Marketing School - Digital Marketing and Online Marketing Tips

4 Types of Workers Right Now

20 min
Feb 17, 20262 months ago
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Summary

The episode discusses a 2x2 framework categorizing workers by their AI usage and judgment capabilities, identifying four types: dead weight, slop cannons, steady hands, and turbo brains. The hosts explore how AI is currently being used primarily for content creation and outreach, while discussing the evolution of AI intelligence and its impact on hiring and business operations.

Insights
  • Only people with good judgment should use AI tools, as AI amplifies existing capabilities - making good people better and lazy people produce more junk
  • Businesses should focus on hiring specialists who excel in specific domains rather than generalists when implementing AI solutions
  • The quality of AI output heavily depends on the expertise of the human operator - specialists in their field produce better AI-assisted results
  • Traditional hiring processes need to evolve to test candidates' ability to do the 20% of work that AI cannot handle
  • High IQ doesn't guarantee business success - practical experience and domain expertise often outperform academic credentials
Trends
AI intelligence is rapidly increasing from average human levels to genius-level IQ scoresBusinesses will need fewer but more specialized employees who can effectively leverage AI toolsAI adoption is consolidating around content creation, outreach, ad creative, and data analytics use casesCompanies are developing internal AI competency teams to customize workflows and toolsThe job market is shifting toward requiring AI proficiency as a baseline skillTraditional SaaS products may become obsolete as AI platforms consolidate multiple functionsHiring processes are evolving to test human capabilities that complement rather than compete with AI
Companies
HubSpot
Featured as sponsor promoting their customer platform and AI tools called Breeze for business growth
Sandler Training
Case study showing 25% click-through rate increase and 4x qualified leads using HubSpot's AI tools
Single Grain
Eric's company that created the 'Beat Claude Challenge' for testing job candidates' AI capabilities
OpenAI
Referenced for Claude AI model and discussions about artificial general intelligence development
Stanford University
Mentioned as example of Ivy League school whose graduates sometimes underperform in business roles
Harvard University
Cited alongside Stanford as Ivy League institution whose graduates may overthink business problems
Arizona State University
Contrasted with Ivy League schools as source of better-performing business hires
Bloomberg
Mentioned as media outlet where Neil conducted an interview during his Mexico City trip
Wired
Referenced as publication that interviewed Neil during his business travel schedule
People
Neil Patel
Co-host discussing AI worker types, hiring practices, and comparing AI vs human outreach effectiveness
Eric Siu
Co-host presenting the 2x2 worker framework and discussing AI implementation in business operations
Ben Affleck
Referenced for his views on AI limitations in creative fields like scriptwriting
Matt Damon
Mentioned alongside Ben Affleck for podcast discussion about AI optimizing for average outcomes
Joe Rogan
Identified as likely host of podcast where Affleck and Damon discussed AI creative limitations
Elon Musk
Referenced as example of leader in field requiring specialized computer science talent
Bernard
Team member who discussed Claude AI as first version of artificial general intelligence
Quotes
"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."
Eric SiuN/A
"Most businesses only use 20% of their data. That's like reading a book with most of the pages torn out."
HubSpot AdN/A
"We don't think AI is going to be amazing at writing a script. We think AI optimizes for the average."
Neil Patel (quoting Ben Affleck)N/A
"You can't run M&A off a spreadsheet."
Neil PatelN/A
"AI could do 80% of knowledge work. Now we need people who can do the 20% that AI can't. Creative leaps, pattern recognition, judgment under uncertainty."
Eric SiuN/A
Full Transcript
2 Speakers
Speaker A

Did you know that most businesses only use 20% of their data? That's like reading a book with most of the pages torn out. Or paying for coffee. That's 1/5 full. Point is, you miss a lot unless you use HubSpot. Their customer platform gives you access to the data you need to grow your business. The insights trapped in emails, call logs and transcripts, all that unstructured data that makes all the difference. Because when you know more, you grow more. And when you get a full cup of coffee, you can do more too. But I digress. Visit HubSpot.com today. Using only 20% of your business data is like dating someone who only texts emojis. First of all, that's annoying. And second, you're missing a lot of context. But that's how most businesses operate today, using only 20% of their data. Unless you have HubSpot, where all the emails, call logs and chat messages from turn into insights to grow your business. Because all that data makes all the difference. I would know because I use HubSpot at my company. Learn more@HubSpot.com Being a know it all used to be considered a bad thing. But in business, it's everything. Because right now, most businesses only use 20% of their data. Unless you have HubSpot, where data that's buried in emails, call logs and meeting notes become insights that help you grow your business. Because when you know more, you grow more. See, being a know it all isn't so bad. Visit HubSpot.com today to learn more. Nobody likes a spoiler unless it's your customers telling you exactly what they need. But too bad. Most businesses miss out on these signals. The hits dropped in emails, the messages hidden in call logs and chats. All of it trapped in the digital ethereum. But with HubSpot, you get all this data in one place. Their customer platform brings together the insights you need to grow your business. And spoiler alert, the more you know, the more you grow. Visit HubSpot.com to find out how.

0:00

Speaker B

Today.

2:01

Speaker A

Cutting your sales cycle in half sounds pretty impossible, but that's exactly what Sandler training did with HubSpot. They used Breeze HubSpot's AI tools to tailor every customer interaction without losing their personal touch. And the results were pretty incredible. Click through. Rates jumped 25%, qualified leads quadrupled, and people spent three times longer on their landing pages. Go to HubSpot.com to see how Breeze can help your business grow. Have you seen this 2 by 2 chart?

2:07

Speaker B

Use the AI slopping cannons. I have not seen it does not use AI. Dead weight, has good judgment. Turbo brains, steady hands.

2:35

Speaker A

Yeah, so what, 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 are 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 and lots of slop because you think just because you know how to use it, you're 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 sloth. And that is not helpful to the world.

2:44

Speaker B

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

3:37

Speaker A

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

3:43

Speaker B

Typically less than 5% in the.

3:47

Speaker A

Does it 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 just amplified at 10x.

3:50

Speaker B

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 a 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. And I mean creative for ads like mass scale producing. Ads, 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.

4:00

Speaker A

I actually want to address that. So 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, 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 know, 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 think I showed this to you. But it's 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. Creative leaps, pattern recognition, judgment under uncertainty. So basically, you can use AI all you want. That's baseline. If you only use AI, like, okay, then you haven't beaten Claude. So we just want to see people, like, fill these out. And then 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 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 talking about use cases, right?

5:05

Speaker B

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.

6:18

Speaker A

Yeah. So I would say in general, yes. But here's what I'll say, Neal, 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?

6:28

Speaker B

Good finance, but yes.

6:50

Speaker A

Good finance, yes. Good finance, but you kind of need the first four before you could finance. Right?

6:51

Speaker B

Yeah. Because you need money.

6:56

Speaker A

But yes, 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 going, Neil? Because Bernard and I were talking today and we're. 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, 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. 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 being used for that, but I think a lot of it's going to become consolidated. Like you don't need all these freaking SaaS products.

6:57

Speaker B

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 for 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 and the output isn't as good, so you really need the specialist. And I know when you were saying four, you're not saying it's going to be four people, you're just using a random number. 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 paid Facebook advertiser to be using this technology for SEO. They're the wrong person. You're going to 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, 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 going to do. And you're not going to end up winning with that.

8:00

Speaker A

Right.

9:00

Speaker B

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 and we're seeing what's going to 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. The 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 an 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 the first call. But so far, no joke, it's setting up way more appointments than I am personally, of course. Cause it's working more hours and it's a machine. The quality, no matter how much 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 10, 15, 20 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. I do believe that output for appointments, that it's setting the quality would be much higher. But I'll quickly see what's going to happen within 30 days from at least appointment to close.

9:01

Speaker A

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

11:16

Speaker B

Me personally, Me personally, I'm doing.

11:20

Speaker A

Five.

11:27

Speaker B

To 15 a week. So I'm not consistently at one a day because it depends on how often I'm traveling.

11:28

Speaker A

Yeah, I mean, if you're traveling, you can do more, right?

11:34

Speaker B

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

11:37

Speaker A

Yep.

11:59

Speaker B

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.

11:59

Speaker A

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

12:05

Speaker B

Um, a hundred, 110 90. I don't know, somewhere around there. Let's go 90 to 110.

12:18

Speaker A

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

12:26

Speaker B

Uh, I would go with maybe 1 20s. And I would say today is probably closer to 130 to 1 40.

12:35

Speaker A

Okay, so pretty accurate here. Let me pull this up real quick, because yesterday 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? Because it's like, oh, like we think just because we're the creator, we're gonna be smarter than it forever. But check this out over here.

12:42

Speaker B

No way it'll be smarter than humans.

13:01

Speaker A

Yeah. You're saying what?

13:04

Speaker B

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

13:06

Speaker A

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 look at this. 70. Okay. Then it jumped up to 70 was initially 2023, and then GPT4 was right in the middle of 2023. 2024, it jumped up to like 90. And then you go a little more to, like, like, early 2024, 100. And then last year, 2025, it jumped up to about 115 or so. And then now we're talking 2026. It's basically genius level. Okay, 130 now. Close. Yeah, you're very close. But check this out. So it's like, oh, it's doing this, like, linear projection over here. So it goes to like, 200 plus. And I'm like, no, no, no, no, no, no. You can't do a linear projection on something that's exponential. So I like, give me an exponential scaling chart. It goes all the way to, like, a thousand.

13:09

Speaker B

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, that kind of iq. I actually think you start seeing diminishing returns after a while.

13:53

Speaker A

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, yo, yeah, it's going like. I don't know if it's going to go to a thousand. But he's like, it should. In a couple years, it should be at like, 300 or 350 or something like that. That I can believe. I don't even know how to fathom 1000 because. Yeah, so.

14:10

Speaker B

And. 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 going to 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. He's nodding his head yes.

14:28

Speaker A

Yeah, I'm sh.

14:58

Speaker B

Yeah, yeah, 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. IQ test doesn't necessarily mean you're really amazing 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 Affleck with this. So Ben Affleck and Matt Damon were on a podcast. I don't know if you saw it.

14:59

Speaker A

Yeah, yeah. With Rogan, right?

15:53

Speaker B

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 going to be amazing at writing a script. We think AI optimizes for the average. You could say the Average could be 136, 140 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. What he meant to say and what I, what I'm trying to say is it's going to be specialists. You're going to take really good people who are good at, let's say, Facebook ads, and they're going to 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.

15:55

Speaker A

All right, so what I'll say, what I'll say is 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 they're, Sometimes it's like, damn, you're really too smart for your own good. Um, and so I'm, I'm generalizing here, like, but it does happen more often than I see. 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.

17:06

Speaker B

So, 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, 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. Who is the person that is in charge of the other relationship? Are they even the decision maker for the service line item that we're discussing or that we have? Are they even part of that organization or part of that department or is it something else and 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. And they want too much of a multiple. And I'm like, I'm going to 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 going to 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, 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. 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 cruncher is going to tell me.

17:35

Speaker A

All right, see you guys. Goodbye.

19:46