Sequoia: Implications of GPU overinvestment

This article analyses the implications of the massive increase in AI model training driven by surging demand for GPUs. The GPU and data center overbuilding will likely waste capital in the short term but lower costs long-term, enabling more experimentation. However, startups must shift focus from infrastructure to concrete use cases.

What's going on here?

There is a disconnect between the AI hype fueling infrastructure overbuilding and the actual end-customer value creation needed to justify it.

What does this mean?

Based on a conservative estimate of Nvidia guiding $50B in GPU revenue, $100B will be spent on data centres annually. For investors to earn a return, $200B in value must be created by AI products leveraging these GPUs. But current known revenue from major tech companies utilizing AI is only around $75B, leaving a $125B+ gap.

Why should I care?

The infrastructure will most likely get cheap with time. Startups need to understand the implications of GPU overinvestment versus end-user value creation and find a balance that works for them and investors. One path forward is to identify specific customer pain points and build narrowly focused AI solutions, rather than building general-purpose LLMs.