2026-04-17 Sexta

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The Next Earthquake in AI: Why the Real Danger Isn't the SaaS Killer, but the Computing Power Revolution?

The next seismic shift in AI is not the threat of "SaaS killers" but a fundamental revolution in computing power. While many focus on how AI applications like Claude Cowork are disrupting traditional software, the real transformation is happening beneath the surface—in the infrastructure that powers AI. Two converging technological paths are challenging NVIDIA’s GPU dominance: 1. **Algorithmic Efficiency**: DeepSeek’s Mixture-of-Experts (MoE) architecture allows massive models (e.g., DeepSeek-V2 with 236B parameters) to activate only a small fraction of "experts" (9%) during computation, achieving GPT-4-level performance at 10% of the computational cost. This decouples AI capability from sheer compute power. 2. **Specialized Hardware**: Inference-optimized chips from companies like Cerebras and Groq integrate memory directly onto the chip, eliminating data transfer delays. This "zero-latency" design drastically improves speed and efficiency, prompting even OpenAI to sign a $10B deal with Cerebras. Together, these advances could cause a cost collapse: training costs may drop by 90%, and inference costs could fall by an order of magnitude. The total cost of running world-class AI may plummet to 10-15% of current GPU-based solutions. This paradigm shift threatens NVIDIA’s valuation, built on the assumption of perpetual GPU dominance. If the market realizes that GPUs are no longer the only—or best—option, the foundation of NVIDIA’s trillions in market cap could crumble. The real black swan event may not be a new AI application, but a quiet technical breakthrough that reshapes the compute landscape.

marsbit02/11 01:58

The Next Earthquake in AI: Why the Real Danger Isn't the SaaS Killer, but the Computing Power Revolution?

marsbit02/11 01:58

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