Alembic and the Future of AI in Marketing - Ep. 263

Foreign.

0:00

Hello and welcome to the Nvidia AI Podcast. I'm your host, Noah Kravitz. A quick note before we welcome Today's guest. The AI podcast has a new home on the web at AI-podcast.Nvidia.com youm can find all of our episodes there as well as links to listen to the show on your choice of podcast platforms. If you like what you hear, please take a moment to follow, subscribe, or even leave us a review. And if we're missing your favorite platform on that page or you just want to tell us something, drop a line@aipodcastvidia.com thanks for listening and let's get right to it. My guest today is working at the leading edge of marketing intelligence. He's got a fascinating backstory, his company does, and today they're using data backed strategy and AI to help brands transform their marketing. Thomas Puig, founder and CEO of Alembic, is here to discuss it all. And I've got just enough of a background in marketing myself that hopefully I can, you know, carry my end of the conversation. We'll see. Tomas, welcome and thank you so much for joining the Nvidia AI Podcast.

0:10

Thank you for having me. Pleasure to be here.

1:12

So I kind of hinted at it, but it's always better coming from the guest than me. We try to do these intros, but interesting story behind Alembic and I'm sure I only know the tip of the iceberg there. So can you tell us what Alembic is and the story about behind founding the company?

1:15

Yeah, so we've been around a little while. We're really an applied science company and so it took us many years to build technology. It began with three people originally and yeah, still with us today. Myself and my background started at Ames Research center originally like when I was a kid. Basically went into quantitative economics and then decided actually I prefer music, the arts and marketing. I went that route for quite a while until I ended up backwards.

1:30

Right.

1:54

The other founder was a guy named John Adams. John Adams, very storied infrastructure engineer in the Valley. He was the 13th employee at Twitter and took the company from the time it was a Mac Mini with a bad Ethernet cable under his desk all the way through the ipo. I think for many years he was the longest serving person not on the board of directors.

1:54

Wow. Okay.

2:11

And then Seth Little, who is a world renowned creative director and designer who has rebuilt, you know, brands for Lego and even done work for Apple, stuff like that over the period of time the three of us Got together. And there were a number of reasons why we really chose this field. But the most important is that we have felt that marketing and anything around the creative and even the arts and trying to promote it had been. And no one had been able to be a storyteller for years. Everything becomes so obsessed with like trying to get that last little click, that last little engagement, that last little performance, that we just about lost the plot. And at the same time, I had a few deep beliefs about where things were going with technology that married into that. And one of the things that. And this kind of brings it to there is the company really came out of a lot of mathematics that were born during the pandemic.

2:12

Okay, how so?

3:05

A lot of people think MRNA is the only big tech to come out of the pandemic. The other thing to come out was a lot of incredible math. It was really one of the first times that we used huge scale compute and modeling to actually be able to analyze something causally in real time. Right. As a, like an emergency was happening.

3:06

Right.

3:23

We've done weather and stuff before.

3:23

Right.

3:24

But nothing like that. And so we're like, well, nuclear weather, disease, drug discovery, everything else is using these type of supercompute modeling and deep learning. Why is everybody who does creative, the arts, marketing, everything else stuck with math in the 1970s? So we're a bit more unique in a company also as well that we actually run our own private cluster in our own cage. Like we physically own our hardware.

3:25

Oh, wow. Okay, you guys, I should have said at the beginning, you're San Francisco based.

3:47

San Francisco based hardware's North Virginia.

3:51

Okay.

3:53

I'm assuming this is a very nerdy audience. And so I'm going to be like, we L2 patch straight into AWS East 1 out of Equinix.

3:54

Yeah, go, go.

3:59

And so when we kind of founded the company, we sat there and we're like, well, what would it take? Well, the problem is, is that what it would take to actually do this was an entire deep offshoot of branch of mathematics. So one of the things we deeply believe and we talk about a lot is that the profit at companies follows the flow of information.

4:00

Okay? So if you want to follow the money, everybody says follow the money, but really follow the information.

4:19

Follow the information. You will find the money.

4:23

Find the money.

4:25

Right, right. And what's so important about that is I believe all the alpha, all the profit that will exist in corporations in the next while will all come from private data sets. We are seeing major models and LLM models In fact, there was a paper just released where they showed that models, when training on the same similar public data sets, end up more than 90% the same all the time. By the way, we are seeing a convergence there. And so, and Jensen spoke to this actually more recently too, where we will buy it like electricity, right?

4:26

Right.

5:00

But that means that it will also be converged and commoditized. The way I put it though, is that these models are converging. Right? And so as they converge, they will be the difference between buying, say, BP and Shell gasoline. Right. No one has the same private data. Now, a private data could be a songwriter writes his own song. That is a private piece of thing that he wrote. That is his. You know what it is, could also be a giant corporation that has a huge corpus of data that nothing else can see. And so I believe that within the next period of time, the thing that will generate the most data, why should be lived human experiences will generate the most brand net new data. Right. I'm not saying that there's not a ton of data that's like derivative or whatever. Net new data. And so this could be, let's take Disney as example, fake example. Got espn, Disney plus, Hulu, the parks, magic bands, all the toys, everything else.

5:00

Sure.

5:52

Marvel a lot. Star Wars a lot, a lot. Monstrous. Right. Like you've got a century of scripted ip. So all of this means that. That being able to take that and feed that and learn from it, you know, understand how it's structurally connected and then act upon it with agents and models of what the world's gonna be like. Right? And that's where the profits can come from.

5:52

Right. And so everybody, the more you can act on that with your data, the more it's gonna inform what you can and perhaps should do and how you do it and everything.

6:11

Yeah. And so one of you know, the first company to kind of really do this in the early days was, I'm a huge fan of Renaissance Technologies. They were the first high frequency trading firm kind of out of New York for that mathematician. Right. And they really. You saw this in the quant space first, then you saw Palantir try to do it in. They were really trying to find a needle in a haystack. Right? Like one actor out of a group.

6:21

Right.

6:41

But what really happens is you have this massive data. And so, you know, there's a few reasons I kind of see this already. You know, first look at open source models, Deepseek, et cetera. They're getting close to matching the premium APIs in terms of performance and everything else.

6:41

Right.

6:56

Second companies, these larger firms, you are seeing larger firms be a much bigger part of this than the mobile revolution in the early days. While you have a couple small companies like Cursor and everything that have a small number of employees that are pushing it through, participating in the large LLM stuff, there's serious amounts of capital that are backed by the large backers.

6:57

Yeah.

7:13

The private data has much higher prominence in the setup and they have huge corpuses to apply this to. And the third is, is that anything that generates derivative data, say you do something and there's an action taken. It's like compounding interest.

7:14

It's a flywheel, which is a cycle.

7:28

Flywheel, just like you're saying. And so it feeds upon itself. And so if we think about like the 2010s, right, 2010s, the advantage went to the team to capture and operationalized new data or audience fire hoses. The rise of Facebook, the rise of these things in 2000s, where we're seeing right now is who could stack the most GPUs. Hardware mattered so much. Right. And you literally see teams being separated, the haves and have nots, by the amount of horsepower they have to apply to these things. Some of that's being offset by innovation. Right. Deep seq, et cetera. But still to a large extent, to serve that, you got to have the power and chips. So I think 2000-30s it's going to be who turns their private data and its exhaust into a deep learning asset is then applied against.

7:29

How do you work with your customers? How do you help brands, you know, start to tap into this data, wherever they're at in that process and do things with it. Gain insights is what we always talk about. But you know, how do you help them?

8:16

So think about like this. I'd say that two things to know about Alembic One, every single piece of data that ever comes into our system is anonymous aggregate data. We allow no PII ever. There's literally never been an instant of it in our system. Just like when you think about where we really based off the biomedical math. Right. You're not, you don't have PII there either for like a third phase trial. Right. You're not tracking great. The second thing to know is that we ingest enormous amounts of it. Yeah. We've brought in 100 billion rows of data in three days for clients. And this could be like every transaction from 17,000 stores, stuff like that. The problem our clients have and anybody has is and this is one of the things I find very interesting about why you haven't seen these whole query. The database tools take off as much as you've seen the generative tools take off. Because the problem actually is. You ever read Hitchecker's Guide to the Galaxy?

8:29

Long time ago. Yeah.

9:19

So you get the very end of the book. Right. They're like, what's the mean of life theory and everything.

9:20

Yeah.

9:24

And they go, well, the problem is we don't know the question.

9:24

Right.

9:27

When you have that much data, you don't even know the question to ask to be able analyze it.

9:27

Or worse yet, you think you know the question, you're so far off, then I'm down a rabbit hole.

9:33

But that's. Oh, and then. And then you make a wrong assumption. Right. And it becomes a huge pa. And so what we have to do first is we have to ingest an enormous amount of data and we have to signal process it. We actually use, and we announced this at GTC actually the way we do signal processing and probably one of the coolest pieces of tech we have is we actually use spiking neural networks to do it. That gives us a lot of superpowers. And the two problems we had to solve for it were this. That's why go build. Spiking neural networks typically have been done on neuromorphic hardware, which usually we think they'll think of wetware like half biological computers. He wrote a simulator for the wetware as a kernel in Nvidia. Just like you wrote a simulator for Quantum. Yep, we wrote the simulator for neuromorphic on the hardware and that's how we run an snn. And so it's actually fully custom to the Nvidia chip.

9:37

Obviously the answer must be good, but how well does it perform?

10:23

Really well. Because the two problems that had to solve were this one. How do you compare apples and oranges?

10:27

Right. Yeah.

10:31

How is a Nielsen rating or a TV or a Spotify view the same as a phone visit or walking into a store. Right. There are different modalities, different mediums. Right.

10:33

They all count as marketing exercises, touch points, something.

10:43

But yeah, marketing has the worst job because their L job is everything. Right? Right. Second reason it's the worst job is everybody on earth thinks they're a marketer. The second thing we had to solve was marketing. You always hear about this, oh, yeah, we'll do a campaign for a couple weeks so you have no time history and no repeatability. But how do you look for outliers with no time history? So those two problems had to be solved and SNNs were the solution for that. We turn everything into spike signals, spiking coatings. That solves the apple sponges for people.

10:47

Who don't know one sentence definition of what an SNN is.

11:15

A spiking neural network is meant to be a digital twin of the human brain to where it can actually process signals like neuron spikes, just like your brain would. And what it's doing is it's seeing signal and then you're firing neurons off a propensity. There's a lot more explanation to that. The reason that they're really well loved is they're incredibly fast and they are online, as we call them. So that means that they are always that and they're evolving. So you don't actually train them in advance. So they're an evolving network. So they really originally were really well loved in people thought about them for Iot and they're fast. We use them to be able to find a way to detect outliers. So a human, a baby can pattern match from the moment it's born? Almost. I'm teaching.

11:19

Yeah.

12:04

So when you're looking for outliers in data, normally what we would do is we'd use prediction, grab all the previous data, see where the highest highs would be predicted to be, lowest lows, and anything outside of that outlier. There's other ways to do it too. But for this audience we'll be overly redactive with what we do is we're actually looking at, at patterns and actually seeing those changes from that neuromorph, like the neuro perspective in SNNs. And so we can do outlier detections with no time history.

12:04

No time history, right.

12:33

The reason this is important is say we actually we presented at GTC a case study with the CMO of Delta and they sponsored the Olympics and there's a great recording of this. Actually we did a session and the problem is the Olympics happens for two weeks every two to four years, depending on how you sponsor it. If you do daily data, that's 14 time steps, what do you do with that? So doing that signal processing, first we had to do that. The second thing is we actually had to figure out how to do the connections, connect the chain of events. So say you watch the Olympics, then you Google for a Delta flight, you click on a Google Flights ad and then you buy it. How do you connect those things in time? So that's where we ended up building all of our causal mathematics. So we use causal inference and transformation mathematics. To be able to do that. So the first thing is we have to see the signal at all we build. Each time series gets its own mini neural network. That neural network then gets chained together with causal links, and then we can build chain reactions. Then you combine the chain reactions for intelligence.

12:34

Right, okay, so let's stay with that example then of Delta and the Olympics. A minute ago you were talking about, you know, with a client being able to ingest their data. And so I'm wondering, can you map, like, these technologies you're talking about in the spike neural networks? And you started talking about causal AI, which was the other, other thing I wanted to ask you about. I need to hit it. What can you do with the data? And how do these chain reactions and, you know, how does it apply? What does the client see? Kind of. I don't say at the end, but, you know.

13:33

You know, it's funny, at the end, I actually believe that all dashboards should never exist. Dashboards only exist to derive intelligence from them. Right. Nobody wakes up in the morning, goes, you know what I want in life? Another dashboard. Yeah. No, so what you do is you, like, look at the dashboard and then you write an email. So the way our intelligence comes out is it actually comes out kind of like. Actually the model for it was like the President of the US Gets an intelligence briefing every day.

14:02

Yeah, yeah.

14:25

Provide them as literal intelligence briefing. Great.

14:25

Yep.

14:27

Oddly enough, we use LLMs at the company, but not in the same way other people do. So there's a game called Mad Libs. I'll play with my kids. It's like you have a paragraph and then you have a blank, and you fill in the blank and make funny sentence. That's how we use LLMs. They never make a decision about data ever. But we do love them for user experience. So they'll write like kind of the by the report, but then the causal. The chains will actually just put in the actual data sets. And that means that it kind of inoculates them against the hallucination issue. Because everything that's an actual real important data point that's not like an is or an and or connector is handled by a deeper method. Right. A deep learning method. And then we use the rest for user experience and communication.

14:28

Oh, interesting.

15:06

Those are derived from the chains. But let's take Delta as an example and talk about the Olympics. Sponsoring the Olympics on a national level is expensive. We're talking eight figures at least.

15:07

Sure.

15:16

I think NBC reported hundreds of millions, maybe even a billion dollars in revenue off of the Olympics alone, I forget what was. I'd have to go look. It was massive.

15:17

Yeah.

15:25

And when you're doing that, there's two types of things that occur. One is you buy a whole bunch of 30 and 60 second AD spots, right? That you do. The other is, is that you see this in sports all the time. You have things that are named after companies. So if you watch the Olympics, you saw that the medal presentation ceremonies were delta, okay? You know what sells a whole lot of tickets to Paris? Watching the Eiffel Tower in the background in a really emotional moment with a player as you put a gold medal on them. That was actually more effective than, than the ads themselves in some circumstances.

15:26

Yeah, no, I, I can see it.

15:57

How do you connect that? Right? Somebody's booking a flight, they're doing some planning, there's a delay between those things. It's not like you do it while you're watching the tv. So you have to calculate the known, we call it optimal lag. Right between the time series. Right. How do you calculate those things?

16:00

Right?

16:14

And so what you do is you go, now we think about this like common sense, of course, I saw that. And then someone's going to buy signs. It's much harder to mathematically prove it. Can't even imagine we work with a finance organization where we actually look at what causes etf, electronically traded fund flows. Conversions can be anything, you know what I mean? You could be selling something or you could be trying to change the volume of something, or you could actually care about the most open source contributors you get. What we do is when we look at these chain reactions, we have nodes and edges as we call them, right? Nodes are kind of the points like connect the dots, the edges, connection between them. We calculate every connection that could possibly exist as we build stuff, right? So then we can dynamically search the intelligence afterwards. And so we can literally morph our reports based off what the user wants to see. So if you're like, I want to know all the things that are about selling a ticket and the next day, you know, I want to know all the things that are about game frequent flyers. All you do is change the focus. It redoes the search in real time, instantaneously. And so that type of stuff. What I find really interesting is a lot of people are working in AI and deep learning right now. The reason why we have such a sticky in these huge, big enterprise customers is, and we're not a cheap system, is that they derive real value out of it. And also we understand that we have to Meet them where they are.

16:15

How do you mean meet them where they are?

17:32

Well, I find that a lot of people are talking about AI and form factor. Should it be hardware, should it be software, how should it speak? Everything else. But I never hear anybody says, is the thing that outputs actually useful. You know, I believe that actually we're having a paradigm shift in the entire thing. For the longest time, there were a ton of studies that said 90% of the world was consumers, 9% was curators, and 1% was creators. And this actually held true for pretty much like as long as people could remember. But now suddenly, with AI, the curators are actually creators.

17:34

Right, right.

18:06

So the 1% is now 10%. So you've 10x the footprint of people who are wanting to build things. And the strategic monstrous shift that occurs.

18:07

With that is large the way you put that. It reminds me of, I mean, it reminds me of the 2000s and the, the web first, you know, coming out and anybody could publish if you wanted to.

18:17

You know, what reminds me of the most is actually when you could start recording your own record in your living room.

18:27

Great.

18:34

It used to be you had to have these ginormous studios. We see them in films. Right. And everything else. And then you have people like Beck and everything being like, I wrote a hit record on my four track.

18:34

Yeah.

18:42

And the democratization of that. I don't think anyone would say music is worse off because of it. The music industry, the monetization may have suffered, but the actual quality of the music and the stuff coming out. I wouldn't say that you've had a. I'd say we have access to more independent music than we have ever had.

18:43

Oh, for sure. No. The, the, the difference between how my kids get music and how I did, you know, couldn't be, couldn't be more different.

18:59

So like, kind of like when we talk about like suddenly, let's take code in this example. We use a lot of AI to write code now, as everybody else. But code used to be or is in a lot of places, like a bespoke thing. Right. Like the most beautiful furniture ever and somebody's handcrafting this beautiful object. Or before the assembly line existed for Ford. Right. Any motor vehicle would be like handcrafted by every single little individual thing. But now it's no longer bespoke. Now you have this sense of like you actually have mass crafted, mass generated objects. In the beginning, I'm sure mass generated cars did not equal the bespoke cars. Of course, nowadays, I would say probably most of the Mass generated cars are probably better than the bespoke cars. From a safety perspective, Code is doing the same thing. And so now you have two fronts. You have everything able to be like process information. And the second thing is the curatorial class. Right. Those with taste are now creators by default.

19:05

The playlist is a veblen good something.

20:04

Right. Like now. I love tools that give people agency to control themselves. I am deeply worried long term about access because that can be hard. But I think about the number of musicians who never would have had anyone hear them at all if a four track didn't exist. Yeah, I think about the number of writers or filmmakers or brilliant people who are never going to be hurt at all if they did not have the democratization of tools. When we bring this back around to marketing attention, everybody's attention. Because really what there is, is there's only, it's a supply side limited. There's only so much attention in the world. There's only like for so long every company in the entire world is competing for the same slice of attention. At the end of the day, the only commodity that can never be increased is a person's heartbeats. How many till the day you die, then you can't sacrifice. So when you're doing that marketing and everything else and we're trying to do that, we're trying to understand the universe and get down to it now we're going a little like theoretical here, but I think it's an important paradigm shift that we're discussing because you can't separate at the end of the day marketing from the rest of the businesses it governs.

20:07

So, so bring it back down sort of to you know, business nuts and bolts level. How is AI, I mean as much as you can and you know, be specific because it's so many ways. But what are some of the biggest ways? You see, maybe that's a better way to ask it that AI is already changing how brands relate to their, their audiences. And you know, what's happening kind of just down the road. I think this idea of. And it's funny, as I was prepping to talk to you, I was thinking about. We've done a lot of episodes on health and medicine lately and this idea of precision medicine, right. And being able to offer, you know, each individual the individualized care, preventative care. You know, it's. We're technology enabling us to provide that level of person personalization. And so there's a similar thing in some ways as I understand it with marketing and I mean to, to really probably not do it justice, but I break it down in my head to cookies following me around.

21:10

Yeah.

22:08

And that being kind of a, you know, to me seems like a brute force, maybe outdated at this point way of doing it. But what's AI doing for all this and what's it going to be doing for brands and engagement and personalization and.

22:09

Take a little bit of an anti stance here. Human beings love building tribes and community and the most visceral experiences that they have. Let's take sports. People will often say sports is the new church. That's not my phrase. It's just what everybody. You know, you want that community, whether that is in that format or it's in the sports format or something else. That means that you need everybody to experience the same thing and be able to respond to it. And if you think about the fondest experiences you can remember, almost, almost always, are they with somebody else? You remember doing things with somebody or how it affected somebody, or how it did something. And so while I believe that personalization is interesting, I actually believe that being able to really build these experiences that people have and to be able to bring people closer together, those are the marketers that are going to win.

22:21

Okay.

23:16

Mathematically, I talk about this a lot, where you're like, what does an LLM mostly do? It predicts the next best token. Inherently, that next best token is the one you would expect. That's what's supposed to do.

23:17

That's right. I think kind of in a weird.

23:30

Way, that's kind of like it's going to give you the median. Right. It's going to give you the answer you should have. But what great artist, what great marketer wakes up in the morning, goes, you know what? I want to be the medium. And I don't know, I can really feel it's gotten better.

23:33

Right.

23:48

But I can still feel the aesthetic of AI imagery and everything that comes out because it is still trying to. It is almost going to have its own aesthetic. And so I think that what will happen is, is that the uniqueness of experiences and everything else and the premium of that will rise and that we will have this kind of push against, where it's not like all about personalization forever. Everybody's going to realize you still are going to want to experience that with other stuff.

23:48

Interesting. Yeah.

24:14

And so for maybe 10% of your life, you want hyper personalized. Like I want to talk one on one with it. Right. Or maybe it's an ephemeral thing you're using. I heard a great founder talk recently about how he uses ChatGPT and he writes a story with his kid at bedtime, and he just doesn't keep. It's audio.

24:15

Okay.

24:32

They're just playing with it. That's femoral. But you don't go to the water cooler the next day or get online and talk on Discord with your buddy about that.

24:33

Yeah.

24:41

And so I feel like often human nature is lost within this. So marketers, our job is to create those experiences, and then when it's a touch point for a preference that you've already created, then try to enhance it with some personalized. But I feel like people have lost the plot a bit.

24:41

Hmm. Yeah. Interesting. I don't disagree. I just. It's interesting to think about that in the context of, as I said, all the personalization that jumps to mind when thinking about these types of things or the potential personalization.

24:57

Well, I always think about personalization is once you've engaged with the brand, how's the brand find the optimal path for you?

25:10

Yeah.

25:16

Deeply important.

25:16

Yeah.

25:17

If I'm already an Amazon customer, I need the happy path. If I am an Apple customer, what iPhone do I buy? If I'm. I have some friends who love Alison Olivia dresses. They've got a ton of different dresses. Which one should they get? Right. They're gonna have preferences within that. But I'm talking about, like, how do you even get them to like the designer?

25:18

Right. You have that shared moment. Yeah.

25:37

Profit and the margin comes from brand. But what is the value of brand? How do you actually price that?

25:39

All right, so I'm gonna flip this on you. And it's not even flipping. I was. I was thinking of an excuse to. To play off of what you just said. How do we value the human in all this going forward? To go to the other side of the process and think about the creator, the ad man and woman, if you will. Whoever's on the side of. Is it curating? What's the role for human creativity? How does that evolve as people in marketing, people in media, whatever part of the process you're working on, you're overseeing, you're collaborating on, how does that role change? And years ago, and it's crazy that I can say years ago referring to this podcast, but we can now. We had somebody on. I don't want to misrepresent his title, but a creative director, basically working in the game, video games industry and talking about how generative AI, I think the title of the podcast, something about making zombie armies with Gen AI, something to that effect. And talking about How Gen AI was enabling this, you know, and it's this metaphor that we keep using. Think of your co pilot as like an internal, right. A highly capable intern who needs a lot of direction, instruction, learning the ropes, but they can generate good work if, you know, correctly prompted, if you will. But he was talking about the ability for one person to kind of give creative director level instructions and have the AI, you know, fill in the rest of the zombie army from a couple of examples or generate landscape or, you know, that kind of a thing. And so when you said, you know, curators.

25:46

No, there's a guy named Andy Warhol.

27:15

Right up front of him. Yeah, right.

27:17

Andy Warhol did not paint all his pieces. How's it different?

27:18

Well, I don't know how to draw the metaphor, but. Because I'm sure Andy Warhol had lots of people working for him as well. But what happens to the human when, if I can generate, you know, the work of five people in a creative setting, does the role become for instead of being a writer who learned how to manage creative teams, I'm just more of a, you know, creative team manager from the get go. And some of my team is AI.

27:21

Or I think sometimes we get so theoretical about this stuff. You know, I had a terrible punk band as a teenager. We were awful nice. Were you called then? There were three. We had four guys and then we ended up with three of them. And so we just called the band and there were three.

27:48

You a Genesis fan?

28:02

Oh, yeah, yeah.

28:04

Okay.

28:04

Yeah.

28:05

It's the whole joke, like there's no way. There's no way you're not.

28:05

I know. So the funny thing about all this is I think back to then, right? If I try to put myself in my young shoes, right, 15, all I want to do is make cool stuff.

28:09

Totally.

28:19

Ezra Klein gives a great talk about this where he says, for those that become creatives, all you have in the early days is taste. And the hardest part about taste is when you do something you suck. But you know, you suck in the early days. And so you have to fight through all of the sucking the hundreds and thousands and awful times to then generate a style and a body of work. And I think about how incredibly demotivating awful that was at the time. And what it will do is for the people starting out, it will create great agency and they will be able to do incredibly cool things and they will get 80% of the way there.

28:20

Right.

29:00

But I also think it's going to create this weird, almost even harder field to get through where you're going to be stuck there way longer at that.

29:00

Sort of glass ceiling of understanding that there's better work out there. I just don't know how to creative.

29:09

Yeah. Because I mean you're. You're not failing as much. Right, Right. And so for me, the interesting conversation about this most is what happens to us as humans when we don't fail enough to improve.

29:14

Right.

29:27

Because failure is what makes us improve. And man, I. I do. I do things the hard way so many times. Yeah, right. Even today, like I'll do things where I'm like, did I really do that? But I have a son who's seven and I've watched him like struggle and get through and learn things and build stuff. We're building Gundam together now. There's Japanese robots.

29:28

Oh nice. Yeah.

29:46

First one he built couldn't get it. He accidentally chopped off a part. Right. Now he can build a whole one by himself. He's seven.

29:46

Amazing.

29:51

And so what happens when failure ceases to exist?

29:52

Does the ability to so quickly generate so many more takes, do you think that desensitizes us to failing?

29:55

I think it means that you're not going to learn from the failures as much. You'll learn different things. Right. You'll learn how to manipulate the AI. We'll become experts in AI manipulation. It's just an interesting thing. Do those small failures where they're not really failures count? Will it have the same effect? I actually don't know the answer to this. I just think it's the most interesting component of what you mentioned from that basis.

30:02

Right. If I can take. I was showing my age, but my mind for some reason jumped to digital photography. And that move from every photo I take literally cost me a roll of film divided by X exposures to. And worse yet than the monetary cost is once my roll's out, you know, I got the rest of my day in Paris and I can't take pictures to, you know, phone in my pocket. It's all digital. Take a whole bunch, pick the best one, you know, delete the other.

30:21

Yeah. And I mean you can see the difference it had on. You can absolutely see the difference it had on what photography looks like now.

30:51

Right.

30:59

You know, you go from Ansel Adams, hyper composed or these beautiful New York style Vogue shoots, right. Where lighting was king and we end up in more of a street photography style, much more naturalistic style. I don't even know if one's worse than the other. Right. I actually like both. But it absolutely will have an effect on the world. And actually I Think that what did do though, is I think it is much harder to make a living as a photographer.

30:59

Right.

31:23

You know, and I don't know what the answer is here. You know, Andreessen, or was it Dreesen or Horowitz?

31:24

I can't remember.

31:29

One of the two of them used to give a talk where they were like, innovation is inherently destructive often. Right. Records got rid of in home, musicians and symphonics, washing machines, displaced house workers, stuff like that. But in the creative side of the space, I've never seen humans be less creative just because they're not paid for it. We're talking about how people are getting paid less to be a photographer and everything. We're not seeing less photographers.

31:29

Right.

31:53

This is what's hard about subjects like this. We're talking about something that's both business and art.

31:53

Exactly. Yeah.

31:58

But let's kind of bring it back to like, kind of a couple things. Just alembic and marketing and stuff.

31:59

Yeah, yeah. Okay, cool.

32:04

So say you have all the spiky neural networks.

32:05

Right?

32:08

Say you have the causal. You can build the chain reactions. The key with what you have to do then, which I think kind of brings it into this, is you have to make it understandable to a human.

32:09

Of course. Right.

32:18

And you have to be able to take action on it. And we talk about this in my company a lot. Every human has their own superpowers and specialties. I don't expect everybody to be great at data and math. I expect some people to be incredible at EQ and be able to keep the office together. Right? Yeah, stuff like that.

32:19

Shout out to those people.

32:33

Seriously, when we're doing this, you have to have the system meet people where they're at, and that's where that last mile comes in.

32:35

Yeah.

32:41

We take the data and instead of being like, I want to find a needle in a haystack, like you're a security company, like, I need to find the one bad actor. We have to surface all the data, have it make sense, and let it help them accomplish their goal. And what that means is we have to do monsters. We have to refine this, like, deep learning, this corpus of monstrous amounts of disparate data that's pseudo structured at best. At best, and be able to pull insights out of it. And with those insights, then be able to let people meet the goals of their group. And I think that that in general with marketing is when you think about it, you're being like, if I can see everything, if I can see all the creative stuff, I'm Doing even the cool stuff. Like I did a cool activation app pop up in New York and I can treat that the same as I did a Google Ad.

32:42

Right? Right.

33:26

Whereas right now one's ignored those ones that will work, those cool pops that work will get credit and we will get more cool things. Whereas otherwise we're just going to get more just whatever. Right. Just some random, just simple thing. Be like, buy this thing. When we talk about like creatives and business and everything, great information is always good for buyer and seller. And so quality information is a plus. Because if companies see, and I've talked to every CMO on earth, right. Chief Marketing Office on earth, they said if companies see that somebody who's creative is making me $2 for every dollar I spend, I'm going to spend as many dollars with that human as possible.

33:27

Of course.

34:01

Whereas if you go, well yeah, we did that, cool pop up, but I can't see what that creator did.

34:02

Right? Yeah.

34:07

Why'd you pay the premium?

34:08

Right. We're going to spend on vibes.

34:09

I think that like AI and everything as we talk about it nowadays can be wonderful and actionable and push ahead and provide real value for people where you could be like, I know for every $12 I spend here, I should sponsor the NBA, I should sponsor the WNBA because it makes me money. Right. Actually I should be paying the WNBA more money because then they can do more for me and then do stuff. Right. It creates quality across the data sets because the things that get ignored are the outliers, are the smaller things because they can't pick up enough signal.

34:11

Right.

34:38

We want to get all the signal. Yeah, do it and then you can distribute correctly.

34:39

Makes a lot of sense. So we've been ending the shows recently asking the guests question, but I, I kind of want to ask you a variant of it question is what tools, what AI tools are you using lately that you know, you really like or might recommend? But I think in this case, I mean, feel free, but also for somebody who's out there, who's whatever, whatever age of their life, but they're newish to marketing or maybe they've been in marketing for a while, but they're new to getting a handle on AI and gen AI and like how to actually start using it in a way that, you know, can be constructive to the work they're doing, their career development, that kind of thing. Tools that you'd suggest, a book that you'd suggest, some resource you might suggest to somebody out there who wants to, you know, get hands and Brain on with AI and marketing right now.

34:43

All right, I'll give two versions. I'll give the I'm just getting into it. And I'll give the I'm the giant advanced nerd.

35:32

Fantastic.

35:39

So for beginners, what I always recommend for any of this stuff is actually you literally can just start with ChatGPT.

35:40

Sure.

35:47

And what I will say about the best of this is plan before you create. And the number one thing that people forget to do is be like, tell the system. You may ask clarifying questions, say, I want to plan. Say anything you like. Be like, please ask clarifying questions.

35:48

Yeah.

36:04

And then we'll just start talking back to you and you two will make the best plan on earth and act. You can do this on how to learn, you could do this on how to do stuff, anything. And I think people forget that. They want a declarative statement, but it can be a conversation.

36:04

Right. That's good. That's good advice. Yeah.

36:18

And it's the number one thing I teach my staff. The second thing is, is, and this sounds really silly for beginners, if you're doing a long conversation, like a big long prompt, put the instructions at the very top and at the very bottom. Both.

36:20

Yeah.

36:34

You have to. When things long enough, you need to put in both places. There's been a lot of studies on this. Those two things are probably the number two things that can get you 80% of the way there for improvement and just explore. The second thing is find voices you like.

36:35

Right.

36:47

Whether that's Scott Bollinger, whether that's Gary Vee, whether that's anything. And you can just use those voices to offset. Right. You do want to talk to actual humans. For the super advanced folks, one thing I'll recommend is choose your top 10 data scientists, whether it's Lacune, whether it's whatever, and tweak your algorithms. Go to LinkedIn and like the last 10 of their posts that are only technical, go feed and train the thing to give you everything you want. I have mine trained to only give me, you know, papers from the archives and like cool algorithms like that. And I actually find it very convenient.

36:48

Yeah, I bet.

37:21

And so I know people get really frustrated with that type of stuff, but it's the absolute best way to get the largest footprint quickly. The second thing I will recommend is that please go look at other disciplines. People get very myopic. We're starting to see people do diffusion models right now for LLMs when we were doing all transformer. I am never precious about where I get methodology from. I'm only precious about what the result is. And so I think that the breadth of learning, the curiosity is the number one thing I see. I feel like people are doing very much. This is my team lately, my LLM that I like to use. This is my algorithm, this thing. And I'm like, it's all hammers and nails, guys, right? Go build something. And so, you know, Ben Franklin, when he discovered the form of electricity, wasn't trying to discover electricity. He was trying to invent the lightning rod so that all the houses wouldn't burn down. So, like, go explore. Those are the two things I would say.

37:22

Excellent. Tomas Fuig, Alembic for listeners who would like to learn more website. Where can they go online to learn more about Alembic, the work you're doing?

38:14

Getelembic.com right? Yeah, a nice little corporate site there. We are very friendly. And then also we are often at several of the industry conferences you'll get at Gartner, Nvidia, Forrester, that type of stuff. Always feel free to say hi to us.

38:24

Fantastic. Tomas, thank you so much for your time. It's been a really. It's been a pleasure talking to you. It was fun to kind of get into the theoretical.

38:38

Yeah, yeah. If you're ever up in the city, drop by the office or wherever and say hello, I'd love to chat with you more. It was just an interesting conversation. Sam.

38:45