Two AI-agents working through lunch. Taotaoju, Shenzhen, China, 2026

A Thousand People Queued to Install OpenClaw

The Chinese internet called it “growing your lobster”. Tencent’s OpenClaw was the software, and roughly a thousand people queued at Tencent’s Shenzhen headquarters to install something that might do work on their behalf.

People were not just turning up to play with a chatbot. They were testing whether software could take tasks off their plate.

Once Tencent began placing OpenClaw’s task-handling logic inside WeChat as a native contact rather than a separate destination, the story stopped looking like a consumer fad and started looking like an economic experiment — not curiosity, but delegation.

The claim I care about is narrower than the hype. By one-person company, I do not mean an internet slogan but software that compresses administration, customer service, bookkeeping, compliance prep, and parts of sales support into a much smaller human footprint — enough that one operator can function a bit more like a small firm.

China has not yet proved that works at scale. OpenClaw matters because it makes the question legible — and it matters to me specifically, because I own the rails. The investable question is who owns them.

Why China May Normalise Agentic Labour First

China may be the first country to make agentic labour feel ordinary because distribution, payments, commerce, and policy legitimacy are already concentrated inside a few domestic platforms.

A seller on Taobao already lives inside Alibaba’s payments, logistics, and merchant services stack. A small business owner already coordinates through WeChat. The fewer new surfaces an agent has to cross, the faster the behaviour compounds into routine.

Policy reinforces the same pattern. Beijing’s 2026 Government Work Report explicitly called for “large-scale commercial application of AI agents” — language that stops short of guaranteeing durable economics but does make policy legitimacy part of the distribution story. If auditability and data locality become binding constraints, that matters: it tilts the infrastructure spend toward domestic rails.

Who Gets Paid First?

I want to be honest about where the evidence stands. Nothing here proves the one-person company exists at scale. What the data does show is that the rail layer is already getting paid for AI-adjacent activity. The question is whether that activity compounds into something structurally new that drives incremental earnings or just makes existing software slightly better.

The demand signal is real. Tencent putting OpenClaw functionality inside WeChat suggests this is being treated as a habit-forming category, not a passing curiosity. Alibaba’s AI-driven commerce disclosures suggest agentic behaviour is already being tested inside a live commercial ecosystem — 140 million users had “experienced” AI-driven shopping by end-February 2026, though “experienced” is doing a lot of work in that sentence.

If the one-person-company thesis works, the first durable profit pool is more likely to sit with the rails than with the flashiest model names. It is the same economic question that runs through all of these positions: not who gets the headlines, but where the economics actually settle.

I may be directionally right and still not make any money from it. What I care about is whether agentic behaviour improves monetisation at the margin inside businesses that already own distribution, trust, and transactions. If agentic AI usage explodes, Tencent monetises activity. Alibaba monetises execution. If trusted domestic hosting becomes a real constraint, the telcos need only be the approved place where the workloads run. Workflow software companies such as Kingdee monetise labour compression. That matters because the thesis has to show up in somebody’s budget, not just in demos and usage charts.

Tencent Owns the Habit Surface

WeChat already bundles communication, payments, mini-programmes, merchant touchpoints, service flows, and daily coordination into one behaviour stack. If agents become habitual, Tencent does not need to teach users a new ritual. It only needs to insert delegation into one that already exists. That is what ClawBot does: it appears as a contact in a user’s chat list, sitting between friends and family, handling tasks through the same interface people already use to coordinate their lives.

That makes Tencent more than a distribution winner. More activity inside WeChat can mean more commercial intent, more transactions, and more paid demand for cloud and AI services inside a surface Tencent already controls. Tencent owns the habit surface. It can stay model agnostic and still capture the economics of rising agent activity.

Alibaba Owns the Commerce Bridge

Alibaba matters less because it can win an AI beauty contest and more because it already sits where intent turns into transactions. More importantly, it owns more of the stack than almost anyone else in China: chips, cloud, models, and interface. If that vertical integration holds, Alibaba should be able to produce and deliver tokens cheaper than most, then monetise them across merchant tools, marketplace traffic, payments, cloud distribution, and enterprise services.

A Taobao seller using agents to rewrite copy, compare suppliers, handle customer messages, prepare VAT paperwork, and adjust campaign spend — that is the one-person-company idea made concrete. It is also the version that matters financially, because Alibaba is sitting inside the transaction. Accio Work, launched in March 2026, extends the same logic to cross-border sellers — autonomous sourcing, supplier negotiations, and customs filings inside the same transaction stack. The seller who once needed a sourcing team, a freight forwarder, and a customs broker can now delegate parts of that workflow to software that already sits where the payments clear.

No other company in this thesis controls the full vertical: chips, cloud, models, orchestration, and now workflow tools that span domestic and cross-border commerce. Alibaba Cloud reported RMB 43.3 billion (~US$6.0 billion) in the December 2025 quarter, with AI-related product revenue posting its tenth consecutive quarter of triple-digit growth. It can produce tokens cheaply because it owns the stack, and it gets paid every time an agent completes a commercial task inside its ecosystem.

Telcos May Be the Cleanest Investment Expression

The telco case matters because trusted deployment may end up being the bottleneck that decides where value pools. If SOEs and other politically sensitive customers are, in practice, going to run a lot of this through one of the big three telcos, then trust is not a soft factor. It is the gate. If you need audit trails, domestic hosting, compliant deployment, and politically acceptable infrastructure, the trusted rail matters.

I do not own Chinese telcos because they are exciting. I own them because the case is getting harder to ignore: too important to fail, real cash yield, consistent profitability, relative insulation from the most obvious US sanction channels, and now a genuine AI tailwind — China Unicom alone reported AI revenue growth of more than 147 per cent year-on-year in 2025. OpenClaw does not create the telco case. It just makes the demand side easier to see.

Workflow Software Is Where the Thesis Becomes Measurable

Workflow software is where the one-person-company thesis becomes testable. If AI is genuinely allowing businesses to operate with fewer human hands, the proof should appear first in the systems that handle finance, administration, procurement, tax, coding, and customer workflows — the places where labour savings can actually be measured.

That is why I keep coming back to Kingdee. It reported RMB 356 million (~US$49 million) of AI contract value for FY2025. That does not prove autonomous firms. What it does show is that businesses will already pay to remove repetitive but necessary work, especially when those tools sit on top of structured finance and customer data.

The more important question is pricing power. A US$1,000 software licence is still software. A US$10,000 workflow bill tied to measurable labour savings is something else entirely. If the customer is genuinely saving US$50,000 to US$100,000 of labour, outcome-priced software stops looking expensive and starts looking rational.

“For the small and micro enterprise market, Kingdee AI achieved bookkeeping efficiency improvements of over 80%, invoicing efficiency improvements of 40%, and tax filing efficiency improvements of 60%.” — Kingdee FY2025 annual results

None of this yet proves durable outcome-based pricing. But it is exactly the sort of evidence I want to see: software moving away from seat-based selling and closer to measurable labour compression.

The bear case that runs through all of these positions is not that model companies will displace the rails — in China, the leading models are open source, so any platform can run them. The real risk is simpler: that none of this amounts to more than incrementally better software. The rails were already getting paid. If agentic AI just makes existing workflows slightly faster without creating a genuinely new category of economic actor, then I am paying for a theme, not a step change. I do not think that is where this lands, but I cannot prove it yet — which is why the retention and operating-proof tests in this piece exist.

What Would Move This from Interesting to Durable?

For me, this thesis becomes durable only if three things show up in the data: retention, monetisation, and operating proof.

On retention, the early signs are encouraging but still adjacent to what I actually need. Tencent and Alibaba can each point to rising AI-related engagement and usage — Qwen’s 300 million monthly active users, nearly 200 million holiday orders facilitated through the app, stronger cloud demand. But none of that is the same as repeat agent-led task completion inside real workflows. The proof I want is habitual delegation, not curiosity.

On monetisation, the question is whether anyone is paying for the behaviour to continue — not just for the software to exist. I want to see closed-loop evidence: agent activity that drives measurable commercial outcomes and shows up as recurring revenue, not just as a line item in a product launch deck.

The third test matters most. Can one human plus software actually function as a one-person company? That is where this thesis becomes an economic claim rather than a technology narrative. And that is why agentic task completion and workflow software still matter more to me than the flashiest consumer AI launch.

The rails were not built for AI. They were built for life — and that is precisely why they may be the first to monetise it. My conviction in Tencent, Alibaba, Chinese telcos, and Kingdee has increased.

As of the date of publication, I hold positions in Tencent (HKEX: 0700), Alibaba (HKEX: 9988), China Mobile (HKEX: 0941), China Telecom (HKEX: 0728), China Unicom (HKEX: 0762) and Kingdee (HKEX: 0268). Positions may change after publication without notice. This is disclosure, not a recommendation. Full disclaimer · About Philip.

Victoria Harbour, Hong Kong, 2025

In April 2025, my portfolio was down six figures in a week and I felt nothing worth acting on. Not because I’ve transcended fear — I haven’t — but because I’d built the portfolio so that a 40% drawdown changes my mood, not my decisions. That distinction is an operational design principle, and it’s the foundation of everything I do with my own capital.

I spent most of my career in and around financial markets — most recently as CFO of a global family office. Every crisis I’ve sat through — and there have been several — looked terrifying in the moment and obvious in retrospect.

The trick is that it’s only obvious if you’ve done the homework beforehand and if your portfolio structure doesn’t force you to sell.

In 2024, I quit that world to become what I call the CIO of my own life — betting my livelihood on the ideas I write about on Cohong Lane. What follows is the operating system I use to allocate capital.

Why does structure matter?

Holding power is the one structural advantage an independent investor has over professional money managers. I don’t have clients calling to redeem. I don’t have a Sharpe ratio mandate forcing me to reduce volatility. I don’t have a career at risk if I’m down 30% for two quarters.

Professionals cannot buy during maximum fear because their clients won’t let them. I can. That’s the edge. But it only works if I’ve structured my portfolio so that drawdowns are an opportunity, not a crisis — and if I’ve trained myself to feel no pain when the red numbers pile up.

None of this comes naturally. I’m still learning. A mentor told me it takes about five major drawdowns before your subconscious mind stops reacting. The first one is torture. By the fifth, it barely registers. The work is in internalising, truly internalising, that price does not hurt. Only value does. If I own a company generating reliable cash flows at a price I know is cheap, a 30% paper loss doesn’t change anything about that company. It just means I get to buy more at an even better price.

How is the portfolio structured?

My portfolio is built around two core buckets — Income and Growth — with a small, emerging allocation to Venture bets. The assets span multiple geographies — Europe, the US, Singapore, Greater China — but this site focuses on where I do the majority of my own research: China and the companies shaped by its industrial policy. The separation is about cognitive clarity and eliminating forced selling.

Income exists for one reason: to generate enough cash flow that I never have to sell anything I don’t want to sell. If my dividend and coupon income covers baseline expenses, every other decision becomes optional. I can hold Growth positions through multi-year drawdowns without being a forced seller. That changes everything.

The principles are straightforward: yield must be real and sustainable, tiered by risk — from European and US corporate bonds to Singapore REITs. When high-quality income assets sell off alongside garbage during dislocations, I rotate up the quality ladder. Once this bucket is set up, I spend maybe 20% of my investment time here. The real intellectual work happens elsewhere.

Growth is where I concentrate on my highest-conviction opportunities — businesses I understand deeply, where I believe the market is materially mispricing the long-term trajectory. I hold growth positions across Asia and the US, but my concentrated bets are in Greater China. Concentration, not diversification. I don’t want to own 50 growth stocks. I want a small number of companies I understand well enough to hold when the price is moving against me — because the edge isn’t information, it’s the ability to sit through the discomfort.

Valuation still matters here. Growth investing isn’t about paying any price for a good story. I model fair value ranges and size positions based on the gap between current price and my estimate. Even the best business is a bad investment at the wrong price. And if my thesis is about where a company will be in five years, I need to be prepared to hold for five years — including through substantial drawdowns along the way.

There’s also a third bucket: Venture. These are small bets on things that could be worth a lot or nothing — new technologies, new competitive dynamics, situations where the range of outcomes is very wide. I size every one for total loss. It’s deliberately small, deliberately early. If any of these bets work, they’ll earn a deeper treatment in a future piece.

What does fair value actually mean?

Every investment decision I make ends with the same question: what is this asset worth?

I don’t calculate before I understand. First comes the industry analysis, the competitive position, the technology edge, the operational reality. Only then do I have enough context to estimate fair value. The number is the final checkpoint, not the starting gate.

For income assets, that means estimating a fair yield given the risk. For growth assets, it means building cash flow models with explicit assumptions I can stress-test. The output is always a range, not a single number — because I’m not that precise and neither is reality.

The point of doing this work is not the number itself. It’s having a pre-built shopping list ready before the crash arrives. When prices collapse and everyone is panicking, I don’t want to be making emotional decisions. I want to be executing against fair value estimates I’ve already pressure-tested in calm weather. The time to decide what to buy in a crisis is not during the crisis.

One discipline worth highlighting because it trips up nearly everyone: my P&L on a position has zero relevance to what I should do next. If I’m sitting on a 50% loss, that’s irrelevant. The only question is whether, at the current price and my current fair value estimate, I should buy more, hold, or sell. The past is sunk. Acting on it is the single most common mistake I see investors make — and it applies equally to gains. If a position has doubled and is now at or above fair value, the fact that I’m “up 100%” doesn’t mean I should hold. It means I should evaluate it as if I were buying fresh today.

The obvious counterargument is that holding power without external accountability is just stubbornness with better branding. Professional managers have risk committees for a reason — someone to tell them their thesis is wrong before the P&L does. I don’t have that. What I have instead is the fair value framework and the discipline to update it. If the business fundamentals deteriorate — not the price, the fundamentals — that shows up in the model and changes the decision. The check on stubbornness isn’t a committee. It’s the homework.

Why does this matter for China?

China’s market is shaped by forces that most investment frameworks don’t account for — and that’s exactly why I’m writing from Hong Kong instead of London. I’ve been investing my own capital in Chinese equities since 2018, and the longer I do it, the more I’m convinced that industrial policy isn’t background noise here. It’s a primary driver of capital allocation at massive scale. When Beijing sets binding targets for factory automation or EV adoption, that creates something I think of as captive demand: procurement that happens regardless of market sentiment. The money gets spent because a provincial official’s career depends on hitting the KPI — and that policy commitment creates demand visibility that market-driven economies simply can’t match. Understanding the transmission mechanism from a Five-Year Plan target to a company’s order book is where much of my Growth research begins. When the 15th Five-Year Plan set binding factory automation targets, the procurement pipeline for companies in that chain was effectively locked in for years — regardless of what the macro headlines said. Tracing those mandates to specific order books is the work I publish on this site.

Then there’s the information problem. Western mainstream media defaults to “China is in structural decline.” Local media defaults to “China’s rise is inevitable.” The possibility that reality might be somewhere in between — messy, sector-specific, and actually quite interesting if you look at the numbers — that’s somehow never on the menu. Sell-side research is conflicted by design — banks won’t publish contrarian China views when IPO mandates are at stake. So you end up with a dangerous context gap: investors have data but lack the nuance to distinguish structural risk from cyclical opportunity.

That’s why I’m here. Not because walking into a NIO showroom in Shenzhen gives me statistically significant data — it doesn’t. My sample of one showroom visit is not going to hold up in a peer review. But it gives me calibration. When I read a bearish report claiming Chinese consumers have stopped spending, I can check that against what I see in Shenzhen malls on a Saturday. It’s not proof. It’s a filter for obvious nonsense — and in China coverage, there is a lot of obvious nonsense to filter.

The combination of holding power, fair value discipline, and physical presence in the Greater Bay Area is the operating system. The homework I share on Cohong Lane — the policy analysis, the valuation work, the ground-truth checks — is how I keep that system honest.

The rest is homework — the policy analysis, the valuation models, the ground-truth checks I publish on Cohong Lane. But the homework only works if the structure underneath it never forces me to sell. Income buys the freedom. Fair value is the compass. And the goal — the one I keep coming back to — is not to manage the pain of a drawdown, but to have built something where the pain doesn’t arrive in the first place. That’s the operating system. The positions are just what it produces.

Liwan District, Guangzhou, China, 2025

Who were the Cohong?

There’s a painting I keep coming back to. It shows a narrow strip of waterfront on the Pearl River, sometime around 1800. Foreign flags — British, American, Dutch — flutter above a row of warehouses called the Thirteen Factories. And standing between those factories and the rest of China were a dozen merchants who controlled every bolt of silk, every chest of tea, and every ounce of silver that moved between East and West.

They were called the Cohong.

The Cohong (gōngháng, 公行) held an official monopoly on foreign trade in Canton from 1757 to 1842. If you wanted to do business with China during the Qing dynasty, you went through them. There was no alternative. Historian Jacques Downs called their world a “golden ghetto” — isolated and lucrative. But the Cohong merchants weren’t just gatekeepers. They were guarantors. Every foreign ship, every person connected with that vessel — the Cohong took full responsibility. Their reputation was their licence.

American consul Samuel Shaw described them as “intelligent, exact accountants, punctual to their engagements, and, though none the worse for being well looked after, value themselves much upon maintaining a fair character.” Trust was their moat.

Howqua, the most famous of them, was one of the richest men in the world — and he got there by being, in Shaw’s words, an “exact accountant” who valued his “fair character.” Not the colonialism that eventually destroyed the system, but the intellectual integrity that preceded it. That’s the tradition I want to honour.

Why am I here?

In 2024, I quit my job as CFO of a global family office. In 2025, I moved to Hong Kong — the capital markets gateway to China’s Greater Bay Area — to become what I call an “independent investor” — betting my livelihood on my ability to allocate capital independently.

If you’re wondering whether that was a rational decision: so am I, most mornings.

My edge, such as it is, lies in being here — in the same Pearl River Delta where the Cohong operated. Physical proximity doesn’t guarantee insight, but it provides something no desk-based analyst has: the ability to check headlines against reality on the ground. Walking into a BYD showroom in Shenzhen doesn’t give me statistically significant data — my sample of one is not going to hold up in a peer review. But it gives me calibration. A filter for obvious nonsense — and in China coverage, there is a lot of obvious nonsense to filter.

The problem facing anyone trying to understand China from the outside is that you’re forced to choose between two flawed sources: mainstream media, which is often alarmist and data-poor, or sell-side research, which is conflicted by design. Neither gives you the context you actually need to make investment decisions.

That’s the job I’m trying to do — except instead of a warehouse full of tea, my tools are a pair of walking shoes, public filings, company visits, and conversations with locals.

What You’ll Find Here

Cohong Lane is the transparent research process of an independent investor whose bills get paid by the markets — I call it open-sourcing the workflow. My livelihood depends on getting these investment decisions right. This isn’t a newsletter that happens to mention investing. I’ll start by walking through the operating system behind my own portfolio — two core buckets, a fair value compass, and why I structured everything so a 40% drawdown causes no pain.

If you’d rather understand the mechanics of a Chinese policy document than read another breathless headline about “China’s collapse” or “China’s unstoppable rise” — Cohong Lane might be for you.

The Cohong merchants earned their monopoly one honest ledger at a time. I don’t have a monopoly on anything — but I do have my own capital on the line and no reason to tell you anything other than what I actually think. That’s the licence I’m betting on.