Meeting Title: Brainforge AI Award Nomination Discussion Date: 2025-12-23 Meeting participants: Gabriel Lam, Hannah Wang
WEBVTT
1 00:00:00.510 ⇒ 00:00:01.630 Hannah Wang: This is working.
2 00:00:01.630 ⇒ 00:00:05.839 Gabriel Lam: Hopefully the transcript will fill in any other information that you need.
3 00:00:05.840 ⇒ 00:00:06.500 Hannah Wang: Oh, yes.
4 00:00:06.800 ⇒ 00:00:10.500 Gabriel Lam: But I’m pretty swamped today, so I was like, I’m so sorry.
5 00:00:10.500 ⇒ 00:00:11.149 Hannah Wang: I’m gonna be done.
6 00:00:11.150 ⇒ 00:00:12.690 Gabriel Lam: They would call.
7 00:00:13.030 ⇒ 00:00:17.650 Hannah Wang: Oh, yeah, of course, yeah. I… I will fill it out, like, you just need to…
8 00:00:17.650 ⇒ 00:00:18.640 Gabriel Lam: I appreciate that.
9 00:00:18.640 ⇒ 00:00:20.000 Hannah Wang: blah blah blah at…
10 00:00:20.000 ⇒ 00:00:20.460 Gabriel Lam: Okay.
11 00:00:20.460 ⇒ 00:00:26.139 Hannah Wang: So yeah, it’s like a award nomination thing, and then…
12 00:00:26.580 ⇒ 00:00:31.319 Hannah Wang: We’re not nominating ourselves, like this one other person said we can put her as the nominee.
13 00:00:31.320 ⇒ 00:00:31.940 Gabriel Lam: Hmm.
14 00:00:31.940 ⇒ 00:00:36.300 Hannah Wang: Yeah. So I did start…
15 00:00:36.630 ⇒ 00:00:40.090 Hannah Wang: filling it out, but I’ve, like…
16 00:00:40.090 ⇒ 00:00:44.210 Gabriel Lam: I think both are… I think that’s pretty accurate, the first one.
17 00:00:44.260 ⇒ 00:00:48.499 Hannah Wang: Like, I’m pretty sure 1, 4, and 5 are not.
18 00:00:48.500 ⇒ 00:00:51.230 Gabriel Lam: No, because we’re not… we’re not really…
19 00:00:52.870 ⇒ 00:00:55.930 Gabriel Lam: At the form, like, for advanced, that’s really, like.
20 00:00:57.290 ⇒ 00:01:01.560 Gabriel Lam: Cutting edge, where we don’t have the resources to do that, so…
21 00:01:01.560 ⇒ 00:01:02.100 Hannah Wang: Okay.
22 00:01:02.480 ⇒ 00:01:05.790 Hannah Wang: Okay, and then it.
23 00:01:05.790 ⇒ 00:01:06.330 Gabriel Lam: That’s good.
24 00:01:06.330 ⇒ 00:01:16.229 Hannah Wang: Brainforge, oh, I guess… Yeah, it was kind of confusing, cuz…
25 00:01:17.430 ⇒ 00:01:25.029 Hannah Wang: like, I’m like, what’s the difference between these two? But whatever. I think it’s fine.
26 00:01:25.030 ⇒ 00:01:29.369 Gabriel Lam: So the nominee is not the person who’s nominating us.
27 00:01:31.780 ⇒ 00:01:35.250 Hannah Wang: Ugh, I always get confused. Nameh…
28 00:01:37.390 ⇒ 00:01:39.709 Hannah Wang: Literally, what is an… what is a nominate?
29 00:01:41.410 ⇒ 00:01:49.439 Hannah Wang: Yeah, a person who’s proposed as something to receive something. So, we are the nominee.
30 00:01:49.440 ⇒ 00:01:51.759 Gabriel Lam: Yeah. So it’s Brainforge.
31 00:01:51.760 ⇒ 00:02:01.800 Hannah Wang: I don’t really know what the name would mean, but that’s okay. Brainforge AI is the company, UTOM’s a contact, LinkedIn, yeah, these are fine.
32 00:02:02.000 ⇒ 00:02:02.500 Gabriel Lam: Yep.
33 00:02:02.500 ⇒ 00:02:09.190 Hannah Wang: And then… Yeah, so this is where I was like, okay, like, I’m…
34 00:02:09.600 ⇒ 00:02:12.559 Hannah Wang: I don’t really know much about our…
35 00:02:14.100 ⇒ 00:02:18.419 Hannah Wang: clients that use AI, except ABC… ABC Home.
36 00:02:18.420 ⇒ 00:02:26.560 Gabriel Lam: Is there any other client that we’re doing AI work for? We’re doing AI work for a company called Lilo Social.
37 00:02:26.930 ⇒ 00:02:32.549 Gabriel Lam: It’s like a… Growth Ecom, growth marketing agency, so…
38 00:02:32.550 ⇒ 00:02:33.160 Hannah Wang: Hmm.
39 00:02:34.010 ⇒ 00:02:38.339 Gabriel Lam: I think either… either or.
40 00:02:38.770 ⇒ 00:02:43.090 Gabriel Lam: But they, like, basically work with, like, Direct-to-consumer brands.
41 00:02:43.380 ⇒ 00:02:47.290 Hannah Wang: Okay. Is that technology? Okay.
42 00:02:48.380 ⇒ 00:02:51.320 Hannah Wang: You see, I’m like, is it professional services?
43 00:02:51.710 ⇒ 00:02:58.459 Gabriel Lam: I think ABC is… So… I’ve always been confused what it actually is.
44 00:02:59.070 ⇒ 00:03:03.939 Gabriel Lam: Because it looks like… What is it?
45 00:03:05.250 ⇒ 00:03:07.870 Gabriel Lam: when I first saw it, I thought it was, like, a…
46 00:03:09.090 ⇒ 00:03:12.619 Gabriel Lam: like a Home Depot kind of thing, but it’s not. It’s like a…
47 00:03:12.620 ⇒ 00:03:13.000 Hannah Wang: Huh?
48 00:03:13.000 ⇒ 00:03:16.549 Gabriel Lam: I guess professional services, because they do, like, HVAC and, like.
49 00:03:16.550 ⇒ 00:03:17.780 Hannah Wang: Boom, yeah.
50 00:03:17.780 ⇒ 00:03:18.450 Gabriel Lam: Yeah.
51 00:03:18.650 ⇒ 00:03:19.490 Gabriel Lam: I don’t know what…
52 00:03:19.570 ⇒ 00:03:21.230 Hannah Wang: I also agree.
53 00:03:22.350 ⇒ 00:03:27.199 Gabriel Lam: other services? I feel like professional services is, like, accounting, or…
54 00:03:27.200 ⇒ 00:03:28.270 Hannah Wang: Hmm.
55 00:03:28.270 ⇒ 00:03:35.630 Gabriel Lam: Yeah, so maybe other services, or utilities. There’s other services, like, under oil and gas.
56 00:03:36.280 ⇒ 00:03:41.699 Gabriel Lam: Then… Lilo, I would also maybe put in marketing and media.
57 00:03:42.480 ⇒ 00:03:44.109 Gabriel Lam: And then there’s…
58 00:03:44.110 ⇒ 00:03:45.300 Hannah Wang: Let me try to find…
59 00:03:45.840 ⇒ 00:03:47.230 Gabriel Lam: What else we’re doing?
60 00:03:53.360 ⇒ 00:03:57.670 Gabriel Lam: One second… I’m gonna look through our engineering chat.
61 00:04:14.270 ⇒ 00:04:15.420 Gabriel Lam: Sorry, hold on.
62 00:04:15.420 ⇒ 00:04:16.170 Hannah Wang: All good.
63 00:04:23.999 ⇒ 00:04:25.179 Gabriel Lam: Where is it?
64 00:04:33.259 ⇒ 00:04:38.179 Gabriel Lam: I think there was a client called, Remo, but I don’t know if…
65 00:04:38.399 ⇒ 00:04:40.629 Gabriel Lam: We have gone over it.
66 00:04:51.090 ⇒ 00:04:52.550 Hannah Wang: Are they a current client?
67 00:04:54.450 ⇒ 00:04:57.699 Gabriel Lam: Oh, I think it might be data.
68 00:04:58.340 ⇒ 00:04:59.939 Gabriel Lam: It might be a data thing.
69 00:05:04.130 ⇒ 00:05:04.810 Gabriel Lam: Yeah.
70 00:05:05.830 ⇒ 00:05:11.600 Gabriel Lam: Is there anyone else? I think that’s it.
71 00:05:12.050 ⇒ 00:05:12.830 Hannah Wang: Okay.
72 00:05:12.970 ⇒ 00:05:13.760 Gabriel Lam: Yeah.
73 00:05:16.610 ⇒ 00:05:19.859 Gabriel Lam: Sam… Sam might be another good resource to ask.
74 00:05:20.290 ⇒ 00:05:23.579 Gabriel Lam: Just to be like, hey… because he, I think…
75 00:05:23.880 ⇒ 00:05:25.950 Gabriel Lam: who Tom sort of wants Sam to lead.
76 00:05:26.330 ⇒ 00:05:31.250 Gabriel Lam: the stand-ups for all the… all the AI stuff.
77 00:05:31.250 ⇒ 00:05:32.010 Hannah Wang: I see.
78 00:05:32.720 ⇒ 00:05:33.939 Gabriel Lam: I’m more internal.
79 00:05:34.480 ⇒ 00:05:35.230 Hannah Wang: Yeah.
80 00:05:35.520 ⇒ 00:05:38.210 Hannah Wang: I’m looking at our Notion…
81 00:05:38.210 ⇒ 00:05:38.710 Gabriel Lam: Hmm.
82 00:05:38.710 ⇒ 00:05:50.980 Hannah Wang: So I guess… I don’t know if we should put, like, churned clients as… part of… this… Okay.
83 00:05:50.980 ⇒ 00:05:51.809 Gabriel Lam: You can.
84 00:05:51.810 ⇒ 00:05:53.119 Hannah Wang: Okay, yeah, so pull.
85 00:05:53.120 ⇒ 00:05:57.389 Gabriel Lam: As in, like, the… they’re turned? As in, like, we finish the work.
86 00:05:57.390 ⇒ 00:05:58.010 Hannah Wang: Yeah.
87 00:05:58.660 ⇒ 00:06:01.370 Gabriel Lam: Yeah, then I don’t see why not.
88 00:06:01.370 ⇒ 00:06:02.040 Hannah Wang: Okay.
89 00:06:02.040 ⇒ 00:06:03.350 Gabriel Lam: So, like, CBDG.
90 00:06:03.520 ⇒ 00:06:06.709 Hannah Wang: So, Pool Parts2Go, they are a…
91 00:06:06.830 ⇒ 00:06:12.210 Hannah Wang: They supply pool part… like, they sell, literally, pool parts.
92 00:06:13.880 ⇒ 00:06:15.850 Gabriel Lam: CPG. It’s Consumer Product Goods.
93 00:06:15.850 ⇒ 00:06:17.520 Hannah Wang: Yeah. Yeah.
94 00:06:17.520 ⇒ 00:06:22.639 Gabriel Lam: I think that’s probably, like, full parts. It looks like, you know, VitaCoco is also…
95 00:06:23.810 ⇒ 00:06:29.219 Hannah Wang: Oh, yeah. And then Interlude, I know they are, like, a deck-making…
96 00:06:29.480 ⇒ 00:06:31.639 Gabriel Lam: Industry. Of technology.
97 00:06:31.640 ⇒ 00:06:35.770 Hannah Wang: Yeah, okay. Default is also SAS, and then…
98 00:06:37.600 ⇒ 00:06:43.659 Hannah Wang: Okay, off the record, I forgot what they are, but I think that’s okay.
99 00:06:43.660 ⇒ 00:06:44.460 Gabriel Lam: That’s fine.
100 00:06:44.460 ⇒ 00:06:50.110 Hannah Wang: Okay, and then type of AI work, yeah, I’m like.
101 00:06:50.110 ⇒ 00:06:55.160 Gabriel Lam: So, there’s Agentic, there’s information retrieval or search.
102 00:06:55.490 ⇒ 00:06:57.909 Gabriel Lam: That’s a big part.
103 00:07:05.540 ⇒ 00:07:09.760 Hannah Wang: Like, automation? We did a lot of automate… process automation?
104 00:07:09.760 ⇒ 00:07:12.459 Gabriel Lam: Yeah, process automation, for sure.
105 00:07:13.980 ⇒ 00:07:18.690 Gabriel Lam: Yeah. The last, like, 4 or 5 are definitely not.
106 00:07:19.450 ⇒ 00:07:23.840 Gabriel Lam: Are we doing recommendations? No, we’re not.
107 00:07:26.520 ⇒ 00:07:32.379 Gabriel Lam: basically for… I know for Lilo, basically, what we’re doing is doing what our platform is doing, and, like.
108 00:07:35.380 ⇒ 00:07:44.280 Gabriel Lam: they have all these, like, Meta and Amazon and Klaviyo, like, information that they want to be able to search through, or to produce, like, reports.
109 00:07:46.330 ⇒ 00:07:47.200 Gabriel Lam: So…
110 00:07:47.510 ⇒ 00:07:49.150 Hannah Wang: like, content…
111 00:07:50.680 ⇒ 00:07:58.250 Gabriel Lam: I think content creation is maybe closer to, like, product videos, or, like, UGC, so maybe not so much.
112 00:07:59.700 ⇒ 00:08:00.740 Hannah Wang: Facebook.
113 00:08:00.740 ⇒ 00:08:02.190 Gabriel Lam: Digital assistance.
114 00:08:02.910 ⇒ 00:08:06.679 Hannah Wang: Yeah, like, pool parts, we… I think we made, like, a chatbot thing for…
115 00:08:06.680 ⇒ 00:08:07.940 Gabriel Lam: Yeah, yeah, yeah, yeah.
116 00:08:08.000 ⇒ 00:08:11.260 Hannah Wang: And then…
117 00:08:13.040 ⇒ 00:08:16.360 Gabriel Lam: Monitoring, I think, is another one.
118 00:08:16.650 ⇒ 00:08:18.359 Hannah Wang: Monitor, oh yes.
119 00:08:18.760 ⇒ 00:08:20.630 Gabriel Lam: I think that’s pretty much it.
120 00:08:20.630 ⇒ 00:08:29.060 Hannah Wang: Yeah. And then I’ll… I’ll have Utam take a look at this. Hopefully he has more time to actually respond to my messages.
121 00:08:29.060 ⇒ 00:08:30.840 Gabriel Lam: denominator, okay, there we go.
122 00:08:30.840 ⇒ 00:08:36.350 Hannah Wang: Yeah, so denominator, I will put in the info of who that person is.
123 00:08:37.080 ⇒ 00:08:43.249 Hannah Wang: Yeah, so it’s like… This is so vague, because we do a lot of work with, like.
124 00:08:43.440 ⇒ 00:08:45.339 Hannah Wang: A lot of our clients, but…
125 00:08:45.950 ⇒ 00:08:49.289 Hannah Wang: You can just literally blab, like.
126 00:08:50.170 ⇒ 00:09:01.940 Hannah Wang: everything you know about the client work that we’ve done, so I’m just gonna be very explicit and be like, okay, so what was achieved with all the AI work we did for our clients?
127 00:09:05.180 ⇒ 00:09:15.530 Gabriel Lam: Yeah, I think the one that I can give the most information on is Lilo, just because…
128 00:09:15.790 ⇒ 00:09:24.980 Gabriel Lam: Yeah. That is the most top of mind. I think if you want to talk about ABC, then Casey or Sam are…
129 00:09:26.500 ⇒ 00:09:29.490 Gabriel Lam: more… better resources.
130 00:09:29.490 ⇒ 00:09:37.310 Hannah Wang: And I think the scope of work there might be bigger, because Lido is just sort of just starting. But what they’ve been trying to do is…
131 00:09:37.510 ⇒ 00:09:47.370 Gabriel Lam: They’ve been trying to create Like, a platform. And this is both in chat and in…
132 00:09:48.770 ⇒ 00:09:50.539 Gabriel Lam: Like, a sort of data warehouse.
133 00:09:51.060 ⇒ 00:09:52.639 Gabriel Lam: So…
134 00:09:53.260 ⇒ 00:10:00.020 Gabriel Lam: the Brainforge platform that we have, the back end of that, which is, like, Subabase, or, all these
135 00:10:00.310 ⇒ 00:10:07.360 Gabriel Lam: tools that we use to call, and, like, MCPs that take The data that we have.
136 00:10:07.590 ⇒ 00:10:13.690 Gabriel Lam: stored in, like, for example, Facebook ads, or Klaviyo, or whatever it might be.
137 00:10:16.040 ⇒ 00:10:20.330 Gabriel Lam: it gets consolidated. And so, when they want to ask, like, hey, what’s the last
138 00:10:21.030 ⇒ 00:10:24.179 Gabriel Lam: You know, 60 days, of…
139 00:10:24.290 ⇒ 00:10:28.540 Gabriel Lam: like, what’s changed in the last 60 days in terms of Klaviyo? Like, who’s buying different things?
140 00:10:28.750 ⇒ 00:10:33.379 Gabriel Lam: Has it been growing? Have there been, like, major sources that were…
141 00:10:34.810 ⇒ 00:10:44.539 Gabriel Lam: neglecting, or have there been certain things that we should… like, trends that we should be aware of? So, if I can find maybe some questions…
142 00:10:44.980 ⇒ 00:10:46.700 Gabriel Lam: That they’ve been asking.
143 00:10:58.550 ⇒ 00:10:59.859 Gabriel Lam: Hold on a second…
144 00:11:00.680 ⇒ 00:11:01.400 Hannah Wang: Oh, good.
145 00:11:06.670 ⇒ 00:11:08.699 Gabriel Lam: Let me try to find again.
146 00:11:48.540 ⇒ 00:11:52.779 Gabriel Lam: Yeah, so… in that sense, what we’ve basically done is…
147 00:11:52.940 ⇒ 00:12:07.150 Gabriel Lam: We’ve created this interface where you can select different data sources, or you can select all the data sources, and basically query what information you have, instead of having to look through all your files, or look through all your documents.
148 00:12:07.150 ⇒ 00:12:08.240 Hannah Wang: To…
149 00:12:08.240 ⇒ 00:12:20.070 Gabriel Lam: to find the relevant information. And so I think that’s part of the, like, whole document search and retrieval. And then the other part is reporting, and so what they want… might want to do is, like, to their clients, they want to send out
150 00:12:20.660 ⇒ 00:12:27.530 Gabriel Lam: you know, periodic… Not in newsletters, but more.
151 00:12:28.480 ⇒ 00:12:33.410 Gabriel Lam: reports, I guess, to be a little more proactive in keeping people in the know.
152 00:12:33.620 ⇒ 00:12:39.890 Gabriel Lam: I think… that’s probably what I would say for what was achieved, and I think why it matters…
153 00:12:40.230 ⇒ 00:12:45.579 Gabriel Lam: is because… We’ve got all these different channels coming in, and…
154 00:12:47.130 ⇒ 00:12:52.900 Gabriel Lam: Either there’s missing data, or things are not structured in a way that is very readable.
155 00:12:53.160 ⇒ 00:12:57.490 Gabriel Lam: And so what often happens is, Either, like.
156 00:12:57.850 ⇒ 00:13:06.700 Gabriel Lam: Either an agent isn’t able to query exactly what you need, or, like, doesn’t know where to find information, or whatever comes in gets sort of all muddled up.
157 00:13:07.390 ⇒ 00:13:17.999 Gabriel Lam: And you really have to sort of do an exercise to make sure everything comes in as you need, and then gets embedded as you need, and then gets retrieved as you need. So that’s been sort of, like, a big…
158 00:13:18.460 ⇒ 00:13:25.810 Gabriel Lam: Yeah, that’s sort of been the big move thus far.
159 00:13:26.950 ⇒ 00:13:33.640 Gabriel Lam: There should be a PRD, so I want to see where it is, but I don’t see it.
160 00:13:46.190 ⇒ 00:13:46.890 Gabriel Lam: Yeah.
161 00:13:47.040 ⇒ 00:13:50.250 Gabriel Lam: I’m sorry, maybe I’m not the best person to ask for this.
162 00:13:50.250 ⇒ 00:13:51.520 Hannah Wang: That’s okay.
163 00:13:51.900 ⇒ 00:13:59.639 Gabriel Lam: Yeah, so for the next one, what was measurable or observed?
164 00:13:59.870 ⇒ 00:14:05.789 Gabriel Lam: impact… I think that’s hard. I don’t think the work there has been going long enough.
165 00:14:05.790 ⇒ 00:14:06.610 Hannah Wang: Yeah.
166 00:14:06.610 ⇒ 00:14:10.689 Gabriel Lam: for me to… Have a good idea.
167 00:14:10.880 ⇒ 00:14:11.500 Hannah Wang: That’s okay.
168 00:14:11.500 ⇒ 00:14:15.939 Gabriel Lam: And then, why is the work a standard example in the selected category?
169 00:14:23.700 ⇒ 00:14:27.490 Hannah Wang: And I think category refers to accelerate or augment.
170 00:14:27.490 ⇒ 00:14:32.670 Gabriel Lam: or upskilling. Yeah, I think the biggest thing is because When you have…
171 00:14:34.840 ⇒ 00:14:39.869 Gabriel Lam: Like, these… this platform is tuned exactly to…
172 00:14:43.160 ⇒ 00:14:56.560 Gabriel Lam: the client, right? So it’s not like… it’s not like you come in and you have a one-size-fits-all solution, and then you teach people how to use it. It’s like, hey, we’re gonna take exactly what you need and get out the information in a way that works for you.
173 00:14:56.980 ⇒ 00:15:00.540 Gabriel Lam: the actual real-world results.
174 00:15:00.740 ⇒ 00:15:09.229 Gabriel Lam: maybe ABC is a better… Example, or a better, like, Case study for that.
175 00:15:10.840 ⇒ 00:15:15.329 Gabriel Lam: I think internally, I can share that
176 00:15:16.510 ⇒ 00:15:19.660 Gabriel Lam: It’s allowed us to get work out a lot quicker.
177 00:15:19.780 ⇒ 00:15:31.519 Gabriel Lam: Like, UTAM has been able to, like, query all our transcripts and all our discovery documentation, and, like, clients have been very receptive to that, so… the…
178 00:15:33.260 ⇒ 00:15:40.070 Gabriel Lam: like, you know, if we send out, like, a SOW or a sort of discovery document to them, and they see it, and they’re like, okay, this…
179 00:15:40.830 ⇒ 00:15:47.679 Gabriel Lam: First of all, thank you for, like, putting in the work to keep us updated and giving us, like, an accurate representation
180 00:15:47.780 ⇒ 00:15:57.770 Gabriel Lam: Or an accurate, like, documentation of what we think we’re gonna do, and then it makes… it sort of builds trust between, like, the company and the client for them to actually
181 00:15:58.160 ⇒ 00:16:09.080 Gabriel Lam: take on bigger work. And then, for the client side, it’s probably more like, hey, now that we know exactly what we need, we’re able to either, build out these systems so we can keep growing, or
182 00:16:10.080 ⇒ 00:16:13.100 Gabriel Lam: We’re able to, like, address pain points, which usually is, like, sort of
183 00:16:13.200 ⇒ 00:16:15.159 Gabriel Lam: Customer data and figuring out, like.
184 00:16:16.150 ⇒ 00:16:25.739 Gabriel Lam: you know, are we making the right decisions that we’re making? Are we, like, selling the right services? Are we selling the right goods? Are we selling on the right platforms? Are we doing campaigns in the right way? Yeah.
185 00:16:26.190 ⇒ 00:16:28.570 Gabriel Lam: I think that’s how I would answer that.
186 00:16:28.570 ⇒ 00:16:34.479 Hannah Wang: Okay, awesome. Yeah, any… any input helps, and I think for, like, the ABC ones.
187 00:16:34.600 ⇒ 00:16:45.909 Hannah Wang: I know we did, like, Andy, which is, like, the assistant for the CSRs, the customer service reps, and then I know we did, like.
188 00:16:46.400 ⇒ 00:16:48.440 Hannah Wang: evals for…
189 00:16:48.740 ⇒ 00:16:57.360 Hannah Wang: that. And I think there’s, like, case study… I made case studies for them. I don’t know how helpful shoving that into AI will be, but…
190 00:16:57.650 ⇒ 00:16:58.340 Hannah Wang: I think…
191 00:16:58.340 ⇒ 00:17:02.010 Gabriel Lam: I think shoving the copy might be more helpful than the actual case study, if that’s.
192 00:17:02.010 ⇒ 00:17:02.450 Hannah Wang: Yeah.
193 00:17:02.450 ⇒ 00:17:03.020 Gabriel Lam: like this.
194 00:17:03.610 ⇒ 00:17:04.470 Hannah Wang: Er…
195 00:17:06.670 ⇒ 00:17:11.720 Hannah Wang: I don’t know, I guess I can have AI, like, read… I don’t know, read the content, in this case.
196 00:17:12.089 ⇒ 00:17:12.429 Hannah Wang: But…
197 00:17:12.430 ⇒ 00:17:13.010 Gabriel Lam: Yeah.
198 00:17:14.750 ⇒ 00:17:25.249 Hannah Wang: Okay, yeah, I don’t really know what Utam wants for this, like, I might put something together and he might be like, change it, so… who knows, but…
199 00:17:25.250 ⇒ 00:17:25.890 Gabriel Lam: Fine.
200 00:17:26.950 ⇒ 00:17:27.800 Gabriel Lam: Yeah.
201 00:17:27.800 ⇒ 00:17:28.690 Hannah Wang: It’s okay.
202 00:17:29.660 ⇒ 00:17:35.630 Gabriel Lam: Yeah, I’m sorry, I think that’s… that’s as much as I… it got in my head right now.
203 00:17:35.630 ⇒ 00:17:41.909 Hannah Wang: That’s okay. Yeah, I asked you because you’re pretty active on Slack, and I was like, I’m sure I can grab…
204 00:17:41.910 ⇒ 00:17:43.040 Gabriel Lam: Yeah.
205 00:17:43.040 ⇒ 00:17:48.189 Hannah Wang: I don’t know about, like, the other folks, like Sam, I know he’s probably busy or something.
206 00:17:48.190 ⇒ 00:17:50.089 Gabriel Lam: Yeah, yeah,
207 00:17:54.530 ⇒ 00:17:55.290 Gabriel Lam: Yeah.
208 00:17:57.250 ⇒ 00:18:01.719 Gabriel Lam: he’s probably… I think, at the moment, he’s probably the best resource.
209 00:18:04.280 ⇒ 00:18:09.099 Hannah Wang: I just know that Casey’s been working on ABC for a while, and so that’s why I was like…
210 00:18:09.720 ⇒ 00:18:10.720 Gabriel Lam: Baby…
211 00:18:11.980 ⇒ 00:18:14.530 Hannah Wang: I mean, Casey’s pretty responsive too, so maybe I’ll just…
212 00:18:14.530 ⇒ 00:18:15.030 Gabriel Lam: Joe.
213 00:18:15.030 ⇒ 00:18:22.379 Hannah Wang: Pay him, okay, this was helpful, especially for the Lilo stuff. I know that’s a newer client, so…
214 00:18:22.830 ⇒ 00:18:29.409 Hannah Wang: Okay, I’m sure I’ll make it sound all nice and fluffy with AI.
215 00:18:29.410 ⇒ 00:18:31.120 Gabriel Lam: I’m sure you will.
216 00:18:31.760 ⇒ 00:18:37.020 Hannah Wang: Seriously, AI is… it’s making me, I feel like, dumber and, like.
217 00:18:37.020 ⇒ 00:18:38.430 Gabriel Lam: Oh, 100%, but…
218 00:18:38.430 ⇒ 00:18:41.910 Hannah Wang: I’m kind of scared, actually, for my brain, but…
219 00:18:41.910 ⇒ 00:18:42.500 Gabriel Lam: Hmm…
220 00:18:42.500 ⇒ 00:18:43.910 Hannah Wang: That’s okay.
221 00:18:43.910 ⇒ 00:18:48.260 Gabriel Lam: I think it’s more just… Like, it helps you context switch a lot faster.
222 00:18:48.260 ⇒ 00:18:48.980 Hannah Wang: Yes.
223 00:18:48.980 ⇒ 00:18:56.720 Gabriel Lam: which has its pros and cons, but I do think the… Consequence is feeling like
224 00:18:56.890 ⇒ 00:19:00.019 Gabriel Lam: we can take on more, when I don’t think we actually can.
225 00:19:00.800 ⇒ 00:19:05.310 Gabriel Lam: So we’re like, oh, we can do 10 different things at the same time, because we have AI now, it’ll, like, help us
226 00:19:05.720 ⇒ 00:19:06.520 Gabriel Lam: of everything.
227 00:19:06.520 ⇒ 00:19:06.950 Hannah Wang: Yeah.
228 00:19:06.950 ⇒ 00:19:07.540 Gabriel Lam: But…
229 00:19:07.830 ⇒ 00:19:13.929 Gabriel Lam: In terms of how much depth my brain is able to go into and, like, ruminate and actually, like, absorb information, that’s…
230 00:19:14.990 ⇒ 00:19:18.449 Hannah Wang: You’re totally right. Depth is not there. Breath is definitely there.
231 00:19:18.450 ⇒ 00:19:18.870 Gabriel Lam: Yeah.
232 00:19:18.870 ⇒ 00:19:24.320 Hannah Wang: depth is decreasing. I’m… like, if it’s a T, I’m… the stem is becoming very small.
233 00:19:24.320 ⇒ 00:19:25.899 Gabriel Lam: Yeah, yeah, yeah, yeah, yeah, yeah, yeah.
234 00:19:25.900 ⇒ 00:19:33.290 Hannah Wang: Yeah, and even, like, during meetings and stuff, I find myself not focusing as much, Transcript is there,
235 00:19:34.350 ⇒ 00:19:37.639 Hannah Wang: And granola’s there, but when they’re not there, it’s, like, over.
236 00:19:37.640 ⇒ 00:19:38.440 Gabriel Lam: dangerous.
237 00:19:38.440 ⇒ 00:19:39.990 Hannah Wang: I don’t pay attention.
238 00:19:39.990 ⇒ 00:19:40.460 Gabriel Lam: Yeah.
239 00:19:40.460 ⇒ 00:19:41.220 Hannah Wang: So…
240 00:19:41.670 ⇒ 00:19:51.819 Hannah Wang: Anyway, alright, thank you for your time, and I know you’re swamped with probably work and friend stuff, so hopefully…
241 00:19:51.820 ⇒ 00:19:57.140 Gabriel Lam: So, today will be probably… the busiest day I’ve had in Ohio, but we’ll get through it.
242 00:19:57.140 ⇒ 00:19:59.600 Hannah Wang: You’re hanging out with, like, a ton of people and stuff.
243 00:19:59.600 ⇒ 00:20:02.319 Gabriel Lam: No, it’s more just, I have a bunch of calls.
244 00:20:02.320 ⇒ 00:20:03.020 Hannah Wang: Oh…
245 00:20:03.020 ⇒ 00:20:06.599 Gabriel Lam: external calls to make today, like, with my old managers.
246 00:20:08.020 ⇒ 00:20:11.100 Gabriel Lam: My old professors.
247 00:20:11.100 ⇒ 00:20:11.879 Hannah Wang: Yeah, trying to…
248 00:20:11.880 ⇒ 00:20:18.590 Gabriel Lam: So I want to be, like… and I want to make sure that they have the right documentation, that they, like, have the things they need to write stuff.
249 00:20:18.860 ⇒ 00:20:20.540 Gabriel Lam: And, like, it’s Christmas, and I’m like…
250 00:20:20.540 ⇒ 00:20:20.909 Hannah Wang: I know.
251 00:20:20.910 ⇒ 00:20:23.590 Gabriel Lam: I know you’re off for Christmas, I gotta get it out today.
252 00:20:24.190 ⇒ 00:20:37.109 Gabriel Lam: My manager was like, I… I have family coming in tomorrow, I’m really sorry I can’t meet you tomorrow. I get it, like, I… you know, like, you already are going out of your way for me to write this, so I’m gonna make your life as easy as possible for you.
253 00:20:37.110 ⇒ 00:20:38.070 Hannah Wang: Totally.
254 00:20:38.400 ⇒ 00:20:46.250 Hannah Wang: Gotcha, I understand. Yeah, it’s like logistically pulling pieces together for the whole puzzle to work, and making people’s lives easier, somehow.
255 00:20:46.250 ⇒ 00:20:48.200 Gabriel Lam: So, yeah, so…
256 00:20:48.200 ⇒ 00:20:48.740 Hannah Wang: Okay.
257 00:20:48.740 ⇒ 00:20:49.679 Gabriel Lam: Check it out.
258 00:20:49.680 ⇒ 00:20:50.020 Hannah Wang: Yeah.
259 00:20:50.020 ⇒ 00:20:59.429 Gabriel Lam: I have to, like, walk their dog for, like, 15 minutes before I hop on my next call. You’re doing everything! I was like, yeah, maybe I’ll do the call-out. Who knows? We’ll figure it out.
260 00:20:59.430 ⇒ 00:21:01.290 Hannah Wang: Alright, well, happy holidays, and we’ll talk to you.
261 00:21:01.290 ⇒ 00:21:09.200 Gabriel Lam: You too, we’ll talk soon. I’ll probably be on tomorrow, just so I’m very limited, but… Yeah. If I see you then, I see you then. If not, happy holidays.
262 00:21:09.200 ⇒ 00:21:10.930 Hannah Wang: Okay, bye-bye.