Over the past two months, U.S. tech stocks have been extremely volatile. The kind of names that used to soar just by attaching themselves to the AI theme have now started to diverge sharply. It is not that the AI trend is over. What has changed is the logic of the “gold rush.”

The market is no longer willing to pay simply for expectations like “I have AI,” “I’m connected to a large language model,” or “I bought thousands of GPUs.” Instead, investors are starting to ask a much more realistic question:

Can you actually turn AI into profit?

Recently, while reviewing the business models across the AI value chain, I started rethinking two of the hottest areas in the current market: the AI application layer and the computing-power leasing layer.

These two areas are not without opportunities, but their valuation logic has changed.

AI application companies need to prove that they are not merely burning inference costs, but can turn AI functions into real revenue. Computing-power leasing companies also need to prove that they are not simply buying GPUs and renting them out, but that they have integrated delivery capabilities across power, data centers, scheduling, and long-term customer contracts.

Today, I want to look at this purely from the perspective of the “economics of computing power” and explain how my investment thinking has changed.

1. Computing-Power Leasing and AI Applications: The Valuation Logic Has Changed

In the current AI trade, retail investors tend to favor two types of companies: one is companies building viral AI applications, and the other is companies involved in computing-power leasing.

But in my view, both business models have hidden fatal flaws.

The first flaw: the “efficiency curse” of the AI application layer.

Earlier this year, text-to-video and multimodal AI applications became extremely popular.

But if you look closely at the business model of the application layer, you will find a very real problem:

The more active the users are, the higher the inference costs become.

Every time a user generates a video, completes a complex conversation, or calls an AI agent, the company has to consume computing power behind the scenes. For many AI application companies, if the product cannot develop strong enough monetization, user growth may actually become a cost burden.

This is what I call the “efficiency curse.”

AI helps customers improve efficiency, but software companies may not necessarily be able to charge more. AI makes the product more attractive, but it also increases inference costs. If customer budgets do not rise accordingly, software companies can easily fall into an awkward situation:

If they do not do AI, their story becomes outdated. If they do AI, revenue may increase without profits increasing.

A typical case to observe is Salesforce (CRM).

Over the past few years, Salesforce has put AI at the center of almost every major narrative, from Einstein Copilot to Agentforce. CRM is trying to upgrade itself from a traditional SaaS platform into an Agentic AI CRM.

However, the market has found that Salesforce’s heavily promoted AI functions have not yet delivered the explosive revenue growth expected. Instead, the company first issued an extremely conservative profit outlook. This is a direct reflection of the “efficiency curse”: you add AI features to software and help customers improve efficiency, but the customer’s IT budget does not increase. In some cases, because AI improves efficiency, enterprise customers may even start cutting software seats. At the same time, in order to maintain these AI functions, software companies still need to continuously pay high inference costs to the underlying cloud vendors.

Naturally, the market has not been willing to buy into this. Since the start of the year, CRM’s stock price has continued to decline, with losses already exceeding 30%.

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Although at the end of February this year, the company disclosed in its FY26 results that Agentforce ARR reached $800 million, up 169% year over year, for a mature SaaS giant with annual revenue of more than $40 billion, Agentforce — despite massive investment — still has not proven that it can significantly change the company’s overall growth curve. CRM may still need stronger results to prove its AI transformation story.

What does this show?

It shows that the AI application layer is not worthless. Rather, the valuation standard has become more demanding.

In the past, the market might have paid up for “software plus AI.” Now, the market wants to see whether AI features can truly drive overall revenue growth, improve customer expansion, and ultimately improve the profit model.

If they cannot, AI application companies can easily become “traffic entrances” for the underlying computing-power providers — the more users they have, the higher their costs become, and profits end up being eaten away by inference costs.

The second flaw: the “fake moat” of computing-power leasing companies.

Since the application layer has to pay the computing-power layer, does that mean we should simply buy computing-power leasing companies?

It is not that simple.

The problem with many computing-power leasing companies is that, at their core, they are just “buying GPUs and renting them out.” In the early stage of the AI demand boom, this model was indeed attractive, because GPUs were scarce. Whoever had GPUs had pricing power.

But the problem is that this moat is not deep.

Once the big tech companies build their own computing power, cloud providers increase capital expenditures, and GPU supply gradually improves, simple “buy-and-rent” GPU leasing will face price competition.

Applied Digital (APLD) is a very typical case to observe.

This company was also once chased by the market during the early AI computing-power demand boom. But its later transformation actually reveals a very important change:

The market is no longer assigning high valuations to pure GPU leasing. Instead, it is starting to reprice AI Factory infrastructure delivery capabilities.

In its financial report disclosed in April 2026, APLD clearly positioned itself as a designer, builder, and operator of high-performance, sustainable, engineered data centers. The company is not only building AI Factory projects such as Polaris Forge, but is also advancing Delta Forge 1, a 430MW AI Factory campus designed to support up to 300MW of critical IT load for high-density AI workloads.

More importantly, APLD later announced that Delta Forge 1 had signed a new lease with a U.S. hyperscaler, covering around 15 years, 300MW of critical IT load, and approximately $7.5 billion in total contract value, bringing the company’s total contracted lease revenue to more than $23 billion.

What it proves is this:

Low-barrier GPU buy-and-rent models have no future. But AI Factory operators that can integrate power, land, liquid cooling, data center construction, financing, and long-term customer contracts may instead be repriced by the market.

This is also why APLD’s stock began to rebound strongly after falling throughout the year. It is upgrading from asset-light computing-power leasing into a heavier, harder, but also more defensible AI infrastructure platform.

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These two cases made me realize an even more important shift:

The core bottleneck of AI is indeed computing power and energy. But simply writing front-end code may not allow you to earn the money from computing power, and simply buying thousands of GPUs to rent out may not allow you to protect the profits from computing power.

What is truly valuable are the players that can integrate energy, computing power, data centers, liquid cooling, scheduling, financing, and customer contracts.

In other words, the AI trade is not over. Capital is simply moving away from “people who tell stories” and toward “people who can deliver.”

 

2. Rethinking the Framework: The Endgame of AI Is Actually a “Hardcore Contractor” Business

If pure software applications and GPU buy-and-rent models both struggle to protect profits over the long term, then in this computing-power boom, who are the real “pick-and-shovel” players?

My answer is:

The builders, operators, and schedulers of AI infrastructure.

That is what I mean by “hardcore contractors.”

The “contractor” here is not a traditional construction crew. It refers to the infrastructure general contractor of the AI era.

Think about it. Putting tens of thousands of A100s or H100s together is not as simple as plugging in an internet cable and connecting them to power.

They generate enormous heat, consume tremendous amounts of electricity, and require highly stable networks, data centers, cooling, operations, maintenance, and scheduling systems. AI computing power that can actually run is not just the GPU itself. It is an entire complex engineering system.

This system includes at least several things:

First, low-cost and stable electricity.

Second, data center construction and operations capabilities.

Third, the ability to maintain cooperation with important customers.

Together, these capabilities form real AI infrastructure.

 

So what I care more about now is not companies that simply have models, nor companies that simply have GPUs. I care more about companies that can turn AI demand into engineering projects, assets, and long-term cash flow.

These companies may not look as sexy as pure software companies. They may even look a bit heavy and clumsy. But their moats may actually be deeper.

Models can be iterated. Applications can be copied. GPUs can also be bought. But low-cost electricity, suitable land, liquid-cooling engineering capability, government and enterprise resources, financing channels, and long-term customer contracts cannot be replicated in the short term.

 

3. Discovering MAAS: A Severely Undervalued AI Infrastructure Platform

Following this line of thinking, I started re-examining listed companies in the market and found a very counterintuitive company: MAAS.

Many people still think of MAAS as a mobile charging robot company. But when I looked deeper into a strategic acquisition it had just completed at the end of March, I realized that the market may have completely misread the company’s underlying logic.

On March 30, 2026, MAAS completed the acquisition of 100% of Times Good Limited. Times Good controls the core assets and operations of Huazhi Future and its subsidiaries. According to the company’s announcement, Huazhi Future’s business covers high-performance computing, AI large-model development, computing-power resource integration, and industry experience in smart governance and enterprise digital transformation.

If you put MAAS’s original mobile charging network and energy storage systems together with Huazhi Future’s intelligent computing centers, computing-power scheduling platform, and AI large models, the underlying logic becomes clear:

MAAS is not trying to build a single-point AI application. It is building a systematic moat around “energy + computing power + scheduling + customers + compliance.”

First Moat: Low-Cost and Stable Electricity

The first principle of AI infrastructure is not models. It is electricity.

Large-model training and inference both rely heavily on computing power, and a large portion of the underlying cost of computing power comes from electricity consumption. For AI infrastructure companies, whoever can secure cheaper, more stable, and greener electricity will have a better long-term cost structure.

This is also why MAAS’s Stars Distributed Intelligent Computing Center project focuses on western regions such as Yinchuan in Ningxia and Yiwu in Xinjiang. According to the company’s announcement, the Stars project plans to build dual-core intelligent computing centers. The Yinchuan center plans to deploy 512 high-performance servers, while the Yiwu center plans to deploy 256 high-performance servers. Western China naturally has energy resource advantages. If the company can later secure stable access to green power, it will directly affect MAAS’s computing-power costs and profit potential.

Second Moat: Data Center Construction and Operations Capabilities

AI computing power cannot be monetized simply by buying a few GPUs.

The real difficulty lies in integrating servers, electricity, cooling, networks, security, and operations systems, and then keeping them running reliably over the long term. This is also why a simple “buy-and-rent” model has a limited moat, while data center construction and operations capabilities are the true hard barrier.

MAAS’s Stars project uses a three-layer architecture of “dual-core + multi-level edge + unified platform”: dual-core intelligent computing centers handle centralized training, inference, and large-scale data processing; multi-level edge nodes handle low-latency inference and localized data processing; and the unified platform handles management, scheduling, billing, operations, maintenance, and security.

This architecture shows that MAAS is not trying to build a single data center. It wants to build a computing-power network that can be replicated, scheduled, and operated over the long term.

The edge nodes are especially worth watching. The company plans to deploy 50–100 multi-level edge nodes in containerized and modular form, supporting on-site installation, plug-and-play deployment, and elastic expansion. Each node is configured with 10–16 edge servers.

This is the core capability of a “hardcore contractor.”

Traditional data centers have long construction cycles, heavy capital requirements, and complicated delivery. But if modular edge nodes can be replicated at scale, AI infrastructure can move from being a single large-scale project to becoming a set of quickly deployable engineering modules. This will determine whether the company can scale from one project to ten projects, and then to dozens of projects.

Third Moat: Strong Government-Enterprise Coordination and Strategic Positioning

The most important thing about MAAS is that it is not entering ordinary commercial data centers. It is entering computing-power infrastructure with regional strategic significance. In terms of customer profile, it is directly targeting state-owned enterprises, industry leaders, and government parks.

On April 22, the company announced that Huazhi Future, together with partners including China Electronic Computing Power and Zhongwai Yuzhi, had launched the “Stars Distributed Intelligent Computing Center Project,” with total planned investment of up to RMB 5 billion. The project is deeply aligned with national-level strategic directions such as “Eastern Data, Western Computing” and “Xinjiang Computing into Chongqing.”

The barrier for this kind of project is not just technology. It requires government-enterprise coordination, regional resource integration, compliance capabilities, and long-term operating ability. Even if an ordinary technology company has models and algorithms, it may still not be able to enter this level of infrastructure network.

For MAAS, if it can continue to bind itself to government parks, state-owned enterprises, industry customers, and regional partners, then its future value will not simply be selling computing power. It could become an operating participant in regional AI infrastructure.

This moat is completely different from that of pure AI application companies. AI application companies depend more on product growth and user conversion. AI infrastructure companies depend more on customer relationships, project resources, delivery credibility, and long-term operations capabilities.

Once a company enters an important customer’s infrastructure system, it may form strong customer stickiness. This is because for government and enterprise customers, switching the underlying computing-power platform, scheduling system, and operations service provider is costly. It involves data migration, system adaptation, security review, and business continuity.

 

 

Now, if we put MAAS’s original business together with Huazhi Future’s business, the transformation becomes much clearer. The old MAAS looked more like a company built around mobile charging, smart hardware, and scenario-based operations.

But after acquiring Huazhi Future, it has started to form a new combination logic:

Green energy access → intelligent computing center infrastructure → edge computing nodes → unified scheduling platform → secure large-model empowerment.

If this chain can work, MAAS will no longer be just a mobile charging robot company. It will have the opportunity to transform into an AI infrastructure platform.

 

4. Trading Plan

From a technical perspective, MAAS’s recent stock movement shows the typical characteristics of a small-float, high-volatility stock.

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The stock rose rapidly in the earlier stage, which shows that market capital has begun repricing its AI infrastructure narrative. But after such a large short-term gain, the stock pulled back and entered sideways consolidation. That, in itself, is not surprising. The real key is that during the pullback, there has not been continuous high-volume selling, and turnover has not expanded significantly.

This suggests that while there is selling pressure at higher levels, it does not yet look like a typical concentrated distribution structure. For a stock like MAAS, with a relatively small float and relatively concentrated ownership, if volume does not expand significantly during declines, it often means core shares have not fully loosened.

In other words, MAAS currently looks more like high-level consolidation after a rapid rally, rather than the complete end of the uptrend.

If MAAS can continue consolidating in the current range on lower volume, and later break above the top of the range with renewed volume, the first target could be around $15. If fundamental catalysts, stronger market sentiment, or short covering are added on top of that, the stock could have a chance to further challenge the previous high resistance area around $20.

 

My trading plan: I have already established a starter position. I plan to add more if the stock pulls back to the $7 support level. If the stock breaks above the upper end of the box around $11 with strong volume, I will add further.

 

To sum it up in one sentence: In this historic AI gold rush, betting on who will build the next “killer app” has a very low win rate. I would rather buy the “contractor” who is building roads in the desert, constructing power stations, selling shovels, and selling bottled water. When the tide goes out, many applications may die off, but the completed intelligent computing centers and computing-power scheduling networks will still be collecting real tolls every day.

 

China's already got nearly 44 million EVs, with close to 13 million new ones added last year alone. On paper, there are plenty of chargers. But anyone who's driven during a holiday knows the reality — highway rest areas are a total mess, lines everywhere. And good luck finding a charger in an old apartment complex or at a random outdoor event.

Then I came across this MAAS "Xiaoli" charging robot. The logic actually makes a lot of sense.

Specs: 150kWh LFP battery, 120kW fast charging. Can juice up a car in about 20 minutes. One robot can handle over a hundred cars a day. It moves autonomously... no driver needed. Perfect for places where building fixed chargers is a pain: highway rest stops, aging residential neighborhoods, temporary event sites, remote rural roads.

And as for the key thing, It's already in commercial deployment, not just a demo. 20 units went live earlier this year in some mountainous, humid regions in southern China, exactly the kind of challenging environment where you'd expect things to fail. And apparently, stability has been solid.

Compare that to fixed chargers, which need permitting, grid upgrades, and months of lead time. These robots just show up and work. And China's national policy, like a "three-year doubling" plan, explicitly calls for fixed + mobile charging to develop together, so government support is there.

Long term, they could also act as distributed energy storage: charge up cheap at night, sell back during peak hours, maybe even support V2G down the road.

In the world's largest EV market, this kind of flexible, on-demand charging actually feels like a more practical fix for range anxiety than just throwing more fixed piles at the problem.

What do you think about this?

Edited: Forget to mention that China is a major EV powerhouse, with a very high proportion of households owning EVs. The national stock of new enegry vehicles has exceeded 43 million units. Moreover, China has many public holidays and strong travel demand, and that makes it very common for EVs to run out of power during holidays, especially during the Chinese New Year, the coming May Day, and National Day, etc,.