$Alphabet(GOOG)$ $Alphabet(GOOGL)$ will definitely be one of the key beneficiaries of global IT spending flipping from on-premise to cloud over the coming years. And Google Cloud's TPU will be leading that charge. Stock price easily going above $200 and then some.
Here's why ... read the 🧵 below ...
1. Vertical Integration and Optimization for AI Workloads
GOOGL, GOOG Tensor Processing Units (TPUs) are likely to gain more traction amid the explosive growth of AI data centers for several strategic, technical, and economic reasons.
TPUs are custom-designed ASICs (Application-Specific Integrated Circuits) optimized for neural network workloads, especially deep learning models like transformers (e.g., BERT, PaLM).
The tight integration with Google Cloud AI stack means TPUs are designed to work seamlessly with TensorFlow and JAX, giving developers streamlined tools to train and deploy models at scale.
2. Scalability and Performance
High throughput at lower power usage: TPUs can outperform GPUs on certain large-scale inference and training tasks, especially for large LLMs, while consuming less energy.
Pod architecture: GOOGL, GOOG TPU Pods scale up to thousands of chips, enabling training of massive models faster than traditional GPU clusters.
Sidenote: This is why $NVIDIA(NVDA)$ partnered with $Navitas Semiconductor Corp(NVTS)$ ... in order to address power efficiency for GPUs. And this is also why we are bullish on NVTS (although it might take a while for our bullish thesis to legitimately play out).
3. Cost Efficiency
Lower Total Cost of Ownership: GOOGL& GOOG claims TPUs offer better price-performance than competing hardware for many AI workloads, especially when used within Google Cloud.
Demand for budget-conscious AI training: As AI models grow, cost becomes a major factor. Organizations seeking alternatives to expensive GPU clusters may turn to TPUs to manage training costs.
4. Supply Chain Factors
Alternative to NVDA GPUs: With global GPU shortages and Nvidia dominating the AI chip market, hyperscalers and enterprises are looking for reliable alternatives.
Custom silicon bypasses GPU bottlenecks: By using TPUs, GOOGL& GOOG can reduce dependence on third-party chipmakers, giving it more control over supply and pricing.
Sidenote: $Advanced Micro Devices(AMD)$ is also one to watch in terms of creating and offering alternatives to Nvidia.
5. AI Model Explosion Requires Hardware Diversity
Different models need different chips: Not all AI workloads benefit equally from $NVIDIA(NVDA)$ GPUs. For certain models (e.g., large-scale matrix multiplication-heavy workloads), GOOGL& GOOG TPUs excel.
Enterprise adoption expanding: As more industries deploy generative AI, TPUs become attractive for specialized workloads like recommendation systems, LLM inference, and multi-modal AI.
Conclusion:
As AI data center growth explodes and global IT spending shifts from on-premise to the cloud, the demand for efficient, scalable, and cost-effective compute will surge. GOOGL& GOOG TPUs are well-positioned to capture more of this market due to their specialization in AI workloads, better integration with Google's software ecosystem, and a strong value proposition for large-scale training and inference.
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