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Opinion: AI growth may create new bottlenecks beyond chips

In this column, the author argues that guessing the next NVIDIA in AI is less useful than identifying emerging bottlenecks. While NVIDIA’s GPUs dominated early AI demand, Google’s TPUs and dedicated chips show how companies may shift from buying hardware to designing specialized solutions. The author warns that AI’s spread into robotics, manufacturing, and logistics will require more than just…

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Key points

  • AI’s expansion beyond computing will drive demand for electricity, cooling, and manufacturing equipment
  • Robotics and automation may create shortages in precision parts and maintenance services
  • Investors should track bottlenecks, not just AI model competition, for long-term opportunities

The piece suggests investors should focus on infrastructure needs rather than individual companies. As AI models become cheaper and more competitive, the real opportunities may lie in supporting industries like robotics (e.g., FANUC, Figure) or infrastructure for automation. The author advises separating long-term tech trends from stock predictions, emphasizing that success depends on anticipating where demand will surge, not just which AI leader will rise.

Full story from note.com · by うさとものひとり言 · via Search: NVIDIAOpen source ↗

[Column] Don't try to guess the 'next NVIDIA'—in the AI era, look for bottlenecks rather than winners

note.com · 3 October 2026

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This text was published by note.com and written by うさとものひとり言. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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NVIDIAGoogleFANUCYaskawa ElectricFigure

The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.

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