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18 results for “ai productivity”
Concept5 mentions across episodes
productivity
less experienced teams kind of try things in a more random way that just takes a long time. And we look across a huge range of businesses, small and large. It feels like there's so much work that could be automated through agentic workflows, But, you
3:47 / 5:21
from transcript3:47 – 5:21
To me, the question is, is AI mostly for cost savings or is it for growth? You know, it's difficult to change workflows. A lot of companies tend to think cost savings, but maybe here's the problem. There's actually one pattern I see. Let's say I have
45:44 / 46:46
from transcript45:44 – 46:46
But typically, when we speak about agents, what do we have in mind? We have in mind a system that's not the transactional calls in an LLM. There's something that's more ongoing. There's, the AI system, you know, the agent can be proactive, not just r
7:30 / 8:36
from transcript7:30 – 8:36
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
And you think this is a tool that was designed twenty plus years ago or has been iterated on for twenty plus years primarily for a human being to write every line of code. When you take a tool like that and you start to introduce AI, you start to int
8:55 / 10:07
from transcript8:55 – 10:07
doing the work, and that's a visual AI that can look at the computer monitor, I think, maybe eventually, you know, but I think at least right now for the next year or two, I think there's a lot of work for human engineers to do, to build more agentic
4:26 / 5:38
from transcript4:26 – 5:38
So much of what we do when we go to work, when we try to build a business is, the actual product and the service we're providing to people. But we all have to do our chores. And cleaning stuff up, normalizing data, just work that humans find monotono
6:02 / 7:11
from transcript6:02 – 7:11
the relationship is, like, totally different within, again, a year or two period where you're using cursor, Winstref, or whatever, and the agent is generating this chunk of an output, and then you're just reviewing it. K. But what's incredible is, li
37:15 / 39:16
from transcript37:15 – 39:16
And the third one is the technical part which we talk about. Right? I think folks that are successful are incredibly obsessed about understanding their workflows very well and augmenting parts, that could be, that could be right for AI versus the one
29:15 / 30:20
from transcript29:15 – 30:20
email marketing or automate okay. You you check the pricing page, now you get an email, like, things like that. Maybe you don't even need AI to do those, but a lot of the next generation tools are all AI enabled. So you probably subscribe to, like, t
17:55 / 19:01
from transcript17:55 – 19:01
And, here's some research. We prepared to reply for you, and and here are the tasks, that we've already delegated. One of the tasks went to the sales team, another went to customer support, and another one went to operations and finance. When will we
3:05 / 4:11
from transcript3:05 – 4:11
the relationship is, like, totally different within, again, a year or two period where you're using cursor, Winstref, or whatever, and the agent is generating this chunk of an output, and then you're just reviewing it. K. But what's incredible is, li
37:15 / 38:35
from transcript37:15 – 38:35
If you're a blue collar worker and you can get 20 people together, you know what's not gonna go away? Like, home repair, handyman jobs, gardening. Like, those jobs are very intricate and difficult and nuanced, and you can learn anything. Long story s
44:13 / 45:19
from transcript44:13 – 45:19
if you say no, you have to be able to articulate a reason. Yeah. So what ends up happening is everybody sets this bar way too high. This is the one of these silly rules that needs to be, looked at. I mean, we we do use it's not so much AI. There's a
59:13 / 1:00:22
from transcript59:13 – 1:00:22
And so we had AI rather than have a project manager put together the whole project plan, which would take weeks or months. We had AI put it together by feeding the AI all these research papers, giving it some guidance, and I did it all myself. Within
3:48 / 4:51
from transcript3:48 – 4:51
...boost in productivity is gonna come from sitting in front of programming environment like Replad and being able to manage, tens of agents, maybe at some point hundreds, but, you know, at least, you know, five, six, seven, eight, nine, ten agents, all
48:44 / 49:46
from transcript48:44 – 49:46
we are basically asking these agents to make indeterminate tasks. Right? Meaning, take a set of actions, look at the output. Based on that, define what the next sort set of actions should be. And so that leads to very interesting possibilities if you
2:19 / 3:22
from transcript2:19 – 3:22
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