Brendan Foody
speaker
1,144 appearances
2 recordings
2 series
first heard Jun 2026
last heard 1 Jun
Brendan Foody’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 2 in all, peaking in Jun 2026 with 2.
Appearances
I think that certainly the models need to have access to data to perform their jobs effectively.
But the caveat is that they'll be able to clean the data themselves fairly effectively as reasoning capabilities go up.
The thing that humans will need to contribute to is all of the tacit knowledge within the organization that isn't written down.
Because I've found that when I try to get agents to do all of these workflows throughout Mercore, there's just an enormous amount of context that lives in people's heads that the agents need to have access to to perform effectively.
And so much of that
is going to be the new job of employees of how do we codify all of this knowledge?
How do we train agents so that they're able to perform these tasks effectively across every function in the organization?
Well, the reason is that if a model is able to, for example, read through every message written in Slack over the last six months, the model can presumably structure a table of here are all the different customer conversations that happened in the CRM, etc.
And so I don't expect humans to be doing like that type of stuff of how do we structure data, how do we classify it, etc.
But I do think that humans will do the things that models inherently can't do, such as the test of knowledge.
Is that how it plays out?
It's interesting.
We're doing a ton of data collection in the physical world as well, especially across skilled domains where you have electricians and mechanics and scientists dropping cameras to their head to record things.
I think that there's always going to be some degree of
value in some of these niche vendors that are able to go really deep in a specific vertical.
But what we're finding is that there's enormous value to aggregation and economies of scale.
And that when we have this talent network of over 5 million people that are able to refer their friends, it's just so much easier for us to find the marginal doctor because we have that enormous talent network that can refer us to their friends.
And even more importantly, that the
kind of data shapes that we would build for a lawyer are often very similar to the kinds of data shapes that we would build for a doctor.
And so all of the tooling that we build is very, very cross applicable.
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