EN0VA’s essay, “Hyperstition Markets: New Psychotechnology for Reality Conjuring,” proposes a radical evolution of prediction markets into engines of “reality construction.” Drawing on George Soros’s theory of reflexivity and J.L. Austin’s speech act philosophy, EN0VA argues that markets can do more than forecast, they can “bootstrap desired futures into existence” through incentives that reward action as well as accurate prediction. Unlike traditional markets that aggregate belief, Hyperstition Markets operationalize belief itself, creating what EN0VA calls “ontological arbitrage,” profit from aligning capital, conviction, and coordinated behavior towards certain outcomes. The paper explores applications from local currency adoption to collective intelligence, describing these systems as “machines for creating collective intentionality at scale.” While acknowledging risks such as manipulation and plutocratic bias, EN0VA envisions these markets as tools to “navigate the polycrisis” by aligning global incentives toward engineered, participatory futures where “the future is not predicted; it is created.”
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ACI Worldwide has partnered with BitPay to integrate digital asset payments into its Payments Orchestration Platform, allowing merchants to accept bitcoin, stablecoins, and other digital currencies alongside traditional payment methods. As reported by Alex Lari, the integration gives businesses the option to settle transactions in either cash or stablecoins, broadening flexibility for cross-border and treasury operations. “As regulated stablecoins evolve from niche to mainstream, they’re creating new opportunities for cross-border transactions and settlement optimization,” said Adriana Jordan, ACI’s Global Head of Merchant. BitPay CEO Stephen Pair added that the collaboration will make “digital currencies more accessible and practical for real-world commerce.” With ACI serving thousands of financial institutions and retailers globally, the partnership signals a meaningful step toward normalizing bitcoin payments and expanding the digital asset industry’s reach within mainstream commerce.
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In a wide-ranging conversation with Dwarkesh Patel, AI researcher Andrej Karpathy argued that artificial general intelligence (AGI) remains “about a decade away,” citing persistent cognitive deficits in large language models (LLMs) such as limited memory, poor continual learning, and over-reliance on reinforcement learning (RL). “RL is terrible,” Karpathy said, describing it as “sucking supervision through a straw,” a noisy, inefficient process that fails to match human reflection or judgment. He emphasized that humans do not truly use RL, instead relying on richer feedback mechanisms and offline distillation akin to “sleep.” Karpathy predicted that future systems will integrate reflection, sparse attention, and long-context memory to mimic human cognitive structures. Despite progress, he warned that current models remain “amazing but still slop,” requiring significant advances in data quality, algorithmic design, and architectural efficiency before AGI blends quietly “into 2% GDP growth.”
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Artificial intelligence may soon aid doctors and families in making life-or-death decisions for incapacitated patients, but experts warn of ethical minefields. Ashley Belanger reports that AI researcher Muhammad Aurangzeb Ahmad at the University of Washington’s Harborview Medical Center is developing “AI surrogates” capable of predicting patient preferences during critical moments. Ahmad told Ars the work remains “in the conceptual phase,” stressing that human surrogates must always remain “in the loop.” Critics, including physicians Emily Moin and Teva Brender, cautioned that such systems could erode human judgment or default decision-making to algorithms under time pressure. Bioethicist Robert Truog emphasized that “AI will not absolve us from making difficult ethical decisions.” While preliminary studies in Europe show AI models can predict patient choices with up to 70% accuracy, doctors warn that preferences are dynamic, contextual, and morally irreducible to statistical patterns, a boundary AI cannot yet cross.
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