The big model development has indeed drawn the internet giants into the same competitive arena.

Tue, 12 May 2026 00:59:12 +0800

My previous article covered the semiconductor cycle, and I feel like there’s a piece of background/context missing.

Your judgment/conclusion regarding this point—the overall direction is correct. Furthermore, I believe it is a prerequisite that is easiest to overlook when trying to understand this current semiconductor boom.

A more accurate way to put it is not that “all internet giants are fighting in the same field,” but rather: Large Models have, for the first time, brought together major players previously scattered across different domains—such as search, advertising, social media, e-commerce, office productivity, cloud computing, and content distribution—into direct competition within the same technical stack.

This technology stack includes models, computational power, inference, cloud, Agents, distribution gateways, and commercialization closed loops. Everyone’s original “moat” is different, but now we must all fill the same gap. Those who fail to do so will see their future search entry points, ad pricing, office suites, e-commerce conversion, and social traffic distribution rewritten by others.

Why is your viewpoint basically valid

Previous internet competition was more like everyone staying within their own established territories (or: was more siloed).

Google / Baidu primarily focus on search.
Meta / Tencent primarily focus on social networking and traffic distribution.
Amazon / Alibaba primarily focus on e-commerce and merchant ecosystems.
Microsoft focuses on office software and enterprise software.
Although AWS, Azure, and Google Cloud are involved in the fight, that is more of a battle over cloud infrastructure and enterprise IT.

It’s different now.

Large models are not a single function; they are more like an “master switch” that will reverse-engineer/supersede all entry points. Search can be rewritten, ad placements can be rewritten, customer service can be rewritten, code generation can be rewritten, e-commerce recommendation can be rewritten, and enterprise knowledge bases can also be rewritten.

Therefore, although these giants are still making money in their respective established domains on the surface, fundamentally they are all competing for the same thing:

\[ \text{AI Competitiveness}=\text{Model Capability}+\text{Computational Resource Supply}+\text{Distribution Channels}+\text{Commercialization Closed Loop} \]

The difference merely lies in who is superior in which aspect.

Microsoft is strong in enterprise distribution and Azure.

Alphabet is strong in search, advertising, and proprietary model stacks.

Amazon is strong in AWS, chips, and enterprise cloud customers.

Meta is strong in traffic entry points and advertising scenarios.

Tencent is strong in super apps, gaming, and advertising implementation.

Ali is strong in e-commerce, cloud, and industrial clients.

Baidu is strong in search, AI Cloud, and ERNIE.

So your judgment is correct, but we need to add one caveat: Everyone is fighting on the same large battlefield, not using the same set of weapons or competing for the same group of users.

This is also why semiconductors are linked/grouped together

The previous text discussed storage and South Korean semiconductors; at that time, I focused on specific product categories like HBM, DDR5, and eSSD.

However, looking at a higher level, what truly fueled all these developments was the way the capital expenditure of internet giants began to concentrate in one direction.

It’s not that Google needs to buy a little more server capacity this year, but Meta might follow suit next year.

Instead, during the 2025 to 2026 phase, Microsoft, Google, Amazon, Meta, Alibaba, Tencent, and Baidu are almost all focusing on AI infrastructure, model training, inference services, Agent platforms, and AI distribution entry points. While their specific terminology varies, money is pouring into areas like GPUs, HBM, networking, SSDs, data centers, and power.

This is uncommon in internet history.

In the mobile internet era, many companies are involved, but not every single one needs to build its own operating system.

The short video era is booming, but not every company needs to build and train its own foundational recommendation model infrastructure.

The era of cloud computing has been long, but traffic platforms and gaming companies like Meta and Tencent have not all shifted their focus to the cloud.

Large models are something that almost every platform giant believes cannot be missed this time. This is what makes this semiconductor cycle different from previous ones.

What Is the Core Area of Competition Among All Players Right Now?

Let’s first clarify the definitions/scope. (Alternative options depending on context:)

We need to establish the parameters first. (If discussing data or technical metrics)
Let’s get aligned on the basis first. (If referring to an agreement or overall strategy)

For the table below, revenue and net profit should use the official disclosures based on each company’s latest completed fiscal year as of May 12, 2026. However, regarding the “AI Investment” column, disclosure methods vary greatly among companies; some report Capex, others provide a three-year investment plan, some report R&D figures, and some only provide quarterly expenditures.

So this column can only be used to view the strength and weakness direction, not for mechanical ranking.

Company	Existing Cash Cow / Core Business	Current AI Leverage Points	Latest Full Fiscal Year Revenue	Latest Full Fiscal Year Net Income	Most Recently Disclosed AI Investment Scope
Microsoft	Office, Windows, Enterprise Software, Azure	Azure + OpenAI Ecosystem + Copilot + Enterprise Agent	FY2025 Revenue $281.7 Billion	FY2025 Net Income $101.8 Billion	Approx. $80.1 Billion in CapEx for the first 9 months of FY2026; AI business annualized revenue reached $37 Billion in Q3 2026
Alphabet	Search, Ads, YouTube, Android, Cloud	Gemini + Search AI + TPU + Google Cloud	FY2025 Revenue $403.0 Billion	Total GAAP Net Income for four quarters of 2025 was approx. $132.2 Billion	FY2025 CapEx $91.4 Billion; FY2026 guidance $175B–$185B
Amazon	E-commerce, AWS, Advertising	Bedrock + Trainium/Inferentia + AWS AI infra + Nova	FY2025 Revenue $716.9 Billion	FY2025 Net Income $77.7 Billion	Approx. $147.3 Billion in CapEx over the last 12 months as of March 2026; Over 2.1 million AI chips delivered in the past 12 months
Meta	Facebook / Instagram / WhatsApp Advertising	Llama + Ad Recommendation + Meta AI + Smart Glasses / Agent	FY2025 Revenue $200.97 Billion	FY2025 Net Income $60.46 Billion	FY2025 CapEx $72.2 Billion; FY2026 guidance $125B–$145B
Tencent	Gaming, Social, Advertising, Fintech	Hunyuan + WeChat /

The most notable thing about this table is not who makes the highest profit, but rather who possesses enough robust “cash cow” resources to withstand the upfront investment period required for AI.

Microsoft, Alphabet, Amazon, and Meta—these four companies are all essentially printing money while expanding production.

Tencent and Alibaba are similar; their old businesses are still providing blood, so they can ramp up their AI investments.

Baidu’s problem is more apparent: they bet early, but their scale and cash flow depth are weaker than the previous few companies, making them naturally less resilient under pressure.

Although all are involved in the AI craze, each company’s approach is actually different

Upon closer examination, it can be seen that although these companies are all engaged in intense competition, their strategic positioning is different.

Category 1: Selling the Shovels While Also Entering the Field

Microsoft, Alphabet, and Amazon belong to this category.

They have cloud, chips or accelerators, enterprise customers, and models or model ecosystems.

What is most frightening about these companies is that, for them, AI isn’t a standalone product, but rather an upgrade tax on the entire platform.

You want to train a model, so you need to buy cloud services.

You need to infer, you need to rent a GPU.

You will need to become/develop an Agent, and you will need to purchase platform services.

You need to provide employees with AI tools, which requires purchasing related capabilities for Copilot, Gemini, and Bedrock.

So, they don’t just want to create a blockbuster AI App; they aim to integrate AI into every part of the entire IT budget (or: IT expenditure).

Type Two: The Traffic King – Optimize Your Distribution Channels First

Meta, Tencent, and Baidu are closer to this category.

What they value most are not enterprise contracts, but rather traffic funnels/entry points, advertising systems, content ecosystems, and high-frequency user time spent.

Meta is the most typical example. It doesn’t need to sell AI to all enterprise clients; merely by applying AI in ad targeting, content recommendation, creative generation, and conversational entry points, it is enough to enhance advertising efficiency and commercialization capabilities.

Tencent is similarly positioned. Areas like WeChat, advertising, gaming, and cloud are inherently natural use cases for embedding AI. While it doesn’t necessarily have to compete with Microsoft by tackling the entire Office suite, it will certainly focus on dominating AI assistants within WeChat, improving ad placement efficiency, enhancing game content production, and deepening collaboration in enterprise WeChat.

Baidu, on the other hand, is more like a traditional search company striving to make a turnaround. It has models, cloud capabilities, and search, but it also faces the greatest pressure from its old advertising business. Therefore, it needs to both maintain its traditional traffic base while simultaneously boosting AI commercialization.

Category Three: Ecological King, Aiming to Transform AI into Trading and Industrial Infrastructure

Alibaba is the most typical example among this category.

Its ambition is not merely to create Qwen, nor is it simply about divesting Alibaba Cloud. What it truly seeks is to integrate AI throughout the entire ecosystem of e-commerce, merchant tools, customer service, marketing, supply chain, and industrial cloud.

So Alibaba has repeatedly emphasized “AI + Cloud” in recent years. These four words are not just a slogan because it realized earlier than many companies that: the model itself might not be the most profitable, but the model will redefine cloud and transaction platforms.

Why Has This Conflict Concentrated Semiconductor Demand So Heavily

Seeing this, we can actually get back to semiconductors.

If only a single company is optimistic about AI, what it generates will primarily be themed investing (or thematic investments).

If there are seven or eight platform companies with the strongest cash flows, and simultaneously converge capex, R&D, models, inference, Agents, and distribution channels onto the same layer/level, then it will no longer be a theme, but actual orders (or: real demand).

But in reality, the final orders will turn into these things:

GPU and customized AI chips.
HBM and high-end DRAM.
Enterprise-grade SSD and higher bandwidth storage.
High-speed networking, switching chips, optical modules.
Data centers, power, cooling.

This is why when analyzing the semiconductor sector lately, you shouldn’t only focus on a single memory manufacturer or a single South Korean company.

The true background is: Internet giants have unusually ramped up their efforts simultaneously in the same arms race.

Conclusion

As of May 12, 2026, my assessment of your point is:

Basically correct.
A more accurate statement should be: internet giants are not completely engaged in “the same product track,” but rather in an arms race over the same foundational AI capabilities.
The importance of this matter is not just because AI will produce blockbuster applications, but because it is drawing originally scattered profit pools—such as search, advertising, social media, e-commerce, office work, and cloud services—back into one common underlying competitive framework.
This is also one of the core backgrounds explaining why the semiconductor industry, especially the chains involving GPU, HBM, storage, and data centers, has been so intense recently.

Well, how do I say it? Previously, everyone could maintain their own separate domains. That’s not feasible anymore. The matter of large models has evolved from merely a new trend into an infrastructure war that platform companies cannot afford to exit.

References

Notes on Writing

Original Prompt

The previous text mentioned high AI demand and surging semiconductor prices, but there is a background that is easy for everyone to overlook: large AI models. This is rare—it’s a field where all major internet giants have started heavily competing. Previously, they developed in their own respective domains; it is uncommon now for them all to be intensely vying within a single sector. First, can I confirm if my viewpoint is correct? Then, let’s outline the sectors each company is developing, along with their respective revenues, net profits, and investments in AI.

Writing Idea Summary

First, judge whether the point of view is valid, and then clarify “to what degree it is valid,” avoiding making sweeping statements with one sentence.
In the main body, rewrite “same industry track” as “same layer of technical stack,” as this better aligns with the true positioning of various companies.
In the middle, use a summary table to place cash cows, AI entry points, revenue, net profit, and AI investment metrics side-by-side, allowing readers to quickly survey the whole picture.
The article intentionally writes out that “AI investment cannot be directly compared,” to avoid forcing rankings using disparate disclosure metrics.
This piece deliberately does not include OpenAI, Anthropic, xAI, and ByteDance together in the table, as they are non-public or have varying disclosure methods, which could easily bias the comparison.

Expanded Brainstorming

Area	Whether to include in the main text	Reason
Including Apple in the comparison?	No	It is certainly affected by AI, but it is not a typical sample within this

Internet Giant on Uncle Xiang's Notebook