The week's essentials in AI and exponential tech · 19 Oct – 26 Oct
This week we learned that, outside the tech sector, most companies are still seeing no financial payoff from AI, and I suspect many will read that as proof the wave has been overblown. I read it the other way. At the same moment, OpenAI is scaling its storage infrastructure to serve more than a billion ChatGPT users, so the capability is clearly here. What is missing is the last mile, the stretch between what the technology can do and what an organization actually manages to change in its processes, its people and its decisions. This is exactly what I discuss in chapter 4, "It Is Already Happening": investment can climb an exponential curve while returns crawl along a straight line, because adoption moves at the speed of habits, not of chips. The problem isn't technical, it's cognitive. Our linear minds look at a flat stretch of results and conclude that nothing is happening, just as the lily pad seems harmless while it covers only a corner of the pond, and the companies that cross that last mile won't be the ones that bought the most AI but the ones that redesigned their work around it. The pond, meanwhile, keeps filling.
This finding illustrates the AI last mile: the critical gap between technical capability and real-world adoption with measurable economic impact. It confirms that the exponential curve of investment does not automatically translate into exponential returns for most organizations, a central challenge of the book.
Exponential infrastructure is not just algorithms: it demands breakthroughs in distributed data architecture and compute. This case shows how real-world AI adoption hinges on solving scale challenges that extend far beyond the model itself.
This case illustrates how AI navigates between automating diagnostic tasks and augmenting human clinical judgment. In exponential times, the question is not whether machines replace doctors, but how to redefine healthcare work when AI reaches team-level specialist competence.
Investment in technical containment and governance of frontier models reflects the core tension of exponential times: as computational power and performance scale, the mechanisms for human oversight and operational boundaries must become equally sophisticated.
Hallucination in generative models stems from fundamental design choices rather than accidental failure. This underscores why human oversight and AI ethics are essential: systems operating with autonomy demand deep understanding of their inherent limitations.
If this week helped, pass it on to someone else living these times.
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