Interesting angle. The mix of research, data, and deployment roles suggests a much more practical bottleneck than model novelty. I'd love to see the same analysis split by company stage.
Useful dataset. The hiring signals point to execution, not just frontier research. It would be interesting to map these roles into a capability stack: data, ops, evals, deployment, and product.
Useful dataset. The hiring signals line up with what we’re seeing in practice: companies want AI-adjacent operators, not just model builders. The workflow layer is where most near-term value is.
Job postings are especially revealing when they describe operational bottlenecks rather than research ambition. The hybrid cloud and self-built data-center roles suggest the competitive unit is becoming the entire deployment stack: procurement, heterogeneous compute, inference kernels, SRE, and product distribution—not just model quality. One useful next cut might separate replacement hiring from net-new capability building, then track how long each role stays open. A persistent CUDA optimization role and a newly created Ascend deployment team imply very different constraints. Do you have historical snapshots that could show which capabilities moved from exploratory hiring into durable organizations?
Strong analysis. The hiring lens is useful because it reveals where execution pressure shows up before it becomes visible in product launches. A stage-by-stage split would make this even more actionable.
Good framing. The job-posting lens catches execution demand earlier than the headline narratives do. A stage split would make this even more actionable.
Super interesting! Great work!!!
Maybe the story of Beijing's demise has been greatly exaggerated
Interesting angle. The mix of research, data, and deployment roles suggests a much more practical bottleneck than model novelty. I'd love to see the same analysis split by company stage.
Useful dataset. The hiring signals point to execution, not just frontier research. It would be interesting to map these roles into a capability stack: data, ops, evals, deployment, and product.
Useful dataset. The hiring signals line up with what we’re seeing in practice: companies want AI-adjacent operators, not just model builders. The workflow layer is where most near-term value is.
Liked your idea about understanding the future of AI by looking at Job Postings in China
Job postings are especially revealing when they describe operational bottlenecks rather than research ambition. The hybrid cloud and self-built data-center roles suggest the competitive unit is becoming the entire deployment stack: procurement, heterogeneous compute, inference kernels, SRE, and product distribution—not just model quality. One useful next cut might separate replacement hiring from net-new capability building, then track how long each role stays open. A persistent CUDA optimization role and a newly created Ascend deployment team imply very different constraints. Do you have historical snapshots that could show which capabilities moved from exploratory hiring into durable organizations?
Strong analysis. The hiring lens is useful because it reveals where execution pressure shows up before it becomes visible in product launches. A stage-by-stage split would make this even more actionable.
Good framing. The job-posting lens catches execution demand earlier than the headline narratives do. A stage split would make this even more actionable.