Aditya "JP" Jayaprakash

speaker

248 appearances 1 recordings 1 series first heard Jun 2026 last heard 29 Jun

Aditya "JP" Jayaprakash’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jun 2026 with 1.

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Uplink: AI, Data Center, and Cloud Innovation Podcast · CI Can't Keep Up With AI · 29 Jun 2026 podcast

So we had one observation, and that was that at that point, we realized that CI as a workload was a completely different beast from what you're describing, which is what I would describe as production workloads.

I would describe production workloads as running a database, running a web server.

And the hyperscalers are fantastic for that.

And the trade-offs that they've made at almost every single level are more geared towards running a web application or a database.

And they subtly nudge you towards using a lot of things that guarantee durability.

An example of this is EBS volumes or network-attached storage.

But realize that CI has a completely different set of trade-offs.

When someone's running CI, the only thing that you care about is performance.

You actually do not care about durability.

It is okay if your machine craps out and your CI job fails.

You can rerun it.

You just need to run it fast, yes.

And the only thing that matters is performance.

The second bit is CI workloads are extremely bursty.

Now, coming back to the example that we were talking about, we have customers...

where every time a developer pushes code, they need access to 1,000 vCPUs worth of compute that run for, say, 10 minutes and then scale down to zero.

And at times, you have seven or eight developers pushing code at the same time.

So if you look at their utilization, there are these, like,

really tall spikes that go all the way to 8,000, come down to zero, go to two, go to four, come down to zero.

That's very different from a production workload.

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