The idea of AI-izing a recruitment platform is actually not complicated:
Use company information and job descriptions to semantically match with job seekers' CVs; then introduce broad industry data and companies' historical hiring records to improve matching accuracy; while also providing intelligent services like median salary, job trends, and career path recommendations.
But the problem is—recruitment platforms are not about facilitating deals; they are about traffic.
Just like social platforms, "successful matching" actually means traffic loss. Once a man and woman become a couple, they no longer log in frequently; once a company and a job seeker achieve a "perfect match," the platform's user activity declines. Therefore, these platforms need to continuously create the possibility of "near success," not "real success"—a structural balance between hope and disillusionment.
The Platform's Algorithmic Goal is Retention, Not Matching
What are the daily operational metrics of a platform? DAU (Daily Active Users), MAU (Monthly Active Users), average session duration, click-through rate, conversion rate.
But actually facilitating a "successful match" means user churn. Just as successful matching on social platforms reduces activity; on recruitment platforms, after an offer is accepted, both the employer and the candidate leave the platform, unless they have hiring or job-hopping needs again.
To maintain activity, the platform must create an "illusion of choice":
- Job seekers repeatedly optimize their resumes, browse jobs, and apply.
- Companies continuously collect resumes but may not make immediate decisions.
- The recommendation algorithm maintains a psychological gap of "within sight but beyond reach."
If AI improves matching accuracy, it means reducing these repetitive behaviors, lowering activity, user stickiness, and ad exposure, which negatively impacts operational goals.
The Platform Sells Not Matching, But "Attention + Data"
Fundamentally, the business model of platform companies is a Two-sided Market:
One side is corporate clients, the other side is job-seeking users.
The platform is essentially a business of "directing traffic to data assets," not "efficient transactions."
- For companies: The platform sells resume exposure, branded recruitment pages, industry data;
- For users: The platform provides "potential opportunities" + employment anxiety relief services.
Not: Real, accurate, timely job matching services.
If AI achieves "ultimate matching," it weakens the platform's "stickiness leverage" over companies and users, undermining their psychological expectation of "always getting closer to something better."
This is similar to the logic of short-video platforms continuously pushing content: not to let you finish one and leave, but to keep you scrolling for "the next one that might be better."
Platforms Rely on Transaction Frequency, Not Transaction Success Rate
The platform's revenue sources are not a commission model based on "one-time successful matching," but rather:
- Advertising revenue (displayed to companies);
- Resume package purchases (displayed to HR);
- Value-added services (user exposure, corporate headhunting, brand building);
- SaaS tools/data service fees;
- Transaction volume commissions (some platforms take a cut).
In other words, the platform profits from "transaction intent," not "transaction results."
Just as securities companies profit from commissions and naturally want clients to trade frequently rather than hold long-term; recruitment platforms also want you to keep refreshing, keep searching for better, not "get it done in one go."
Therefore, the essence of AI improving matching efficiency is compressing the platform's profit margin. The more accurate the AI, the more it undermines the platform's strategy of extending the transaction cycle.
Creating Anxiety is the Social Mechanism Platforms Rely on for Survival
Sociologist Zygmunt Bauman proposed an important point: "Modern society is a system where uncertainty is institutionally manufactured." Recruitment platforms are an excellent example of this theory.
1. Creating Mobility Anxiety
The logic of recruitment platforms is: You can always find a better opportunity.
This creates a structural contradiction where "scarcity anxiety of opportunities" coexists with "over-supply of information." The platform reinforces this structure by controlling information flow through algorithms.
2. Fictional Nature of Information
High-salary positions may not be real at all, just used to attract traffic. Job seekers' information also contains exaggerations and embellishments. Neither side wants to reveal "real information," and the platform has no incentive to screen and calibrate.
If real AI were applied to value calibration and authenticity verification, it would touch the bottom line of this "collective fiction" ecosystem.
3. Platform's Avoidance of Responsibility
The platform is not a "matchmaker," but an "information intermediary." It is not responsible for companies' hiring failures or for misleading job seekers. AI-ization means taking on more moral and technical responsibility for matching quality, which platforms naturally resist.
AI is a "Structural Threat" to Platforms
AI technology inherently tends toward value regression and efficiency improvement, which naturally conflicts with the platform's current logic of attention economy, data monetization, and matching delay.
Only when the platform's revenue model shifts from "frequency-oriented" to "result-oriented" will the value of AI-ization be truly unleashed.
In today's commercial world where "traffic growth" is the only virtue, expecting platforms to use AI to achieve matching efficiency is actually a misunderstanding of their business essence—it's not that they don't know how to do it, but that they simply don't want to.