If Consensus Hong Kong 2026 had an unofficial theme, it wasn’t Bitcoin or regulation. It was artificial intelligence and the struggle to figure out what that actually meant for cryptocurrencies.
AI was present at almost every turn: main stage keynotes, side event panels, venture capital meetings, and even the post-conference atmosphere. However, the conversation was not uniform. They range from Hong Kong government officials championing a machine economy to venture capitalists declaring the hype cycle for AI in cryptocurrencies is over.
Enterprise AI agent is already deployed
At a side event at The Gate, Sophia Jing, Hong Kong tech director at Byteplus, ByteDance’s enterprise technology arm, revealed that several major crypto exchanges are already using the company’s AI agent product. She outlined three use cases in production. One is intelligent customer service that incorporates detailed research and transaction scenario matching. A multi-agent research system with parallel data collection. Automate AML workflows with human oversight at decision points.
The most notable detail was the safety architecture. Byteplus places guardrails outside of the agent orchestration layer. This is a kill switch that can immediately stop an agent if it breaches the defined boundaries. Jin predicted that within two years, every exchange employee will have an enterprise-grade AI assistant, and onboarding new users will be dramatically easier through personalized AI-powered education.
Two years until AI surpasses you
Ben Goertzel, CEO of decentralized AI marketplace SingularityNET, presented the most provocative timeline of the conference. He gave humans about two years before AI surpassed humans in strategic thinking.
“The human brain is good at making leaps of imagination to understand the unknown,” Goertzel said in a consensus statement. But it doesn’t last long. “I should be able to enjoy it for a few more years.”
Although his Quantium project is already able to predict short-term Bitcoin volatility with high accuracy, Goertzel noted that long-term strategic thinking is currently unique to humans. He described the current bear cycle as a “stress test” for the infrastructure that will eventually host artificial general intelligence.
Bitget CEO Gracy Chen offered a more grounded view. During a panel discussion on agent trading, she compared current AI trading bots to interns. It’s faster and cheaper, but requires supervision. She noted that historical data-driven models have never encountered events like the 10/10 liquidation, so human intervention is essential in unfamiliar situations. But within three to five years, she predicted, AI could replace many human roles.
Saad Nagy, CEO of agent trading startup PiP World, countered that humans may not be the correct baseline. He noted that 90% of day traders lose money and said, “As humans, we’re too emotional. We can’t compete with AI solutions.”
Building a payment layer for agents
If the main stage provided the vision, side events tried to build the plumbing.
At the Stablecoin Odyssey event held at Soho House, a panel called “Building Payment Blockchains for Agent Economies” focused on the infrastructure that AI agents actually need. Nellie Tan, head of payments at Monad, introduced Coinbase’s X402 protocol, an HTTP-native on-chain payment standard, and argued that agent payments generate transactions “at the speed of data” and require throughput of thousands to millions per second.
Eddie, CEO of payment middleware AEON, characterized this transition as an interface transition. When consumers interact through an AI agent rather than an app, all commercial interactions pass through a single point and the last mile is always payment. His company processes 80% of cryptocurrency payments through partnerships with OKX, Bybit, and others.
The question of which blockchain AI agent to choose remains open. Mate Tokay, CMO of OP_CAT Layer, said no one knows yet whether agents will choose chains based on training data, experience, speed and security. The answer probably depends on the deal. Security is a priority for large-scale asset transfers, and speed is a priority for consumer purchases.
Crypto as the currency of AI — or just a hype cycle?
The most impressive support came from outside the industry. Hong Kong’s finance minister, Paul Chan Mopo, has used his appearance to frame AI agents as an economic force in which cryptocurrencies have a unique position.
“Once AI agents are able to make and execute decisions independently, we may begin to see the early forms of what some call a machine economy, where AI agents can hold and transfer digital assets, pay for services, and transact with each other on-chain,” Zhang said.
Binance CEO Richard Teng took it even further. “If you think about agent AI, if you think about booking a hotel or a flight or any kind of purchase, how do you think those purchases are going to be made? It’s going to be done through cryptocurrencies and stablecoins,” he said. “So, if you think about it, cryptocurrency is a currency for AI.”
But venture capitalists have poured cold water on the broader “AI + Cryptocurrency” narrative. Anand Iyer of Canonical Crypto described this moment as a trough. “We went through a period of bubbles and now it’s about finding out where the real strength lies,” he said. Spartan Group’s Iyer and Kelvin Koh criticized over-investment in GPU marketplaces and attempts to build decentralized alternatives to OpenAI and Anthropic, projects that require far more capital than cryptocurrencies can raise.
Instead, both see the potential in specialized solutions that start with specific problems. Unique data, regulatory advantages, or go-to-market advantages are now more important than technological novelty. Mr. Koh’s advice to founders was straightforward. “Twelve months ago, a wrapper on ChatGPT was enough. That’s no longer true.”
what is formed
Conversations among industry participants noted that frameworks are taking shape, including stablecoins that serve as value rails for agent trading, predictive markets that process information prices, AI systems that execute trades and operations, and physical robotics that extend the loop into the real world. This is not a single project or protocol. This is a thesis about where cryptocurrencies and AI intersect productively, without relying on the speculative cycles that drove previous bull markets.
Parallel threads run through distributed AI. The current system is centralized and opaque. The idea of transparent, verifiable, community-managed AI networks is consistent with the founding principles of cryptocurrencies, and Goertzel in particular pointed to the growth of such projects at the event as evidence of growing convergence.
The pure speculative cycle may never return. But in Consensus Hong Kong, the argument that AI will give cryptocurrencies a reason for existence beyond trading was made simultaneously from government podiums, exchange boards, and venture capital meetings. It’s a different kind of consensus.
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