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What do you think is the most underrated problem in AI infrastructure right now?

This is a test of whether you have a position you can defend, not of which problem you pick. What makes a thesis defensible, two worked examples with the arithmetic behind them, and the counterargument you have to be able to state before the interviewer does.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

This is a test of whether you have a position you can defend, not of which problem you pick. What makes a thesis defensible, two worked examples with the arithmetic behind them, and the counterargument you have to be able to state before the interviewer does.

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The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

🧭 Ownership & Judgment🔒 Premium

Your View on Where AI Infrastructure Is GoingSomewhere in a senior or staff loop an interviewer asks what you think happens next: to GPUs and their challengers, to training at scale, to inference economics, to the tools. It looks like small talk and it is scored. The answer that works is a thesis with a date on it, a reason grounded in numbers you can derive, the counterargument you find strongest, and the thing you would watch to know you were wrong. This page shows how to build such a thesis from the material on this site, gives three worked examples, and lists the answers that sound informed and fail.

🧭 Ownership & Judgment

Deciding Under Incomplete InformationMost infrastructure decisions are made before the evidence is complete, and the skill being assessed is not judgment in the abstract but classification: whether the decision can be undone cheaply. Reversible decisions deserve speed and a scheduled review; one-way decisions deserve the delay and a second opinion. Engineers who apply the same deliberation to both are slow where speed is free and careless where it is not.

🕸️ Distributed TrainingSign in

MFU and HFUModel FLOPs utilization is the fraction of a GPU's peak that goes into the model's own forward and backward math, computed from 6ND and the step time; hardware FLOPs utilization also counts recomputation. Production LLM training lands at 35 to 45% MFU, and knowing where the other 55% goes is the job.

🗂️ Scheduling & OrchestrationSign in

MIG, MPS and Time-SlicingA whole H100 is far more than a notebook, a small inference service or a CI job needs, and giving each of them a card leaves most of the fleet idle. Three mechanisms share a GPU, and they differ in what they isolate: MIG partitions the hardware into up to seven slices with their own memory and compute, MPS lets several processes share one GPU's SMs concurrently with no memory isolation, and time-slicing context-switches between processes with no isolation at all. The choice is the isolation the workload needs against the utilization the platform wants.

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UP NEXT ON YOUR JOURNEY

Next in this trackThree incidents are open, your pager is going off, and a customer is escalating. What do you do first?Next in this trackTell me about a decision you had to make without enough information.Next in this trackYou think your tech lead's design is wrong. How do you handle it?

FEDITOR'S NOTE

Scored on the thesis being specific enough to be wrong, on carrying a number, and on the candidate stating the counterargument themselves.

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