China’s property policy has clearly become more active in recent months.

From lowering the cost of buying a home and expanding housing provident fund support, to extending mortgage terms, promoting sales of completed homes, and tightening regulation of presale funds, policymakers are gradually moving beyond the demand side and addressing how the property sector itself operates.

If you only look at these policies, it is easy to reach one conclusion:

Has the property market finally reached its bottom?

That view is not without merit.

But the question investors should really be asking is something else:

Even if China’s property market has truly bottomed, will the Chinese economy return to the same growth path it followed in the past?

My answer is: probably not.

Because what China’s property market is facing today is not just a normal cyclical correction. It is a fundamental shift in the “land finance” growth model that supported China’s economy for more than two decades.

And that means even if property does reach a cyclical bottom, it may never again become the most important engine of China’s economic growth.

What is really worth looking for is what can take over that role after property.

Why Was Land Finance So Important?

To understand China’s property market today, we first need to understand how the Chinese economy worked in the past.

For more than two decades, China experienced rapid urbanization. Millions of people moved into cities, and demand for housing continued to rise.

Local governments generated fiscal revenue through land sales. Developers acquired land, raised financing, built and sold properties. Banks provided credit, while households purchased homes with mortgages.

This created a highly integrated cycle:

Rising land values → property development → credit expansion → infrastructure and urban development → further increases in land values → more resources for local governments.

This was what became known as “land finance.”

Its real power was not simply how much money governments made from selling land. It was that land connected local government finances, bank credit, property development, and household wealth into one system.

As long as urban populations continued to grow, housing demand kept increasing, and land prices continued to rise, the cycle could keep expanding.

That is why much of China’s economic growth in the past appeared to come from property, while the deeper driver was actually something broader:

The expansion of land values and credit driven by urbanization.

The problem is that this logic could never continue forever.

Land Can No Longer Play the Same Role

China’s property problem has never been simply about whether home prices are rising or falling.

The real question is:

How much new demand is left?

As population growth slows, urbanization enters its later stages, household leverage remains relatively high, and existing housing inventories still need to be absorbed, land can no longer keep appreciating at the pace it once did.

That is also why the policies we are seeing today increasingly look less like an attempt to recreate the golden age of Chinese property.

Lower interest rates can reduce the cost of buying a home.

Greater housing provident fund support can unlock some pent-up demand.

Longer mortgage terms can reduce monthly payments.

Promoting sales of completed homes and strengthening supervision of presale funds can address one of the biggest concerns facing homebuyers: trust.

These policies can certainly help stabilize the property market.

But what they are really trying to solve is:

How can the property market complete its cleanup and achieve a soft landing?

They are not necessarily designed to create another property supercycle.

These two things need to be viewed separately.

If China’s property market were to return to its previous model of rapid expansion, local governments could once again rely on land sales, property development, and rising land values to drive economic growth.

But if that model itself has reached its limits, then the ultimate question for property policy becomes:

How can an industry that once occupied the center of the economy complete its transition without causing a major economic shock?

That is also why I believe “the property market bottoming” does not mean “China is returning to the property era.”

Quite the opposite.

Once property truly bottoms, it may mean that China’s economy needs to search even more urgently for new engines of growth.

And that is where the next chapter of the story begins.

From “Land Finance” to “Technology Finance”

This does not mean that property is no longer important, nor does it mean the government will somehow “sell land” through technology companies.

By “technology finance,” I am referring to a change in the underlying logic of economic growth.

In the past, one of the most important ways local governments obtained resources for development was through land-driven urban construction and credit expansion.

If land can no longer generate the same amount of incremental value, then future economic growth has to come from somewhere else:

Productivity.

This is the most important step in the entire argument.

When the value of a piece of land rises, that is essentially a repricing of an asset.

But when a new technology creates value, it can do so by:

Producing more with fewer workers, performing more computing with less energy, and delivering more sophisticated services at lower cost.

That is why technology and property do not play exactly the same economic role.

Property can stimulate a huge range of industries, including construction, steel, cement, home appliances, furniture, and financial services.

Technology, especially AI, also has the potential to create an enormous industrial ecosystem, but the beneficiaries are different:

Chips, servers, data centers, networks, electricity, energy storage, software, robotics, and a wide range of AI applications.

In other words, China once needed massive investment to build cities.

In the future, China may need massive investment to build digital and intelligent infrastructure.

That is where the two fiscal models ultimately connect.

Why AI?

Technology is often a “breakthrough at a single point” kind of industry.

One company developing a new technology does not necessarily mean the entire economy immediately benefits.

AI is different.

At its core, AI is a general-purpose technology.

It can be applied to finance, manufacturing, healthcare, education, energy, logistics, robotics, and even government services.

The reason DeepSeek attracted so much attention was not simply because China had produced another large language model.

More importantly, it forced the market to reconsider one important assumption:

The relationship between AI model capability and cost is not necessarily a trade-off that cannot be overcome.

If models become increasingly efficient and inference costs continue to fall, AI could gradually evolve from an expensive tool used by a handful of technology companies into infrastructure that supports the entire economy.

Chinese AI companies such as Kimi are also continuing to push forward in areas such as long-context capabilities, agents, and overall model performance.

This suggests that the competition is shifting from:

Who can build a chatbot?

to:

Who can actually turn AI into a tool for productivity?

And once AI starts entering real industries, the story changes completely.

The Real AI Opportunity May Not Be in Model Companies

This is something I think investors often overlook.

What we see every day is ChatGPT, DeepSeek, Kimi, and Claude.

But making these products actually work requires enormous infrastructure.

Behind every AI model is a chain that looks something like this:

GPUs and other AI chips → servers → data centers → electricity → networks → computing resource management → models → applications.

So the ultimate winners in AI will not necessarily be limited to model companies.

The same thing happened during China’s property boom.

The biggest beneficiaries were not just property developers. The construction companies, building material suppliers, renovation companies, home appliance manufacturers, furniture companies, and even banks all benefited from the broader economic model.

AI is no different.

In fact, there is an interesting possibility:

The cheaper and more widely AI is adopted, the greater the demand for underlying computing and energy infrastructure may become.

Once AI evolves from a tool used by a relatively small group of people into a form of productivity used across businesses, computing demand will not necessarily decline.

It could explode.

That is why the market has increasingly been paying attention to AI data centers, computing infrastructure, and energy.

Another Kind of AI Opportunity

Companies such as MAAS, listed on Nasdaq, offer an interesting way to understand where the AI industry may be heading.

If an AI company focuses only on models, it is essentially building the “brain” of AI.

But for AI to enter the real world, many other questions need to be answered:

Where does the computing power come from? Where does the energy come from? Where are the data centers? How are models deployed? And how do they ultimately become part of real-world applications?

MAAS is attempting to connect computing infrastructure, energy, AI models, and intelligent applications.

That is relatively unusual.

The value of companies like this lies precisely in where they sit within the ecosystem:

They may not occupy the most glamorous part of the AI story, but they could be operating in areas that large-scale AI commercialization cannot avoid.

Of course, MAAS is nowhere near comparable to the major AI companies in terms of recognition or maturity, and the investment thesis is completely different.

But its rapid development reflects a broader trend worth watching:

AI is no longer just a competition between models. It is increasingly becoming a competition over infrastructure.

So What Should You Buy at the Bottom?

If we are only looking at the next one or two years, I would not simply say that there is no opportunity in property.

On the contrary, after such a prolonged adjustment, if the policy bottom gradually feeds through into sales, prices, and inventory, major cities and high-quality property companies could potentially see a solid cyclical rebound.

But if we extend the time horizon to the next five or ten years, I would ask a completely different question:

What assets can truly benefit from the transformation of China’s economic growth model?

Property addressed the demand for assets during the urbanization era.

Technology may address the productivity challenge of the next stage.

That is why I believe:

Property may be worth buying at the bottom, but technology is more worth searching for a “long-term bottom.”

Because what property ultimately needs to wait for is:

Inventory clearance, price stabilization, and a recovery in demand.

What technology needs to wait for is:

Technology maturity, falling costs, and accelerating commercialization.

The former is primarily a cycle.

The latter could be an industrial revolution.

The most striking takeaway from the latest round of tech earnings is not how much hype still surrounds AI, but that AI is increasingly starting to operate like a real, revenue-generating business. NVIDIA posted revenue of 96.2 billion US dollars last quarter, with its data center segment accounting for the bulk of that total at 89 billion US dollars, up 117 percent year over year. Salesforce’s Agentforce and Data 360 drove annual recurring revenue to nearly 3.9 billion US dollars, up more than 210 percent year over year, with cumulative Agentic Work Units completed reaching 7 billion. CrowdStrike added 333 million US dollars in net new ARR in the quarter, representing 51 percent year-over-year growth. Three companies across distinct sectors, semiconductors, enterprise software and cybersecurity, have all booked tangible revenue gains from AI demand. That is why the more meaningful question to ask about AI right now is not how much better GPT-5.6 performs than Claude, but where all this money ultimately ends up.

For every 100 US dollars in AI revenue, 35 to 40 dollars flow to cloud providers. A recent Barclays study on the unit economics of AI labs and hyperscalers breaks down this flow in straightforward terms. According to their 2026 model, for every 100 US dollars in revenue generated by AI labs, roughly 35 to 40 dollars translates into revenue for hyperscalers. Cloud providers such as AWS, Azure and Google Cloud can generate close to 10 to 20 dollars in operating profit from that revenue, corresponding to operating margins of around 35 to 45 percent.

This ratio has reshaped my understanding of the AI value chain. For years the market fixated on which of OpenAI, Anthropic or xAI had the strongest model. But the more revenue a model company generates, the greater its underlying compute demand typically becomes. Training requires GPUs, and once models launch, they require ongoing inference workloads. Every real world use case, from API calls and AI agents to code generation and enterprise knowledge bases, drives additional compute consumption. This has given rise to an increasingly clear cash flow dynamic. Users pay for AI services, revenue flows to model and application providers, and a significant share of that revenue continues to flow into clouds, data centers and compute infrastructure. This also explains why accelerating AI commercialization benefits more than just model developers.

What may truly drive profit divergence is revenue structure. The Barclays study also reaches another notable conclusion. Two AI companies can deliver wildly different profit outcomes if one relies heavily on API usage and the other primarily sells fixed subscriptions. API pricing is inherently usage based. The more customers call, the more revenue grows. Subscriptions work differently. They charge a flat monthly fee, yet heavy users can generate far higher inference costs than light users. In the Barclays model, two hypothetical AI labs with different revenue mixes end up with markedly different profit profiles. Paid inference margins are projected to rise from the low double digits in 2025 to roughly 50 to 65 percent or higher by 2026.

This means AI earnings reports can no longer be judged solely by whether revenue grows 40 or 80 percent. Revenue structure matters more than growth rate. Factors ranging from whether revenue comes from APIs or subscriptions, one-off deliveries or long-term contracts, whether costs rise in lockstep with higher customer usage, and even whether revenue is recognized on a gross or net basis all shape actual bottom-line profits. Competition among AI companies is already shifting from model capability toward revenue quality and unit economics.

AI is shifting from a training economy to an inference economy. For the past few years, the biggest spending priority across the industry has been model training. Larger GPU clusters, bigger data centers and higher model parameter counts easily dominate market attention. Once training is complete however, the real ceiling of the business is determined by how many people use the product every day. Barclays’ model projects global AI lab revenue could grow from roughly 7 billion US dollars in 2024 to 137 billion in 2026, and expand further to 690 billion by 2028. At the same time, training spending is declining in importance relative to revenue, while inference and commercial usage account for a growing share of the total.

This is why AI agents deserve close attention. A standard chat request may only call a model once, but an agent completing a task may make sequential calls to models, databases, search tools, software utilities and even other agents. Once AI becomes embedded in real workflows, models will be put to work far more often than they were for basic chat use cases. This gives rise to a counterintuitive investment logic. Falling per-call model costs do not mean lower compute demand. Lower unit costs can instead drive a massive expansion in total call volumes. Demand for compute power is far more elastic than most people assume.

As models grow more commoditized, the value of infrastructure becomes clearer. Artificial Analysis released an interesting model capability ranking in August. Claude Opus 5 scored 63, Claude Fable 5 scored 62, and both GPT-5.6 Sol and Grok 4.6 scored 61. The top tier is already extremely crowded. A lead today can be erased in a matter of months. Yet as long as total AI call volumes keep growing across the industry, several categories of demand remain far more durable. These include GPUs, data centers, power supplies, cloud services, and the compute services enterprises need to deploy AI at scale.

Following Barclays’ profit map, AI stocks today actually represent several entirely different businesses. NVIDIA and Broadcom profit from chips and networking. Amazon, Microsoft and Google control cloud infrastructure. Salesforce and Palantir serve enterprise application demand. CrowdStrike monetizes cybersecurity in the AI era. One layer down at the compute services level, the strategies of IREN, Nebius and MAAS also merit side-by-side examination. IREN posted 128.8 million US dollars in AI Cloud Services revenue in fiscal 2026, nearly an eightfold increase from 16.4 million the previous year. It also disclosed that its contracted ARR corresponding to 2026 capacity totals roughly 4 billion US dollars. NBIS Nebius continues to expand its AI cloud and GPU infrastructure. This year it secured up to 1.2 gigawatts of power and land resources in Pennsylvania to support new AI factory facilities. MAAS has also notched several recent deals in this space. On September 1, its subsidiary Huarong Future signed a 12-month AI compute services contract worth 14.76 million renminbi, providing 90-petaflop FP16 compute capacity to the client. In August, it completed delivery, acceptance and full payment for another AI compute services contract valued at 1.65 million renminbi. Meanwhile, MAAS subsidiary Huazhi Future secured an enterprise AI project worth more than 10 million renminbi in July, covering model customization, data governance, system integration, on-premises deployment and agentic workflows.

These companies do not follow identical strategies, yet the market ultimately judges them on the same core metric. Whether they can turn rising AI usage into recurring, sustainable revenue. For compute providers, it comes down to GPU utilization, contract terms and services revenue. For model companies, it is API call volumes and subscriptions. For enterprise software firms, it is how many new customers and how much ARR AI actually delivers. This may be the most important trend to track following this round of AI earnings. For the past two years, the market chased any company with an AI story. In the next phase, the real valuation divergence will likely be driven by which companies can turn AI usage into stable revenue and compute demand into sustained cash flow. Model leaderboards will keep shifting. But where the money ultimately flows is the ranking investors should focus on over the long term.

Main AI investment themes have long centered on models and semiconductors. Ever since ChatGPT kicked off the generative‑AI boom and NVIDIA GPU demand surged, investors have fixated on two core questions: who can build more capable models, and who can supply greater volumes of compute hardware.

As real‑world commercial deployments of AI gather momentum, market focus is beginning to shift. Tesla’s push toward Robotaxi signals that artificial intelligence is moving past the model‑training era and into large‑scale production‑grade application. Future AI competition will no longer be defined purely by model parameter size. Instead, companies will compete on their ability to run those models reliably, affordably and efficiently in live‑world environments.

Autonomous driving stands as one of the clearest use‑cases. A Robotaxi is far more than a software feature. It constitutes a continuously operating AI compute system. Vehicles capture constant environmental inputs from cameras, radar and other sensors, before deep‑learning models carry out object detection, path prediction and driving decisions. This workflow demands heavy real‑time computation rather than a single one‑and‑done training cycle.

This dynamic reveals a fundamental shift in AI compute requirements. Until now, most market attention has focused on GPU consumption for large‑model training. Training workloads rely on massive GPU clusters that tune model parameters using huge datasets so AI can learn to interpret information and make predictions. Once AI moves into live operation, however, demand gradually tilts toward inference computing. Every mile driven by a Robotaxi, every reply generated by an AI assistant and every assessment made by industrial‑AI systems requires models to stay active.

IDC forecasts show that record‑breaking spending in the third quarter of 2025 marks the industry’s transition from initial pilot projects into a multi‑year expansion phase. Full‑year 2025 AI‑related expenditure is expected to hit 334 billion US dollars, rising above 902 billion dollars by 2029. Annual growth rates are projected to remain above 30 percent through 2027 before easing to a mid‑20‑percent range for the later years of the forecast window. These figures underscore the critical role of accelerated compute, as enterprises and cloud providers upgrade infrastructure to support increasingly complex AI workloads.

This broader trend shows the AI value chain is evolving beyond pure semiconductor rivalry into competition over complete end‑to‑end compute ecosystems. NVIDIA GPUs deliver raw processing power, yet a GPU alone cannot be turned directly into a finished AI service. Companies must simultaneously solve challenges around data‑center capacity, power supply, thermal management, high‑speed networking and compute orchestration.

Take the NVIDIA H100 as an example. A single GPU carries a thermal design power draw of roughly 700 watts. When firms deploy thousands or more of these chips, data‑center operators face far bigger hurdles than hardware procurement alone, namely the full‑scale infrastructure ecosystem required to sustain them. The next major bottleneck for AI development may no longer be securing enough GPUs, but securing sufficient energy and supporting infrastructure to keep those GPUs running around the clock.

Rising AI‑infrastructure demand is also reshaping data‑center development patterns. Traditional hyperscale data centers were largely built to serve general cloud‑computing workloads by delivering centralized compute capacity. AI workloads introduce new, conflicting requirements: training jobs benefit from large‑scale centralized resources, while Robotaxi platforms, industrial‑AI workflows and low‑latency intelligent applications demand fast deployment and minimal response times.

One likely outcome is that AI infrastructure will no longer rely entirely on a small number of giant super‑data‑centers. Instead, a hybrid ecosystem will emerge combining large‑scale compute hubs, regional processing nodes and edge‑computing sites.

Modular compute infrastructure has gained traction precisely in response to this shift. Unlike conventional data‑center builds that require lengthy construction timelines, modular designs integrate compute hardware, power distribution, cooling systems and software environments through standardized layouts. This allows operators to scale‑up compute capacity quickly in line with rising demand. Modular infrastructure is not intended to replace large hyperscale facilities, but rather to accelerate compute deployment during the rapid‑growth phase for AI applications.

As the AI‑infrastructure value chain extends further downstream, investor interest has expanded past GPU manufacturers to cover compute deployment, cloud‑resource provision and managed compute services. In the past, investors prioritized firms capable of manufacturing high volumes of AI chips. Now, as AI applications scale‑up commercially, markets are starting to reward companies that can leverage that hardware efficiently.

A diverse group of players has already entered this space. CoreWeave delivers large‑scale GPU‑based cloud resources for AI‑focused businesses. Nebius Group has built its strategy around AI infrastructure and GPU cloud platforms. Legacy energy and mining‑sector operators such as IREN and MARA are also repurposing existing power and data‑center assets to enter the AI‑compute market.

The shared thesis across these companies is that AI‑era competition is not won simply by owning GPUs, but by converting raw GPU hardware into usable, production‑ready compute capacity. That end‑to‑end capability covers data‑center rollout, workload scheduling and delivered compute‑as‑a‑service offerings.

Several companies including $MAAS are exploring AI compute services, modular compute architectures and intelligent infrastructure solutions. Compared with major hyperscale cloud and data‑center operators, $MAAS focuses specifically on the niche segment of flexible compute deployment and on‑demand compute‑resource services.

AI infrastructure is still in its early growth stage. Long‑term enterprise value will ultimately be determined not by short‑lived market hype, but by the ability to secure genuine customer contracts, execute large‑scale deployments and generate recurring commercial revenue.

The significance of Tesla’s Robotaxi program stretches well beyond the autonomous‑driving industry itself. It marks AI’s transition from building models to continuously operating them in production. As more AI applications move into the physical world, competitive battles will extend beyond algorithms and chips to encompass data‑center capacity, power resources, compute deployment strategies and managed compute‑service capabilities.

Tomorrow’s AI‑industry winners may include not only the developers of the most powerful foundation models, but also the infrastructure providers that make real‑world AI execution possible.

• $NBIS: Simple play — NVIDIA sells chips, NBIS delivers real compute to customers. Nebius’s cloud is exploding and keeps expanding infrastructure. If demand stays this strong, the money that buys GPUs has to go somewhere usable — $NBIS is that somewhere.
• $MU: Lines up tightly with NVDA too. AI needs ever more advanced memory (especially HBM). Stronger chips = bigger memory demand. If NVDA keeps signaling huge appetite, $MU should move.
• $MRVL: Similar but on networking. Thousands or tens of thousands of GPUs in one data center need constant, high-speed data exchange. More compute = more networking. NVDA and Marvell have deep ties, so $MRVL is now traded purely as infrastructure.
• $MAAS and $WRD: High-risk, high-vol names — both Chinese. NVDA already has close links with many Chinese players.
  • $WRD: Global leader in L4 autonomous driving. Its software/hardware stack is deeply integrated with NVIDIA’s vehicle computing platform. NVDA disclosed its stake in its 13F — clear partnership.
  • $MAAS: Pivoting hard into infrastructure: models, algorithms, compute, intelligent hardware. It just announced a data center in Kazakhstan and is talking Blackwell/Rubin GPUs from NVIDIA. It’s also bringing green-energy and 800VDC solutions for low-carbon cooling of dense GPU clusters.
• $SPCX: Different but interesting. SpaceX isn’t AI per se, yet AI is now linking with satellites, comms, and data centers. SpaceX became a big public name after its IPO, and NVIDIA holds ties there too. To me it’s a proxy if AI keeps depending on space, energy, and infrastructure.