Jeff Bezos: "Never Hire Your Friends Under 40"

0:00Eric Siu:Does your experience with AI sound a little something like this? You've been prompting for 20 minutes. The output is polished, confident, and completely useless. That's what happens when AI doesn't know anything about your business. HubSpot's AI works with your actual customer data, every interaction and every signal your team has generated over time. So instead of prompting and hoping, you ask once and ship it. Check out HubSpot.com, the agentic customer platform for growing businesses. Let me give you a Jeff Bezos quote. I don't know if you saw this interview. Neil's about to fall asleep, but it's okay, guys.

0:30Eric Siu:I got you. Which interview? CNBC one? No, I forgot where it was. It was with Walter Isaacson, who wrote the Elon Musk biography. So he said this. I thought this was really interesting. So Jeff Bezos said, never hire your friends if you're under 40, but after 40, always hire your friends. And so with Blue Origin right now, he has an executive that was sitting next to him that worked with him for 20 years or so. He's like, I couldn't have chosen like a better partner to work with on this. And Neil, let's think about this. If we think about some of the people that we work with now, that we work, we're both over 40 now, right?

1:04Eric Siu:So do you think this rings true? Because I tend to think that after 40, you kind of know what you want and what you don't want. And you know who you trust and who you don't trust. So what do you think?

1:15Neil Patel:So I would say for me, particularly in my 20s, I made way too many mistakes with hiring as well. In my 30s, I made less. But now that I'm in my 40s and I'm still young, I'm 41, I kind of know what's a good fit, what's not a good fit, what works, who will work. And I still make mistakes, but my chances of making mistakes are a lot less. And what I found is a lot of the people I've worked with over the years that I've learned to trust and I've enjoyed working with them, they're good out what they do and I would work again with them, but they were first colleagues and now they're friends, right? But they started off as colleagues and I would work with a lot of them all over again.

1:58Eric Siu:You know what? Um, I think about, for example, like today you're sick, right? But we're supposed to record with Noah. So Noah I've known for a while. Right. And so, you know, it's like, I, it's tried and trusted. Like I know how he's going to do. I know what to predict. Um, and I know he's, he's reliable, like he's going to show up on time. Um, and so that's what it. And I know sometimes he might forget a thing or two when it comes to recording, but for the most part, it's been good, right? And so I think what Jeff Bezos here is true. I actually hadn't thought about it, but I do believe that, yeah, if you're under 40, you kind of don't know what you want or you kind of don't have enough pattern recognition.

2:33Eric Siu:So you end up making the same stupid mistakes and then eventually you'll realize them. Yep.

2:38Neil Patel:Yeah, I agree with you on that. But yeah, I never really thought about it that way. In general right now, I don't really think about hiring friends or non-friends. I just try to hire people who are really good at specific things that I'm trying to get done. I never really hired generalists, especially in marketing. You know, like people are like, oh, I'm really good at paid ads. I'd be like, no, no, no, which platform? I'm good at all the platforms. It's okay, I don't want to work with you. But if someone's like, I'm really amazing at Facebook. Look what I can do on Facebook. Cool, sounds good.

3:05Neil Patel:You just run Facebook. But I tend to still hire very targeted specialists who don't just specialize in a form of marketing, but specialize in a channel, a very specific channel or platform. And I found that to work way better than hiring generalists.

3:24Eric Siu:Dude, speaking of which, yeah, I would just say the company we were talking about earlier, they have a lot of people that are looking for jobs. Really? Yeah, we can talk about it later. But yeah, we can talk about it later.

3:42Neil Patel:okay so eric's talking about another popular marketer they have a massive social following uh we won't go into their name they do really well on the view count on social media but why would you say there are a lot of people who are looking for a job is it culture because i know

3:55Eric Siu:the pay is pretty decent culture hours of work are hard though culture which leads to a lot of churn um especially within that team got it yeah so anyway that's it that's that's i'm i'm you know So it's good to know. So anyway, I want to pull this up from Ramp. So Ramp does a really good job. You want to talk about data storytelling. They have so much data around company spend in general. It's not all the data in the world, but I think there's some interesting stuff. So let me show this to you, Neil. So they have this over here.

4:27Neil Patel:They have a lot of startups and small businesses. So it's good to look at those kind of businesses through the lens of Ramp.

4:34Eric Siu:So it says you're spending too much on AI. You're also using too little, okay? So a big AI bill doesn't mean you're using too much on AI. It means you're buying it wrong. So most companies I talk to have two contradictory problems. They overspend on AI they use, but use far less AI than they should. So for example, everyone just using the latest Fable 5 for what's the freezing temperature for water, right? Are you serious?

4:59Neil Patel:That's one of the worst things to use it for, and it's expensive.

5:03Eric Siu:Yeah, I'm saying people are using it in stupid ways, right? So let's start with a simple question. How much does$100 ,000 buy an AI? So for those of you that can't see this, if you're spending 100 grand on Fable 5 for 5 billion tokens, this is what you get, right? And then Kimi 2.6, which is what you can call an open model or open-ish model, you get 210 billion tokens, so 21 times more, right? Opus 4.7 is a little higher than Fable 5. GPT 5.4 is a little more generous. Look at this. Gemini 3 Flash. Remember you were talking about how Google is just going to undercut everyone? They are undercutting quite a bit here.

5:41Eric Siu:So$89 billion, right? Who's Kimi 2.6? Kimi 2.6, that's more of an open source model. That's one of the Chinese models. Got it. Yeah. So for the same$100 ,000...

5:53Neil Patel:And you watch Gemini 3 and Microsoft and all of them, they'll get even cheaper and cheaper because they just print so much cash.

6:00Eric Siu:Yeah, Microsoft doesn't even register right now. But at least you just pay attention to Gemini. And like, honestly, Neil, I still continue to think that we're going to have to buy a lot more infrastructure. Your team's already buying computers. I'm looking at buying more computers because you just keep running all this stuff locally. Like GLM 5.2 came out, and that's like equivalent to 4.8 or pretty close, and a lot of people are talking about it. So I think we're just going to buy more. And I think this too, Neil, I think clients, because clients aren't going to want us to just do this for them.

6:25Eric Siu:So what we're going to do is we're going to sell them some of our computers, and we'll probably mark it up a little bit too.

6:30Neil Patel:Dude, and what we've been starting to do is not buy computers. and hardware, we've been starting to lease it. Because the big fear is you buy it and then it's not powerful enough for the future models or not enough RAM or whatever it may be. So it's just like, eh, might as well just lease it.

6:47Eric Siu:Remember I told you I was leasing all my Apple devices? Uh-huh. Yeah. So anyway, so for 100 grand, you can buy 5 billion tokens on the smartest model on the market or 210 billion tokens, 42X more, right? So anyway, you guys should read this one, but$100 ,000 drawn as finished work. So Fable 5, it can do like support tickets and sales calls analyzed. It'll cost you for$5 ,000, oh, sorry, 5 ,000 tasks,$20 each, okay? Now for Kimmy 2.6, you want 500 ,000 tasks, 20 cents per task, okay? I like how this is modeled out. This is pretty useful.

7:22Neil Patel:Yeah, that is. And on a side note, when it comes to leasing computers, you know not all computer companies do it, but there are third-party companies that will buy it for you and lease it to you.

7:31Eric Siu:No, I didn't know that. Can you name one of them?

7:33Neil Patel:I can find out from Tracy. Okay. She was telling us in all hands. I was like, oh, I don't know that. She's like, or all hands for leadership. And she was just like, yeah, it's just a more efficient way. I was like, oh yeah, that's smart. And she's like, yeah. She's like, you buy it and then what? Because she's just like, some of the hardware that we bought a year ago is useless for some of this stuff. It's like the last thing you want to do is make a big investment and then be like a year later, two years later, we all need new stuff.

7:56Eric Siu:I told you, this device that I'm showing on my screen right now, this is a DGX Spark from NVIDIA. I'm actively getting this repaired right now. They're sending me a replacement. Some of this stuff, it dies pretty quickly too. I want to go a little more on this. Model type, Frontier models for Frontier problems. Ideally, if you're doing a level, an S-tier problem, you're using the S-tier model on it. right? So you're going to have a better time, it's going to succeed on a tougher problem, versus if you run for like a tough problem, you're using a cheaper model, it's going to take longer to solve it.

8:33Eric Siu:And it might take like a few more turns to do it. So you're better off using PhD level intelligence there, right? So all that to say is, it's just they have a conclusion here, where you should actually spend more, okay? So for easy work, work that is well understood and scoped gets the cheapest model that clears your benchmarks, hard work, so novel, ambiguous, is high stake work gets the best model on maximum effort sampled as many times as you like with much more leeway on spend i think a lot of people just aren't thinking about that so they they think that and this is what i mentioned earlier neil a lot of companies just leave the smartest one on by default and that ends up burning a lot of tokens right and so why they do it i know why i think a lot of people just one i think there's a lot of reason but also i just think people don't know any better real quick if you want to acquire customers faster and more efficiently this year with the latest strategies and tactics, then check out singlegrain.com.

9:24Eric Siu:That is my ad agency. Again, www.singlegrain.com. Check it out. And if it seems like a fit, we'll get in touch and help you with a free marketing plan.

9:33Neil Patel:I think that's part of it. But I think a bigger one is it's not their money.

9:37Eric Siu:Well, there's that too. Yeah.

9:39Neil Patel:Right? Because if people had to be accountable for it and it affected their comp, you bet people would be really quick at figuring out ways to save money and figure out how to get more done with less money.

9:49Eric Siu:You know what I noticed, Neil? So on our Claude usage, I don't know if this is correlation or causation. And I love, by the way, this is no knock. I think if you speak Spanish, typically you're going to be a little more wordy. Would you agree or disagree with that? Just their language in general involves the use of more words, yes? I think so, but I don't know. Okay, so the answer is yes. You can research this. We're looking at our Claude. So the native Spanish speakers, their usage is higher, significantly higher. English speaking speakers. Yeah. And then like, you know, people have said, one of our mutual friends said that like, if you use Chinese, called like Chinese words, like it actually takes up less tokens, but I don't think that's true anymore because the number of letters counts as like one character versus each Chinese character is just one, right?

10:36Eric Siu:So it's like more efficient to write in Chinese, but I don't know if that's necessarily true. But I can see with my usage, at least the different language, like just how you do things as a native speaker is a little different than the English speaker.

10:49Neil Patel:The other thing that I'm seeing in marketing is people are using AI when they don't need to use AI. And I'll give you a great example of this. I'm seeing a lot of people go out there and creating things like social content and cranking out versions of it. And then sending it to other people in the team like, here's just some random ideas that I got from AI. Hope this helps you come up with other ideas. And I'm like, so you just spent all this money creating images and videos that won't be published. I'm like, you could have just shared a list of ideas without going there. And they're like, no, I want to show that I go above and beyond for a company.

11:30Neil Patel:And I'm like, this is not going above and beyond. This is just wasting money. And then I was talking to another friend at a conference. They work very closely with Salesforce. Some of the compensation supposedly, and I don't know this firsthand, but some of the compensation is supposedly based on token usage, depending on the department you are, or how good you look. So people are just burning up tokens and building stuff to just randomly use tokens and do it for them so that way they can get praise. And I'm like, this is just terrible ways to compensate people. People should be compensated on how efficient they are, the results that they're bringing in the most efficient way, not just on quote unquote token usage, because then people just token max.

12:09Eric Siu:Let's talk about that more because I've had a thought. I'm like, hey, I'm realizing there's a problem here that I've caused my own company, Neil. And so, yes, there's so much like every like not everyone, but a lot of people right now are trying to automate first before they even understand the problem. And I'm like, whoa, whoa, whoa, like, yes, I talk about AI all the time. But if you're gonna like you have to nail it before you scale it. Because if you don't understand the problem, and you scale out using AI, it's going to be a bunch of garbage, right? And for some reason, it's hard for me to get that point across.

12:40Eric Siu:Like, I'm like, guys, I don't want you to be a slot cannon, right? And the other thing is, there's still like, I still see pockets of AI theater, right? You know, I'm like, guys, like don't make dashboards just to make dashboards. And then yet, like I still continue to see like dashboards. I'm like, guys, we need to make things that lead to an outcome. It needs to lead to an outcome. Like ideally it's a business outcome. Whether you're saving time or whether you're driving more leads or whatever it is exactly, it needs to lead to that. And not only that, like we're big on making skills that other people can use.

13:10Eric Siu:We're like, guys, okay, it's really cool that you made this skill for yourself, but if not, at least one person is using it, it's useless, right? So we have a rule now where it's like, we're doing this training right now where it's like, okay, you have to at least upload two skills to our skills dojo, and at least one other person has to be proven using it. So that's the verifiable proof. Now, token maxing is one thing. I think we can look at token use as one thing. It shouldn't be the only thing, but we need to look at, that's the quantifiable piece. But the qualifiable piece is, hey, can you prove to me that you're using it in a smart way?

13:43Eric Siu:And maybe that's your manager that's actually talking to you about it. But again, this goes back to nail it before you scale it. Because right now I'm starting to see documents like artifacts and things like that, that we're showing to clients where it's clearly AI generated, and we're not putting thought in front of it. And people are just reading off the slides. And I'm just like, dude, enough.

14:02Neil Patel:Dude, I was in a meeting with a financial firm. And when I was meeting with them, we're talking about new ways to grow. So this company specifically targets rich people to market to, to try to convince them to park some of their money with them, and they would manage their money. So think of like a wealth manager, wealth advisor, whatever you want to end up calling it, or financial advisor. So I was like, what do you send reports? it's like how do you get clients what do you send them before and they're showing me some of the workflows and systems that they do so like one of them was showing me how they analyze clients current portfolio and they'll tell them what their stock portfolio looks like what numbers it can hit do projections and all this kind of stuff and i was just like she'll show me an example of it so they were loading up uh how they're using chat gpt and asking chat gpt about a stock and it was a tech stocks.

14:59Neil Patel:I know quite a bit. At least I track quite a few of the tech stocks out there. And they were wanting to know how likely a tech stock was to hit like 100 and something, like maybe like 120 or 150. I forgot what the exact number was. And it's only like 10-15 % away from that current number. So they're showing me this analysis from ChatGBT that it shows that it's somewhere around 60 to 70 % likelihood that it would hit that number for the next six plus months this year. And I'm like, this stock moves up and down 5 % in days so many times you can't even count on two hands, just in a matter of a month or two.