Why Applying Has Never Been Easier And Getting Hired Has Never Been Harder
A data-driven case study exploring how digitization, ATS systems, and rising application volume have reshaped modern hiring dynamics in the U.S. labor market.
How often have you applied for a job before receiving a non-automated reply inviting you to an interview?
Hundreds? Maybe even thousands?
It's no secret that the job market has hardened dramatically in recent years.
Having graduated in 2022, I found myself in a situation that felt both challenging and confusing. Like many others, I had been brought up to believe that a degree from a well-regarded university would translate into job offers that would be handed to me on a silver platter. But this couldn't have been further from reality.
I graduated from the University of Waterloo, known for its rigorous STEM programs, and proceeded to apply to hundreds of roles without hearing back from a single one. I wasn't alone in this experience. Many of my peers who didn't convert internships into full-time roles were facing the same silence.
Seven months later, after relocating to Chicago, I decided to approach the problem differently. I started going to local networking events and job fairs. At one event just ten minutes from my apartment, I made a warm connection with a woman hiring for apprentice roles. That single interaction led to a recruiter call the same day, and within a few weeks, and several interview rounds later, I had received my first full-time offer.
The contrast was striking:
Months of online applications resulted in zero interviews and zero leads, while a single in-person interaction immediately created opportunity.
Since then, through conversations, articles, books, and reflection, I've remained deeply curious about this apparent paradox in today's job market.
Job seekers describe constant rejection, ghosting, and silence. At the same time, companies and founders often express frustration about how difficult it is to find the "right" talent.
How can both of these realities exist at once?
How has the job market changed in the last 10-15 years and how can we better understand what drives this disconnect?
And is this truly a supply–demand problem, or something else entirely?
This project explores these questions through data and patterns observed in today's job market and hiring landscape.
Why does understanding this market dynamic matter?
For startups and companies, misunderstanding this dynamic can lead to inefficient hiring processes, missed talent, poor hiring decisions, and slower growth.
For candidates, it can mean prolonged job searches despite strong qualifications, as well as mental fatigue and loss of motivation throughout the job search, which in turn lowers the chances of finding the right role.
For this analysis, I investigate the U.S. job market, with a focus on knowledge work and non-farm work. While this analysis focuses on the U.S. job market, the patterns may offer useful direction when exploring similar tensions in other countries.
I also wanted to note that while I dove into this topic as deeply as I could, the data available for job openings, hires, unemployment rates, and applicants per role is not in a perfect or complete condition, so I will focus on the main patterns and trends, while acknowledging that due to the nature of the topic there are gaps in the data available.
My main hypothesis is that the number of applicants per role has increased dramatically with the digitization of the job application process. As internet-based applications and applicant tracking systems (ATS) reduced friction for both candidates and employers, they enabled a surge in both job postings and application volume. The result is a growing signal-to-noise problem: companies face greater difficulty identifying qualified candidates, while candidates struggle to stand out.
20 years ago, applying for a job required meaningful effort. Candidates printed resumes, focused on local opportunities, and either mailed applications or delivered them in person. This natural friction limited both the number of roles an individual could apply to and the number of applications a company would receive.
Today, the whole application process has become almost frictionless, just a few clicks away. Applying often takes only a few minutes and can be done from anywhere, regardless of location or familiarity with the company. The process is increasingly standardized: enter basic personal information, attach a resume, and click submit. Cover letters are often optional, and with the rise of AI tools, the time and effort required per application continue to decline.
As a result, applying has become nearly frictionless. This shift likely drives a substantial increase in applicants per role compared to 20 years ago. In turn, companies must filter through a much larger volume of candidates, leading to higher rejection rates. What was once a manageable evaluation process has become a problem of scale.
In reducing friction, the system has optimized for volume without accounting for signal and clarity. This dynamic lies at the heart of the modern job market paradox: increased efficiency in the posting of jobs and in submitting job applications, yet greater difficulty in matching the right candidates with the right roles and an increasing sense of frustration on both sides of the application process.
My analysis explores how these dynamics have evolved over the past 20 years and examines the factors contributing to this shift.
To understand how the job market has evolved over the past two decades at a macro level, I use data from the Job Openings and Labor Turnover Survey (JOLTS), published by the U.S. Bureau of Labor Statistics.
JOLTS provides monthly estimates of job openings, hires, and separations across the U.S. economy. Rather than surveying every business, it collects data from a sample of roughly 21,000 establishments and uses statistical weighting to produce nationally representative estimates. The survey covers all non-agricultural industries across both the public and private sectors.
For this analysis, I focus on private sector job openings and hires. Government hiring tends to be more stable over time. As a result, private sector demand often better reflects broader labor market dynamics.
Like any survey-based dataset, JOLTS is not a perfect measurement of reality. Because it relies on a sample, the reported values are subject to sampling variability, meaning the estimates may differ from the true population values. There are also non-sampling sources of error, such as reporting inconsistencies or data collection issues.
However, these limitations matter much less when looking at long-term trends. While the precise levels may not be exact, the overall patterns such as rises, declines, and structural shifts over time, are generally reliable. In other words, JOLTS may not tell us the exact number of job openings at any given moment, but it provides a strong signal of how the labor market is moving. JOLTS is one of the most widely used datasets for analyzing U.S. labor market dynamics.
Figure 1 shows U.S. annual job openings and hires over the past 20 years. The numbers are based on seasonally unadjusted data. While the Bureau of Labor Statistics also provides seasonally adjusted estimates (useful for analyzing month-to-month or year-over-year changes), for annual totals, both approaches produce nearly identical time-series patterns.
As mentioned, these estimates are derived from a sampled set of businesses. Within JOLTS, job openings are defined as positions that are open on the last business day of the month, could be filled within 30 days, and are actively being recruited for. Hires represent all additions to payroll during the month, including both new and rehired employees.
In Figure 1, monthly estimates are aggregated to the annual level to highlight longer-term trends in labor demand and hiring activity.
Prior to 2015, hires in the U.S. private sector consistently exceeded job openings. Around 2015, we observe an inflection point where job openings begin to surpass hires for the first time in the data. From that point onward, the gap widens, indicating a growing divergence between labor demand and realized hiring. This trend briefly breaks during the 2020 pandemic, when economic activity slowed sharply, but resumes immediately after.
The post-pandemic period marks the most extreme imbalance. Job openings surge dramatically in 2021 and 2022, exceeding hires by roughly 50% and 70%, respectively. These levels were not observed elsewhere in the time series.
Looking across the full period, three major declines in job openings stand out. The first occurs during the 2008 Financial Crisis, when openings fall from 51 million in 2007 to 26 million in 2009. The second occurs in 2020 during the COVID-19 pandemic, with openings dropping from 77 million to 68 million, followed by a sharp rebound to 122 million in 2022.
The third decline begins after this peak, with job openings falling to 99 million in 2023 and continuing downward through 2025. A plausible explanation is that pandemic-era demand, driven by remote work, digital services, and restricted in-person activity, temporarily inflated hiring needs. As these conditions normalized, so did labor demand, leading to a correction in job openings.
To better understand this 2015 inflection point, when job openings began to surpass hires, we need to look at how the recruiting process itself has evolved. In the last few decades, hiring processes started shifting from paper to digital in several waves.
In the 1970s, the main channels for companies to advertise jobs were through newspaper ads and word of mouth. Candidates would either physically mail or hand-deliver their resume. Recruiters would read through the physical resumes, trying to look for the candidates with relevant skills, certifications, and experience.
By the 1980s, the first Applicant Tracking Systems (commonly referred to as ATS) emerged. They were basic tools back then, storing candidate information. Recruiters could search resumes using keywords, but functionality relied on exact string matches, often overlooking qualified applicants. These systems were not integrated with broader recruiting workflows, and due to their cost, adoption remained limited.
With the rise of the internet in the 1990s, the first online job boards such as Monster.com emerged. This was the first opportunity for companies to post jobs online instead of in newspapers. The ATS tools evolved alongside the internet driven changes, and slowly started integrating with online job boards. It is worth noting here that the adoption of companies using online application forms was slow. Most companies, especially smaller and mid sized ones, could simply not afford the cost of these platforms and tools.
This slow adoption continued in the 2000s. On top of the high cost, recruiters were resistant to change. They were used to the manual processes and found it hard to transition to a digital process. Concerns around data security also slowed adoption.
Online recruitment processes started to pick up in the 2010s.
A wave of ATS and recruiting SaaS platforms emerged around 2012, reflecting a shift toward cloud-based hiring infrastructure. Several major integration-ready HR platforms with ATS tools were launched in 2012, including Greenhouse and Lever. Online job platform Indeed, which was founded in 2004, experienced massive growth in the mid-2010s, and became the #1 job site globally in the mid-2010s. LinkedIn, which launched in 2003, grew from ~90 million users in 2010 to ~450 million users in 2016.
LinkedIn's recruiter workflows and application tools likely experienced rapid adoption in this exact period of the early to mid 2010s, inferring from the rapid growth of users. This scaling coincides with the normalization of digital recruiting pipelines in that time period. Deloitte's 2016 Human Capital Trends report highlights that by this period, digital recruiting platforms and integrated talent systems had become central to how organizations manage hiring, indicating that widespread adoption had already occurred by 2016.
The data suggests that digitization reduced friction on both sides of the market. Posting jobs became easier and more scalable, while applying required significantly less time and effort. As a result, both job openings and application volume increased.
However, this increase in volume introduced new bottlenecks. As more candidates applied to each role, the signal-to-noise ratio declined. Companies now had to filter through hundreds of applications, while candidates faced increasing competition and lower response rates.
In effect, the system became more efficient at generating activity, but less efficient at matching the right candidates to the right roles. This helps explain why job openings began to outpace hires in 2015: job postings and applications scaled faster than the system's ability to pair candidates to roles.
We can add this context of the mid-2010 digital hiring adoption to our JOLTS graph, visualizing the split between predominantly offline hiring pre-2012, and predominantly online hiring post-2015.
To approximate the growth of digital hiring, we can use LinkedIn membership as a proxy for the expansion of online recruiting activity. Figure 3 overlays U.S. job openings and hires with estimated global LinkedIn membership and the timeline of digital hiring adoption.
LinkedIn membership numbers are based on publicly reported global milestones rather than U.S.-specific data. While this is an imperfect proxy, the U.S. represents a significant share of LinkedIn's user base, and the overall growth trend provides useful directional insight into the shift toward digital recruiting.
LinkedIn membership accelerates significantly throughout the 2010s, reflecting the rapid expansion of digital professional networks. This growth continues into the early 2020s, reaching 1 billion members globally by 2023.
The timing of this growth aligns with the widening gap between job openings and hires, suggesting a potential relationship between the expansion of digital hiring platforms and increased application volume.
While we don't have perfect numbers reporting average applications per job posting, several anecdotal data points (CNBC, LinkedIn) suggest that recruiters today often receive 200+ resumes within the first 24 hours of posting a job. Estimates from Glassdoor indicate that a typical corporate job attracts around 250 applications on average. In contrast, seasoned recruiters report that years ago, a role might have received only 4 to 5 strong resumes. LinkedIn data also shows that U.S. applicants per open role in 2026 have doubled since the spring of 2022.
While these figures come from different sources and methodologies, they consistently point in the same direction: a large-scale increase in application volume per role.
With standardized tracking on applications not being present in the pre-digitalization era, we cannot know for sure how many applications recruiters received per opening. However, given the higher friction to apply, geographic constraints, and limited distribution channels (such as newspapers and word of mouth), it is reasonable to assume volumes were significantly lower, likely just a handful of applications per role.
The shift is not incremental, it is structural. Recruiters once reviewed a handful of applications per role. Today, they face hundreds, often within hours.
The system changed so much that the metric itself wasn't even tracked before. What once felt like a manageable matching process has turned into a problem of scale.
While the narrative of the digitization-driven impact on the job market is intuitive, it's observational rather than deterministic. That is, we did not conduct a controlled experiment of isolating one factor while keeping all other variables constant. Instead, we observe a strong correlation between the growth of online recruiting and the increasing inefficiency in hiring. This observed pattern is strong, but it's important to know that it does not prove causation.
There are several other factors that likely contributed to the growing job market tension.
One such factor is the increasing level of educational attainment. According to Forbes, the proportion of working-age adults with bachelor's degrees increased by 9% from 2008 to its current level of 47.1%, which is roughly a 24% increase in degree holders. While this does not translate directly into a one-to-one increase in competition, it significantly expands the pool of candidates competing for knowledge-based roles. As degrees became more common, they shifted from being a differentiator to a baseline requirement.
This shift is particularly visible in early-career hiring. Roles labeled as "entry level" increasingly require prior experience, often listing 2–3 years as a baseline expectation. In practice, "entry level" has come to mean "non-senior" rather than "no experience required." As a result, true entry pathways into the workforce have narrowed. According to Farah Sharghi, a well known career mentor and Ex-Google recruiter, apprenticeship and traineeship roles are becoming some of the few structured entry points left for new graduates.
At the same time, job requirements themselves have become more rigid. Many postings specify highly detailed skill sets, certifications, and tool experience that may exceed what is necessary to perform the role effectively. This appears to be a response to increased application volume: as more candidates apply, companies rely more heavily on credential-based filtering to manage scale and reduce perceived hiring risk.
However, this approach introduces its own inefficiencies. Strict requirements can filter out capable candidates who do not match exact criteria, while increasing the likelihood of overqualified hires who may leave quickly. Over time, this dynamic can weaken internal learning and development, as hiring begins to replace training rather than complement it.
Lastly, the rise of "ghost jobs" may further contribute to market inefficiency. Ghost jobs are online job listings that appear legitimate but are not intended to be filled in the near term. Companies may use such listings to gauge candidate availability and salary expectations, build future talent pipelines, signal growth to investors, or reassure employees that help is coming. At scale these practices can inflate perceived opportunity, contributing to job seeker frustration and application fatigue.
While not exhaustive, these factors provide additional context for the growing gap between job openings and hires, alongside the impact of digital hiring.
While outside of the scope of this case study, it is worth noting that with AI entering the mainstream public with OpenAI's release of ChatGPT in 2022, job market dynamics are likely to undergo even more structural change in the years ahead.
Both applicants and employers are beginning to adopt AI-powered tools at increasing scale. However, this adoption is unlikely to happen symmetrically. While candidates are largely free to use emerging tools to optimize and automate their applications, companies face tighter regulatory, legal, and reputational constraints in hiring processes, which may slow widespread adoption on the employer side.
At the same time, early signals are already visible. LinkedIn, for example, has introduced a "Hiring Assistant" designed to identify and surface relevant candidates to recruiters across its platform. As these tools evolve, one possible trajectory is a form of "AI vs. AI" dynamic, where candidates and hirers increasingly interact through automated systems.
Whether this leads to a new equilibrium or further amplifies existing inefficiencies remains an open question. It is conceivable that core elements of today's hiring process, such as resumes and job postings, could be fundamentally redefined or even phased out over time.
This represents a compelling area for future exploration, perhaps best revisited in a few years as these technologies and their real-world implications continue to mature.
What can this information help us reveal? How can we use the insights to inform our hiring and job seeking efforts?
For companies and hirers, the root issue is scale. When applications become frictionless, volume increases faster than the ability to evaluate candidates. One way to address this is to intentionally reintroduce friction earlier in the process: a filter before the filtering.
Thoughtful application questions, such as situational or experience-based prompts, can require candidates to invest effort upfront. This naturally reduces low-intent applications and improves the signal-to-noise ratio. Culture-fit questions can further strengthen this pre-filtering.
As AI tools become more common, these prompts should focus on lived experiences and specificity rather than generic responses. While AI can generate plausible answers, it cannot replicate detailed personal context.
For job seekers, the challenge is the inverse: not reducing volume, but increasing signal.
To stand out in a world where companies receive hundreds, if not thousands of applications, alternative channels become powerful levers. Showing your skills instead of simply claiming them is one of the most effective ways to build signal. A portfolio of projects, a blog, or any form of documented work can serve as proof of ability and help establish a personal brand.
That being said, not everyone wants to be a content creator, and that's not the point. The goal is simply to make your work visible in a way that feels natural to you.
Think about it: if you were hiring a photographer for an important event (like your wedding), who would you choose? Someone with a rich portfolio of beautiful past events' photos, or someone who simply claims they're great?
Sharing your skills can take many forms. Some people write about their ideas and experiences, others create short-form content, and others build simple websites that reflect their thinking and work. The format matters less than the visibility. The goal isn't to become sales-driven, it's to give others a clear way to understand what you bring.
Additionally, leveraging your network of friends, family, past coworkers, and acquaintances often remains one of the most effective paths to opportunity. And if your network doesn't immediately offer what you're looking for, reaching out intentionally to people or companies you admire can open unexpected doors. A strong portfolio makes this approach significantly more powerful.
Navigating today's job market can feel difficult, for both hirers and job seekers. It can be discouraging, especially when effort doesn't immediately translate into outcomes. But rejection, even repeated rejection, is not a signal that your contributions lack value. More often, it's a signal that the way value is discovered and evaluated has changed.
Linear career paths are becoming less common. You may need to take a less direct route, explore adjacent industries, or build experience in unexpected ways. But as long as you continue learning and engaging with what genuinely interests you, opportunities tend to re-emerge, often in forms you didn't initially expect.
Sometimes, the hardest challenges and uncertainties reveal the most valuable life lessons. If the job market feels more difficult today, it is not necessarily because opportunity has disappeared, but because the system itself has changed.
This case study aims to provide a clearer lens into that shift, and to show that even within a changing system, people still have the ability to shape their own path forward.
As AI-generated content becomes more common, I want to be transparent about how I used AI (and how I didn't use AI) in the creation of this case study.
This project has deep personal meaning to me. The idea for the topic, as well as all section and sub-section concepts, were developed by me independently.
One section, Looking Ahead: When AI Meets AI, was inspired by a thoughtful suggestion from my friend Lynette, which I chose to incorporate as a forward-looking extension of the analysis.
The data analysis, interpretation of sources, and overall narrative are also my own.
I used AI as a supporting tool in two main ways. First, to help me find relevant data sources faster, such as discovering the JOLTS dataset and the LinkedIn membership milestones in public releases.
Second, AI assisted in refining and polishing my writing. I wrote every section of this case study myself in its initial draft, and used AI as a "reviewer" to help tighten sentences and improve clarity.
AI also created the 'Figure 2: Evolution of Hiring from Paper to Digital' image.