Diana Hu

speaker

322 appearances 4 recordings 1 series first heard Jun 2026 last heard 5 Aug

Diana Hu’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 4 in all, peaking in Aug 2026 with 2.

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Y Combinator Startup Podcast · Jeff Dean: The 1% Rule for Building in AI · 1 Aug 2026 podcast

That's a good tip.

Now, for everyone here who doesn't know, years ago, Jeff wrote a very famous list called the latency numbers every engineer should know.

And these are numbers around, for example, how long a cache miss takes, a disk seek, a network package traveling, let's say, from California to Netherlands.

Lots of numbers like this about distributed systems and systems engineering.

And it's been sort of taped and become the bible for a lot of distributed systems engineers.

Now, fast forward, that list is up for an update.

Give us the AI edition for now, 2026.

And one interesting thing that I've heard you talk about is that nowadays, the unit that you measure everything is energy.

You pointed out that doing a calculation or math costs about one picojoule, but moving the data and doing data I.O.

costs a thousand times that.

That gap kind of quietly decides what products are possible and how these algorithms in AI are built.

So what are the kinds of problems that founders keep calling model problems but are in fact actually energy or data I.O.

problems?

A very concrete example is just how training models is done.

There's this whole concept of batching the data sets and running epochs.

That's basically, people perhaps may confuse that as a model problem, but it's really a systems data IO problem, right?

Do you think it's possible for, I know you're well known for taking off on a long week or weekend and coming up with this brilliant solution.

Is there such things of Jeff Dean going and working on it for a couple of weeks and getting batch size equals one training done?

What are some of those interesting things around inference that you're really thinking a lot about?

Which I think it brings down to a core analogy I heard from famous computer scientists that really the whole process of AI is a big compression problem.

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