Meeting Title: Uttam Date: 2025-03-03 Meeting participants: Uttam Kumaran, Amber Lin
WEBVTT
1 00:02:08.630 ⇒ 00:02:09.970 Amber Lin: Hi! There!
2 00:02:18.430 ⇒ 00:02:19.080 Uttam Kumaran: Hello!
3 00:02:19.880 ⇒ 00:02:20.739 Amber Lin: Hi! Can you.
4 00:02:20.740 ⇒ 00:02:31.870 Uttam Kumaran: Hi, yes, I can hear you so sorry. I just my dog, needs to go out really quick, so I just have to take him just to the park nearby. I’m walking home right now, but sorry.
5 00:02:31.870 ⇒ 00:02:32.559 Amber Lin: So do you.
6 00:02:32.560 ⇒ 00:02:33.729 Uttam Kumaran: I’ll be on video and like.
7 00:02:34.430 ⇒ 00:02:38.530 Amber Lin: Sure. Do you wait a little bit until you get home and get set up? I can.
8 00:02:38.530 ⇒ 00:02:41.009 Uttam Kumaran: No, I’m I’m I’m literally like 2 min away. I
9 00:02:41.950 ⇒ 00:02:50.039 Uttam Kumaran: there’s a park across the street from me and usually around. Around. This time I take him to go through the ball, and I try.
10 00:02:50.040 ⇒ 00:02:50.660 Amber Lin: Oh!
11 00:02:51.050 ⇒ 00:02:55.260 Uttam Kumaran: I tried to. Yeah. I tried to embargo it for a little bit, but.
12 00:02:55.677 ⇒ 00:02:58.180 Amber Lin: He will go back, I understand.
13 00:02:58.180 ⇒ 00:03:01.520 Uttam Kumaran: He’s a big dog, and he’s a bully. So yeah.
14 00:03:01.520 ⇒ 00:03:02.180 Amber Lin: I see.
15 00:03:02.180 ⇒ 00:03:06.549 Uttam Kumaran: Good, though it’s for me. Otherwise, like I’ll just be sitting and working all day, so I guess it’s.
16 00:03:06.550 ⇒ 00:03:20.060 Amber Lin: No, no! And throughout the throughout the few months I’ve been applying to jobs, I’m like, I need to get out of the house because I get so sucked with work. And then at night I’m like, I don’t know why I feel this way, and then I realize.
17 00:03:20.060 ⇒ 00:03:21.120 Uttam Kumaran: Like I haven’t seen the sun.
18 00:03:21.120 ⇒ 00:03:21.510 Amber Lin: Oh!
19 00:03:21.510 ⇒ 00:03:22.400 Uttam Kumaran: All day. Yeah.
20 00:03:22.710 ⇒ 00:03:30.770 Amber Lin: Yeah. So I was, actually, I was just outside. I went to return an Amazon package. And I was like, Okay, gotta sprint. I gotta walk.
21 00:03:30.770 ⇒ 00:03:31.749 Uttam Kumaran: Oh, okay, nice.
22 00:03:32.910 ⇒ 00:03:41.278 Uttam Kumaran: Well, I appreciate it. Thank you for thank you for taking the time. I’m really excited to to chat. Robert told me. There are a lot of great things. So
23 00:03:41.540 ⇒ 00:03:42.060 Uttam Kumaran: Hi! Everyone.
24 00:03:42.060 ⇒ 00:03:43.260 Amber Lin: Excited, too, and.
25 00:03:43.260 ⇒ 00:03:44.480 Uttam Kumaran: I’m excited to catch up.
26 00:03:44.790 ⇒ 00:03:45.280 Amber Lin: Yeah.
27 00:03:45.880 ⇒ 00:03:48.029 Amber Lin: And actually.
28 00:03:48.624 ⇒ 00:03:58.399 Amber Lin: I, after we connected Linkedin shows me the post you interacted with. And right after we connected, I saw that you reposted.
29 00:03:59.240 ⇒ 00:04:01.880 Amber Lin: I think it was. His name is Paul.
30 00:04:02.010 ⇒ 00:04:05.880 Amber Lin: and he did. He did this. L 6.
31 00:04:05.880 ⇒ 00:04:06.480 Uttam Kumaran: Yes.
32 00:04:06.480 ⇒ 00:04:18.119 Amber Lin: Course, and I was like, Oh, my God, that is so interesting because I don’t know if Paul told you about something that I really wanted to do. I’ve been. I’ve been brainstorming my own startup idea of having this
33 00:04:19.006 ⇒ 00:04:24.490 Amber Lin: Ai project management breakdown tool so that we can make a platform
34 00:04:25.030 ⇒ 00:04:36.009 Amber Lin: that essentially connects the college students where people looking for work that’s willing to do work for maybe a lower cost or just to build experience. And I was thinking that something
35 00:04:36.240 ⇒ 00:04:53.140 Amber Lin: that has a backing of Alm system that breaks tasks down really, really, specifically, to one or 2 h chunks so that it can automatically manage all the task completion. And then I saw your post and say, Okay, I
36 00:04:53.760 ⇒ 00:04:55.720 Amber Lin: I guess I’ll do it. But like.
37 00:04:55.720 ⇒ 00:05:00.049 Uttam Kumaran: Well, we’re we’re like one. The post that post is really good. If you haven’t.
38 00:05:00.050 ⇒ 00:05:01.940 Amber Lin: It is really really good. I
39 00:05:02.590 ⇒ 00:05:05.060 Amber Lin: I spent the whole weekend on it because.
40 00:05:05.060 ⇒ 00:05:06.100 Uttam Kumaran: Oh, nice!
41 00:05:06.100 ⇒ 00:05:19.350 Amber Lin: Yeah. And honestly, it’s like learning a new language, a lot of different new languages. Because, you know, I’m from the same program as Robert and I was also in Canada, so I had to learn French, Italian, and
42 00:05:20.100 ⇒ 00:05:25.860 Amber Lin: and I was reading the blog post, and I was like this feels like reading in the new language, because
43 00:05:25.860 ⇒ 00:05:29.380 Amber Lin: yes, up something. Every single line.
44 00:05:29.590 ⇒ 00:05:34.479 Uttam Kumaran: Yeah, I mean, that’s like, basically, all of AI right now is that I mean, I feel really.
45 00:05:34.480 ⇒ 00:05:34.840 Amber Lin: Movie.
46 00:05:34.840 ⇒ 00:05:39.929 Uttam Kumaran: Because we have 2 parts to our business. We have the AI part of the business, and we have.
47 00:05:39.930 ⇒ 00:05:40.620 Amber Lin: The data.
48 00:05:40.620 ⇒ 00:05:41.180 Uttam Kumaran: Apartment.
49 00:05:41.180 ⇒ 00:05:41.660 Amber Lin: No.
50 00:05:41.660 ⇒ 00:05:46.199 Uttam Kumaran: The data part is where, like, I feel great like, I come to work every day. I’m like.
51 00:05:46.200 ⇒ 00:05:46.650 Amber Lin: I know.
52 00:05:46.650 ⇒ 00:05:49.660 Uttam Kumaran: Doing. In fact, our challenge is, there
53 00:05:49.920 ⇒ 00:06:01.130 Uttam Kumaran: are not data related at all. They’re all people related which you’d be surprised like, and not in a bad way. It’s just like we have, like 10 people, and they’re all need to be project managed. And so we just like are
54 00:06:01.610 ⇒ 00:06:25.760 Uttam Kumaran: growing up there. But the AI side of the business is is like all brand new, and it’s all like I’m self taught on everything there. And we have 3 AI people that work at the company. Initially, I I hired AI developers to come work on Automating Brainforge. So their task was to come automate the company.
55 00:06:26.170 ⇒ 00:06:33.580 Uttam Kumaran: Like that, was it? I was like, I wanna automate 50% of the company. I need to have someone who their whole work. All they do is work on automating the company.
56 00:06:33.580 ⇒ 00:06:34.060 Amber Lin: Yeah.
57 00:06:34.342 ⇒ 00:06:56.130 Uttam Kumaran: When we started doing that, I started doing that in September of last year I found out that, hey? A lot of what we do can definitely be sold. Like, what a lot of the stuff we’re learning about building agents. Doing a bunch of stuff like we should just package this and sell it. And so yeah, that’s how we sort of started getting into AI as well. Initially, I just wanted to automate.
58 00:06:56.260 ⇒ 00:06:58.885 Uttam Kumaran: I just wanted to automate our business.
59 00:06:59.260 ⇒ 00:07:01.379 Amber Lin: I feel, I feel that pain cause I.
60 00:07:01.380 ⇒ 00:07:02.000 Uttam Kumaran: Yeah.
61 00:07:02.315 ⇒ 00:07:06.730 Amber Lin: Sorry. I’m a little teeny little bit sick. Everybody’s getting the flu right now.
62 00:07:06.730 ⇒ 00:07:07.220 Uttam Kumaran: Yeah.
63 00:07:07.710 ⇒ 00:07:17.660 Amber Lin: And I, I really get that, because right now, cause I’m in the period when I’m looking for different different paying jobs, I’m doing unpaid work. And I’m
64 00:07:18.170 ⇒ 00:07:25.709 Amber Lin: I’m helping 2 foundations, actually, 2 foundations in the branding agency doing product and project management.
65 00:07:26.210 ⇒ 00:07:34.169 Amber Lin: It is. It is. It is so much so hectic. I did because one of them
66 00:07:34.470 ⇒ 00:07:45.419 Amber Lin: it’s a agricultural intelligence dashboard data. Blah, blah financial insights, that kind of, and they want to scale from 40 to 80.
67 00:07:45.550 ⇒ 00:07:55.640 Amber Lin: And so this this morning I was just doing interviews for people, and coordinating that from.
68 00:07:55.640 ⇒ 00:07:56.280 Uttam Kumaran: Oh, yeah.
69 00:07:56.280 ⇒ 00:08:14.840 Amber Lin: I was like, this is, their system is so bad. They used to spend per applicant. They spent 3 h because they don’t. Their their application portal and their Hr. And the hiring managers. Everything is disconnected, and they and then applicants, Mister, interviews hiring managers don’t.
70 00:08:14.840 ⇒ 00:08:20.189 Uttam Kumaran: Yeah, this is so common. And it’s a, it’s a such a failure of like software. But.
71 00:08:20.190 ⇒ 00:08:20.690 Amber Lin: Cool.
72 00:08:21.040 ⇒ 00:08:43.010 Uttam Kumaran: But it’s it’s reality. Because people come to work and they don’t know where to get information. And all of those problems are in our information retrieval problems. Right? So you should. Totally. If you if you know about AI, you’re like, okay, if we just make sure it has the right information and the right tools it should go do those things. And that was my whole thesis, and practical, though
73 00:08:43.309 ⇒ 00:09:00.329 Uttam Kumaran: it’s hard because I don’t have a ton of. I don’t have a ton of money, and I’m sort of like building this data business the same time, I really know that AI is like the future. So we want to build the AI business as well. And it’s like sort of trying to do both at the same time. You know.
74 00:09:00.330 ⇒ 00:09:19.080 Amber Lin: Yeah, I totally get it. Honestly, a lot of these improving system solutions don’t necessarily require that much. AI, because what I did for them. And it was, it was really fast, because I essentially got thrown into the whole interview process from the day one. And Hi.
75 00:09:20.170 ⇒ 00:09:22.679 Amber Lin: yeah, thank you for being on video.
76 00:09:22.680 ⇒ 00:09:23.736 Uttam Kumaran: Of course, of course.
77 00:09:24.000 ⇒ 00:09:51.550 Amber Lin: And essentially from day one I spent 8 h interviewing people. I was like, this is not gonna work. I’m not gonna spend this much time on this, and so I told the I told the founder I was like, you are going to give me the person that handles this, and I. Essentially, I gave them a new system, and I set up a new project management and click up. So it’s a funnel. So everybody’s on board. And then I
78 00:09:51.870 ⇒ 00:09:53.569 Amber Lin: change it to a group interview.
79 00:09:53.700 ⇒ 00:10:06.820 Amber Lin: And so before it was 1 h, each candidate back to back. And now today, we just did a group interview, and we knocked out half of the candidates. As that was scheduled between now and the end of March, so.
80 00:10:06.820 ⇒ 00:10:07.460 Uttam Kumaran: Nice.
81 00:10:07.790 ⇒ 00:10:19.139 Amber Lin: It. It felt so good, I was so proud, I was like, oh, we did all of that in 2 h instead of 20 HI was very proud.
82 00:10:19.370 ⇒ 00:10:25.499 Uttam Kumaran: Nice. So tell me like, what? Tell me about your background and like what you’re interested in doing.
83 00:10:25.750 ⇒ 00:10:55.369 Amber Lin: Oh, definitely, I found eventually that what I’m interested in doing is improving processes, making them more efficient and getting new initiative started because I talked to a lot of people, especially in a startup space, and everybody has the idea. But the idea is never realized, and hence the my own starter idea that I told you I want people to get things started by breaking breaking tasks down. And so
84 00:10:55.580 ⇒ 00:11:22.510 Amber Lin: my 2 main focuses is improving processes and implementing new things. And I do that through. Say, project management. I do that through data analysis. And these things are tools. But overall my values and principles is that I want things to. I want people to spend time where their time is deserved and not on monotonous tasks, and I want people to realize
85 00:11:22.740 ⇒ 00:11:29.390 Amber Lin: what they can do and what they want to do. So that’s sort of my my philosophy behind what I do.
86 00:11:29.680 ⇒ 00:11:48.539 Amber Lin: And my background is, I was in the same program as Robert. So I went to 3 different continents teaching in top business schools and did work in each of these continents. So I really know how to work with a lot of different people, and I really got to know their work styles, because
87 00:11:48.610 ⇒ 00:12:07.370 Amber Lin: people in China, people in Hong Kong work very differently from people in Europe or Italy. Italy is very slow, and so also, knowing from that experience how to motivate different people and what they really want and what their values are, so that education part, I would say, improved my people
88 00:12:07.550 ⇒ 00:12:13.689 Amber Lin: knowledge more than my technical knowledge, because a lot of the technical knowledge I learn on a job.
89 00:12:14.156 ⇒ 00:12:29.289 Amber Lin: So I did, consulting for ui, and then I’m still kind of doing projects here and there for them now, because I I just know the person, and I was like, I need some income. I’ll do this on the side for you occasionally.
90 00:12:29.290 ⇒ 00:12:30.479 Uttam Kumaran: Type of work? Is it for them.
91 00:12:30.890 ⇒ 00:12:35.319 Amber Lin: So before, when I was full time interning, it was consulting work.
92 00:12:35.820 ⇒ 00:12:54.550 Amber Lin: So it was from the basic Dr. Market research to eventually make doing the analyses and then presenting the final solutions. So the whole consulting pipeline I was in. Specifically, I was in the life, science and sector. So, working with a lot of
93 00:12:55.226 ⇒ 00:13:08.810 Amber Lin: research. Firms consume healthcare, pharmaceutical and sort of more on the consumer side of consumer health over the counter products. So working with that.
94 00:13:09.280 ⇒ 00:13:15.639 Amber Lin: And now the the projects that I do for them is mostly a quick
95 00:13:15.920 ⇒ 00:13:28.180 Amber Lin: Dr. Quick. Market research, pulling some quick insights together is much more granular right now, because I don’t have that much time to devote to the entire project.
96 00:13:28.960 ⇒ 00:13:51.399 Amber Lin: So there’s that I did consulting, and then I did a lot of I have a very wide range of backgrounds. I also did a ib internship investment banking internship. I did internships in marketing. I did one in sales door to door B, 2 B sales, which I know that I will not be doing door to door sales anymore.
97 00:13:51.836 ⇒ 00:14:08.490 Amber Lin: But it is good experience. And then, now, right now, I’m just helping a lot of startups and nonprofits with project management, and they have new initiatives. They have new products. I’m helping push them forward. So the branding agency hired me
98 00:14:08.550 ⇒ 00:14:11.049 Amber Lin: to tell them what to do.
99 00:14:11.420 ⇒ 00:14:11.910 Uttam Kumaran: Good night.
100 00:14:12.222 ⇒ 00:14:33.187 Amber Lin: Cause their founder wants to. Their agency has been 20 years old, and they want to do this for 10 years, and they haven’t got it done. They wanted to have a product size offerings to their branding service. He hasn’t got it done, so I’m like, I think you should do this. I think you should do this thing now. So that’s what I do.
101 00:14:33.500 ⇒ 00:14:37.369 Uttam Kumaran: And what is so? What is your what is the day to day for them like, what are you typically doing.
102 00:14:38.367 ⇒ 00:14:39.379 Amber Lin: The branding agency.
103 00:14:40.150 ⇒ 00:14:42.300 Amber Lin: Oh, so the day to day.
104 00:14:42.310 ⇒ 00:14:51.440 Amber Lin: The team is very dispersed. So technically, the day to day is 24 h. Because we have people in India. We have people in Latvia, so
105 00:14:51.440 ⇒ 00:15:14.550 Amber Lin: it’s very spread out. And so the day to day would be someone. Is managing the sales funnels we get that we check in with every the, with the designers and web developers to see how the progress is going. They go to work. I work on what needs to be done next, and I check all the projects that check their status. And then, before I go to sleep.
106 00:15:14.880 ⇒ 00:15:30.669 Amber Lin: I summarize everything, I push it out to them, and when I get up I get all their summaries of what they have done in the past day, and then I start my day again. And so it’s a very international team. And so it kind of feels like work never ends.
107 00:15:30.670 ⇒ 00:15:34.030 Uttam Kumaran: Yeah, yeah, I mean, that’s kind of similar to what we have, although I.
108 00:15:34.750 ⇒ 00:15:37.209 Uttam Kumaran: I tell our team to like, relax, like I’m like.
109 00:15:37.210 ⇒ 00:15:37.970 Amber Lin: Yeah.
110 00:15:37.970 ⇒ 00:15:41.560 Uttam Kumaran: Work, and then like log off, because I’m up like all the time.
111 00:15:42.200 ⇒ 00:15:48.137 Uttam Kumaran: So I catch people when they go to bed, and then when they wake up, I’m up the whole time.
112 00:15:48.420 ⇒ 00:15:49.020 Uttam Kumaran: Go ahead.
113 00:15:51.520 ⇒ 00:15:58.910 Uttam Kumaran: But that’s awesome, I mean, like, so so is are you doing paid work for them? Or is that like a side? What is that? What is it for the branding agency?
114 00:15:58.910 ⇒ 00:16:08.819 Amber Lin: The branding agency. I started office on paperwork, but I’m discussing with them, if it’s possible, because I told them
115 00:16:08.970 ⇒ 00:16:21.240 Amber Lin: with unpaid work. I have limited hours I can commit. It is interesting to me, and it is experience. But I’ve already gotten a lot of experience from unpaid work at this point, and.
116 00:16:21.240 ⇒ 00:16:26.339 Uttam Kumaran: No, you don’t wanna do unpaid work, I’m telling you, because someone is making money off of your back.
117 00:16:26.780 ⇒ 00:16:34.750 Uttam Kumaran: You don’t have a path to making money. What is the point like we? So we wanna process that we do for a lot of folks is.
118 00:16:35.390 ⇒ 00:16:46.529 Uttam Kumaran: I don’t really like to have people like I like, I’m I’m I would say I’m an okay interviewer. But I like to give people tasks to take on. So for everybody on our team, we do contract to hire, meaning, I’m like.
119 00:16:47.120 ⇒ 00:16:57.109 Uttam Kumaran: Well, here’s 10. Come, spend 1020 HA week. Take on a couple of things if you like it, then it’ll work out. If it doesn’t, then it doesn’t like, you know. But
120 00:16:57.220 ⇒ 00:17:01.219 Uttam Kumaran: people have asked to do unpaid stuff. And I I always find that it’s like
121 00:17:01.690 ⇒ 00:17:05.150 Uttam Kumaran: it’s people aren’t interested because they’re not making money, and maybe they may.
122 00:17:05.150 ⇒ 00:17:08.800 Uttam Kumaran: Yeah, they’re like interested. But in something else comes up and you’re immediately.
123 00:17:08.800 ⇒ 00:17:09.609 Amber Lin: Yeah, I know.
124 00:17:09.619 ⇒ 00:17:11.579 Uttam Kumaran: Put down the totem pole right.
125 00:17:11.930 ⇒ 00:17:23.569 Amber Lin: Yeah, totally. That’s a big problem. I’m facing right now with volunteer management for that foundation, because a lot of people get inactive. And then we have have to hire new people we have to spend time interviewing have to spend
126 00:17:24.250 ⇒ 00:17:32.930 Amber Lin: onboarding them. So it’s the founder doesn’t want to spend anything we asked them so many times to invest in a project management system.
127 00:17:33.450 ⇒ 00:17:36.830 Amber Lin: He doesn’t want to spend money, so it is what it is
128 00:17:37.000 ⇒ 00:17:42.609 Amber Lin: we have to deal with that, but it’s really awesome to hear that this is your philosophy.
129 00:17:43.130 ⇒ 00:17:53.749 Uttam Kumaran: Yeah, I mean, I I feel very similar in that one. I see most of my business ultimately. Brain forge at the at the at the peak is just a broker for talent.
130 00:17:54.040 ⇒ 00:18:04.180 Uttam Kumaran: like we find really amazing people. And we pair them with really amazing problems. And everybody makes money and solving that. That’s it, that’s all we do very simply.
131 00:18:04.830 ⇒ 00:18:31.790 Amber Lin: Yeah. And honestly, everybody wants to get into that space. I think this is a very attractive sector for talent, specifically, data. And AI, these 2 things are just what people want to work for. Because today for the applicants, I think 80% of what I interviewed today was for the big data analyst role, and the other was like copywriting design and stuff. So I really think that’s a possible solution.
132 00:18:31.970 ⇒ 00:18:35.149 Uttam Kumaran: Yeah. And but the tough part is is like
133 00:18:35.340 ⇒ 00:18:52.220 Uttam Kumaran: everybody wants to. However, the date, the problem solving part of this business is the easiest part. It’s the everything around process information people that’s really difficult. And so one of the things that you know we’re really struggling with is we? We’re now sort of managing around 7 clients.
134 00:18:52.220 ⇒ 00:18:52.820 Amber Lin: Yeah.
135 00:18:53.092 ⇒ 00:18:59.900 Uttam Kumaran: You know. And when we started it was like one or 2 clients. And so we have a path forward to growing. But really, the
136 00:19:00.030 ⇒ 00:19:12.999 Uttam Kumaran: the problem we’re having now is we need to move from a mindset of, especially for me and Robert’s time, moving from doing a lot of the work to building the machine. And we’re trying to find really amazing people who want to come in and own
137 00:19:13.150 ⇒ 00:19:17.330 Uttam Kumaran: clients multiple clients and own their success
138 00:19:17.440 ⇒ 00:19:32.670 Uttam Kumaran: and basically do whatever it takes to to win for them. Right? We have enough people on the data team to execute problems. Really, one of our biggest challenges right now is sort of project management. It’s also sort of like making sure people have the right things.
139 00:19:33.330 ⇒ 00:19:38.839 Uttam Kumaran: And it’s tough because we’re growing. We’re growing from this phase of like me and Robert were doing a lot of the work to now
140 00:19:39.230 ⇒ 00:19:50.239 Uttam Kumaran: filling in our shoes. But also we want people who would come in and think in a process. Mindset. Right? Like, you have 2 types of people. You have people that come. They wake up and they see what’s on the to list, execute to go to bed.
141 00:19:50.700 ⇒ 00:19:51.040 Amber Lin: Yeah.
142 00:19:51.040 ⇒ 00:19:56.119 Uttam Kumaran: Me. I’m the person I execute. If I do something twice, I’m like
143 00:19:56.310 ⇒ 00:20:08.759 Uttam Kumaran: we’re gonna automate it before we do it a 3rd time, like every single thing right? So I don’t like. I’m not a fan of doing the same thing multiple. In fact, I have to find a way to do it faster. And so.
144 00:20:08.760 ⇒ 00:20:09.320 Amber Lin: You know.
145 00:20:09.320 ⇒ 00:20:37.669 Uttam Kumaran: That’s helped us. But the problem is like we we. I want to enable our team to be that way, too, and our customers ultimately win if we do that. And so, yeah, I mean, we have open roles, sort of on doing data analysis work which may sort of fit your background. We also are trying to bring on people who are sort of both, who can go own, who can help with like client relationships, but break down problems like sort of work with the team on being like running those meetings.
146 00:20:38.032 ⇒ 00:20:41.699 Uttam Kumaran: I kind of am not a really big. I’m not very interested in like
147 00:20:41.820 ⇒ 00:21:11.219 Uttam Kumaran: people having fixed roles, although that’s just pretty traditional. So we still do that. But I don’t know, like I don’t have a role here like I do every job. And so I don’t wanna hire. Yeah, I don’t wanna hire people who come in and they’re like, I only do one thing, cause that’s sort of like a limiting mindset. So I I like to bring on people who are like, maybe I do this today, and maybe I do the next thing tomorrow. Maybe I do 3 things, and I want to pay people for the value they bring. Not like what they’re like. Badge
148 00:21:11.540 ⇒ 00:21:27.669 Uttam Kumaran: says right? And so we’re sort of looking for more generalist people. People that sort of like can sit back and see the problems and like, go solve solve them whatever they are we, of course, have really technical people, like data engineers, analytics, engineers, analysts.
149 00:21:29.100 ⇒ 00:21:33.409 Uttam Kumaran: But there’s always a difference between the people executing the work, and then people managing the clients.
150 00:21:33.410 ⇒ 00:21:33.950 Amber Lin: Yeah.
151 00:21:33.950 ⇒ 00:21:35.830 Uttam Kumaran: Ability to think in systems, you know.
152 00:21:36.270 ⇒ 00:21:47.429 Amber Lin: Yeah. And really, when I when I talked to Robert, he was mentioning that they need project manager, that you guys need project managers and that you guys are building an internal AI system.
153 00:21:47.430 ⇒ 00:22:11.599 Amber Lin: And when I heard that was when I was like, Okay, I really, wanna I wanna do this because, yes, I can do a lot of data analytics. But, as I said earlier, at the very beginning of our call, my interest really is in the more holistic making things happen, getting project and products started dealing with people interfacing with clients.
154 00:22:11.610 ⇒ 00:22:18.009 Amber Lin: I think that’s my stronghold versus I. I can do data analysis. I’m pretty good at it.
155 00:22:18.070 ⇒ 00:22:20.080 Amber Lin: but I think it doesn’t.
156 00:22:20.440 ⇒ 00:22:28.219 Amber Lin: It doesn’t let me show my full potential. Yeah. So if guys would like me to do that, that will be
157 00:22:28.510 ⇒ 00:22:31.460 Amber Lin: even better. I would really appreciate that.
158 00:22:31.460 ⇒ 00:22:41.830 Uttam Kumaran: Yeah, I mean, I mean again, I’m probably out of everyone you may work with for me. I’m an engineer. So my background is engine I studied. I guess I’ll give you. I didn’t give you much of my background.
159 00:22:41.830 ⇒ 00:22:42.470 Amber Lin: Okay. Okay.
160 00:22:42.470 ⇒ 00:22:42.940 Uttam Kumaran: I
161 00:22:43.720 ⇒ 00:23:00.299 Uttam Kumaran: so my name is Tom. I grew up in the Bay area. I went to school in Pennsylvania and Bucknell, and then I lived in New York for about 5 years and then, now, I live here in Austin. Yeah, I worked as a data engineer. I studied computer engineering. I worked as a data engineer.
162 00:23:00.530 ⇒ 00:23:15.899 Uttam Kumaran: I led data teams. I led product at a startup and then I quit. And then I started Brain Forge. And yeah, we’ve been running. I’ve been running the company since 2023 like April, but I quit my job in April, and we got our 1st client in July. So.
163 00:23:16.300 ⇒ 00:23:18.399 Amber Lin: That is awesome, that is awesome.
164 00:23:18.400 ⇒ 00:23:21.648 Uttam Kumaran: Yeah. So still alive. Still going?
165 00:23:22.570 ⇒ 00:23:37.299 Uttam Kumaran: yeah. And I don’t know. I think a lot about the people we hire. I think a lot about building a sustainable business. I think a lot about finding ways. I feel really lucky to have started a company during AI because we use AI every single part of our process.
166 00:23:37.430 ⇒ 00:23:46.679 Uttam Kumaran: I use cursor every day when I do development work. All of our meetings. We built our own, you know, like a otter, or like Firefox.
167 00:23:46.680 ⇒ 00:23:59.660 Amber Lin: I I today I was telling the other foundation that I’m helping for the product. And she was like, Oh, all of you product managers need to send meeting notes and then make sure it’s updated here. I was like, why are people spending?
168 00:24:00.540 ⇒ 00:24:21.669 Amber Lin: And then I and I politely suggested to her that we should. We should do a thing called Meet Geek, because she doesn’t want to pay for anything. Firefly is not in not possible for her, and so I was like, Okay, if every project manager has that meeting assistant, it sends out automated meeting notes after the meeting.
169 00:24:21.940 ⇒ 00:24:30.759 Amber Lin: then they will save 30 h when all of them are volunteers who only have 4 HA week. It will. It will let them do a lot more work.
170 00:24:31.370 ⇒ 00:24:34.259 Amber Lin: And she was like, Okay, make sure I don’t pay. And I was like, Okay.
171 00:24:34.548 ⇒ 00:24:49.270 Uttam Kumaran: You’ll find it funny is that like I paid. And then I got angry because it wasn’t good enough, and we built our own. So we we built our own agent that connects to Zoom Api for. So this meeting, for example, all of our meetings are recorded. I’ve been recording.
172 00:24:49.660 ⇒ 00:24:50.150 Uttam Kumaran: I don’t.
173 00:24:50.470 ⇒ 00:24:52.799 Amber Lin: So I assume that’s why.
174 00:24:52.800 ⇒ 00:24:56.540 Uttam Kumaran: Yeah, I’ve been recording all of our meetings for like almost a year and a half.
175 00:24:57.390 ⇒ 00:24:57.950 Amber Lin: Sure.
176 00:24:58.360 ⇒ 00:25:18.359 Uttam Kumaran: And basically, we, I wanted to build like, I knew that we would eventually be able to shove all this into context and build really great AI agents. And so we record every meeting when this meeting finishes it not only has context about what we talked about, but me and you are on the call. It’ll tell me that it’ll know that it’s an interview. It knows
177 00:25:18.360 ⇒ 00:25:33.490 Uttam Kumaran: what our business, what my business does, and so it’ll send a summary with like context about, Hey, this is an interview. Here’s a template like we have an interview prompt. So it gets auto routed into an interview, prompt, summarize or like a interview summarizer prompt.
178 00:25:33.720 ⇒ 00:25:37.359 Uttam Kumaran: And then I get the notes, basically so. And then that runs, it’s probably like a
179 00:25:37.660 ⇒ 00:25:41.229 Uttam Kumaran: probably like half of a cent. Per run to do the whole thing.
180 00:25:43.000 ⇒ 00:25:43.490 Amber Lin: Good.
181 00:25:43.490 ⇒ 00:25:46.689 Uttam Kumaran: Because the Zoom Api is free as part of our plan.
182 00:25:47.550 ⇒ 00:25:51.210 Uttam Kumaran: The agent runs, and it’s open. AI calls so.
183 00:25:51.210 ⇒ 00:25:51.900 Amber Lin: What’s up?
184 00:25:51.900 ⇒ 00:26:00.560 Uttam Kumaran: That’s like one example. We have that running for every meeting we are. The other thing is one of our okrs, for this quarter is to build a junior Pm. Agent
185 00:26:00.840 ⇒ 00:26:01.940 Uttam Kumaran: all through AI.
186 00:26:01.940 ⇒ 00:26:05.520 Amber Lin: I wanted to talk to you about that. That is something that I really really want.
187 00:26:05.520 ⇒ 00:26:09.820 Uttam Kumaran: So we broke because I was a Pm. For a while. And so I,
188 00:26:10.140 ⇒ 00:26:17.049 Uttam Kumaran: I was like, Okay, I. We wrote a Jd like a job description for a Junior DM, and I basically was like all of these things.
189 00:26:17.180 ⇒ 00:26:18.580 Uttam Kumaran: we can automate
190 00:26:18.940 ⇒ 00:26:42.469 Uttam Kumaran: the task for our AI team. I said, between one or many agents. I want you guys to automate the entire role of a junior Pm, which includes creating tickets, summarizing meeting notes like asking questions about timelines. And so we built a lot of that we connect directly to. We use a notion. SDK, we pull a lot of information in all of our all of our AI agents are in slack.
191 00:26:42.610 ⇒ 00:26:48.469 Uttam Kumaran: so you could just add them and be like, Tell me about something. So we have like.
192 00:26:49.210 ⇒ 00:27:00.739 Uttam Kumaran: yeah. And then and then we also pipe in all of the Github code for clients, all of our meeting summaries, all of our emails, all of our like. Everything gets sent into there. So it has all the contacts of the business.
193 00:27:00.740 ⇒ 00:27:02.510 Amber Lin: Oh, that is great!
194 00:27:02.510 ⇒ 00:27:15.189 Uttam Kumaran: The only problem we have is like only me, and, like one other person, are using it because we have an adoption problem, right? And that’s the thing you’ll learn about AI is that it’s actually not that the technology isn’t great. It’s that people aren’t used to it.
195 00:27:15.320 ⇒ 00:27:16.120 Uttam Kumaran: So
196 00:27:16.660 ⇒ 00:27:28.799 Uttam Kumaran: one of the things I told AI team you have to go sit with people and train them on how to use these things right. But that’s their task, because I want to automate that I want our Pm. To be doing serious pm, work like spending time with clients.
197 00:27:29.450 ⇒ 00:27:33.070 Uttam Kumaran: I don’t want them to be sitting writing tickets for like 6 HA day.
198 00:27:33.070 ⇒ 00:27:34.930 Amber Lin: I know, I know, and
199 00:27:35.080 ⇒ 00:27:45.600 Amber Lin: I tried to. I tried to build a really simple agent. I saw a few tutorials, one with link chain and one with the open AI
200 00:27:45.710 ⇒ 00:27:48.370 Amber Lin: Api, and
201 00:27:48.800 ⇒ 00:28:11.999 Amber Lin: it’s more for process management. And it’s very much. Oh, you’re gonna break down tasks. You’re gonna assign it to different people. You’re going to check all the dependencies. And honestly, it’s great, because a lot of the times. It’s a great starting point for brainstorming, and it really lets the Pm. To just use their expertise to guide the AI instead of having to type everything out.
202 00:28:12.540 ⇒ 00:28:18.480 Amber Lin: And the problem you said with adoption. I really see that, too. I’m really trying to get them to adopt.
203 00:28:18.480 ⇒ 00:28:19.440 Uttam Kumaran: It’s hard, it’s hard.
204 00:28:19.440 ⇒ 00:28:21.187 Amber Lin: As as Pm.
205 00:28:22.673 ⇒ 00:28:27.919 Amber Lin: Cause. It’s instead of sending emails back and forth.
206 00:28:27.920 ⇒ 00:28:34.519 Amber Lin: The lovely thing about my company is that I’m in charge. So I love AI, and we’re gonna everybody’s gonna use it like from the top.
207 00:28:34.520 ⇒ 00:28:37.829 Amber Lin: Not everybody’s gonna use it. What’s just what you said.
208 00:28:38.290 ⇒ 00:28:48.799 Uttam Kumaran: Well, no, they’re not used because I don’t see that’s the thing. I don’t have any time to go train people. I’m hiring everybody and building a team. The problem is actually the people aren’t the problem right? People. People in AI.
209 00:28:48.800 ⇒ 00:28:50.439 Amber Lin: Feel like you’re the bottleneck.
210 00:28:50.720 ⇒ 00:28:53.660 Uttam Kumaran: I’m the. I’m 100%. I’m the biggest bottleneck in this whole.
211 00:28:54.055 ⇒ 00:28:54.450 Amber Lin: Okay.
212 00:28:54.450 ⇒ 00:28:57.419 Uttam Kumaran: I’m the number one person at fault with most of our problems.
213 00:28:57.655 ⇒ 00:28:57.890 Amber Lin: Bye.
214 00:28:57.890 ⇒ 00:29:05.269 Uttam Kumaran: Right like. So I acknowledge that. And but also I know the technology is there, and we’re building it to automate ourselves.
215 00:29:05.380 ⇒ 00:29:09.670 Amber Lin: And it’s great because we have our own problems. And we can just solve them using AI,
216 00:29:09.670 ⇒ 00:29:12.690 Amber Lin: no, and it’s a fantastic case study for clients.
217 00:29:12.690 ⇒ 00:29:13.230 Uttam Kumaran: Yeah.
218 00:29:13.230 ⇒ 00:29:18.930 Amber Lin: You’ll see. Oh, you were like this. Oh, we have the same problem. Oh, you did that. Okay, great. I’m willing to pay for that.
219 00:29:18.930 ⇒ 00:29:28.410 Uttam Kumaran: Yes, exactly. So that’s really like our ambition there. And then the data stuff again. We’re 1st we we automated a lot on sales, and then we automated a lot on marketing.
220 00:29:28.540 ⇒ 00:29:34.540 Uttam Kumaran: And we’re next moving into Pm automation. And then after that, we’ll move into engineering like trying to automate a lot of engineering.
221 00:29:34.540 ⇒ 00:29:45.259 Amber Lin: Yeah, I’ve been experimenting with cursor recently. I’ve been trying to build a financial auto audit tool. It’s kinda hard.
222 00:29:45.890 ⇒ 00:29:46.800 Uttam Kumaran: It’s hard. Yeah.
223 00:29:46.800 ⇒ 00:30:00.289 Amber Lin: This mostly because I am not a software engineer. And so I rely very heavily on cursor to tell me, okay, this is what the error means. This is what I need to do. So it’s very much of like I’m learning the whole.
224 00:30:00.290 ⇒ 00:30:00.980 Uttam Kumaran: Yeah.
225 00:30:01.340 ⇒ 00:30:04.140 Amber Lin: I believe in, I believe in what it does.
226 00:30:04.140 ⇒ 00:30:04.910 Uttam Kumaran: Yeah.
227 00:30:05.130 ⇒ 00:30:11.329 Uttam Kumaran: so tell me, like, what’s your availability? And like, I don’t know out of everything I mentioned, like, what’s interesting to like work on.
228 00:30:11.940 ⇒ 00:30:38.350 Amber Lin: Totally I can start anytime, especially when you pay me, and the others don’t. They can go, and what I’m most interested on is one I hear. Okay, there’s a lot of things that I’m interested in. I think above the data analytics, which is the basic individual contributor work that we do. I’m very interested in
229 00:30:38.640 ⇒ 00:30:56.270 Amber Lin: the whole project product management, driving products forward, working with the people to get them to adopt things. And also, lastly, you mentioned that you feel like you’re the bottleneck a lot of times. That is essentially what I’m doing with a branding agency
230 00:30:56.570 ⇒ 00:31:04.639 Amber Lin: because they’re also a team of around 5 to 10 people fluctuating with a lot of freelancers or contractors, and
231 00:31:04.990 ⇒ 00:31:12.790 Amber Lin: they are not insured of clients. But however they’re insured of, everything is, is in the CEO’s head.
232 00:31:12.790 ⇒ 00:31:13.709 Uttam Kumaran: Yes, it is.
233 00:31:13.710 ⇒ 00:31:28.820 Amber Lin: His inboxes. So my half of my role is to essentially get things out of him, to get what he is stuck with to the team. So a lot of times
234 00:31:29.120 ⇒ 00:31:33.759 Amber Lin: I’ll tell he was like, no, I’ll I’ll look into that next week. He’s like, no.
235 00:31:34.170 ⇒ 00:31:38.120 Amber Lin: I’m gonna check back with you on this meeting.
236 00:31:38.320 ⇒ 00:31:51.919 Amber Lin: I’m gonna have something a basic draft for you. And so you are going to use your expertise to modify that and not waste your time. Well, not waste your time and spend your time creating it from scratch. So
237 00:31:52.010 ⇒ 00:32:10.629 Amber Lin: I I don’t know if you need that kind of help, but I’m open to whatever the business needs, because I really do see that on the general list. And I learn things really, really fast. So whether it’s Pm data analytics, AI machine learning thing. I can pick those up. But I think
238 00:32:11.010 ⇒ 00:32:15.879 Amber Lin: the most important thing is that I know how to get things started, get things.
239 00:32:15.880 ⇒ 00:32:22.009 Uttam Kumaran: Yeah, we’re actually like, I I don’t think we’re ever gonna be in shortage of great engineers. In fact, like.
240 00:32:22.260 ⇒ 00:32:42.670 Uttam Kumaran: if I went in like I think I could bring on any type of engineer. What I’m more looking for is like systems, thinkers like, I want people to come in and work on things and understand. Okay, what is the system I can build to then have this be cheaper, faster, more efficient. The next time? Right? I think part of that role is like Pm.
241 00:32:42.670 ⇒ 00:32:52.950 Uttam Kumaran: But also it evolves like. For example, we have AI clients, too, where we do AI work for them. I can’t go. I don’t know, if, like what. Pm, I could even hire to go do that, because.
242 00:32:53.240 ⇒ 00:32:53.880 Amber Lin: There’s no.
243 00:32:53.880 ⇒ 00:33:03.460 Uttam Kumaran: That knows what those do. Yeah. So I so Ipm that client. And it’s like, it’s ridiculous. I shouldn’t be doing that. There’s like there’s more, there’s other things that are falling
244 00:33:04.230 ⇒ 00:33:11.139 Uttam Kumaran: times going there. But so there’s 2 things. There’s 1 also working on the business like finding ways to adapt AI
245 00:33:11.210 ⇒ 00:33:35.690 Uttam Kumaran: to use it in every nook and cranning in the business. Right? I said. Sales marketing, finance, operations, legal executive. I want us to adopt AI in every single piece of the business, and, in fact, the way we’ve architected the business and the tools we use have all been pointing towards that. Like we, we only slack. We only use notion. We only use zoom because I wanted the information to be constrained.
246 00:33:36.210 ⇒ 00:33:37.180 Amber Lin: Yes.
247 00:33:37.180 ⇒ 00:33:47.530 Uttam Kumaran: Right. And I wanted there to be tools that we they have sdks. They’ll have Apis, and it’s really the directive of the AI team is to automate the business like 100. That’s their job.
248 00:33:48.080 ⇒ 00:33:54.266 Amber Lin: Totally, and that once we do that, you just sell this package as a as a product.
249 00:33:54.750 ⇒ 00:34:01.650 Amber Lin: you sell it to every single startup or company that needs it. And then
250 00:34:01.830 ⇒ 00:34:10.669 Amber Lin: we benefit ourselves. And we make a future thing future product. That is not a service, because service requires time, but product doesn’t.
251 00:34:10.840 ⇒ 00:34:21.800 Amber Lin: So that’s the scalability. And that is tremendous. And really, once we accomplish that task of automating our internal processes, this is a great potential.
252 00:34:21.800 ⇒ 00:34:22.489 Uttam Kumaran: Yeah.
253 00:34:22.590 ⇒ 00:34:26.700 Uttam Kumaran: So how about? Let me talk to? Let me talk to Robert and see if there’s anything
254 00:34:26.969 ⇒ 00:34:32.259 Uttam Kumaran: short term that we could use your help on, I mean, I think, especially, I think, one.
255 00:34:32.699 ⇒ 00:34:40.619 Uttam Kumaran: The data analytics, tasks. I think definitely, there’s a couple of things, I think, more importantly, we have immediate like Pm needs, we’re basically coming in and like
256 00:34:40.899 ⇒ 00:34:46.449 Uttam Kumaran: just making sure that things are organized. Our engineers know what they’re working on the next day.
257 00:34:46.709 ⇒ 00:35:10.875 Uttam Kumaran: of course, like being a friendly, like everybody in the company you’ll meet is extremely friendly, and is like so kind. And so this isn’t like a sweatshop, or this isn’t like a dev shop. It’s all like human beings, but our pace is high. And and then yeah, I mean, let me see if there’s any opportunity to come on that. I mean, I could see that your background can help definitely with one of our AI clients that we’re managing
258 00:35:11.300 ⇒ 00:35:14.929 Amber Lin: And I can send you my Pm. Resume, if you would like, cause.
259 00:35:14.930 ⇒ 00:35:15.880 Uttam Kumaran: Please, please.
260 00:35:15.880 ⇒ 00:35:18.609 Amber Lin: Very, very tailored resumes.
261 00:35:18.610 ⇒ 00:35:30.619 Uttam Kumaran: Please. You’d be surprised, though, is like I don’t. I don’t know. I didn’t ask you for your degree. I didn’t ask you for where you went to school. I didn’t ask how old it doesn’t. That stuff doesn’t matter like.
262 00:35:30.620 ⇒ 00:35:31.320 Amber Lin: I know.
263 00:35:31.320 ⇒ 00:35:36.000 Uttam Kumaran: It just doesn’t for me. We’ll both find out if you get to do the thing.
264 00:35:36.000 ⇒ 00:35:36.679 Uttam Kumaran: Yeah, I hope.
265 00:35:37.972 ⇒ 00:35:38.820 Amber Lin: Project is for.
266 00:35:38.820 ⇒ 00:35:49.810 Uttam Kumaran: Exactly, but also for folks, you know, like you, and even for folks like me, like you want to show. You know the talking is helpful. But again the resumes and stuff are helpful for people who need that as a barometer
267 00:35:50.180 ⇒ 00:35:53.790 Uttam Kumaran: for us. I’m like I speak with you. I know you can the way you think.
268 00:35:53.970 ⇒ 00:36:03.429 Uttam Kumaran: And we have stuff. So take it on. And that’s it like, it’s so. It’s so so simple a lot of interview process, I think, is backed by these like, where did you go to school? Oh, like.
269 00:36:03.430 ⇒ 00:36:03.790 Amber Lin: You’re.
270 00:36:03.790 ⇒ 00:36:16.829 Uttam Kumaran: I’m like. I can’t tell you the last time I like asked someone where they went to school, or what their degree. I don’t think I I don’t know any. I actually don’t know for any of our people like I know one or 2 people the people that went to the best schools because they
271 00:36:17.070 ⇒ 00:36:20.209 Uttam Kumaran: someone mentioned it, but like otherwise I don’t know. That’s
272 00:36:20.600 ⇒ 00:36:29.260 Uttam Kumaran: I didn’t go to. I didn’t go to like a super crazy school. I didn’t have great grades. I was very young, and so how can I turn around and have those same.
273 00:36:29.620 ⇒ 00:36:30.050 Amber Lin: I know.
274 00:36:30.050 ⇒ 00:36:32.159 Uttam Kumaran: Barriers. If someone gave me the benefit.
275 00:36:32.160 ⇒ 00:36:48.550 Amber Lin: Yeah. And especially, I know people who didn’t end up in the top 20 schools. They work really hard because they know that the market wants the top schools. And really, if we still discriminate against that, we’re gonna miss out on a lot of great people.
276 00:36:48.550 ⇒ 00:36:56.050 Uttam Kumaran: Totally. I mean, I’m 100%. Yeah. So okay, let me chat with Robert and see if I can get you a couple of things short term.
277 00:36:56.421 ⇒ 00:37:19.800 Amber Lin: I I think immediate help could be some data projects. But I really want to show you guys how I house how I think systematically, and how I look at the different processes. So if in these, if in the short term project, do you think it’s possible for me to get a better sense of the system, because essentially, I can only
278 00:37:20.080 ⇒ 00:37:22.510 Amber Lin: improve the system. If I know the system.
279 00:37:22.510 ⇒ 00:37:29.930 Uttam Kumaran: Oh, yeah, I mean, like, I’m I’m telling you the only reason I like the only reason I don’t give anyone any scope, really, let’s.
280 00:37:29.930 ⇒ 00:37:30.370 Amber Lin: I can.
281 00:37:30.370 ⇒ 00:37:37.119 Uttam Kumaran: The scope I give people is like, you’re an engineer on this project, but I do that because some people they need blinders.
282 00:37:37.120 ⇒ 00:37:37.910 Amber Lin: It’s the.
283 00:37:37.910 ⇒ 00:37:51.169 Uttam Kumaran: There’s other people who I’m like dude. You just go run toward my day to day, is running into the Bernie building right, and I’m trying to hire people. That that’s all they want to do is run towards the most challenging problem.
284 00:37:51.720 ⇒ 00:37:57.660 Uttam Kumaran: But then also understand that if we solve problems multiple times there should that screens process
285 00:37:57.760 ⇒ 00:38:02.100 Uttam Kumaran: right and that screens automation immediately after. You know.
286 00:38:02.100 ⇒ 00:38:02.710 Amber Lin: Holy.
287 00:38:03.269 ⇒ 00:38:09.529 Uttam Kumaran: I do have. So I do have some stuff that’s definitely both technical, but also things that are around.
288 00:38:09.730 ⇒ 00:38:20.270 Uttam Kumaran: Pm, like, understand? Again, for me. It’s also like understanding. Okay, what are the playbooks when we execute work for clients? How do we dissect that to find the the bottlenecks.
289 00:38:20.270 ⇒ 00:38:20.650 Amber Lin: Hmm.
290 00:38:20.650 ⇒ 00:38:22.289 Uttam Kumaran: Right? How do we find that critical path
291 00:38:22.440 ⇒ 00:38:24.760 Uttam Kumaran: around all of our operations? Basically.
292 00:38:24.960 ⇒ 00:38:30.349 Uttam Kumaran: right? I again, I think if you’re in engineering, or if you’re in science, you understand the concept of like critical path and.
293 00:38:30.350 ⇒ 00:38:31.140 Amber Lin: Employees.
294 00:38:31.140 ⇒ 00:38:31.970 Uttam Kumaran: And sort of.
295 00:38:32.080 ⇒ 00:38:52.179 Uttam Kumaran: you know. So that’s that’s a lot of the ultimately, this, the data stuff and the AI stuff we do, it’s gonna stay the same. It’s gonna be innovative. But that’s not for me. Where the real, like creativity goes. The creativity is going into building a sustainable, effective business that can serve our clients well and serve our employees really well, like.
296 00:38:52.180 ⇒ 00:38:52.720 Amber Lin: Yeah.
297 00:38:52.720 ⇒ 00:38:55.369 Uttam Kumaran: That’s all. Brain forge is a vehicle for right.
298 00:38:55.370 ⇒ 00:39:22.300 Amber Lin: And that’s that’s kind of the thing that I want to do is I all the data because I did a major in data analytics. I picked all I picked up all the data stuff, the machine learning stuff on my own because it was part of when I was working for the consulting firms. When I was working for these projects, I was like, Oh, I can. I can do that. I can do a demo for you. So the data engineers
299 00:39:22.440 ⇒ 00:39:27.279 Amber Lin: can do it fast can know what they want to do. So
300 00:39:27.460 ⇒ 00:39:32.629 Amber Lin: those are skills I use to enhance my core beliefs
301 00:39:33.330 ⇒ 00:39:40.200 Amber Lin: for things. So I’m really excited. I’m really excited to take on a short product with you guys, because I just.
302 00:39:40.360 ⇒ 00:39:44.249 Amber Lin: I hope that I demonstrated to you that I really enjoy.
303 00:39:44.625 ⇒ 00:39:44.920 Uttam Kumaran: Right.
304 00:39:45.355 ⇒ 00:39:51.009 Amber Lin: Challenges. I really enjoy new things and just making things more efficient and.
305 00:39:51.010 ⇒ 00:39:54.360 Uttam Kumaran: Yeah, it has its downfalls. I will not lie.
306 00:39:54.360 ⇒ 00:40:01.319 Uttam Kumaran: Yeah, I’m the same way. It’s like, you can automate something too much, and it just becomes harder than just doing the work.
307 00:40:01.320 ⇒ 00:40:08.089 Uttam Kumaran: And then you spend a lot of time automating a teeny, tiny task that you don’t do that much.
308 00:40:08.950 ⇒ 00:40:10.949 Uttam Kumaran: Okay, awesome. Where are you based, by the way.
309 00:40:11.310 ⇒ 00:40:13.439 Amber Lin: I’m based in la, so.
310 00:40:13.440 ⇒ 00:40:13.900 Uttam Kumaran: Nice.
311 00:40:13.900 ⇒ 00:40:17.019 Amber Lin: We wouldn’t. We will have like a 3 h time.
312 00:40:17.020 ⇒ 00:40:21.170 Uttam Kumaran: We have people in La. There’s people in La in New York. I’m here.
313 00:40:21.300 ⇒ 00:40:24.640 Amber Lin: There’s people in Spain in Asia, I know.
314 00:40:24.910 ⇒ 00:40:40.629 Uttam Kumaran: It’s just for me. It’s wherever the smart people that we can afford in the moment are, and my goal is to pay them as much as humanly possible, and to to ideally not hire at the same rate at which we’re growing, because then nobody
315 00:40:41.100 ⇒ 00:40:42.210 Uttam Kumaran: more money right? So.
316 00:40:42.210 ⇒ 00:40:49.550 Amber Lin: Yeah, can you give me a general idea of what the budget you guys have for, like a short term project.
317 00:40:49.870 ⇒ 00:40:59.680 Uttam Kumaran: I need to scope what the project is with Robert before I can give you a sense of that. Because I know you. You’re sort of interested in some Pm stuff, but also maybe taking some data stuff.
318 00:40:59.990 ⇒ 00:41:04.119 Uttam Kumaran: So I’m gonna see something that at least can take up like 10 to 20 h of your time
319 00:41:04.290 ⇒ 00:41:05.660 Uttam Kumaran: to start.
320 00:41:06.670 ⇒ 00:41:25.949 Uttam Kumaran: And we’ll give you a rate. And then again we’ll see how it goes. And then again, it’s a negotiation on both sides. So for you. I want you to come in and know that we’re the real deal, that there are hard problems, and that there’s definitely money to be made. And then for us, it’s to find out like, can we work together? And can we solve stuff for clients? So that’s that’s all. The trial period is, you know. So.
321 00:41:26.090 ⇒ 00:41:29.359 Amber Lin: Yeah, totally. And how long would it last? Approximately.
322 00:41:29.750 ⇒ 00:41:34.989 Uttam Kumaran: I think we I mean, we would try to just do something for like 2 weeks before seeing. If you want to come on full time.
323 00:41:35.200 ⇒ 00:41:37.070 Amber Lin: Okay, 1, 2 weeks.
324 00:41:37.300 ⇒ 00:41:37.850 Amber Lin: Seriously.
325 00:41:38.287 ⇒ 00:41:39.510 Uttam Kumaran: I think that’s
326 00:41:39.650 ⇒ 00:41:45.840 Uttam Kumaran: I mean for me. That’s more than enough time we can if you want to keep going trial longer. That’s totally fine, but
327 00:41:46.050 ⇒ 00:41:52.299 Uttam Kumaran: I feel like, you know, I don’t know when you once you work for a lot of people in in a week or so. You know whether.
328 00:41:52.680 ⇒ 00:41:57.220 Amber Lin: Yeah, I have sometimes in the 1st call. I’ll know, because.
329 00:41:57.220 ⇒ 00:42:04.700 Uttam Kumaran: Yeah, so it’s it’s and for me, I also like, if you’re like, I’ve if if you’re amazing and the work you’re doing for us amazing.
330 00:42:04.820 ⇒ 00:42:13.109 Uttam Kumaran: I’ll give you offer the next day like this, just like these are all made up bureaucracy things like, I say, 2 weeks, because I feel like that’s like a nice number.
331 00:42:13.110 ⇒ 00:42:13.460 Amber Lin: Yeah.
332 00:42:13.460 ⇒ 00:42:21.040 Uttam Kumaran: But, like I don’t know, I think ideally, we’ll know sooner, and we’ll be able to give an offer sooner and sort of like. How do you join us as as fast as possible, so.
333 00:42:21.040 ⇒ 00:42:28.170 Amber Lin: Totally. And when you say join us, you mean a full time, say 40 plus 40 or 40 around that. Okay.
334 00:42:28.510 ⇒ 00:42:29.270 Uttam Kumaran: You know.
335 00:42:29.270 ⇒ 00:42:31.970 Amber Lin: That’s good to know, or maybe.
336 00:42:33.320 ⇒ 00:42:39.663 Uttam Kumaran: Yeah. And then you also, you have my email. If you have any questions, I it may take me a day or 2 to get back to you.
337 00:42:40.252 ⇒ 00:42:50.040 Amber Lin: I can. I can ping you, ping you in one or 2 days, because I know what it’s like to have so many messages on your plate.
338 00:42:50.040 ⇒ 00:42:54.939 Uttam Kumaran: No, no, it’s fine, but just don’t take it. Don’t take it the wrong way. If if anything we are.
339 00:42:55.050 ⇒ 00:42:57.999 Uttam Kumaran: we’re yeah. We’re just working hard, so.
340 00:42:58.000 ⇒ 00:43:03.970 Amber Lin: Yeah, you know, one of the things I know you might have to go, but I found this really interesting insight of.
341 00:43:04.640 ⇒ 00:43:10.249 Amber Lin: I don’t have to feel bad when I’m reminding people, because I know it’s their call for help
342 00:43:10.470 ⇒ 00:43:15.989 Amber Lin: for me to remind them, because they’re really busy, and they need me to help them.
343 00:43:16.473 ⇒ 00:43:17.440 Uttam Kumaran: Tell everybody.
344 00:43:17.440 ⇒ 00:43:18.100 Amber Lin: About it that way.
345 00:43:18.100 ⇒ 00:43:24.270 Uttam Kumaran: Exactly. I’m like you can ping me at any hour with any question like I don’t. I want this to be open.
346 00:43:25.260 ⇒ 00:43:42.199 Uttam Kumaran: Because lack of transparency and lack of openness leads to like resentment and stuff like that. So I try to be like if I don’t get back to you. Please do not take it the wrong way. I’m either like sleeping, eating, or working, or like mix of both. And so like you just need to
347 00:43:42.810 ⇒ 00:43:46.190 Uttam Kumaran: hear me, please like ask me again. So.
348 00:43:46.410 ⇒ 00:43:55.040 Amber Lin: Same goes for me. I I really appreciate your time, and I had a great conversation with you. I think I think I really get.
349 00:43:55.170 ⇒ 00:44:01.119 Amber Lin: I like your energy because I had a lot of interviews, was very much corporate, even though.
350 00:44:01.752 ⇒ 00:44:03.017 Uttam Kumaran: Me, too.
351 00:44:04.170 ⇒ 00:44:10.670 Uttam Kumaran: It’s so weird I don’t know. I’m not like that, but maybe I have to become like that. But I don’t know, and none of that.
352 00:44:10.670 ⇒ 00:44:20.099 Amber Lin: There’s an interesting balance to interviewing, because I’m interviewing a lot of people, and I can see their different responses to how I act. So I’m still finding that out, too.
353 00:44:20.100 ⇒ 00:44:21.680 Uttam Kumaran: Yeah, yeah, same.
354 00:44:22.170 ⇒ 00:44:22.850 Amber Lin: It’s okay.
355 00:44:22.850 ⇒ 00:44:23.780 Uttam Kumaran: Okay. Cool.
356 00:44:23.780 ⇒ 00:44:24.220 Amber Lin: Hey! Michael!
357 00:44:24.550 ⇒ 00:44:25.840 Uttam Kumaran: It’s really great to meet you.
358 00:44:26.690 ⇒ 00:44:27.580 Amber Lin: Bye.