By Jim, MSX Maitong
A year ago, if someone told you that Dell, Nokia, Cisco, Corning, Western Digital, etc., would once again become hot stocks in the AI trade, you'd probably think they were out of touch...
After all, for a very long time, the market's first reaction when talking about AI was typically NVIDIA, memory, optical modules, power, and data centers. They were either close enough to GPUs or directly in the hottest part of the computing power expansion. In contrast, old-guard tech companies like Dell, HPE, Nokia, Cisco, Corning, and Seagate were more often labeled as 'slow growth,' 'old story,' and 'no valuation elasticity.'
Yet, this very group of old-guard tech stocks, which previously seemed less sexy, has recently performed quite brightly, leading the market to start re-discussing them.
The market quickly and adaptively found the appropriate angle of interpretation: As AI moves from model parameters to real data centers, the market naturally begins to re-evaluate companies with proven delivery and infrastructure capabilities. This is why Dell, HPE, Nokia, etc., are being seen anew.
So, is this a genuine industry revaluation, or just a new narrative temporarily slapped onto these old tech stocks by the market?
I. The AI Rally Shifts Gears: Why Revalue Old-Guard Tech Stocks?
Over the past few years, the core thread of the AI trade has been very clear: first look at the models, then look at the computing power.
This is easy to understand. Those with the strongest models or access to the most GPUs received the most direct market premium. In this phase, investors were most willing to buy AI imagination, the computing power supply gap, and core beneficiaries like NVIDIA.
However, the issue is that AI cannot remain solely in press conferences and model parameters. After all, models need data centers to be trained; inference needs servers, networks, storage, and power to be deployed at scale; and for enterprises to truly use AI, they need complete IT architectures and delivery capabilities.
In other words, AI isn't a problem solved by a single GPU; it's an entire complex systems engineering challenge. This is also the starting point for the re-pricing of old-guard tech companies.
Previously, the market might have seen Dell and thought of PCs and traditional servers; seen HPE and thought of enterprise hardware; seen Nokia and thought of the old 5G equipment story; seen Cisco and thought of traditional networking gear; seen Corning and thought of glass and fiber materials; seen Western Digital and Seagate and thought of the hard drive cycle.
These labels aren't wrong, but in the AI infrastructure build-out cycle, their roles have changed. Building AI data centers requires rack-scale servers, liquid cooling, storage, network switches, fiber connectivity, data management, power infrastructure, and enterprise-grade delivery capabilities. The larger the AI cluster, the higher the demands on system integration, network transmission, storage capacity, and operational capabilities.
Therefore, the essence of this revaluation isn't a sudden wave of market nostalgia, nor old companies collectively jumping on the AI bandwagon. Instead, as AI moves into the order, revenue, and delivery phase, the market is starting to re-evaluate 'who can actually build the AI infrastructure.'
These companies may not be the sexiest, but they share a common advantage: their decades of accumulated customers, channels, supply chains, delivery experience, and infrastructure capabilities are becoming valuable again during the large-scale AI deployment phase.
In other words, AI is placing a batch of 'old assets' into a 'new demand' context for re-pricing.
II. From Servers, Networks to Storage: Old-Guard Tech Stocks Are Being Placed in the AI Infrastructure Chain
Overall, this round of AI-driven revaluation of old-guard tech stocks can roughly be divided into three lines: Servers & System Integration, Networking & Connectivity, and Storage & Data Management.
The first line is servers and system integration.
Dell is the most typical example. In its latest quarterly earnings, Dell delivered very strong numbers: Q1 FY27 revenue reached $43.8 billion, AI orders reached $24.4 billion, and it recognized $16.1 billion in AI server revenue. The company also raised its full-year FY27 AI server revenue expectation to $60 billion and raised its full-year revenue guidance midpoint to $167 billion.
These numbers are significant because they change how the market views Dell. Previously, investors looked at Dell mostly through the lens of the PC cycle, traditional servers, and enterprise hardware demand. But now, the market is starting to see if Dell can become the general contractor in building AI factories.
Its advantage is not in making its own GPUs, but in its supply chain, delivery capability, enterprise customer base, server system design, and integration capabilities within the NVIDIA ecosystem. An AI server isn't finished when a GPU is sold; it needs to be installed into racks, connected to networks, power, and cooling systems, and then delivered to cloud providers and enterprise customers.
Dell is capturing this phase from chip to system deployment. The logic for HPE is similar.
HPE's stock surged after its latest earnings, also primarily due to strong AI infrastructure demand. The company's Q2 revenue reached $10.68 billion, up 40% year-over-year; cloud and AI-related business revenue reached $7.71 billion, and it raised its full-year FY2026 growth outlook. More importantly, HPE also benefits from the networking capabilities brought by Juniper, making it less of just a traditional server company and more like an 'AI networking + enterprise infrastructure' platform.
Therefore, the revaluation logic for Dell and HPE is not that 'they are turning into NVIDIA,' but that they are becoming crucial system integrators in the AI factory construction crew.
The second line is networking and connectivity.
One of the most easily overlooked aspects of AI infrastructure is connectivity. Computing power does not exist in isolation. Data centers require high-speed internal interconnection, data centers need fiber optic connections between them, and as AI applications move to the edge and end devices, stronger telecom networks and wireless infrastructure are needed. The larger the scale of AI training and inference, the less networking and connectivity are mere supporting roles; they become the critical infrastructure determining computing efficiency.
This is also why Corning, Nokia, and Cisco are being discussed anew. Corning is a very typical example; it's not a traditional AI chip stock, but its fiber optics, optical connectivity, and optical communication materials happen to be important supporting components for AI data center expansion.
The company's Q1 2026 core sales reached $4.35 billion, up 18% year-over-year; within that, optical communications business sales reached $1.846 billion, up 36%. The company also mentioned that Gen AI product demand and new long-term agreements with large hyperscale customers were key growth drivers, indicating that AI data centers don't just need GPUs, but also the foundational materials that truly connect the computing power.
Nokia's story extends from traditional 5G equipment to AI-RAN, 6G, and AI-native wireless networks. NVIDIA previously announced it would invest $1 billion in Nokia, and the two would collaborate on advancing AI-RAN and the transition from 5G to 6G. This signal is important because AI traffic won't remain solely in data centers; it will enter end-user scenarios like phones, cars, robots, and AR/VR. As long as AI applications continue to spread to the edge and mobile networks, telecom infrastructure companies will regain narrative space.
Cisco's logic leans more toward data center networking. The company's Q3 FY2026 revenue reached $15.8 billion, up 12% year-over-year; data center switch orders grew over 40% year-over-year. It's important to remember that in AI clusters, the network is not just a simple cable but a critical component affecting data transfer efficiency, computing power utilization, and cluster stability.
The common logic for this category of companies is: the larger the scale of AI deployment, the more valuable networking and connectivity become.
The third line is storage.
This line has become widely known to the market in the past two months: AI doesn't just lack computing power, it also lacks storage. Previously, the market focused most on HBM, DRAM, and NAND. But now, high-capacity HDDs are back in the spotlight because AI model training, inference logs, video data, enterprise data, and cold data archiving all create larger storage capacity demands.
Western Digital is one representative. The company's latest quarterly revenue grew 45% year-over-year to $3.34 billion, and it provided next-quarter revenue guidance above market expectations. More importantly, the market noted that its high-capacity hard drive demand primarily comes from AI and cloud data centers. Seagate is similar, clearly benefiting from high-capacity nearline hard drives, with data center customers accounting for a growing share.
Of course, the AI era doesn't require all data to sit on the most expensive high-speed storage. Vast amounts of cold data, training data, log data, video data, and archival data still need cost-effective, high-capacity hard drives. Therefore, the revaluation logic for WDC and STX is not a sudden 'hard drive revival,' but that the AI data explosion is making storage a necessity once again.
III. What Constitutes a Genuine Revaluation?
However, the AI revaluation of old-guard tech stocks does not mean all old companies deserve blind optimism.
The most important distinction here is that some companies have genuinely entered the AI infrastructure chain. Therefore, judging whether such a company is truly being revalued involves at least three criteria:
- First, Is there order and revenue realization? For example, Dell's AI orders and AI server revenue, HPE's cloud and AI-related business, Corning's optical communications revenue, Cisco's data center switch orders, WDC's high-capacity hard drive demand—these matter more than simply telling an AI story.
- Second, Is there guidance revision upward? If AI remains only in press releases and product descriptions, stock prices can easily rise and then fall back. But if management is willing to raise full-year revenue expectations, business growth outlooks, or key product shipment forecasts, it suggests AI demand is no longer just short-term sentiment but may be altering the company's growth trajectory. This is also why the market is re-pricing companies like Dell and HPE.
- Third, Can profit quality keep up? The biggest issue for old-guard hardware companies has always been gross margins and cyclicality. Fast growth in AI server revenue does not necessarily mean high profit elasticity; rising storage prices may also be just short-term supply-demand mismatch; increased networking equipment orders also need to translate into sustainable profits.
A truly good revaluation should see improvement in revenue growth, order visibility, and profit quality together.
If only revenue goes up but gross margins are squeezed thin, or if demand is merely a short-cycle inventory restocking, then valuation re-rating will be limited. Ultimately, the market isn't buying 'old companies telling new stories,' but whether 'old assets plus new demand can become new profits.'
This is also the most noteworthy aspect of this round of 'old trees blossoming anew': AI will not turn all traditional tech companies back into growth stocks. It will only screen out those truly positioned at key infrastructure points and capable of converting AI demand into orders, revenue, and profits.
In Conclusion
Objectively speaking, at this stage of the AI rally, it's no longer just about 'whose model is stronger' or 'who has more GPUs.' The real change is that AI is entering the actual construction phase.
As more AI data centers are built, server companies will be re-priced; as computing clusters become more complex, networking companies will be re-priced; as data centers need more fiber connectivity, materials companies will be re-priced; and as AI data continues to explode, storage companies will also be re-priced.
This is why old-guard tech stocks are being seen anew by the market: they haven't suddenly become young again; rather, the AI era once again needs the infrastructure they possess.
But this also means this round of revaluation won't be evenly distributed among all 'old dogs.'
Only by truly entering the capital expenditure chain of data center and enterprise deployment can old-guard tech companies potentially move from 'valuation repair' to 'logical revaluation.'
