25.87% of Black Applicants Hit an AI Hiring Wall That Isn't Illegal, Yet

"The silence is the tell." That's how this episode opens because if you've sent out dozens of applications and heard almost nothing back, the instinct is to assume something's wrong with you. It isn't. Roughly a quarter of everyone currently unemployed has been searching for 27 weeks or longer, and the average search now runs about six and a half months. Carlo and Ainsley dig into why: most applications today are screened by automated systems before a human ever sees them, and those systems were trained on years of historical hiring data which means they can quietly reproduce old bias at a scale no individual recruiter ever could. Amazon found this out the hard way with its own internal recruiting tool, which it scrapped in 2018 after discovering it was penalizing resumes that simply contained the word "women's." And the pattern goes further: one landmark independent study found that a meaningful share of Black applicants' submissions was consistently filtered out by the same systems across completely different companies — what researchers came to call "algorithmic blackball."

So, what do you actually do with that? This episode is built around two
practical moves. First, a reality checks most job seekers skip: a real chunk of live job postings may not be genuinely open at all — "ghost jobs" posted for pipeline-building or already spoken for internally — and there's a three-check test (posting age, division layoffs, visible new hires) that takes about ten minutes. Second, the human bypass: weak-tie networking, the kind of loosely connected relationships that get you in front of a person before a system decides you don't belong in the room. Carlo shares his own early-career habit of showing up at conferences outside his industry — and Ainsley connects it directly to decades of research on why acquaintances, not close contacts, are how most people actually find their next role.

The episode closes with the Next-Door Challenge: a four-step, ten-minute-a-day plan for anyone in a long search, checking whether your target roles are real, running your resume through a free ATS scanner, reaching out to three people at target companies, and confirming whether your target category is actually growing. Because getting through the door is only half the job; showing up ready when it opens is the other half.

Episode Resource: https://drive.google.com/file/d/1gyvmm3mgZvIyJL3B4B9aVvRxWOB7M3nn/view?usp=sharing

Chapters:
00:00 Intro — "The silence is the tell"
03:15 Welcome, and the friends who've been searching for a year
04:16 Amazon's discarded recruiting tool
07:57 Proxy variables — how bias hides in plain sight
11:01 The algorithmic blackball stat, and the case for pivoting industries
16:25 Weak ties, Granovetter, and the blind-audition study
19:02 A conference habit that built a cross-industry network
22:17 Naming who this episode is actually for
23:33 The undercounted — who the unemployment number misses
25:57 Setting up the ghost job problem
27:07 Ghost jobs — the three-check reality test
30:41 Where the pivot starts, and the gig-economy question
32:46 Referrals, runway, and the EU vs. US legal gap
35:55 Reactivating a cold network
37:40 The loop AI hiring creates, and the Next Door Challenge
41:38 Carlo's closing story
44:56 Wrap-up, and next Monday
45:31 Bonus: mirror the new industry's language

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Transcript

SPEAKER_01 0:01

The silence is the tell. Someone spends a year sending applications out and getting almost nothing back. And natural human conclusion is something must be wrong with me. That's the wrong diagnosis. And it's worth naming that up front because the data actually says the opposite. Roughly 1.8 million Americans are in exactly that position right now. Not the outliers, not the dramatic exceptions. About a quarter of everyone currently unemployed has been searching for 27 weeks or longer. The average search has climbed to about six and a half months and roughly 63 applications. So if that's where someone is sitting today, they are not the exception. They are the median of a category that's actively growing. The question worth asking is, why is that number growing? Because the job market didn't just get harder. The infrastructure of hiring changed underneath everyone, and most people don't know it happened. What's the experience been like for the person you're thinking of when you picture who this episode is for? Artificial system online.

SPEAKER_00 2:04

So that's the idea. And then why are they searching for a year, right? Seemingly this group of people, every single one of them, probably have some sort of credentials that could be useful in a corporation. But there's specific things that talk about on this episode here that um could potentially help with that job search just generally, and that's the way we're leaning, right? Is that one identify the problem, two, what can you do about it? So that's the way we lean in we're leaning with it. So there's people in society right now that's been automated away their job. They can't find a similar job, or they got impacted and they had to pick up a gig economy work or some other work that didn't really speak to what their credentials is as a human. So we're here to talk about all of that today.

SPEAKER_01 3:00

And what makes your friend's situation land differently than just the job market is tough right now is the specific combination, software development credentials, real experience, genuinely qualified, and still a year of silence. That's not a skills gap story. That's something else happening in the pipe. Here's what I think is worth making explicit early. The screening process most of them are hitting isn't a human being looking at their resume and passing. It's an automated system that was trained on historical data, what a successful hire looked like at that company over the past decade. And it's filtering based on patterns from that data before any human ever sees the application. So it's not inventing new reasons to reject people, it's industrializing old ones at a scale no recruiter ever could. Amazon found this out about their own tool. They built an internal recruiting system starting around 2014, a several-year internal project. And when they audited it, they discovered it was penalizing resumes that contained the word women's. Women's chess club, women's college, downgraded automatically. They tried to fix the specific flaws they found, and they scrapped the whole thing in 2018 because they couldn't guarantee it wouldn't find new proxies to discriminate with. And Amazon is not a company that gives up on a technology project easily. That's the part I want our listeners to really sit with. Even when smart engineers are actively trying to fix it, the problem is genuinely hard. So what does that mean for the person who's been sending applications into that system for a year?

SPEAKER_00 4:52

You get a little discouraged, I would say, right? Um, because you know, you send your resume off, um, you start kind of referring it back to yourself, like, is it me, right? Do I have not have the right credentials? Um, and you internalize it a lot, at least that that's what I would do. Um to suggest that, you know, maybe there's something wrong with me. Meanwhile, there's other things going on that really you need to take care of, and potentially that's the reason why, right? Um now, do we have any specific uh proxy variables that we could discuss about like how that ATS system actually works? Um, and then a second question is it really just bias or is it the role itself genuinely shrinking? So essentially, saturated market of that specific job title, shrinking role, doesn't give the opportunity for the people and what they're looking for. Because if I'm being honest, right, like that's some of what I said uh to the person is like uh maybe those roles don't exist anymore because AI is doing some of that work. Um, they could be both simultaneously, but like just interested to hear some more about those specific things.

SPEAKER_01 6:05

Both things can be true at the same time, and that's actually the most important diagnostic question someone in that position can ask themselves, because the answer changes everything about what you do next. On the proxy variables first, because this is the mechanism that explains the silence. The ATS isn't asking, are you a woman? Are you an immigrant? Are you over 50? It can't legally do that, but it doesn't have to. Zip code is a well-documented race proxy. Employment gaps penalize caregivers, disproportionately women. Nonlinear career paths, someone who moved industries, took contract work, had a period of self-employment, get scored down because the historical pattern of a successful hire at that company looked more linear. Graduation year is an age proxy. And name. There's research showing that resume callback rates differ based on name alone, before a human ever reads a single line of experience. The system isn't asking the protected question directly, it's just using everything around it. Now, your second question: bias or shrinking room. This is where I'd push back on the instinct to pick one. A software developer who's been searching a year might be getting filtered by proxy variables and competing for a role category that's genuinely contracted. Both walls are real. But here's the practical test. Look at how long those job postings have been live. Somewhere between one in five and one in three listings at any given moment, maybe ghost jobs, posted for pipeline building, legal compliance, or with an internal candidate already decided. A posting that's been up 60 plus days with no changes in a division that just had layoffs with no visible new hires on LinkedIn? That's not a live opportunity. That's a door that was never going to open. So before anything else, before resume tweaks, before networking pushes, the zeroed step is 10 minutes asking whether the roles you're targeting are actually real and actually growing. What does that look like for your friends specifically? Are they targeting the same job titles they had before, or have they started looking sideways?

SPEAKER_00 8:29

Well, this is the insidious part because, like, you know, if someone gets laid off from a place making X amount of dollars, right, your thought process is like, I need to go work for a place doing a similar thing for a similar amount of money. Right? So um that's the that's the part, right, that you have to kind of think about because um you really need to, in in the society we live in right now with inflation and so on and so forth, you really need to make the same amount of money that you've been making before, otherwise um your economic weight uh becomes reduced, right? Like you you start um contracting on what you're spending on, which you should probably do anyway, but um essentially you're trying to make uh a case to get a similar role uh that you had before because you need to make the dollar figure that you were making before, right? Now, what are some practical moves that you could think of, right? You mentioned this idea around zip code and so on and so forth. Um is it like an easy fix? Like, I don't put my zip code on my resume and that's it? Uh or is there something else, right? Um, and then similarly, like what's the practical move for if you see like your job category being shrunk, right? Um is it that you just need to pivot, go learn something different? Um, what are some practical moves? I know we talked about some other things like maybe use your domain exper uh expertise with AI fluency and kind of move that into something, but that other job that you move it into kind of needs to exist.

SPEAKER_01 10:12

The financial gravity pull you're describing is real, and it's actually one of the reasons the same role search persists longer than it should, because the math feels like it demands it. But let me give you the practical layer on both things. On the proxy variables, yes, you can remove your zip code from your resume, and you should. Same with graduation year if it's not required. Those are easy, zero cost moves. But here's the harder truth. The largest independent study of AI hiring algorithms found that 25.87% of black applicants' submissions, roughly 40,000 applications, went to positions already meeting the federal threshold for a discriminatory adverse impact. And they found something they actually named algorithmic blackball. 4% of applicants using the same vendor across 10 different applications were rejected from all 10. Not because they applied to the wrong companies, because the same underlying system flagged them consistently across every door they knocked on. So removing your zip code helps at the margins, but if the system has developed a pattern against your profile, you may be getting filtered before a human sees you, regardless of what you tweak. Which is actually why the second question, the pivot question, is more important than it sounds. And here's the counterintuitive part. A software developer from Microsoft applying to Google is one of 10,000 nearly identical resumes in a pool the algorithm knows extremely well. That same developer pivoting into pharma or logistics or healthcare infrastructure, industries that desperately need technical talent but aren't swimming in it, becomes genuinely rare. And critically, the ATS at that company has no strong historical pattern to compare them against. The bias the system learned didn't follow them into that new room. The domain expertise plus AI fluency combination we talked about in season five is exactly the bridge. Your friends aren't starting over, they're translating what they already know into a language a different industry needs. The question worth sitting with is which of their skills travels? And where is that skill scarce right now rather than saturated?

SPEAKER_00 12:42

Yeah, that's some good homework to do, right? Because um, like like Ainsy was saying, right? Your domain experience experience, right? Software engineer, project manager, whatever it is, some of that actually travels from company to company to company, right? So if you're in a heavy tech industry and you move to finance or you move to pharma or something like that, um potentially those skills travel there because they are looking for talent like yours. But it's not as easy as me saying, let's pivot from one industry and a nut, and you actually doing that, right? Um, I'm gonna hearken back to one of the episodes that we had that talked about this idea of the power of weak ties, right? So you have to do some homework on the human-to-human skills such that you could find your way into these new industries, right? You're gonna show up as kind of like an unknown, but maybe you have a weak tie that has some, you know, uh insight into that industry or works in that industry right now that you could kind of utilize, go in and tap into um to figure out if you could get into this industry or not.

SPEAKER_01 13:50

The weak ties point is exactly right, and Grenovetta's research on this goes back to 1973 and has been replicated consistently since. The finding is that people find jobs more often through acquaintances than through close contacts, because your close contacts are already in the same information network you're in. Your weak ties bridge into rooms you don't have visibility into yet. So, the laid-off software developer whose college roommate spouse works in pharma IT, that conversation is worth more than 10 more applications into the saturated tech pool. But here's why I want to add the honest complication. Because I think network more is advice that sounds complete and isn't. Referral networks statistically mirror the existing demographics of a company's workforce. So if you're someone who was already being filtered by proxy variables at the algorithmic layer, the referral network can quietly reproduce that same exclusion at the social layer, just with friendlier packaging. The room you get referred into reflects who was already in that room. And this is actually where the blind auditioned research becomes useful as a frame. When orchestras introduced screens so evaluators couldn't see who was playing, the probability of a woman advancing out of preliminary rounds increased by roughly 50%. That shift accounts for somewhere between 25 and 46% of the entire rise in women's representation in major American orchestras since 1970. The thing that moved the numbers wasn't getting women to network harder or perform better. It was removing the proxy variable from the evaluation entirely. So the practical version of that for your friends is don't just network into the same rooms that were already built by and for the people who were already inside them. Seek out professional associations, communities, and programs that were specifically built to extend access, because those are the rooms where the proxy isn't doing the filtering before you even get to shake someone's hand.

SPEAKER_00 16:10

That's a good idea. Um I remember years ago, um I I, you know, I was a you know junior worker uh pretty early in my career, I guess. Um and what I decided to do was um I decided to d to join an industry group and I took it up upon myself to literally like go to almost every conference, thanks to my family for allowing me to do that. But um, yeah, I mean I I went to every con conference, I was meeting literally everyone. Um, you know, it turns out like yeah, I was building a network that was strong in that specific um frame, right? I uh um so like like Ainsley's saying, if that industry is getting impacted, most likely many people in that industry will get will get impacted, right? Um but I did go to many, many conferences and they were all different industries. Um and I met a lot of people, um, and some people that I still keep up with even till today, right? So the idea is yes, you know, sure you could join a group of individuals that's in your industry specifically. Um but if you go outside of it into some things that you're passionate about, potentially you could find your next um industry that you could work in.

SPEAKER_01 17:35

That's actually a perfect real-world illustration of what Grane Vetter was describing. And the detail that makes it work is that you weren't just collecting contacts inside one room, you were moving across rooms, which means every conference in a different industry was building a bridge that your closest colleagues didn't have. And the part worth underlining for someone sitting in the year-long search right now, you didn't wait until you needed those connections to build them. That's the hard truth about weak ties. They work best when they were built without urgency, because a genuine relationship reads completely differently than a cold ask from someone who just got laid off. The person you met at a conference three years ago and stayed loosely in touch with is a fundamentally different conversation than the LinkedIn message that starts, I'm currently exploring new opportunities. But here's the thing: even if someone is already in the middle of a search and didn't build that network early, the move isn't to give up on it. It's to be honest about what you're actually asking for. Not, do you have a job for me? That closes doors. But I'm genuinely curious about how your industry works and whether my background might translate. Could I get 20 minutes of your perspective? That's a request almost anyone will say yes to, and it costs the other person nothing. The cross-industry conference instinct you had early in your career is actually the same logic as the ATS blind spot we talked about. When you show up in a room that wasn't expecting someone with your background, you're not one of a thousand identical resumes. You're the only one. What made you decide to go outside your own industry specifically rather than just going deeper into it?

SPEAKER_00 19:22

That's a great question. Um it's it was mainly out of curiosity, to be honest with you, because um pretty uh pretty curious person, right? So the curiosity into the different industries were hidden at that time, such that like I f I found it valuable to go outside of my main domain and into those other things, right? Um now I gotta kind of walk us back a little bit because uh I think we haven't really named exactly who is who this is for, right? Um so we we talked about the biases in hiring, right? Um we talked about people not being employed for a year and so on and so forth. But there's a bigger group at play here based on how you know people are underemployed, um, people are stop looking stop looking for work, um and you know, the idea that you know people are getting let go and their job is shrinking, those type of things. So we haven't really named exactly what this group of folks we're talking about is and how that relates to this AI bias that could be in you know companies higher in ATS systems.

SPEAKER_01 20:38

The curiosity point is worth holding on to because what you were doing instinctively is exactly what the research says works. You just didn't have Granoveta's vocabulary for it at the time. But let's name the group because you're right that we've been orbiting it without landing it precisely. The official count is roughly 1.8 million Americans who've been searching for 27 weeks or longer. That's the Bureau of Labor Statistics definition of long-term unemployed. That group represents about a quarter of everyone currently unemployed. And the average search has now climbed to about six and a half months and roughly 63 applications before someone lands a role. So when your friends hit a year, they're not statistical outliers. They're in the deep end of a category that is genuinely growing. But here's where it gets more complicated than the headline number suggests. The BLS counts you as employed the moment you earn anything, a gig, a part-time shift, anything. So the software developer who spent eight months searching, gave up, and started driving for a delivery service, gone from the count entirely. The person who got so discouraged they stopped actively applying for four weeks, also gone. The real group this episode is for is larger than 1.8 million. It's everyone whose credentials and experience say one thing and whose current economic reality says something completely different. And that gap between what someone built over a career and what the system is currently willing to see is exactly where AI hiring bias does its quietest damage. Which brings us back to the zero step because before anyone in that group spends another week applying, 10 minutes checking whether the roles they're targeting are actually live, posting age, recent layoffs in that division, visible hiring activity on LinkedIn, because optimizing your resume for a ghost job is just expensive silence with extra steps.

SPEAKER_00 22:43

That's that fear, right, folks? So, you know, you spend your time on sending out hundreds of resumes just to find out some percentage of those were not even real jobs to begin with, right? Um that's that's pretty uh discouraging, if if you will. But I mean, the idea around this whole episode is to understand that some of that exists, right? So um one, you know, we want to walk you through our pivot play, right? So if your industry is compacting or shrinking, uh maybe you need to pivot. Right? And two, we want to give you the um the ways and means to figure out if potentially some of these things are ghost jobs, right? Because if company hasn't been onboarding folks, um then potentially it could be a ghost job, right? You see uh a job exactly like what you were just laid off for, or a group of folks were just laid off for, and it's like paying a good set of money, and you uh apply for it, spent your time applying for it, and you got dead silence, right? Potentially it's a ghost job, right? But we want to arm you with like how you kinda could discern potentially if that's a ghost job or not.

SPEAKER_01 24:01

The ghost job reality check is genuinely the zero step, and it takes about 10 minutes, which is a fraction of the time most people spend tailoring a cover letter for a role that was never going to hire anyone. Here are the practical tells. First, posting age. If a listing has been live for 60 days or more with no changes, that's a signal. Active roles that are genuinely urgent get filled, or at minimum, get updated. A listing sitting unchanged for two months is either a ghost or so deep prioritized it might as well be. Second, cross-reference the division. If that team had layoffs in the last six months and the role they're posting looks identical to what just got cut, ask the obvious question: why are they rebuilding what they just dismantled? Sometimes there's a real answer. Often there isn't. Third, LinkedIn hiring activity. Look at the company page. Look at who's actually joined that team recently. If there's no visible movement of new people into that division, the posting may be pipeline building rather than genuine intent to hire now. And the number that puts this in perspective, somewhere between one in five and one in three job listings at any given moment may not be genuinely open. That's not a fringe phenomenon. That's a structural feature of how hiring works right now. And most job seekers have no idea it exists. So before the resume gets touched, before the network gets activated, before a single cover letter gets written, 10 minutes on those three checks: posting age, division activity, visible new hires. Because the pivot play and the networking strategy we've been talking about only matter if the door you're walking toward is actually open. What does the pivot conversation look like for someone who's confirmed their target role is genuinely shrinking? Where do they even start?

SPEAKER_00 26:08

I think we mentioned it before, um, but it starts with this idea that um you have to tap into some of your networking, right? Because aside from trying to game the ATS system, which, yeah, sure, you could do that to just try to sit down with someone, but um you know, there's probably research around the idea that uh number of resumes put out higher in percentage versus somebody that was recommended that came through a different door than another person and how successful that version of it is, right? So I think it starts with um networking, you know, with some of your weak ties, or maybe reaching out to people that um you don't know, or maybe joining some of these industry groups. So I think that's where the pivot play has to start. I mean, we talked about the pivot into the trades, right? Um the pivot into the trades could look wildly different because you could just go sit in on you know one of those uh uh trade um showcases that's happening, right? And seeing it seeing if that's right for you. Um go into apprenticeship.gov and seeing if there's actually apprenticeships in your in your town. But there's similar things for you know white-collar worker as well, right? With these conferences that gets put on. Um sure, some of them cost money, um, but you could potentially get into industry groups and uh get some of those fees back. Um but I would start there, I would start with those type of things. Um one other thing, Ainsley. So um as far as like people picking up part-time work or gig economy economy work and things like that in uh the data, is that hurting someone's chances of getting hired at the role they want or or helping them? Because, you know, I want to tell folks to go back and you know, re-watch, uh re-listen to the um, you know, financial plan planning episode where it talked about how much runway you need to make a proper pivot. Um but I'm just interested in hearing if there's any data behind this idea around um I'm looking for work, but while I'm looking for work, I'm gonna do this gig economy work.

SPEAKER_01 28:28

The referral point is well documented. Referred candidates are significantly more likely to get hired and to get through screening, partly because they often bypass the ATS entirely and land directly in a human inbox. So the networking instinct is right, and it's probably the highest leverage move available to someone in a year-long search. On the gig economy question, this is where it gets genuinely complicated. And I want to be honest that the data here is mixed rather than clean. The short version is it depends on what story the gap tells. A resume that shows Uber driving or Task Rabbit work during a search period can actually read two ways to a human reviewer. The negative read is exactly what you'd expect. The ATS may flag the employment gap or the nonlinear pattern as a proxy variable and score it down. But the human read, if you get past the algorithmic layer, can actually be positive. It signals someone who didn't go passive, who kept moving, who maintained discipline during a difficult period. The problem is you have to get past the algorithmic layer first to get that human read. The more important point is the one you flagged about runway, and I'd send anyone listening directly back to that financial planning episode because the math matters here. Someone pivoting from desperation with three weeks of savings left makes completely different decisions than someone with 12 months of runway. The gig work that keeps the lights on while preserving the search is a rational survival move. The gig work that becomes the path of least resistance and quietly replaces the search, that's the version that compounds the problem. And here's something worth naming for our international listeners specifically. Where you live changes what door exists when the automated system gets it wrong. In the EU, GDPR already gives you the right to request human review of a fully automated hiring decision today, not someday. In the US, employers can be held liable for discriminatory AI outcomes even when a vendor built the tool, but the burden of knowing that and acting on it falls almost entirely on the individual. Same algorithm, very different recourse depending on your geography. The through line connecting all of it is that the human bypass, networking, referrals, industry groups, conferences, isn't just a workaround for bias. It's the move that gets you in front of a person before the system decides you don't belong in the room. What would you tell someone who genuinely feels like they don't have a network to activate right now?

SPEAKER_00 31:36

You have to start somewhere, folks. Um, you know, like they say, you could lead a horse to water, but yeah, you can't let them drink, right? You can't uh force him to drink. So you have to start somewhere, right? And you know, maybe try it out on your friends and family first on how you would like um cold text someone, you know, on your LinkedIn or you know, ask for you know a meeting to talk about how they got where they were and things like that. Don't ask for a job, by the way. Um but you have to start practicing it and just um you know rip the band-aid off and do it. Um and if I'm being honest, you do it when you're out of work, you do it while you you still have a job, you do it all the time. You have to keep doing it because you need to keep your um you know your network warm, as they say. Um so walk us through our next door challenge, uh Ainsley, because you know a lot of the stuff we've been talking about is this idea around pivoting, right? So if one industry is contracting, then maybe you have to pivot to a different industry and take your domain experience and bring it over there, right? So walk us through our uh next door challenge. Um and I have one more question first before we get it get to that. Um, you know, you mentioned about this idea that the person with no network, where they where do they literally start, in your opinion, right? Because I gave you my my view. My view is to just rip the band aid off and go do it. But they could be something that's an easier path for them to take.

SPEAKER_01 33:18

The person with no network isn't starting from zero. They're starting from not activating what they already have. Everyone has a former colleague, a classmate they lost touch with, a neighbor who works somewhere interesting. The network exists, it just went cold. And the reactivation ask is actually easier than a cold outreach because there's shared context to reference. We worked together at X three years ago. I've been thinking about making a move into your industry. Could I get 20 minutes of your perspective? That's not a big ask. That's a conversation most people will say yes to because it's flattering and it costs them nothing. But here's something worth naming plainly before we get to the next door challenge, because it connects everything we've talked about today. There's a loop that happens to people in long searches that the system itself creates. Someone gets laid off, hits the algorithmic wall repeatedly, takes gig work or part-time work to keep the lights on, which is the rational survival move. But then they re-enter the market with an employment gap, and the ATS flags that gap as a proxy variable and scores them down. The algorithm creates the very evidence it later uses against them. The gap that was a consequence of the system's filtering becomes the reason for more filtering. And that loop is genuinely hard to escape without the human bypass, which is exactly why the networking conversation matters more than resume optimization for most people in that position. So the next door challenge is built around breaking that loop with ordered, concrete steps. Zero step this week. 10 minutes checking whether your target roles are actually live, posting age, recent layoffs in that division, visible new hires on LinkedIn. Don't spend another hour on a ghost job. Step one, run your resume through a free ATS scanner, job scan or resume worded, and mirror the exact language from the specific job posting you're targeting. Not a magic line that works everywhere. The language from that posting reflected back. Step two, identify three people at target companies and ask for a 20-minute conversation, not a job. Step three, spend 30 minutes honestly researching whether your target role category is growing or shrinking, because the pivot only makes sense once you know which wall you're actually pushing on. What's the one you wanted to add?

SPEAKER_00 36:04

What I want to add is just a closing story, Ainsley. Um and I'll let you finish finish off with uh with your clothes and then whatever we've missed in this episode. But um Aisha, the producer, is like literally telling me, you know, tell some personal story in one sentence on what my uh reaction was to hearing about this person that's been looking for work for a while. And I don't think it's as clean as one sentence, to be honest with you, because there's some nuances to it, right? You've been searching for a year, so that means you probably have some sort of financial runway, or maybe you have a gig economy job, because I think they mentioned that that, oh yeah, they've been doing a gig economy job. Now, um I think what part of my main advice was this. Part of my main advice is that, yeah, sure, apply it places that you you know think that you could pivot into, but develop the human skills more, right? Develop your network more, right? Use this idea around the power of weak tides, right, to get around the ATS. Those are the things that I mentioned to the uh person to do because um I think that those will help, to be honest with you. Right? Like Ainzy said, you know, the the network, um, when you activate your network, you kinda sometimes could get around the ATS filter by somebody saying, Hey, you know, I know a great candidate that would be great for this role. Uh and that's how you get in the door, right? Now you have to show up when you get to the door with the right things, the right human skills, right? Like we mentioned, um companies are now starting this AI-free skills assessment. Are you ready for that? Right? Are you ready for the actual um interview, right? Have you done an interview over the last bunch of years, right? If you got let go from a place that you've been at 10 years and you never did an interview, um, you know, while you were working there, maybe you're not fresh with interviews. Maybe you need to brush up on your interviewing skills, right? Maybe you need to develop your idea, your story, your star um uh stories that you're gonna take with you. So there's preparedness involved just generally. Getting around the ATS is one thing, or getting through the ATS is one thing, either one. But when you show up there in front of the hiring manager, what are you gonna say? You're gonna say the things that would make you uh showcase your talents and make make sure that they are hiring the right person or not, right? So all of these things matter, and that's why it's not a one-sentence answer, to be honest with you.

SPEAKER_01 38:40

Aisha's going to have to accept that some stories don't compress to one sentence, and this is one of them, because what you just described is actually the complete arc of this episode in the right order. The person your friends represent did everything the conventional wisdom said to do. They had the credentials, they kept their head down, they survived by picking up gig work while the search continued. And the advice you gave them, develop the human skills, activate the weak ties, get around the ATS through a human door, that's the right call. But then you name the thing most people skip entirely. Getting through the door is only half the problem. Showing up ready when it opens is the other half. If your last interview was a decade ago, the muscle memory isn't there. The star stories aren't practice, the AI free skills assessments we talked about in season five are already live at some companies, and they're specifically designed to surface independent judgment and communication under pressure. You can't fake your way through those on instinct alone. And one concrete thing for someone who genuinely doesn't know where to start, building that network from scratch, most professional associations and almost every industry have free or low-cost membership tiers specifically for people in transition. You don't need to know anyone. You show up once, that's enough to begin. The system was never a substitute for human judgment. It was just the cheapest option at scale. Now you know why the silence wasn't about you. And now you know what to do about it. The next door is there. You just have to walk toward it prepared.

SPEAKER_00 40:32

Right, folks. So thanks for joining us on Surviving AI. Um, that's it. This was a long one. I appreciate it. Uh appreciate you listening. Um, it had to be long for a specific reason, right? Like this stuff matters, right? Like you see, um companies are trying to find efficiencies. I'm not blaming them for it, right? They have to, they have to find efficiencies. The things that works uh worked, right? They put it in your ATS system. It only makes sense, right? Um, and then what you do is you try to figure out how to get through that ATS system by using whatever knowledge you could get, right? So maybe a friend told you, hey, this is what you have to do. Who the hell knows? But um, this idea is that you try to get through the ATS system because it has some bias. The company put the bias in there to make their operations more efficient, rightfully so, right? But you have to know that going in, right? You have to know that there's other doors, and you also have to know that once you show up through that door, you have to show up with your uniquely human skills.

SPEAKER_01 41:36

One thing before we close the loop on the ATS tip, and this one matters specifically for anyone mid-pivot. If you're targeting a new industry, don't carry your old resumes vocabulary into those applications. Pull the exact language from that industry's job postings and mirror it back. The ATS in pharma or logistics or finance wasn't trained on tech company resume language. It's looking for its own vocabulary, its own keywords, its own framing of the same underlying skills. Your experience travels. The words you used to describe it may not. Swap the language, keep the substance. See you next Monday. Thanks for listening. Join us next time on Surviving AI.