Greg Parker

Why are AI researchers so hard to find? Why are they so highly paid? To try to answer this, let’s rewind back to something from the Carnegie Mellon PhD program. At Carnegie Mellon's PhD program, there's a legendary oral exam question that's deceptively simple: "What happens when you type google.com into a browser?" It's a masterpiece of pedagogical design. You can spend hours traversing the stack—from keypress interrupts to browser event loops, DNS resolution to TCP handshakes, TLS negotiation to HTTP parsing, CDN routing to datacenter load balancing, all the way down to electrons moving through silicon. The beauty is its fractal nature. Each layer reveals another universe of complexity. A strong engineer can navigate these depths, moving fluidly between abstraction levels. Now consider the equivalent question for our current moment: "What happens when you type a prompt into GPT-5?" I estimate fewer than 500 people globally can answer this with comparable depth. Think about what comprehensive understanding requires: transformer architecture internals, attention mechanisms at scale, distributed training orchestration across thousands of GPUs, RLHF implementation details, constitutional AI approaches, inference optimization, quantization trade-offs, not to mention the labyrinthine data pipelines and evaluation frameworks. Unlike traditional systems—which evolved over decades with extensive documentation, courses, and industry knowledge transfer—the modern LLM stack emerged in just a few years within a handful of organizations. The field is simultaneously too new and too vertically integrated. The people who truly understand these systems end-to-end are essentially the early engineers at a small set of frontier labs: OpenAI, Anthropic, DeepMind, Meta's FAIR, and a few others. This explains the talent market dynamics. When the total addressable pool of people who can architect and debug these systems is smaller than a single Bay Area high school, the economics become inevitable.