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    Home»Markets»XYO’s Markus Levin: Why a data-native L1 may develop into AI’s “proof of origin” spine
    XYO’s Markus Levin: Why a data-native L1 may develop into AI’s “proof of origin” spine
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    XYO’s Markus Levin: Why a data-native L1 may develop into AI’s “proof of origin” spine

    By Crypto EditorDecember 31, 2025No Comments3 Mins Read
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    Within the newest SlateCast episode, XYO co-founder Markus Levin joined CryptoSlate’s hosts to unpack why decentralized bodily infrastructure networks (DePIN) are transferring past area of interest experiments—and why XYO constructed a purpose-built Layer-1 to deal with the sort of information AI and real-world functions more and more demand.

    Levin’s ambition for the community is blunt: “First, I believe XYO is gonna have eight billion nodes,” he stated, calling it a stretch aim—however one he believes matches the place the class is headed.

    DePIN’s “each nook of the world” thesis

    Levin framed DePIN as a structural shift in how markets coordinate bodily infrastructure, pointing to speedy development expectations for the sector. He cited a World Financial Discussion board projection that DePIN may broaden from roughly at this time’s tens of billions to trillions by 2028.

    For XYO, scale is just not hypothetical. One of many hosts famous that the community has grown “with over 10 million nodes,” setting the stage for a dialog targeted much less on “what if” and extra on what breaks when real-world information quantity turns into the product.

    Proof of origin for AI: the information downside, not simply compute

    Requested about deepfakes and the collapse of belief in media, Levin argued that AI’s bottleneck isn’t solely computation—it’s provenance. “Whereas DePIN, what you are able to do is you may, uh, show the place information comes from,” he stated, outlining a mannequin the place information might be verified end-to-end, tracked into coaching pipelines, and queried when methods want floor reality.

    In his view, provenance creates a suggestions loop: if a mannequin is accused of hallucinating, it might test whether or not the underlying enter is verifiably sourced—or request new, particular information from a decentralized community relatively than scraping unreliable sources.

    Why a data-native Layer-1 issues

    XYO spent years making an attempt to not construct a sequence, Levin stated—working as middleware between real-world alerts and good contracts. However “no person constructed it,” and the community’s information quantity pressured the problem.

    He defined the design aim merely: “Blockchain can’t bloat… and it’s simply constructed for information actually.”

    XYO’s Markus Levin: Why a data-native L1 may develop into AI’s “proof of origin” spineXYO’s Markus Levin: Why a data-native L1 may develop into AI’s “proof of origin” spine

    XYO’s strategy facilities on mechanisms equivalent to Proof of Good and “lookback” model constraints meant to maintain node necessities light-weight, whilst datasets develop.

    COIN onboarding: turning non-crypto customers into nodes

    A key development lever has been the COIN app, which Levin described as a option to rework cell phones into XYO community nodes.

    Reasonably than pushing customers into quick token volatility, the app makes use of dollar-tied factors and broader redemption choices—then bridges customers into crypto rails over time.

    Twin token mannequin: aligning incentives with XL1

    Levin stated the twin token system is designed to separate ecosystem rewards/safety from chain exercise prices. “We’re extraordinarily enthusiastic about this twin token system,” he stated, describing $XYO because the exterior staking/governance/safety asset and $XL1 as the interior gasoline/transactions token used on XYO Layer One.

    Actual-world companions: charging infrastructure and mapping-grade POI information

    Levin pointed to new partnerships as early “killer app” momentum contained in the broader DePIN ecosystem, citing a take care of Piggycell—a big South Korean charging community that wants proof-of-location and plans to tokenize information on XYO Layer One.

    He additionally described a separate proof-of-location use case involving point-of-interest datasets (hours, pictures, venue data), claiming a serious geolocation companion discovered points in its personal dataset “in 60% of the instances,” whereas XYO-sourced information was “99.9% right,” enabling downstream mapping for giant enterprises.

    Taken collectively, Levin’s message was constant: if AI and RWAs want reliable inputs, the subsequent aggressive frontier could also be much less about quicker fashions—and extra about verifiable information pipelines anchored in the true world.



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