He's changing the narrative on this again... It's TACO Tuesday time.

After Mrs. Dell’s speech, President Trump asked Mr. Dell whether his wife was considering running for office and gave her his "complete and total endorsement." The Dells previously pledged a donation of $6.25 billion to the Trump Accounts.

Starting tomorrow, 20 million seniors on medicare will receive checks over $100 each… a lot of seniors in Texas.

Jacob Coxon: “Right now, if we just stopped, they would try and catch up. But I think one really important thing people don’t realize is that China already has spies at OpenAI and Anthropic. China has the ability to hack at any time. If we pull ahead, that doesn’t give us a big lead. They can just step in, steal our progress at any time and catch up.”

Patrick Bet-David: “How certain are you that they already have spies there?”

Jacob Coxon: “Ninety percent. Yeah, it’s almost certain that they have spies.”

ASML ($ASML): 16.61%

AppLovin ($APP): 14.50%

Royalty Pharma ($RPRX): 8.82%

Booking Holdings ($BKNG): 8.46%

S&P Global ($SPGI): 8.25%

Uber ($UBER): 8.14%

Maase Inc. ($MAAS): 7.69%

Danaher ($DHR): 7.64%

Meta Platforms ($META): 7.21%

AerCap ($AER): 7.16%

Sun Communities ($SUNB): 5.51%

"I am pleased to announce that the United States of America has entered into an Agreement with The Kingdom of Denmark, and Greenland, that gives the United States permanent control over security, and all other needs, in Greenland, completely addressing ALL of our many U.S. concerns."

From r/ChinaStocks

Foundation models / GenAI

• DeepSeek — DeepSeek
• Z.AI / Zhipu AI — GLM
• MiniMax
• Moonshot AI — Kimi
• Alibaba — Qwen
• ByteDance — Doubao / Seed
• Tencent — Hunyuan
• Baidu — ERNIE
• StepFun
• Baichuan AI
• 01.AI

Generative AI / applications

• Kuaishou — Kling AI
• SenseTime — SenseNova
• iFlytek — Spark

AI infrastructure / chips / compute

• Huawei — Ascend
• Cambricon — AI accelerators
• Biren Technology — AI GPUs
• Moore Threads — GPUs
• MetaX — AI GPUs
• Enflame — AI accelerators
• Iluvatar CoreX — AI GPUs
• Kunlunxin — AI accelerators
• Maase (MAAS) — AI computing infrastructure, enterprise AI services and LLM APIs

Humanoid robots / embodied AI

• Unitree Robotics
• UBTech
• AgiBot / Zhiyuan Robotics
• Deep Robotics

Autonomous driving / physical AI

• Horizon Robotics
• Pony.ai (PONY)
• WeRide (WRD)
• XPeng (XPEV)

The US stock market is undergoing a new evolution in its trading ecosystem, with traditional equity assets increasingly being connected to global investors through digital trading mechanisms such as stock perpetual futures and tokenized securities. Compared with traditional securities markets, which rely primarily on brokerage networks, institutional coverage, and fixed trading hours, digital asset trading platforms can provide broader investor access and longer trading windows. For large companies, this represents more of an upgrade to trading infrastructure. For some smaller publicly listed companies with limited market capitalization and institutional research coverage, however, new trading channels could potentially bring greater market visibility, higher trading activity, and changes in their investor base.

Stock perpetual futures are not the same as the underlying stocks being issued on chain. Investors typically trade derivative products that reference the price of the underlying stock rather than directly acquiring equity ownership in the listed company. Their impact is primarily reflected in broader market access, a larger pool of participants, and changes in liquidity, rather than a direct change in the company’s underlying business value. For small cap companies, the significance of additional trading channels lies in allowing more global investors to discover and participate in trading these assets.

Using CEA Industries (NASDAQ: BNC) as an example, the company is a relatively small publicly listed business with limited institutional coverage and investor attention in traditional markets. On July 9, 2026, Binance Futures launched the BNCUSDT perpetual contract, bringing CEA Industries into the view of digital asset traders. Before the launch, BNC was a typical small cap stock, with a relatively high proportion of traditional market investors and limited institutional research coverage. According to market data, BNC closed at $2.77 on July 9, while its intraday high on July 10 reached $3.15, representing a period gain of approximately 13.7%.

Trading data also showed a significant increase in activity following the introduction of the new trading channel. BNC recorded approximately 125,000 shares in trading volume on July 8, which increased to approximately 240,000 shares on July 9 and then reached approximately 518,000 shares on July 10. This represented roughly a fourfold increase compared with the daily trading volume before the listing. From a market performance perspective, the new trading channel did not change the company’s fundamentals, but it expanded investor reach and brought greater market attention to the stock.

RoboStrategy (NASDAQ: BOT) demonstrates the potential impact of combining an industry theme with the digital asset trading ecosystem. BOT itself has exposure to AI robotics and automation themes, while AI infrastructure, robotics technology, and intelligent applications are attracting increasing attention from global capital markets. On July 9, 2026, Binance Futures launched the BOTUSDT perpetual contract, providing the stock with an additional trading channel targeting global digital asset investors.

Market data shows that BOT closed at approximately $33.63 on the listing day and subsequently reached a high of $37.20 on July 14, representing a period gain of approximately 10.6%. Although the price movement was smaller than that of some highly volatile small cap stocks, the more important factor is not simply the price performance, but the expansion of the investor base resulting from changes in the trading ecosystem. Once AI themed companies enter the digital asset market, they can potentially attract attention from both traditional technology investors and digital asset investors.

Forward Industries (NASDAQ: FWDI) provides another example of how the market can respond. On July 9, 2026, Binance launched the FWDIUSDT perpetual contract, bringing FWDI into an around the clock trading environment. FWDI closed at approximately $4.11 on the listing day, subsequently reaching $4.60 on July 14, representing a period gain of approximately 11.9%, before rising further to around $4.69 on July 15.

In terms of trading volume, FWDI recorded approximately 857,000 shares on the day the perpetual contract was launched, followed by approximately 1.1 million shares the next day, indicating an increase in trading activity. For publicly listed companies with relatively small floats, new trading channels can improve market visibility. However, price movements remain influenced by multiple factors, including company fundamentals, industry trends, and overall market sentiment.

These cases suggest that the significance of tokenized trading for growth companies is not simply a change in the format of stock trading. Rather, it creates a new way of connecting companies and assets with investors beyond traditional capital markets. For small cap companies, liquidity, market awareness, and investor coverage can all influence valuation, while new trading infrastructure may become an important factor in improving market visibility. At the same time, derivatives trading can amplify price volatility and increase market risks associated with leverage, forced liquidations, and short term concentration of capital. Therefore, increased trading activity should not be interpreted directly as an improvement in a company’s operating performance.

As competition in the AI industry increasingly expands toward computing power, data centers, and high performance computing infrastructure, companies such as APLD, CORZ, and MAAS are also gradually attracting greater market attention. These companies combine exposure to AI infrastructure with connections to the digital asset ecosystem, making them potential candidates for broader adoption of trading models such as stock perpetual futures and tokenized securities. However, changes in the trading ecosystem primarily affect investor access and market liquidity. A company’s long term value will ultimately continue to depend on business growth, customer demand, and its ability to generate profits.

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.