30d • Learn AI
The AI Wildfire Is Coming.
I read an article taken from a recent event where a guest asked a veteran Silicon Valley CEO the burning question - Are we in an AI Bubble?
His reply really resonated with me so here is my summary:
Silicon Valley’s periodic crashes act like forest fires. They burn away excess, reallocate talent, and create the infrastructure for the next generation of growth.
1. The Function of the Fire
- Each tech boom ends when growth chokes itself.
- Crashes redistribute talent, culture, and infrastructure.
- Survivors emerge leaner, faster, and better equipped.
2. The First Two Fires (2000 and 2008)
- 2000 (Web 1.0): Total wipeout of speculative startups. Yet data centers, fiber, and key firms like Amazon, eBay, and Google survived and built lasting systems. The fiber glut became the backbone for Web 2.0 and cloud computing.
- 2008 (Web 2.0): The recession killed weak business models but rewarded resilient ones—Apple, Amazon, Netflix, Google, and Salesforce integrated hardware, software, and services into sustainable ecosystems.
3. The Current Fire (AI Cycle)
- Today’s bubble centers on the biggest players: Nvidia, OpenAI, Microsoft.
- The danger is a “canopy fire” at the top—mutual overinvestment in compute creating an industrial bubble.
- When demand cools, compute utilization could collapse, exposing dependence on a few large buyers.
4. Compute Overbuild and Abundance
- Massive GPU and data center spending mirrors the 1990s fiber boom.
- Overcapacity will drive compute costs down, seeding future innovation.
- Two compute markets matter:
5. Why This Bubble Is Productive
- Overinvestment in compute infrastructure will leave behind real assets.
- Inference demand—AI applied to productivity, cost reduction, and decision-making—is durable and measurable.
- The correction will shift capital from speculative training to practical deployment.
6. The Depreciation and Energy Problems
- GPUs age fast; unlike fiber, they lose value in a few years. Survivors with the latest hardware will hold an edge.
- True bottleneck: energy. Compute equals electricity. Power generation and grid capacity, not chips, will decide who leads the next cycle.
- Energy infrastructure takes decades to build, creating strategic advantage for those investing in it now.
7. Fire-Resistance Metrics
- Model labs: revenue must outpace compute costs.
- Enterprise AI: retention must come from AI adoption, not legacy tools.
- Application companies: must prove deep integration and strong unit economics.
- Inference providers: need pricing power and efficiency.
- Energy firms: survival depends on low-cost, reliable power.
8. The Sequoia Lesson
- Fires are essential for renewal. Suppressing them builds dangerous fuel.
- Regular corrections prevent catastrophic collapses.
- True resilience comes from deep roots—long-term strategy, adaptability, and strong fundamentals.
9. The Takeaway
- The coming AI correction is likely a productive burn, not a collapse.
- Capital will vanish, but infrastructure and capability will remain.
- Survival depends on endurance during scarcity and investment in lasting systems—especially energy.
- The real winners are those building roots deep enough to thrive after the flames.
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The AI Wildfire Is Coming.
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