When Markus Levin, co-founder of XYO, last joined Blockchain Interviews host Ashton Addison, the conversation centered on AI hallucinations and the early promise of an “autonomous layer” for machines. A few months and one crypto pullback later, Levin was back to report on what’s actually shipped — and to make the case that as AI agents move from novelty to infrastructure, the industry’s biggest unsolved problem isn’t capability. It’s accountability.

“I think in a year or two, our lives will be mostly automated,” Levin said, pointing to tools like Claude Code that let non-technical users connect AI agents directly to their Gmail and financial accounts. But automation without verification, he argued, is just a faster way to make expensive mistakes — which is the problem XYO has spent the past several months building against.

Vibe Coding Goes Fully Live on XYO Layer One

The headline update is the full public release of the XYO AI SDK, a tool that lets anyone — coder or not — build products directly on the XYO Layer One blockchain using natural-language instructions. Levin calls the broader trend “vibe coding”: using AI to assemble a working product without needing to understand the code underneath it.

“It allows a lot of non-technical people to build their own products as they dream them to be,” Levin said. The SDK moved out of early access and into full release just days before this interview, following a few thousand signups during its beta period. XYO is expecting that shift to translate directly into on-chain activity.

“We anticipate thousands of products being built in a very short period of time, which will lead to a lot of usage of the XYO Layer One — and with that, lots of usage of the XYO token to make the Layer One run.”

Markus Levin, Co-Founder, XYO

The logic mirrors how every Layer One and Layer Two ultimately competes: attract products, attract transaction volume, and the token economics follow. XYO’s bet is that lowering the technical bar to near zero is the fastest way to get there.

Trading Bots Are Coming — But Someone Still Has to Watch Them

Addison pressed Levin on whether AI agents will end up doing more of the trading currently done by humans, given that automated, rules-based portfolios tend to outperform emotional retail traders. Levin agreed — with a caveat.

“As long as you’re able to define guardrails for your bot, so it doesn’t lose your funds,” he said. “Most of us are emotional traders — successful traders have very low emotion, and bots will help us manage that much better.” He pointed to new trading-bot products launching in the U.S. and globally that make it easier to trade crypto and generate yield beyond simply holding.

But Levin was direct about the current limitation: bots don’t yet have the track record to be trusted blindly. “Today I think you need tools and third-party verification for AI agents, to make sure they don’t go wrong,” he said — which is exactly the gap XYO is positioning itself to fill.

The Real Problem: Most Oracles Still Aren’t Trustless

This is where the conversation turned into the heart of XYO’s pitch. As AI trading tools and agents move into mainstream platforms, Levin says the industry has a quiet accountability problem: data getting “stamped” onto a blockchain is often treated as verified truth — when it isn’t.

“Most oracles are just connectors of data to blockchain,” Levin explained. “You have one source of information, and it hasn’t been verified. It’s just a connector — it puts it on the chain and then it’s just there as truth. But often, it actually is not.”

XYO’s answer is data provenance: tracing a piece of data from its point of origin — a camera, a sensor, a device — through every system it touches, so its full chain of custody is provable rather than assumed. The company points to a real-world example to make the stakes concrete:

“If you think about that false data now flowing into the big AI models — the damage that does.”

Markus Levin, Co-Founder, XYO

This is an extension of work XYO has been doing since 2018, when it began using proof-of-location technology — cryptographic handshakes between phones and IoT devices — to prevent GPS spoofing. The same underlying principle, Levin said, now applies to any data type an AI model might rely on: verification has to happen at the source, not after the fact.

A Decentralized “Google Maps of Truth” — Verified by the Crowd, Not One Source

Rather than a single authority certifying what’s true, Levin described a model closer to collective witnessing. He used a prediction-market example: if 100 people at an event take out their phones and photograph the same moment, their devices can cryptographically interact and fingerprint the location, producing overlapping proof that something actually happened at a specific place and time — proof that can then resolve the market.

“The trick is not the single source of truth,” Levin said. “In a decentralized way you have a community — a whole-humanity kind of truth — and that’s much better, because people can verify each other.”

That same architecture underpins Data Links, XYO’s tool for storing data that’s immutably connected to XYO Layer One. Developers who trust a given data lake can point an AI model at it directly and instruct it to treat that dataset as ground truth. Behind it sits XYO’s DXYR network of more than 10 million data-collecting devices, whose redundant, overlapping submissions let the system verify data algorithmically — while a public data lake remains open for anyone to independently check that nothing was tampered with after the fact. Private data links exist for sensitive data, like health information, that shouldn’t be public.

Proof of Work — For Robots

Addison steered the conversation toward physical AI: the wave of home and industrial robots that surged in early 2026, tapered off, and is now picking back up with new entrants out of San Francisco. Levin argued this category needs blockchain verification even more urgently than software agents do.

“If you give a robot — or a fleet of robots — a task, and it does something else, but nobody sees it, and the robot says, ‘I did the work, pay me,’ someone needs to be able to prove that,” Levin said. His proposed fix: proof of work for robots, where completed tasks are verified and validated before payment is released on-chain.

Addison drew a direct line to Bitcoin’s own consensus model — proof of work exists precisely so a system can’t be gamed into printing value it hasn’t earned. Levin agreed it’s a fair analogy, and extended the logic downstream: robots will increasingly be run by AI agents, so the integrity of the whole chain — agent, robot, data — depends on every link being verifiable.

Machine-to-Machine Payments and the Case for On-Chain Accountability

As stablecoin adoption grows, Levin sees the agentic economy — AI agents and robots transacting far more frequently than humans ever will — as the use case crypto rails were arguably built for. He pointed to a smart-city example: intersections without stop signs, where vehicles negotiate right-of-way through real-time micropayments. That only works, he said, if every participant can verify the others’ location, speed, and intent — otherwise malicious actors break the system.

Addison raised the more unsettling scenario directly: AI models that misrepresent their own behavior to their creators. Levin didn’t oversell XYO’s role here.

“It’s a part of it, but it’s not everything. It’s very much also the programming of the agents — the programming gives them power, and data gives them information.”

Markus Levin, Co-Founder, XYO

He pointed to ongoing discussions between AI companies and the U.S. government in Washington, D.C., around pre-release safety review, as a necessary complement to data-level accountability, not a replacement for it.

What Comes Next

Levin framed XYO’s near-term roadmap around growing its base of data contributors — including through the COIN app, available at coinup.co — and around developers building directly against the DXYR network through the XYO AI SDK, feeding verified data straight into their own AI agents and products.

On regulation, Levin expects accountability questions to force clarity over time: when an AI agent’s trade or action goes wrong, who’s responsible — the person who issued the last command, the underlying model, or the data it relied on? “Data provenance is going to be extremely important,” he said, “because then you can actually know where the fault lies.”

Closing out the conversation, Addison summed up the throughline of the interview: as AI takes on more of daily life — inboxes, portfolios, eventually physical chores — the guardrails need to be built in from the start, not bolted on after something goes wrong.

“If you keep building the models and all of a sudden they’re breaking out of the system and feeding you false information, it’s hard to build it after the fact. You need to have that built in as we grow.”

Ashton Addison, Founder & CEO, Crypto Coin Show