Speaker 1: I'm t T and I'm Zakiyah and this is Dope Labs.
Speaker 1: Welcome to Dope Labs, a weekly podcast that mixes hardcore
Speaker 1: science with pop culture and a healthy dose of friendship.
Speaker 1: We don't really show our faces a lot or at
Speaker 1: all with the podcast. We don't do because I know
Speaker 1: a lot of podcasts now are like, oh, you can
Speaker 1: come and watch us, Yeah, you can come and watch
Speaker 1: us on YouTube or whatever, and we don't do that
Speaker 1: because we don't feel like we need.
Speaker 2: To do that.
Speaker 3: But what I have said this is one of the
Speaker 3: things that I did do in preparation for this lab
Speaker 3: was I asked chat GBT to make a photo of us,
Speaker 3: and so I uploaded some reference photos and I just
Speaker 3: sent you what they were, and I want your initial reactions.
Speaker 1: That's the first draft, right because this is giving AI,
Speaker 1: you know it is it is. And I'm confused because
Speaker 1: I see other people that are like, I generated this
Speaker 1: using AI and it doesn't look like it looks very realistic.
Speaker 2: Uh huh.
Speaker 1: There's this black woman, her name is Jessica E Boyd,
Speaker 1: So she's on Instagram at Jessica E Boyd and she
Speaker 1: does these incredible AI images. It's just a great job
Speaker 1: and it looks like real afro texture hair, you know,
Speaker 1: but this one you created no shade. But how many
Speaker 1: fingers do I have?
Speaker 3: It looks like you've either got fourteen or six.
Speaker 2: I can't really tell.
Speaker 3: All the fingers are melting together, and I'm like, this
Speaker 3: is fun.
Speaker 1: I see people that have been making themselves, like not barbies,
Speaker 1: but like action figures in the plastic.
Speaker 2: I've been seeing that. I've been seeing that.
Speaker 1: I've seen people do themselves in animation.
Speaker 2: Yeah, the studio ghibli ones. Those are very cute.
Speaker 1: But I think like, no, don't get me wrong. I
Speaker 1: love all the latest technology, and you know, I've been
Speaker 1: out here trying everything okay, but sometimes I have to
Speaker 1: stop and wonder, like who's doing all this stuff? Whoever's
Speaker 1: doing it, or wherever's being done. It's just so far
Speaker 1: away from us, and it makes me think, like, what
Speaker 1: is the cost to generate these outputs?
Speaker 2: Honestly?
Speaker 3: And I think that's something that has been talked about
Speaker 3: a lot on social media. Most recently, I've been seeing
Speaker 3: people saying it takes sixty seven trillion gallons of water
Speaker 3: to just if you say hello, chat GPT, And I'm like,
Speaker 3: is that how that works.
Speaker 2: I don't know if that's how that works.
Speaker 1: I don't think that's how that works. But it's time
Speaker 1: to pull back the curtain.
Speaker 2: Let's jump into the recitation.
Speaker 1: So today we're diving into AI. We're gonna talk a
Speaker 1: little bit about automation, large language models like chat, GPT
Speaker 1: and their impacts, specifically their environmental impacts. So with what
Speaker 1: we know TT.
Speaker 2: So we know what automation is.
Speaker 3: That's the use of technology to perform tasks without human intervention.
Speaker 3: So it's often rule based, so like if then if
Speaker 3: I do this, then it'll do this or do this
Speaker 3: until and it's also repetitive, so it's able to keep
Speaker 3: going over and over and over again without need for
Speaker 3: a human.
Speaker 1: But I think a step above that or beyond that
Speaker 1: is artificial intelligence, and sometimes we see people conflating those two.
Speaker 1: Artificial intelligence is when you have systems that are designed
Speaker 1: to mimic human intelligence and so unlike automation, which is
Speaker 1: just a rule and it just every time it encounters
Speaker 1: this one thing, it does whatever you told it to,
Speaker 1: artificial intelligence is capable of learning and problem solving, and
Speaker 1: so it'll say, okay, if you put this input, you
Speaker 1: tend to like this, so the next time I see
Speaker 1: that input, I'll bring it up. Even if that's not
Speaker 1: a rule, it's a learning. It's able to expand its
Speaker 1: knowledge based on what the inputs you give it.
Speaker 3: Right, So like if you had a button factory and said,
Speaker 3: every time this piece of plastic comes here, poke three
Speaker 3: holes in it, and it does that, and it does
Speaker 3: it over and over again, that's automation.
Speaker 2: Now if you had a machine that was able.
Speaker 3: To say, based on fashion trends, I'm going to make
Speaker 3: this button out of wood instead of plastic, or I'm
Speaker 3: gonna make this button bigger because people started liking bigger buttons.
Speaker 2: Now that would be aim.
Speaker 1: Then you have large language models, and that's advanced AIS
Speaker 1: systems that are trained on vast amounts of data, particularly
Speaker 1: text data, to understand and generate human like language. So
Speaker 1: that's CHAT, GPT, Gemini, all of that.
Speaker 3: Yeah, they're calling the Internet and gathering all of this
Speaker 3: information and creating context around all of that information so
Speaker 3: that they're able to learn. The next thing is proprietary models,
Speaker 3: and what they do is they highlight that companies develop
Speaker 3: specialized models.
Speaker 2: So open AI they have.
Speaker 3: The GPT variant, so we know open AI, most commonly
Speaker 3: for chat GPT and chat GBT within it, that's just
Speaker 3: the big umbrella. There's multiple types of chat GPT, so
Speaker 3: they have four oh they have I think it's four
Speaker 3: and four mini. They also have deep research now, so
Speaker 3: it's tailored specific for a different task.
Speaker 1: So now that we all have kind of the same
Speaker 1: understanding of what these terms mean, let's talk about what
Speaker 1: we want to know.
Speaker 3: I want to know how the energy consumption of training
Speaker 3: these AI models and running these AI models compared to
Speaker 3: other technologies, like is it one to one or is
Speaker 3: AI the big bad wolf?
Speaker 1: And then like what are the implications? So like water
Speaker 1: usage is one that I've seen people talking about on
Speaker 1: social media, so about cooling data centers and all of
Speaker 1: this stuff. I was like, I thought we were all
Speaker 1: in the water cycle. So all the word is here
Speaker 1: is here, but I don't know we did an expert
Speaker 1: for that. I also want to know the mechanics. You know,
Speaker 1: my background isn't engineering all of those things that it's
Speaker 1: just interesting to me.
Speaker 2: So what processes occur when you're.
Speaker 3: Using a large language model like chat GBT or Gemini.
Speaker 1: We also want a future cast and the same way
Speaker 1: that we looked at what policies are around new technology.
Speaker 1: We thought about this in our Space episode. You know,
Speaker 1: we say, like, all right, now that all these people
Speaker 1: are going to space, who's making the rules, who's said
Speaker 1: in the speed limit? The same thing for AI? You know,
Speaker 1: who's making the rules, who's saying how much is too much?
Speaker 2: What do you use?
Speaker 1: What's the costs, what's you know, there's a cost and
Speaker 1: benefit for all these things, and so what is it
Speaker 1: for AI? It's out of site, out of mind really.
Speaker 3: Right, Well, I think that sets the stage perfectly for
Speaker 3: us to jump into the dissection.
Speaker 1: For today's dissection, we are talking to doctor Challet Wren.
Speaker 1: Doctor Wren, we wanted to start by kind of setting
Speaker 1: the stage about them and some measurement. You know, I
Speaker 1: think for all tech and innovation, there's a cost and
Speaker 1: a benefit, and often those costs are immutely presented in
Speaker 1: price and things you buy to maintain the technology. Sometimes
Speaker 1: it's subscriptions for software, Like I remember when I had
Speaker 1: a game Boy, it was the batteries. Okay, yeah, burn
Speaker 1: was like burning through batteries. For cars, it's gas, it's maintenance,
Speaker 1: it's inspections for emissions. But when we're physically removed from
Speaker 1: the technology, it can be harder to have a good
Speaker 1: grasp of like what it costs to run these things.
Speaker 1: How do we understand and appreciate costs now.
Speaker 4: So this is a huge energy consumption, and I mean,
Speaker 4: by the way, using energy itself, I think is a
Speaker 4: great thing because it just shows that we have more productivity.
Speaker 4: You know, usually energy consumption is a indicator of the
Speaker 4: economic activity, but the consequence of the using energy that's
Speaker 4: not always good. For example, the energy produces heat and
Speaker 4: we need water to take away the heat, and that
Speaker 4: consumes our natural resources. And also when we generate how
Speaker 4: we generate the electricity, how we generate the energy. Usually,
Speaker 4: even in the United States, a large fraction of the
Speaker 4: energy is coming from fossil fuels, and that comes with
Speaker 4: the carbon emission problem. Right. So this, you know, it
Speaker 4: has this long term impact on the climate change as
Speaker 4: many of the research that they have shown.
Speaker 1: I think that's a great point. What doctor Renna is
Speaker 1: saying is it costs even before we get to water,
Speaker 1: there's just energy. This is something I'm always talking about
Speaker 1: the people who drive electric cars. I'm like, Okay, you're
Speaker 1: not using gas, but what do you think how you
Speaker 1: think we get the energy that's coming to you.
Speaker 3: Right, That is one of the laws of thermodynamics. Energy
Speaker 3: cannot be created or destroyed. So we're not creating this
Speaker 3: energy out of nowhere and you can't get rid of it.
Speaker 2: It all goes somewhere.
Speaker 3: It's a closed loop like energy is flowing from one
Speaker 3: thing to another.
Speaker 1: And I think the other part that he mentioned that
Speaker 1: I didn't really think about for these data centers is
Speaker 1: the air quality. So PM two point five that means
Speaker 1: particular matter that is two point five micrometers or smaller
Speaker 1: in diameter. Now, biologically, when you're that small, you can
Speaker 1: get very deep into the lungs, okay, and so whatever
Speaker 1: you have can get into the lungs and it can
Speaker 1: potentially enter the bloodstream. Not always, but potential is enough
Speaker 1: for me to be like humhm, right, that's enough for me.
Speaker 2: That sounds scary.
Speaker 4: So the air pllutants includes particular matter two point five
Speaker 4: PM two point five and also nitrogen dioxide soultur dioxide.
Speaker 4: So these type of air pollutants can travel hundreds of miles.
Speaker 4: So even though we don't leave next to a data
Speaker 4: center or next to some power plants, we are the
Speaker 4: air pollutants that we breathe in could be coming from
Speaker 4: those places, and so this air just have a direct
Speaker 4: toll on people's health. They could create asthma, heart attack,
Speaker 4: lung cancer, and also even premature deaths. And there's been many,
Speaker 4: many research studies showing that causal relationship between these air
Speaker 4: pollutants and people health.
Speaker 3: But what about the water that's associated with running these
Speaker 3: AI models?
Speaker 2: Is it really as bad as folks are saying.
Speaker 4: Our study find based on the very limited data in
Speaker 4: the public domain, we show that if you have a
Speaker 4: conversation with a large language model, you likely consume about
Speaker 4: five hundred million water for ten to fifty queries, depending
Speaker 4: on where have you random model, And that's for the
Speaker 4: inference and also when you train the model, it's also
Speaker 4: consumed quite a bit of water.
Speaker 1: Okay, so we're talking to doctor Charlet Wrenn about AI
Speaker 1: and water usage, and doctor Rand said, basically, with ten
Speaker 1: to fifty queries, you use about five hundred millileaters of water,
Speaker 1: which is sixteen point nine ounces or a Deer Park
Speaker 1: water bottle. Now, I think there are two things to
Speaker 1: unpack their tt the water usage and the note that
Speaker 1: he said about it depending on where you are when
Speaker 1: you run the model. Let's start with the water usage.
Speaker 3: Okay, so before we get too deep into the water consumption,
Speaker 3: I really want to take a step back and like
Speaker 3: set the stage for everybody about what actually happens when
Speaker 3: you type something into chat GPT. When you put a
Speaker 3: prompt into CHATGPT, it travels through the Internet to a
Speaker 3: data center where that model. Remember we talked about those
Speaker 3: different specific models associated with different organizations, So open ai
Speaker 3: has the GPT models. So where that model is hosted,
Speaker 3: it goes to that data center, and then the servers
Speaker 3: process your input by running it through that model, which
Speaker 3: consists of layers of neural networks, and then the servers
Speaker 3: generate a response and send it back to your device,
Speaker 3: so your phone or your laptop or however you're using
Speaker 3: that AI model.
Speaker 2: And so it's a very fast process.
Speaker 3: If you've ever used any of these AI tools, you
Speaker 3: know it happens in seconds. So when we're talking about
Speaker 3: water consumption, I think knowing where that water comes in
Speaker 3: because it's like, what are we talking about here? Why
Speaker 3: is water associated with the Internet. Data centers use a
Speaker 3: significant amount of water to cool those servers down because
Speaker 3: they generate a lot of heat.
Speaker 2: We all know that.
Speaker 3: This technology gets hot when we're on our laptops for
Speaker 3: a long time, on a computer for a long time,
Speaker 3: when we're on our phones for a long time, they
Speaker 3: start to feel a little bit warm, right, And that's
Speaker 3: because you know, all of the technology inside is working
Speaker 3: and it's generating heat to do that work. And so
Speaker 3: what they do is they're running this cooling system which
Speaker 3: uses water to cool down these servers.
Speaker 4: When we cool down the data center facility, it involves
Speaker 4: two typically involves two stages. The first stage is moving
Speaker 4: the heat from the servers to a heat exchanger or
Speaker 4: to the facility. This process either uses air or uses
Speaker 4: a closed loop with some special liquids so there's no
Speaker 4: water loss, and there shouldn't be any water loss unless
Speaker 4: there's a leak, okay. And the second stage is moving
Speaker 4: the heat from the heat exchanger to the outside environment,
Speaker 4: to the atmosphere, to the sky and this process, of course,
Speaker 4: there are different options for moving the heat, but one
Speaker 4: Cammer approach is using water evaporation. And water evaporation just
Speaker 4: is the water that is temporarily lost into the sky,
Speaker 4: and that's what we call water consumption. So of course
Speaker 4: the water is still within our planet. It doesn't go
Speaker 4: to the sound whatever somewhere. Yeah, yeah, within our globe.
Speaker 4: But it's just you know, when the water comes back
Speaker 4: and where it'll come back is highly uncertain, especially in
Speaker 4: those route regions, and also even when the water comes back,
Speaker 4: it may not be in the in the freshwater state.
Speaker 1: But I think in the same way that they're using
Speaker 1: water to cool down a service. Isn't that the same
Speaker 1: way like basically air conditioning works and cooler and stuff
Speaker 1: in there.
Speaker 3: And the other thing is like the Internet been using servers,
Speaker 3: like AI using servers. That's not new. That's not new technology.
Speaker 3: We've always had servers. AI is just advancing and using
Speaker 3: those exame, exact servers that we're already being used for
Speaker 3: the Internet, like the same types of servers.
Speaker 2: They just have different models in it.
Speaker 3: But when you type in www, dot Dope Labs, podcast
Speaker 3: dot com, that information has to go to a server
Speaker 3: in order.
Speaker 1: To type it in right now.
Speaker 2: He tied it right now and click on stuff. Oh
Speaker 2: my goodness. Yeah, it's it's they're all doing the same things.
Speaker 1: I think one of the things that was really interesting
Speaker 1: to me that Charlett explained is that sometimes people are
Speaker 1: comparing apples and oranges because that water that's those servers
Speaker 1: are using is being recycled.
Speaker 2: It's being used.
Speaker 1: Over and over again. And they were comparing it to
Speaker 1: like eating a hamburger, but they were looking at the
Speaker 1: water to cow, the water to water the grass.
Speaker 4: Like a few weeks ago, the CEO of a leading
Speaker 4: AI company said, you know the study by the study
Speaker 4: on as water US. It was something made up by
Speaker 4: those anti AI crowd. But this, I would say, this
Speaker 4: is really misleading comparison because the Hamburg's water days quoted
Speaker 4: includes the rainwater in the in the soils to grow
Speaker 4: the grasses, to feed the cattle, to make the paddy.
Speaker 4: So that's really live cycle water using and it's mostly
Speaker 4: water which we call green water in the technical community.
Speaker 4: But the air water consumption is only for the operational
Speaker 4: stage when we actually random all, we're not talking about
Speaker 4: the water used for mining the rare earth or making
Speaker 4: the AI chapes or recycling the summer. So it's not
Speaker 4: life cycle for water for AI, and so it's a
Speaker 4: different scope of water comparison, different type of water. And
Speaker 4: the air water is mostly blue water, that's the water
Speaker 4: in the rivers, lakes, and groundwater sources that human can
Speaker 4: directly use. So I think those type of comparison are
Speaker 4: just showing that they either do not have the necessary
Speaker 4: knowledge in this space or they're having some other agendas.
Speaker 4: So yeah, those are not really scientifically correct comparison.
Speaker 2: Oh okay, I think I understand.
Speaker 3: So the water consumption is the water that doesn't make
Speaker 3: it into the drain and into our sewage. So that
Speaker 3: would be the water that in the washing your hands example,
Speaker 3: is being absorbed into your skin a little bit and
Speaker 3: then also absorbed in like a paper towel or a
Speaker 3: dish towel, or however you wipe your hands off after
Speaker 3: you wash your hands. I hope y'alla washing our hands.
Speaker 4: Yeah, So technically that's called water withdrawal, and the water
Speaker 4: you put it that goes into the sewage is called
Speaker 4: water discharge. The difference in water wizdow and water discharge
Speaker 4: for washing our hands is very minimum, so okay, the
Speaker 4: difference is called water consumption.
Speaker 3: So Challet did a study about how AI and human
Speaker 3: work compared to each other, about the efficiency and about
Speaker 3: the energy required to perform a task, and what they
Speaker 3: found was is that of course, you know, in a
Speaker 3: lot of situations, AI is more efficient. It can work faster,
Speaker 3: it can work longer. Know where humans require you know,
Speaker 3: lunch breaks and sleep. AI doesn't it's constantly running. But
Speaker 3: the energy consumption of AI is a lot higher, you know.
Speaker 3: So there's like pluses and minuses to both our studies.
Speaker 4: Not trying to say, let's AI is bad and that's
Speaker 4: not our purpose. Its AI is great. It's why why
Speaker 4: it's bad. It's it's using some resources. It's just like
Speaker 4: anything else, right, So we need to have an understanding
Speaker 4: of the cost. And turns out the water cost is
Speaker 4: just one part of the cost, and where there are
Speaker 4: also other costs like the climate, the energy, the public health.
Speaker 4: So we need to look at this, evalu the cost
Speaker 4: more holistically and think about what is the best way
Speaker 4: to support the technology. That's our purpose. Our research goal
Speaker 4: is not like hey, please stop using it now, No,
Speaker 4: we shure to use AI for good purposes and you
Speaker 4: know more response way.
Speaker 3: Can you talk about that a little bit more the
Speaker 3: cost to the environment, public health, so that people can
Speaker 3: understand what you mean by that.
Speaker 4: So because this is a huge energy consumption, and I mean,
Speaker 4: by the way, using energy itself is not I think
Speaker 4: is a great thing because we want I mean, it
Speaker 4: just shows that we have more productivity. You know, usually
Speaker 4: energy consumption is the indicator of the economic activity. So,
Speaker 4: but the consequence of using energy that's probably not always good.
Speaker 4: For example, the energy produces heat, and we need water
Speaker 4: to take away the heat, and that consumes our natural resources.
Speaker 4: And also when we generate how we generate the electricity,
Speaker 4: how we generate energy. Usually even in the United States,
Speaker 4: a large fraction of the energy is coming from fossil fuels,
Speaker 4: and that comes with the carbon emission problem. Right, So this, uh,
Speaker 4: you know, it has this long term impact on the
Speaker 4: climate change. As many of the research I've shown, I feel.
Speaker 1: Like so much of the conversation has been framed as
Speaker 1: an all or nothing is don't have any costs or
Speaker 1: use AI, And I'm like, what people don't think about
Speaker 1: is how many people are using Zoom, Google Meet, Skype
Speaker 1: if that's still operational. I don't know Microsoft teams and
Speaker 1: you know, when the pandemic hit, Zoom saw elite from
Speaker 1: ten million daily users to over three hundred million daily users.
Speaker 1: And there was a study from Purdue University. A group
Speaker 1: there said that an hour on zoom generates one hundred
Speaker 1: and fifty two one thousand grams of CO two. Nobody
Speaker 1: was thinking about that when we were trying to all
Speaker 1: stay connected and stay saying now turn off your camera
Speaker 1: can help with that is whether this study is in.
Speaker 1: But I think us considering, you know, there's a cost
Speaker 1: to all of these things that we often don't think about,
Speaker 1: and so I think it just becomes important to consider, like, hey,
Speaker 1: everything here has a cost.
Speaker 3: Right, and the cost varies depending on where you are.
Speaker 3: So the amount of water required for the AI chatbots
Speaker 3: using chat GBT four to generate a one hundred word
Speaker 3: email varies by location. The amount goes up depending on
Speaker 3: where you are, Like in Washington State, it'll require almost
Speaker 3: fifteen hundred milli liters of water, while in Texas it's
Speaker 3: only two hundred and thirty five. So we can't just say, oh,
Speaker 3: all of it requires all this water, and that's not
Speaker 3: necessarily true.
Speaker 2: Is it using water?
Speaker 3: Yes, in a closed loop, because the water we have
Speaker 3: is the water we've got, But also other forms of
Speaker 3: technology are also using water as well. It takes six
Speaker 3: nine hundred and nine liters of water to produce one
Speaker 3: pound of beef one pound of beef six ninety two liters.
Speaker 3: Training a chat GBT three has the same water cost
Speaker 3: as producing one hundred pounds of beef.
Speaker 1: So if one hundred people do meatless mondays yea, then.
Speaker 3: It's nearly double the amount and average American eats in
Speaker 3: a year. So the amount of water you're using to
Speaker 3: eat beef is way more than you're using to run
Speaker 3: a CHATGBT model. So in another comparison, is it takes
Speaker 3: nine and fifty six liters of water to produce one
Speaker 3: pound of rice because rice has grown completely submerged underwater.
Speaker 2: Requires a lot of water to grow rice.
Speaker 3: But we haven't been hearing nothing about that, or maybe
Speaker 3: people been talking about it, and I just don't know that.
Speaker 1: I know that I'm.
Speaker 3: People been talking about AI and how all this water
Speaker 3: and all these things like that, and I'm just like, man,
Speaker 3: we really got to think about everything else that requires water.
Speaker 3: I remember we talked about it when I said I
Speaker 3: was drinking almond milk and they were like, you know,
Speaker 3: text that growing all men, And I was like, damn.
Speaker 1: You know how much water it takes up. If I
Speaker 1: have a reaction to where I'm lactose.
Speaker 2: Intolerant I know.
Speaker 1: People, you know, I just think I really, when it
Speaker 1: comes down to it, I think we are just we're
Speaker 1: bringing in so much information, but we're not grounding it
Speaker 1: at anything. No, really, I blame the media. Uh and
Speaker 1: not our media, but like Hollywood, because everything you see
Speaker 1: represented about AI, nothing is really talking about this kind
Speaker 1: of stuff, or you would never see. I don't think
Speaker 1: there's a lot of grounding what the cost is to
Speaker 1: the benefit and convenience of things, and so when people
Speaker 1: start talking costs, it's like, oh my goodness. I'm sure
Speaker 1: people thought the same thing when the radio hit the
Speaker 1: market or when the calculator started being used. I mean,
Speaker 1: I feel like AI, the way people talk about AI
Speaker 1: in the education system, they always just put it in
Speaker 1: this thing where it's like they're cheating, They're cheating, they're cheating.
Speaker 2: I'm like, first of all, that's the thing in the past.
Speaker 3: That was something that was an issue with early AI,
Speaker 3: early chat GBT.
Speaker 2: That's no longer an issue anymore.
Speaker 1: I think the main thing I can tell people I
Speaker 1: would want our listeners to consider is to consider all
Speaker 1: of this in the grand scope of the natural resources
Speaker 1: that we have available. Think about it when you turn
Speaker 1: it on your ring light when you are live streaming
Speaker 1: but not even looking at your device, when you are.
Speaker 3: Like scrolling on TikTok for hours and hours and hours
Speaker 3: and falling asleep with the TikTok running and the TikTok
Speaker 3: keeps looping and looping.
Speaker 1: Yes, I think there is a cost to all of
Speaker 1: the things that are entertaining, convenient and brought to us
Speaker 1: through our devices, and I think we should be considering
Speaker 1: that for everything. I think one of the key things
Speaker 1: that I'm interested in is how public policy begins to shape,
Speaker 1: Like are we going to have Now this may be
Speaker 1: a little dystopian, I'm like, are we gonna have AI tokens?
Speaker 1: You know, not only certain can you use it? But
Speaker 1: who knows?
Speaker 3: Yeah, I mean I think that that is what's up next.
Speaker 3: Agentic ai is the next big thing where you know,
Speaker 3: you can make your own personal assistant. You can create
Speaker 3: these AI models that do a task for you, that
Speaker 3: can you know, read emails and populate your calendar and
Speaker 3: really work as a personal assistant for you. And that's
Speaker 3: where where a lot of industries are moving towards. A
Speaker 3: lot of these corporations they want agentic ai to be
Speaker 3: a part of everyone's daily life. So as soon as
Speaker 3: you wake up and log into your computer, your agent
Speaker 3: can tell you, Hey, this is what you missed. This
Speaker 3: is what you need to do, this is what you
Speaker 3: should be prioritizing.
Speaker 1: No, this is what you missed.
Speaker 2: I was sleep.
Speaker 1: I think that is definitely already in the works, So
Speaker 1: I think it'll just be the next wave of adoption.
Speaker 1: Shouldn't that we see next? Yeah.
Speaker 3: And the other thing that I think people should be
Speaker 3: thinking about and like keeping an eye on is the
Speaker 3: fact that a lot of regulations don't exist yet, you know,
Speaker 3: and we've already seen it with like the actors strike
Speaker 3: because of folks being able to use their face and
Speaker 3: make them do things, and like it's a it's a
Speaker 3: really big issue when it comes to creativity because now,
Speaker 3: you know, artists are feeling like, okay, art is going
Speaker 3: to be lost if you can just take Michael B.
Speaker 3: Jordan's face and body and get him to do whatever
Speaker 3: you want him to do a jail, like he should
Speaker 3: have a say in that, Like there should be loss
Speaker 3: that say that that is illegal, and the laws have
Speaker 3: not caught up yet.
Speaker 2: The regulations have not caught up yet, So keep trying.
Speaker 1: There yeah. Yeah.
Speaker 3: You can find us on X and Instagram at Dope
Speaker 3: Labs podcast, tt.
Speaker 1: Is on X and Instagram at d R Underscore T Shoe.
Speaker 2: And you can find Takiya at z said So.
Speaker 1: Dope Labs is a production of Lamanada Media.
Speaker 3: Our senior supervising producer is Kristin Lapour and our associate
Speaker 3: producer is Isara Savez.
Speaker 1: Dope Labs is sound design, edited and mixed by James Farber.
Speaker 1: Lamanada Media's Vice President of Partnerships and Production is Jackie Danziger.
Speaker 1: Executive producer from iHeart podcast is Katrina Norvil. Marketing lead
Speaker 1: is Alison Kanter. Original music composed and produced by Taka
Speaker 1: Yatsuzawa and Alex sugi Ura, with additional music by Elijah Harvey.
Speaker 1: Dope Lab is executive produced by US T T Show
Speaker 1: Dia and Nakia Wattley.