Red Flags Every Marketer Should Know

AI hype is creating a marketing technology minefield. Tom Chavez, Founding General Partner at super{set}, shares his expertise from building companies acquired by Salesforce and Microsoft. He reveals how to identify AI posers versus genuine innovators, emphasizes the importance of systems thinking over technical expertise, and explains why vertical AI applications offer better business opportunities than building foundational models. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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  The Martech Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast? I Hear Everything delivers podcast production, growth and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit iHereEverything.com.

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  From advertising to software as a service to data, across all of our programs and clients, we've seen a 55% to 65% open rate.

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  Getting brands authentically integrated into content performs better than TV advertising.

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  Typical life span of an article is about 24 to 36 hours.

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  If we're reaching out to the right person with the right message and a clear call to action, then it's just a matter of timing.

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  Welcome to the Martech Podcast, a member of the I Hear Everything Podcast Network. In this podcast, you'll hear the stories of world-class marketers that use technology to drive business results and achieve career success. Here's the host of the Martech Podcast, Benjamin Shapiro.

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  I'm Benjamin Shapiro and joining me today is Tom Chavez, a co-founding general partner at Superset. Tom has built and sold startups to some of the biggest tech companies in the world. And now he runs a venture studio that funds data-driven AI applications to deliver practical business results. And today, Tom and I are going to delve into what's real and what's not when it comes to AI's impact on marketing. All right, let's move on. How do you identify founders and vendors who are just riding the AI hype wave versus truly understanding the technology?

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  This is a biggie because in our shop, we're constantly on the hunt for and hiring kick-ass founders and engineers. There are, to your point, a number of posers out there who talk the talk, right? They read the latest blog post or they're on Twitter and they're just kind of getting the buzzwords under their belt, but they're not actually going to the shed. So the first thing we look For is, are you going to the shed? Are you learning these new frameworks? Are you prototyping and building against them? Are you actively using to our earlier topic? Are you actually using it? Or are some other people doing it for you? No, no, no, no, no. You've got to embrace these new technologies in an up close and personal way. Part of the thing that matters there the most, we find, is curiosity.

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  Some engineers especially are just less curious about these new things. They want to stick to the mac and cheese and the thing they did the prior 20 years. Oh, my God, I don't know how these folks cross over, particularly in the context where you have companies like Microsoft and met another saying that they plan to lay off 30, 40% of their mid-level engineering staff within the coming one or two years. So if you're out there and you're a builder of one of these kinds of systems, you've got to be tooling up. You've got to stay curious. You've got to be ambitious enough to repot yourself and learn these new methods.

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  Yeah, it's interesting. I understand that obviously you want people that have the experience that have already done it. But it's really hard right now to have people that have done it because everything is moving so quickly. So I think that a little bit of it is like, if you can talk the talk, you could probably figure it out as well as anybody else can. So I imagine that it's very hard to figure out who are the snake oil salesmen? And who actually found gold? Part of this new technology and mostly because the interface is so text-based and even chat-based, you just have to sit there and fumble with it until you find something that works. And then all of a sudden you have data. The interface is so much easier that almost anybody could do it. It's got to be really hard to figure out who actually is effective as opposed to who is just a talker.

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  Well, it is tricky. And I'm not going to tell you that we nail every one of these. But I think my crew and I have gotten pretty good at discerning the snake oil salesman from the true builders for real. And the main, main question is, we're sitting at a whiteboard in the room, like the one I'm in right now. And we push people like, no, no, no, diagram it out. Teach me exactly how that works. And if they just sprinkle some buzzwords and arrows pointing nowhere in a make-believe picture, then we know they're full of shit. The ones who can actually give us a systems of systems kind of view as to how things work together without necessarily unpacking every last little piece. That's the other thing for builders of systems. In the, when these L L Ms can write a huge amount of your code,

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  it really elevates the systems thinkers who can think architecturally about how all the pieces hang together. That's high ground.

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  You just took the words out of my mouth. I don't think you have to be an artificial intelligence expert to be an expert at using artificial intelligence. I think you have to be a great, whatever you are, a great systems thinker, right? A great engineer, a great copywriter, a great business analyst. If you are that, then artificial intelligence, if you mess with it enough, you'll know when you're getting the right response. I'm a pretty good content creator. I understand when I am using artificial intelligence and writing my prompts and doing my research, what is actually good research, what that looks like, how to use it in a system to get it to reproduce and use it consistently. Doesn't mean I'm an LLM expert, doesn't mean I understand how that part works, but I understand

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  the process of what I do well. And I think that is an example that most people, you don't have to be an AI expert, you just have to be an expert.

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  That's exactly right. We've said, if you have an extra six to eight billion dollars burning a hole in your pocket, you want to build a new LLM from scratch, go for it. But our general view is leave the science of LLMs to the anthropics and OpenAI's of the world, be all about the engineering of AI, right? Use these systems to create new systems. And by the way, some people have wondered, well, should the OpenAI's of the world start opening up their APIs so that you can set the attentional weights at the third layer of the neural network? Like, I don't know, I don't care. Leave that to you guys. We're engineers who build on top of those systems and we're barely in the first inning of that. That's where the money's at.

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  That's why, to your earlier example about a verticalized AI application company, that's where the smart money is. And coming into the large LLMs, the perplexities and so on of the world, like, some of them are going to win. And if you can write a large enough check such that you could own a non-trivial percentage of one of those companies, good on you. But again, from my perspective, that's not really where the biggest opportunities lie.

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  Two more points for Yellow Pad. And that wraps up this episode of the Martech Podcast. Thanks to Tom Chavez, the co-founder and general partner at Superset for joining us. If you'd like to contact Tom, you could find a link to his LinkedIn profile, which is in our show notes or on martechpod.com. Or you could visit his company website, which is superset.com. You can also find Tom on his podcast, which is Closed Session. If you haven't subscribed yet and you want a daily stream of marketing and technology knowledge in your podcast feed, hit the subscribe button in your podcast app or on YouTube and we'll be back in your feed next week. Okay, that's it for today, but until next time, my advice is to just focus on keeping your customers happy.

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