These are not merely two different keynote styles: they’re two power structures fighting over the terms of AI use and stewardship. Used here as illustrations of common narratives, the argument concerns systems and incentives, not individuals. On one side, Big Tech scale-first camp (and its admirers) treats AI as the engine of extraction-led growth. Progress in this narrative means shipping models fast, pushing telemetry into every role, and celebrating access to the same tools that, in the next breath, are used to justify head-count cuts, normalize surveillance as productivity, saturate information spaces with synthetic media, and push energy and water costs onto the public. It is blitz-scale automation as civic virtue, asking people to be grateful that where managers once monitored, an automated, polished, and fluent dashboard now does the watching, scoring, and judging.
Read MoreAt every conference, someone reaches for the tranquilizer line: “AI is just a tool—like a camera.” It sounds sensible because cameras calmed us once: art didn’t die; it changed. But cameras point at the world and capture what’s there. Modern AI points at us and proposes what comes next—labels, scores, sentences that other systems treat as facts.
Read MoreThe AI Investment Paradox Generative AI is dominating headlines, while predictive AI quietly powers businesses behind the scenes. Despite delivering far greater returns in efficiency and cost savings, predictive AI receives nearly the same level of investment as its flashier counterpart. Why? The answer lies in perception: generative AI dazzles with its creative outputs, while predictive AI quietly drives results. Yet for businesses seeking measurable ROI, predictive AI remains the unsung hero.
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