OpenAI Switches To $Alphabet(GOOGL)$ AI Chips For ChatGPT - What does this mean for $Alphabet(GOOG)$ and $NVIDIA(NVDA)$ ?!?!?
Been seeing lots of folks getting out of their long position in Google and posting about how Google is not worth the investment as it loses ground in their search business. That might have been the wrong move, and staying in the position or loading up would have been the better choice.
Here are the Top 4 reasons why ...
Reason #1 - The Competitive Edge of TPUs Over GPUs
Google may be entering a new phase of revenue generation, driven by the accelerated adoption of its custom AI infrastructure. A key development signaling this shift is OpenAI's reported move toward utilizing Google’s Tensor Processing Units (TPUs) instead of Nvidia's industry-leading GPUs.
Google’s TPU (Tensor Processing Unit) is an application-specific integrated circuit (ASIC) purpose-built for machine learning workloads. Compared to general-purpose NVIDIA GPUs, TPUs can offer:
▫️ Higher efficiency per watt for large-scale model training and inference.
▫️ Tighter integration with Google's cloud infrastructure, offering end-to-end optimization.
▫️ Cost advantages at scale, especially for organizations like OpenAI that need massive compute resources to train frontier models like GPT-5 and beyond.
With OpenAI being one of the largest consumers of AI compute globally, this transition could significantly boost utilization of Google Cloud’s TPU-based infrastructure, resulting in a direct increase in cloud-related revenue.
Reason #2 - Strategic Cloud Growth Amid Search Weakness
Google Cloud is one of the fastest-growing segments for the overall business, generating over $9 billion in quarterly revenue as of early 2025. The TPU partnership with OpenAI could drive:
▫️ Higher demand for Google Cloud Compute services.
▫️ New high-margin enterprise AI services anchored by TPU usage.
▫️ Long-term customer lock-in, as large models trained on TPU infrastructure are less portable than those trained on commodity GPUs.
Even if Google's core advertising business continues to face competition from TikTok, ChatGPT, Amazon search and others, the potential multi-billion-dollar scale of AI compute revenue from foundational model developers can meaningfully offset that decline.
Reason #3 - Undermining NVIDIA's Dominance
While Nvidia still dominates the AI hardweare landscape, its data center growth may begin to plateau if major players like OpenAI diversify their compute strategy. Google’s TPU hardware:
▫️ Reduces dependency on Nvidia's supply chain, potentially easing bottlenecks in AI training.
▫️ Enhances Google’s negotiating power across the AI ecosystem.
▫️ Establishes Google as a full-stack AI provider, spanning hardware, software (TensorFlow, JAX), and infrastructure.
This hardware adoption positions Google as not just a cloud vendor, but a deep tech infrastructure company with strategic leverage in AI.
Reason #4 - Long-Term Strategic Leverage
If OpenAI’s shift becomes permanent and successful, it sets a precedent for other AI firms to adopt Google’s TPU stack. This could evolve into:
▫️ Recurring AI compute revenue akin to SaaS models.
▫️ Hardware-as-a-service models, with Google charging premium pricing for TPU access.
▫️ Stronger ecosystem control, pushing developers into Google's software tools and APIs.
Even as Google’s core ad revenues may stagnate or decline due to competitive pressures in traditional search, this pivot to foundational infrastructure gives the company a long-term growth vector in one of the most valuable tech segments of the next decade.
Conclusion:
While Google faces real headwinds in search monetization, its TPU ecosystem offers a credible, high-margin growth path. OpenAI’s move to Google's custom silicon is more than a technical choice — it’s a strategic realignment in the AI value chain, positioning Google to capture a bigger slice of the next era of internet infrastructure revenue.
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