Google releases its first AI laptop: a revolution from operating systems to intelligent systems

Byline: Techub News Compilation

On the eve of the annual Google I/O developer conference, Google unexpectedly released a series of major AI products and strategic partnerships ahead of schedule, with its most eye-catching offering being its first laptop designed specifically for AI, “Google Book.” This is not only a hardware breakthrough; it also represents Google’s comprehensive evolution from “operating systems” to “intelligent systems.” At the same time, Google’s moves in AI infrastructure and cutting-edge applications have been just as rapid, including partnering with SpaceX to build space-based AI data centers, and its biotech subsidiary Isomorphic Labs securing $2.1 billion in funding. Together, these initiatives outline Google’s full-stack advantages in the next wave of AI competition.

AI Laptop: Google Book and the Revolution of Intelligent Systems

The “Google Book” laptop that Google rolled out this time was designed to be built for AI from the ground up. Its core idea is to create an “intelligent system” rather than a traditional operating system. This means that AI capabilities—especially its flagship model, Gemini—will be deeply integrated into every interaction layer of the device, becoming the system’s core.

One of the device’s signature features is the “Magic Pointer.” Users simply hover the cursor over a date in an email, and Gemini can automatically schedule a meeting; hover over a living room photo, and Gemini can synthesize a rendering effect of a new sofa. Users can even ask it to plan a family gathering, and it can automatically generate a real-time dashboard including flight and hotel information and a countdown timer. All these features are natively integrated into the new device.

From a hardware perspective, Google Book is a natural evolution of the Chromebook concept. In 2011, Google introduced Chromebooks with the browser at the center, reshaping how laptops are used. Today, Google Book represents a new round of transformation: AI is “consuming” the browser and becoming the new core interaction layer. The device itself is beautifully designed—something of a hybrid of MacBook Air and MacBook Pro—and priced between $200 and $500, making it highly competitive.

More importantly, Google Book is deeply integrated with the Android mobile ecosystem, giving users a coherent experience similar to the Apple ecosystem, while delivering AI software capabilities that Apple had once promised but failed to deliver. For iOS users, it may feel more like an interesting experimental device, but the native AI experience it demonstrates signals the direction of personal computing devices.

Ecosystem Expansion: Encroaching on Apple’s Market and Forming an AI Alliance

This release from Google is not only about hardware, but also a full-scale ecosystem play. They introduced “Gemini Intelligence,” an AI model system that can carry out actions across all of Google’s applications, tools, and products (such as Gmail, Google Maps, and G Suite). Google’s vertical integration advantages are on full display here: they have the model layer (Gemini), the computing layer (GPU), and leverage an enormous product matrix to achieve unparalleled distribution.

Google is actively simplifying the process of data migration for users moving from the Apple ecosystem to the Google ecosystem. This reflects Google’s Android-style open philosophy. Analysis suggests that as Apple appears to be stumbling in AI progress, Google is taking the opportunity to chip away at its market share. Although Apple has signed licensing agreements for Gemini models worth billions of dollars, there are no signs that it is building foundational models on its own. Google, on the other hand, is seizing the timing—releasing new products a week ahead of major launch events to actively compete for market share.

In addition, an “AI alliance” composed of companies such as SpaceX AI, Anthropic, Tesla, Google, and Cursor is taking shape. This is a mutually beneficial symbiotic relationship: Google gains a low-cost route to space and supposedly unlimited solar energy; Anthropic obtains 300 megawatts of inference compute from SpaceX’s “Colossus One” data center; SpaceX earns about $5–10 billion in revenue through transactions with Anthropic and Cursor; and Cursor gains access to flagship coding model compute power that they would otherwise be unable to afford. Notably, OpenAI currently appears to be excluded from this alliance.

Space Ambitions: Google and SpaceX Collaborate on AI Data Centers

Another major development this week is the partnership between Google and SpaceX in the field of AI data centers. SpaceX appears to be collaborating with multiple AI giants to deploy data centers in space. After last week’s agreement with Anthropic, Google also joined this week. SpaceX will use its launch capabilities to send Google’s TPUs (Tensor Processing Units) into space.

This is not a sudden novelty. Google CEO Sundar Pichai announced about six months ago that Google is developing radiation-hardened TPUs for use in space. They need a way to send these devices into orbit, and SpaceX provides the most economical “space highway.” Google is already a shareholder of SpaceX (holding 6.1%), and both sides have a shared foundation of interests. Google also has an existing space machine learning project called “Project Suncatcher,” and it collaborates with other rocket launch companies and satellite design firms such as Planet Labs.

As SpaceX nears its IPO, one of its clear goals is to become an infrastructure provider for this new space race. The partnership between Google and SpaceX marks the formal extension of competition in AI computing infrastructure into the space domain.

Biotech Frontiers: Isomorphic Labs and AI-Driven Drug Discovery

Google’s biotech subsidiary Isomorphic Labs, focused on AI drug discovery, announced this week that it secured $2.1 billion in funding, led by Thrive Capital. The company is led by Demis Hassabis, CEO of Google DeepMind—often dubbed the “DeepMind of the biotech sector.”

Its breakthrough results stem from early work on the “protein folding” problem. Proteins are key regulators of human bodily functions, and understanding their structure is crucial for developing therapies targeting specific diseases. The AI models developed by Demis Hassabis’s team (such as AlphaFold and AlphaGo) can predict the three-dimensional folded structures of proteins and, based on that, design molecular drugs that match specific protein “locks” with the precision of a “key.”

Isomorphic Labs’s core technology is a model called “ISO DDE” (Isomorphic AI Drug Design Engine), which can identify vast numbers of new molecules. The technology has already been made freely available to about 300,000 top-tier research personnel worldwide, and it has helped identify a range of new molecules that may be used to treat major diseases such as Alzheimer’s disease and cancer. This massive round of funding will be used to push these discoveries into human trial stages, and it is expected that the first real therapies could be produced within the next few years.

This technology is not only about blocking diseases; in the long run, it may also open the door to manipulating human bodily functions—such as enabling people to see infrared light with the naked eye—bringing “science-fiction” capabilities closer to reality. Demis Hassabis’s long-term commitment to this field makes him a key driver of this revolutionary product.

Hardware Marvels and Progress in Chinese Robotics

Beyond software and infrastructure, there is also notable progress in hardware. China has developed a giant mech robot that costs about $50,000 and can be “driven” by a human. Weighing over 500 kilograms, it even demonstrated in a showcase that it could knock down a brick wall. While its practical use may seem unclear (“just because it can”), it showcases possible future forms of devices and also reflects China’s traditional strengths in robotic hardware manufacturing and large-scale production.

At the same time, Thinking Machines Labs, founded by former OpenAI CTO Mira Murati, released its new model this week after nearly two years of silence. This model is not a traditional LLM (large language model), but an innovative “interaction model.” What makes it unique is its “single-modal” design, which can process audio, video, and text inputs at the same time—enabling human-like real-time, two-way interaction. It can listen to the user’s interruptions while speaking and respond immediately, overcoming the “one-way call” limitations present in many current AI interactions.

However, the model has roughly 12 billion parameters, which is smaller than some frontier models currently rumored to be as large as 1.5 trillion parameters. That suggests its intelligence may be limited. A few hours later, Meta also released an AI voice dialogue product with similar capabilities. This reflects that even with well-known founders and large rounds of funding, small AI labs still face major challenges when trying to catch up with large AI labs like OpenAI and Anthropic.

Investment Market Dynamics and Equity Disputes in AI Companies

Anthropic, one of the hottest AI labs right now, has raised hundreds of billions of dollars while also triggering an investment controversy in the secondary market. Since its shares are not publicly listed, an active “secondary market” has emerged. Through special purpose vehicles (SPVs), some investors who received allocations can resell their shares to ordinary retail investors.

This week, a user named Ash Aurora claimed on social media that, through an intermediary facilitating an Anthropic secondary market transaction, they profited more than their net worth accumulated over their entire career in their 20s. The incident drew widespread attention. Shortly after, Anthropic quickly updated its support page, stating that “any sale or transfer of shares without board approval is invalid and will not be recognized by the company.”

OpenAI has also issued similar statements. This means that many investors who bought into these SPVs via unofficial channels may not be able to cash out their equity when the company goes public in the future, putting their funds at risk. This move has, to some extent, cooled the speculative heat in the secondary market. Meanwhile, on the blockchain, Anthropic’s equity has been tokenized and traded at a valuation of as high as 1.5 trillion USD—far above its actual valuation (currently rumored to be about 90 billion USD in a new funding round). The company’s official clarification helps the market recalibrate.

Global AI Political Economy: US Business Leaders Visit China

This week, SpaceX AI CEO and others arrived in Beijing with a US delegation for a visit. The delegation includes tech leaders such as Elon Musk and Jensen Huang. Their agenda includes discussions under bilateral frameworks covering trade rebalancing, energy security (especially pushing for Iran to open the Strait of Hormuz to reach a peace agreement), rare earth supply chains, and AI risk and safety.

Choosing Musk and Jensen Huang to accompany the delegation is not a coincidence. Musk’s Tesla has been deeply involved in the China market for a long time, and Jensen Huang has been working to sell NVIDIA GPUs in China. He had previously said that understanding China’s AI progress is crucial, and that the level of China’s AI models can be inferred from the hardware they use (especially US-made hardware).

However, current US policies tend to ban the sale of advanced GPUs to China and require manufacturing and GPU production to be “reshored” back to the United States. This puts both sides in a stalemate: China needs to purchase GPUs, but the US intends to restrict them. In response, the Chinese government has ordered its major AI labs to use domestically produced hardware and GPUs to train models. Recent models such as DeepSeek V4 and Kimi K2 are mainly trained on domestically produced GPUs from companies like Huawei, and they perform quite well—comparable to Claude Opus 4.7 in some aspects, but with lower cost and faster speed. This visit may aim to ease tensions and open up the huge China market for companies like NVIDIA.

As these technology leaders visit, the global landscape of competition and cooperation in the AI field is undergoing subtle yet significant changes.