Gen Z Uses ChatGPT to Practice Salary Talks
March 22, 20266 min readStandard
Fazen Markets Research
AI-Enhanced Analysis
Gen ZChatGPTsalary negotiationsAI roleplayHR policy
Gen Z professionals are increasingly turning to conversational AI to rehearse difficult workplace conversations, including salary negotiations, performance reviews and conflict resolution. Fortune reported on March 22, 2026 that a growing segment of the cohort—approximately 35% in the sample cited—has used ChatGPT for roleplay scenarios to prepare for real-world interactions. The rise of AI as a rehearsal tool dovetails with the platform-level scale achieved by large language models since late 2022, and coincides with employers taking notice of candidate behaviour during hiring and onboarding. For institutional investors and corporate leaders, the phenomenon has implications for talent management, recruiting efficiency and the design of training programs.
The adoption curve for conversational AI tools accelerated after OpenAI released ChatGPT in late November 2022; OpenAI disclosed roughly 100 million monthly active users by January 2023, a benchmark that demonstrated rapid consumer adoption and a scalable user base for downstream behavioural trends. Within three years of launch, the technology has migrated from novelty to utility in personal productivity, technical coding assistance and, increasingly, social-skill rehearsal. Fortune's March 22, 2026 coverage highlights a qualitative shift: younger workers who grew up on screens are repurposing AI roleplay as a low-cost, low-stakes rehearsal gym for negotiations and other emotionally fraught exchanges.
This behavioural shift intersects with broader labour-market dynamics. Wage negotiations have become more frequent as turnover remains elevated in many sectors; Glassdoor and LinkedIn have documented shorter tenure and more dynamic job-changing behaviour since 2020, putting a premium on early-career negotiation skills. Employers and talent acquisition teams are responding—some with policy updates and others by embedding coaching into candidate experiences. That response loop creates a feedback mechanism: as candidates arrive better prepared, employers recalibrate evaluation benchmarks and compensation bands.
Historically, mock negotiations and in-person roleplay were confined to career centres, executive coaching or HR workshops, often with a measurable cost per participant. The new AI-driven model reduces marginal cost to near zero, effectively democratizing access to rehearsal. That means differences in negotiation preparation across socio-economic cohorts may narrow if the tools continue to be free or low-cost, but it also raises questions about standardized assessment when employers compare candidates who have access to AI tutoring to those who do not.
Fortune (Mar 22, 2026) provides the primary observational datapoint for this article: a reported 35% of Gen Z respondents indicating use of ChatGPT or similar models to roleplay workplace conversations. This single figure is significant because it signals mainstream use rather than niche experimentation; to provide context, OpenAI's January 2023 disclosure of 100 million monthly active users illustrates the baseline scale of these models. ChatGPT's public launch on November 30, 2022 and subsequent iterations set a timeline where substantial adoption could plausibly develop within three years, aligning with Fortune's March 2026 report.
Beyond raw penetration, the qualitative data in Fortune's reporting suggests that use cases are concentrated in specific scenarios: salary negotiation rehearsals, preparing for performance feedback, and practicing exit conversations. Employers in the Fortune piece reported noticing conversational fluency and more assertive compensation requests in candidate interviews. A useful comparison: employers who reported formal negotiation coaching programs in 2024 versus those in 2026 documented differing outcomes—companies that invested in coaching saw a 10–15% higher internal offer acceptance rate, per HR surveys referenced in industry reporting—suggesting that rehearsal, whether human-led or AI-enabled, materially affects outcomes.
Publicly available metrics on AI usage and labour outcomes remain fragmented. Where the Fortune article fills a gap is in behavioural observation rather than causal proof. The correlation between AI rehearsal and higher requested compensation is plausible and reported anecdotally, but robust causal studies are not yet published. Institutional investors should therefore treat the current dataset as indicative of directionality—growing use and observable employer response—rather than definitive proof of long-term structural change.
For talent-heavy sectors—technology, professional services and finance—the rise of AI roleplay changes the dynamics of entry-level hiring and early-career retention. Recruiters may see higher variance in candidate presentation, with firms differentiating on candidate-sourcing strategies, employer branding and transparent compensation frameworks. This matters for corporate margins: if candidates secure higher starting offers more frequently because of advanced preparation, companies with tight compensation bands could face margin compression or need to reprioritize investments in productivity-enhancing tools.
Education and training providers stand to be disrupted. Traditional career services, paid coaching firms and enterprise learning vendors may face competition from AI-first offerings that can deliver scaled, asynchronous rehearsal. Some incumbents will integrate AI into their platforms—an approach that creates bundling opportunities and recurring revenue streams—while others may lose share. Institutions that pivot successfully could benefit from cross-selling assessment and credentialing services tied to demonstrated conversational proficiency.
For HR policy and compliance, increased use of AI for negotiation rehearsal raises questions about fairness and benchmarking. Companies that lack transparent salary bands may see larger outside-market offers accepted by candidates trained by AI, pressuring salary structures. Conversely, firms that publish pay ranges and use structured interviews will be less exposed to variance in candidate preparedness. Investors should therefore consider human capital strategy as an input in operating models, and monitor which firms publish pay transparency metrics and which invest in internal coaching programs.
Several risks accompany the rise of AI roleplay for negotiation. First, there is model fidelity risk: large language models can produce plausible but inaccurate scripts or advice that might backfire in live interactions. Candidates who rely solely on AI guidance without situational judgment may overreach, harming long-term employability. Second, regulatory and legal risk exists if employers interpret AI-prepared materials as deceptive representation; while no broad legal consensus exists yet, class-action risk could emerge around recruitment misrepresentation if factual inaccuracies are systemic.
Operational risk for employers is non-trivial. Interview evaluation rubrics predicated on spontaneous communication may need recalibration when a higher share of candidates arrive rehearsed. This can increase time-to-hire if companies feel the need to add technical or behavioural assessments to validate authenticity. From an investor perspective, firms that cannot adapt talent acquisition processes may experience higher recruitment costs and volatility in compensation spend.
Finally, reputational risk is present for both candidates and companies. If the public perceives that AI-assisted negotiation confers an unfair advantage to certain cohorts, there may be pressure for policy remedies such as expanded salary transparency or regulated disclosure about AI usage in candidate prep. The policy landscape is nascent but evolving; prudent stakeholders are monitoring legislative activity around AI and employment practices in multiple jurisdictions.
Fazen Capital views the use of ChatGPT and similar models as a structural accelerant to an existing trend: the professionalization of early-career negotiation. However, our contrarian read is that AI roleplay will compress rather than expand long-term wage dispersion if firms respond with greater transparency. In other words, while short-term observed outcomes may include higher initial asks and greater variance, the natural employer response—published pay bands, structured interviews and algorithmic compensation calibration—will likely mute disparities over a multi-year horizon.
From an investment lens, the more actionable insight is not the prevalence of AI roleplay per se but which companies adapt productively. Firms that integrate AI into learning and development, that publish compensation frameworks, or that deploy AI to streamline recruitment workflows stand to benefit from lower marginal hiring costs and improved retention. Conversely, companies that ignore the shift may face elevated recruiting expense and worse hiring outcomes. For this reason, Fazen monitors disclosure around pay transparency, L&D spend, and recruitment process metrics as leading indicators of human-capital quality.
We also see an opportunity in enterprise vendors that can package AI roleplay with compliance, evaluation and credentialing tools. These vendors can create defensible business models by tying roleplay outcomes to verifiable skill signals that hiring firms value, creating a two-sided marketplace effect. Our recommended focus is not on betting on raw user adoption but on identifying businesses that monetize the intersection of AI rehearsal and employer verification.
Q: Will AI roleplay make salary negotiations automatic or replace human coaching?
A: No. AI roleplay increases accessibility to rehearsal but does not replace the interpretive, personalized guidance a human coach provides for complex negotiations. Human coaches can incorporate firm-specific dynamics, personality calibration and non-verbal cues that LLMs currently cannot reliably emulate. Practical implication: companies offering blended human-plus-AI coaching may capture the highest willingness-to-pay.
Q: How has employer behaviour historically adapted to improved candidate preparation?
A: Historically, when candidate preparedness rose—such as after the proliferation of online interview resources—employers responded by standardizing interview formats and publishing clearer role expectations. Over time, transparency and structure reduced variance in hiring outcomes. That pattern suggests a similar equilibrium could emerge with AI roleplay, leading to more standardized compensation frameworks and reduced arbitrage from rehearsal alone.
Gen Z's use of ChatGPT for negotiation rehearsal is a measurable behavioural shift with operational implications for employers, training providers and investors; monitor pay-transparency disclosures and L&D monetization as leading indicators of which firms will gain.
Disclaimer: This article is for informational purposes only and does not constitute investment advice.
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