I own three Chinese state telcos. They are paying me roughly 6 per cent a year while I wait to find out whether the state’s AI infrastructure build shows up in the earnings — or eats them. That is the entire trade. Everything else in this piece is the work I did to convince myself the wait is worth it.

In the United States, a telco is a pipe. Traffic flows through it, built by others. In China, the operator owns the build site, holds the state procurement, and writes the cheque. That is not a nuance. It is a different business. China Mobile (CM), China Telecom (CT) and China Unicom (CU) are not pipes for AI built elsewhere. They are the licensed infrastructure inside which the state’s computing build is being capitalised.

The last time these three were asked to build national infrastructure at scale, shareholders got less cash for six years. The 2008 to 2013 3G build-out is the analogue. The state has done the naming again. That is the risk I am underwriting against, and it has not gone away. The market has stopped treating these three like dead phone utilities; it has not yet decided whether they belong inside China’s AI+ infrastructure stack. That undecided gap is the trade.

All three are dual-listed: H-shares in Hong Kong, A-shares in Shanghai. Same underlying company, same annual report, same dividend cheque. Different price. A-shares trade at a premium that implies roughly 4 to 5 per cent yield for mainland investors. H-shares imply 5.6 to 6.5 per cent for Hong Kong investors on the same dividend. I hold H-shares for that reason.

I started buying all three in 2023 and 2024, and added again on sell-offs in 2025 and on the 2026 VAT reclassification. The reasoning then was simple: depressed prices, attractive yields, three state-owned enterprise (SOE) backbones inside the national network. A value investor did not need to believe anything else about the future to find that combination interesting. One of my mentors had bought a year earlier, at the 2022 low; his work was done. Mine was not, and I waited.

Why a Chinese telco is not a Western telco

A Chinese state telco is a licensed builder of national infrastructure under a Five-Year Plan mandate, sitting beneath a single shareholder — the State-owned Assets Supervision and Administration Commission (SASAC) — with state procurement directed to domestic providers by both regulation and incentive. A Western telco is a pipe for compute built elsewhere. Same word on the stock screen, different business behind it.

Verizon’s AI infrastructure is a low-latency fibre product for third-party hyperscalers. British Telecom’s (BT) AI thesis is cost-out inside a regulated incumbent with pension obligations. The compute is somebody else’s. Deutsche Telekom (DT) is the closest Western analogue: sovereign-compute pivot, regulated domestic procurement, premium multiple for the digital-sovereignty story. Even DT is buying capacity that others operate.

In China, the 15th FYP names the National Unified Computing Power Network as core infrastructure and writes the operators into the chapter. SASAC owns the controlling stake in all three. The shareholder, the regulator and the procurement counterparty are the same balance sheet.

The 15th FYP screen, applied to the three

China Telecom’s 2025 Annual Results Presentation guides total capex down 9.2 per cent to RMB 73 billion (~US$10.7 billion), and inside that smaller envelope computing infrastructure rises from 25 to 35 per cent of mix while network infrastructure falls from 51 to 41 per cent. Cash reallocated to where the plan says the build will happen, not added on top of where it was already going. That single slide is what Five-Year Plan execution looks like inside a corporate budget.

I have a rule on Chinese equities: I will not own one unless I can see how it rides the current Five-Year Plan. The transmission from chapter language to dividend cheque runs in five steps. The state names a priority. The Ministry of Industry and Information Technology (MIIT) defines the network architecture. The telco moves capex. The segment line shows revenue. The dividend tells me whether shareholders are still being paid while that happens. Break any one and the trade weakens.

The chapter language is unambiguous. Chapter 7 of the 15th FYP mandates the National Unified Computing Power Network: ultra-large-scale clusters, compute-energy coordination, allocation scheduled across regions. The AI Plus action lists model, chip, cloud and application as a coordinated stack. The 2026 Government Work Report names both as priorities. CT’s capex slide is what step three of the transmission looks like in a filing.

Stage three: the growth gets rails

In late April and early May 2026, all three operators stood up within a fortnight and named what they were building: a Token factory. The phrase each used varied. The structure was identical — IaaS compute at the bottom, a scheduling and model layer in the middle, Token billing at the top. Stage three of this thesis is the moment that build went from inferred to publicly committed.

For two years before that, the growth half of this thesis ran on inference. The FYP chapters were clear. The capex mix was shifting. The segment lines were appearing. But no operator had stood in front of an audience and named what they were building.

At the 9th Digital China Summit, China Telecom’s cloud subsidiary Tianyi Cloud launched what it calls a one-stop Token service system, structured around serving AI Token demand at scale. The IaaS layer is already at 91 EFLOPS of self-owned and accessed compute; the stated ambition is a full-stack national infrastructure for Token production. China Mobile followed on 8 May at its annual cloud conference in Suzhou. The chairman announced a Trillion-Token Service Trial Package. The company launched MoMA, a model management platform integrating over 300 AI models with real-time Token billing. China Mobile’s management said explicitly that Token operations cannot yet replace mobile data revenue. I found that last disclosure more useful than the headline number. It tells you the growth runway is still early, and management knows it.

China Unicom announced Token-based computing packages and said it will drive Token volume growth through computing power sales and model access.

The state has provided the framing. In May 2026, Xinhua and CCTV both described the national computing power network as the “computing version of the state grid.” Daily Token calls in China exceeded 140 trillion in March, more than 1,000 times the level at the start of 2024. Computing power has been classified as one of China’s “six networks,” alongside water, power, and logistics. This is what the 15th FYP chapters look like when they are being executed in real time.

Yield in 2023. Yield plus inferred growth in 2024 and 2025. Yield plus named, capitalised, publicly committed growth in 2026. The Token is the unit of account the industry has chosen, and the state has ratified it. Whether this translates to a sustained earnings upgrade is what the August interims will begin to answer. I do not know the answer. What follows is the sizing the arc earned.

The sector bet, in order: CM > CT > CU

I size CM largest, CT second, CU smallest. CM is the cohort’s cleanest income position — largest stack, 77 per cent payout, the operator the state would call on first if shareholder cash were needed. CT is the cloud and government-enterprise growth play, where the August 2026 interim results — the first H1 earnings print since the Token factory was named in April — are the first read on whether the Token scheduling layer carries margin. CU is the capital-discipline tail — the thinnest growth disclosure, the most room to disappoint, and the cleanest payout-convergence optionality. I own all three. The differences inside the cohort earn the sizing.

China Mobile is the scale anchor. Backbone operator, 92.5 EFLOPS of computing capacity at FP16, and the largest balance sheet of the three. Payout 77 per cent in 2025, up from 71 in 2023, with 2026 guidance flagged as stable-to-rising at the Capital Markets Day. Its growth play is what the chairman named in Suzhou: the Trillion-Token Service Package and the MoMA platform, sold on the largest stack in the cohort. If the income thesis breaks anywhere in the cohort, it breaks here last. The live alternative is not a revenue miss — it is a payout decision: CM is the operator the state would call on first if the build required shareholder cash, and it has done exactly that before.

China Telecom is the cloud and government-enterprise position. Cloud revenue RMB 120.7 billion (~US$17.8 billion) in 2025, the cohort’s first RMB 100 billion milestone. AIDC RMB 34.5 billion (~US$5.1 billion). Security revenue RMB 16.6 billion (~US$2.4 billion) as a standalone line. Its growth play is the Tianyi Cloud Token service named at the Digital China Summit: five layers from compute to application. The scheduling layer is the structural differentiator: guaranteed Token throughput sold with SLA-level commitments, not raw capacity. Whether the margin model follows is the August question. The disclosure says the model exists, not that it is profitable. CT is the cohort name where the story is easiest to believe. That is why I am hardest on it.

China Unicom is the capital-discipline tail. Smallest position. Free cash flow RMB 36.0 billion (~US$5.3 billion) in 2025, up 28.5 per cent on net profit growth of only 1 per cent. I read this as capex restraint rather than revenue momentum; the live alternative is that CU cannot find shovel-ready AI spend at its scale, which would make restraint a tell. The 2027 budget separates the two. Payout sits at 63 per cent against 77 per cent at the other two. That is where the re-rating optionality sits: if CU’s payout drifts upward toward 77 per cent, the dividend grows, and the price has to re-rate to hold the yield in line with CM and CT. Its growth play is the Token-based computing packages announced alongside the others, sold for computing power and model access, with smaller scale framed as a flexibility advantage. The growth story has the most room to disappoint and the disclosure quality is the thinnest.

I am not picking the telco that wins the computing build. I own the listed SOE layer through which part of the build flows: income prices in 2023, growth prices in 2026.

The valuation anchor: CM 11.95×, CT 13.70×, against Deutsche Telekom at 14.02×. The income re-rating from the 2021 trough — CM from 6.85× — is largely in. The next leg of return, if it comes, depends on whether the growth named in April converts to segment-line earnings inside the August interims and the FY2026 Capital Markets Days. CU sits inside the same valuation logic but trades on a free-cash-flow story rather than a clean trailing multiple, which is part of why it is the smallest of the three weightings. That is the valuation case. The reason I will not pay for it naively is what happened the last time these three were asked to build.

Why 2009 still sits in the middle of my thinking

Across the 2008 to 2013 3G build-out, China Mobile held its payout ratio at 43 per cent for six consecutive years; China Telecom held DPS flat through 2008 and 2009 at an inferred 30 to 35 per cent payout; China Unicom’s 2012 payout sat at 25 to 30 per cent against RMB 99.79 billion (~US$14.7 billion) of capex. Payout suppression of 25 to 40 percentage points lasted six years. The only thing that historically broke the income on these three was the state asking them to build something, and these are the numbers from the last time it happened.

I went to the HKEX primary filings to read what that looked like underneath the headline. CT’s DPS sat flat at HK$0.085 across 2008 and 2009 while the C-network integration ran. CU’s 2012 DPS was RMB 0.12 (~US$0.02) against that RMB 99.79 billion capex envelope. Board language across all three referenced “investment needs” and “long-term sustainable development”. Calm. Administrative. The payout ratio did the talking.

No SASAC or MIIT directive appears in any board disclosure I retrieved. The instruction may have existed. It was not in the public text.

BT cut its dividend in 2020 while funding a national fibre roll-out the UK government had defined as strategic infrastructure. When a company is the national network, shareholder cash becomes the balancing item when the build requires it. China is more direct about that balance.

That is the risk control argument for not paying 12× earnings naively.

Why 2026 is not 2009

Three things have changed.

Capex direction. In 2008, the operators were ramping into a build. In 2026, total capex is flat to declining across the cohort. CM cut 2025 capex 8 per cent to RMB 150.9 billion (~US$22.2 billion). CT guided 2026 down 9.2 per cent to RMB 73 billion. CU’s free cash flow rose 28.5 per cent on net profit growth of only 1 per cent, with the capex burden falling. The mix inside the smaller envelope is shifting to AI infrastructure, but the envelope itself is not exploding.

Revenue visibility. The 3G build created the network first and the monetisation later. The AI+ build is already showing up in cloud, AIDC, computing services and security revenue, year by year, in the segment lines. That is a different sequence inside the income statement.

SOE reform direction. The SASAC value-creation framework, often referenced as Document 79, and the later SASAC market-value management push, are distinct documents and I treat them so. The first is a profitability and value-creation campaign for central SOE managers. The second pushes listed central SOEs to manage market value, capital returns and shareholder communication. CM’s payout moved from 71 per cent in 2023 to 77 per cent in 2025 inside that framework. The trajectory tracks the framework dates, CT and CU have moved in the same direction over the same window, and management cites shareholder-return language at the Capital Markets Day. Declining capex is the live alternative driver. I read the SASAC direction as the most plausible cause; I cannot separate it cleanly from the cash-flow effect on the public disclosure.

That is not a guarantee. It is the changed incentive set I am underwriting on top of the income floor.

What I am watching for, on a five-year horizon

I am watching three things across three release windows: capex mix at the August 2026 interims and the FY2026 Capital Markets Days; payout trajectory across the same prints; the 2027 budget disclosures, where guided intent either becomes durable or does not. If the capex mix and payout trajectory hold through that window, the cohort thesis is intact. The horizon is the FYP duration, not a single earnings print.

I am a Bayesian investor on long positions. I update on what the data tell me, in proportion to how informative each release actually is. The August interims are one input, not the picture.

On CM, the indicator is the disaggregation of the AI services line. If management starts separating infrastructure services (computing, cloud, security) from bundled content (MIGU, e-commerce), the disclosure quality improves and the underwritten thesis gets cleaner. If the bundle hardens, the underwritten thesis weakens whether or not the headline number grows.

On CT, the indicator is the capex mix continuing to shift toward computing infrastructure inside a flat-to-declining envelope. The 26 per cent to 35 per cent guided shift for 2026 is the signal of intent. The 2027 budget will tell me whether the intent is durable.

On CU, the indicator is payout convergence. If the payout drifts from 63 per cent toward 70 per cent, the yield compresses through price and the re-rating optionality earns out. If the payout stays low while capex obligations rise, the income story weakens before the growth story has had a chance.

Across all three, the indicator that would change my view of the cohort, not the timing, is SASAC’s central-SOE performance assessment. CU’s 2025 Sustainability Report ties performance assessment for central enterprise managers to AI computing power targets. If a future round of criteria encodes computing-power floors as a hard performance measure, the cohort-wide capex discipline is at risk and I would revisit the sector weight before any individual name. That is the single most important unresolved data point in the position.

The trade I am still in

Adding now is harder than buying in 2023. The 2023 buy was easy. Price was low, yield was high, and the underwriting required nothing more than valuation discipline. The 2026 hold is harder. The easy part has played through, and the remaining return needs a state-mandated growth story to deliver where chapter language and filing disclosure currently disagree.

That is the trade I am still in. The work bought me the position. The work is what tells me whether to keep it. The 2009 filing stays on the desk for the same reason: not because history repeats cleanly, but because it tells me what to watch when a national infrastructure cycle reaches the shareholder.

As of the date of publication, I hold positions in China Mobile (HKEX: 941), China Telecom (HKEX: 728), and China Unicom (HKEX: 762). Positions may change after publication without notice. Cohong Lane is a periodical publication made generally available to the public; this is disclosure of my positions, not a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.

When ServiceNow (NOW) printed Q1 2026, the number was unambiguous. Revenue acceleration. No margin collapse. No evidence that AI-native start-ups were cannibalising the installed base. The print read like the thesis I had been holding for months — and the market sold the stock off seventeen per cent on the news. Analyst downgrades printed alongside the move. The good-till-cancelled (GTC) orders I had set months earlier at the lower end of the fair-value range filled themselves into the drop. The only decision I had to make in that hour was whether to cancel the unfilled tranches. I did not.

The piece of the institutional desk that nobody can buy is not the research. It is the calm. I learned this over twenty-five years in and around institutional finance, including the CFO seat at a large global multi-family office. I left in 2024, and the question I could not yet answer was whether the disciplines I had absorbed could survive without the desk that had taught them to me. The orders that filled themselves while NOW was seventeen per cent lower on a thesis-affirming print are part of the answer. Not the buying — the not-cancelling. Passing that test once does not close the question. The next sell-off may be the one I cancel into.

I am two years in on my full-time investment journey. The research half of the desk is no longer the bottleneck. Five custom AI agents on $32 a month of reasoning models, sitting on top of a Notion workspace that remembers everything, get me to a depth that used to require several people collecting sources, reconciling filings, and building the dossier. That does not make them a research team. It gives me research-team reach, with one human still responsible for judgement. The other half — the calm that lets the calm-weather version of me do the buying while the in-the-moment version sits on his hands — is not solved. The market is engineered to take that calm away from a retail investor, and the engineering is getting better.

The structural edge of running alone

Running a liquid book alone gives me three structural freedoms an institutional desk does not have: no benchmark to hug, no redemption risk, no career risk. Those are not minor. An institutional desk that tries to run the habits in this piece runs them into a headwind — quarterly performance windows, redemption cycles, the politics of underperforming a peer for two quarters in a row. I have none of those pressures, so the habits compound instead of fighting to survive. That is where the asymmetry comes from, and it is structural — but only when the habits are in place. Without them, the freedoms are just unsupervised drift. The family office taught me the habits. Leaving it taught me they were always the asset, not the headcount. The two scenes below show them under live fire.

Scene one: buying into the SaaSpocalypse

Through late 2025 I held the bull case on three US enterprise-software names — Salesforce (CRM), NOW, and Veeva Systems (VEEV). The market had moved to the opposite view. The narrative was the SaaSpocalypse: large language models would cannibalise the installed base, AI-native start-ups would bypass the incumbents, and the seventy-plus per cent gross margins these businesses had earned for two decades would compress as AI features became table stakes. I disagreed with the market on the business case, not on price. The agentic-workflow case for the incumbents looked stronger to me in an LLM world, not weaker. On price we agreed — these names had been too expensive for me to act on, for months. Valuation was the binding constraint.

What follows is the dossier I built to make sure I was not the one missing something. A logical bear case deserves a proper answer before you bet against it. The answer below is what got me to fair-value ranges I was willing to act on. It is not a full underwriting note for the three positions — I owe that piece separately — and the load-bearing question for this scene is not whether the dossier is right. It is whether the habits held when the print arrived.

My answer had three parts, in the order I built them.

The first part is the data moat. A large language model reasons probabilistically — it works in distributions. Enterprise workflows are not probabilistic. When a customer asks for a refund, the CRM workflow has to be deterministic: same input, same output, every time. The next generation of enterprise systems is precisely that combination — agents reasoning probabilistically, calling deterministic workflows for the actions that have to be exact. CRM, NOW, and VEEV are the systems of record the agentic stack has to call into, and they own the deterministic workflows around that record. Ten years of sales history, customer relationship maps, service interaction logs — context no foundation model can replicate from general training. That is what the sell-off had missed.

The second part is security and complexity at scale. Inside a large enterprise in 2026, point solutions proliferate — one agent for HR queries, another for IT helpdesk, another for sales outreach. Within a year there are fifty independent AI systems from fifty different start-ups running against core data. Every one is a potential prompt-injection vector. Every one requires IT approval, security certification, user retraining, and the political buy-in of the SVP whose department it touches. The CTO ends up insisting on a single system of coordination: a pre-approved, enterprise-grade, security-audited platform the team already knows, one that connects to the systems of record they already run. That is ServiceNow’s AI Control Tower. That is Salesforce’s Agentforce. The bear narrative systematically ignores this constraint because it is modelled by people who have never sat in an IT governance meeting or only care about next quarter’s numbers.

The third part is where the asymmetry actually lives. Today, CRM and NOW compete for IT and software budgets — measured in tens of billions. If AI agents automate work previously done by people — inside sales, tier-one IT support, compliance workflows — the relevant budget shifts. It is no longer the software line item. It is the labour line item. Corporate headcount budgets are measured in trillions. An enterprise deploying agents at multiples of the output-per-dollar of a human employee is not buying software any more. It is replacing payroll. The platform does not automatically take that budget. The incumbents that climb from systems of record into the system of action — the layer that executes tasks rather than stores data — become the toll roads across that flow. Once a customer is hooked on agents that work, the switching cost is the institutional memory embedded in the data model, not the software contract. Existing-book gross margins may still compress as agentic pricing pressure works through. The labour-budget asymmetry is what makes the platform worth owning anyway.

That was the dossier I had on the desk by late 2025. Fair-value ranges, on my numbers, well below market price for months. Sizing decided in calm weather. Then the SaaSpocalypse arrived: a multi-month software capitulation, deepened by Middle East war pressure on risk assets. CRM and VEEV filled close to the peak of the bearish sentiment — bought, in the moment everyone else was selling, by orders the calm-weather version of me had set months earlier. The starter tranches in NOW filled in the same window. That part was uneventful — calm-weather work doing what it is supposed to do.

The eventful part was NOW. The Q1 2026 print landed inside the SaaSpocalypse, not after it. Revenue accelerated. The CFO commentary described exactly the agentic-workflow adoption I had spent six months underwriting. Guidance for the remainder of the year flagged slightly lower margins on the cost of integrating recent acquisitions — acquisitions that, on my reading, strengthened the data moat the case rests on. The market read it as margin pressure. The moat point was ignored. The stock dropped seventeen per cent. Analyst downgrades followed. The GTC orders I had set in calm weather, sitting in tranches at the lower end of my fair-value range, started filling against a tape that was telling me I was wrong.

I did not sit through those hours calmly. A tight pull behind the sternum. The urge to cancel before they embarrassed me further. A low, hot kind of regret — the suspicion that someone else was seeing something I had missed, and the orders were going to keep filling all the way down to a number that would look very stupid in a month.

I named what I was feeling. Then I did the cognitive move the Budapest test had taught me a year earlier: separate the feeling from the state of the business. The print was the business. The price was the print being processed by a market that had decided in advance what the print was going to say. The dossier had not changed. Three questions. Had the data-moat case weakened on this print? No — the CFO commentary and the acquisitions had strengthened it. Had the security-and-complexity case shifted? No. Had the labour-budget asymmetry moved? No. The price had moved. Nothing else had.

I did not cancel the orders. The remaining tranches filled over the next two days. The position is currently above my entry; that is not the point. The point is the decision-shape, which has to be the same whether the stock immediately rewards it or sits underwater for two quarters. I do not know which it will do next, and writing this piece is not predicated on the answer.

What this scene shows is the four habits running into a moment that tests them. Investment policy: the fair-value range was set in calm weather, in writing, weeks before the print. Primary-source research: the bear case got a proper answer, not a dismissal. Calm-weather decision-making: the rule was if a thesis-affirming print arrives and the market sells, let the orders fill inside the range. Execution discipline: the tranches sat in the book and filled themselves while the screen was red. None of this was AI-enabled in the moment. The AI desk wrote the policy. The policy set the orders. The orders built the position. What the moment required of me was not action. It was the discipline not to cancel.

That is the harder of the two registers. The next one is easier — and shows what the system does when I can mostly stay out of its way.

Scene two: scaling China Everbright Water through the 14-to-15 Five-Year Plan bridge

Late 2025. China’s Fourth Plenum hits the wires in the early afternoon, Hong Kong time, on a desk twenty minutes from HKEX. Within the hour the outline of the 15th Five-Year Plan — the country’s economic priorities for the next half-decade — is on my desk. Beautiful China. Water governance named a structural priority. Priority regions made explicit. I am already long China Everbright Water (CEW), a position I initiated in September on work my AI desk ran through August. The Plenum did not trigger the trade. It extended the visibility of the policy the trade was already inside — same direction, longer clock, regions named. That earned the scaling decision.

The interesting moment in this scene is not the Plenum. It is September — buying the position with nothing external yet rewarding me for it.

Under the still-running 14th Five-Year Plan, the research desk worked open-ended. I do not read Chinese well enough to underwrite a policy thesis unaided. The workflow had to be built around that fact. Build a longlist of regulated water-treatment operators that fit the income book. Map the transmission chain from policy mandate to operator cashflow. Work that used to take a team a month. I did it in hours. That speed mattered: it freed August for the policy-to-cashflow work itself, not the data collection. When CEW emerged at the top of the longlist, the agent’s instructions changed: filings only, Plan clauses returned with Chinese alongside English, missing data flagged rather than guessed. The workflow is deliberately suspicious — clause references preserved, any gap treated as a gap rather than filled with fluent nonsense. Conviction does not survive a summary. It survives a source — and on a Chinese-policy thread the source is in Chinese.

By September the numbers were clear. Fair-value range, size band, starter conditions written into the policy in August, while the thesis was still cooling. Looking at the quote screen in September, the in-the-moment version of me wanted to wait. CEW is a smaller operator in a sector I know less well than Chinese banks or insurers; the instinct was to size conservatively and watch another month. Nothing on the tape rewarded the decision — no catalyst, no upgrade, no peer move. Waiting another month felt like the disciplined thing. The August version of me had already overridden that. I bought. Calm-weather decision-making is not the absence of doubt. It is a policy with the authority to override doubt — written when the doubt was not in the room.

When the Plenum outline landed, the same deterministic pipeline ran against the new text. Mandate confirmed and extended; priority regions named; cashflow profile re-tested. The rule fired: if visibility extends, scale to the upper band. I had already decided in August. I executed. By the time the National People’s Congress formally approves the Plan in March 2026, the position has been at its new size for nearly five months. The news prints. I do nothing. Execution discipline, in this case, is the absence of action.

Scene one was holding my nerve while the screen ran red. This was overriding quiet doubt while the screen showed nothing. Same discipline, different register. What the AI did was depth — a team’s worth of policy reading, primary-source verification, and Chinese, in hours instead of months. What I did was decide twice, in calm weather, against written rules.

How the desk actually runs

Two reasoning environments. $32 a month. The tools are not what matters; the architecture is. The architecture turns on one distinction: probabilistic versus deterministic mode. Probabilistic mode is hypothesis work — building the regulated water-treatment operators longlist from a sector universe, ranking by fit to the income book, mapping how a policy mandate plausibly transmits to operator cashflow. The model reasons across distributions; the answer is honestly uncertain and meant to be.

Deterministic mode is the opposite. When CEW emerged at the top of the longlist, the agent’s instructions changed — filings only, Plan clauses with Chinese alongside English, missing data flagged rather than guessed. No improvisation. No fluent inference papering over an absent source. The same model on the wrong mode on the wrong task is worse than useless: it sounds confident about the thing you most need it to admit it does not know.

The full architecture — five agents, the Notion memory layer, how the mode distinction is enforced in practice — is a future piece. What matters here is that the $32 buys the discipline of mode, not the discipline of the operator.

What the two scenes earned

What the two scenes earned, between them, is the documented version of the four habits twenty-five years in markets installed in me — six of them inside a family office: write the policy in calm weather; read primary sources, not paraphrases; make the hard call when nothing is rewarding it; set the trade up before the moment arrives. The market is engineered against all four — which is why the moment is the wrong place to make them.

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The half the $32 does not buy

The architecture is cheap, deliberately so. When the tool is expensive, I justify using it whether or not it is working. The discipline that decides when to use it is the part that took twenty-five years to build.

What I left the family office with was not a system. It was the knowledge of what good looked like — and a trained instinct for the difference between a market that is genuinely wrong and a portfolio that is genuinely broken. The AI stack is what let me rebuild the depth without rebuilding the headcount.

No Pain to Begin With sets out the structure. Volatility Is the Admission Price sets out the psychology. This is the operating layer that sits beneath both.

But the more I run this desk, the clearer it becomes that the binding constraint is not analytical depth. It never was. The binding constraint is what I described sitting in Budapest a year ago — the gap between what I felt and what I knew, and the willingness to act on the latter while the former was still screaming. That gap is not closed by better models. It is closed, slowly and unevenly, by accumulating enough experience that the subconscious mind starts to recognise the pattern rather than flinch at it.

The test is whether the calm-weather version of me is still in the room when the moment arrives — and whether I can tell, in that moment, the difference between a price moving and a business changing.

The $32 builds the first. The second is built more slowly, on real tests like the one I described at the top of this piece.

The discipline was always there. The question was always whether I could run it alone. The answer, so far, is yes — but the words so far are doing real work in that sentence.

As of the date of publication, I hold positions in China Everbright Water (HKEX: 1857), ServiceNow (NYSE: NOW), Salesforce (NYSE: CRM), and Veeva Systems (NYSE: VEEV). Positions may change after publication without notice. Cohong Lane is a periodical publication made generally available to the public; this is disclosure of my positions, not a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.

In March 2026, the National People’s Congress (NPC) adopted the Outline of the 15th Five-Year Plan (FYP). The Outline is the screen I will run every name in my China book through for the next five years.

By the time the vote landed, I had been reading its signals for half a year: the Party plenum communiqué in October 2025; the Central Committee’s Recommendations on the 15th FYP in November; MIIT’s AI Plus and Digital Infrastructure action plan in January. The published Outline did not come out of nowhere. It came out of a paper trail that anyone with an indecent quantity of pu-erh tea could read in advance, which is what I did.

The financial press treated the document as a slogan inventory the morning after the vote: AI Plus, Beautiful China, RMB internationalisation, Belt and Road, technological self-reliance. That is not how I read it.

What you will find in this piece

• The one rule: no Chinese stock in my book that I cannot find in the FYP

• Why the Plan binds: cadre evaluations, promotion risk, and career consequences attached to the chapters, and where the mechanism breaks

• Four chapters that have already moved names in my book, and the names they moved

• Plan-right is not stock-right: when the 14th FYP was correct and property still fell 75%

• The first test of the 15th, late 2026: what I will be reading for

My one-line rule on Chinese equities

I have invested my own money in Chinese equities since 2018. Eight years in, one rule does most of the work: I will not own a Chinese company, public or private, unless I can see how it rides the current Five-Year Plan.

That is not because the FYP is destiny. The 14th FYP correctly called the property deleveraging in writing, and the Hang Seng Mainland Properties Index still fell roughly 75% from its 2021 peak. Plan-right does not equal stock-right. The cautions are part of the rule, not an afterthought, and I will come back to them. The reason the rule still does the work is narrower.

In China, the FYP is the closest thing to an operating manual that the state ever publishes about itself. The chapters tell you what gets funded, what gets licensed, what gets procured, and what the cadres running provincial economies will be evaluated against in five years’ time. If a company I am thinking of owning is not in the chapters, the structural tailwind I am trying to underwrite is not there. If it is in the chapters, I have at least bought myself a fair fight.

The confirmation arrives in chairman letters. Read the FYP closely enough to learn its house style, and you start seeing the same wording show up, almost verbatim, in the next round of SOE annual results, capital-markets days, and chairman letters. The phrase is not coincidence. It is the company telling its regulator, its shareholders, and the cadre evaluating its Party committee that it has read the manual and is acting on it. When China Mobile uses the FYP’s exact framing on AI Plus or computing-power networks in its annual results, I am not reading marketing. I am reading translation from state policy to corporate strategy, in print, and free of charge. That tell carries more signal than almost anything else an outside investor gets in this market.

Why these things bind, and when they don’t

Five-year plans bind, when they bind, because the people executing them get graded on them. The 2019 Regulations on the Evaluation of the Work of Party and Government Leading Cadres are still in force. The mechanics are unromantic. A Party committee evaluates a cadre once a year and at the end of each term. Indicators include implementation of Central decisions and “high-quality development” outcomes, which is the technical phrase for “the things written in the FYP.” A “basic competence” rating triggers a formal admonishment and a deadline to improve. A “poor” rating costs the cadre a rank. Sustained underperformance can cost the principal responsible person the job. That is why the manifesto is a manual. “The state has decided X” in China is not a Western press statement; it is a behavioural instruction tied to a cadre’s evaluation, promotion prospects, and, eventually, job security. The Outline itself tags only some indicators as binding and most as expectative. That is the system telling you which lines a cadre will actually be punished against. The mechanism is real but uneven. Provincial governments game the binding numbers, particularly the environmental ones. Cadre rotation can break term-end accountability before it tightens. Chapters the Party prefers not to enforce get under-resourced rather than rejected. The screen relies on it where it bites.

The horizon is the other thing that makes the FYP serious. The 15th nests inside the 2035 long-range objectives, which were adopted with the 14th FYP in 2021 and which speak in the directional language of “by 2035, China will basically achieve socialist modernization.” Two five-year plans, one strategic horizon. The 16th FYP will be the third leg.

I run my own portfolio with an Income and Growth structure and a five-year minimum horizon on positions, and the FYP-2035 stack tells me the policy direction I am underwriting will not be unpicked at the next reshuffle. That is unusual in most markets, useful in investing, and worth more than people give it credit for.

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What the 15th FYP told me to look at, and what it did to my book

Roughly twenty-five things in the 15th FYP would interest an investor. Four have already changed something on my screen. Here they are, in the order they hit my book.

New productive forces, AI Plus, and a computing build that someone has to actually pay for

The 15th FYP carries one phrase that does more work than any other for an investor: “new productive forces”. This is Beijing’s official language for the strategic shift out of the concrete-and-rebar growth model and into technology-led productivity, advanced manufacturing, and the AI-enabled industrial stack. The state has decided to spend the next five years levelling its economy up the value chain rather than rebuilding the apartment blocks.

AI Plus is the most concrete instruction inside it. The Outline’s Chapters 11 and 14 direct the “full implementation of the AI Plus initiative” and the construction of “a nationally integrated computing power network”. The same chapters state explicitly, in the official English translation, that the state will “support meeting compute demand through multiple mechanisms including government procurement of compute services and compute leasing”. That is industrial policy as a purchase order. It is not subtle.

Between an FYP chapter and a corporate result sits the State Council’s implementing notice. In March 2026, the State Council instructed ministries to issue sector plans against the Outline and provincial governments to issue local execution plans. Sector plans become provincial budgets, become procurement, become next year’s loan book at ICBC, become, eventually, the line in China Mobile’s results that says “computing services revenue +11.1%.” I cannot watch every step of the cascade. I can watch the company at the bottom.

The transmission line into my book is China Mobile, the largest of the three Mainland state-owned-enterprise (SOE) telcos and the cleanest example I have of how this works. (For the avoidance of doubt: I own all three; China Mobile is the primary AI Plus position, with China Telecom and China Unicom smaller weights for the same reason.) China Mobile spent RMB 150.9 billion on capex in 2025. It built up to 92.5 EFLOPS of intelligent computing capacity at FP16 (the standard half-precision benchmark for AI compute) and more than 1.5 million standard racks. Computing services revenue grew 11.1% to RMB 89.8 billion. AI services revenue grew 5.3% to RMB 90.8 billion. None of that says “AI Plus mandate” on the income statement; the Chinese state does not insist companies file in the language of state planning. It shows up in the next year’s results as bigger numbers in the sub-segment that does the work.

The cross-check sits next door. China Telecom last year reported industrial digitalisation revenue of RMB 147.3 billion, group capex of RMB 80.4 billion, and 91 EFLOPS of intelligent computing capacity. China Unicom’s 2025 results show the same shape at smaller scale. Three SOEs, comparable direction, comparable cadence. The AI Plus build is industry-wide and Plan-driven, not idiosyncratic to any one name. When I see three SOEs doing the same thing at the same time, I assume someone at the State Council told all three of them to do it. That assumption is rarely wrong.

The demand side is where my auto and sensor exposure earns its place. I covered the supply side in 50,000 Factories: What China’s AI Mandate Means for My Portfolio when MIIT’s January action plan landed. The demand side is the same wave hitting different income statements. BYD shipped over 4.6 million New Energy Vehicles (NEVs) in 2025. Geely crossed CNY 345.2 billion in revenue. Hesai tripled its LiDAR shipments year-on-year on RMB 3 billion of revenue. None of those companies sells to “new productive forces” as a line item. All of them sell into the demand wave the FYP frames at the chapter level. That is the difference between a slogan and a screen.

The Beautiful China indicators and a power generator nobody talks about

The Outline carries a table called Box 1. It lists the Plan’s quantitative majors over the next five years and tags each one as binding or expectative. The bound ones are the numbers a provincial cadre will be measured against at term end. The expectative ones are directional aspirations the system will work towards but will not punish on. The ecological cluster is the bit I take most seriously, and the targets are explicit, sourced, and date-stamped: PM2.5 below 27 micrograms per cubic metre by 2030, and non-fossil energy at 25% of primary energy consumption by 2030. For a utility, that is not mood music. It is the policy mechanism above the income statement: permits, dispatch priority, grid connection, local approvals, and the career incentives of the officials who sign them. The longer-horizon directional commitments, peak carbon before 2030 and carbon neutrality by 2060, sit in the 2035 Long-Range Objectives, which is a different document.

If you have not heard of Beijing Jingneng Clean Energy, that is fine. It does not market itself outside Hong Kong investor presentations and does not need to. It is a piece of Beijing’s municipal-SOE clean-power plumbing that happens to be listed. At end-2025 it held 18,365 megawatts of consolidated installed capacity. Renewables were more than 72% of it. It generated 42.45 billion kilowatt-hours over the year, of which wind contributed 15.97 billion, gas 19.02 billion, and PV 6.49 billion. The chapter says the share of non-fossil in primary energy has to reach 25% nationally. Beijing Jingneng moves the share inside its own book in the same direction, faster, and files its disclosures into HKEX where I can read them. The Plan does not name Beijing Jingneng. It does not need to.

People-centred urbanisation and the household balance sheet

The Outline’s Chapter 31 is titled “In-Depth Advancement of People-Centred New Urbanisation.” It is one of the more consequential chapters and one of the easier to misread. People-centred urbanisation is not a return to the property boom. It is the explicit policy frame for moving people from villages into county towns, from county towns into mid-tier cities, with formal hukou (the household-registration system that gates access to schools, healthcare, and other public services in a given city) access, formal credit access, and formal social services. It is the chapter that tells you the next five years of Chinese domestic demand are going to come from a different pool of households than the last five. Four names in my book sit on this line.

China State Construction International (CSCI) is the build side. Its order book is heavily Modular Integrated Construction (MiC) work, the urban-renewal modality the Outline names directly. MiC is one of the cleanest examples of new productive forces inside construction: industrialised, factory-built, technology-upgraded. The business is built for the post-property version of construction. That is why it is in the book.

Two bank franchises earn at opposite ends of the same migration. Agricultural Bank of China (ABC) banks it from the bottom: the largest county-level branch network of any Chinese bank, founded on the “Three Rurals” mandate (agriculture, rural areas, rural households), already on file with the village and county households the Outline says will be moving up the urbanisation ladder over the next five years. The county economy is where hukou reform, infrastructure spending, and township-enterprise upgrades land first; ABC banks that economy as a matter of original mandate. China Merchants Bank (CMB) earns at the other end. It is the leading retail and wealth-management franchise in Mainland China by client assets under management and by private-banking penetration, structured to capture the wealth-management fees, retail credit spread, and asset-management margin as the middle and upper-middle class that the Outline’s domestic-demand chapters describe continues to grow. The Plan does not mention either bank. The Plan describes, chapter by chapter, the household whose financial life each of them is already structured to serve.

One leg would not earn the size; two legs do. One leg is the ageing chapter (Chapter 40), where Ping An’s life and health insurance franchises sit directly on the structural tailwind: 7.6% operating profit growth in 2025 and resilient new business value in life are what the demographic line is supposed to look like inside an income statement, long before it shows up in the obituary pages. The other leg is the wealthier-household thread that lifts CMB: insurance, retirement, and life-stage savings products for a richer customer. Ping An is the only name in this section that earns on both legs of the same chapter cluster.

None of these four is a moonshot. All four are FYP-aligned. The urbanisation chapter does not promise any of them a return. It tells me they are running into the wind the state has decided to keep blowing in their favour for the next five years.

RMB internationalisation, the Belt and Road, and the part where boring banks earn their keep

Part Seven is mostly about external opening. Chapter 21 on autonomous opening-up. Chapter 23 on the high-quality co-building of the Belt and Road. Chapter 24 on the shared-future framework. This is the chapter cluster that sounds the most like a slogan inventory and the least like a procurement order, which is precisely why most foreign investors give it the lightest reading and miss what it does in practice.

I own Bank of China (BoC) for it. BoC’s 2025 results show continued growth in cross-border RMB settlement and a higher share of operating income from overseas, which is the literal income-statement signature of Chapter 21. I also own China Merchants Port (CMP). Port assets along the Belt and Road Initiative (BRI) corridors are the listed surface of Chapter 23, and CMP’s 2025 overseas terminal throughput and core-port profitability are what a multi-year corridor build is supposed to put on an income statement. Neither name will ever be a multi-bagger. Both have already done what they are meant to do, which is keep delivering operating cashflow while a chapter of the FYP says, on government letterhead, that what they do is to be encouraged. Names such as these fit my Income book.

If “RMB settlement bank with HK depth” and “overseas port operator with corridor exposure” are not the most exciting sentences in this article, that is the point. Boring is what I want at the policy-aligned end of my income book. Excitement lives in the AI line.

Where the FYP got it right and the stocks still lost money

The FYP being decision-useful is not the same as the FYP being a return engine. Two cautions.

Plan-right does not equal stock-right at the sector level. The 14th FYP correctly diagnosed that the property cycle had to deleverage. It said so in writing, and the system acted on it. The Hang Seng Mainland Properties Index then fell from a peak near 4,000 in early 2021 to below 1,000 by 2024. The macro thesis was right. The equity outcome was a 75% drawdown. If you had bought sector exposure on the official narrative alone, you would have lost three quarters of your money being correct. FYP-aligned is the screen. It is not the position.

Geopolitics overrides the FYP at the firm level. The US Department of Commerce added Yangtze Memory Technologies Co. (YMTC, China’s leading memory-chip maker and a 14th FYP self-reliance flagship) to its Entity List in December 2022, and tooling-export controls have continued through 2024. No FYP can write its way around an export-control regime in another sovereign’s jurisdiction. If you cannot live with that risk, do not own these companies. I can, but I size these exposures as options on the self-reliance thesis, not core income positions.

I write my one-line rule with these caveats inside it, not after it. The rule is “FYP-aligned and worth what I am paying,” not “FYP-aligned.” The first half gets the company on the screen. The second half is the rest of the work, industry by industry, company by company.

The screen at work, and the test that comes next

Slogans tell you what the state wants you to think. Manuals tell you what the state will do. I read the 15th FYP as a manual because that is what owns the next five years of my book.

The names in this article are not a recommendation list. They are the screen at work. The screen keeps me out of the wrong fights. It does not win the right ones for me. My actual book is several dozen Mainland and Hong Kong listings deep, and the screen runs across all of them, every quarter, against every important disclosure HKEX puts out. Names that do not sit in the chapters do not make the book, even when they screen cheap on backward-looking metrics. The Mainland property names that screened cheapest on book value through 2022 and 2023 were precisely the ones the 14th FYP had already de-rated; the screen kept me out, and the 75% drawdown happened to other people.

The first real test of the screen lands over late 2026 and 2027, when State Council sector plans and provincial execution plans start translating Outline language into procurement, licensing, and capital allocation, and when SOE annual results begin echoing the 15th FYP’s wording the way China Mobile’s already echo the 14th. That is what I will be reading for over the next five years. The bet is simple: the manual gets executed by people whose jobs depend on it.

As of the date of publication, I hold positions in Agricultural Bank of China (HKEX: 1288), Bank of China (HKEX: 3988), Beijing Jingneng Clean Energy (HKEX: 579), BYD Company (HKEX: 1211), China Merchants Bank (HKEX: 3968), China Merchants Port (HKEX: 144), China Mobile (HKEX: 941), China State Construction International (HKEX: 3311), China Telecom (HKEX: 728), China Unicom (HKEX: 762), Geely Automobile Holdings (HKEX: 175), Hesai Group (NASDAQ: HSAI), and Ping An Insurance Group (HKEX: 2318). Positions may change after publication without notice. Cohong Lane is a periodical publication made generally available to the public; this is disclosure of my positions, not a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.

I went to Chongqing in November to understand a city where three of my positions converge. I wrote about that trip in City Mechanics: What Chongqing Taught Me About My Portfolio. After it published, several readers wrote in about one detail I had folded into a single paragraph: the freight train from Chongqing to Budapest, opened the week I was there.

This piece is the answer. It is also, honestly, an example of the kind of detail I like to understand about a business before I invest. Not trains specifically, but how a company actually operates. That kind of understanding is what lets me tell fact from fiction, and hold through volatility. I own BYD. BYD’s first European assembly plant — Szeged, Hungary, EUR 4 billion, 300,000 vehicles a year at full scale — sits 160 kilometres from where those trains stop. The corridor opened on 30 November 2025: biweekly, eleven days, fixed timetable. Six months old.

When I formed my view on BYD’s European build-out, this line did not exist. There is now a logistics input sitting next to labour, tariffs, and local content that was not there before.

The Budapest Line

Chongqing has been running freight trains to Europe since 2011 — the inaugural China-Europe Railway Express train left Tuanjie Village for Duisburg on 19 March that year. Most of those trains run on demand, with variable transit times. The Duisburg route went onto a fully fixed bilateral timetable some years back, China’s first. Budapest, opened on 30 November 2025, is the second. Biweekly, eleven days, run by Yuxinou (Chongqing) Logistics. Chongqing rail officials frame the line as a gateway to Slovakia, Austria, and Serbia, not only Hungary.

A reality check before going further. China shipped 108 TEUs to Hungary by rail in all of 2025. Not 108,000. 108. The corridor exists on paper and on a biweekly timetable; the volume does not yet exist at all. What follows is the work I did to figure out whether the corridor matters. If the volume gap closes, it adds a logistics input I had missed. If it does not, my view on BYD before discovering the train still holds.

Why Budapest specifically. BYD’s EUR 4 billion Szeged plant is in trial production since January 2026, with mass production targeted for Q2 2026. CATL’s 40 GWh Debrecen gigafactory sits 250 kilometres from the rail terminal, with cells already shipping. On the Chinese side, Chongqing’s automotive parts cluster — over 1,000 suppliers, with BYD’s own Fudi Blade Battery factory at its centre — is the natural first-tier supplier base for Szeged. I walked the Chongqing supplier cluster in November. The geometry is unusually clean: one cluster, one plant, one fixed-timetable corridor between them.

As one enterprise representative told state media outlet Xinhua:

“Goods destined for Central and Eastern Europe previously required multiple transshipments or sea freight, with large lead-time variability. Now with fixed-schedule trains, we can even plan production schedules and inventory management by the timetable — supply chain resilience is greatly enhanced.”

Eleven days versus thirty-plus by sea. For just-in-time automotive supply chains, where carrying-cost savings on inventory partially offset the freight premium, the lead-time gap is what makes the corridor commercially relevant. Not the price. The timetable.

What It Costs

I built out the door-to-door freight economics myself, using current forwarder quotes and the Drewry container index. The short version: rail is expensive. The longer version follows. Rail Chongqing-Budapest runs roughly US$9,600 per forty-foot container, end to end. Sea, routed via the Yangtze to a Northern European port and trucked inland to Hungary, comes in at roughly US$6,425. Rail is around 49% above sea on a like-for-like basis. These are post-subsidy quotes — central per-container support for China-Europe rail wound down through 2023, and what’s left in Chongqing is land, tax, and infrastructure support to operators, not cash per container.

The premium does not vanish on a spreadsheet, but it does not fully reverse there either. Three weeks of vehicle parts sitting on water is working capital tied up; carrying-cost arithmetic recovers something like a tenth of the rail premium, not all of it. The rest is paid for something else: corridor geometry. A fixed-timetable train between one supplier cluster and one assembly plant lets a 300,000-vehicle plant schedule production against the corridor, not the swell on the Indian Ocean. Whether that optionality is worth a forty-plus-percent net premium is the live question, and the answer probably depends on which parts. Heavy, low-value-density components keep going by sea. Time-sensitive battery and powertrain content moves by rail. That is the split I would expect to see, and the one that will show up in volume data over the next eighteen months if the corridor is going to matter.

What I’m Forecasting

Biweekly is not a supply chain. It is a gesture.

If you are scheduling production for a plant targeting 300,000 vehicles a year, two trains a month does not feed an assembly line. The Chongqing-Duisburg corridor took three years and a month from inaugural train to weekly cadence — March 2011 to April 2014. Budapest opened in November 2025. My base case is weekly cadence somewhere in 2028 to 2030, once Szeged is past trial production and the corridor is pulling volume from Chengdu and Chongqing together.

The math is bottom-up. Take Szeged at half nameplate — 150,000 vehicles — as a deliberately conservative short-term steady state. Per vehicle, roughly 600 kg of China-sourced battery and powertrain content: about 450 kg for a Blade Battery pack, 80 kg for an e-axle, 70 kg for motors and ancillaries. Across 150,000 vehicles, divided by about 13 tonnes per dense-cargo TEU, plus a 20% packaging and consumables buffer. That gets to roughly 8,300 TEUs a year of inbound Chinese content into Szeged.

The split by Chinese origin is the part I am least sure of. BYD has not published a supplier-by-origin breakdown. My working assumption is that around 35% of Szeged’s inbound Chinese content originates in Chongqing — the Fudi Blade Battery factory, the adjacent powertrain capacity, and the broader 1,000-supplier cluster across Chongqing and Chengdu make Chongqing the obvious anchor. That puts roughly 2,900 TEUs from Chongqing alone at the 150,000-vehicle steady state. The exact share is illustrative; the load-bearing claim is that Chongqing-rooted suppliers carry the bulk. Drop the share to 15% and Chongqing still carries roughly 1,250 TEUs at steady state — an order of magnitude above all of Hungary in 2025; push it to 55% and you get 4,600. The load-bearing claim survives at either end.

All of Hungary took 108 forty-foot containers by rail from China in 2025. At the BYD plant at Szeged steady state, Chongqing alone could send around 2,900 a year — roughly twenty-five times the entire 2025 baseline, from one inland Chinese city. The corridor is six months old. The plant is not yet in mass production. That is the shape worth watching.

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What Would Prove Me Wrong

Two ways the corridor stops mattering.

The 35% Chongqing-origin share assumes Szeged sources predominantly from Chongqing-rooted suppliers. If the Chinese content actually flows from Shenzhen, Xi’an, or BYD’s other hubs, the rail link is largely irrelevant to how I read BYD’s European build-out — relevant to someone else’s read, not mine. A supplier-by-origin disclosure from BYD, or freight-manifest sampling at the Budapest terminal, would close it.

The other way is faster than I expect. EU local-content rules and the Brussels push for European battery and component sourcing could pull Szeged’s parts demand into Hungary, Poland, and Czechia faster than the corridor scales. In that scenario the 49% rail premium loses its carrying-cost offset, because the comparison stops being rail-from-Chongqing versus sea-from-Chongqing and becomes rail-from-Chongqing versus a 400-kilometre truck from a Polish supplier. The logistics input I added washes out. The view I had on BYD’s European build-out before discovering the train still holds.

What This Changes for BYD

Not the thesis. I did not buy BYD because of trains, and one biweekly freight line does not move the underwriting on a company I have written about at length elsewhere.

What it changes is one input. How BYD’s European build-out actually runs now includes a Chinese rail-logistics line that did not exist before — small today, potentially material at Szeged steady state, contingent on the volume actually showing up. That is a thing to watch, not a thing to act on.

What it changes more durably is how I read China’s manufacturing reach into Europe. A fixed-timetable corridor between an inland Chinese supplier cluster and a Central European assembly plant is what the policy documents have been describing for years. Now there is a train. Whether it carries 108 containers a year or 8,000, whether the volume comes from BYD or someone else, the geometry has shifted. China is no longer manufacturing at the coast and shipping out. It is manufacturing inland and railing in.

This is the kind of work I left institutional finance to do, and the reason I publish what I learn. The longer story: Why Cohong Lane? A CFO’s Bet on Independent Investing.

As of the date of publication, I hold positions in BYD Company (HKEX: 1211) and Contemporary Amperex Technology (HKEX: 3750). Positions may change after publication without notice. Cohong Lane is a periodical publication made generally available to the public; this is disclosure of my positions, not a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.

I went to Chongqing to stress-test 3 China EV positions I already hold: BYD, Geely, and XPeng. I’ve written separately about what that trip taught me about my portfolio — but the question that kept forcing itself into my notebook wasn’t about any of them. It was about Huawei. More precisely: how Huawei gets paid by an industry whose private P&L will never tell you. The answer is on file in Hong Kong — not in Huawei’s accounts, but in the procurement ledger of the assembler that builds the cars Huawei sells.

I keep seeing what people call “Huawei cars” on the street — whether I am in Chongqing, Guangzhou, or Shenzhen. More precisely: AITO vehicles displayed and sold inside Huawei’s own retail footprint. In Chongqing I went into one of those stores. The staff lead with HarmonyOS, ADS, and the cockpit experience. But Huawei doesn’t build a single car. SERES — the Chongqing-based assembler — does.

SERES has now reported its FY2025 audited results, filed on HKEX on 30 March 2026: revenue RMB 164.9 billion (~US$22.7 billion), up 13.6% year-on-year; attributable net profit RMB 5.96 billion (~US$0.8 billion), essentially flat. Overall gross margin expanded approximately 3 percentage points to 26.88%. Top line growing, gross margin expanding, attributable profit flat. Standing in that Chongqing store, the question I couldn’t shake was simpler: if the assembler is generating real gross profit on a far larger revenue base, where is the operating leverage going at the net line? The number, in SERES’s procurement lines, is larger than I expected.

What follows is my attempt to answer that with primary filings and careful inference. Huawei is private, so I cannot prove its unit margin from any primary source — the case I can build is one of dependency rather than profit extraction, and dependency is the more important variable. This is how Huawei captures EV economics without building a car, and what it means for the Chinese manufacturers I hold.

What does a “Huawei car” look like on the ground?

On the ground, a “Huawei car” is an AITO vehicle displayed and sold inside Huawei’s own retail footprint, pitched entirely around HarmonyOS, ADS, and the cockpit experience. The assembler — SERES — is never mentioned. The brand on every wall is Huawei. The product being sold is the cabin, not the car.

The Huawei store in Raffles City Chongqing sits on an upper floor overlooking the confluence of the Jialing and Yangtze rivers — premium real estate, premium context. It’s set up less like a showroom and more like a personalised tech consultation. A staff member walked me through the car as if the vehicle itself were almost a formality: HarmonyOS, the ADS stack, the cockpit display, the seat experience, the audio environment. Horsepower was never mentioned. Neither was the drivetrain. Neither, notably, was SERES — the company that actually built the car sitting on the floor. The brand on every wall was Huawei. The pitch was the experience. The car was hardware.

That observation is not proof of anything on its own. But it calibrates how seriously to take what the industry calls the “third living space” — the idea, now mainstream in Chinese premium EV marketing, that the cabin itself is the product: screens, audio, lighting, near-flat bed seats with heating, massage and cooling, voice and gesture controls, and the ambient sense that the car is woven into your daily life. The drivetrain is assumed. The shell is almost incidental.

That framing tells you who holds the structural advantage. Huawei already wins in two living spaces — the home and the pocket. The car is the next one.

The open strategic question — whether HarmonyOS becomes the premium default in China the way CarPlay and Android Auto became defaults in the West — runs underneath everything that follows.

How does Huawei get paid without building a car?

Through procurement, not profit-sharing. In 2022, SERES purchased RMB 5.8 billion (~US$0.8 billion) from Huawei. In 2023, RMB 7.2 billion (~US$1.0 billion). In 2024, RMB 42 billion (~US$5.8 billion) — 30.2% of total purchases. In FY2025, RMB 56.1 billion (~US$7.7 billion) — 33.78% of total purchases, a new high. The FY2025 annual report names the largest supplier only as “IT, communications, and hardware equipment”; Chinese financial media identify it as Huawei. Cumulative procurement from 2022 through FY2025: approximately RMB 131 billion.

The SERES IPO prospectus is explicit:

“Our collaborations with Huawei do not involve any arrangements regarding profit-sharing, which are consistent with industry norm according to the Frost & Sullivan Report.”

The FY2025 annual report does not amend that arrangement. “No profit-sharing” does not mean “no economic transfer”. It means the transfer happens through ordinary operating lines — procurement and, likely, the retail channel. RMB 56 billion flowing to a single supplier tells me exactly who controls SERES’s cost stack — and it isn’t SERES.

To understand what that number actually means, it helps to put it in context. Apple’s dependence on Foxconn at its peak — the period that prompted Apple to spend a decade deliberately diversifying toward Pegatron, Luxshare, and Indian assembly — ran at roughly 25–30% of COGS. SERES is already at 34%, and rising. The difference is that Apple owned the brand and the customer relationship. In the Huawei store in Raffles City, it was impossible to tell who did.

The strongest counter-argument to this framing is that procurement is not profit. RMB 56 billion is top-line revenue for Huawei's automotive business, not a margin number, and I cannot prove Huawei's unit economics on those components and services from any primary filing. Huawei could in principle sell at or near cost, treating SERES as a platform anchor rather than a profit centre. I doubt it: Huawei's 2024 annual report disclosed that its intelligent automotive solutions unit turned a profit for the first time, and software-and-cockpit revenue carries software-margin economics, not commodity-component economics. What I can prove is the dependency, and dependency is the more important variable. Whether Huawei is currently extracting profit or simply locking in the platform position, the result for SERES is the same: a third of every yuan it spends on inputs goes to one supplier, and that share is climbing.

SERES’s selling expenses ran RMB 24.2 billion (~US$3.3 billion) in FY2025 — 14.7% of revenue, up from 13.2% in 2024. Consistent with what you see on the ground: premium stores, premium service, and a sales story led by the platform brand, not the assembler. SERES still posted a 3.6% attributable net margin in FY2025 (4.1% in 2024) — proof the assembler can be profitable in the Huawei era. But profitable with a cost structure where Huawei is the largest single supplier, where the share of inputs going to that supplier rose by another 4 percentage points in the most recent year, and where revenue grew 13.6% while attributable profit did not move at all.

What happens when Huawei replicates the platform?

Huawei is already doing it, and the cumulative volume across the Harmony Intelligent Mobility Alliance (HIMA) — Huawei’s smart car joint-venture framework through which it supplies ADS, HarmonyOS, and hardware to OEM partners without taking an assembly stake — settles the question. HIMA delivered 112,700 vehicles in Q1 2026 (+41.9% YoY), bringing cumulative deliveries to approximately 1.35 million units by March 2026. The AITO M9 alone has now passed 280,000 cumulative deliveries and outsold BMW X5, X7, Mercedes GLE and GLS in China on a cumulative basis. The AITO M6 took 60,000 pre-orders in the first 24 hours of pre-sale on 23 March 2026. The platform isn’t trying to scale. It has scaled.

The share split tells the same story. Five brands now sell across 4 assemblers: AITO at SERES, LUXEED at Chery, STELATO at BAIC, MAEXTRO at JAC, SHANGJIE at SAIC. AITO’s share of HIMA fell from above 85% in 2024 to roughly 63% by November 2025 as new brands joined; it has since recovered to roughly 69–76% in Q1 2026 as the non-AITO ramp ran slower than expected.

The structural implication is not “Huawei wins”. It’s that platform scale changes OEM bargaining power over time. SERES can be profitable and still become more dependent — and those two things are not in contradiction.

SERES has responded on 3 fronts — none of them the moves of a comfortable partner. They are the moves of a company managing a dependency it knows is structural.

Trademark reclaim. In July 2024, SERES paid RMB 2.5 billion (~US$0.3 billion) to buy 919 AITO trademarks back from Huawei. A company paying to own the customer identity it built in partnership tells you something unambiguous about who the brand equity originally belonged to.

Technology stake. SERES invested RMB 11.5 billion (~US$1.6 billion) for what was originally a 10% stake in Huawei’s Yinwang autonomous driving unit, now diluted to 9.36% as Huawei admitted other partners. The FY2025 annual report recognised approximately RMB 170 million of equity-method income from this stake — accretive at the income line, but a 1.5% return on capital implies the investment is a strategic lock-in, not a financial one. Changan’s AVATR made the same move independently at the same percentage. This is becoming standard practice for HIMA partners who want a seat at the technology table rather than just a supply agreement.

Capital raise. SERES listed in Hong Kong on 5 November 2025, raising approximately US$1.7 billion in fresh capital.

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The platform toll and my portfolio

The thesis is simple: Huawei has inserted itself into the value chain, the customer relationship, and the standard-setting layer of Chinese premium EVs — without taking assembly risk.

I didn’t go to Chongqing to find a new stock. I went to stress-test positions I already hold — and Huawei is now inside that underwriting.

The risk isn’t that BYD, Geely, or XPeng can’t build EVs. It’s that Huawei is redefining what Chinese consumers are paying for in the premium segment. Not the drivetrain. The cabin.

Two mechanisms are at work, and they break differently. For HIMA partners — SERES, Chery, BAIC, JAC, SAIC — the toll is procurement, visible at SERES’s 33.78%. For the three I hold — BYD, Geely, XPeng — the risk is competitive displacement: if HarmonyOS becomes the premium default, their in-house cabin stacks become an expense rather than a moat.

BYD has the scale to respond — but only if Denza and YangWang can match the Huawei experience baseline, not just approach it. Geely’s Zeekr is betting in the opposite direction: building its own independent cabin stack (ZEEKR AI OS, Qualcomm Snapdragon 8295) rather than paying the Huawei platform toll. The risk isn’t dependency — it’s irrelevance. If Chinese consumers decide HarmonyOS is the definitive premium standard, Zeekr’s in-house software investment becomes a sunk cost. XPeng built its own stack as a moat; that same stack becomes an expense if HIMA reaches parity fast enough.

I’ll be direct about the uncertainty here: I don’t know which of these resolves first, or how fast. But these are the 3 data points that would change my view:

1. The experience gap. The market is already bifurcating along integration lines. My base case is that the gap widens through 2026 as HIMA launches new models at pace. The break condition for my OEM positions: BYD (Denza or YangWang), Geely (Zeekr), or XPeng demonstrably matching HIMA cockpit benchmarks in independent comparisons by H2 2026. Until then, Huawei is setting the floor.

2. HIMA’s spread. AITO fell from 85%+ of HIMA volume to roughly 63% in November 2025, then recovered to 69–76% in Q1 2026. If it falls below 50% on a sustained basis, Huawei no longer needs SERES to anchor the platform — and SERES loses whatever negotiating leverage it currently holds. If it stabilises above 60%, the relationship is maturing into something bilateral enough to be manageable. The current data point sits in the second camp, but Q1 monthly figures swing widely.

3. The procurement mirror. 33.78% and rising at SERES. As of late April 2026, none of the FY2025 annual reports filed by Changan, BAIC BluePark, JAC, or SAIC show Huawei or Yinwang as a top-five supplier or named related-party transaction at the parent-group level — most likely because the brand-level volumes are still too small to register in parent-group COGS. AVATR has filed for a Hong Kong IPO at a reported US$4.6 billion valuation; when it lists, its prospectus and first annual filing will be the next clean read. Three HIMA partners showing 25%+ procurement dependency would be the clearest possible signal that Huawei has restructured the cost stack of the Chinese premium segment — invisibly, without building a single car.

I hold my positions. But I’m now measuring each of them against one question: can they compete on the thing Huawei is actually selling? SERES’s procurement lines already show what the platform toll costs. The next 18 months will tell me which other OEMs have been paying it all along.

Cohong Lane is where I publish the work I do on the China positions I actually hold — written from Hong Kong, sourced from HKEX filings, calibrated against what I see in the cities where these companies operate. The next piece from the Chongqing trip is I Own BYD. Chongqing’s 11-Day Train Changed My Math. — on the rail corridor to BYD’s Szeged plant that I had not modelled before visiting.

As of the date of publication, I hold positions in BYD (HKEX: 1211), Geely Automobile (HKEX: 0175), and XPeng (HKEX: 9868). Positions may change after publication without notice. Cohong Lane is a periodical publication made generally available to the public; this is disclosure of my positions, not a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.

In a car from Chongqing East station to the hotel, I spotted a vehicle I had never seen before. Low, sculpted, with lines that belonged in a concept video rather than on a road. The badge read AVATR — a joint venture between Changan Automobile, Chongqing’s anchor state-owned automaker, CATL, and Huawei. Three of China’s most consequential industrial players, converging in a single vehicle, designed and assembled in a city roughly 2,000 kilometres from the nearest major seaport.

I hold BYD, CATL, and China Mobile. All three have operations here: BYD’s Blade Battery plant, CATL’s first “factory-in-factory” production line, one of China Mobile’s 8 national data centre hub nodes. The mountain topography over the Yangtze is genuinely dramatic, but I did not come for the scenery. I came to work out what that overlap in my own book actually meant.

What Role Does Chongqing Play in China’s Economic Plan?

My working view after the trip: Chongqing’s logistics stack is a potential structural edge for BYD’s and CATL’s European supply chains that was not in my model. The rest of this piece is the case, and the one signal that would falsify it.

Chongqing is one of only 4 direct-administered municipalities (直辖市) in China — alongside Beijing, Shanghai, and Tianjin. That status means it retains a larger share of tax revenues, issues its own municipal bonds, and competes directly for central government capital without provincial intermediation. When Beijing allocates RMB 800 billion (~US$110 billion) in ultra-long special treasury bonds for infrastructure in 2026, Chongqing sits at the same table as Shanghai.

“Boost the development capacity of the Chengdu-Chongqing economic zone.” — CPC Central Committee 15th Five-Year Plan Recommendations, Section 29 (October 2025)

The policy stack underneath is what matters: direct municipality status, the Liangjiang New Area, a Pilot Free Trade Zone handling roughly 70% of the municipality’s foreign trade across just 0.1% of its land, and the China-Singapore Demonstration Initiative — a government-to-government project giving Chongqing privileged access to Singapore’s capital networks and ASEAN markets. No peer inland city has this many stacked in one place. Not Chengdu, not Wuhan, not Xi’an.

The Numbers That Matter — and Where the City Is Fragile

Chongqing’s 2025 GDP reached RMB 3.376 trillion (~US$485 billion), growing 5.3% against a 5.0% national average — enough, for the first time, to overtake Liaoning, the old industrial heart of the northeast. The city missed its own 6% target by 0.7 percentage points, and has lowered the 2026 target to “above 5%”. The automotive industry kept doing most of the lifting: smart-connected NEV value-added rose 13.4% on the year, and the city’s NEV output has stepped up from roughly 43,000 in 2020 to 1.296 million in 2025, reclaiming the title of China’s top auto-producing city.

Three numbers cut against the headline.

First, the NEV growth rate has already halved. The 90.5% print in 2024 dropped to 36% by 2025, and I expect Chongqing will converge toward the national rate within 2 to 3 years.

Second, the concentration is brutal. The automotive industry alone contributed two-thirds of Chongqing’s entire industrial expansion in 2024. Two-thirds. From one sector.

Third, the population is shrinking. A net loss of 92,000 people in 2024, against an economy that still grew 5.7%. If your China model starts with demographics, Chongqing is the city that breaks it — the growth is coming from productivity and capital upgrading, not headcount, and that is a harder trick to repeat than a demographic tailwind.

Taken together: if automotive growth decelerates to 10–15% before the named replacement sectors — electronics, advanced materials, AI, biomedicine — reach scale, Chongqing faces a growth gap. Not a crisis for the city, but a reason to read every subsequent Chongqing data release more carefully than I used to.

Is the Advantage Structural, or Just Subsidies?

Every major Chinese city offers subsidies, land deals, and tax breaks to attract manufacturers. By CSIS’s Red Ink estimate (Scott Kennedy, 2024), China’s EV industry received roughly US$230 billion in state support from 2009 to 2023, with local governments accounting for about 80% of industrial policies. BYD’s largest megafactory is in Zhengzhou, not Chongqing. CATL’s main cell production hubs are in eastern China.

The answer is logistics — the kind that cannot be replicated by writing a bigger cheque.

Chongqing is the only city in China to hold all five categories of national logistics hub designation simultaneously — port, land-port, airport, production-service, and trade-service. A manufacturer here can route goods east down the Yangtze via Guoyuan Port, west to Europe by China-Europe Railway Express, south to ASEAN via the New International Land-Sea Trade Corridor, or by air through Jiangbei Airport. Four export corridors from a single inland hub. Xi’an moves more CRE rail volume, but Xi’an does not have the Yangtze. Chengdu shares the western rail corridor, but Chengdu does not have the river port. No peer city replicates the combination.

The detail that made me sit up: while I was in Chongqing last November, Xinhua’s Chongqing wire reported (30 November 2025) that China had launched its second fixed-schedule freight rail service to Europe — from Chongqing to Budapest, Hungary, running biweekly in approximately 11 days. The first route, to Duisburg in Germany, has been running since 2011. Why does Budapest matter? Because BYD is building a €4 billion plant in Szeged, Hungary — 160 kilometres from Budapest — its first European passenger-car factory, targeting 300,000 EVs a year at full capacity, with pilot production in Q1 2026. CATL’s Debrecen gigafactory — a €7.34 billion plant, initial capacity 40 GWh with cell production from early 2026 and designed for 100 GWh at full build-out — is 250 kilometres away. When Chongqing’s 1,000-plus automotive parts suppliers need to ship components to those European assembly lines, the timetable now exists. 11 days by rail versus 30-plus by sea makes rail competitive for just-in-time supply chains. For the first time, an inland Chinese city has a direct, timetabled logistics link to the Central European EV cluster.

Ground Truth — What You Won’t Read Elsewhere

To understand Chongqing, you have to leave it.

One morning we drove 90 minutes southwest to the Dazu Rock Carvings. The carvings are extraordinary — a UNESCO site, 50,000 sculptures carved into cliff faces since the 7th century. But it was the drive that changed how I think about the data.

Within 40 minutes of leaving the urban core you are in a different economy. The road narrows. The buildings are older, lower, unfinished. This is the other Chongqing: 82,400 square kilometres of mountain terrain where the main urban district produces 78% of GDP. The per capita income gap between urban residents (RMB 49,778, ~US$6,800) and rural residents (RMB 22,221, ~US$3,000) is more than 2-to-1. An investor assuming the headline applies evenly is making an error I nearly made myself.

Restaurants were packed — not the performative busy of a new development, but the organic busy of a consumer economy actually spending. Wholesale and retail grew 9.5% in 2024. Accommodation and catering grew 7.3%. You feel these numbers in the queue for a hotpot table at 9pm on a Tuesday, not in a spreadsheet. (If you visit: skip the tourist hotpot and try 烤匠麻辣烤鱼. It is a Chengdu-born chain — and their sea bass, buried under a small mountain of dried chillies and green Sichuan peppercorns, is the best thing I ate in China in 2025. 不吃火锅，就吃烤匠.)

One evening walking through Chongqing’s malls is not statistically significant. I know that. But what on-the-ground observation gives you is not data — it is a filter for obvious nonsense in the data. It does not prove a bearish report wrong. But it tells me to ask better questions about which consumers, where.

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What Does This Mean for My Portfolio?

I came to Chongqing to understand a city. Two of my positions got sharper. One did not.

BYD operates a dedicated Blade Battery factory here through its Fudi subsidiary: 8 production lines, 20 GWh annual capacity, running at or near full utilisation since 2020. What I had not connected was the logistics. The Budapest rail link means BYD can ship battery components to Szeged in 11 days without routing through Shanghai. For a company building out its first European passenger-car production base targeting 300,000 EVs a year, that is a potential structural cost advantage — contingent on the Budapest schedule going weekly — that was not in my model. It is now.

CATL entered Chongqing in June 2025 with a factory-in-factory model: 2 battery pack lines physically inside the SERES Super Factory in Liangjiang, a 5-year exclusivity agreement for all AITO vehicles to use CATL batteries, planned annual output value of RMB 7 billion (~US$960 million). I knew the concept on paper; being in the city where it runs forced me to focus on what it means — switching costs that are physical and contractual, not just commercial. Beyond that, the Chongqing presence did not change my view of the position; I hold CATL on the global platform thesis, not on a single-site read-across.

China Mobile also runs a Chongqing node — one of 8 national “Eastern Data, Western Computing” hubs. Nothing on the ground changed my view of the position; I hold it on the telco thesis, not on a Chongqing read-across.

The Signal I Am Watching

In that first car ride from Chongqing East, I thought I had spotted an unfamiliar vehicle. What I had actually spotted was my own blind spot. The AVATR was the whole city in miniature: Changan, CATL, Huawei, capital, policy, manufacturing, all compressed into one thing moving down the road.

The question for me now is whether Chongqing's logistics stack can turn that industrial density into a durable edge for my BYD and CATL positions, rather than just an impressive story. The Budapest rail schedule is the test. If it goes weekly by end-2026, I will treat that first sighting as an early clue I was lucky enough to notice. If it does not, the car was still real, but the edge I thought I saw behind it was smaller than I believed. And the deeper question, which I unpack separately in Why Huawei Now Sits Inside My China EV Underwriting, is how much of that value is ultimately being captured not by the assembler, but by the platform layers sitting inside the same machine.

As of the date of publication, I hold positions in BYD (HKEX: 1211), CATL (HKEX: 3750), and China Mobile (HKEX: 0941). Positions may change after publication without notice. Cohong Lane is a periodical publication made generally available to the public; this is disclosure of my positions, not a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.

In April 2025, I was in Budapest, meant to be on holiday, and instead spent an indecent amount of time staring at falling prices on my screen. I felt the drawdown in the only way that matters: as real money, in my own account, disappearing by six figures while every instinct in my body suggested that doing something, anything, would feel better.

Budapest is a beautiful city, which is not especially helpful when tariffs hit. More important than the trades I did or did not make was the thing going on in my own head: I felt fear, I felt regret, and I felt the very human urge to convert uncertainty into certainty, even at a bad price.

The problem is that my instincts do not distinguish particularly well between actual danger and falling share prices. Falling share prices are not automatically danger. They are information, and the job is to work out what kind. The market was not telling me my thesis was wrong. It was telling me sentiment had turned.

I did not sell, though, and the reason was not that I am some sort of monk in a linen shirt who has transcended loss aversion. I am not. The reason was simpler and less glamorous: I knew what I owned, I knew why I owned it, and I knew what I thought it was worth.

What is volatility, and what is it not?

Howard Marks made the distinction plainly in “Risk Revisited Again“: what investors actually fear is not price movement but the possibility of permanent loss. Volatility became the academic proxy because it was quantifiable, but it was always the wrong measure.

I agree with Marks — but I want to take the distinction one step further, because in my own portfolio the useful question is not what volatility is but when it becomes dangerous. The answer, as far as I can tell, is: through only two mechanisms. Either my structure forces me to sell, or my psychology does. Permanent loss itself is narrower than most drawdowns suggest — fraud, obsolescence, terminal competitive decline, or regulatory destruction. Four ways a business becomes permanently worth less. If none of them is in play, the price is moving without the business moving, and moving prices are noise, however unpleasant.

The first mechanism is structural. This is where a lot of institutional pain comes from, and it is also why private capital with the right design has a real edge. If you are running leverage, a drawdown can become a margin call. If you are managing outside money, a drawdown can become a redemption. If you are judged quarterly on relative performance, a temporary decline can become a career problem long before it becomes an investment problem. Time horizon is not a decorative preference in investing. It is often the whole game.

That is why my own portfolio is built to eliminate forced selling. My living expenses are covered by income-generating assets. I do not use leverage. I do not manage external capital. That removes the first channel almost entirely.

The second mechanism is psychological, and this is the harder one because it follows you around. You can design away leverage. You cannot design away being human. If I sell simply because I cannot bear the discomfort of seeing prices down another 10%, even though my underlying thesis is intact, then I have voluntarily converted volatility into permanent loss. Nobody forced me. I did it to myself.

Part of what makes the psychological channel so difficult is that the fear feels rational. A falling price appears to carry information — the market is selling, so it must know something you do not. That instinct is not stupidity; it is pattern recognition misfiring. Price movement is not the same as informational superiority. When the market sells, it tells you about the constraints of the people selling — not about the business.

What actually constitutes permanent loss?

In my experience, most drawdowns have the same basic explanation: the market is selling off for reasons that are only loosely connected to the underlying business. I don’t buy stocks — I buy tiny pieces of what I believe to be great companies, at prices below my estimate of intrinsic value. When prices fall but the business hasn’t changed, that’s not a warning. That’s a price decline worth sitting through, not running from.

When a position I own is in a genuine drawdown, I run four questions before I touch anything. Has management integrity changed — because I have been wrong about management before, and it is usually the first thing I am too optimistic about. Has the industry structure worsened, not cyclically, but in a way that permanently favours someone else. Has regulation impaired the model — not inconvenienced it, impaired it. Has the balance sheet become a real problem, or am I just watching a line item worry me. If none of the four has shifted, I am almost certainly looking at volatility, not impairment.

Why does homework create holding power?

Deep research creates holding power because conviction anchored in business reality can survive a falling share price, whereas conviction anchored in price action cannot. If I understand the industry, the competitive position, and a sensible fair value range, then a drawdown changes my emotional state, not my estimate of intrinsic value.

This is the bit that gets romanticised in investing books and then brutalised by actual markets. People say “just hold through volatility” as if that was a personality trait. Like being tall. It is not.

Holding power is downstream of preparation.

If I have done the work properly, a 30% decline tells me the market’s mood has changed. It does not automatically tell me the business has deteriorated by 30%. Those are wildly different statements, but markets present them in the same font, which is unhelpful. The screen says the bid is lower. Fine. The screen does not tell me whether the bid is rational.

That, to me, is the real reason most investors cannot hold through a continued decline: they do not truly understand what they own. If your conviction is anchored in price action, a further decline dissolves it. If it is anchored in the business, the decline is just the price changing, which is a different thing entirely.

I should be honest about the obvious objection: the diagnostic is only as good as the analyst running it. If I have spent months building a thesis, I am not a neutral examiner of the evidence — I am a motivated one. The risk is not that I ignore the checklist but that confirmation bias shapes what I see when I run it. I do not have a risk committee to overrule me. What I have is the discipline to name, in advance, the specific evidence that would change my mind — and to write it down before the drawdown arrives, when my judgement is not yet compromised by the desire to be right. It is not a guarantee against self-deception, but it is the best structural defence I have found so far.

The time to do the work is in calm weather — deciding what I want to own, at what price, in what size. A GTC limit order is exactly that: a standing instruction placed before the storm that fires automatically when the price arrives, without requiring my emotional state at 3am to agree. When the sell-off arrives, the calm-weather version of you is already at the desk.

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Familiarity, Not Heroics

Budapest tested whether that preparation held. The episode mattered because I did not just endure it; I used it as deliberate psychological training. The useful skill in a sell-off is not emotional numbness but the ability to observe fear, regret, and the urge to act without automatically obeying them.

The Budapest drawdown felt awful. I did not enjoy it. I was not floating above it in enlightened detachment, whispering about intrinsic value while the minibar mocked me from across the room.

What I did differently was pay attention. Most investors respond to a sell-off in one of two ways. They either stop looking entirely, or they stare at the screen in a state of rising panic and let each downtick rewrite their beliefs. I tried to do neither.

I watched the losses accumulate. I named what I was feeling: fear, regret, frustration, the desire for relief. And then I separated those feelings from the actual state of the businesses I owned. That gap — between what I felt and what I knew — was the whole exercise. My feelings were screaming catastrophe. My research was saying, in effect, “the market is upset; the businesses are still the businesses.”

Budapest was not my first sell-off. The first time a position of mine dropped 30% I spent three weeks second-guessing every decision that led to the buy. By Budapest I spent a lot less time doing that. The gap is not willpower. It is evidence. My subconscious has seen enough of these chapters to know how they usually end.

I want to be honest about this: it is genuinely difficult. Anyone who tells you they feel nothing when watching large drawdowns accumulate on real money — not paper money, not a simulation, and with no salary arriving at the end of the month to soften the edges — is either exaggerating or has not yet been tested with enough capital at stake. The goal is not to feel nothing. The goal is to feel it, understand it, and choose not to act on it. That, to me, is the real edge of lived experience.

Not heroics. Familiarity. The first major drawdown feels like an emergency. By the fifth, it starts to feel more like weather.

Where I Paid the Admission Price Most Recently

China equities are where the admission price is highest — and where confusing it with risk is most expensive. Sentiment swings are amplified by policy headlines, geopolitical friction, and Western media narratives that default to catastrophe. If you conflate that volatility with risk, you will never hold through the drawdowns that create the best entry points.

In April 2026, when the Iran headlines hit and the Hang Seng sold off, I noticed the familiar sequence almost immediately. The screen made everything feel more urgent than the facts justified. The impulse was the same as in Budapest: do something, reduce the discomfort, convert uncertainty into action.

So I ran the checklist before I let myself touch the order ticket. Had anything changed about the businesses I was watching? Had the competitive position worsened? Had the policy structure shifted? The answers were no, no, and no. What had changed was sentiment.

That distinction matters because the psychological danger in a sell-off is not fear by itself. It is fear smuggling itself into the analysis and pretending to be new information. Once I could see that happening, the job was simple, if not pleasant: slow down, go back to the work, and act only if the gap between price and value had genuinely widened.

That was the setup with Kingdee. I had done the homework months earlier, when I traced how Beijing’s binding factory-automation KPIs were creating a mandate-backed upgrade cycle for enterprise software. The thesis was ready. The fair value range was set. When the Iran sell-off pushed the price into that range, I bought. Same with Hesai — the production wins were already in the work, the certification path was already in the work, and the question in front of me was not whether the headlines felt alarming but whether the business had changed. It had not.

The important part, though, is not that I bought them. It is that the decision did not come from adrenaline. It came from homework done in calm weather, checked again under pressure, and executed only after I had separated what I felt from what I knew. That is the admission price in practice.

If you’re new to Cohong Lane, that is the whole point of the publication: I invest my own capital from Hong Kong and publish the process, the pressure, and the mistakes in public so readers can inspect the reasoning, not just the conclusion.

I have sat through drawdowns that lasted quarters, not days — the 2022 China panic ground on for quarters while every Western headline declared the market uninvestable. I ran the same diagnostic then. I held. The framework held.

Volatility is the admission price. Risk is what happens if I have built a portfolio, or a mind, that cannot afford to pay it.

As of the date of publication, I hold positions in Hang Seng Tech ETF (Xetra: H4ZX), Kingdee International Software Group (HKEX: 0268), and Hesai Group (NASDAQ: HSAI). Positions may change after publication without notice. Cohong Lane is a periodical publication made generally available to the public; this is disclosure of my positions, not a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.

BYD ended 2025 by telling shareholders that China is no longer enough

I own BYD, and this piece is a check on whether the reason I own it still holds.

I did not buy BYD because I thought it would post a neat set of annual results in one particular year. I bought it because I thought it might become something much rarer: a globally localised, cycle-resilient, structurally profitable auto-industrial platform with the scale, staying power, and foreign industrial presence that make the Toyota comparison worth taking seriously. It is the same lens I use across the rest of my portfolio, which I laid out in How I Invest My Own Capital.

BYD sold 4.60 million new-energy vehicles in 2025, exported more than 1 million of them, and lifted revenue to just over RMB 804 billion. On the surface, that still looks like a company scaling.

Underneath it, the year tightened. Gross margin fell to 17.74 percent from 19.44 percent. Net profit fell 18.97 percent to RMB 32.6 billion. Operating cash flow dropped from RMB 133.5 billion to RMB 59.1 billion. Capex jumped to RMB 156.8 billion. Borrowings rose from RMB 28.6 billion to RMB 113.4 billion.

The Toyota comparison demands more than scale. It does not mean BYD gets huge and keeps shipping cars out of China. It means BYD becomes the sort of company that can build at scale across regions, make decent money through a cycle, localise production when politics harden, absorb tariffs and price wars without breaking the balance sheet, and keep compounding because the industrial system underneath it is real.

What management is saying is revealing

Management is clear on one point: the domestic market is brutal. BYD's 2025 annual report describes domestic competition as having entered a "fever pitch" and a brutal "knockout stage" — unusually direct language for a corporate filing. Beijing is signalling the same thing: regulators have moved to curb destructive price competition and below-cost selling in autos. That tells me the company knows the home market is now a stress test, not a comfortable source of compounding.

Second, overseas is not just a volume story. It is an economics story. Overseas revenue rose 40 percent to RMB 310.7 billion while China revenue fell 11.2 percent to RMB 493.2 billion. Overseas now accounts for roughly 38.7 percent of group revenue, up from roughly 28.6 percent a year earlier. Management also confirmed that overseas profitability is structurally stronger.

Those two facts together are enough to tell me the direction is right. The magnitude is the hypothesis I am watching.

I want to be careful with that.

Management commentary is not segment disclosure. BYD does not yet report overseas as a separate segment, which means I cannot verify the full economics directly from the filing. What I can say is that the revenue mix shift is real, the directional commentary is consistent, and the hypothesis makes industrial sense in markets where BYD is not fighting a domestic price war. The first clean test will be whether BYD breaks out an overseas segment in the 2026 interim results. If that happens, the hypothesis becomes a fact or a disappointment. Until then, it is the part of the thesis I watch most carefully.

If that profitability gap holds as the business scales and localises, overseas is not just the faster-growing segment. It becomes the part of BYD that could simultaneously be more profitable and more durable — insulated from domestic pricing dynamics by geography and brand positioning.

That leaves China as the base case: still dominant, still under pressure. And overseas as the part of the thesis that has to prove itself.

You could reasonably argue that BYD does not need to pass this test at all — that a company selling nearly five million vehicles a year with vertical integration and deep cost advantages can compound perfectly well as a domestic champion that exports on the side. That is a defensible case, and it may turn out to be the right one. But it is not the case I am underwriting. What I bought was the potential for something rarer: a company that can build durable economics in foreign markets, not just ship volume into them. If BYD turns out to be only a domestic champion, it will still be a good company. It will not be what I paid for.

Third, management is signalling that the build-out will take precedence over distributions. The proposed 2025 dividend was RMB 0.358 a share, down from RMB 3.974 in 2024, and management tied the cut to operating cash flow and future development needs. As a long-term shareholder, I think that is the right call. This phase has to be funded, not wished into existence.

What management is doing matters more than what it is saying

Global ambition is cheap. Industrial proof is not.

What I look for when I read a chairman’s letter is the gap between what management emphasises and what the numbers actually show. Wang Chuanfu’s language — “fever pitch”, “knockout stage” — is significant not because it is alarming, but because it is honest. It is a chairman telling shareholders that the home market no longer offers a comfortable base from which to fund overseas expansion. That forces you to ask a different question: if domestic economics weaken further, can the overseas business grow fast enough and profitably enough to absorb the pressure? That question is what this piece is really about.

BYD is starting to supply the industrial proof: trial production commenced at the Szeged, Hungary plant in January 2026, with full series production scheduled for Q2 2026 — the first locally manufactured European BYD. The Brazil plant is operational. Cambodia is in progress. Turkey’s 150,000-unit plant is scheduled to open by end-2026, and Spain is under active evaluation as a third European site, favoured for its clean energy infrastructure and manufacturing cost base. The company now runs eight roll-on/roll-off vessels and has established a European headquarters in Hungary.

That is not proof on its own. It is a footprint. Anyone can export. The Toyota test is whether exports turn into factories, suppliers, service networks, and local political staying power. That is a harder path than SAIC’s MG-style push into Europe, and more self-built than Geely’s ability to lean on Volvo’s foreign industrial base. What makes BYD’s path distinctive — and more fragile — is that it is building the industrial infrastructure itself, from shipping to charging to assembly, without an acquired brand to backstop the learning curve.

That is also why capex matters. BYD spent RMB 156.8 billion in 2025 on overseas plants, proprietary shipping, charging infrastructure, energy storage, and R&D simultaneously. The bear case is that management is trying to build five moats at once and cannot fund them all without the domestic cash engine running at full pressure. The bull case is that this is what a real moat often looks like while it is being constructed.

The 2026 test for the Hungary plant is not whether it produces cars. Trial production has already started.

The real test is whether it runs at a utilisation rate and cost structure that can compete with Chinese production after European labour and logistics costs are taken into account. Underutilised overseas plants drag margins. If Hungary starts reporting at meaningful capacity — initial design is 150,000 units, scalable to 300,000 — before the domestic business stabilises, the thesis gains a genuine second leg.

For me, localisation does not mean putting pins on a map. It means local suppliers, after-sales capability, political acceptability, demand that holds up without brute-force discounting, and eventually economics good enough to survive tariffs or policy tightening.

The strategic direction only matters if BYD can export a platform, not just cars

BYD’s technology roadmap matters only if it helps the company win outside China — in markets with weaker charging infrastructure and harder politics.

Fifth-generation DM and longer-range PHEVs matter because large parts of the world still do not have the charging infrastructure that makes pure EV ownership easy. Management is pushing larger-battery PHEVs with up to 210km of pure-electric range and more than 2,100km of combined range. That is aimed at markets where charging is patchy and drivers still need ICE-like convenience. It is a product designed for the world as it actually exists, not the world as EV evangelists wish it did.

The individual technologies — Super e-Platform, flash charging, the second-generation Blade Battery, Gods Eye — are not the point. Management is bundling them: charging speed, battery range and durability, driver intelligence, local assembly, and shipping control, packaged as a single offer that is harder to replicate than any one feature. If you are a competing OEM, you do not get to pick which part to match. I explored a related policy-to-cashflow angle in 50,000 Factories: What China’s AI Mandate Means for My Portfolio, where BYD also appears as part of the EV manufacturing and automation build-out.

One reason BYD might become more resilient than a normal carmaker is that it may not end up being only a carmaker. The company delivered more than 60GWh globally in 2025, ranked first worldwide in energy storage system shipments, and won the 12.5GWh Saudi Electricity contract. That is enough to tell me energy storage is not a side business. What I still cannot see clearly is the economics. Because storage sits inside the broader auto segment disclosure, I cannot tell whether it is already a margin enhancer, a temporary drag while the business scales, or simply too small to matter yet. Management has given me enough to be intrigued and not enough to underwrite. That is the real gap.

Premiumisation matters too, but I want to be disciplined about it. Yangwang, DENZA, and FANGCHENGBAO together approached 400,000 units in 2025 and more than doubled. That is encouraging. It is not yet the same as saying BYD has become a premium company. It has become a company that also sells some premium cars, which is a different sentence.

What 2025 actually changed for me

The annual report did not kill the thesis. It raised the hurdle.

Before this report, I could still tell myself a simpler story: China scale first, exports later, and operating leverage eventually doing the rest. I do not think that is the story any more.

The domestic business weakened, cash flow deteriorated, capex surged, and borrowings nearly quadrupled. Those numbers do not decide the thesis on their own. But they leave management with far less room for strategic error.

I remain invested because BYD has already proved enough to keep the thesis alive. But from here, the next leg has to be earned.

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How I am positioned — and what would change that

BYD currently represents approximately 2 percent of my growth portfolio, with additional indirect exposure via the Hang Seng Tech ETF. The combined position is meaningful but not yet sized to reflect full conviction — which is deliberate.

My current approach is to use cash-secured short puts to build toward the position gradually, targeting a lower entry point if the stock comes in to a price that reflects more of the execution risk now visible in the 2025 numbers. If management continues to execute as I expect — overseas volume growing, Hungary demonstrating viable localised economics, and cash flow recovering from what looks like a trough year — I would be comfortable growing the combined growth portfolio stake toward 5 percent over time.

The sizing logic is straightforward: the thesis is intact but unproven at the critical overseas step. A 2 percent position reflects a live hypothesis. A 5 percent position would reflect a thesis entering confirmation. I am not going to 5 percent until I can see the overseas economics in the numbers, not just in management commentary.

The mechanism — short puts rather than direct purchase — is not about being clever. It is about being paid to wait at a price I would genuinely be happy to own more stock at. If the puts expire worthless, I collect premium while the thesis develops. If I get assigned, I own more BYD at a price that builds a larger margin of safety into the position. Either outcome works for me.

If Hungary is approaching 100,000 units of annualised production before Q3 2026, or if BYD breaks out energy storage as a reportable segment, I would treat either as a signal to build the position faster.

What would make me reduce is more specific: overseas revenue share continuing to rise while operating cash flow stays depressed. Revenue mix shifting while cash does not follow is not a healthy transition. It is a sign the overseas business is absorbing costs faster than it is generating returns. At that point, patience stops being a thesis and starts being a habit.

What would make me believe more — and what would kill the thesis

I do not need BYD to look perfect in 2026 or 2027. I need the future path to become more legible.

In practical terms, I want to see three things in the numbers: overseas revenue per vehicle holding up as volume scales, at least one overseas plant moving from symbolic output to economically meaningful utilisation, and operating cash flow recovering without another stretch in supplier payables.

The kill condition is not that China stays difficult for another year. China probably will stay difficult.

The kill condition is that BYD keeps growing abroad and still fails to become more resilient — that overseas volume expands but overseas margins compress toward domestic levels as price competition follows the brand into new markets, or that localisation turns into a permanent cost burden rather than a moat. If that happens, the thesis is not early. It is wrong.

What I think now

The real question is not whether BYD can sell more cars abroad. It is whether it can become locally durable there.

The forward signals I am watching: the Szeged plant’s capacity utilisation in the Q3 2026 interim report — if it begins approaching 100,000 units annualised without a margin deterioration narrative attached, that is the first localisation proof point. And separately, whether BYD breaks out energy storage as a reportable segment in the 2026 interims. If it does, the platform thesis has enough commercial weight to require standalone disclosure. If it does not, I will continue to treat storage as optionality rather than underwriting it as a value driver.

A future Toyota is not just a company that can export everywhere. It is a company that can belong everywhere.

As of the date of publication, I hold positions in BYD Company (HKEX: 1211) and Hang Seng Tech ETF (Xetra: H4ZX). Positions may change after publication without notice. Cohong Lane is a periodical publication made generally available to the public; this is disclosure of my positions, not a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.

On 6 and 7 January, while most of the Western financial world was still shaking off the holidays, Beijing published 2 documents that quietly shifted the ground beneath four sectors I care deeply about: the AI + Manufacturing Special Action Implementation Opinions and the Industrial Internet and AI Integration Empowerment Action Plan.

I’m not writing about this because it’s “news.” I’m writing because this is one of the first times I’ve seen the 15th Five-Year Plan translate from aspirational language into specific ministerial KPIs with binding targets — and the implementation clock is now ticking. That distinction matters — because when eight ministries set binding KPIs with SASAC enforcing SOE compliance, the result is captive demand: revenue that exists because policy mandates it, not because customers chose it freely. That captive demand is now flowing into EVs, telcos, chips, and software. I want to be positioned in the sectors where Chinese firms are already competitive enough to turn mandated procurement into real earnings growth — and honest about where I’m not confident enough to buy.

The short version: I already held EVs and telcos. These documents sharpened my conviction that software and the sensor layer deserve direct exposure — so when the Iran sell-off gave me the price I wanted on Kingdee and Hesai, I executed. I did not add semis. The question running through the whole piece is whether captive demand accelerates dominance or subsidises mediocrity — and the answer depends entirely on the sector.

What Actually Changed

Beijing has moved from telling us what it wants to telling us exactly how much, by when, and who’s responsible for delivering it.

The numbers are concrete:

By 2027: 3–5 general-purpose large AI models deeply applied in manufacturing, 1,000 high-level industrial intelligent agents, 100 high-quality industrial datasets, 500 typical application scenarios, and 1,000 benchmark enterprises. By 2028: at least 50,000 enterprises implementing new industrial network transformation, with high-quality datasets across 20 key industries. — AI + Manufacturing Special Action Implementation Opinions and Industrial Internet and AI Integration Empowerment Action Plan, January 2026

These aren’t the kind of round numbers bureaucrats throw around when they’re being aspirational. 50,000 enterprises. 20 industries. 2027 and 2028 deadlines. This is execution-phase policy with ministerial accountability attached.

For investors in EV, telco, chips, and software, this is where captive demand stops being a concept and starts being an order book. When SASAC directs central SOEs to integrate AI into strategic planning and build intelligent computing centers, that’s not market-driven adoption — it’s mandated procurement with budget lines attached.

Why Now: The 15th FYP Implementation Phase

The 15th Five-Year Plan (2026-2030) was formally adopted in late 2025, but until these January documents, the guidance was directional — strategic priorities, aspirational language, the “China will become a leader in X” type of framing. Useful if you’re trying to figure out which way Beijing is leaning — less useful if you’re trying to size an order book.

These are implementation-level policy. The eight-ministry joint issuance of the AI + Manufacturing Opinions (MIIT, CAC, NDRC, Ministry of Education, MOFCOM, SASAC, SAMR, National Data Bureau) signals whole-of-government coordination. That’s not a single ministry floating a trial balloon—it’s cross-functional accountability with SASAC in the room enforcing SOE compliance and NDRC controlling investment approval. And SASAC doesn’t just coordinate — it controls executive appointments and compensation for state-owned enterprises. When a policy document says “integrate AI into strategic planning,” and the people saying it decide whether executives get promoted or fired, that’s not a suggestion. It’s a career incentive.

Add compliance mechanisms with teeth — data governance certifications, model safety assessments, subsidy eligibility tied to targets — and the enforcement structure has both carrot and stick.

Implementation-phase policy tends to be stickier. Once ministerial KPIs are set and budgets allocated, reversing course becomes bureaucratically expensive. I’m comfortable with multi-year exposure here because the commitment runs through 2030 — unwinding this is not just unlikely, it’s bureaucratically painful for every ministry that signed on.

But there’s a tension worth naming first. Mandated procurement creates revenue visibility — but it can also compress margins, misallocate capex, and produce political winners instead of commercial ones. The test I apply sector by sector below: are the domestic firms capturing this demand already globally competitive, or are they being shielded from the iteration pressure that makes products world-class? Where the answer is “already competitive,” captive demand is an accelerant. Where it isn’t, I want to see margin quality and receivables before committing capital.

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Who Gets Paid

4 sectors sit in the direct procurement path: EVs, telco, chips, and software. Each captures captive demand differently — and each has a different way of punishing the wrong positioning.

Electric Vehicles

This policy creates two distinct EV value channels.

The first is factory efficiency. AI-powered production lines — real-time optimisation, predictive maintenance, defect detection — can cut EV assembly times by 15–30%. In a margin war measured in hundreds of RMB, that shows up in gross margins.

The second is autonomous driving — the policy supports L3/L4 with clearer regulatory pathways and calls for “AI chip hardware-software coordination.” XPeng’s CEO is forecasting a 2026 leap in autonomous driving capability from L2 to L4, and policy clarity is part of why. That is also part of why I added Hesai. Lidar is not optional for L3 in practice — new safety regulations already require redundant sensing, and Hesai’s nomination list of 160+ production programs across China’s top ten automakers reflects that reality. L4 certification requirements are still being formalised, but regulators are unlikely to accept a camera-only architecture.

The immediate order flow sits with smart factory suppliers — industrial robots, manufacturing software, machine vision — and leading EV makers who already invested get policy validation and subsidy access.

For my EV holdings, the signals I’m watching are specific to each name: export volumes for BYD and Geely (proof that manufacturing competitiveness travels), and licensing revenue for XPeng (proof that the autonomy stack has value beyond its own vehicles). If those numbers are growing, the factory-efficiency and autonomy theses are showing up where it matters — in revenue lines that don’t depend on domestic policy.

Telecommunications

The telco story is straightforward but underappreciated: 50,000+ enterprises need new or upgraded industrial networks, and state-owned carriers are the ones building them. MIIT is targeting 10,000 5G-powered factories by 2027, with 35%+ 5G penetration in large industrial enterprises.

The interesting part isn’t the capex — it’s the revenue mix. Consumer voice and SMS are in structural decline, but industrial internet services — factory connectivity, computing power leasing, AI platform hosting — are a fundamentally different business. The national computing power network positions the big three as infrastructure providers.

When SOEs are mandated to adopt AI + industrial internet and the carriers are themselves SOEs, the loop is circular — procurement mandates, not consumer adoption.

There’s a second reason: trust. Compliant AI deployment with audit trails and domestic hosting requires politically acceptable infrastructure — and the big three are already inside the gate. I explored why trusted rails may matter more than model performance in My One-Person-Company Bet: Who Gets Paid First in China?. China Unicom alone reported AI revenue growth of more than 147% year-on-year in 2025 — and these policy documents just handed the carriers a mandate-backed order book.

For my positions in China Mobile, China Unicom, and China Telecom, the question is whether mandated B2B revenue converts into sustainable margin — or whether it’s low-margin infrastructure buildout dressed up as a growth story. The signal I’m watching: revenue mix and segment margins. China Telecom now reports Industrial Digitalisation as a discrete revenue line; China Mobile breaks out computing and AI services. What I need to see is the resource-based, infrastructure side of that revenue growing faster than the project-based system integration work — and eventually, margin disclosure to confirm the quality is there, not just the top line.

Semiconductors

Chips are where this policy gets complicated — and where I have the most questions. The documents call for breakthroughs in “high-end training chips, edge inference chips, AI servers, high-speed interconnect,” require “lightweight computing modules” across production equipment and automated vehicles, and push for a “national integrated computing power network” requiring domestic chip supply to work around export controls.

Here’s the math: 50,000+ enterprise upgrades need industrial control chips and edge AI accelerators — procurement budgets, not aspirations. 10,000 5G factories need baseband and RF chips. The computing power network needs datacenter GPUs or domestic equivalents. That’s a multi-year order book for domestic foundries — structural, not cyclical, and distinct from consumer electronics. Whether the supply side can deliver is the harder question.

The policy phrase “safe and reliable supply” is doing a lot of work here.

安全可靠 — “safe and reliable” — is Beijing’s standard procurement euphemism for domestic substitution.

When SOEs are told to use domestic chips where available, that creates a protected market where domestic suppliers can achieve scale without competing head-to-head with TSMC. The funding is substantial: Big Fund III at ~US$47.5 billion, plus the National AI Industry Investment Fund and government subsidies.

There’s also support for chiplet architectures — where smaller chip components are combined rather than fabricated as a single piece — which lets Chinese firms work around older manufacturing nodes. I find this more interesting than the headline GPU race, because it’s a realistic path to “good enough” rather than a moonshot to match TSMC. And a focus on smaller AI models that reduces computing requirements extends the viable market for those nodes.

The performance bar for factory-floor inference is lower than datacenter training — domestic technology is good enough for what’s being asked of it. Captive demand plus “good enough” is how protected industries achieve scale — but this is where the tension bites hardest: a decade of Big Fund money and the gap with TSMC hasn’t closed. The policy creates the order book; whether it produces competitive products is what I’d need to see in margins and design wins before buying.

I don’t hold AI/compute semis. The domestic positioning risk gives me pause: fabless designers dependent on TSMC for advanced nodes face supply constraints policy can’t solve, and firms optimised for consumer chips may find themselves outside the tailwind. What would change my mind: a domestic edge-AI vendor showing improving gross margins and non-SOE design wins. One open question that could shift the picture: how the ~US$70 billion chip support package splits between datacenter AI accelerators and industrial/edge chips. If the bulk flows to edge, the order book for domestic foundries gets materially larger — and the “good enough” thesis strengthens. Until then, I’m watching.

Software

Every traditional enterprise software category — ERPs, manufacturing execution systems, product lifecycle tools — is about to go through a policy-mandated upgrade cycle. If your software doesn’t have AI features, you’re off the SOE vendor shortlist. That’s not a competitive dynamic — it’s a procurement filter, and it reshapes who captures the upgrade budget. That is one reason I added Kingdee. It reported RMB 356 million in AI contract value for FY2025, including SOE clients like Shenzhen Energy, with AI tools cutting bookkeeping time by over 80% and tax filing by 60%. Not a demo — a paid enterprise SaaS motion, and this policy just handed it a mandate-backed upgrade cycle.

The policy calls for vendors who are both “intelligent and industry-familiar” — 赋能应用服务商. Firms that bridge AI with domain knowledge in steel production or chemical processing are suddenly in demand.

Industrial internet platforms are positioning themselves as hosts for “model pools” and intelligent agents. This is part of why I hold Kingdee rather than a point-solution vendor — Kingdee already has the enterprise client base and the ERP infrastructure inside SOEs like Shenzhen Energy. When those clients deploy AI agents on top of an existing platform, switching costs compound with every agent added. A point-solution vendor has to win the client first; Kingdee is already inside the building.

And then there’s the compliance layer. Mandatory data governance certifications, Chief Data Officer requirements, data asset registration systems — all of this creates a software category that didn’t exist before: data cataloging, lineage tracking, governance platforms. Compliance software has reliable demand curves.

Foreign incumbents are under pressure. The “technology self-reliance” language is procurement guidance, not decoration. Data localisation complicates foreign cloud offerings, and domestic competitors are iterating faster — a market share risk that compounds.

Of the four sectors, software is where the accelerant dynamic looks clearest after EVs. The products are already usable, the switching costs are real, and the compliance layer adds recurring revenue that doesn’t depend on the next policy cycle. The signal I’m watching: whether Kingdee’s AI contract value converts into retained enterprise subscriptions — revenue that renews because the product works, not because SASAC said so.

How I’m Positioned

My approach and portfolio structure haven’t changed structurally. The Iran sell-off gave me the entry I wanted on Kingdee (workflow software, measurable labour compression) and Hesai (lidar on the L3/L4 certification path). Full exposure in and around this theme: XPeng, BYD, Geely, China Mobile, China Unicom, China Telecom, Kingdee, Hesai, and Innoscience. I also hold Pony AI, though it is less central here.

The common thread: every position I hold in this space is in a sector where I believe the accelerant dynamic applies — firms already competitive enough to convert mandated demand into real earnings growth. The positions that depend on China’s domestic policy for their thesis — telcos, Kingdee — are balanced by positions where China’s global competitiveness is already proven independent of mandate: BYD, Geely, XPeng’s licensing deals. If policy reverses, the latter group doesn’t need it. I may be wrong about that balance, and I’ll be testing it against the signals I’ve named above.

The Thing I Can’t Figure Out

I’ve been running the “accelerant or subsidy” test through every sector above — and the results are mixed. EVs and software pass; telcos are ambiguous; chips I’m not buying. The broader uncertainty remains: does the SOE mandate structure that makes this policy enforceable also limit the upside? Captive customers who must buy domestic regardless of price can distort market signals and slow the iteration pressure that produces world-class products.

I’ll be watching the execution data — order books, margins, and whether captive demand shows up in earnings with healthy receivables or just in revenue with deteriorating collection cycles. The investment case depends entirely on which dynamic you’re buying into — and being honest about which one you’re holding. The next test arrives with H1 2026 earnings: that’s when the 2027 KPI deadlines should start showing up in procurement pipelines. If the revenue is there with clean margins, the thesis is working. If it’s there with deteriorating receivables, I’ll need to rethink which sectors deserve the capital.

As of the date of publication, I hold positions in XPeng (HKEX: 9868), BYD Company (HKEX: 1211), Geely Automobile Holdings (HKEX: 0175), China Mobile (HKEX: 0941), China Unicom (HKEX: 0762), China Telecom (HKEX: 0728), Innoscience Technology (HKEX: 2577), Kingdee International Software Group (HKEX: 0268), Hesai Group (HKEX: 2525 / NASDAQ: HSAI), and Pony AI (NASDAQ: PONY / HKEX: 2026). Positions may change after publication without notice. Cohong Lane is a periodical publication made generally available to the public; this is disclosure of my positions, not a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.

A Thousand People Queued to Install OpenClaw

I own Tencent, Alibaba, three Chinese telcos, and Kingdee — and I own them because I believe the companies that already control daily habit, payments, and enterprise workflows are the ones that get paid when digital labour becomes routine. That is the bet — and everything in this piece tests whether I should be increasing it or reconsidering the sizing. A thousand people queuing at Tencent’s Shenzhen headquarters to install software that might do work on their behalf is either early evidence that the bet is right, or the kind of demand signal that flatters before it fades.

The Chinese internet gave it a name: “growing your lobster” — feeding a software agent tasks until it starts handling the workload for you. Tencent’s OpenClaw was the software, and those thousand 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.

By one-person company, I do not mean an internet slogan but software that compresses administration, customer service, bookkeeping, 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. Once the behaviour is plausible, the investable question becomes who owns the rails it runs on. That is the same logic I use in No Pain to Begin With: How I Invest My Own Capital: structure first, then thesis.

Why China May Normalise Agentic Labour First

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

A Taobao seller 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”. That language does not guarantee durable economics. But it does lower the adoption friction — and in China that matters, because bureaucratic customers follow the signal. If auditability and data locality become binding constraints, the infrastructure spend tilts toward domestic rails.

Who Gets Paid First?

When I evaluate a technology shift, I start with who owns the surface where the behaviour has to happen. Models are commoditising fast — DeepSeek-V3 cost under six million dollars to train, and Chinese domestic API prices range from five cents to fifty-five cents per million tokens versus five to fifteen dollars for OpenAI. When the model layer compresses by more than 97 per cent, value does not vanish. It re-pools into whoever controls where the task ends. Four layers matter: the habit surface, the transaction bridge, the trust infrastructure, and the workflow data. Those are the rails. Here is where I think the early profit pool sits in China — and why I have my money there.

Nothing here proves the one-person company exists at scale. What it does show is that the rail layer is already getting paid for AI-adjacent activity. Tencent putting OpenClaw functionality inside WeChat suggests this is being treated as a habit-forming category, not a passing curiosity. Alibaba says 140 million users had “experienced” AI-driven shopping by end-February 2026, though “experienced” is doing a lot of work in that sentence.

I may be directionally right and still not make any money from it. The question is whether agentic behaviour improves monetisation inside businesses that already own distribution, trust, and transactions. If usage explodes, Tencent gets paid on activity inside a surface it already monetises. Alibaba earns across the full vertical — chips, cloud, transaction, take-rate. The telcos collect rent on trusted domestic hosting. Kingdee is the one with the most to prove: whether businesses really will pay on outcomes, not seats. That is the real test: does the behaviour show up in revenue, or does it just make existing software slightly better? It is the same question I asked in 50,000 Factories: What China's AI Mandate Means for My Portfolio: not whether policy sounds ambitious, but where the economics actually settle.

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. It means more agent activity inside WeChat can flow through revenue channels that already exist. The list is straightforward: AI-targeted advertising — Marketing Services revenue grew 17 per cent in Q4 2025, driven by closed-loop ads that convert inside Mini Programmes and Mini Shops rather than sending users elsewhere; e-commerce technology service fees on Mini Shops GMV already in the trillions of renminbi; enterprise subscriptions through WeCom’s 14 million business clients; and cloud compute, where Tencent reached operating profit at scale in 2025. I cannot yet isolate how much of the advertising growth is agent-driven versus ordinary targeting improvements. But the structural point is simpler: Tencent does not need a new business model. It needs agents to increase commercial intent inside a surface it already monetises.

Alibaba Owns the Commerce Bridge

Alibaba matters not because it can win an AI beauty contest but because it already sits where intent turns into transactions — and it owns more of the stack than almost anyone else in China: chips, cloud, models, and interface. If that vertical integration holds, Alibaba can produce tokens cheaper than most and monetise them across merchant tools, marketplace traffic, payments, 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. Think about what that seller’s cost structure used to look like: a part-time assistant, a freight coordinator, and a tax filing service. Call it RMB 150,000 a year in overhead. If software compresses that to a RMB 20,000 annual subscription, the seller is not choosing software over labour. They are choosing freedom over headcount. 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. That is the one-person company made concrete — and Alibaba is sitting inside every transaction. It is also the version that matters financially.

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. The vertical integration matters for economics: each agent call that ends in a Taobao purchase generates revenue at multiple layers — inference fees on Alibaba Cloud, commerce take-rates on the transaction, and payment processing through Alipay. T-Head has shipped 470,000 chips — if Alibaba is running meaningful inference workloads on its own silicon, the cost per token should be meaningfully lower than renting compute from third parties. I don’t have a clean per-token number, but the directional logic holds. The Alibaba Token Hub binds models to applications across the ecosystem, creating switching costs even for customers running open-weight Qwen. That is why 140 million users completing their first agentic shopping by February is a transaction-linked metric, not a vanity number — it means agents are already driving activity inside the revenue stack.

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 an SOE or a bank is, in practice, going to run sensitive workloads only through a domestically auditable stack, that is not a soft factor — that is the gate. And the gate is a telco. 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: real cash yield, consistent profitability, and now a genuine AI tailwind. China Telecom’s AI revenue reached RMB 12.3 billion in 2025 — that is what it looks like when regulated enterprises pay for compliant domestic deployment. China Unicom 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

This is where the thesis has to show up in somebody’s budget, not just in a launch deck. 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 RMB 1,000 software licence is still software. A RMB 10,000 workflow bill tied to measurable labour savings is something else entirely. If the customer is genuinely saving RMB 50,000 to RMB 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 (for now), 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.

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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 adjacent to what I actually need. Tencent and Alibaba can each point to rising AI engagement — Qwen’s 300 million monthly active users, 200 million holiday orders, 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 agent activity that drives measurable commercial outcomes and shows up as recurring revenue, not just in a launch deck.

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

My conviction in Tencent, Alibaba, Chinese telcos, and Kingdee has increased — not because the evidence is conclusive, but because each earnings cycle narrows the gap between “interesting hypothesis” and “visible in somebody’s revenue.” The specific signal I am watching next: Alibaba’s Q1 FY2027 results in June, where repeat agentic purchase rates — not first-time usage, but whether those 140 million users come back — will tell me whether the commerce bridge is habitual or promotional. If it is habitual, the thesis graduates from inference to fact. If it is not, I will need to revisit the sizing.

That is also, frankly, how I am trying to operate my own research and capital allocation — with fewer intermediaries, more direct ownership of the process, and software compressing the overhead. The thesis is not just about companies. It is a template.

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 International Software Group (HKEX: 0268). Positions may change after publication without notice. Cohong Lane is a periodical publication made generally available to the public; this is disclosure of my positions, not a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.

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 newsletter 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 daily 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 threshold is simple: minimum 5% net of withholding tax. If an income asset doesn’t clear that bar after tax drag, it doesn’t earn its place. Beyond that, yield must be real and sustainable, tiered by risk — from European and US corporate bonds to Singapore REITs and dividend-paying Chinese state-owned enterprises. When high-quality income assets sell off alongside lower-quality names 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. Every Growth position is underwritten for a minimum five-year horizon. If the thesis doesn’t hold over that timeframe, the position doesn’t belong here. 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. That’s the price of entry — and it means the only positions worth taking are ones where I can see a credible path to 5–10x over the investment horizon. If the asymmetry isn’t there, the bet doesn’t belong here. It’s deliberately small, deliberately early.

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 XPeng 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.

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 — and that's what I'll be writing about next. How the income bucket is constructed to clear 5% net. Which growth positions I'm holding through a five-year lens and why. And the live question I can't yet answer: whether this structure holds when the drawdown is not 40% but 60%. That's the test I haven't sat through yet.

Cohong Lane is a periodical publication made generally available to the public. Nothing here is a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.

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.

The most famous of them, Howqua, became one of the richest men in the world — and he got there not through political favour but through what the American consul Samuel Shaw called being 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. I’d spent 25 years in and around institutional finance — close enough to the allocation decisions to see how real money moves, and close enough to know that the smartest capital in the room is often the most constrained. In 2025, I moved to Hong Kong — the capital markets gateway to China’s Greater Bay Area — to test a hypothesis: that one serious person, doing the work on the ground with their own money at risk, can sometimes see more clearly than a conflicted institutional machine.

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.

When I read a bearish report on Chinese consumer spending, I can check the claim against what I see on the ground — and the picture is almost always more nuanced, more sector-specific, and more investable than the headline. The filter doesn’t replace the data. It tells me which questions to ask next — and in China coverage, knowing the right question is half the work.

That’s the job I’m trying to do — except instead of a warehouse full of tea, my tools are public filings, policy documents, dealership visits, and the kind of conversations you only have when you’re physically here.

What You’ll Find Here

Cohong Lane is the transparent research process of an independent investor whose bills get paid by the markets. My livelihood depends on getting these investment decisions right. This isn’t a newsletter that happens to mention investing — I call it open-sourcing the workflow.

What I publish falls into three areas. Portfolio & Process — how I structure and manage my own capital, with every position, every sizing choice, and every mistake in the open. You can watch each thesis play out in real time. Investor’s Mindset — the psychology, frameworks, and mental models behind investing independently, because the hardest part of this job isn’t the analysis — it’s the discipline. China Mechanics — tracing how a government mandate becomes a line on a company’s P&L, verified on the ground through dealership visits, city walks, and the kind of checks you can only do from the Greater Bay Area.

If you manage your own money — or you’re building towards it — and you’d rather understand how a serious independent investor actually operates than read another set of tips or hot takes, Cohong Lane might be for you. I don’t have all the answers. But I do the work, I put real money behind it, and I share the whole process so we can figure it out together.

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.

Next, I walk through the operating system behind my portfolio — two core buckets, a fair value compass, and why I structured everything so that a 40% drawdown changes my mood, not my decisions: No Pain to Begin With.

Cohong Lane is a periodical publication made generally available to the public. Nothing here is a recommendation to buy, sell, or hold any securities. Full disclaimer · About Philip.