The week's essentials in AI and exponential tech · 26 Oct – 2 Nov
AWS now projects one trillion dollars in revenue within the next decade, and the most telling part is not the size of the number but what lies beneath it: AI capacity is being consumed almost as fast as it can be installed. That is the signature of an exponential curve, where generative AI, cloud computing and specialized accelerators stop advancing on separate tracks and start feeding one another. Our linear mind reads a trillion as a destination; the curve reads it as one more square on the chessboard, with the next one already waiting to double. This is exactly what I discuss in chapter 4, It Is Already Happening: the future rarely announces itself, it shows up as a shortage, and today that shortage is compute. For companies, the bottleneck is no longer desire or imagination but the physical capacity to run what they already want to build. Yet the same week brought deepfake lawsuits, synthetic content threatening scientific integrity and warnings about the robustness of encryption, reminders that trust, as I argue in chapter 15, does not scale at the speed of data centers. The ground is shifting faster on one side than on the other.
AWS's exponential growth exemplifies the convergence of exponential technologies—generative AI, cloud computing, and specialized accelerators—that reinforce each other. The fact that AI capacity is consumed almost as fast as it is installed underscores that compute infrastructure is the critical bottleneck in real-world AI adoption by enterprises.
This story illustrates a core tension in the book: while AI amplifies human capabilities, it also industrializes disinformation production at scale. Without rigorous human oversight (human-in-the-loop) at critical junctures like scientific publishing, the risk of eroding trust in knowledge itself becomes exponential.
As AI amplifies processing and analytical capacity, a systemic risk emerges: the possibility of breaking the cryptographic defenses that today underpin digital trust. This exemplifies how exponential growth in computing power demands governance and anticipation, not mere reaction.
This case demonstrates how intelligent agents move beyond reactive generative AI: they require human oversight at decision points that matter (human-in-the-loop) and represent a step toward automating entire layers of professional work in legal and administrative services.
This case demonstrates how the exponential capability of generative AI to create false content erodes social trust and exposes governance gaps: technology advances faster than institutions can protect fundamental rights such as reputation and identity.
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