Treat explainability as infrastructure. Ask vendors about their substrate, not their dashboard. Hire the librarians. Give the project a year. The AI deployment that wins the next decade is the one whose conclusions you can defend in a deposition. That deployment is not the one with the best reasoning-display screen. It is the one whose foundations were built to hold the trail, and whose builders were given the time to lay them.

It's good work. Five forces - technology, economics, geopolitics, demographics, climate - each with their own core shifts and key uncertainties.

The work isn’t prediction. It’s perseverance. It’s staying with the complexity long enough to recognize patterns instead of imposing them. It’s building systems that make it easier for the parent with the 13-point cognitive tax to access the same quality of care, information, and decision-support as the investor reading Amy Webb’s report at $10,000 a seat.

That’s not a technology problem. It’s not even an AI problem. It’s a recognition problem — who we see, what we count, and whether we’re willing to build for the ground conditions that already exist instead of the convergence we hope is coming.

When we released "AI for the Rest of Us" two years ago, we stood in a liminal space, observing as artificial intelligence seemed poised to transform not only Silicon Valley but the entirety of human experience. In hindsight, we recognize that our predictions have been realized in both expected and surprising ways.