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
A step forward that could influence how smart contracts are designed and verified.
Updated
January 8, 2026 6:32 PM
ChainGPT's robot mascot. IMAGE: CHAINGPT
A new collaboration between ChainGPT, an AI company specialising in blockchain development tools and Secret Network, a privacy-focused blockchain platform, is redefining how developers can safely build smart contracts with artificial intelligence. Together, they’ve achieved a major industry first: an AI model trained exclusively to write and audit Solidity code is now running inside a Trusted Execution Environment (TEE). For the blockchain ecosystem, this marks a turning point in how AI, privacy and on-chain development can work together.
For years, smart-contract developers have faced a trade-off. AI assistants could speed up coding and security reviews, but only if developers uploaded their most sensitive source code to external servers. That meant exposing intellectual property, confidential logic and even potential vulnerabilities. In an industry where trust is everything, this risk held many teams back from using AI at all.
ChainGPT’s Solidity-LLM aims to solve that problem. It is a specialised large language model trained on over 650,000 curated Solidity contracts, giving it a deep understanding of how real smart contracts are structured, optimised and secured. And now, by running inside SecretVM, the Confidential Virtual Machine that powers Secret Network’s encrypted compute layer, the model can assist developers without ever revealing their code to outside parties.
“Confidential computing is no longer an abstract concept,” said Luke Bowman, COO of the Secret Network Foundation. “We've shown that you can run a complex AI model, purpose-built for Solidity, inside a fully encrypted environment and that every inference can be verified on-chain. This is a real milestone for both privacy and decentralised infrastructure”.
SecretVM makes this workflow possible by using hardware-backed encryption to protect all data while computations take place. Developers don’t interact with the underlying hardware or cryptography. Instead, they simply work inside a private, sealed environment where their code stays invisible to everyone except them—even node operators. For the first time, developers can generate, test and analyse smart contracts with AI while keeping every detail confidential.
This shift opens new possibilities for the broader blockchain community. Developers gain a private coding partner that can streamline contract logic or catch vulnerabilities without risking leaks. Auditors can rely on AI-assisted analysis while keeping sensitive audit material protected. Enterprises working in finance, healthcare or governance finally have a path to adopt AI-driven blockchain automation without raising compliance concerns. Even decentralised organisations can run smart-contract agents that make decisions privately, without exposing internal logic on a public chain.
The system also supports secure model training and fine-tuning on encrypted datasets. This enables collaborative AI development without forcing anyone to share raw data—a meaningful step toward decentralised and privacy-preserving AI at scale.
By combining specialised AI with confidential computing, ChainGPT and Secret Network are shifting the trust model of on-chain development. Instead of relying on centralised cloud AI services, developers now have a verifiable, encrypted environment where they keep full control of their code, their data and their workflow. It’s a practical solution to one of blockchain’s biggest challenges: using powerful AI tools without sacrificing privacy.
As the technology evolves, the roadmap includes confidential model fine-tuning, multi-agent AI systems and cross-chain use cases. But the core advancement is already clear: developers now have a way to use AI for smart contract development that is fast, private and verifiable—without compromising the security standards that decentralised systems rely on.