It's Data, Stupid

Welcome back to the HYPEWORKS Newsletter.

It’s Data, stupid.

Here’s the thing about data science, it’s supposed to make us smarter. But half the time, it just makes things noisier. Everyone’s swimming in dashboards, metrics, and “insights,” but almost nobody’s making better decisions.

“The real skill isn’t collecting more data. It’s interpreting less, better.” That’s what our guest this week on the pod, Mia Umanos, helped explain (see below).

Every company now says they’re “data-driven.” Sounds nice, but being data-driven without context is just analysis cosplay. You don’t win by knowing the numbers you win by knowing which numbers actually matter.

“The best data scientists I know are basically translators.” said Umanos. They turn patterns into sentences, and sentences into action. They don’t talk about models or regression co-efficients they talk about what’s really happening and what to do next.

The better you get at data, the less you show.

Thanks again for subscribing.

— Alex/Jake, team HYPEWORKS

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Latest episode: Mia Umanos on Data Science (Watch/Listen)

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Latest episode: Mia Umanos on Data Science (Transcript)

Alex MacGregor (00:01.27) Alright everyone, welcome back to episode number 52 of the Fibers Podcast Oratory. Yeah, we'll figure it out. Great to have Mia on the pod today calling from the US, so welcome to pod Mia.

Jake Hissitt (00:05.473) four three.

Mia (00:07.758) or 50 days.

Mia (00:18.68) Thank you, happy to be here.

Alex MacGregor (00:20.737) Yeah, we were just talking before about your kind of roots. You were saying you're kind of families from Philippine background, but you're in US now. So we were just talking about cross-culture, like US and Asia. And yeah, just kind of like, you got any additional thoughts on that? you know, in terms of like, you were talking about work culture in Italy. Like maybe have you got stories from Italy aside from not working there?

Mia (00:46.956) Yeah, mean, I'll say like, you know, having, you know, growing up in the States, having lived in Europe, having lived in Asia, my background in marketing has always been on the data side. And so my job for the last several decades, well, several, few decades has been to interpret marketing data to tell...

businesses, how they're doing, and then also help them do what they want to do with the data. And so with a lot of our customers in America, was very advanced, very, very, very forward about what we were going to do with it. So for example, people coming to a website or return on ad spend, and these are the things by campaign, what is the psychology behind how people are making decisions and what they're clicking on is my job.

When I moved to Asia, the questions were more to how can I discover who is moving their bank money from one account to another? And then because I own the mall and the main stores, can I give them a coupon right away as soon as I know that there's money in their bank? Which you would just never do, right?

in the state. So you would never use your data in such a sort of like questionably ethical manner. Yeah, super sketchy. But then in Europe, when I lived in Europe, like they don't even want to touch the data. They're like afraid to touch it. And so the data relationship and marketing across business cultures is really reflective of like that.

Alex MacGregor (02:07.051) Mmm.

Alex MacGregor (02:12.803) Sketchy, sketchy, yeah.

Yeah.

Alex MacGregor (02:21.985) Yeah. Yeah.

Mia (02:30.83) companies politics, like, you know what it's like to be in Asia. It's very like, you know, dog eat dog. And so their data practices are very, very sketchy. Like most, everything that people are afraid of is happening. And in the United States, they do like a little bit of a balance, but I just think that they're better at, mean, better, corporations are better.

Jake Hissitt (02:56.769) Hiding it.

Mia (02:58.542) at finding that balance. what is it?

Jake Hissitt (03:02.753) They've got bigger lawyers.

I'm joking, making a joke. saying they've got bigger lawyers. You know, I really wanted to like talk about AI for a second there and the privacy and how much, you know, these companies really mine from people. So, okay, there's things that help us every day as marketers, like Google analytics, which we're going to get an absolute storm on today, I'm sure. But, you know, there's like large language models, like open AI. How much do you, should we trust these people with our data?

Mia (03:13.411) Mm-hmm.

Mia (03:34.286) Well, there isn't much you can do about what it's ingesting, unfortunately. Like lot of these, what they're calling them is frontier AI. like anthropic, know, perplexity, chachi BT, like Gemini, they're just going to take whatever they want, right? They're going to, they're grabbing from Quora, they're grabbing from, so if you are a participant in social media, which 90 % of us are, they have that data.

And so.

What you should be trusting out of an LLM is a very good question. I you how these work is that they're actually, they're not just, I mean, there are some things right now where they're actually looking up and there's like, talk about agentic AI that are doing things for you. And, you know, even now, I mean, I tried to look for a cybersecurity professional in the Philippines. cause that's where my team is based. And Chachi BT gave me some recommendations, one of which was a complete fraud.

but this person had answered so many questions inside like a, I can't remember, like Quora, that content was there. And so it looked it up and said, try this guy. And he was a complete fraud. Like he actually had no certifications. He was a teacher, but this is the thing, like it is, it is just very much like, you think of it as a very, very junior resource for now.

Jake Hissitt (04:42.826) Reddit.

Jake Hissitt (04:56.057) in the system.

Mia (05:04.338) So, you know, do you want to have your content or do you want to have your content found by an LLM? If you're a human as a person, probably not so much. You have to definitely think about what you're putting out there. For me as a business, like we have

a PR person, you know, I'm making content. I a school about data and analytics, a school about website experimentation. And so, you know, I'd be curious what comes up for you.

Jake Hissitt (05:34.327) Does it change your relationship because you've expressed on the content side like we're going into content marketing, but you initially got that person. So for that person whose name we shall not refer to on this channel, but that person was gaming the system, right? So it does actually benefit them to game the system. So there's like an ethical question around, well, you actually use this data, but some people might not be as savvy as

data ready, have the knowledge that you have, and thus will fall into the trap of being this customer of somebody who's not got all the credentials, perhaps could be fraudulent. So has it changed your relationship with also trusting the information that the LLM gives you?

Mia (06:21.206) yeah, absolutely. mean, you have to, the thing about it is I don't think humans are prepared to question it as deeply as they should be. think LLMs are very good at like, okay, what was the cause of the fall of the Roman Empire? Right? It's like been written about many times. We know the causes, like, but to find things that are current, like who is the best marketer in Thailand right now? You can't.

Trust it, It's Jake and Alex. Which, right? So from a marketer standpoint, Jake and Alex needs to figure out how to get into the LLM through Quora, but on the user perspective, they have to be really diligent about vetting the information they're in.

Jake Hissitt (07:14.388) You should write a LinkedIn post about this. You've got one life of me.

Mia (07:16.846) Okay. Well, we do talk about it a lot. I mean, there's a few things that we're doing. Like number one, you know, we've built an AI to analyze data for marketing. Why? Because computers are very good at mathematics, but it's not an LLM. I well, it's not.

Not really in LLM. LLMs are not traditionally very good at mathematics, but what we do is connect it to writing code. LLM can be very good at writing code. So basically, we've built an AI very specific to shopper behavior analysis in e-commerce experiences. So if you go into, maybe not in Thailand, but in the grocery stores in the United States, like they're looking at your RFID, where you stop, what you put in your car, what you put back, how you pause, do you

you pause at the end cap, that kind of stuff is not happening in the e-commerce environment, even though it's much easier to do it. And so most companies are building out, like the Shopify customers and things like that are typically building out these experiences and making decisions about their store and their merchandising without knowing what the behavioral economics changes are. Here's an example. Like one of our customers selling like,

you know, $800 addresses online. They had sale in the top navigation of their website. So you're buying traffic, right? And you're buying traffic. It's going to e-commerce experience. And what is the user going to do? We're humans. We are attracted to that sale navigation item. And so what they had been doing over time was training their visitors that they paid for to go shop for the lowest ticket items. And so by leveling that up in the data,

Again, this is like stuff that you probably would have guessed, but if you're not looking at the data, you don't know. And so by looking at the data, you can see, okay, everybody is clicking there. That's like the number one click navigation item. If you walk into a store, they bury the sale. It's in the back of the house. It's not at the front. And so we put that sale navigation in the subcategories of, you know, tops and dresses and pants and so forth.

Mia (09:31.375) and didn't jump up and down about it. And as a result, their average order value went from $300 to $700 in 45 days, just by training the visitors that they hard earned through earned media and paid media and SEO to shop for things that are current and not on sale. those are the kinds of things that we do with our AI to

to be able to level that up pretty quickly, like most of the time, and this is where cultures differ on what they want to track, but most of the time businesses are really looking at all this hardened traffic and the behavioral science behind what they're doing once they get there.

Jake Hissitt (10:22.258) Yeah. Cool. I can't wait to see more. You know, you should give it a shout out to be honest, give it a shout out right now. We're not sponsored. it's okay. You've got a shout out. Like you can shout your AI name out if you've got one.

Mia (10:30.851) what do you mean?

Mia (10:35.65) OK. Yeah. Well, the company is called Clickvoyant. It's got very witchy vibes about the branding. But Clickvoyant is the company. And we do a combination of data, AI software, and data services. So.

Jake Hissitt (10:38.697) Okay.

Mia (10:54.434) People just have marketers in particular have actually pretty bad relationship with math. So we find that you can't quite take the human out of the experience yet delivering.

Alex MacGregor (11:04.355) Yeah. So do you have like a data science background Mia or how did you get into this?

Mia (11:11.51) Yeah. So, my God, you guys, this is, so I've been in data analytics in marketing since, my space. So, I actually recall a time when somebody had to teach me how to use Facebook. So, it's been, it's been a minute. So I've been doing this for a long time. It started out, actually, my background is in journalism. I studied journalism at school and I wanted to.

Alex MacGregor (11:22.659) Thank you.

Mia (11:41.357) be a science journalist working for a magazine, a very specific magazine called Nature Magazine. And that sort of fire in the belly was rooted in an ability to tell a story, to lay people on something that is highly complicated. So, you know.

Science is my first love, but then storytelling is my second. so, you know, coming from, I actually worked for a PR company. I was a journalist for maybe six months, but then I got sick of making no money. And I worked for a PR firm and there I was.

Right, I was working on an account for a person called Bo Jackson, who was a famous baseball player, NFL player in the United States.

And he, there was like, at the time there was a lot of like professional athletes getting embroiled in like steroids, right? It was like, at this time, like he's on steroids and he's on steroids and he's on steroids. So Bo Jackson was named, named and shamed. And I was on the PR team that was sort of combating that, the news. And I was in a

in a newsroom somewhere faxing my press releases, faxing to newsrooms all over the United States, like beep, beep, beep, boop, boop, know, faxing this damn thing and not knowing where it's going. And I had discovered a website called prweb.com, which last I checked about a year ago, it's still up there where I could see, holy shit, like a hundred people looked at my press release.

Mia (13:29.494) And having that one metric of how many people viewed my press release was the turning point. was like, I put cash all my chips in. Like I am not going to be a PR person. Like I am a data person from here on out for like 500 views to a Jackson press release. And that's pretty much.

Alex MacGregor (13:46.465) Yeah. There's always, there's always that argument, right? Like in agency world, like creative versus the data people. There's a famous quote from a football manager and he said like statistics are like a bikini, you know, you get to see a little bit, but you don't get to see the, what you want, right? Like it's that kind of, that kind of vibe, you know.

Mia (14:08.014) Yeah, yeah. I mean, I think that's a, think I pushed back on that. I think that is a, I believe that there is an art and a science to creativity, particularly for marketing, because like in the, in, you know, the quote about, can only see like part of the bikini or you'll see part of what you want to see. The use case for that kind of data is prove that you did your job.

Right. It's like, I'm going to look at this KPI and I'm going to make some decisions about my performance. Right. That's like, that's very different from looking at all of the data to understand the customer. So like, that's what's different to me is like, you've got.

we've been taught, keep it simple, stupid, like kiss metrics or like use your KPIs, which is your key performance indicator. And I'm saying all of it is important. Not just. Right. So, so yeah, if you're, if you're, if you're just looking at your KPIs, you're probably, you're just navel glazing. You're just looking at your own value.

Jake Hissitt (15:07.68) This is a great analogy. Yes.

Jake Hissitt (15:17.462) Hmm.

Alex MacGregor (15:18.851) Do you not think this happens, at least personally for me, like everyone's got a data warehouse, right? And they have some tools that can, like every Friday my manager would get me to use a tool called Metawater. It's like a PR software, you probably know. So I would go into this tool and I would like download all the metrics, like ShareVoice and mentions and like all this stuff. It would crunch all the data and I would give it to her in the report every Friday. And literally nothing happened off the back of this report for like the whole time I was at the company.

Mia (15:31.598) Mm-hmm.

Alex MacGregor (15:48.995) So, yeah. Yeah. So how do you deal with that as a data scientist? Like, how do you deal with

Mia (15:49.314) That was my life like 20 years. I'm like, I'm a fucking...

Well, you know, like data science in technology has the biggest churn rate for employment. The average life span of a data scientist is like 12 to 18. Oh, well think about the churn of like, if you, if you, if you had one employee that was trying to like analyze all of your data for one year, and then after a year they left and you had to get a new guy in.

Alex MacGregor (16:05.763) Right. I didn't know that.

Jake Hissitt (16:10.868) Lifestyle. Okay.

Alex MacGregor (16:13.568) Yeah.

Mia (16:26.274) That's a pain in the ass. That means that...

Jake Hissitt (16:27.638) Bad management though, you know, like we had a data guy and the boss really loved him and listened to him and then we made every all the changes around this guy and the company like blew up, you know, that guy. that drives me crazy when they don't listen.

Mia (16:45.378) They don't, well, okay, so math, I've discovered, is like a religion. It's like, you know, it's like, I'm just gonna like, I know a couple of stories about the Bible, David versus Goliath, whatever, Jesus dude, but you don't go to church because of the Bible, you go because you like the preacher. And in math and marketing analytics in particular, if they don't like you,

no one's gonna believe you. And so you have to have, and that's where the storytelling is. I tell you, I got fired once by Moen Faucets. You know Moen, the faucet manufacturer?

Alex MacGregor (17:26.43) yeah, yeah, I heard of it, yeah. M-O-E-N, right? Yeah, yeah, yeah.

Mia (17:29.932) So, you know, back in the day, there were no attribution tools. Once upon a time we had no fire. There were no tools to attribute a purchase to a channel, you know, paid media versus paid search versus organic search. And I was working in a product called Adobe Analytics and we had, my team had invented this new thing because the chief marketing officer wanted it. And I'm like, okay, well, I know how to do that.

I'm a data scientist. I know how to architect the data. I know how to get the data to you in your lab. So we came up with this very, I mean, it wasn't that complex, but it was a smart use and an off-label use of a feature on that software. So basically, nobody had done it before.

So we were able to create this attribution model to show where the revenue was coming from. And then we built these dashboards. We presented it at eMetrics that year, was one of the biggest, used to be one of the biggest analytics conferences. And then right after we presented, I got fired. And it was primarily because while the CMO wanted this work,

person in charge of the digital marketing hated it the whole time. Fucking hated it. She was just like, I don't understand it. Why is it so complicated? I mean, somebody on the team died during this time period. was like very, very weird time. So there was like weird feelings around it. But if you don't believe, like if you're a person who believes that, data is going to solve all the problems, like

you don't really understand the full relationship and the feelings that people catch when they're looking at data.

Mia (19:26.624) And you have to be a good storyteller. And sometimes what is doing the right thing from a science standpoint isn't necessarily what that company or that client is ready for. And so sometimes you have to do less smart things. you know, I mean, that's just when I say that mathematics is like religion is like, you can't just like dump the Bible on a person.

Alex MacGregor (19:53.283) Also, it's fair to say some companies operate with a very data-focused, like Google, example, like A-B testing. And you've got companies like Apple that's more like Gap Field, right? I think that's also fair.

Mia (20:05.454) Oh yeah. Yeah. I, I worked at Apple actually. I thought that that was going to be the best job ever. I was like, Oh, that's my, that's my dream job. It was the most boring job ever because I worked on my data. My data science skills was placed on one thing and one thing only that was site search. Like it was so much. Sorry.

Alex MacGregor (20:06.562) Yeah.

Alex MacGregor (20:26.431) Okay, not so obvious to be honest.

Not the most obvious thing, to be honest. Like on apple.com, basically. Okay. I mean, who searches on apple.com, right?

Mia (20:33.954) Well, yep, apple.com is like my all my... Dude, like think about your all day, every day, working on just that one feature of one website, it was the most, and then all the...

Alex MacGregor (20:48.801) I Amazon, Amazon I could understand because everyone's searching on Amazon, right? They have that thing they want to buy, but Apple is like, who searches on Apple,

Mia (20:58.358) Well, I can't say much more, I think it's more like when we were talking, you start out talking about different cultures and the culture of data science and Apple A has deep pockets. They can put one data scientist on one feature of the website as a result. But for me, my sweet spot and my heart song is in this mid-market.

Alex MacGregor (21:01.986) Okay.

Mia (21:23.264) mid-market e-commerce because I feel I'm a founder of a company, right? I mean, and that's fucking hard. It's probably one of the hardest things I've ever had to do coming from a corporate job working for Omni-com or I think for JWT working for publicist. Like, you know, something goes wrong or somebody's not performing. You can't just put them on a performance plan. Like they got to go. And that takes a certain amount of

Alex MacGregor (21:43.927) Mm-hmm. Mm-hmm.

Mia (21:49.987) character development to be that leader of your business. And so with Shopify customers, I see that in my customers. I see that they're you know, they're like working hard, they're trying, they're trying to figure it out. They don't know what to do. You know, they're grateful. Whereas working for, you know, companies like Toyota and Mattel and, or Apple, like you're just doing this sort of like...

Alex MacGregor (21:54.573) Yeah.

Alex MacGregor (22:15.351) You're just refining, you're just refining, Yeah.

Mia (22:18.316) You're doing hamster wheel work. And in some cases, like you mentioned, like they don't do anything with the insights. They don't do anything with the data. But whereas with this other dress company, it's like, we just basically doubled your money with one website change. That's meaningful to them. And they don't need to go through, you know, seven different departments and eight leadership decisions to make that change on a website.

Alex MacGregor (22:35.267) Mmm.

Alex MacGregor (22:45.315) How is, when Jake mentioned AI, how is AI Smup and this expansion of AI platforms and everyone's optimizing AI chat, how has that changed your work recently?

Mia (23:00.768) Well, for an on one hand, like the delivery of our work is made very inexpensive and accessible. So we have an AI ML team that is automating the e-commerce behavioral research. Otherwise, companies, know, bigger companies have to get a team of data scientists to do that. So that's like at minimum, like, you know, at least just with the people, the three hundred thousand dollar expense annual.

plus overhead, right? If you had two data scientists, that's what you're paying. And you're probably going very slow. For us, our e-commerce clients are spending $3,000 to $5,000 a month, depending on if they're doing A-B testing, and they're getting unlimited work. And that's what changes it for us.

is that we can now deliver the kind of smart and science to meet art and science of what marketing is.

Alex MacGregor (24:02.529) It's definitely flattening the agency landscape. You see that with the layoffs, right? The big guys are laying off and the small guys are going faster. So it feels like everyone's kind of being leveled, which is, it's kind of good. Yeah.

Mia (24:12.098) Yeah, mean, yeah, we can be competitive to our customers because I think our next nearest neighbor might do like CRO experimentation for like $7,000 a month for like up to three tests. And for us, like we're $5,000 a month and we're doing unlimited. So we're kind of averaging about 15 tests a month.

So, and that's really how you have to, that's the pace that you have to go to be able to learn from an experience or to.

Alex MacGregor (24:46.305) Who's like in this area, like it's not, there's not many, how you'd say like well known names. So who's some people that you kind of admire and like in this space that people can kind of research.

Jake Hissitt (24:57.736) talk about competitors.

Alex MacGregor (24:59.437) Come on, come on. No, I mean, I just mean like could be data scientist or yeah, just some people. Cause there's not a super well known industry really.

Mia (25:00.426) Jake Hissitt (25:03.272) You mean in like an industry?

Mia (25:10.356) No, in fact, CRO is kind of a new name for it. Like, used to be called just experimentation. CRO is like a new thing where like, it's conversion rate optimization.

Alex MacGregor (25:19.063) Yeah.

Alex MacGregor (25:23.491) I saw the Facebook guy, CMO, launched a book last week and he apparently was like the data scientist guy, Alex Schultz was his name. He tests the hell out of the whole Facebook website like every day and apparently this book is full of all his like hacks, his like data science growth hacks. Yeah.

Mia (25:33.71) Mmm.

Mia (25:38.094) yeah.

Mia (25:41.646) Ooh, I also, I feel like every time I log into Meta Business Center, it's different. That's also like, they test so much at fucking stages, every week. I'm like, what, where is this? Like, how do I get to that, that location?

Jake Hissitt (25:54.167) It's still not amazing. Like, you know, I had this issue actually, Alex, you brought it to my mind. You're talking about like AI and optimization, but I've been getting so many errors and recently also made a recommendation of something that I should do within my advertising set. And I took its recommendation and the cost per lead has gone up. It's like, actually it's doubled and I'm leaving it because I'm like, well, I'm hoping it comes down because that's what it told me to do.

Alex MacGregor (26:22.874) Okay

Jake Hissitt (26:22.966) But actually I'm like furious. I'm like, oh, now I have to maybe restart the same set of ads to get the same cost per lead. Like, okay. understand logically why it told me to do what it did, but it seems to have not had a positive impact on the cost per acquisition. So I was like, maybe, maybe it's triggered me. That's all.

Mia (26:26.914) Yeah.

Mia (26:33.442) Mm-hmm.

Mia (26:43.755) I really thought a lot.

Mia (26:47.926) No, I have heard that. There are some agencies that we partner with. we're focused on analytics only. We don't do any media buying. We don't do any creative work. mean, unless you call CRO experimentation creative work, but it's a fine line. So we do partner with agencies. some performance marketing agencies have said it's not as good. Meta is not as good as we are. And I'm not an expert.

in ad buying, but I do know that that's some feedback. However, I have heard that PMAX is very good, that PMAX cannot beat, or PMAX will always

Jake Hissitt (27:28.096) I tell you, in e-commerce, in e-commerce, PMAX is killer.

Mia (27:34.255) Yeah. you know, I mean, I think it's interesting. know, AI, AI plus humans has always been where the rock meets the roll. You know what I'm saying? Like, I don't think that, I mean, I do think that if, you know, if you're a marketer that's like sort of sticking your head in the sand about it and is not AI forward, that marketer will fall behind.

But the marketer that is like constantly experimenting and trying to figure out, when is it like we, one agency partner always had a program to discover who wins. And they had, you know, they had enough clients and enough budgets at like A-B test. My person against their AI, what decisions would they make and who wins? And then that's like, that's like a program that they run all the time to just understand when is it trustworthy.

Jake Hissitt (28:29.385) Agree.

Mia (28:29.44) So yeah, kind of similar to like the own user of ChachiBT. Like you have to take everything with a grain of salt, but also understand that there are certain things that might help you do your job better, faster, smarter.

Jake Hissitt (28:44.552) Okay Alex, you know what time it is.

Alex MacGregor (28:48.268) Yes.

Jake Hissitt (28:49.454) I also, yeah. So it's the big one, Mia, prepare yourself. gets people every single time. But it's the final question of the show. What are your future predictions? It could be anything. It could be work, it could be life, it could be the world.

Mia (28:56.684) Okay.

Mia (29:08.494) Okay, well, you know, I do have podcast with my daughter about AI and we talk about this a lot. So I actually have an answer. I think I have an answer. We can't really peek around the corner of AI right now. Like we can't see it. But if you think about the strides that it's made from even last year to now.

It's quite big and we're at the beginning stages of it, the very beginning. And so I'll tell you something that I've seen in the data that across the board on all sites. we, you know, we mostly do e-commerce. have some higher education customers as well that we've had for a long time, but across the board.

the referral from chat, GPT and AI into a website has gone up from May, like hockey sticks since May. that last May was kind of the turning point, I think. It's not, you know, it's not commanding like even 10 % of website traffic, but it is grown quite a bit.

Alex MacGregor (30:05.389) I heard about this. Yeah.

Mia (30:24.794) across all industries, across all websites that we work on, which is more than 50. And in addition to that, the conversion rate of that traffic is often double what the other channels are. So I have this hypothesis that people are kind of like me with the, you know, the fraud, cybersecurity professional in Philippines are

pre-qualifying themselves before arriving to your site. I think that, I mean, what we've already seen like SEO, that people are taking a blood bath on website traffic.

Alex MacGregor (31:06.083) Hands up, I spawned Reddit for my app, like for like three months, non-stop. And I did.

Mia (31:11.456) You did. mean, like, listen, you know, if people are coming to the site and I think my prediction is that more and more people will inform themselves like like SEO is dead, like traffic will be a metric that, you know, it will become more of a vanity metric, the quality of the people coming to the site. So like informational marketing is going to change quite a bit. And as well.

I think that humans, I mean, I'd like to think the best, I think humans are going to get dumber. I do. think, I mean, have you seen the movie Idiocracy? you should take a look at that movie. It's a 20 year old movie, but it's like, you know, my, my modern day, like Nostradamus.

Alex MacGregor (31:55.233) No.

Jake Hissitt (32:02.036) Alex, didn't someone else mention that film on one of these podcasts? I think one of our last like three or four guests has mentioned this film.

Alex MacGregor (32:06.295) I dunno, it rings a bell, it rings a bell.

Mia (32:13.408) You need to pick up that. You need to watch that on Netflix or wherever it's streaming. So it's about, it's an American movie about, American, like very average Joe in the military. And he, as an, yes, yeah. So as an experiment, he is put in a cryogenic freezer and then he wakes up accidentally, like a hundred years later and

Alex MacGregor (32:27.021) Definitely. For sure.

Mia (32:38.518) When he went in, he was the most average man on earth. And when he came out, he's the smartest man on earth.

Alex MacGregor (32:43.811) Three podcasts ago, for sure. That's weird. Yeah, we should watch it.

Mia (32:46.07) Yeah, so I mean, we see it happening all the time. mean, you know, when we when I think the humans, I mean, we saw what happened with social media and how humans behave with social media. Now we're having this, you know, pre processed information. Research is no longer a thing that we do as humans. It's a thing that we do less and less. And

Critical thinking, think as a result is suffering. So, yeah, I'm like, buy all the guns, stock up. I mean, I don't, I'm not usually, right. I am American. was, I did grow up here, but no, I I, I like to think the best, but I do, you know, I do think that there has been a bit of a devolution and critical thought. And I think that AI is definitely, you know,

Jake Hissitt (33:23.446) It's taken after your parents.

Alex MacGregor (33:42.381) But the AI maximalist would say it's augmenting our intelligence, right?

Jake Hissitt (33:47.22) guess in some way it depends how much you lean in on it. The reasons why.

Alex MacGregor (33:50.755) Mm.

Mia (33:51.277) Listen, sure. And you know, I mean, if you're just being fed the answers all of the time as humans, we've classically throughout history been very good about that, right?

Jake Hissitt (34:04.276) You know, that MPC, sorry, sorry, Alex, okay. I'll admit, I know we're going, I know we're going.

Alex MacGregor (34:04.525) That's a good note to end on, the way. A bit positivity.

Alex MacGregor (34:11.669) No, no, I'm glad we got to something that's not a doomsday scenario.

Jake Hissitt (34:17.366) Okay, we can call it.

Mia (34:19.15) Is everybody in a doomsday scenario when you ask that question?

Alex MacGregor (34:22.857) Mmm, a lot. A lot.

Jake Hissitt (34:24.458) I tell you what, we do get quite a few. We'd say at minimum 50%.

Mia (34:29.89) Wow. Well, you know, mean, Sam and founder of OpenAI is working on a basic income plan.

Jake Hissitt (34:31.755) Time to go.

Jake Hissitt (34:37.93) Yeah, I'm not sure I want to be on it.

Mia (34:40.126) No.

Alex MacGregor (34:41.645) Yeah, we'll get you back in a year and we'll see if we're still here, hopefully.

That was awesome though, enjoyed it, it was a good talk.

Mia (34:50.402) Thank you. Yeah, appreciate you staying up very late for this podcast.

Alex MacGregor (34:54.307) Nah, normal for the I-Works. Like and subscribe, please. All right, bye.

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