Why AI Won’t Follow the Internet Bubble
💡See why AI’s demand curve, hardware constraints, and enterprise adoption may defy the internet-bubble playbook.
⚡ 30-Second TL;DR
What Changed
Historical comparisons are unreliable because today’s fiscal systems, industrial policy, information infrastructure, and state involvement differ substantially from earlier eras.
Why It Matters
For AI builders and founders, the analysis suggests that demand planning should not rely solely on an internet-bubble analogy or wait for one dominant application. Products that reduce deployment friction and capture many enterprise workflows may drive substantial aggregate inference demand.
What To Do Next
Instrument your application’s token demand by workflow, model size, and latency, then run a capacity forecast that includes falling inference costs and enterprise adoption growth through 2028.
Key Points
- •Historical comparisons are unreliable because today’s fiscal systems, industrial policy, information infrastructure, and state involvement differ substantially from earlier eras.
- •AI adds general-purpose intelligence as a new technological dimension, extending beyond the energy, transportation, and communications changes of prior revolutions.
- •Current evidence points to continued AI hardware constraints rather than infrastructure oversupply; meaningful capacity relief may not emerge until the second half of 2027 through 2028.
- •Falling inference costs and improving small or open-source models could accelerate token demand faster than compute supply expands.
- •Enterprise AI deployment may provide distributed demand after coding tools, without requiring a single consumer-scale killer application.
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Original source: 虎嗅 ↗
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