Discus Fish of Cobo and Samuel Tse of Animoca Brands discuss how AI agents, digital identity and blockchain could change the way money moves.
Updated
November 2, 2026 12:03 PM
The Agentic Economy Summit at CVCF 2026 explores how AI agents and digital assets could shape the future of business and finance. IMAGE: CYBERPORT
For an AI agent to manage money on someone’s behalf, it needs more than the ability to follow instructions. It must be able to establish whom it represents, access funds and transact within agreed limits.
These are among the questions informing the discussion of AI and digital assets at Cyberport Venture Capital Forum (CVCF) 2026. Ahead of the forum, Discus Fish, CEO and Co-founder of Cobo, and Samuel Tse, Head of Investment and Corporate Finance at Animoca Brands, shared their views on how this convergence could develop—and where they see opportunities for businesses.
Discus Fish points to a longstanding challenge: blockchain can be difficult for everyday users to navigate, but AI agents may find it easier to interact with these networks.
“Blockchains have always been hard for ordinary people to use. AI agents may be better users: the chain is fully transparent and every record is traceable, which suits agents well.”
His outlook comes with a qualification. Both the agents and the networks they would use still need to develop.
“Agents aren’t capable enough yet, and chains are still adapting. But once agents truly run on-chain, the number of users and what’s possible will grow by an order of magnitude.”
Discus Fish says blockchain could reshape how financial assets are issued, traded and settled over the next five years. He also notes that institutional adoption has progressed faster than Cobo initially expected, with individual users no longer leading the shift. “Since 2017, we have believed blockchain will become part of the financial infrastructure of the future. We expected individual users to lead, but institutions moved faster.”
For Fish, the next stage involves a broader range of assets and financial clients.
“What gets redefined next is how assets are issued, traded, and settled. As traditional assets like US equities come on-chain, a much wider range of financial clients will follow.”
Samuel Tse, Head of Investment and Corporate Finance at Animoca Brands, focuses on investment opportunities across digital assets and agentic AI. He sees digital identity as essential to allowing AI agents to act on behalf of people while keeping transactions within the permissions they set.
He believes users should set clear rules for what an AI agent can do, allowing it to complete transactions without requiring approval for each one.
“Once agents can prove who they represent, hold digital assets, and move value under clear rules, the economy stops being click-by-click. You set the policy once—who, how much, for what—and the agent transacts inside it.”
He describes applications ranging from everyday payments and transfers to subscriptions, swaps and earning returns on idle funds. “That’s agentic transactions: payments, transfers, subscriptions, swaps, even chasing a better rate on idle cash. Identity is the trust layer. Assets and credentials are the spending authority—always accountable to the human who granted permission.”
In Tse’s account, the human remains the source of that authority. The agent acts within the permissions granted to it.
To illustrate how AI agents, digital identity and digital assets might work together, Tse offers a hypothetical Hong Kong parent's weekday.
“A Hong Kong parent’s weekday: top up the child’s Octopus, book a weekend football slot, translate a school notice for the helper, check weather before pickup, restock milk at the member price, and keep leftover budget earning until tomorrow.”
At present, he says, completing these tasks requires navigating services that often operate separately.
“Today that means many apps that don't talk to each other, and rails that struggle with micro payments.”
His proposed alternative brings the tasks together under instructions established by the user.
“With an agent, they set the rules once and let it decide what to do. Identity unlocks trust and member prices without oversharing. Digital assets fit the money—per-use fees, hard limits, yield until spend.”
For Tse, this combination could create opportunities across payments, merchants, identity providers and digital-asset platforms. He compares the potential scale of agentic commerce to an earlier change in how consumers accessed services.
“The iPhone in 2007 rewired how people buy and use services for 20 years. We are just at the beginning of a similar paradigm shift, with outcomes we can barely imagine.”
Discus Fish and Samuel Tse offer two perspectives on the emerging agentic economy, from the blockchain infrastructure enabling transactions to the digital identity and permissions that allow AI agents to act on people's behalf. At CVCF 2026, these ideas will become part of a wider conversation with the leaders building and investing in this evolving space.
Be part of the conversation. Join industry leaders, founders and investors at CVCF 2026 on 4–5 November and explore the technologies and opportunities shaping the next phase of business.
Exclusive for Ventureport readers: Use code cvcf26vp to access a Full Access Pass for HK$300, subject to confirmation by the organiser.
Keep Reading
The hidden cost of scaling AI: infrastructure, energy, and the push for liquid cooling.
Updated
January 8, 2026 6:31 PM
The inside of a data centre, with rows of server racks. PHOTO: FREEPIK
As artificial intelligence models grow larger and more demanding, the quiet pressure point isn’t the algorithms themselves—it’s the AI infrastructure that has to run them. Training and deploying modern AI models now requires enormous amounts of computing power, which creates a different kind of challenge: heat, energy use and space inside data centers. This is the context in which Supermicro and NVIDIA’s collaboration on AI infrastructure begins to matter.
Supermicro designs and builds large-scale computing systems for data centers. It has now expanded its support for NVIDIA’s Blackwell generation of AI chips with new liquid-cooled server platforms built around the NVIDIA HGX B300. The announcement isn’t just about faster hardware. It reflects a broader effort to rethink how AI data center infrastructure is built as facilities strain under rising power and cooling demands.
At a basic level, the systems are designed to pack more AI chips into less space while using less energy to keep them running. Instead of relying mainly on air cooling—fans, chillers and large amounts of electricity, these liquid-cooled AI servers circulate liquid directly across critical components. That approach removes heat more efficiently, allowing servers to run denser AI workloads without overheating or wasting energy.
Why does that matter outside a data center? Because AI doesn’t scale in isolation. As models become more complex, the cost of running them rises quickly, not just in hardware budgets, but in electricity use, water consumption and physical footprint. Traditional air-cooling methods are increasingly becoming a bottleneck, limiting how far AI systems can grow before energy and infrastructure costs spiral.
This is where the Supermicro–NVIDIA partnership fits in. NVIDIA supplies the computing engines—the Blackwell-based GPUs designed to handle massive AI workloads. Supermicro focuses on how those chips are deployed in the real world: how many GPUs can fit in a rack, how they are cooled, how quickly systems can be assembled and how reliably they can operate at scale in modern data centers. Together, the goal is to make high-density AI computing more practical, not just more powerful.
The new liquid-cooled designs are aimed at hyperscale data centers and so-called AI factories—facilities built specifically to train and run large AI models continuously. By increasing GPU density per rack and removing most of the heat through liquid cooling, these systems aim to ease a growing tension in the AI boom: the need for more computers without an equally dramatic rise in energy waste.
Just as important is speed. Large organizations don’t want to spend months stitching together custom AI infrastructure. Supermicro’s approach packages compute, networking and cooling into pre-validated data center building blocks that can be deployed faster. In a world where AI capabilities are advancing rapidly, time to deployment can matter as much as raw performance.
Stepping back, this development says less about one product launch and more about a shift in priorities across the AI industry. The next phase of AI growth isn’t only about smarter models—it’s about whether the physical infrastructure powering AI can scale responsibly. Efficiency, power use and sustainability are becoming as critical as speed.