The AI Market Is Entering a New Phase
Over the past year, AI has undoubtedly become the central topic in global technology markets.
From chips and cloud computing to enterprise software, advertising platforms, autonomous driving, and robotics, nearly every major tech company is reshaping its strategy around AI. In the early stages, the market’s pricing logic for AI was relatively straightforward—anything related to AI tended to attract investor attention.
However, this phase-driven logic is now shifting.
The market is moving from being "concept-driven" to "results-driven." Investors are increasingly focused not just on whether a company has an AI business, but on questions like:
- Can AI truly drive revenue growth?
- Will AI improve profit margins?
- Can large-scale capital investments create lasting competitive barriers?
- Is the AI business model sustainable?
This marks the industry’s entry into the second stage of the AI cycle.
In the first stage, the market traded on "future imagination." Now, it’s demanding more concrete commercial outcomes from companies.
This shift has also led to clear differentiation within the tech sector.
Computing Infrastructure Remains the Core Focus
Although profitability is gaining attention, AI infrastructure is still one of the strongest areas in the current market.
The reason is simple.
Whether it’s large model training, AI agents, or generative AI applications, all require massive computing power. GPUs, data centers, high-speed networks, and energy systems continue to form the foundation of the entire AI industry chain.
As a result, a new wave of capital expenditure around AI infrastructure is still underway.
Key areas currently in focus include:
- GPUs and AI chips
- Cloud computing platforms
- Data center development
- High-speed network equipment
- Enterprise-level AI computing services
Many major tech platforms are still ramping up AI-related capital spending, aiming to secure computing resources for the coming years. However, compared to the previous "growth-at-any-cost" phase, the market is now concerned: If AI demand growth slows in the future, will large-scale data center investments become excessive?
The cost of building AI infrastructure is also rising, with issues like electricity, cooling systems, and high-end chip supply potentially impacting industry profit margins. So even if some companies continue to deliver strong earnings, their stock prices may not surge as before—market expectations are already very high.
Clear Divergence Among Tech Giants
Another noticeable trend is the growing divergence in performance among leading tech platforms. Previously, the market viewed large tech companies as a unified group of "growth assets," but now, differences in AI commercialization capabilities are directly influencing valuation logic.
Some companies have successfully integrated AI deeply into their product ecosystems, generating stable revenue streams. Examples include:
- AI Copilot features in enterprise productivity software
- AI-powered advertising recommendation systems
- AI-driven automation tools
- Intelligent customer service and data analytics platforms
In contrast, other companies may have strong AI narratives, but struggle to prove their commercialization ability in the short term.
This means the market is shifting from "who is investing in AI" to "who can actually monetize AI."
At the same time, competition among tech platforms is intensifying.
In the future, the focus of AI industry competition may not be just on model capabilities, but also on:
- User ecosystems
- Data resources
- Cloud service capabilities
- Developer networks
- Ability to build closed business loops
Whoever can establish a complete ecosystem will have a better chance of gaining an edge in the next phase.
Competition in the AI Application Layer Is Heating Up
While the first phase of the AI market centered on "selling shovels"—the infrastructure—attention is now shifting to the AI application layer.
The reason is that long-term industry value is determined not by underlying technology alone, but by who can build sustainable business models.
Current areas of focus include:
Enterprise AI
More companies are experimenting with AI in office workflows, data analytics, automation, and customer service, aiming to boost efficiency and reduce operating costs.
AI Advertising and Recommendation Systems
AI is transforming advertising models on internet platforms. By improving content recommendations and user analysis, platforms can enhance ad conversion rates.
AI Agents
AI agents are seen as a potential major direction for the next phase. The consensus is that AI will move beyond simply answering questions to actively helping users complete tasks.
Content Generation
Video generation, AI programming, design assistance, and automated creative tools are expanding rapidly. However, competition in this space is fierce. Overall, the market is realizing:
The "AI concept" alone is no longer enough to support high valuations over the long term—what matters is the ability to generate sustained revenue.
Robotics and Autonomous Driving Regain Attention
Beyond software and cloud computing, robotics and autonomous driving are once again market hotspots.
As AI inference, visual recognition, and hardware performance improve, the market is revisiting topics such as:
- Humanoid robots
- Autonomous driving systems
- Industrial automation
- Intelligent logistics
- AI integration with physical industries
Many investors believe robotics could be the next extension of AI, with AI not only transforming the digital world but also making deeper inroads into the physical world.
However, the robotics industry is still in its early stages, with significant uncertainties around commercialization paths, cost control, and regulatory issues.
As a result, assets in this sector tend to be highly volatile.
Why Capital Markets Are Becoming More Cautious
A clear change in the current market is that capital is becoming more rational.
Previously, any company associated with AI could easily command a high valuation. Now, the market is increasingly focused on:
- Sustainability of profit margins
- Health of cash flow
- Whether AI investments are truly generating returns
- Stability of business models
This explains why many tech companies, despite reporting strong earnings, have seen muted stock price reactions.
The market has already priced in much of the expected future growth.
To some extent, the market is no longer satisfied with "storytelling"—it cares more about whether companies can actually deliver on their growth narratives.
Tokenized Stocks Are Bridging Tech Narratives and On-Chain Trading
As the tech market heats up, the digital asset industry is exploring new product formats that combine traditional markets with blockchain.
For example, Gate Tokenized Stocks offer stock tokens linked to tech themes, allowing users to track global tech trends and innovation stories through on-chain trading.
The emergence of tokenized stocks reflects a new industry trend:
Traditional financial assets and blockchain trading ecosystems are gradually converging.
For users interested in AI, tech platforms, and innovative industries, these products provide new ways to participate in the market.
However, it’s important to note that tokenized stocks may involve factors such as liquidity, volatility, product structure, and market risks. Participants should fully understand the relevant mechanisms before getting involved.
Risks to Watch Out For
Despite the ongoing AI boom, market risks persist.
High valuations remain a key concern in the tech sector. If future growth falls short of expectations, market volatility could increase significantly. Regulatory issues also deserve attention, including:
- AI data regulation
- Privacy and copyright concerns
- Platform antitrust measures
- Cross-border tech restrictions
Additionally, there’s ongoing debate about whether large-scale AI infrastructure investments could lead to temporary oversupply.
As more companies enter the AI space, competition may intensify further, and profit margins may not remain elevated over the long term.
So, even though the long-term outlook continues to attract market attention, short-term volatility risks should not be overlooked.
Conclusion
The tech market is shifting from "AI concept-driven" to the "commercialization and results phase." The first stage was about imagination; the second is about profitability, ecosystem barriers, and long-term competitive advantages. Computing infrastructure remains crucial, but AI applications, enterprise software, advertising platforms, robotics, and autonomous driving are becoming new focal points.
Going forward, the real question for the market is no longer "who has AI," but:
Who can truly turn AI into long-term, stable commercial value.
