Brandon (Host)

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390 appearances 3 recordings 1 series first heard Jun 2026 last heard 16 Jul

Brandon (Host)’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 3 in all, peaking in Jul 2026 with 2.

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Latent Space: The AI Engineer Podcast · 🔬 The Lab of the Future Should Feel Like a Data Center — Andy Beam & Rafa Gómez-Bombarelli, Lila Sciences · 16 Jul 2026 podcast

or some other idea, but, you know, getting broad data quickly and efficiently at some cost.

Or scaling is, you know, lower throughput, but just parallelizing, you know, wildly.

So, like, in general, I would approach those as two different sets of problems.

I don't think the same strategy really works for them in general.

So, like, what types of scaling is more important for you as a scientist?

So iteration time is really the single thing.

Yeah.

Okay.

So does that limit the domains that you want to focus on?

Like, you know, now do you think like...

if we were going to try to tackle a new problem, do we ask, can we just solve this problem with fast iteration versus something where maybe the answer is, will you scale up by massively multiplexing something, but with month-long turnaround?

FRANCESC CAMPOY- Paralyzing and multiplexing are somewhat different, right?

There's a joke that all biotech is just mapping whatever readout you want to on NGS sequencing.

Tag it, you know, multiplex.

There you go.

You can get lots of data.

We've had guests who have had both of these themes of, first of all, none of the devices you buy are set up to do high throughput AI science.

And also that there are new scientific devices which come up every day, which just like open up something which was impossible five, ten years ago.

Okay, so kind of switching topics a little bit.

So you were talking about your scientific pile of 10 trillion tokens.

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