The article argues that people have become the new bottleneck in the AI development landscape, replacing compute as the primary constraint. In the past, compute resources were scarce, limiting the ability of researchers to test their ideas. For instance, in 2019, OpenAI trained a significant model for around $50,000, a cost equivalent to nearly four years of average American rent. Now, that model can be replicated in just five days on a single consumer GPU. As compute becomes more accessible and commoditized, the focus has shifted to the human talent behind the ideas. OpenAI's massive investment of $500 billion over four years in a single compute cluster underscores this shift. The financial breakdown highlights how researchers are a small expense in the grand scheme, with major labs operating between 512 to 2,048 high-end GPUs per researcher. In this context, startups are adapting by hiring more talent rather than investing heavily in additional compute resources. For instance, they might choose to pay a researcher $175,000 instead of spending the same amount on an eight-node GPU setup from Lambda Labs. This strategic shift emphasizes that diverse perspectives and innovative ideas from individuals can outpace hardware advantages. The article notes that breakthroughs in AI aren't coming from the labs with the most hardware but rather from individuals with unique insights. The example of the NanoGPT Speedrun illustrates this; the record is broken not through superior compute but by someone thinking differently. The key takeaway is that as resources like oil and compute become abundant, the focus must shift to nurturing human talent, as that will drive the next wave of innovation in AI.