Original · Unique Research · 2026-03-24 · Shanghai
Editor's note: This complete English edition preserves the author's narrative and Gao Zhou's March 2026 interview. Zhongju Intelligence is a romanized rendering of 中聚智能; an official English corporate name has not been confirmed. The founder's fund-management background, demonstrations and commercial claims are source reports, not independently verified results. Fund integration with OpenClaw is described as in progress, the Shopify 1% share is a target, and the 99% technology-copyability statement is a personal judgment. The source's remarks on AI honesty, creativity and “deep learning” terminology are retained as interview framing, not guarantees of reliability or a technical history of AI. Proposed security and compliance practices do not prove regulatory compliance or trading safety. This historical interview is not investment advice or authorization to automate financial activity.
Unique Awards · Guest Interview
A Quant Trader of 10 Years Goes All In on AI: Turning OpenClaw from Conversation into Action
"99% of technology can be copied, except creativity and imagination—things AI still does not possess."
As Gao Zhou said this, I was staring at the screen behind him. It was not displaying stock candlestick charts, but lines of OpenClaw invocation logs: "Agent executing: automatic replies to Shopify orders."
10 years ago, in a Hong Kong quantitative trading room, he used deep-learning models to capture market arbitrage opportunities. Back then, AI was not called AI; it was called deep learning. Today, he manages a private investment fund in China while leading Zhongju Intelligence, a company that makes AI agents work for humans rather than merely chat.
At a late-night review meeting, he pointed to a red marker for a failed task on the screen and suddenly said, "Look, that is AI's honesty. If it messes up, it has messed up. It does not make excuses."
After a decade navigating financial markets, that honesty is the quality he values most.
From Arbitrage Models to End-to-End Agents: A Quant Trader Transfers His Methodology
Gao Zhou's background is thoroughly financial: 10 years of overseas quantitative trading, managing a private fund in China, and using algorithms to seek alpha in millisecond-level fluctuations.
But his entrepreneurial choice is unconventional.
"Before AI, I worked in financial markets. You could call that AI too, though people called it deep learning back then," Gao says with a smile. "The fund is still operating. I am integrating OpenClaw into it as well. It should be ready soon—something to look forward to."
This style of placing bets on two fronts has run through his career.
Quantitative trading taught him that a genuine moat is never the model itself, but a deep understanding of market microstructure: the feel and judgment that cannot be standardized or copied.
When ChatGPT set off a wave, he saw an opportunity larger than financial markets: "For idealists of our generation, the emergence of AI is a chance to change the world. I absolutely have to be involved. I love creativity."
Zhongju Intelligence was founded a few years ago.
But this is not a story of chasing a hot trend. Gao's central judgment is that most AI tools stop at conversation, whereas companies truly need action.
"We are not making a more usable SaaS product," he says. "We put agents at the center of an end-to-end process: understand the goal, break down the task, call tools to execute it, and keep records for review. We deliver results directly, not just tools."
What Has He Been Working on Lately? OpenClaw
"Recently, I have mainly been studying OpenClaw. It is an impressive framework, taking our AI collaboration from being able to chat to being able to execute and deliver results."
They are not just studying it; integration is already underway.
This is not a concept, but one of 2026's strongest signals of practical deployment. In Gao's vision, OpenClaw is not just a tool, but a new operating-system layer, placed on top of agents and rewriting the entry points for content creation, distribution and interaction.
"Imagine," he says, his eyes lighting up, "not opening the WeChat Official Account dashboard, the Xiaohongshu App or Notion. Instead, you tell an agent in a chat box: 'Turn today's thoughts on Shopify operations into a post in a style suitable for overseas sellers. Use recent screenshots from my photo library as illustrations. After publishing it, monitor the first two hours of engagement for me.'"
Distribution, creation and interaction, all completed through one instruction.
But Gao also raises a sharp concern: "In traditional security models, we have clear trust boundaries—application sandboxes, permission levels and API key scopes. What is frightening about agents like OpenClaw is that they break through all of those boundaries."
His advice to OpenClaw's core contributors: "Do not compete only on feature iteration. Security mechanisms must be treated as first-class citizens."
Why Shopify? "ROI Can Be Calculated Directly"
This year, Gao is concentrating all his efforts on one scenario: an end-to-end growth process within the Shopify ecosystem.
The product is Upsello, an AI agent intended to automate growth for cross-border sellers, from marketing to customer service.
Why Shopify? His answer is characteristically quantitative: "ROI can be calculated directly."
The purchasing decision chain is long, involving budgets, IT, security and business teams. How can it be shortened?
"Express the value as how much labor is saved or how much additional revenue is generated, and make the results visible during the trial."
There are three monetization layers: subscriptions for the basic AI agent, enterprise integrations involving extensive customization, and project work for high-value plus clients. But this year's strategy is focused: develop scalable, repeatable product lines such as Upsello in depth.
Small teams need speed; large teams need compliance. Upsello targets growth, the most painful area for cross-border sellers.
"If ByteDance Builds the Same Product Tomorrow, What Do You Have Left?"
Gao's answer is unexpectedly candid: "99% of technology can be copied, except creativity and imagination—things AI still does not possess."
Behind that answer lies a quant trader's deep understanding of alpha. While everyone else competes on model parameters, he competes through three connected layers:
① Standardize the foundation—permissions, audits, workflow engines and tool interfaces.
② Localize the strategy—language, compliance templates and regional habits.
③ Make delivery outcome-based—show how much labor is saved or additional revenue generated during the trial.
"Differences in regulation are the biggest challenge in global enterprise collaboration," Gao says. "Technology and culture can be adapted iteratively, but compliance mistakes can be fatal."
His solution is engineering-oriented: data minimization, tiered permissions, audit trails and configurable regional policies, with stricter compliance modes as the default for critical processes.
AI's Boundary: Support Decisions, Replace Repetitive Work
"What role does AI play in collaboration?"
Gao's answer: support important judgments and trade-offs, while replacing low-value synchronization, organization, follow-up and repetitive question-answering. It does not replace human responsibility or boundaries.
When AI can generate meeting minutes, assign tasks and follow up on progress automatically, what value remains for managers?
"Set direction, make trade-offs, build mechanisms, take responsibility and develop people," Gao says. "The stronger AI becomes, the better humans must become at judgment and commitment."
What holds a team together when it works remotely with AI assistance?
Transparency: clear goals, clear progress and clear responsibilities. AI makes information transparent; people align values.
The 2026 Make-or-Break Test: From AI That Can Talk to AI That Can Act
His one-sentence prediction for the end of the year: "Become an AI collaboration-engine company that makes global teamwork executable, auditable and capable of growth, with Upsello as its most representative deployment in the Shopify ecosystem."
The core goal is to move from AI that can talk to AI that can act and produce results.
If it fails, what is the most likely reason?
"Microsoft, Google and Alibaba squeeze the global enterprise-collaboration space from every direction."
Plan B is already in place—
"Do not compete on platform breadth; focus on depth in vertical scenarios. Make Upsello a growth component Shopify sellers cannot do without. Deepen the moat through scenario-specific data, end-to-end workflows and measurable results, build a user-data flywheel, and capture 1% of the Shopify market."
Ecosystem Ambitions: Three Pieces of an Agent Operating System
Among fellow guests at the event, SimplexAI works on AI Workflow Automation, Refly.ai on AI writing, and Zhongju Intelligence on enterprise collaboration.
Gao predicts that the three directions will converge into an organization-level agent operating system: writing as the expression layer, workflow as the execution layer, and collaboration as the governance layer.
This year, he most wants to connect with three types of partners: Shopify ecosystem participants such as service providers and agencies, cross-border brand sellers willing to co-develop case studies, and international-expansion infrastructure providers in logistics, payments, ERP/Klaviyo and related areas.
"We are willing to use Upsello's Shopify scenario as a concrete deployment point to get the ecosystem working first."
He also researched another field last year: advertising automation.
"The strategy is very similar to our quantitative trading work," he says. "I think it is still somewhat difficult, but enormously valuable."
In 2026, while most people are still asking whether AI will replace them, Gao has moved to a different question: How can I use OpenClaw to make AI genuinely work?
He is still managing that private fund, still studying OpenClaw integration, and still wrestling with agents' security boundaries.
Perhaps the most authentic relationship between humans and AI has never been replacement, but jointly turning conversation into action: challenging each other, helping each other succeed, breaking old rules together and then establishing new ones.
And are you ready to let AI work for you?
Selected Interview Q&A
About the Founder
Q: Introduce yourself in one sentence. What are your three keywords for 2026?
A: I am Gao Zhou, Founder and CEO of Zhongju Intelligence. Recently, I have been studying OpenClaw. It is an impressive framework that takes our AI collaboration from being able to chat to being able to execute and deliver results. This year, we are especially focused on making those capabilities work in Shopify scenarios through Upsello.
Q: What was your background, and what prompted you to enter AI enterprise collaboration?
A: Before working in AI, I spent 10 years in overseas quantitative trading. You could call that AI too, though people called it deep learning then. We still manage a private fund in China and are in the process of integrating OpenClaw. As for the opportunity, the emergence of AI is a chance for idealists of our generation to change the world. I absolutely have to participate. I love creativity.
About Positioning
Q: What is Zhongju Intelligence's core business, and how is it differentiated?
A: We are not making a more usable SaaS product. We put agents at the center of an end-to-end process: understand the goal, break down the task, call tools to execute it, and keep records for review. Our differentiation is executable collaboration, deeply integrating user context and delivering results directly rather than just tools.
Q: How do you define intelligence's role in collaboration: assistance or replacement?
A: Decision support plus replacement of repetitive work. Support important judgments and trade-offs; replace low-value synchronization, organization, follow-up and repeated question-answering. Do not replace human responsibility or boundaries.
Q: If ByteDance builds the same product tomorrow, what is your moat?
A: Accumulated industry-specific data and knowledge graphs, and in-depth optimization of customer solutions. General-purpose large models struggle to adapt quickly to specialized vertical scenarios. But I think 99% of technology can probably be copied in the AI era, except creativity and imagination, which AI still does not possess.
About Commercialization
Q: How do you monetize today, and how do you shorten purchasing decisions?
A: Primarily subscriptions, with enterprise integrations and add-on modules. Project work mainly provides customized agent services to high-value plus clients. We shorten the decision chain by expressing value as how much labor is saved or additional revenue generated, with results visible during the trial. Upsello is on this year's main track because ROI in Shopify scenarios can be calculated directly.
About Globalization
Q: What is the biggest challenge in global enterprise collaboration? How do you address data compliance?
A: Ultimately, regulatory differences are the hardest part. Technology and culture can be adapted iteratively, but compliance mistakes can be fatal. Data minimization, tiered permissions, audit trails and configurable regional policies; stricter compliance modes by default for critical workflows; and significant compliance issues must always be aligned with the legal function.
About AI Native
Q: Do you agree with the idea of AI Native? Is Zhongju Intelligence AI Native?
A: Very much so. AI Native means treating AI as the brain of the main workflow from the outset, with tools as its hands and feet. Upsello is our clearest AI Native deployment this year. Adding AI to traditional software suits upgrading existing systems. AI Native better suits complex scenarios such as global collaboration that require an end-to-end decision, execution and audit process, and has a higher ceiling.
About the Ecosystem
Q: Will the three directions (Workflow/ writing/ collaboration) converge?
A: They will converge into an organization-level agent operating system: writing is the expression layer, workflow the execution layer, and collaboration the governance layer.
Q: Who would you most like to collaborate with at Unique Awards?
A: Partners who can deliver global deployment and ecosystem integration reliably. The collaboration centers on Upsello: connecting logistics, payments, ERP, Klaviyo and other toolchains within Shopify's ecosystem so that agents do not just answer questions, but actually carry growth actions through to completion.
About 2026
Q: Where do you see the greatest new opportunity over the next 12 months?
A: AI-driven end-to-end growth processes, especially cross-border e-commerce, where ROI is clearest. We will continue focusing on Upsello and developing repeatable growth templates. Another field is advertising automation. Its strategies are very similar to our quantitative trading work. I researched it last year; it is somewhat difficult, but enormously valuable.
Q: Describe Zhongju Intelligence at the end of 2026 in one sentence. If it fails, what is the most likely reason?
A: Become an AI collaboration-engine company that makes global teamwork executable, auditable and capable of growth, with Upsello as its most representative deployment in the Shopify ecosystem. If Microsoft, Google and Alibaba squeeze global enterprise collaboration from every direction, Plan B is clear: Do not compete on platform breadth, but focus on depth in vertical scenarios. Make Upsello a growth component Shopify sellers cannot do without, and capture 1% of the Shopify market.
This article is based on Unique Research's in-depth interview with Gao Zhou.