Sean Neville, CEO of Kadena Labs and co-founder of Circle, says stablecoins will evolve beyond payment rails into “machine-native money” that enables AI agents to transact autonomously.
In an interview with Benzinga, the Circle co-founder and Kadena Labs CEO traced his crypto journey to viewing “money as just data” that should “flow globally like any other kind of data on the internet.”
This philosophy drove Circle’s 2013 founding with the vision of creating “open protocols and open rails for value transfer” that would be “almost free” and operate without borders.
Circle’s pivot to USDC in 2018 addressed a fundamental payment reality that “wasn’t necessarily as obvious in 2013-2014.”
While Bitcoin and Ethereum served as interesting assets, “when it comes to payments people mostly want dollars,” Neville explained.
The breakthrough insight was creating “a new form of a dollar” that could utilize blockchain rails.
Regulatory compliance became central to Circle’s strategy from inception.
AI agents present unique identity and trust challenges that existing financial infrastructure cannot address. “How do I even know if I’m talking to an agent?” Neville asked, noting that traditional KYC and AML processes don’t apply to artificial entities.
The convergence of stablecoins and AI agents creates new transaction paradigms. AI agents “don’t struggle as much with managing things like signing cryptographic messages.”
Stablecoin interoperability remains crucial as the ecosystem fragments across multiple issuers. “A dollar is a dollar is a dollar.” Neville emphasized, noting that agent workflows often involve “multiple stables” requiring seamless conversion capabilities.
The Lummis-Gillibrand Act provides regulatory clarity that could accelerate adoption. While it “defines a pretty clear regulatory path” for payment stablecoins, it doesn’t address interoperability challenges between different issuers, leaving room for technological solutions.
Traditional banking faces disruption as institutions integrate both stablecoin infrastructure and AI automation.
Banks increasingly deploy “AI actors for things like FX managing effects markets effectively, not just negotiating contracts but closing them, treasury management capabilities,” creating efficiency gains impossible with legacy systems.
Neville’s vision extends beyond current use cases to “brand new kinds of products that simply can’t exist because the old system is too inefficient.”
Real-time dynamic microtransactions become feasible when “the speed and the economic efficiencies are there,” potentially unlocking innovations comparable to early internet developments.
The infrastructure buildout parallels electric vehicle charging networks, requiring ecosystem-wide adoption for maximum effectiveness.
The timeline for mainstream adoption remains uncertain, echoing early internet development.
However, the fundamental transformation appears inevitable as both technological capabilities and regulatory frameworks mature.
