could compromise student outcomes.
We agreed to run controlled tests for AI-enabled content curation. The approach involved validating all AI recommendations against existing data quality standards while building hybrid systems that used both AI capabilities and the company's content advantage. The results exceeded expectations. The AI-enhanced experience proved significantly more engaging for users while maintaining content quality. Rather than replacing human expertise, AI amplified the value of curated content through more personalized and responsive delivery.
The team that had resisted AI became its biggest champion - not because we forced the technology on them, but because we created a safe environment to test their concerns against reality.
Confidence Over Competence
Both experiences reinforced the same lesson. The 17-year-old business didn't fail because AI was too powerful. It failed because the team couldn't adapt. The content company didn't succeed because AI was easy. It succeeded because we built confidence through controlled experimentation.
Yes, most AI implementations are failing right now. But most projects of any kind fail to meet executive expectations. There's no big surprise that a brand new technology being rolled out by people who have no idea what they're doing has a high failure rate. The teams that get it right start with hands-on experimentation, not abstract training. They let people play with AI tools on real problems. They celebrate learning over perfection. The human element becomes more critical as AI capabilities advance, not less - humans provide the context, judgment, and creative insight that turn AI possibilities into valuable products.