Crypto’s AI Pivot: Hype, Infrastructure, and a Two-Year Countdown

If Consensus Hong Kong 2026 had an unofficial theme, it wasn’t Bitcoin or regulation. It was artificial intelligence — and the scramble to figure out what it actually means for crypto.

AI surfaced in almost every context: main-stage keynotes, side-event panels, venture capital meetings, and even the post-conference mood. But the conversations weren’t uniform. They ranged from Hong Kong government officials endorsing the machine economy to venture capitalists declaring the AI hype cycle in crypto already over.

Enterprise AI Agents Are Already Deployed

At the Gate’s side event, Sophia Jin, Hong Kong Tech Director at Byteplus — ByteDance’s enterprise technology arm — revealed that multiple major crypto exchanges are already using the company’s AI agent products. She outlined three use cases in production: intelligent customer service that incorporates deep research and trading scenario matching; multi-agent research systems with parallel data collection; and AML workflow automation with human oversight at decision points.

The most notable detail was the safety architecture. Byteplus places guardrails outside the agent orchestration layer — a kill switch that can halt agents immediately if they breach defined boundaries. Jin projected that within two years, every exchange employee will have an enterprise-grade AI assistant, while onboarding new users will become dramatically easier through AI-powered personalized education.

Two Years Until AI Outthinks You

Ben Goertzel, CEO of decentralized AI marketplace SingularityNET, offered the conference’s most provocative timeline. He gave humans roughly two years before AI surpasses them in strategic thinking.

“The human brain is better at taking the imaginative leap to understand the unknown,” Goertzel said iat Consensus. It won’t last, though. “We should enjoy it for a couple more years.”

While his Quantium project can already predict short-term Bitcoin volatility with high accuracy, Goertzel noted that long-term strategic thinking remains uniquely human — for now. He described the current bear cycle as a “stress test” for infrastructure that will eventually host artificial general intelligence.

Bitget CEO Gracy Chen offered a more grounded view. On a panel about agentic trading, she compared current AI trading bots to interns — faster and cheaper but requiring supervision. Historical data-driven models have never encountered events like the 10/10 liquidations, she noted, making human intervention essential in unfamiliar conditions. But within three to five years, she projected, AI could replace many human roles.

Saad Naj, CEO of agentic trading startup PiP World, countered that humans may not be the right baseline. “As humans, we are too emotional. We can’t compete with AI solutions,” he said, noting that 90% of day traders lose money.

Building the Payment Layer for Agents

If the main stage provided the vision, side events tried to build the plumbing.

At the Stablecoin Odyssey event at Soho House, the panel “Building Payment Blockchains for the Agentic Economy” focused on what infrastructure AI agents actually need. Nellie Tan, Payment Head at Monad, introduced Coinbase’s X402 protocol — an HTTP-native on-chain payment standard — and argued that agentic payments would generate transactions “at the speed of data,” requiring throughput of thousands to millions per second.

Eddie, CEO of payment middleware AEON, framed the shift as an interface transition. When consumers interact through AI agents rather than apps, every commercial interaction funnels through a single point — and the last mile is always a payment. His company processes what he described as 80% of crypto payments through partnerships with OKX, Bybit, and others.

The question of which blockchain AI agents would choose remained open. Mate Tokay, CMO of OP_CAT Layer, noted that no one yet knows whether agents will select chains based on training data, experience, speed, or security. The answer likely depends on the transaction — large asset transfers prioritize security, while consumer purchases prioritize speed.

Crypto as Currency for AI — or Just Another Hype Cycle?

The most striking endorsement came from outside the industry. Hong Kong Financial Secretary Paul Chan Mo-po used his appearance to frame AI agents as an economic force that crypto is uniquely positioned to serve.

“As AI agents become capable of making and executing decisions independently, we may begin to see the early forms of what some call the machine economy, where AI agents can hold and transfer digital assets, pay for services and transact with one another onchain,” Chan said.

Binance CEO Richard Teng pushed it further. “If you think about the agentic AI, so the booking of hotels, flights, whatever purchases that you would make, how you think that those purchases will be made — it’ll be via crypto and stablecoins,” he said. “So, crypto is the currency for AI, if you think about it.”

But venture capitalists poured cold water on the broader “AI + crypto” narrative. Anand Iyer of Canonical Crypto described the moment as a trough. “We went through a frothy period. Now it’s about figuring out where the real strength lies,” he said. Both Iyer and Kelvin Koh of Spartan Group criticized overinvestment in GPU marketplaces and attempts to build decentralized alternatives to OpenAI or Anthropic — projects that require capital far beyond what crypto can muster.

Instead, both see potential in purpose-built solutions that start with a specific problem. Proprietary data, regulatory edges, or go-to-market advantages now matter more than technical novelty. Koh’s advice to founders was blunt: “Twelve months ago, it was enough to have a wrapper on ChatGPT. That’s no longer true.”

What’s Forming

Conversations among industry participants pointed toward a framework taking shape: stablecoins serving as value rails for agent transactions, prediction markets handling information pricing, AI systems executing trades and operations, and physical robotics extending the loop into the real world. It’s not a single project or protocol — it’s a thesis about where crypto and AI intersect productively, without relying on the speculative cycles that drove previous bull runs.

A parallel thread runs through decentralized AI. Current systems are centralized and opaque. The idea of transparent, verifiable, community-governed AI networks aligns with crypto’s founding principles — and Goertzel, among others, pointed to the growth of such projects at the event as evidence that convergence is underway.

The pure speculation cycle may not return. But at Consensus Hong Kong, the argument that AI gives crypto a reason to exist beyond trading was made simultaneously from the government podium, the exchange boardroom, and the venture capital meeting. That’s a different kind of consensus.