Shiv Rao
speaker
693 appearances
1 recordings
1 series
first heard May 2026
last heard 16 May
Shiv Rao’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 — 1 in all, peaking in May 2026 with 1.
Appearances
I don't know if it's vertical AI company, like we are an AI company in my head.
serving one of the biggest opportunities that is out there.
In this market, you can go millions of miles deep, especially on the enterprise side of the spectrum of healthcare.
You can go millions of miles deep in a regulated industry with proprietary data sets that you can build into very, very specific workflows in a way that's really, really hard to replicate.
But being fast, getting scale very, very quickly is crucial because, like, the scale enables you to not just build more products and just get more surface area, but we do a lot of mid-training and post-training in the company.
On the post-training side, it's being able to learn from all the users' edits on a daily basis.
It's being able to be useful for all the different types of doctors in all the different settings they deliver care in all the different spoken languages they might use with their patients.
And so people, I think, in 2023 thought that was like last mile.
That's actually most of it is that part.
The model piece is much less.
But even on the model side, I think the vertical AI companies that have the most leverage, that have the most upside, are the ones that can reach farther down into the stack and own their destiny in a different differentiated way that can kind of control their P&L.
So about 40% of our model outputs inside our product are generated by in-house models.
And that varies from month to month.
Next month it might be 60% because we've distilled a new open source model and fine-tuned it and gotten some feedback and we are convicted and we've just replaced a frontier model with this new in-house model.
What do you expect that to be in two to three years?
It's a great question.
So I think this is where we're all kind of nobody knows the answer, but you just have to be principled in how you approach this.
So there's any number of different problems we solve as a company with our product.
Some of those problems you can kind of imagine ringing the bell relatively easily.
Like, okay, if we just help the nurse with this piece of data getting into this discrete field at this time in their workflow, we won the game.
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