Episode 228

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Published on:

15th Sep 2026

228: AI Ruined The Job Search. Here’s How To Fix It.

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Former LinkedIn exec explains why mass applying with AI is getting you nowhere. I asked him what works instead.

🧑‍💼 See 60+ recruiters who are hiring data analysts right now 👉 https://findadatajob.com/recruiters

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⌚ TIMESTAMPS

00:00 – The AI arms race

04:33 – What are we solving for?

10:57 – Referrals were 10x in 2016

21:12 – Anyone can refer anyone

22:18 – Elevator pitch or questions?

36:36 – Is AI useless in the job search?

42:24 – Quality beats volume

🔗 CONNECT WITH JEREMY

🤝 LinkedIn: https://www.linkedin.com/in/schifeling/

📚 Check out Jeremy's books 👉 https://www.amazon.com/stores/author/B00AB7IEX2

🔗 CONNECT WITH AVERY

🎥 YouTube Channel

🤝 LinkedIn

📸 Instagram

🎵 TikTok

💻 Website

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Transcript
Speaker:

You could use Claude CoWork,

you could use GPT Codex.

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Literally say, "Here's my resume.

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Go apply to 1,000 jobs.

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Don't stop till you're done."

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It'll do it for you.

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Is that if we're just applying

for random jobs in a completely

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mechanical way, it's not really 0.01%,

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it's 0% period.

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Do you know what the bonus, the top

bonus was at Google when I worked there?

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$25,000.

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Oh my gosh.

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All right, Jeremy.

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You are a former LinkedIn executive

and an early OpenAI partner who

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now helps job seekers get hired.

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Uh, so thank you for joining us today,

and you're gonna talk about how AI

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has changed the job hunting process.

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So let's just start with an easy

question: Has AI changed the

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job hunting process, yes or no?

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100%, absolutely.

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Okay.

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And is that, like, uh, how has it changed?

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Yeah.

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And so it's changed on

both sides of the equation.

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You know, if you're a job seeker,

you have all these tools now.

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"Build me a resume, ChatGPT.

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Write me a cover letter, Claude."

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But it's also changed on the other

side, where recruiters have all these

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tools to review your applications.

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And so there's kind of this AI arms

race where both sides are trying to pump

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up the volume, pump up the rejections.

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And I think that ultimately that's

left us in a place where both

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sides are a little frustrated.

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And hopefully what I can share with your

listeners today is how to get out of

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this arms race and actually connect with

real humans the way that we used to.

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When, when we talk about recruiters

using AI and the job seekers using

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AI, who's winning in that battle?

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Yeah.

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I'd say no one, to be honest.

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Like, so I talk to job

seekers all the time.

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Whether you're a brand-new grad and you

feel like all the jobs have evaporated or

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a mid-career changer and you feel like,

"What happened to the Great Resignation?

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What happened to all the opportunities?"

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They feel super depressed.

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But even the recruiters who you

might think have all the power say,

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"I've never hated my job as much as

I used to because all of a sudden

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I'm dealing with way more volume.

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So much of this stuff is fake

because it's AI generated.

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This is not why I got into recruiting."

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And so I really do feel like it's

left both sides more miserable.

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Would, would you say that AI has

made job seeking in the world worse?

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Absolutely.

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I mean, in the sense that, like,

I think the best way to think

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about it is for anyone who's been

using AI, at first glance, you're

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like, "Wow, this is magical."

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Let's say you're trying to sort of

build out your data analysis skills,

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and you're like, "Wow, I don't even

have to really understand the SQL or

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the Python as much anymore because

I can just have AI bang it out."

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But then you actually run the code,

and you're like, "Uh-oh, we got

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some bugs, we got some problems."

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I think the same thing is happening with

job searching, where it seemed like it

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was gonna be this panacea, but actually

it's turning more into a nightmare.

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That's super interesting

that you mention that.

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Um, do you think, like, that's

a total, uh, summary of AI in

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per- i- i- as like a totality?

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It's like it has great promises

to actually do interesting things,

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and it gets you, like, maybe, like,

66% of the way there, but then

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it, the last third is missing.

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And so you either manually have to go

fix the last third, and that takes you

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maybe just as much as time to, to do the

last third, um, or it just kinda sucks.

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Is that kinda what you've been seeing?

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Yeah.

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You know, there are all those

interesting studies from early, the

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early days of AI, where programmers

were reporting to the surveyors, "I'm

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saving at least 20 or 30 hours a week."

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And then they were actually measuring

the progress the programmers were

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making towards code completion, and

they were, like, 20 or 30 hours a

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week slower than they expected to be.

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And so there really is this disconnect

between the promise and the potential

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and the actual on-the-ground reality.

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Yeah.

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That's super interesting that, that

you mention that because I've been…

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One thing I…

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You know, I love AI.

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I'm super fascinated by AI.

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One thing I've been trying to do with

AI is for, for example, use AI to edit,

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you know, the YouTube videos I make.

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Uh, 'cause, like, theoretically, like,

if you Claude the transcript and the,

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the timestamps, and then it, there's

all these different libraries that you

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can use to, you know, render different

HTML and, uh, SVG images and stuff, and,

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like, you could theoretically, like,

edit your videos with, with Claude.

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And that's, like…

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I feel like that's, like, if you go

on, like, YouTube or you go on TikTok

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or you go on Instagram, especially,

like, in, like, the business AI world,

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it's like, it's all these promises.

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Like, these videos of, like,

"This is how I build…

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I applied to 500 jobs with ChatGPT,"

and you're like, "Wow, that's amazing.

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I wanna do that."

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And they show you how to do it, but then

no one really shows you the results.

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And so you're just- Yes … like,

"Oh, what an amazing promise.

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I wanna do that."

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And then you, like, spend all the time

to actually do it, and then there's,

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like, kinda lackluster results.

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So you're seeing that as well?

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Absolutely.

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'Cause here's the deal.

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I think the number one thing

that we all need to do in this AI

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moment is to step back and try to

realize what we're solving for.

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If our goal in using AI is just to

basically throw more sort of, um,

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darts at the board, but the board has

now moved, like, 1,000 feet away, just

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throwing an incremental extra dart is

not gonna actually increase our chances.

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But if we say, "Hey, the whole

point of getting a job is to

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connect with a human on the inside.

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There is a hiring manager out there

who needs my talent to succeed.

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I gotta find them, and I have to convey

how I can help them direct into that

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person," well, AI can help with that.

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AI can help you track that person

down, but it's not by generating

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1,000 random applications.

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It's by getting targeted on what

you're actually trying to solve for,

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which is to find this human in the

universe, understand their needs,

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and connect with them in a deep and

meaningful way, and that is so different

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than what you're seeing on TikTok.

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Okay.

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I wanna make sure I'm

understanding this correctly.

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Yes.

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So I, I think, I think- Uh, for those

of you who, who are listening and

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maybe haven't figured out how AI…

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Maybe you haven't even used AI

to do, uh, job applications yet.

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Um, I think, I think, Jeremy, what you're

kind of talking about when you're saying,

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like, sending hundreds of resumes, is

there's a bunch of, like, tools out there

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that will essentially mass apply for you.

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So, you know, if it took you, let's just

say it took you 10 minutes to apply to

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a job, now you can apply to, like, let's

just say 100 jobs in 10 minutes using AI.

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That's kind of what you're

talking about, right?

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Exactly.

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I mean, you don't even need

a fancy tool now, right?

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You could use Claude Cowork.

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You could use GPT Codex.

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Literally say, "Here's my resume.

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Go apply to 1,000 jobs.

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Don't stop till you're done."

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It'll do it for you.

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Wow.

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Okay.

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That's super interesting.

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So, um, but, but you, you bring up a good

point that it's like, what's actually

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the problem that we're trying to solve?

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And the thing that you said that

was interesting was we're trying

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to talk to a hiring manager.

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Um, and, and is that because

at the end of the day, hiring

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is still happening by humans?

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Yes.

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And there may be a day, I know

that today Stripe announced

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that the singularity is here.

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Um, and so maybe that day when AI does all

the hiring and the working is not far off.

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But as long as humans are still involved

in the hiring and the actual doing of

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the tasks, it comes back to human nature.

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Imagine that there is a boss, say,

at Google, hiring for a data analyst.

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That person is sticking their neck out and

hiring when everyone else is doing layoffs

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because they have a massive pain point.

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They have too much to get

done and not enough talent

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on their team to do it today.

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And so when they put out a job

description, they are really putting

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out a cry for help from inside the

Googleplex, saying, "Hey, I need awesome

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data analysts so I can get my job done,

so I can be successful in the world."

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But if you just blast them with 1,000

generic applications, that doesn't

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give them any signals of the fact

that you're committed, dedicated,

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invested, all the things that we would

be hungry for as the hiring manager.

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It just says, "Hey, you're wasting

my time, and now I'm on to the next."

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On the other hand, just to give

you the sort of comparison, imagine

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that you did a little research.

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You understood who this person

was, what their challenges were.

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And again, you can use AI for this.

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You could put into ChatGPT,

"Here's the job description.

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What are the three pain

points behind this hire?"

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And then you reached out and

said, "Hey, this is exactly

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how I can solve your problems.

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Here's how I've done in the past.

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Here's what I can do

for you in the future."

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That's speaking the human language

of why they're hiring in the first

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place, not just this AI game of let

me apply for as many jobs as possible.

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That's the ultimate end goal,

and I want your listeners to keep

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that north star in their minds.

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That's really interesting that you mention

that because sometimes it is really hard

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to remember what, what the end game is.

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I remember in college, um- I, I don't

know why this was the case, but, uh,

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when, in my undergrad at University of

Utah, it seemed like the, the STEM career

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fair always l- overlapped with midterms.

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It was like always like the same week.

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And, um I remember there was m- there

was me, and then there was this other

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girl in, in the program, and I talked

to her, and she was like, "Oh, I

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gotta study for this, this midterm.

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I can't go to the career fair."

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And f- me, I was like, "Well, I'm

only here 'cause I wanna get a job.

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Like, I don't really care about my grades.

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I just wanna get a job."

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Um, and you know, and, and I went

to the career fair, and she didn't.

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She got an A on the test.

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I literally got an F, um, but I got a job

from that career fair, and she didn't.

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Uh, and she struggled.

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And so I think it's so easy,

for some reason as humans, we

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like to think that, like, the

intermediate step is the end goal.

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But you're right, the end goal

is to actually land a job.

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The applying for the job is just so

we can get in front of a human's eyes.

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But, but it sounds to me like what

you're saying is it's not the only

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way to get in front of a human's eyes.

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So how else can we get in

front of a human's eyes?

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Yeah, absolutely.

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So I think at the end of the day, what

we should remember is that we are living

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at this incredibly opportunistic moment.

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Like, there was a time probably 20 or

30 years ago where it was still that

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old boys' club of like, okay, you don't

know that individual hiring manager

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inside Google, therefore you are not

sort of eligible for this opportunity.

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But now that we're living in this age

of LinkedIn plus AI, what's to stop you

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as a University of Utah student or a

community college student or someone who

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never even went to college at all from,

like, literally going on LinkedIn and

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say, "I don't know that hiring manager at

Google, but I know someone who knows him.

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Could I ask for an introduction?"

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And I think if you think about what

AI is doing to all these digital

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documents, whether it's your resume,

your cover letter, your LinkedIn

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profile, it's destroying the trust

and the differentiating power of

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those signals because everyone's

resume and cover letter and LinkedIn

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profile can look amazing right now.

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But if you have the trust of someone

who that person knows and has a

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relationship with, that is so much more

powerful, so much more differentiating

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than another mass-produced application

like everyone else out there.

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That's how you stand out even if

you're not part of the, quote-unquote,

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"old b- old boys' network."

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Okay, so it's about…

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sounds like it's about trust, um, gaining

these, these people's trust on the inside.

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Um, okay, so one of the examples

you gave was like, oh, maybe I don't

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know the hiring manager, but I know

someone who knows the hiring manager.

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What are some other ways that we can

gain these magical human beings' trusts?

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Yeah, absolutely.

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So I think a lot of it is through

relationships, and so one of the really

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fascinating things is if you had to

guess, Avery, what do you think was

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the power of a referral back in 2016,

10 years ago, to give you an edge

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compared to online job applicants?

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So imagine someone applies

to a job at Adobe online,

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someone else gets a referral.

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How much likelier is the

referred candidate to be hired?

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Uh, I mean, I would say a lot more likely.

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I would say, like- Five times more likely?

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Turns out it was 10X.

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Wow.

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1,000% boost, which is crazy for

like a coffee chat with someone.

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Yeah.

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Now that's 2016.

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If you fast-forward to today,

:

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advantage of a referred candidate

over an online applicant?

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Well, I would say that since it's

gotten so much easier to apply for

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jobs, you have a lot more applicants.

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So just by, just like by default,

I would say it has to be way more.

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So I, I mean, I, I would

probably say more than double.

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I would guess more than 20 times.

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Yeah, so it's 20X today.

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Yeah.

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Which just gives you a sense of like, wow.

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Like, remember what we're talking about.

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This is not a game.

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This is not a game show.

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This is your life.

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Mm.

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If you could get a 20X advantage

on an opportunity that changes the

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course of your career and maybe your

life, why wouldn't you go for that?

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So that goes back to your question,

which is how do you get that advantage?

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Yeah, maybe you know someone who

knows someone, or maybe you went to

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the same school, maybe you volunteer

with the same organizations.

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If you literally went on LinkedIn today

and said every Googler who volunteers

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with Habitat For Humanity, you would

find over 1,000 Googlers who spend

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their time on that kind of thing.

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That is reason for you to reach out and

say, "Hey, we've got this in common.

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Could I learn whether your time at

Google feels consistent with your values?

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I want to compare apples to apples."

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And I think the reality is we stop

having to put ourselves in this sort

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of easy button mentality of let me

just press a button in ChatGPT and

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get the job, and let me instead build

a real connection with a real person.

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Okay.

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I have, uh…

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I agree with you, and I have

two, two follow-up points.

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One- Yeah … um, you're 100%

right, like, with the alumni

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or with, like, a nonprofit.

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Like, when I get, you know, with having,

like, 150,000 LinkedIn followers, I, I

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get a decent amount of messages inbound,

and, uh, I try to respond to as many as

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I can, but obviously it's an overload.

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Uh, it's an overbearing amount

of, of work sometimes, so I

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don't reply to every single one.

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But if you went to my alma mater,

I'm very likely to respond.

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And it has nothing…

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I don't know you better.

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It's just like, oh, you and I lived at

the same place for a little while and

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we cheered for the same football team,

and I, for some reason, there's, like,

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school spirit in the United States,

and I wanna, I wanna help this person.

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Um, so that's awesome.

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I agree with that.

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And two, yeah, like, nonprofit's a

really good one, or interests like,

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um, like, like for instance, uh, you

know, I have my church on my LinkedIn.

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If you're from the same church

as me, I- Yep … I'm probably

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more likely to respond.

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Uh, just little human connections like

that can, can make a big difference.

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Um, I wanna go to, okay, so, like, we can

do this, but why aren't people doing it?

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You, you mentioned- Yeah … that, like,

it just feels easier to press a button.

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Is that the number one

primary reason why you think?

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Like, even though I know this action is

20 times more likely to land a result,

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I wanna still press this button even

though I know it's not the right choice?

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Yeah.

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So I don't know that people are thinking

about it quite that rationally, but

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I think when you're in this moment of

like, oh, I need a job, the single best

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sort of, like, quick dopamine hit is

that spin of the roulette wheel, right?

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Yeah.

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Apply.

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Apply.

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Apply.

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Hey, at least I'm making progress.

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At least I'm putting

stuff out into the world.

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Versus what I'm talking about

is a much slower game, right?

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You've gotta find the person, you've gotta

find the mutual connection, you've gotta

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ask for the info, you gotta have a chat.

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None of this is instant gratification.

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And one thing we've learned about our

species in:

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to want that quick dopamine hit

versus that long-term thinking.

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So even though every single person

knows that referrals matter, very

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few get them for that exact reason.

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Do, do you think people also are just

like, they just like shrug off the

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idea of networking 'cause they're

like, "Oh, I don't know the hiring

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manager at Google, I'm a nobody."

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Yeah.

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And so they're just like, "There's

no point in trying 'cause I…

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Who am I?

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I live in this little, you know, podunk

area and I only know podunk people, and

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I, you know, I come from this family,

we've never been to college, we don't…"

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Like, I don't know.

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Do you think people get like in

their heads about it that way?

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Yeah.

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Yeah.

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So I try not to psychoanalyze the

students that I work with, but I think

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that there's a lot of imposter syndrome.

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Yeah.

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A lot of sense of like, "I'm

not worthy of a referral."

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Mm.

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"Someone else should get that opportunity

because they deserve it more."

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And I think the reality is, and I

think this is what you teach, you know,

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through, um, Data Career Jumpstart,

is anyone out there could add

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tremendous value to an organization.

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If you've put in the time, if you've

built the skills, there is that hiring

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manager who has that cry for help, but

you have to believe in yourself first and

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foremost before they can believe in you.

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Mm.

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And I know that's easy for me to say,

harder to do, but I do think that

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is an internal barrier that a lot of

folks face Yeah, the imposter syndrome

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is, is really hard to get over.

351

:

That's, uh, something we all have to face.

352

:

I, I think…

353

:

A- and, and I was being a little

trivial earlier that, you know,

354

:

from being from, uh, you know, maybe

a middle-of-nowhere area, but…

355

:

And maybe you don't know

anyone, but I think there's

356

:

opportunities to know someone.

357

:

So for, just for example- Yes … um,

you know, once upon a time, I, I

358

:

post every day on LinkedIn, and this

guy literally commented on my posts,

359

:

like, every day for six months.

360

:

And I'm like, "Who the heck is this guy?"

361

:

And so finally I reached out to him

and I was like, "Hey, who are you?

362

:

W- why you keep commenting on my posts?"

363

:

And we got on a call, and I ended up

hiring this guy, and it's like this

364

:

guy is from, literally from Africa.

365

:

Like, he literally…

366

:

Him and I couldn't be further

apart almost in the entire world.

367

:

Um, but, you know, through the power

of LinkedIn, just from adding value

368

:

and networking, y- you know, that, that

really gave him a good opportunity to,

369

:

to work remotely in the United States.

370

:

But I think the problem is, is he

had to do that, what, 89 times in a

371

:

row without any benefit basically.

372

:

How, how do you overcome, like, when

networking feels pointless, when

373

:

you're like, "I'm planting all these

seeds and I'm getting no plants"?

374

:

Yeah, I love that story.

375

:

But again, that's the hardest case

scenario, where he had absolutely nothing

376

:

in common with you- That's it … maybe

except for shared interests, and so he

377

:

had to win you over one post at a time.

378

:

I wanna go back to what you

mentioned about, um, being a member

379

:

of the Utes, being a member of

LDS, whatever the connection is.

380

:

There's this concept in human

psychology called generativity.

381

:

You can look it up on Google.

382

:

And it basically says that we as a species

are hardwired to actually wanna pay it

383

:

forward to the next generation, both

because someone likely helped us back

384

:

when we were starting out, and because

there may be this evolutionary sort of

385

:

bias towards you wanna see people in

that same tribe, that same walk of life

386

:

also be successful And so I think the

change of mindset for folks is less of,

387

:

"Oh, I have to believe in myself 100%,"

but to understand that I'm actually

388

:

giving a gift to the other person.

389

:

You might feel like, "Oh,

I'm wasting their time.

390

:

They're super senior,

they're super successful.

391

:

What could I possibly offer them?"

392

:

But that sense of being generative, that

as an old guy, I get a chance to feel

393

:

like I'm leaving value in the world, I'm

creating a legacy in the world, that is

394

:

massively powerful for me, and if you

give me that gift as a younger person,

395

:

I'm gonna wanna go to bat for you as well.

396

:

So don't forget that you're giving

that gift as well as receiving it.

397

:

Important perspective.

398

:

Yeah.

399

:

Once again, just trying to go through and,

and, and see if I felt this in my career.

400

:

When I, when I worked for ExxonMobil,

I was one of three, uh, alumni from

401

:

the University of Utah that worked

for- Wow … a 70,000-person company.

402

:

Uh, maybe it was, like, five actually.

403

:

Um, but one in three on

a 10,000-person campus.

404

:

And yeah, I would've

done, I would've gone…

405

:

I, I really wanted more Utes and

more Utah people at ExxonMobil.

406

:

Yes.

407

:

I would've totally, uh, tried

to help people that, you know,

408

:

that, that needed the help.

409

:

So that, that, that rings true.

410

:

Um, also, th- we should also point

out that sometimes, not always, but

411

:

sometimes, there's also financial

incentives for these people within

412

:

the company that if they refer someone

who gets hired, they get, like, a

413

:

bonus of $500 or $1,000 or whatever.

414

:

Um, so maybe- Yeah.

415

:

Let me, let me just stop

you right there, Avery.

416

:

Yeah.

417

:

Do you know what the bonus, the top

bonus was at Google when I worked there?

418

:

No.

419

:

If you referred someone with,

like, very specialized skills?

420

:

No.

421

:

$25,000 Oh my gosh.

422

:

And then you think about it

from the employer's standpoint.

423

:

Data shows that referred candidates

are likelier to accept the job offer

424

:

because they've got that friend

on the inside, likelier to stay

425

:

longer, and then actually likelier

to outperform non-referred candidates

426

:

because they've been hand-selected.

427

:

The reality is it's a bargain

for an employer to pay that bonus

428

:

to automatically have access to

the best talent in the world.

429

:

So again, lots of gifts going on here.

430

:

It's not just purely all one,

you know, um, zero-sum thinking.

431

:

That's, that's actually interesting

that you bring that up because, um, I

432

:

run a job board where I post data jobs

and, um, I had, uh, an ex-colleague

433

:

reach out to me who's at a different

company now, and she said, "Hey, my

434

:

company's hiring someone in data.

435

:

Do you have any recommendations?"

436

:

And so I kind of asked the people that

I kind of know that are, that are in

437

:

the market, uh, for, for a job, and

this was a more senior role, so not

438

:

necessarily like a, like a, a good fit

for a lot of my boot camp students.

439

:

Um, it was like a senior data

scientist, machine learning role.

440

:

Um, and so I asked kind of some of my, my

friends that I know are in the job market.

441

:

No one was super interested.

442

:

So I replied, "Hey, sorry,

I don't know anyone.

443

:

But if you'd like, I'll post it on my job

board for free, and we- we can promote it

444

:

for free just 'cause I wanna kind of get

that, that side of the job board going."

445

:

And she said, "No, thank you.

446

:

We're only looking for referred,

re- re- referred candidates."

447

:

Oh.

448

:

And I was like, wow.

449

:

They're-- They don't even

want the application mess.

450

:

They only want people who were referred,

and I think it's for the reasons you

451

:

mentioned earlier, that a referred

candidate is more likely to stay, an

452

:

easier hire, like, better performer.

453

:

It just works out better for them, so it's

really interesting that you mention that.

454

:

Is, is that true?

455

:

Have you seen that, like, at other

companies as well, other than Google?

456

:

Yeah.

457

:

It's so funny.

458

:

I hear that all the time actually.

459

:

I'm always off- offering to post

stuff 'cause I have, you know,

460

:

all this LinkedIn stuff going on.

461

:

People say, "No, that's more work for me."

462

:

"That's more strangers in my queue.

463

:

I want less work and more trust,

not the other way around."

464

:

Okay.

465

:

Uh, referred candidates, guys.

466

:

It's important.

467

:

Try to get referred.

468

:

Yes.

469

:

So what can people kind

of do to get referred?

470

:

So we mentioned, you know,

we're trying to connect with,

471

:

with hiring managers if we can.

472

:

If we can't, maybe connections

of hiring managers.

473

:

And we're sending them LinkedIn messages?

474

:

What type of…

475

:

Like, how are we contacting

them, would you say?

476

:

Yeah.

477

:

Absolutely.

478

:

So first of all, if you find someone

inside an organization, obviously the

479

:

closer they are to the hiring team,

the more leverage they're gonna have.

480

:

But if you…

481

:

You know, when I worked at Google, I

would refer people for jobs in India, for

482

:

legal team jobs when I was a marketer.

483

:

And so anyone can refer anyone.

484

:

That's the first thing to know.

485

:

Just gotta find someone on the inside.

486

:

One ute inside ExxonMobil.

487

:

Then when you wanna get in touch with that

person in the first place, don't send them

488

:

a cold outreach on LinkedIn, 'cause most

people are not that active on the site.

489

:

Um, basically it could sit there in their

LinkedIn inbox forever Don't send them a

490

:

cold email because everyone's inboxes are

exploding and they don't know your name.

491

:

Instead, wherever possible, find that

mutual connection on LinkedIn, that

492

:

warm intro opportunity where, say, Avery

introduces you to Jeremy, and now you're

493

:

coming with that nice halo effect.

494

:

Okay, so that's the dream scenario.

495

:

You've been introduced to an insider.

496

:

You've got something in common.

497

:

Now what's the game plan?

498

:

I'm gonna pitch it to you, Avery.

499

:

You have two choices.

500

:

All right.

501

:

This is like choose your

own adventure style.

502

:

I'm ready.

503

:

Do you, do you give this insider

an elevator pitch telling them

504

:

how awesome you are, or do you ask

them questions about themselves?

505

:

Oh, yeah.

506

:

That's a good question.

507

:

Okay.

508

:

I, I would probably say, you know,

everyone likes to talk about themselves,

509

:

so that's probably the better path.

510

:

I'd be tempted to talk about

myself 'cause I love talking

511

:

about myself, but, uh- Oh, me too.

512

:

And everyone does, right?

513

:

Yeah.

514

:

Sure.

515

:

But the reality is, even though

business school-- Like, when I was an

516

:

MBA student, they were like, "Gotta

have your elevator pitch ready," you

517

:

know, this mythical elevator ride

when you're gonna sell yourself.

518

:

The reality is no one wants to be sold to.

519

:

No one wants to get a, you know,

call during dinner offering

520

:

them a new cell phone plan.

521

:

Don't be that guy.

522

:

Instead, bring it back to the human stuff.

523

:

Like, "Hey, what…

524

:

How did you go from, um,

University of Utah to ExxonMobil?

525

:

What surprised you along the way?

526

:

What do you wish you had known

when you were starting over?"

527

:

That's catnip to any alum out there.

528

:

And then make sure that when you

close that call, you don't just

529

:

say, "Hey, have a great life.

530

:

Thanks for, thanks for the chat."

531

:

You keep the conversation going.

532

:

So I'm gonna share my, my golden

question with all of your listeners.

533

:

I want you to have the confidence to

say, "Hey, Avery, this has been awesome.

534

:

If I could ask you one last question, if

you were back at the University of Utah

535

:

today trying to break into ExxonMobil

all over again, knowing what you know,

536

:

what would you be doing- Mm … to get

the best shot, um, at this organization?"

537

:

And why do you think that works so well,

Avery, based on everything we discussed?

538

:

Uh, I mean, people, people love to

be-- Like, people love to talk about

539

:

themselves, and then also people love

to, to give advice, um, and say, you

540

:

know, help the younger generation.

541

:

And I think, I think a lot of the

times when you, when you ask for

542

:

advice, you actually kind of end up

getting a referral, 'cause they're

543

:

like, "Oh, this is what I would do,

and maybe I'll help you along the way."

544

:

Yes.

545

:

I cannot tell you the number of times

just by throwing that out there,

546

:

they said, "Look, I got referred.

547

:

My whole team got referred.

548

:

You gotta get a referral.

549

:

Oh, by the way, we're hiring right now."

550

:

Yeah.

551

:

"I get a five thousand

dollar referral bonus.

552

:

Send me your resume."

553

:

Yeah.

554

:

So bottom line, like, I know it's

almost like Inception style, but,

555

:

like, give them a chance to lead and

play that mentor role versus, like,

556

:

transactionally, like, "Give me a referral

just because I went to your alma mater."

557

:

Yeah.

558

:

We had a, another recruiter on the

channel, uh, one time kinda talking

559

:

about networking, and one thing they said

is if you ask for, uh, a referral, you

560

:

end up with advice, and if you end up

for advice, you end up with a referral.

561

:

Um, so it's good to hear it coming

from, you know, some-someone like you.

562

:

Okay, so we can do these, like, we can

get these, like, warm introductions

563

:

from mutual connections and then, you

know, ask for-- be interested in them

564

:

and then ask for advice ultimately,

and that can lead to, to good things.

565

:

Is there anything else that we can

be doing to, to fight this good fight

566

:

against, you know, the applicant tracking

system and the AI slop and the AI volume

567

:

that's basically making it impossible

for us, you know, to actually have our

568

:

resumes be seen by a hiring manager?

569

:

Yeah.

570

:

Couple things.

571

:

So first of all, know that there

are two kinds of referrals.

572

:

The sort of basic one that everyone's

familiar with is someone goes inside

573

:

an applicant tracking system, this

is the big sort of like applicant

574

:

database that every company has,

and they basically check a box.

575

:

So you say, "Avery, um, I recommend you

for this data analyst role at ExxonMobil."

576

:

Um, and that's nice.

577

:

You know, the recruiter will see

it, the hiring manager could see it,

578

:

but it's still just one little box.

579

:

The ideal, and this is where it's helpful

to sort of steer that alum or that insider

580

:

a little bit, is don't just check a box.

581

:

Like, go directly to the recruiter.

582

:

Go directly to the hiring manager,

ideally if you know them well,

583

:

and, like, make that case, because

that is way more persuasive than,

584

:

oh, there's a little extra bonus

point attached to this profile.

585

:

So that's the first thing to know, is

there are two flavors of referrals.

586

:

One is very robotic, one is more human.

587

:

The latter's more powerful.

588

:

And then number two, don't be

afraid, as we talked about, to go

589

:

directly to the hiring manager.

590

:

Like, if you think about what the

recruiter's job is, their job is not

591

:

to find the best talent in the world.

592

:

Their job is to put a warm body in

that seat as fast as possible so

593

:

they can move on to the 39 other

roles that they have to fill.

594

:

Versus the hiring manager is the one

person in the organization who actually

595

:

has the incentive to find the best

person- Mm … because it's their butt

596

:

on the line, their career that's gonna

be yoked to yours for the rest of time.

597

:

So that's why if you can actually build

a connection with them versus just the

598

:

recruiter, there's a lot more alignment

between what you want and what they want.

599

:

This, this is so interesting that you

bring, you bring this up because, um

600

:

You know, once again, just going back

to job boards for, for- Yeah … Uh,

601

:

and maybe, maybe this is the sign

that it's time to change job boards.

602

:

Because, you know, for as long as I've

been an adult, job boards have been

603

:

what they are, a list, a directory

of opening job positions that us,

604

:

the applicants, can apply for.

605

:

It goes to someone for review,

they interview us, we get hired.

606

:

But it's…

607

:

And, and people spend, you know, hours

scrolling through these job boards and

608

:

looking and applying for all these jobs.

609

:

But what we're saying, what you're

saying, if I'm understanding correctly,

610

:

is that's not super effective.

611

:

The more effective thing is

to do this referral thing.

612

:

So, like, why, why don't you

think that there's more, like,

613

:

software oriented towards this?

614

:

Like, why aren't there, like, job

boards that only show the, like,

615

:

hiring managers and recruit- Like,

why is the process it is what it is?

616

:

All right, this is Avery

coming to you from the future.

617

:

I was ending this video and, uh,

I asked this question because I've

618

:

been thinking about this previously.

619

:

Like, why isn't there more, like,

directories of just recruiters and

620

:

hiring managers of people who want

to hire data analysts, who are, like,

621

:

literally looking for someone right now?

622

:

And so I actually built

it on finddatajob.com,

623

:

and I wanted to talk to Jeremy

about that, but I got distracted.

624

:

I never had the chance to tell

Jeremy that I actually built it.

625

:

Um, and so I want to tell you now.

626

:

That's why Avery from the future

is here interrupting this episode

627

:

to tell you that it's built.

628

:

So let me show you.

629

:

So, um, if you go to finddatajob.com,

630

:

at the top up here, you'll basically

have something that says recruiters.

631

:

If you're listening audio only,

don't worry, you should be able

632

:

to do this on your own on your

computer or, or later, whatever.

633

:

So you click on recruiters, and

then bam, right here is a list of

634

:

55 recruiters and hiring managers

that are literally hiring right now.

635

:

And so you can scroll through these,

you know, get their name, their

636

:

title, their company, uh, where

they got their degree, and then of

637

:

course their, uh, LinkedIn profile.

638

:

Um, and there it is,

that purple hiring sign.

639

:

So you can literally reach out to

these people, you know, send them

640

:

a cold message, send them an email,

um, and you know, give them your

641

:

elevator pitch, or maybe better

not give them the elevator pitch.

642

:

Give them…

643

:

Ask for advice, right?

644

:

'Cause we're, we're learning that when

we ask for advice or we connect with

645

:

them emotionally and humanly first, we

actually get more results in the end.

646

:

And, uh, this is actually part of

Premium Data Jobs, um, which is the

647

:

premium version of finddatajob.com.

648

:

So on, on Premium Data Jobs we've always

had these premium jobs right here.

649

:

We have 121 right now posted.

650

:

And these jobs, for instance,

like let's just do this entry

651

:

level data analyst role at Aflac.

652

:

This was posted a day ago and, uh,

has a pretty l- uh, friendly score.

653

:

You got three out of 10, uh, on

our, uh, guess on how senior it is.

654

:

Um, it does require a decent

amount of skills here.

655

:

But anyways, if you click on contact

hiring team, that's literally gonna take

656

:

you to a LinkedIn post where, you know,

some sort of person said they were hiring.

657

:

In this case, uh, looks like this

post got pretty popular, 231.

658

:

Um, but typically we try to find jobs

that don't have very many comments

659

:

on them, so you can leave like a

really mean- meaningful comment.

660

:

And I mean, like, you can definitely

outdo a lot of these comments here

661

:

and send a little bit better of a cold

message, um, than these people have.

662

:

And you can also, of course, you know,

click on this person's profile and send

663

:

them a cold message, uh, here on LinkedIn.

664

:

So if you're kind of enjoying this

type of style of job hunting, um, then

665

:

you need to check out findadatjob.com

666

:

because we're doing good things.

667

:

All right, back to the episode.

668

:

Jeremy, you know, why aren't

there more recruiter directories

669

:

like the one on findadatjob.com?

670

:

Yeah.

671

:

I think the fundamental, uh,

problem is Economics 101, right?

672

:

Think about what we know about

how marketplaces work, where as

673

:

soon as someone increases the

price, it drives down demand.

674

:

As someone is-- As soon as someone

increases the supply, it drives down

675

:

the price, et cetera, et cetera.

676

:

There's a equal and opposite reaction.

677

:

Well, from the minute that monster.com

678

:

turned on in 1995 or whenever it launched

and the first online job board was

679

:

born, all of a sudden the amount of

applicants just completely exploded.

680

:

And so then it went from

let's have monster.com

681

:

to linkedin.com.

682

:

Maybe recruiters can go

out there and find people.

683

:

Well, now LinkedIn has one

point three billion profiles.

684

:

And so as a result, every signal kind

of gets ruined or maxed out by that

685

:

perfect competition over time, which

is why I think we're coming back

686

:

to some of these natural signals.

687

:

When you are reaching out to me as

the hiring manager or reaching out to

688

:

me as a referrer, it's not just about

the actual conversation, it's about

689

:

what that conversation says about you.

690

:

Mm.

691

:

Out of 1,000 people who wanted

this job, you were the only

692

:

one who had the cleverness, the

guts, the drive to actually reach

693

:

out and do something different.

694

:

If I want the one data analyst who's

gonna think differently about this role

695

:

versus being a cog in the machine, you've

just walked the talk when everyone else

696

:

has only talked it Such a good point.

697

:

Um, we just, uh, posted a role that

we're hiring, uh, a salesperson to

698

:

do some of our sales calls for our

boot camp, and we wanted to hire

699

:

internally from our boot camp, so I

posted it in our boot camp community.

700

:

I think we had 10 people interested.

701

:

Um, and I said, you know, "Comment 'me'

if you're interested on this page."

702

:

So 10 people commented me.

703

:

You know, out of those 10 people,

great, I like all 10 of them.

704

:

They're good candidates.

705

:

One of them sent me an email,

"Hey, I know, like, you haven't

706

:

told me how to apply yet.

707

:

You haven't given me any other

instructions, but I just wanna t- I

708

:

already wanna tell you about myself

and why I'm a good fit for this role."

709

:

And if, if nothing else, like, h-

his name's at the top of my mind now

710

:

'cause it's like he's the only one

that stood out out of those 10 names.

711

:

Um, and literally, in this case, you know,

maybe they were waiting for instructions,

712

:

but all of them had my email.

713

:

All of them had the chance to do it.

714

:

Um, but yeah.

715

:

It's just like, that's just…

716

:

You're right.

717

:

That doesn't even matter what he

said, it's just the fact that he

718

:

took initiative and said something.

719

:

That actually says maybe more

than any message could say.

720

:

Yeah.

721

:

I think that's the important thing

here is like I know when people think

722

:

about this stuff at a theoretical

level, they get exhausted, right?

723

:

No one likes job searching.

724

:

It's ego destructive.

725

:

It wears you down in all

these different ways.

726

:

And yet if you sort of like

pull out all the feelings, all

727

:

the emotion from it, we're not

talking about massive investments

728

:

of time or energy or creativity.

729

:

Just the little spark compared

to the average person is

730

:

enough to put you out there.

731

:

So hopefully, we can lower the temperature

on that and say, "Hey, how do you step

732

:

one step further than everyone else?"

733

:

And man, Jeremy, you're firing me

up here 'cause now, now I'm excited.

734

:

Now I'm, now I'm like- … how

do we tell people this?

735

:

How do we, how do we make it more fun?

736

:

Because I think that's what it is.

737

:

I think it's like going back to the

dopamine earlier, like if I have

738

:

Claude CoWork go out there and apply

for 100 jobs for me, that feels good.

739

:

That feels like I did something today.

740

:

I did something cool.

741

:

I did something neat.

742

:

And if I went out there and I sent,

you know, let, let's even say 10

743

:

cold messages, you know, to…

744

:

I mean, we'll call them warm messages,

'cause maybe we're messaging people

745

:

we're already connected with.

746

:

Let's just say none of them reply.

747

:

Like, that doesn't feel good.

748

:

But, but the…

749

:

And I, let's say I spent the same

amount of time on each- The networking

750

:

one, even though it doesn't feel

good, is actually valued more,

751

:

but it, but it doesn't feel good.

752

:

So how do we convince people to do that?

753

:

I don't know.

754

:

Yeah.

755

:

I mean, how do we change

our species, right?

756

:

Like, how do we go from a species that's

like, "We're gonna live today and burn

757

:

down this planet if we have to," to like,

"We're gonna build a sustainable society

758

:

that lasts forever because we're thinking,

like, 10 generations into the future"?

759

:

And I don't know the answer to that,

but I do know that here's another

760

:

psychological concept I really like.

761

:

It's this idea of locus of control.

762

:

Have you heard of it?

763

:

Uh, I've heard of it, but

I don't know what it is.

764

:

Okay.

765

:

So basically, external locus of

control is how most people operate.

766

:

You look around at the headlines, 'cause

we're so saturated them, in them today,

767

:

and you're like, "The job market sucks.

768

:

AI's taking all the jobs.

769

:

You know, geopolitics is crazy," whatever,

and you're like, "Oh, I'm screwed."

770

:

Especially if you're Gen

Z, you're doubly screwed.

771

:

And you're like, "Okay, now I'm stuck.

772

:

I can't do anything about that."

773

:

But then there's a handful of people when

times get tough, whether it's the Great

774

:

Depression or the Great Recession or

even this moment, who are like, "I can't

775

:

control any of that stuff that's swirling

around, but I can send that email today.

776

:

I can reach out to that person today."

777

:

And if you can just sort of focus on,

here is the one little thing that I

778

:

can control in my life to take control

of my destiny in a way that no one

779

:

else is doing, then I think that

you focused on the most realizable,

780

:

most magical part of our ability as

humans, which is we do have free will.

781

:

We do have the ability to make

these choices, and that's the choice

782

:

that I want your listeners to take.

783

:

Yeah.

784

:

That's, that's a, that's a hard

thing where it's like, okay, we

785

:

can't control the results, but I

can control the actions I take.

786

:

And so instead of measuring, you

know, the results, my new result is

787

:

just going to be how many efforts,

how many actions I put in today.

788

:

Uh, and I just trust that the results

will take care of themself if I stay

789

:

consistent in taking these actions.

790

:

And I, I guess I would just tack on that

if you don't trust, you know, the, the

791

:

stat that Jerry brought up earlier about,

you know, you're 20 times more likely to

792

:

land a job- Yeah, yeah … when referred.

793

:

Let's say you don't trust it.

794

:

Let's say you don't trust me.

795

:

Maybe you can just, like, instead

of just applying to 100 jobs, maybe

796

:

you just apply to 80, and then

you send two, you know, messages.

797

:

Like, maybe it's not like-

Yes … you just send 10 messages

798

:

and you don't apply to any jobs.

799

:

Like, maybe you can

gradually work your way in.

800

:

Does that, does that kind

of sound fair to you?

801

:

I love that.

802

:

It's kind of like a

balanced portfolio, right?

803

:

Don't put all your eggs in one basket.

804

:

Spread them around.

805

:

Okay.

806

:

So we've talked about, like, how AI

basically is flooding the applicant

807

:

tracking systems, how everyone has the

same looking, the same sounding resumes

808

:

'cause they're all using ChatGPT that's

trained on all the same data to write

809

:

the same bullet points, and we're

not making fun of hiring managers.

810

:

So we can kind of like network

our way in essentially and try

811

:

to get these, these referrals.

812

:

Um, is there anything

that, like, AI is good for?

813

:

Like, is AI useless in the job search?

814

:

Yes.

815

:

Well, let's just start

at the very beginning.

816

:

I find that a lot of folks, and I

understand it if you're unemployed

817

:

right now, and you're like, "I need a

job yesterday," you don't have time to

818

:

fool around with career exploration and

finding your path and all that good stuff.

819

:

But even if you're time-pressed, I think

everyone now using AI could do a very

820

:

simple exercise to make sure they're

actually aiming in the right direction.

821

:

Like, how frustrating would it be to spend

all this time applying and networking

822

:

just to land a job that you hate?

823

:

Mm.

824

:

And if you want to prevent

that, this is where AI comes in.

825

:

You can go to AI and lim- simply say,

"Hey, I want you to take everything

826

:

that you know about me in terms of my

strengths, everything that you know

827

:

about me in terms of my passions, and

I want you to figure out the 10 job

828

:

titles that are a perfect fit for who

I am and what the world needs from me."

829

:

This is a Japanese concept called ikigai.

830

:

And because AI's trained on every job

that's ever existed, not just consulting,

831

:

not just data analytics, it can say,

"Hey, you might think that you should

832

:

be a data scientist, but actually,

maybe you should be doing BI in the

833

:

healthcare space because that would be

a way better fit for all these reasons."

834

:

And I think if people spent even 10

minutes at that initial step before they

835

:

started applying willy-nilly, not only are

they gonna get better results in terms of

836

:

the applications 'cause they are a better

fit, but they're gonna be way happier

837

:

down the road, which is the whole point of

applying for the job in the first place.

838

:

Mm.

839

:

Okay.

840

:

So AI can help us, you know, try

to figure out what ac- what job

841

:

we actually are interested in.

842

:

Um- Mm-hmm … what about,

like, what about, like, cover

843

:

letters or- Uh, resume bullets.

844

:

What do you think about those?

845

:

Yeah, for sure.

846

:

So just to be clear, I'm not

saying throw the baby out with the

847

:

bathwater, but the reality is, is

that AI is sort of this thin red line

848

:

that you have to be careful about.

849

:

So if you went to ChatGPT or Claude

or Perplexity or whatever and said,

850

:

"Here's the job description that

I want, here's my current resume.

851

:

Which important keywords am I missing?"

852

:

It'll be really good

at that level analysis.

853

:

That's essentially what an ATS does.

854

:

However, where people cross that red line

is they say, "Great, now give me credit

855

:

for all of those skills in my experience

bullet points so I can apply immediately."

856

:

And then someday they're sitting

in front of their future boss, and

857

:

the boss says, "Hey, tell me about

this amazing thing that you did."

858

:

And the only problem is they never did

it- Mm … because AI hallucinated it.

859

:

So again, I think it's totally fine to

use AI for the research and the analysis,

860

:

but if AI is telling our story without

our input, now we've got a big problem.

861

:

Mm.

862

:

Okay.

863

:

Um, and what about, like,

in the education space?

864

:

'Cause y- you worked in the

education space with AI.

865

:

Do you feel like AI makes a good tutor?

866

:

When, when does it do good things,

and when does it do bad things?

867

:

Yeah, and again, it's one of these

things where we have to kind of like

868

:

bind ourselves to the mast, Odysseus

style, uh, to quote, um, someone who's

869

:

in the theaters these days, where

basically we say, if we're gonna use

870

:

AI to learn or to develop skills, we

gotta give it very clear instructions.

871

:

We gotta say, "Hey, don't

just give me the answer.

872

:

Don't just rush to the output.

873

:

Help me walk through the process."

874

:

And a lot of the tools have

these study modes built in.

875

:

Gemini's got it.

876

:

Uh, GPT's got it.

877

:

I think Claude may have it now, where

basically we'll never give you the answer.

878

:

It'll just keep coming back to you

with questions, Socratic style.

879

:

But again, I think we have to resist that

temptation of the easy button, the instant

880

:

gratification- Mm … if we really wanna

have a true tutorial-type experience.

881

:

I, I mean, it goes back to what we

were talking about earlier in the

882

:

episode, where it's like AI's decent

at like 66% of what task you give it.

883

:

Um, and also depending on the prompt

and the data you give it as well,

884

:

like those things can increase.

885

:

Yeah, it's, it's really hard.

886

:

I'm in a weird place with AI where

it's like, oh, no matter what I

887

:

have you do, you do an okay job.

888

:

And, and oftentimes that's like

a great starting place where it's

889

:

like, okay, I'll take over manually,

and I didn't have to do like the

890

:

first half of this project, great.

891

:

Um, and other times it's like, man,

I spent so much time writing this

892

:

prompt and giving all this data,

and you just kinda sucked at this,

893

:

that I've spent all this time.

894

:

You know, I'm not getting any

results from what you're telling me.

895

:

It's like I sh- should've probably

just done this on my own or

896

:

with a human, uh, a human there.

897

:

And I, I guess that's kind of what

you're saying is like- Yeah … AI can

898

:

be useful, but also like keep humans

in the loop and just know it can

899

:

make mistakes, it can l- hallucinate.

900

:

So take a hybrid approach where it's like

helping you along the path, but maybe

901

:

not, you're not riding it as the vehicle.

902

:

Yes.

903

:

I'm gonna give you a little analogy here.

904

:

Um, I think that in some ways AI reminds

me a little bit of a British accent.

905

:

I don't know if you're like me, A-

Avery, as a typical American, but

906

:

when I hear someone t- speak to me in

a posh British accent, I immediately

907

:

give them like 20 extra IQ points.

908

:

Yeah.

909

:

Wow, they're so brilliant.

910

:

Yes.

911

:

And in fact, when you stop and actually

like think about what they just said,

912

:

you're like, "No, that's totally

dumb," but it just sounded so good.

913

:

I think AI's the same, right?

914

:

Because it writes in correct sentences,

because it can be opinionated

915

:

sometimes, you're like, "I'm not

talking to an intern, I'm talking

916

:

to, you know, a boss, a CEO here."

917

:

But it makes those intern-level

mistakes even with that posh accent.

918

:

Love that analogy.

919

:

Stealing that analogy.

920

:

100% agree.

921

:

I … Anyone who has a British accent,

I trust them- … 20% more, like

922

:

20% smarter, like- It's some weird,

like, colonial holdover, right?

923

:

We're like totally in thrall.

924

:

That, that's so funny.

925

:

And it's funny 'cause I lived, I lived

in Europe for, for over two years.

926

:

Ah.

927

:

And, uh, they love American accents

'cause they learn British English in

928

:

school, and they're always like, "Oh,

you sound like you're from the movies."

929

:

So it's just so funny.

930

:

They like American accents, we

like British accents, I guess.

931

:

Um- Grass is greener.

932

:

Yes.

933

:

Okay.

934

:

That's amazing.

935

:

Um, okay, Jeremy, riddle me this.

936

:

You know, we talked about networking,

but outside of networking, if you

937

:

were to talk to a job seeker right

now in this AI-flooded world with

938

:

all these different applicants, what

is the one piece of advice you'd

939

:

give them on how they could land a

job quicker than they are right now?

940

:

Yeah, absolutely.

941

:

I think the number one thing

is that you have to be focused.

942

:

Um, again, I know that there's this

mythical math equation that's running

943

:

in the back of our minds where we

say, "Yeah, we know that our chance

944

:

of landing any job online is like .01%

945

:

these days, but if we just apply to 1,000

jobs, we'll get that one job, right?"

946

:

I think what we don't appreciate

is that if we're just applying

947

:

for random jobs in a completely

mechanical way, it's not really .01%,

948

:

it's 0% period.

949

:

Because again, especially in a labor

market as tough as this one, companies

950

:

are always gonna have their pick

of really qualified driven people.

951

:

And so you're fooling yourself if

you believe, "I just need to have

952

:

enough lottery ticks and I'll--

tickets and I'll win the lottery."

953

:

And so my number one rule is, hey, play

fewer games, get into fewer of these

954

:

application matches, but have a winning

rate of 1% or even 10%, and now the

955

:

quality piece is gonna carry you so much

further than the quantity piece ever did.

956

:

And again, I know that's hard

'cause it takes time, it takes

957

:

a little self-reflection, but

that's the overarching rule.

958

:

It's fundamental mathematics

at the end of the day.

959

:

And so even if you hate relationships,

if you're an introvert the, like

960

:

way that I am, think about it like,

"Hey, I just need to give myself

961

:

reasonable odds and this will work out

quicker than applying at a 0% rate."

962

:

I, I love it.

963

:

It's, don't buy a, a bajillion

lottery tickets, buy the most likely

964

:

to be winning lottery tickets.

965

:

Yes.

966

:

Uh, I love that, Jeremy.

967

:

If you guys enjoyed this episode with

Jeremy, we'll have his LinkedIn and his,

968

:

uh, website in the show notes down below.

969

:

Jeremy is an awesome follow on LinkedIn.

970

:

He has his own podcast, and he's

written, what, three, five books?

971

:

How many books have you written

about landing a data job?

972

:

Or not landing a data job, landing

a job in, in today's market.

973

:

Yeah, too many.

974

:

But don't worry about

reading all the books.

975

:

Just get out there, start

connecting with folks.

976

:

That's what I want for

anyone listening right now.

977

:

Hey, that's perfect, Jeremy.

978

:

So if you guys wanna connect with Jeremy,

look at the show notes down below.

979

:

Jeremy, thanks so much for coming

on the Data Career Podcast.

980

:

Thanks for all you're doing, Avery.

981

:

Good luck to everyone.

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About the Podcast

Data Career Podcast: Helping You Land a Data Analyst Job FAST
The Data Career Podcast: helping you break into data analytics, build your data career, and develop a personal brand

About your host

Profile picture for Avery Smith

Avery Smith

Avery Smith is the host of The Data Career Podcast & founder of Data Career Jumpstart, an online platform dedicated to helping individuals transition into and advance within the data analytics field. After studying chemical engineering in college, Avery pivoted his career into data, and later earned a Masters in Data Analytics from Georgia Tech. He’s worked as a data analyst, data engineer, and data scientist for companies like Vaporsens, ExxonMobil, Harley Davidson, MIT, and the Utah Jazz. Avery lives in the mountains of Utah where he enjoys running, skiing, & hiking with his wife, dog, and new born baby.