4 Types of Workers Right Now

Neil and Eric break down the viral AI 2x2 chart: dead weight, slop cannons, steady hands, and turbo brains. They explain why AI amplifies judgment, how most marketers misuse AI for content and outreach, and what actually drives revenue growth. From AI outbound testing to hiring for judgment over IQ, this episode covers real-world AI marketing strategy, specialist roles, and the future of lean teams powered by automation. Key Takeaways: AI amplifies your strengths and weaknesses Most AI marketing is low-quality content and outreach Specialists + AI beat generalists every time Chapters: (00:00) AI 2x2 Explained (01:27) AI Amplifies Intelligence (02:32) AI Marketing Use Cases (03:35) Hiring for Judgment (06:29) AI vs Human Outreach (10:06) AI IQ Growth Debate (14:18) Specialists vs Generalists

• 0:00Speaker 00

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• 0:35Speaker 01

  Uh, use the AI. Slop can cannons, I have not seen it. Does not use AI, dead weight. Has good judgment. Turbo brains, steady hands.

• 0:45Speaker 00

  Yeah. So what Neil's calling out, let me, let me just map it out for people that are listening. So this is a two by two chart. And so you have on the very bottom, it says, you know, people who do not have good judgment. And then to the right of that, you have people who have good judgment. Okay. And then right above, you have people who do not use AI versus people that use AI. So let's just put it this way. If you're someone that you do not have good judgment, nor do you not, and you, and you don't know how to use AI, you are what? You are dead weight, guys. You are dead weight. I'm sorry. But if you're listening to this, for sure, you're not dead weight, because you have good judgment, because you listen to this podcast. So if you have, if you don't have good judgment, and you know how to use AI, guess what? You are a slop cannon, meaning you produce lots and lots of lots of slop, because you think just because you know how to use it, you're

• 1:29Speaker 00

  adding a lot of value, which is, I think, a lot of what, what Neil kind of alludes to on this podcast, is like, people using it to create slop. And that is not helpful to the world.

• 1:38Speaker 01

  That's the majority, by the way. That's more than 95% of what I see is dead weight, just creating more junk.

• 1:44Speaker 00

  But Neil, hey, question for you. What percent of people do you think in the world have good judgment typically?

• 1:49Speaker 01

  Less than 5% in the corporate world.

• 1:51Speaker 00

  Doesn't that add up? Does, we, and we talked about this last week, when we're like, hey, if you're using it, it amplifies your, your intelligence, basically. Like, whatever you tend to do, you've just amplified it 10x.

• 2:01Speaker 01

  So I've been stealing Eric's quote recently, at events. AI brings out the best in you, and it brings out the worst in you. The lazy people are getting lazier, and they're producing more junk. And the good people are accelerating and becoming even higher performers. That we see. But when I look at marketing, right now... From an AI perspective, people are only really doing two main things with AI. And you can add, if you know any others that are taking up big buckets. One is creating content. And two is doing outbound to just generate more leads. Those are the two main things we see. If I had to add in a third, I would add in more creative for AI.

• 2:44Speaker 01

  Uh, and I mean creative for ads, like mass scale producing ads. Uh, if I had to add in a fourth and it's a much smaller percentage doing this is using it for data and analytics. But the main two are content and outreach. Those are the big two that I'm seeing. I don't know what you're seeing, but those are the main two that I'm seeing.

• 3:06Speaker 00

  I actually want to address that. So, um, let me, let, let me finish the two by two here. So the two by two here, so let's say someone has good judgment, okay? But they do not use AI, they are steady hands, okay? So meaning that they're, you know, kind of more or less stabilizers. Um, but if they have good judgment and they use AI, they are turbo brains. And so I think the reason I'm pulling this up is any of you watching this right now, you should be thinking about looking at this chart all the time. Whoever you're looking at hiring, you should run them through this, okay? And you're like, oh, well, how do I quickly test if they have good judgment or not? You know what we do? We have this thing now, Neil, called, I, I think I showed you this to you, but it's, uh, literally the single grain beat Claude challenge, right? And so it's basically like, hey, AI could do 80% of knowledge work now. We need people who can do the 20% that AI can't.

• 3:50Speaker 00

  Creative leaps, pattern recognition, judgment under uncertainty. So basically you can use AI all you want. Um, that's baseline. If you only use AI, like, okay, then you, you haven't beaten Claude. So we, we just want to see people like fill these out. Um, and then, you know, these challenges over here, here's like a brief and the situation and all that stuff. And they'll send it over to us and that's basically it. So your, your hiring process is going to have to become tougher. That's what I generally think. Now, going back to what Neil said, I think Neil, you're, you're talking about use cases, right?

• 4:18Speaker 01

  Yeah, because at the end of the day. Yes, people can use air for whatever they want. As a business owner and as a marketer, we really care about revenue growth and profitability.

• 4:29Speaker 00

  Yeah, so I would say in general, yes. But here's what I'll say, Neil, that's interesting. So what typically are the main kind of funnels that that matter for a business, right? It's how good is your recruiting? How good is your sales? How good is your marketing? And if you have a good product, you need product fulfillment, product delivery, right? You kind of need those four main things. Am I missing anything?

• 4:49Speaker 01

  Hmm, good finance, but yeah.

• 4:52Speaker 00

  Good finance, yes, good finance. But you kind of need the first four before you're in good finance, right?

• 4:56Speaker 01

  Yeah, because you need money, but yes.

• 4:59Speaker 00

  So we agree on that. So I I do think a lot of stuff is like, okay, create content creation or outreach right now, whether it's for outreach for sales or outreach for recruiting or like ad creative. Now, what where I think this open clause is going, Neil, because Bernard and I were talking today, and he's like, dude, this is the first version of AGI. I was like, you know what? This is like the MS-DOS version of AGI, right? It's like the very like, you know, in the 1970s, 80s, where you're just using command line interface. And so I think you're going to be able to have your own version of OpenClaw that allows you to do all these things as one. And then you just have really smart people. Neil, the way I'm looking at it is you have four deployed marketers, like four deployed engineers, and these people are working maybe full time embedded into a company, and they're just focused on customizing these things.

• 5:44Speaker 00

  And these people know how to talk business, they understand the latest in marketing, and they're creating new skills and new workflows within like a mission control dashboard. That's where I think a lot of this is going. So I don't disagree with you. Like, a lot of it's big news for that, but I think a lot of it's going to become consolidated. Like, you don't need all these freaking SaaS products.

• 6:01Speaker 01

  I I agree with you that that's a big chunk of the future, maybe not all of it. But I don't think it's going to be four people. I think it's going to be many more than that. Because what we see is unless you have people who are really good specialists, when they use these products, the output isn't as good. So you really need the specialists. And I know when you were saying four, you're not saying it's going to be four people. You're just giving a random number, right? I just want to clarify that for anyone listening, because I already know Eric thinks the similar way to I do. We wouldn't want to pay Facebook advertiser to be using this technology for SEO. They're the wrong person. You're gonna have a really mediocre output, even if they're amazing at Facebook ads. And if they're amazing at Facebook ads, but they haven't done much in Google Ads,

• 6:44Speaker 01

  we wouldn't want them to be using this technology to help with Google Ads. We want them to stick in their lane. The moment they start spreading outside of the lane, that's when you start getting mediocre outputs. Mediocre outputs don't work because that's what the majority of the competition is gonna do, and you're not gonna end up winning with that, right? And what we're seeing is, we're running a test right now. I should end up knowing more, call it first week of March. All right? So my team set up a system where we're having AI outreach as me and set up meetings. At the same time, I am, which we've talked about, I am setting up my own meetings,

• 7:23Speaker 01

  and we're seeing what's gonna end up driving more revenue by the end of, by sometime in March, right? At least from a pipe perspective, because we know, based on how many meetings we get, based on how the first call goes, that after the first call, we usually know what's in our pipe that's qualified and what percentage will close. But we need the first call. Just a email intro or someone willing to take a call doesn't tell us enough, even if it's company size or right title. We genuinely need the first call. Sometimes we need two calls. But if we need two calls, it's because typically they need to bring in someone else, where we weren't able to get all the information we need on the first call. So in that case, we still classify the second call, even though it's the second one, we still classify it as a first call.

• 8:07Speaker 01

  Um, but so far, no joke, it's setting up way more appointments than I am personally. Of course, because it's working more hours and it's, you know, a machine. Um, the quality, no matter how much.

• 8:22Speaker 01

  Input we give and training we give, the quality is not there yet. And I want to use the word yet because no matter how many qualifications and criteria you give, it's not as simple as inputting ten, fifteen, twenty years worth of relationships from your head. That's not out there in email or that's not out there on your LinkedIn or whatnot. And having the AI learn all of it, like it does take time. But I do believe if I kept doing it through the AI for call it another year, two years, which I know people think is a long time, but in reality it's not like look how long you and I have been entrepreneurs for. It's like short term pain for long term gain.

• 9:06Speaker 01

  I do believe that output for appointments at its setting, the quality would be much higher. But I'll quickly see what's going to happen within thirty days from at least appointment to close.

• 9:17Speaker 00

  And Neil, just to clarify, you're sending one per day? Is that what it is?

• 9:20Speaker 01

  Uh, me personally?

• 9:23Speaker 01

  Me personally, I'm doing

• 9:27Speaker 01

  five to fifteen a week. So I'm not consistently at one a day because it depends on how often I'm traveling.

• 9:35Speaker 00

  Yeah. Frictionless Growth Marketing hosted by Sonya Thompson is brought to you by the HubSpot Podcast Network, the audio destination for business professionals. Frictionless Growth Marketing hosted by Sonya Thompson shows how modern brands grow by removing the friction in their marketing and customer experience that pushes customers away every single day, often without leaders even realizing it. Each episode delivers practical insights, real brand examples, and conversations with marketing leaders showing brands how to win with the new rules of growth, identity-driven marketing, cultural relevance, and frictionless customer experiences. Listen to Frictionless Growth Marketing wherever you get your podcasts. I mean, if you're traveling, you can do more, right?

• 10:13Speaker 01

  No, it's really hard because like let's say I'm in Mexico City.

• 10:16Speaker 00

  Yeah.

• 10:16Speaker 01

  Morning meetings.

• 10:18Speaker 00

  Yeah.

• 10:18Speaker 01

  Morning event after that, then interview with Wired, then another speech at a university, then meeting with the university because we work with them. Then I had to go to Bloomberg for another interview. Then I had to do a meeting with my team to catch up with them and then here doing this.

• 10:35Speaker 00

  Yeah.

• 10:35Speaker 01

  So like I haven't had much chance to work at all today. That's the hard part. When I'm on a plane, it's different, but if I'm not on a plane.

• 10:41Speaker 00

  That's what I mean. On a plane. Yeah. Not when you're on the ground. Um, by the way, Neil, I, I just let, let me, let's, let's do this for fun. So what do you think the L L M I Q scores were on average in 2023?

• 10:54Speaker 01

  Um, a hundred, a hundred and ten, ninety. I don't know, somewhere around there. Let's go a hun- ninety to a hundred and ten.

• 11:01Speaker 00

  Okay. Which is like, you know, that's actually average is, is a hundred. Okay. So, so get this and then let, let's, let's go a little more. 2024. What do you think it was?

• 11:11Speaker 01

  Uh, I would go with maybe one twenties and I would say today is probably closer to one thirty to one forty.

• 11:17Speaker 00

  Okay. So pretty accurate. Here, let me pull this up real quick because yesterday, um, some of my team was like, they're just robots. They're just robots. I was like, are you kidding me? Like, so, and, and, and I'll explain why, why I said, are you kidding me? Cause it's like, oh, like, we think just because we're the creator, we're going to be smarter than it forever. But check this out over here.

• 11:37Speaker 01

  No way. It'll be smarter than humans.

• 11:39Speaker 00

  Yeah. You're saying what?

• 11:41Speaker 01

  There's no way it won't be smarter than humans. Like humans.

• 11:43Speaker 00

  Yeah, yeah. We're, we're on the same page. I'm like, I'm like, there's, this, it's just going to crush our intelligence, right? And so, um, look at this. Seventy. Okay. Then it jumped up to seventy was initially twenty twenty-three and then G P T four was um, right in the middle of twenty twenty-three, twenty twenty-four. It jumped up to like ninety and then you go a little more to like, like early twenty twenty-four, hundred. And then last year, twenty twenty-five, it jumped up to about one fifteen or so. And then now we're talking twenty twenty-six. It's basically genius level. Okay. One thirty.

• 12:10Speaker 01

  Ah, of course.

• 12:12Speaker 00

  Yeah, you're very close, right? Um, but check this out. So it's like, oh, it's doing this like linear projection over here. So it goes to like two hundred plus. And I'm like, no, no, no, no, no, no, no. You can't do a linear projection on something that's exponential. So like, give me an exponential scaling chart. It goes all the way to like a thousand.

• 12:28Speaker 01

  Uh, so, so here's the hard part. For these models to get much, much better and smarter, it's so. Much more work than it was at the early days. I don't think it's exponential for them growing to like that kind of IQ. I actually think you start seeing diminishing returns after a while.

• 12:45Speaker 00

  I don't know enough. Neither Neil or I are AI scientists. I just heard on a podcast that one of the scientists was like, oh yeah, it's going like, I don't know if it's gonna go to a thousand, but he's like, it should, in a couple of years, it should be at like three hundred or three fifty or something like that. That I can believe. I don't even know how to fathom one thousand because, yeah, so.

• 13:04Speaker 01

  And I want everyone to keep this in mind. Having a really high IQ, whether it's a human or AI, doesn't mean that they're gonna produce amazing results for you. And Eric and I can attest to this, or at least I can attest. I don't know what your hiring has been like over the years. But I've tended to have the worst luck with people who graduated from Stanford or Harvard or Ivy League schools. When you look at their IQ tests, they tend to be really high, just generally speaking. Would you agree with this, Eric? He's nodding his head yes.

• 13:34Speaker 00

  Yeah, I'm, yeah, yeah.

• 13:35Speaker 01

  Yeah, so, or shaking his head yes, whatever it is. What I found is my hires from like Arizona State University tend to outperform the people. Eric's giving me the thumbs up. They tend to outperform the people in most cases who graduated from Stanford. Keep in mind, I'm not in a field like Elon Musk where I need all these computer scientists. So it could vary field by field. But when you look at general IQ, IQ tests test you on a wide variety of things. My kids have taken IQ tests. An IQ test doesn't necessarily mean you're really amazing

• 14:16Speaker 01

  at one specific industry or field. And yes, AI can learn a lot, but here's a problem, and I'm a big believer of Ben Athlack with this. So Ben Athlack and Matt Damon were on a podcast. I don't know if you saw it.

• 14:29Speaker 00

  Yeah, yeah, with Rogan, right?

• 14:31Speaker 01

  Uh, I think it was Rogan. And they were talking about the averages. I only saw clips of it. And they were just like, we don't think AI is gonna be amazing at writing a script. We think AI optimizes for the average. You could say the average could be a hundred and thirty-six, hundred and forty. IQ, but that's just IQ. What is the output that they're producing for specific industry? Someone could be really book smart, but they could be a terrible script writer. Someone could be a really amazing marketer, but they could be terrible at math. And what I'm getting at is they're scraping the internet, learning from everyone. A lot of the crap that is out there on marketing is junk. And they're pulling from a lot of that. And that's why when Eric and I were talking about how future organizations were looking like, and Eric mentioned four people, but he didn't really mean four people.

• 15:15Speaker 01

  What he meant to say, and what I, what I'm trying to say is it's going to be specialists. You're gonna take really good people who are good at, let's say, Facebook ads. And they're gonna be using AI to do a lot more damage. And instead of you needing a team of five or six, you may be okay with the team of two amazing Facebook ad specialists combined with AI instead of needing five or six people.

• 15:41Speaker 00

  All right. So what I'll say, what I'll say is, um, when it comes to Ivy League people I've either worked with or hired, they kind of, I'm generalizing here, but in many cases I've seen they, they kind of struggle to get out of their own way sometimes. And they tend to overthink a lot of situations, which there, it sometimes it's like, damn, you're really too smart for your own good. Uh, and so I'm, I'm generalizing here, like, but it does happen more often than I see. Um, and I, I, I think, I, I, I don't know. Maybe it's just a pattern that we've seen in our, like, hiring. So.

• 16:11Speaker 01

  Dude, my favorite one is, we've had a lot of Ivy League people in M and A. And with M and A, a lot of these people are amazing at Excel. Like just, they're rocket scientists when it comes to just using Excel. And it is a skill, believe it or not. And what we'll find is like, they'll show us deals and they'll be like, Neil, this company right here. Oh my God, if you bought this, the business is gonna change. And they show us a spreadsheet of the synergies with the business. You'll be able to cross sell this to these customers. They'll be able to cross sell, uh, you know, their stuff to your customers. You can just cut this fat right here. Look at the additional profit. And they tend to struggle to take into account things like culture. Cross selling doesn't always work that simply.

• 16:55Speaker 01

  Who is the person that is in charge of that other relationship? Are they even the decision maker for the service line item that we're discussing or that we have?

• 17:06Speaker 01

  Are they even part of that organization or part of that department? Or is it something else? Is it in a different country? Like, there's so many nuances to M and A. And I'm like, you can't run M and A off a spreadsheet. And we bought companies that people told us like, ah, it's not growing that much. It's declining. I don't think it's a good company to buy.

• 17:29Speaker 01

  And they want too much of a multiple. And I'm like, I'm gonna go buy the company. And people are like, you're telling me you want to buy this company that's not growing as much? They have quality problems and you want to buy them? I'm like, they're one of the biggest in that region. They have a name. I'm gonna go in and fix it. And they're like, why would you go through all that headache? I'm like, they have the brand recognition where they're getting hit up by most of the companies in the space. I can fix other stuff. You're telling me to go buy another company that's growing fast, that doesn't have the brand recognition, that doesn't have the right customer profile, but from financial, you think it's better? And I'm like, I'm telling you, to go get the right companies to hit you up because of your name brand is much harder to do, in my opinion, than it is to fix operations for someone like me.

• 18:12Speaker 01

  And that's, you know, the skill set of my team and I. So I go with the route that I think I have the edge on versus what a number of countries are gonna tell me. All right. See you, guys. Goodbye.