Cloud PCs Get a Second Chance at Life as Google, Alibaba, and Microsoft Battle It Out in the Cloud AI Space
Just before the Google I/O Conference, Google held a pre - event for Android 17 in the early hours of May 13th. Unexpectedly, at this event, Google suddenly announced a brand - new product series: Android computers. Different from Chromebooks, Android computers are positioned at the high - end market, with productivity as their core selling point. Google is no longer satisfied with the entry - level market and aims to seize more shares in the PC field beyond netbooks.
The concept of AI PCs has been very popular in recent years. Countless PC chip and terminal manufacturers have been emphasizing the AI features of their products, repeatedly touting the new changes brought by AI to PC usage scenarios. The emergence of Android computers presents a brand - new solution for AI PCs: no longer relying on traditional desktop systems, with cloud - based AI as the core rather than an accessory, from which all related functions are derived.
(Image source: Google)
If Android computers succeed, cloud computers are likely to become the definitive answer in the AI era.
Current AI PCs are not "AI" enough
Currently, AI PCs in the PC industry are more like traditional PCs with an AI layer added. In terms of chips, both Intel and AMD have added independent AI computing units to PC processors to enhance their edge - side AI capabilities. In terms of systems and ecosystems, terminal manufacturers have been building their own AI applications in the system, including their own computer management software and intelligent agents, and have connected to external large - scale models.
However, these AI PCs are essentially still traditional Windows computers, and AI is more of an icing - on - the - cake feature. Moreover, most of the AI scenarios implemented on AI PCs are based on cloud - based AI, including document summarization and modification, image generation, and various "lobster" tools.
Although chip manufacturers have been promoting the local AI capabilities of their chips and emphasizing scenarios where open - source models are deployed using heterogeneous computing with CPU, GPU + NPU. In reality, the AI computing power provided by consumer - grade PC chips is always limited. After all, not every consumer has a 5080 graphics card and at least 32GB of memory.
(Image source: JD.com)
In this situation, an ordinary consumer - grade PC can hardly run large - parameter local models, and thus cannot truly undertake more complex AI tasks.
Recently, OpenClaw became extremely popular, causing Mac mini to be out of stock and its price to increase. However, most people are using cloud - based models to "raise lobsters". Various lobster deployment tutorials mention which AI's tokens are cheaper and how to reduce token consumption.
(Image source: Gitbook)
As a result, a new question arises: Since AI PCs still rely on cloud - based AI to implement AI scenarios, what is the hardware value of AI PCs themselves?
After all, theoretically, a traditional PC without the premium of an AI chip can also transform into an AI PC as long as it can connect to the cloud - based AI via the Internet.
Even more radically, we can significantly reduce the hardware configuration of a PC. As long as it has a screen, a keyboard, and Internet connectivity, it can become a cloud - based AI computer. The rapid development and popularization of AI seem to provide an opportunity for the not - so - new "cloud computer" to thrive.
Cloud computers + AI: The future of AI PCs?
Cloud computers are not a strange concept to us. The cloud gaming that was very popular a few years ago was essentially implemented in the form of cloud computers. At that time, with the full popularization of 5G, its low - latency and high - throughput characteristics were regarded as a panacea for the popularization of cloud computers.
However, the reality is harsh. The concept of cloud gaming has never really taken off. Google's cloud gaming service Stadia, launched in 2019, was shut down in less than three years. According to overseas media reviews and user feedback, for Stadia to achieve a smooth experience close to that of local game platforms, it has extremely high requirements for network quality. For example, it requires a high - speed local broadband for wired connection. Even using WiFi will significantly reduce the gaming experience, not to mention using a more volatile 5G mobile network.
(Image source: Google)
However, cloud gaming is highly sensitive to network latency, while online AI has a much higher tolerance. As ordinary users, we are already used to AI taking time to "think" when answering questions and processing tasks, and we are not as eager for AI results as we are for game responses.
Ultimately, the bottleneck in AI response speed lies not in network speed but in computing power. Even if you install a local large - scale model, it still needs sufficient inference time to generate an answer.
Therefore, we believe that the form of cloud computers is naturally suitable for AI PCs. Google's Android computers are creating AI PCs in a mode different from traditional PCs. On Android computers, AI is not an accessory but the core function. Google says that most current AI tools are independent apps, and users have to copy data into the AI interface to use AI functions. Android computers integrate AI into every aspect of the system. Most intuitively, wherever the mouse pointer moves, AI appears. AI will capture information such as text, images, and code near the pointer and directly process and operate them.
(Image source: Google)
In addition, the implementation solutions for Android computers are very diverse. For Android computers, Google mainly provides product ideas and implementation forms, and the hardware itself still needs to be built by partner manufacturers. According to the partner brands announced by Google, they are mainly divided into two categories: chips and terminals. The former includes Intel, Qualcomm, and MediaTek, and the latter includes HP, Lenovo, Acer, ASUS, and Dell.
From the perspective of chip brands, it can be seen that Google doesn't care what architecture of chips are used in Android computers. Both X86 and ARM are acceptable. After all, currently, the implementation of AI scenarios on Android PCs still highly depends on the cloud - based Gemini, and the computing power of local hardware is relatively less important.
In addition, Internet and cloud service providers have been providing cloud computer services and evolving towards AI PCs.
Take Alibaba for example. In 2024, it launched the Wuying AI cloud computer, which not only has powerful cloud - based hardware configurations but also provides comprehensive support for large - scale models. By 2026, the Wuying AI cloud computer was further upgraded to provide full support for OpenClaw lobster - raising, enabling one - click deployment, direct access to Qianwen, and connection with communication tools such as DingTalk, Feishu, and WeChat.
(Image source: Alibaba Cloud)
It's also worth noting that AI giants are engaged in a fierce arms race in AI infrastructure construction, which is the "culprit" for the increase in storage prices. Moreover, there is no sign of storage price reduction in the short term. As a result, the configuration upgrade of consumer - grade PCs will be further restricted. If we still use the traditional PC iteration model to build AI PCs, it will be extremely difficult. Instead of investing high costs in local AI configurations with an obvious ceiling on computing power, it's better to directly hand over AI tasks to the cloud.
The times have changed. How should PC manufacturers respond?
The AI transformation of PCs is an irreversible trend. All players in the PC industry chain are racking their brains on how to board the AI PC bandwagon. Due to their different roles, their approaches to promoting AI PCs also vary.
First, chip manufacturers are still constantly emphasizing the AI computing power of consumer - grade chips and building AI scenarios around it. More importantly, both Intel and AMD are continuously making efforts in the server market, constantly competing for orders from AI giants.
After all, AI manufacturers need to purchase a large number of AI chips for AI infrastructure construction. Besides NVIDIA, the main players capable of fulfilling these orders are traditional CPU brands like Intel and AMD.
AMD's latest financial report shows that the "Data Center" business segment contributed $5.8 billion in revenue in the first fiscal quarter, accounting for more than half of the total. Moreover, neither Intel nor AMD can meet the order volume. AMD has been seeking assistance from other wafer foundries such as Samsung in addition to TSMC.
(Image source: AMD)
Second, terminal manufacturers include traditional PC brands such as Lenovo, ASUS, and HP, as well as emerging brands like Huawei, Xiaomi, and Honor. Currently, their efforts in building AI PCs are mainly based on the traditional architecture of Intel/AMD chips + Windows systems, enhancing the AI capabilities of PCs by implanting software such as computer management software and intelligent agents.
Meanwhile, mobile phone brands have an advantage in the AI PC field. They can connect PC products with various devices in their hardware ecosystem, such as mobile phones, in - car systems, wearables, and smart home devices, enabling seamless transfer of AI capabilities across devices. Take Xiaomi for example. Super Xiaoai, a tool with multiple capabilities such as an intelligent agent, an AI assistant, and a voice assistant, can appear on various devices in the Xiaomi ecosystem.
(Image source: Xiaomi)
In addition, Apple is a special case in the AI PC field. Apple Intelligence was announced a long time ago, but its implementation progress has been slow, making the AI transformation of Macs rather awkward. However, Apple still has an unparalleled advantage in the PC field: its integrated hardware - software capabilities, with absolute control over M - series chips and the macOS system.
Recently, Apple increased the production of the MacBook Neo from 5 million to 10 million units and spared no expense to maintain the production of the A18 Pro chip. Thanks to the success of this laptop, according to the Q1 online laptop market data released by Lotu, Apple has become the PC brand with the second - largest market share in the domestic market after Lenovo.
(Image source: Lotu)
Against the backdrop of soaring storage prices, the budget - friendly MacBook has shown amazing appeal. Frankly speaking, the MacBook Neo was not initially well - regarded and seemed more like a product to consume the inventory of A18 Pro chips. This shows that Apple is capable of creating successful budget - friendly PCs. Once it has a solid user base, MacBooks powered by Apple Intelligence have the potential to catch up and lead in the AI PC era.
Finally, Microsoft, as the dominant player in the PC system, cannot be ignored. Microsoft's actions regarding AI PCs mainly focus on three aspects: defining AI PC hardware standards, system reconstruction, and diversifying hardware architectures.
Microsoft requires AI PCs to have a computing power of over 40 TOPS and at least 16GB of memory. It has introduced Windows Copilot Runtime at the Windows core and integrated multiple small - scale models. At the same time, Windows provides AI functions such as real - time subtitles and recall.
(Image source: Microsoft)
One crucial point is that Copilot uses the large - scale model technology of GPT and the Internet - connection ability of Bing, and is deeply integrated into the Windows system, Edge browser, and Office 365, fully leveraging its ecological advantages. And this still mainly relies on cloud - based AI capabilities.
In conclusion
The emergence of Android computers challenges the traditional PC form that has remained unchanged for many years. It represents another product concept for PC development in the AI era: emphasizing the cloud while reducing reliance on local hardware.
In today's context where storage costs remain high and local consumer - grade computing power has reached a bottleneck, this solution that breaks through hardware barriers and directly entrusts core productivity to cloud - based large - scale models is undoubtedly more imaginative.
Of course, the PC form transformation triggered by AI has just begun. Microsoft