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

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


Within the newest SlateCast episode, XYO co-founder Markus Levin joined CryptoSlate’s hosts to unpack why decentralized bodily infrastructure networks (DePIN) are shifting past area of interest experiments—and why XYO constructed a purpose-built Layer-1 to deal with the sort of knowledge AI and real-world purposes more and more demand.

Levin’s ambition for the community is blunt: “First, I believe XYO is gonna have eight billion nodes,” he mentioned, 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 fast development expectations for the sector. He cited a World Financial Discussion board projection that DePIN may develop from roughly as we speak’s tens of billions to trillions by 2028.

For XYO, scale isn’t 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 knowledge quantity turns into the product.

Proof of origin for AI: the info drawback, 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 possibly can, uh, show the place knowledge comes from,” he mentioned, outlining a mannequin the place knowledge will be verified end-to-end, tracked into coaching pipelines, and queried when techniques want floor fact.

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

Why a data-native Layer-1 issues

XYO spent years attempting to not construct a series, Levin mentioned—working as middleware between real-world indicators and sensible contracts. However “no person constructed it,” and the community’s knowledge quantity pressured the difficulty.

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

XYO’s method facilities on mechanisms reminiscent of Proof of Excellent 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 technique to remodel cellphones into XYO community nodes.

Somewhat than pushing customers into speedy 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 mentioned 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 mentioned, describing $XYO because the exterior staking/governance/safety asset and $XL1 as the inner fuel/transactions token used on XYO Layer One.

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

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

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

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



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