Cisco, Nokia, BlackBerry and other veteran tech giants hitting collective new highs? The next round of AI opportunities is hidden in these legacy assets

In the past few years, the main theme of US technology stocks has almost been dominated by one word: computing power.

NVIDIA's GPUs, the data centers of Microsoft and Google, and the large models of OpenAI and Anthropic have formed the most prominent narrative of this round of AI bull market. The capital market once believed that as long as the models continued to grow, data centers continued to expand, and GPUs remained in short supply, technology stocks could continue to be re - valued upwards.

However, since 2026, a new change has been taking place.

The market is no longer just focusing on the "cloud brains". It has also begun to re - examine those long - neglected "nervous systems": network switches, optical communication links, wireless base stations, edge computing nodes, and in - vehicle and industrial - grade operating systems.

As a result, technology companies like Cisco, Nokia, and BlackBerry, which were once labeled as "old - fashioned stocks", have once again come into people's view.

This round of change certainly has an emotional component and also has the flavor of valuation repair. But if it is only understood as a catch - up rise of low - priced stocks, one may miss more important industrial signals: AI is moving from the cloud to the physical world, and the physical world needs new network, edge, and security infrastructure to support it.

In this article, the RockFlow research team will answer the following questions for you: Why these old companies? Why now? And who will be the next beneficiary?

The main investment theme of AI is spreading from "cloud computing power" to "physical - layer infrastructure"

In the past three years, the core logic of AI investment has not been complicated.

Training large models requires more GPUs, higher - bandwidth video memory, more dense data centers, and also sufficient and stable power. Naturally, capital has flowed to companies such as NVIDIA, TSMC, Broadcom, Super Micro, as well as Microsoft, Amazon, and Google, which are at the core of the cloud computing power chain.

At this stage, the market is pricing based on the idea that "whoever controls the training computing power controls the entrance to the AI era".

This logic has not become invalid; it has just become incomplete.

Large models are no longer confined to chat windows. They are starting to enter cars, robots, industrial equipment, power grids, communication networks, medical terminals, and urban infrastructure. AI is no longer just answering questions; it also issues instructions, controls equipment, coordinates resources, and even participates in real - time decision - making in the real world.

The problems have also become more specific.

If a chatbot is 200 milliseconds slower, most users will only feel a slight lag. However, if an autonomous vehicle is 200 milliseconds slower, the situation is completely different. If an office software crashes, it can be restarted. But if an industrial robot carrying heavy objects loses control, the consequences cannot be simply described as "poor user experience".

After AI enters the physical world from the screen, the bottleneck is no longer just computing power but also includes latency, bandwidth, stability, security isolation, and real - time control.

This is also the reason why physical - layer assets are being re - priced.

The physical layer here is not just about optical fibers and base stations. It is a complete set of underlying systems that support the implementation of AI, including:

High - speed switching networks inside data centers;

Routing, optical transmission, and backhaul links between the cloud and the edge;

5G/6G wireless access networks and communication base stations;

Edge computing nodes close to end - users;

Real - time operating systems in cars, robots, and industrial equipment;

Network security, identity authentication, and trusted execution environments for devices.

In the past, the common labels for these assets were slow growth, strong cyclicality, and limited imagination. Especially in the decade when cloud computing and software subscriptions swept the market, traditional network equipment manufacturers and communication equipment manufacturers once seemed cumbersome.

However, the physicalization of AI is changing this perception.

When AI agents, autonomous vehicle fleets, industrial robots, and smart power grids start to generate high - frequency, real - time, machine - to - machine data interactions, the network is no longer just a background facility. It is also a prerequisite for the normal operation of the AI system.

In other words, in the past three years, the market has been pricing the formation of the "AI brain". In the future, the market may gradually price the reconstruction of the "AI nervous system".

The reappearance of Cisco, Nokia, and BlackBerry is just a side view of this process.

This time is different from 1999: "Infrastructure ahead of demand" VS "Demand driving infrastructure"

Whenever there is a sharp rise in old - fashioned technology stocks, the market naturally thinks of the 1999 Internet bubble.

The story from that year is familiar: With the explosion of the Internet narrative, telecom operators and network equipment manufacturers made large - scale investments in optical fibers, routers, switches, and base stations. The capital market believed that traffic would grow indefinitely, so infrastructure construction far outpaced real demand.

However, the problem was that the number of Internet users, application complexity, and data throughput at that time were far from sufficient to absorb these investments. Eventually, a large amount of communication assets were left idle, the balance sheets of telecom operators deteriorated, and companies like Cisco and Nokia also experienced a long - term decline in valuation.

So, the core contradiction in 1999 was that infrastructure was ahead of demand.

Today's situation is the opposite. In the AI era, the generators of traffic are no longer just humans but also machines.

Although humans generate a large amount of traffic through daily Internet use, video watching, and message sending, their behavior frequency is still limited by human time, attention, and physiological rhythm. AI agents, autonomous vehicle fleets, industrial robots, and Internet of Things devices are different. They can operate 24/7, interact in milliseconds, continuously upload environmental data, call model interfaces, synchronize status logs, and perform local inferences.

This means that the nature of network traffic is changing:

From "human - to - human" to "machine - to - machine";

From low - frequency interaction to high - frequency interaction;

From content consumption to real - time decision - making;

From centralized cloud processing to cloud - edge - end collaboration;

From tolerable latency to low - latency or even ultra - low - latency.

This is the fundamental reason why physical - layer assets have regained investment value.

No matter how powerful the cloud models are, they must connect to terminals through the network, reduce latency through edge nodes, and control devices through secure operating systems. Without these infrastructures, it is difficult for AI to truly enter cars, robots, factories, and cities.

This is also the most fundamental difference between this round of market and the situation in 1999:

In 1999, roads were built in advance for the demand that had not yet arrived. In 2026, the demand is already on the way, but the roads are starting to get congested.

What new scripts have the three old - fashioned giants got?

In this round of physical - layer re - valuation, although Cisco, Nokia, and BlackBerry seem to belong to the category of "old - fashioned technology stocks", the directions in which they truly benefit are different.

Cisco: From an enterprise network equipment provider to an AI data center network platform

Cisco's core opportunity lies in the AI data center network.

If GPUs are the engines of the AI factory, then switches, routers, and network management systems are the transmission systems between the engines. The higher the transmission efficiency, the higher the overall computing power utilization rate.

Cisco's advantages are mainly reflected in three aspects:

A long - term accumulated customer base in enterprises and data centers;

Self - developed network chips such as Silicon One and high - performance switching capabilities;

After the consolidation of Splunk, software reinforcement in observability, security monitoring, and log analysis.

Especially Splunk is of great significance to Cisco.

In the past, Cisco was more regarded as a hardware company, and its valuation center was suppressed by the hardware cycle. The addition of Splunk enables it to combine network equipment, security monitoring, traffic analysis, observability, and automated operation and maintenance to form a higher proportion of software subscription revenue.

This means that Cisco's story is not just about "selling switches". It also provides a set of network efficiency, security monitoring, and operation and maintenance management solutions for AI data centers.

For institutional investors, the indicators really worth tracking include:

The proportion of orders related to AI data centers;

The growth rate of software subscription revenue;

The change in gross profit margin;

The concentration of large customers;

The competition and cooperation relationships with ecological partners such as NVIDIA and Broadcom.

Cisco has relatively higher certainty but may have less elasticity than BlackBerry. It is more like a "stable core asset" in the physical - layer re - valuation.

Nokia: From a communication equipment provider to a participant in AI - RAN and edge networks

Nokia's opportunity lies in the AI - enabled transformation of telecom networks.

It owns assets such as wireless access networks, core networks, optical networks, and communication patents and still holds an important position in the global operator system. If AI - RAN enters the large - scale deployment stage, Nokia is expected to benefit from base station upgrades, edge computing, network intelligence, and patent licensing.

However, Nokia's challenges are also obvious.

The capital expenditure cycle of telecom operators is strong, and the global communication equipment market is highly competitive. Whether operators are willing to make large - scale investments in AI - RAN depends on the actual commercial return, not just the technical feasibility.

Therefore, two issues need to be observed in Nokia's re - valuation logic:

First, whether AI - RAN can really help operators make money. If it only increases base station costs without bringing in new revenue, it will be difficult for operators to pay for it in the long run.

Second, whether Nokia can obtain sufficient profits in the AI - RAN value chain. If computing power chips, cloud platforms, and the application layer take away most of the value, equipment manufacturers may still only earn hardware profits.

So, Nokia's investment logic is more of an "industry inflection point type". It has strong narrative elasticity but also requires more strict tracking of order fulfillment and profit margin improvement.

BlackBerry: From a once - popular mobile phone brand to a provider of secure real - time operating systems

BlackBerry's change is the most dramatic and is also most likely to be misinterpreted by the market.

Its value lies not in mobile phones but in its QNX and network security businesses. Especially QNX has strong scarcity in intelligent vehicle, industrial control, and robot systems.

BlackBerry's room for imagination comes from two directions: the upgrade of the intelligent vehicle electronic architecture; the large - scale development of robots and industrial intelligent devices.

In the past, the value of QNX per vehicle was limited, and it mainly served dashboards, infotainment systems, and some control modules. In the future, as intelligent vehicles evolve towards central computing, cockpit - driving integration, and autonomous driving, the importance of the underlying secure operating system will increase, and the value per vehicle is expected to expand.

If embodied intelligent robots enter large - scale mass production, QNX may also spill over from the automotive market to a wider range of industrial and robotic scenarios.

However, BlackBerry also has risks.

Its business scale, profit stability, and customer conversion rhythm still need time to be verified. When the market gives a high - elasticity valuation, it often reflects the growth expectations for many years in advance. Once the order rhythm fails to meet expectations, the stock price will fluctuate more violently.

So, BlackBerry is more like a "high - elasticity option" in the physical - layer re - valuation. Its upside potential comes from the platform - based expansion of QNX, while its downside risk comes from the commercialization rhythm and the pre - overdrawn valuation.

External beneficiaries: The physical - layer revolution will not only belong to three companies

If AI infrastructure spreads from the cloud to the edge and the physical layer, the beneficiaries will not be limited to Cisco, Nokia, and BlackBerry.

The more complete industrial chain also includes:

High - speed switching chips, represented by companies like Broadcom and Marvell. The benefit logic is that the upgrade of Ethernet in AI data centers brings demand for switching chips and PHY chips;

Optical modules and optical interconnections, represented by companies such as Coherent and Lumentum. The benefit logic is that higher - bandwidth optical connections are needed for internal data center and cloud - edge transmissions;

Communication companies and edge real - estate, represented by companies like American Tower and Crown Castle. The benefit logic is that AI - RAN and edge nodes increase the value of site locations, power supply, and machine rooms;

Network security, represented by companies like Palo Alto, Fortinet, and CrowdStrike. The benefit logic is that AI agents and edge devices increase the attack surface;

Industrial automation, represented by companies like Siemens and Rockwell. The benefit logic is that the implementation of industrial AI requires end - side control and security systems.

This round of physical - layer re - valuation is not an isolated market trend. It is expected to benefit an entire industrial chain that spreads from cloud computing power to networks, base stations, edges, terminals, and security.

The capital market often trades the most easily understandable targets first and then gradually spreads to more hidden links.

Therefore, the RockFlow research team believes that one should not only look at the stock price increase but also break down the real industrial causal chain:

Who really has the pricing power?

Who only benefits from short - term orders?

Who can convert hardware revenue into software subscriptions?

Whose patents and standards are irreplaceable?

Whose valuation has already overdrawn the next three to five years?

These questions are more important than simply judging whether the stock price has risen too much or not enough.

Conclusion: The next stage of AI is not just about the cloud

In the past three years, the capital market has been used to understanding AI as models, chips, and data centers in cloud servers.

However, the real way for AI to change the world will not stop at browser windows. It will eventually enter cars, robots, factories, power grids, cities, and communication networks. At that stage, what determines whether AI can be implemented is not only model parameters and the number of GPUs but also network latency, bandwidth, security, real - time control, and system stability.

This is the reason why Cisco, Nokia, and BlackBerry have been rediscovered by the market.

They represent a horizontal expansion of the AI investment framework:

From cloud computing power to physical - layer infrastructure;

From training large models to deploying agents;

From "making AI smarter" to "letting AI truly control the real world".

The long - term logic of this main theme is worthy of attention.

However, we also need to note that industrial trends and stock price rhythms are not always synchronized. The companies that can truly cross the cycle are those that can occupy key nodes in data, networks, security, standards, and operating systems and continuously convert technical barriers into cash flows.

In the first stage of AI, the market rewarded the cloud brains.

In the second stage of AI, the market has begun to re - price those infrastructure companies that have long been overlooked.

The rise of Cisco, Nokia, and BlackBerry may only be the beginning of this re - valuation. After the boom, the companies that can stay must prove that they are not only in the narrative but also on the basis of orders, profits, and irreplaceability.

This article is from the WeChat official account “RockFlow Universe”, author: RockFlow, published by 36Kr with authorization.