Trevor Koverko, the Canadian entrepreneur best known for co-founding Polymath and its regulated asset blockchain Polymesh, is now turning his attention to one of AI’s most pressing problems: the quality of training data. His latest venture, Sapien, is building what he describes as a decentralized, incentive-driven data labeling network.
“We’re putting humans back at the center of AI,” Koverko said. “Large models are only as good as the data they’re trained on, and right now, much of that data is unverified, inconsistent, or coming from opaque pipelines. Sapien flips that model by rewarding contributors directly and holding them accountable for quality.”
At the heart of Sapien’s system is a staking and peer review model. Contributors stake tokens as they label data, and peers evaluate the quality of their work. Poor performance carries penalties, while accurate, consistent contributions build reputation and rewards.
“AI developers don’t just want more data, they want trustworthy data,” Koverko explained. “By combining incentives with transparent reputation, we’re creating an economy where good labeling is valuable and bad labeling is costly.”
This framework also aims to make the entire labeling process auditable. By recording reputation scores and reviews on-chain, Sapien provides model builders with traceable, verifiable datasets—an increasingly urgent requirement as governments and regulators scrutinize how AI systems are trained.
Koverko acknowledges that maintaining quality at scale is the hardest challenge. “It’s easy to build a labeling system with 100 people. It’s much harder with 100,000,” he said. To address this, Sapien is layering automated checks, randomized peer review, and penalties for poor work. The goal is to ensure that as the network grows, quality doesn’t collapse under the weight of volume.
The demand is clear. Major enterprises, from tech giants to automakers to NGOs, are under pressure to improve the transparency of their AI pipelines. “When you’re training models in sensitive sectors like healthcare or finance, you can’t afford black-box data,” Koverko said. “We see Sapien as an answer to that.”
Sapien’s model also highlights a trend: blockchain tools being deployed beyond finance and token trading to enforce trust and governance in AI workflows. In Koverko’s view, this represents a natural evolution. “Blockchain was built to solve trust problems in finance,” he noted. “Now we’re applying those same principles to AI, where trust in the data is just as critical.”
This aligns with the wider crypto market, which is in the midst of recalibration. After years dominated by speculation, there’s a growing shift toward real-world utility, from tokenized assets to decentralized identity to blockchain-backed AI services. With regulators pressing for clearer rules and investors demanding more sustainable models, projects that demonstrate practical, revenue-generating use cases are standing out. Sapien’s focus on solving a concrete bottleneck in AI training is emblematic of how blockchain ventures are repositioning themselves: less about hype cycles, more about infrastructure that can power industries beyond crypto.
As AI adoption accelerates, pressure for provenance, accountability, and high-quality training data is only increasing. If Sapien’s decentralized approach gains traction, it could set a new standard for how humans and machines collaborate in building the next generation of intelligent systems.
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