AI post-training
Alex Brogan
The AI race has two bottlenecks: compute and people. One has a clear scaling path. The other doesn't.
While labs pour capital into chips, clusters, and energy infrastructure, they face a subtler constraint that money alone can't solve: the shortage of domain experts needed for post-training work. This is the phase where raw foundation models get refined into actual tools—radiology assistants, contract analysis systems, financial compliance engines.
That refinement requires a specific breed of researcher. Not just machine learning engineers, but PhD-level experts who understand clinical reasoning, case law, or regulatory frameworks well enough to evaluate model outputs, design safety protocols, and catch the edge cases that matter in deployment.
The United States doesn't have enough of these people. Latin America does.