JBL Resources blog

Why the Job Market Feels More Like Online Dating

Peter Schnelle — Mon, 24 Aug 2026 18:56:39 GMT

Somewhere along the way, the job market turned into online dating. Endless options, snap judgments made from a two-page profile, and a lot of people on both sides wondering why nobody writes back.

It wasn't always like this. For a long time, one of the biggest challenges in a job search was simply finding the right opportunities. Candidates had to know where to look, decide which openings were worth pursuing, and usually put a fair amount of time into each application. Employers faced the opposite problem: reaching enough qualified people to build a strong candidate pool.

Technology has changed that balance pretty dramatically. Today, candidates can find and apply to hundreds of jobs with very little friction, while employers can post openings across multiple platforms and reach large audiences almost instantly. AI has accelerated that shift by making it easier to tailor resumes, create applications, screen candidates, and automate parts of the communication process.

It's a comparison we've been talking about at JBL Resources for a while, and the more we think about it, the more useful it feels. Not because recruiting should be treated casually, and not because we think the comparison needs to be played for laughs. It's simply a good way to describe what happens when technology gives people access to far more options than they can realistically give attention to.

More access has also created more noise

Dating apps made it possible to see an almost endless number of potential matches. Job platforms have done something similar for employment. A candidate no longer has to choose from a handful of openings they happened to hear about. There may be hundreds of positions available within minutes, and in many cases applying requires very little effort.

The increased access is a good thing in a lot of ways. The difficulty is that when applying becomes almost frictionless, people naturally begin applying more broadly. A candidate may apply to a role where they meet nearly every requirement, and then apply to another where the fit is much less clear simply because it takes another minute to do so. When that behavior is repeated across thousands of job seekers, employers can end up with several hundred applications for one position, many of which are not especially close to what they need.

The employer then has a practical problem. Someone has to review all of that information, and there's only so much time available. Automated screening, keyword matching, and AI-assisted review become attractive because they help make the volume manageable. In some cases, they may be the only realistic way to get through it.

Here's where the cycle starts to reinforce itself. A thoughtful candidate might begin a job search by selecting four or five positions that genuinely make sense, tailoring a résumé, writing a cover letter, and putting real effort into each application. If all of those applications are rejected quickly and automatically, the lesson they take from the experience is fairly predictable. Applying selectively starts to feel inefficient, so they begin applying to 20 or 50 jobs instead.

From the candidate's perspective, that's a rational response to the system. From the employer's perspective, the additional volume creates even more pressure to screen aggressively. Neither side is necessarily doing anything unreasonable, but together the behavior creates much more noise and much less confidence that any individual application will receive serious attention.

A résumé can only tell so much of the story

The online dating comparison also becomes useful when thinking about how quickly people are being evaluated. A dating profile is a compressed version of a person. A handful of photos and a short description are expected to provide enough information for someone else to decide whether a conversation is worthwhile.

A résumé works in a similar way. Someone may have 15 years of professional experience reduced to two pages, and there may be several completely accurate ways to describe the work they have done. The problem is that only one of those descriptions may immediately make sense to the person reviewing a particular job.

We see this often in recruiting. Someone may have experience that is highly relevant to a position but describe it using terminology from a different company, industry, or internal process. In conversation, the connection may be obvious. On paper, it may not be. This matters even more when an employer is reviewing hundreds of résumés and has very little time to investigate candidates who don't immediately look like an exact match.

This is also where an abundance of options can start to change judgment. If a hiring manager has 300 applications in front of them, it is understandable to assume that someone who matches every line of the job description must be in there somewhere, which can make employers more hesitant to take a chance on someone who's clearly strong but doesn't fit the specification perfectly.

The problem is that 300 applications don't necessarily mean 300 realistic candidates. Once you account for experience, interest, location, compensation, timing, and all of the other things that determine whether someone can actually take the job, the true pool may be quite small. The apparent size of the candidate pool can make the market feel more abundant than it really is.

The system changes behavior on both sides

One of the more frustrating parts of the current job market is that it can make sensible people behave in ways that make the experience worse for everyone else. Candidates apply more broadly because they don't know which applications will receive meaningful attention. Employers screen more aggressively because they can't realistically give every résumé a detailed review. Recruiters send more messages because response rates decline, and candidates begin ignoring more messages because so many of them are irrelevant.

Ghosting is an obvious parallel with online dating, but it's really just another consequence of the same issue. When the number of possible interactions becomes larger than the amount of attention people can give them, communication starts to break down.

AI didn't create this problem on its own. One-click applications, large job boards, and recruiting automation were already moving the market in this direction. AI has simply increased the amount of activity that both sides can generate. Most companies are not trying to make the candidate experience worse, and most candidates are not intentionally flooding employers with irrelevant applications. The technology just makes it much easier to operate at a scale that creates side effects for everyone else.

It's why we keep coming back to the idea that access is no longer the scarce resource. Attention is.

Why human connection still matters

At JBL, we don't think the answer is to reject technology or pretend these tools are going away. We use AI and automation ourselves, and there are plenty of areas where they help us work more efficiently. What we try to be careful about is where that efficiency stops being useful.

Our role isn't to send a hiring manager hundreds of résumés and leave them to sort out which ones matter. The value we bring is in taking the time to understand the position, talking to candidates, and narrowing the field before it reaches the client.

It starts with understanding what an employer actually needs, including the parts that may not be obvious from a written job description. It also means understanding the candidate beyond the résumé: what they've actually done, what they're looking for next, whether they're genuinely interested in the opportunity, and whether the practical details of the role make sense for them.

Ideally, a client doesn't receive 300 résumés from JBL and discover that only two or three are worth talking to. They receive those two or three candidates in the first place. Those people have already been spoken with, they understand the opportunity, and someone has taken the time to determine whether there is a real fit on both sides.

This is the problem firms like ours exist to solve. When both sides are drowning in options, somebody has to be the connection piece: the one who actually spends the time to know the companies, know the candidates, and figure out which introductions are worth making. Technology has made it easier than ever for candidates and employers to find each other, but finding each other isn't the same as understanding each other. When someone takes the time to close that gap, the results are careers that last and working relationships that hold up long after the search ends. That's the part no algorithm has figured out how to automate, and it's the part we intend to keep doing ourselves.

How JBL Uses AI in Recruiting, and Where We Draw the Line

Peter Schnelle — Mon, 24 Aug 2026 18:06:53 GMT

Over the last decade, technology has changed the way we work at JBL Resources in ways we probably would not have predicted when many of our current processes were first built. Search tools have improved, data has become easier to work with, and automation has taken a meaningful amount of manual work out of recruiting. More recently, AI has accelerated that shift. Like most companies, we have spent a lot of time thinking about where it actually helps, where it simply creates more noise, and where we do not want it involved at all.

Our view of AI in recruiting has become fairly straightforward. We want to use it to reduce the administrative weight that surrounds recruiting, not to reduce the amount of actual recruiting being done. If technology can save a recruiter time on work that is repetitive or procedural, that is useful to us. The value comes from what that recruiter can then do with the time that was saved.

How we currently use AI in recruiting

Much of our use of AI happens behind the scenes. It helps with things like creating backend forms, organizing information within internal processes, summarizing recruiter notes after candidate conversations, and reformatting résumés into a more consistent presentation. These are all necessary parts of the work, but they are not generally the parts where a recruiter's experience or judgment adds the most value.

The same is true on the business development side. We have a large amount of information in our database about companies, contacts, prior conversations, hiring activity, and past relationships. AI can help us sort through that information and identify people who may be worth reaching out to based on what is happening at a company or what we already know about the relationship. In that case, the technology is helping narrow the field. It is not deciding what the relationship should look like or carrying it forward on its own.

That distinction is important to us because the more capable these tools become, the easier it is to automate things simply because they can be automated. We have tried to resist that instinct. Some parts of recruiting are inefficient for a reason: they involve people, context, incomplete information, and judgment. Those are not always problems that need to be removed from the process.

What we want technology in staffing to give back

When we think about technology in staffing, the most useful question for us is not how much more activity it can generate. It is how much meaningful time it can give back to the people doing the work.

A recruiter may finish a candidate conversation with pages of notes that need to be organized. If AI can reduce the time spent cleaning those notes up, that is valuable. If a résumé needs to be reformatted before it can be sent to a client, there is little benefit in having someone spend 20 minutes manually correcting spacing and headings. If we are trying to determine which contacts in our database are most relevant to an active hiring need, there is no reason to ask a recruiter to comb through hundreds of records one by one.

In each of those cases, we are comfortable allowing technology to do more of the administrative work because it leaves more room for the parts of recruiting that require a person. That might mean having another conversation with a candidate, asking a hiring manager a better follow-up question, or spending more time understanding why someone's experience does or does not fit an opportunity.

That has gradually become the standard we use when looking at AI automation in recruiting. If the technology creates more time for judgment and conversation, it is probably helping. If it starts replacing those things, we become much more cautious.

Where we have chosen not to automate

There are several areas where we have decided that the efficiency gain is not worth what would be lost.

One is recruiter outreach. AI can help identify who may be worth contacting, but we do not want it writing and sending emails in place of the recruiter. Outreach is the beginning of a relationship, and we believe the person whose name is on the message should actually be the person behind it. That means taking the time to understand why someone is being contacted and writing the message accordingly.

We take a similar view of candidate conversations. There are tools available that can automate more of the screening process or replace parts of an interview with an AI-driven interaction. That is not a direction we are interested in pursuing. A résumé gives us useful information, but a conversation often changes how we understand it. Candidates explain decisions, add context, ask questions, and describe experiences that do not always translate cleanly to a document or database field. The human connection in hiring is not separate from the evaluation. In many cases, it is how the evaluation becomes accurate.

The clearest boundary for us is candidate rejection. We will not allow AI to automatically reject a candidate. Technology can help organize information, identify areas that deserve attention, or compare a résumé against known requirements. The final judgment belongs to a person.

Part of the reason is practical. Recruiting is full of imperfect matches that turn out to be very good ones. Job titles differ between companies. Skills transfer between industries. People take unconventional career paths. A candidate who looks slightly off-target on paper may make complete sense after a conversation. We do not want to build a process that becomes more efficient by removing the opportunity to notice those things.

There is also a broader principle behind it. As AI becomes more capable, keeping a human in the loop will require more deliberate choices, not fewer. It will often be possible to automate another step. The question for us is whether doing so actually improves the quality of the work.

We expect our use of AI to continue growing. There are almost certainly administrative tasks we do manually today that we will not be doing manually a few years from now. We see that as a positive development. The part we are more interested in protecting is the time that sits on the other side of that efficiency.

If a recruiter spends less time formatting documents, organizing notes, or searching through records, we would like that time to show up somewhere else in the process as a better conversation, a more thoughtful evaluation, or a stronger relationship. That is the balance we are trying to maintain as the technology changes, and it is likely to remain the way we think about AI for some time.