Google's latest AI model, Gemini 3, has sparked significant discussion within the tech industry, drawing attention from major players like Nvidia and OpenAI. The model, powered by Google's custom-made Tensor chips, is setting new benchmarks in AI performance, suggesting a shift in the competitive landscape.
This development comes as tech giants increasingly invest in their own hardware to support advanced AI capabilities. The implications extend beyond immediate sales, potentially influencing the future direction of AI infrastructure and investment portfolios globally.
Key Takeaways
- Google's Gemini 3 model leads in several AI benchmarks.
- Google's Tensor chips are custom-made ASICs for specific AI tasks.
- Rivals like Nvidia and OpenAI acknowledge Google's advancements.
- Meta and Anthropic are reportedly exploring Google's chip technology.
- The AI chip market is becoming more diverse with increased ASIC adoption.
Gemini 3 Sets New Performance Standards
Google's Gemini 3 model, launched on November 18, has quickly risen to the top of various benchmark leaderboards. It excels in tasks such as text generation, image editing, and converting text into images. This places it ahead of competitors like OpenAI's ChatGPT and Anthropic's Claude in these specific areas.
The model's rapid adoption highlights its potential impact. Google reported that over one million users engaged with Gemini 3 within its first 24 hours. This engagement occurred across the company's AI coding program and tools designed for digital service integration.
"The leap is insane — reasoning, speed, images, video… everything is sharper and faster. It feels like the world just changed, again," wrote Salesforce CEO Marc Benioff on X after trying Google's new model.
AI Model Usage
- ChatGPT boasts at least 800 million weekly active users.
- Google's Gemini app has 650 million monthly active users.
Despite Gemini 3's strong performance, the AI ecosystem remains varied. Different AI models often serve different purposes. For instance, models from xAI and Perplexity currently rank higher in search performance benchmarks compared to Gemini 3.
Angelo Zino, senior vice president and technology lead at CFRA, notes that Google is "just kind of another piece to this AI ecosystem that continues to get bigger." This suggests a diversifying landscape rather than a single dominant player.
The Role of Google's Tensor Chips
Central to Gemini 3's capabilities are Google's Tensor chips. These are Application-Specific Integrated Circuits (ASICs), designed for particular functions. Google began developing these custom chips long before the recent surge in AI interest.
ASICs differ from the Graphics Processing Units (GPUs) that Nvidia and AMD primarily produce. GPUs are versatile and powerful, capable of handling vast, complex calculations across many applications. ASICs, by contrast, are optimized for narrower, more specific workloads.
Understanding AI Chips
GPUs (Graphics Processing Units): Developed by companies like Nvidia and AMD, these chips are highly versatile and excel at parallel processing, making them ideal for a wide range of AI training and inference tasks.
ASICs (Application-Specific Integrated Circuits): Custom-designed chips, like Google's Tensor, are optimized for specific tasks. While less versatile than GPUs, they can offer superior efficiency and performance for their intended purpose.
Jacob Feldgoise, a senior data research analyst, explains that ASICs are typically designed for "narrower workloads" compared to GPUs. This distinction is crucial in understanding the competitive dynamics of the AI hardware market.
Despite their specialized nature, these chips are gaining traction. Reports indicate that Meta is in discussions with Google to purchase its Tensor chips. Additionally, Anthropic announced in October its plans to significantly increase its use of Google's technology.
Nvidia's Enduring Dominance and Market Shifts
Nvidia remains the dominant force in the AI chip sector. The company reported a 62% year-over-year sales growth in the October quarter, with profits up 65% compared to the previous year. This leadership is largely due to the broad utility and power of its GPUs, along with its comprehensive technology packages for data centers.
Nvidia's offerings include not just GPUs but also essential components like networking chips and a robust software platform. This platform allows developers to optimize their code to better utilize Nvidia's hardware, creating a strong ecosystem for long-term customers. Even Google itself is an Nvidia client.
"If you look at the magnitude of Nvidia’s offerings, nobody really can touch them," stated Ted Mortonson, technology desk sector strategist at Baird.
However, the increasing adoption of ASICs, combined with growing competition from AMD, suggests a broader industry trend. Companies may be seeking to reduce their reliance on a single provider and diversify their AI infrastructure.
Ben Barringer, global head of technology research at Quilter Cheviot, believes that Google will not be the only new competitor in the AI chip space. He doubts that any single entity will achieve Nvidia's current level of dominance. Instead, he sees a future where various types of chips and providers contribute to a more balanced ecosystem.
The Broader Implications for the AI Race
The recent developments in AI models and chips highlight the rapidly evolving nature of the artificial intelligence race. Google's advancements with Gemini 3 and its Tensor chips signal a strong internal capability that could reshape the market.
The responses from competitors like Nvidia and OpenAI, while congratulatory, also underscore the competitive pressure. Nvidia emphasized the "greater performance, versatility, and fungibility" of its GPUs compared to ASICs like Google's.
The stakes in this competition are high. AI is projected to significantly impact various sectors, from investment portfolios to daily life. The company that ultimately leads in AI innovation and infrastructure could shape future technological progress and economic trends.
Google's stock saw an increase of nearly 8% last week, while Nvidia's experienced a slight dip of just over 2%. These market movements reflect the ongoing adjustments as investors react to the dynamic landscape of AI development and hardware innovation.