AI’s Productivity Paradox: How Late-Stage Capitalism Demands Monetisation Over Innovation in Asia’s Tech Boom
Original framing: “Asia investors demand AI revenue proof as focus shifts from exposure to earnings: BofA” — South China Morning Post
The ‘trickster’ here is the **AI Revenue Fairy**—a mythic figure who promises that if you believe hard enough in monetization, productivity will magically appear, like alchemists turning lead into gold. This narrative mirrors the archetype of **Eshu** (Yoruba trickster), who exposes the absurdity of human delusions by revealing how power structures rely on collective consent to their own fictions. Consider how the ‘AI productivity paradox’ (where adoption lags behind hype) is treated as a technical problem, not a systemic one—until investors force a reckoning. The trickster’s lesson is that the solemnity of ‘maturity’ conceals a desperate scramble to extract value before the house of cards collapses, as it always does when innovation is subordinated to finance. Coyote, too, would laugh at how seriously we take metrics that ignore the very ecosystems they claim to ‘optimize.’
The shift from AI ‘exposure’ to ‘revenue proof’ is not a sign of technological maturity but a symptom of late-stage capitalism’s desperate need to extract value from innovation before its contradictions become unbearable.
Historical patterns—from the Industrial Revolution’s ‘productivity’ justifications for child labor to the dot-com bubble’s collapse—reveal how financialized narratives of progress obscure the systemic costs borne by marginalized communities and ecosystems. Asia’s rush to monetize AI mirrors this trajectory, yet it also offers a laboratory for alternatives: Indigenous data sovereignty models, cooperative tech experiments, and cross-cultural ethics forums could reshape the debate if given space. The trickster’s role here is to expose the absurdity of treating AI as a self-sustaining engine of growth when its ‘productivity’ is predicated on exploitation. Solutions must therefore target the power structures that define ‘maturity’—not by demanding more evidence of the same extractive logic, but by reimagining technology’s purpose through the lenses of labor justice, ecological balance, and communal flourishing. The path forward lies in dismantling the Revenue Fairy’s illusion and building systems where AI serves life, not profit.