Research Summary

The report discusses the rapid growth of AI and the increasing demand for GPUs, driven by Nvidia’s strong Q2 earnings. It raises questions about the purpose of these GPUs and the value they need to generate to justify the investment. The report also highlights the significant capital expenditure in data centers by tech giants and the potential for startups to fill the revenue gap in AI.

Key Takeaways

AI’s Rapid Growth and GPU Demand

  • AI’s Acceleration: The AI wave has accelerated, driven by Nvidia’s strong Q2 earnings, which signaled a high demand for GPUs and AI model training.
  • Public Interest in AI: Consumer launches like ChatGPT, Midjourney, and Stable Diffusion have raised AI awareness among the public.
  • Investment in AI: Following Nvidia’s results, investments in AI are happening at a rapid pace and at record valuations.

Questions Surrounding GPU Usage

  • GPU Purpose: The report questions what these GPUs are being used for and who the end customers are.
  • Value Generation: It also questions how much value needs to be generated for the rapid rate of investment in GPUs to pay off.
  • Cost of GPU Operation: For every $1 spent on a GPU, approximately $1 needs to be spent on energy costs to run the GPU in a data center.

Capital Expenditure in Data Centers

  • Data Center Expenditure: The report suggests that for Nvidia to sell $50B in run-rate GPU revenue by the end of the year, it implies approximately $100B in data center expenditures.
  • Big Tech’s Role: Much of the incremental data center build-out is coming from big tech companies like Google, Microsoft, and Meta.
  • Future Contributors: Companies like Amazon, Oracle, Apple, Tesla, and Coreweave are expected to be important contributors in the future.

Revenue Gap in AI

  • Revenue Generation: The report suggests that even with generous gains from AI, there’s a $125B+ hole that needs to be filled for each year of CapEx at today’s levels.
  • Opportunity for Startups: There is a large opportunity for the startup ecosystem to fill this revenue gap.
  • Shift in Thinking: The report suggests a need to shift thinking away from infrastructure and towards end-customer value.

Actionable Insights

  • Follow the GPUs: Investors and startups should “follow the GPUs” to find the next generation of startups that leverage AI technology to create real end-customer value.
  • Focus on End-Customer Value: The AI community needs to shift its focus from infrastructure to creating value for end customers.
  • Leverage AI Innovations: The AI community needs to figure out how to translate AI innovations into products that customers use every day, love, and are willing to pay for.

Visit the full report here


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