A potential multibillion-dollar chip deal between Google and Meta could challenge Nvidia's dominance in the AI hardware market. At the same time, new AI models from Anthropic, Google , and OpenAI are setting new records in coding and reasoning, showing how quickly the technology is advancing. These developments, alongside a looming memory chip shortage and new satellite internet competition, signal a period of rapid change across the tech industry.
Google's TPU Deal with Meta May Reshape AI Chip Market
According to reporting in The Information, Meta Platforms is in advanced talks with Google to invest billions in Google’s tensor processing units (TPUs). Meta plans to use the chips in its data centers starting in 2027, making Google’s custom silicon a credible rival to offerings from Nvidia . The market reacted quickly, as Alphabet's stock climbed over 4% while Nvidia shares fell, signaling that the AI chip market is becoming more competitive.
This move validates Google's long-term investment in its own AI chips. The company's new Gemini 3 AI model, which was trained on TPUs, recently became the first model to pass a key performance benchmark. Other major AI companies like Anthropic and OpenAI are also using or testing Google's hardware, proving TPUs are a legitimate alternative for top AI developers.
For Meta, which expects to spend nearly $72 billion on infrastructure in 2025, finding an alternative to Nvidia's expensive and hard-to-get GPUs is a smart financial move. While Nvidia still controls over 90% of the AI chip market, growing competition from Google, Amazon (with its Trainium and Inferentia chips), and Microsoft (with its Maia processor) points to a shift in the industry. This could lead to better prices and more chip availability for everyone developing AI.
Frontier AI Models Redefine Performance and Coding
November 2025 was a big month for large language models, with new releases like Google's Gemini 3 Pro, OpenAI's GPT-5.1, and Anthropic's Claude Opus 4.5 setting new performance records. According to benchmark analyses, Claude Opus 4.5 scored an impressive 80.9% on SWE-bench Verified, the industry’s toughest test for software engineering. Anthropic announced that the model even outperformed every human who has ever taken its internal engineering hiring exam, establishing new standards for AI-assisted programming.
A key advance in this generation of AI is its efficiency. Claude Opus 4.5 delivers its top performance while using nearly half the processing power of its predecessor, which helps lower the high cost of running AI applications. A new "effort parameter" allows developers to adjust the model's resource use, enabling them to balance cost and performance for different tasks. This could save large companies hundreds of thousands of dollars a year in infrastructure costs.
Google's Gemini 3 Pro offers its own unique strengths, especially in understanding and combining different types of information. The model introduces "generative interfaces" that create visual layouts instead of just text and can pull in real-time information from Google Search. The different strengths of these new models suggest that companies will increasingly use a mix of AI systems, choosing the best one for each specific job.
Memory Chip Shortages Could Limit AI Growth Through 2026
The semiconductor industry is facing a severe and widespread memory chip shortage. For the first time in about 30 years, supplies of DRAM, NAND flash, and high-bandwidth memory (HBM) are all running low at the same time, a situation one industry executive called unprecedented. The shortages, driven by huge demand for AI hardware, have caused DDR4 memory prices to jump by about 50% in some markets, with further increases expected.
The problem stems from a strategic shift by major manufacturers like Samsung, Micron, and SK Hynix. These companies have moved their production focus from older memory types to more profitable HBM, which is essential for AI chips. This has created a supply gap for mainstream memory, just as AI data center construction is driving record demand.
This shortage affects more than just consumer electronics. Industries like automotive and medical technology, which use older hardware designs for many years, are struggling to find the parts they need. PC and server makers are reportedly stockpiling memory, which only makes the price increases worse. Analysts believe these supply problems will continue well into 2026 and will not be resolved until new factories can be built.
Amazon's Leo Ultra Enters Satellite Internet Competition
Amazon has officially entered the satellite internet race against SpaceX's Starlink with its new Leo Ultra terminal. The service targets businesses and governments, promising download speeds up to 1 Gbps and upload speeds of 400 Mbps. While Starlink has a much larger network, Amazon aims to compete on performance and tight integration with its Amazon Web Services (AWS) cloud platform.
The competitive landscape is still tilted in Starlink's favor, with nearly 9,000 active satellites compared to Amazon's 150. However, Amazon has approval to launch over 3,200 satellites and has secured launch partners to do so. The company has already signed initial deals with partners like JetBlue and L3Harris and is launching a preview program for select customers before a wider rollout in 2026.
Amazon's key advantage may be its integration with AWS. A "Direct to AWS" feature sends data straight to the cloud without using the public internet, a major benefit for customers concerned about security and speed. For businesses in remote locations like mining, shipping, or emergency services, this secure, high-speed connection makes Leo a compelling alternative to Starlink.
Evolving AI Regulations Create New Timelines in US and EU
Rules for artificial intelligence are changing in Europe and the United States, creating new challenges for tech companies. On November 19, 2025, the EU proposed the "Digital Omnibus on AI" to simplify its upcoming AI Act. The proposal extends key deadlines for compliance, giving companies more time to adapt to rules for high-risk AI systems used in applications like credit scoring and hiring tools.
The EU's goal is to ensure that clear standards are in place before enforcement begins. The proposal also simplifies some rules, such as removing a registration requirement for certain systems, and gives providers more flexibility to use sensitive data to check for and correct bias in their AI.
In the US, the situation is more fragmented, with at least 16 states developing their own AI laws. The Trump administration considered an executive order to challenge these state laws but put the plan on hold after facing opposition. This uncertainty means that companies must navigate a complex patchwork of different regulations, creating a significant compliance challenge for any firm operating nationwide.
What This News Means for You
For people unfamiliar with these technologies, here is what these developments mean in practice. The battle to supply the "brains" for AI, which are powerful computer chips, is intensifying. For years, Nvidia has dominated this market, but now Google is emerging as a strong competitor. If big companies like Meta start buying Google's chips, this competition could lead to more innovation and lower costs, making the AI tools you use every day better and more affordable.
At the same time, the AI models themselves are becoming dramatically more powerful. New versions can now pass difficult programming exams that were once only for expert human engineers. This means that in the near future, software could be developed and improved much more quickly, leading to better and more reliable apps and websites. However, the huge demand for AI is causing a global shortage of computer memory. This could make all kinds of electronics, from laptops to cars, more expensive and harder to find for the next year or two.
Finally, new satellite internet services from companies like Amazon are creating more options for fast, reliable internet access, especially for people in remote areas. As all this technology grows, governments in the US and Europe are working to create rules to ensure AI is used safely and fairly. This is a slow process, and companies must adapt to different laws in different places, which is a major challenge. The key trend to watch is how this competition and regulation will shape the next wave of technology that impacts our daily lives.