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reflections on ai-infra from a private lunch

A16z hosted a private lunch last week with AI researchers and infrastructure engineers, where attendees identified agent sandboxes and AI-driven auto-research as key areas of focus. Multiple labs have developed proprietary methods for recursive self-improvement, likely using reinforcement learning-based post-training to enable agents to explore and discover new knowledge at super-human speeds. The discussions highlighted a growing need for CPU compute in ML training loops and the emergence of ML inference as the hottest vertical in the industry.

read2 min views11 publishedMay 26, 2026

Last week I attend a lunch event hosted by a16z. The people in attendance were AI-researchers and AI-infra engineers. I compiled a list of companies where I think the talent density is ridiculously high (based on my interactions with them):

The people from the above companies were kind enough to entertain my questions and the reflections are based on my discussions with them. There were more companies (Apple, OpenAI etc) but I couldn't talk to all of them cuz I had to rush back to work (it was literally my lunch break).

Agent sandboxes and AI scientists (or AI driven auto-research): Seems like a lot of labs have figured out some secret-sauce for recursive self-improvement. They wouldn't tell me exactly what that is but they are really interested in agent-sanboxing. If I were to guess I'd say that it has to do with the intelligence unlocks from RL based post-training methods. We have run out of data to train on so to discover new knowledge, you drop an agent into an RL environment and let it perform tree-of-thought exploration. This is a lot like the human trial and error process. But the agent can try out "several" actions in parallel and discover new insights at super-human speed. My personal take-away is that the ML training loop is going to need a LOOOTTTT more CPU compute. What will you do with this information?

ML Inference seems to be the hottest vertical and it makes sense due to the sheer demand. Interesting challenges that I heard were:

Compiler researchers + AI kernels: Seems like all big-labs employ compiler researchers. Perhaps because AI chips are seeing a rapid evolution. Also MTIA folks mentioned that all of their kernels are written by AI and out-perform human experts. Seems like a fun space to be in and I honestly need to read up more on this space (i'm a noob rn)

ML Training infra (10k+ nodes): Seems like people use Kubernetes for ML training. GPU stragglers are still puzzling. Distributed ML training has a lot of interesting problems; Checkout Shaksham's work

All neo-labs are on multiple clouds. Situational Awareness will continue to thrive for a few more years I guess

Note: The observations above are already out there in public and in no way "exclusive" to this event. And all opinions are personal.

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