Elad Gil
speaker
6,339 appearances
58 recordings
1 series
first heard Sep 2024
last heard 10 Sep
Elad Gil’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 — 22 in all, peaking in May 2026 with 4.
Appearances
And so I feel like in Silicon Valley there's too much, and outside of Silicon Valley, there's too little.
So it's this interesting.
you know, spread of uh different models that sort of stick.
Maybe the way to to think about it is in in Sarah's context, like if you haven't say you're a YC founder, you haven't been at Google, you haven't been at Meta, you haven't been at Twitter, you don't have this network of engineers, you're a complete unknown, you haven't worked with very many people, you're straight out of school.
How do you then attract that talent?
And to your point, you can
tell a story of how you're gonna build things or what you're gonna do or but it is a harder um obstacle to basically convince others to join you or for others to come on board or to have money to pay them if you haven't if you don't have long work at history.
So I think maybe that's the point Sarah's making.
So I guess um with the initial surge team, it sounds like you had sort of a small initial tight engineering team.
You guys started building product, you were bootstrapping off of revenue.
You know, at this point you're at over a billion dollars in revenue, which is amazing.
Um how do you think about the future of how you wanna shape the organization, how big you wanna get, the different products you're you're launching and introducing?
Like what what do you view as sort of the future of Surge and how that's all gonna evolve?
is the measurement through human evaluation?
Is it through model-based evaluation?
I'm I'm a little bit curious like how you create that feedback loop since
To some extent, it's a little bit this question of how do you have enough evaluators to evaluate the output relative to the people generating the output or do you use models or how how do you approach it?
If you assume eventually um
some form of superhuman performance across different model types relative to human experts.
How do you think about the role of humans relative to data and data generation versus synthetic data or other approaches?
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