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14 results for “ai productivity”
Concept5 mentions across episodes
productivity
Take take the four month case just to be a little aggressive. That's three doublings a year. That's eight x task length increase per year. That would mean you go from two hours now to two days in one year from now. And then if you do another eight x
59:59 / 1:01:01
from transcript59:59 – 1:01:01
what is happening is very quickly, that three days is getting cut down to half a day to half an hour to fifteen minutes. And that's, like, what the entire industry or lots of people in the industry are starting to work on is making that cut down. Bec
54:35 / 55:52
from transcript54:35 – 55:52
how much? They they then asked them afterwards, how much do you think this sped you up? And they're, yeah. Yeah. It sped me up. And I I feel it does reveal, actually, like, it might be hard for us to judge, whether we were sped up or not. Yeah. One t
17:11 / 18:16
from transcript17:11 – 18:16
you need to spend a decent amount of time to build something that helps you, in your in your actual environment. To give you an idea, so in October, we took 30 senior executives to Scotland. We had three days, and they all built their own agentic env
54:00 / 55:01
from transcript54:00 – 55:01
85% of the companies we talked to said they really believe they only have the next eighteen months to either become a leader or fall behind. You know, we have our little group chat where we have another friend who's like, oh, all this stuff is overhy
0:00 / 1:02
from transcript0:00 – 1:02
A friend of mine basically, believes that you know Nyquist theorem Mhmm. Which is, basically, if you're sampling a signal, like, you need to be able to sample it twice the frequency in order to be able to actually extrapolate what it is. Otherwise, y
20:20 / 21:24
from transcript20:20 – 21:24
But there's this more complicated sense of, like, how much is being written by AI? Probably not 90%, but it's it's hard to tell. Yeah. And I think that is a very meaningful distinction. Yeah. Like, if you were to measure how many lines of code are be
16:24 / 17:39
from transcript16:24 – 17:39
...productivity improvement. Another thing is also very widely. And I think that people do tell me that they notice different buckets of of employees, like, different reactions to AI assistive tools. Like, first of all, I I keep going back to coding bec
44:43 / 46:04
from transcript44:43 – 46:04
My fear was, yeah, but it they won't get corrected until the next day. So the whole first day, you've got the lie going out there, and then it only gets corrected. That's a little well, like, what happens with newspapers. Like, if the New York Times
42:00 / 43:15
from transcript42:00 – 43:15
Sometimes we do intellectually really easy tasks. Like if someone asked me, like, yesterday my daughter, she misspelled the word butterfly, so I need to tell her how to spell butterfly. It's a low, you know, it's an easy intellectual task. And someti
33:39 / 34:48
from transcript33:39 – 34:48
But, you know, is it, like, perfectly prioritized and tasked out and sequenced so I'm not bottlenecked on a particular thing? No. Right? And I would absolutely expect that the assistant can do that for me. Totally. Well, and it goes to the point earl
30:05 / 31:06
from transcript30:05 – 31:06
...productivity. So I do ask people to ask their managers, would you rather have give everyone on the team very expensive, coding agent subscriptions, or you get an extra headcount. Almost everyone in managers would say headcount. But if you ask VP leve
0:00 / 1:09
from transcript0:00 – 1:09
system card. They showed a jump from, like, low to mid single digits to roughly 40% of PRs actually checked in by research engineers at OpenAI that the model could do. So prior to o three, not much at all. You know, low to mid single digits. As of o
38:14 / 39:14
from transcript38:14 – 39:14
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