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14 results for “goodwill optimization”
“This AI connector optimizes for goodwill instead of traditional metrics”
...that is goodwill. Right? How much goodwill can I actually accumulate across my entire network? And can I make intelligent bets in the introductions that I make that will lead to good conversations and
19:49 / 20:26
framework19:49 – 20:26
We know who is gonna make the purchasing decision to buy Prosperous' product, and we know why they're going to buy it. Because they want to save money, and they want to go faster. Perfect. How do you do this for them? Yeah. So we we look at their int
29:12 / 30:15
from transcript29:12 – 30:15
“How AI agents could save factories millions by making them bid against each other”
all through AI agents. And so the way that I, look at it longer term for for the business is if we're able to get this in real time, we can consistently get, all these factories to bid against each ot
24:03 / 24:23
market insight24:03 – 24:23
“The hidden trust currency that makes or breaks startup introductions”
...a goodwill sort of trust bank with everybody I've ever met. When I introduce two people, I am gambling that goodwill with both people. Right? I'm like, okay. Well, I think those two people will will g
18:49 / 19:36
framework18:49 – 19:36
predicting your next edit and saving you some time. And we want to serve both of those demographics. And so I think to pick a single acceleration factor is difficult because it depends where you were previously. And I think compared to many other com
25:32 / 26:37
from transcript25:32 – 26:37
One thing that worked really well for us, Talia, is we have a small research team now, and most of the people we hired for that research team is through our research grants program. So we we it's open invitation. You can basically just send us an ema
16:30 / 17:31
from transcript16:30 – 17:31
“The startup dilemma: optimize for revenue or product data?”
what would you wanna see on the go to market side? Because I feel like I maybe lately missed the mark. What are you optimizing for? Are you optimizing for revenue? Are you optimizing for usage and dat
24:16 / 24:52
tactical advice24:16 – 24:52
But the key difference is that in the long run, you can just keep increasing the supply of compute or of robots. And so if it is the case, so if an h 100 costs a couple thousand dollars a year to run, but the value of an extra year of intellectual wo
32:58 / 34:02
from transcript32:58 – 34:02
organic customer demand, you know, high value of dollars that they're willing to pay relative to how much it costs to acquire them either via marketing or sales. And so if you have things that are sort of, like, being pulled off the shelves and you h
24:43 / 25:45
from transcript24:43 – 25:45
So we looked for candidates who could clearly articulate how their skills would move us closer to our mission, which is to create AGI, artificial general intelligence, that benefits all of humanity, not just those who are excited by the vision. The n
5:54 / 7:03
from transcript5:54 – 7:03
And if and when you do introduce and commissions, think about the behaviors that you want to drive. For example, at Slack, we wanted to drive new logo adoption. So we centered 75 of our comp plan on new logo versus existing customer upsells. And lo a
27:00 / 28:06
from transcript27:00 – 28:06
It was like when you took a thing where you were paying piecemeal every single time you wanted the experience to a flat subscription monthly, have this experience as many times as you want. Obviously, from a consumer perspective, that's the better de
16:23 / 17:33
from transcript16:23 – 17:33
The other element is margin, and the margins are pretty terrible in a lot of cases, especially when you take into account free user giveaways, which there's a lot of. Should we give a shit about margin structures given how early we are in the cycle?
36:40 / 37:51
from transcript36:40 – 37:51
is most legible. And by legible, I mean, you can basically write it down. It's relatively replicable, mostly deterministic, so it's more likely that AI will do it well. And we actually built the agent, and then we keep a human in the loop. But from t
16:30 / 17:32
from transcript16:30 – 17:32
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