In October 2025, the Future of Work Research Lab for SAP SuccessFactors published our seminal report, “The road ahead: Predictions and possibilities for the Future of Work,” where we explored the Future of Work across three key pillars: the future of working, the future of the workforce, and the future of work practices. Across all three pillars one truth was abundantly clear, the proliferation of AI technology is forcing HR to fundamentally rethink everything. We are seeing this play out strongly now, particularly in the recruiting space, where already 39% of candidates are leveraging AI tools according to our research.

As candidates adopt AI to optimize applications at scale, traditional screening signals become noisier, forcing employers to automate even more aggressively in response. We call this shift the “AI standoff,” where candidates and employers are both battling to maximize efficiency in a race to the bottom. In this article, we will check in to see how this topic has progressed since October based on more recent data and business press literature, discuss how this approach might harm both candidates and organizations, and identify a better approach forward.

How the AI standoff is harming employers

“Applicants per requisition” metrics have been increasing for years as technology has enabled organizations to reach broader candidate pools (e.g., through online job boards, one-click apply, etc.). This trend was largely seen as positive for employers, yet candidates now having access to their own AI tools has made identifying legitimately qualified talent from that pipeline much more challenging.

In an over-saturated job market, candidates may become desperate to find ways to get their application to the top of the pile. According to our predictions report, 68% of employees believed that they would need to use AI in the future if they wanted to stand out from other applicants.

Even if candidates using AI to help refine resumes or CVs may be viewed as permissible (or even a potential indicator of candidate AI skills), this is the least of organization’s concerns. For example, 36~38% of respondents said they would consider using AI to fake application materials such as references and work samples. 51% of respondents said they would consider leveraging an AI avatar to complete a job interview on their behalf.

The result of this is not simply the nail in the coffin of the resume, it’s a complete breakdown of the toolset organizations have historically used to narrow down an ocean of applicants to a more manageable set of qualified candidates. AI-powered candidate screening and matching tools are developing better signal detection capabilities to help recruiters see through the noise. Still, we see an increase in the business press literature discussing how top talent is getting buried while organizations are shifting resources towards identifying candidate cheating in an increasingly uphill battle.

How the AI standoff is harming candidates

Although it would be easy to assume that the employers “losing” the AI standoff means candidates are “winning,” evidence suggests that this is not the case. On the contrary, things only seemed to have gotten worse for candidates as well.

According to system data collected through SmartRecruiters¹, an SAP company, the average number of applicants per early-career job opening has doubled since 2021. Although a portion of this is likely due to general macro-economic conditions, this trend coincides with the rapid adoption of generative AI tools that candidates can use to streamline their application process.

“Serial applicants” (defined here as candidates who submit 10+ applications/day, 50+/month, or multiple applications within the same hour) are on the rise, particularly in industries such as public sector & social services, healthcare, and media & entertainment, according to system data from SmartRecruiters.

But more isn’t always better. That same data from SmartRecruiters indicates that compared to typical applicants, “serial applicants” are actually 2x less likely to ever reach the interview stage. This suggests that the pure quantity-based approach may be ineffective at helping candidates be considered, particularly if their application quality suffers as a result.

In an increasingly competitive job market, competition among candidates is fiercer than ever before. Yet the increase in candidate AI use seems to have only increased the amount of noise in the system without having the desired effect of helping them be considered for open roles.

The road ahead—less noise through better measurement

The Future of Work Predictions report outlined two possible paths forward for recruiting. Today, it is clear that the system is drifting toward the “AI standoff” future, one defined by escalating automation, growing mistrust, and diminishing returns for both employers and candidates. As each side responds to the other with more scale, speed, and sophistication, the result is not better matching, but more noise and more distance between people and opportunity. Perhaps it’s time to call a “truce” and explore another potential future.

Calling a “truce” in this case does not mean rejecting AI in recruiting. It means using it more deliberately. Rather than continuing to invest in surveillance, fraud detection, and ever-more aggressive filtering, organizations have an opportunity to redirect their efforts toward higher‑fidelity signals of potential such as work samples, skill assessments, and verifiable credentials that more directly reflect a candidate’s ability to succeed. Done well, these approaches can reduce noise without shifting even more burden onto candidates who are already feeling overwhelmed and burnt out.

This path forward is not without challenges. It requires new infrastructure, new standards of trust, and a willingness to move away from familiar but increasingly broken practices. But if employers and candidates agree to prioritize quality over scale, recruiting can shift back toward mutual discovery; restoring trust and making hiring not just faster, but more accurate and meaningful for everyone involved.