The week's essentials in AI and exponential tech · 24 Aug – 31 Aug
The paper-folding experiment keeps coming back to me this week, and for good reason: if you fold a sheet of paper 42 times, the stack reaches the Moon, yet almost no one believes it until they do the arithmetic themselves, because our intuition is wired for addition, not multiplication. That cognitive miscalibration is not a curiosity — it is the central problem of our moment, and it is exactly what I explore in Chapter 1 of the book, where I argue that the greatest risk of exponential times is not the technology itself but the gap between how fast the curve moves and how slowly our minds accept it. This week that gap showed up in three places at once: Meta began testing autonomous robots inside its data centers to handle technical tasks that humans used to perform, Caterpillar extended its mining automation expertise into full AI deployment across heavy operations, and meanwhile students in some classrooms are now co-designing the very policies that govern how generative AI is used in their own education — a sign that adaptation is beginning, however unevenly. At the same time, U.S. export controls are raising walls around drones and robotics, but China's manufacturing scale means competition simply migrates to other markets, which is precisely the access gap I describe in Chapter 13: barriers slow adoption in one place while accelerating it somewhere else, and the net effect on the global curve is close to zero. Two new tools, ChatGPT Work and Claude Cowork, landed on the map this week, quietly extending AI into collaborative professional workflows where, not long ago, no one was asking for it. The lily pad does not announce itself before it covers the pond.
This classic example of exponentiality reinforces a central thesis of the book: our intuition is calibrated for linear change and fails systematically when facing exponential curves. Understanding this cognitive gap is essential to navigating the times we live in.
This research illustrates the institutional adaptation crisis: as generative AI adoption accelerates, educational organizations still lack clear governance frameworks. Student-led co-design reveals the gap between technical capability and real-world governance, confirming that AI ethics is not merely top-down regulation but lived negotiation within institutions.
Regulatory fragmentation of critical automation technologies deepens the access gap between regions and accelerates the formation of parallel technological ecosystems—a central dynamic in unequal adaptation to exponential times.
This case demonstrates how mature industrial automation—machines operating without real-time human control in remote locations—provides the foundation for AI adoption in contexts where reliability, safety, and remote operation are critical. It confirms that automation is not merely software: it requires infrastructure, governance, and decades of accumulated learning.
A concrete example of how automation evolves from supervised subtasks toward systems executing complete workflows with minimal human intervention. It illustrates the shift from augmentation to automation in critical infrastructure.
If this week helped, pass it on to someone else living these times.
Create your free account and get Exponential Times every Monday: the news interpreted, threaded through the book that explains it.
Privacy
We use necessary cookies for the interactive book to work, and we'd also like to use anonymous analytics to improve it. Your reflections are always private and we never analyse them. More details in the cookie policy.