As the world's largest mobile operating system, Android has built a near-zero entry for Google's traffic. Through pre-installed search, Chrome, and YouTube applications, Alphabet has transformed 3 billion device users into sustained cash flow. More importantly, the massive amount of behavioral data (search records, location information, etc.) generated by the system has become the exclusive nutrient for training the Gemini large model, forming a self-reinforcing flywheel for "data AI-monetization". In fact, Google's seven major products have a user base of over 2 billion, creating the largest digital ecosystem in human history. When users leave their footprints on platforms such as search, YouTube, and maps simultaneously, the data synergies are exponentially amplified. This ecological col
Amazon Network Services (AWS) announced a plan on Monday to build and deploy for the first time a system dedicated to artificial intelligence and high-performance computing for the US government. The plan promises to invest up to $50 billion to enhance the company's artificial intelligence and supercomputing capabilities to US federal government customers. This investment will launch in 2026, when it will build data centers on AWS's Top Secret Area, AWS Secret Area, and AWS Government Cloud (US) Regions, using advanced computing and networking technologies, adding nearly 1.3 gigawatts of artificial intelligence and supercomputing capabilities.
Some AI bubbles exist in some area or companies already. Mag 7 sound ok at this moment The AI startups of "potential unicorns" are on the brink of bankruptcy due to failure to raise enough funds, including the once-scented Builder.ai and Rodin AI. Builder.ai, which has been backed by Microsoft (MSFT), Qatar Investment Authority (Qatar Investment Authority) and SoftBank, claims to be able to develop applications with AI, with a valuation of over $1.5 billion at one point. However, in May this year it was exposed that it had exaggerated sales by a full 300%, and its so-called "artificial intelligence" writing app was actually relying on Indian outsourcing engineers to write code manually and use human intelligence to tease artificially. As a result, the entire AI concept valuation
Huang emphasized that energy constraints are the core physical boundary of current AI development, and the upper limit of all calculations is ultimately constrained by bit flipping and the energy required for information transmission. "We are far from reaching those fundamental bottlenecks that truly limit development," he said. At the same time, our task is to build a more energy-efficient computing platform. Meanwhile, Huang mentioned that improving the energy efficiency of AI computing is NVIDIA's current priority. He emphasized that since 2016, the energy efficiency of AI computing has increased by 10,000 times, and this progress is comparable to the technological singularity in the automotive and lighting industries in terms of energy density improvement. To build smarter systems must