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16 results for “ai rollups”
“Why AI-rollups must force standardization to succeed where traditional buy-and-build fails”
fit your product? That's amazing question. That's exactly the latter. And I think that's exactly where the difference between your typical kind of buy and build and what we're doing is coming up. Bec
22:29 / 23:04
contrarian take22:29 – 23:04
So Charles Rolllet, that's r o l l e t one. That's my handle on x. Ah, okay. So so there's another Charles Rolllet out there. Yeah. There's an imposter. Actually, Charles Rolllet is not an uncommon name in French. There's a bunch of us. So make sure
20:59 / 21:36
from transcript20:59 – 21:36
“How data reveals the optimal operational processes by ditching industry best practices”
the selection of the best practices of operational processes across The UK with not just selecting the best one of the variety, but also quite often selecting none of the existing and reviewing all of
23:40 / 24:15
framework23:40 – 24:15
Rippling handles all the can't get it wrong admin work of payroll and benefits, giving you back hours every week. But it does a lot more than that. Rippling is a game changer for the entire company, with tools for HR, IT, and spend, all built from th
3:44 / 4:46
from transcript3:44 – 4:46
But I'm excited and glad that you were able to hop on the mic and kinda go over this with me. No. I mean, that it's great talking to you, Marianne. And, yeah, you've done great coverage on this case as well. And it can be really hard to keep track of
20:14 / 21:16
from transcript20:14 – 21:16
...rollups and automate those. Getting them to understand what agents are sometimes can be, you know, an obstacle course, but those things are happening. Right? People see them as opportunities. Coming up, Mark answers some of your questions, and we div
17:16 / 18:02
from transcript17:16 – 18:02
The whole idea is human data is kinda tapped out. We don't care. We all care about self play, verifiable data. The self play set off, which is an R and D problem. Does not make a lot of money on each individual machine. And the same can be said for t
4:27:44 / 4:28:46
from transcript4:27:44 – 4:28:46
So that solution isn't really gonna work. But AI has been used in analyzing traffic for at least over a decade. It's just it was called machine learning. And so you start with machine learning, and the question is, well, what does the the new generat
22:27 / 23:31
from transcript22:27 – 23:31
So you made the enormously expensive, ridiculous, oversized, hard to use thing, and then simply let that train the smaller model, which is cheaper, faster, easier to run, and therefore was a better fit for a GPU cluster you might find at a bank versu
8:11 / 9:16
from transcript8:11 – 9:16
I, I almost felt like I was like processing some sort of like ethical boundary, but we were just talking about like the stock market, but it still felt different in the interesting way. Almost like I was talking to a person, even though I knew better
51:42 / 52:50
from transcript51:42 – 52:50
on the Internet. There was that famous line in the nineties that on the Internet, nobody knows you're a dog. Yeah. And that is very, very true now in 2025 where, like, on the Internet, nobody knows you're human. Like, you could be anything. You could
23:54 / 25:04
from transcript23:54 – 25:04
You won't be able to have these sub two hundred millisecond interactions. I was completely wrong. And so on about the twentieth customer that us at Eleven Labs had helped build these conversational AI agents, we were like, okay. Let's actually turn t
1:05:59 / 1:07:06
from transcript1:05:59 – 1:07:06
is excited about that because if someone can go in and validate or attest to it that the AI is correct, could even be AI itself in the future, that's an enormous paradigm shift in how you can originate truths from source events. Ultimately, both of t
25:30 / 26:31
from transcript25:30 – 26:31
episode, everyone's building agentic AI at the moment. Y Combinator's Spring Batch alone had over 70 startups in that space out of a 144 startups in the cohort. The industries include solopreneur support, home buying, health care developer tools, som
15:43 / 16:47
from transcript15:43 – 16:47
And the other is more of an application layer thing. So generative AI for ChargeGPT or for our systems, etcetera, is used to build a conversational application. Right? So by and large conversational AI is about applications and it lives in the applic
27:00 / 28:02
from transcript27:00 – 28:02
handles your entire digital life and is way smarter than everybody. It's like it's operating in a and so it's an interesting leap of faith to go from cloud code becomes that, which it, like, in some ways is there's some avenues for that, but I do thi
3:42:00 / 3:43:01
from transcript3:42:00 – 3:43:01
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