Lately I’ve been thinking about how AI infrastructure spending is starting to feel less like normal cloud usage and more like long-term capital investment (similar to energy or telecom sectors).
Big tech companies are already locking in massive compute capacity to support AI agents and large-scale inference workloads. If this trend continues, just having reliable access to compute could become a serious competitive advantage not just a backend technical detail.
It also makes me wonder if startup funding dynamics could change. In the future, investors might care not only about product and model quality, but also about whether a startup has secured long-term compute access to scale safely.
Of course, there’s also the other side of the argument. Hardware innovation is moving fast, new fabs are being built, and historically GPU shortages have been cyclical. So maybe this becomes less of a problem over time.
But if AI agent usage grows really fast and demand explodes, maybe compute access will matter more than we expect.
Curious to hear your thoughts:
If you were building an AI startup today, would you focus more on improving model capability first, or on making sure you have long-term compute independence?
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