A recent listing on Mercor asks for something specific: an experienced financial advisor, wealth manager, or CFP, CFA, or CPA charterholder, to help design, test, and refine AI systems built to replicate real-world wealth management, retirement planning, and investment advisory work. The listing advertises pay in the range of $90 to $150 an hour, with bonuses possible for the right résumé — a figure taken directly from the posting and not independently verified. The work involves auditing how an AI model constructs a portfolio, profiles risk, and plans a retirement drawdown, then telling the model where it got it wrong.
A financial advisor, in this arrangement, gets paid to help build the thing that might eventually do their job.
Mercor is not alone in recruiting from the advice profession. A parallel listing seeks financial and investment analysts to write, review, and validate prompt-based questions used to train AI, with a preference for CFA Level I or II candidates who can build discounted cash flow models and interpret earnings filings well enough to grade an AI's version of the same work. A related junior buy-side analyst listing advertised roughly $105 an hour for 10 to 20 hours a week, again as advertised by the platform and not independently verified.
These postings sit inside a much larger industry that has drawn scrutiny from The New York Times ("The Work of Helping A.I. Destroy Work," Lora Kelley, July 10, 2026), The Wall Street Journal's The Journal podcast ("How AI Is Being Trained to Do Your Job," June 4, 2026), and the Financial Times: a fast-growing "human data" business in which credentialed professionals are paid to feed their expertise into the models most likely to compete with them. According to the Times, Mercor, founded in 2023, was reportedly in talks this month about a funding round that would value the company at roughly $20 billion, twice its valuation from just months earlier. Handshake, a recruiting startup that pivoted into AI data work only in 2025, told the Times its annualized revenue run rate crossed $1 billion in April, up from $550 million at the start of the year — a figure this article has not independently verified.
What began with low-paid overseas workers tagging images has moved up what the industry calls the value chain. Brendan Foody, Mercor's 23-year-old chief executive, told the Times that capturing not just individual expertise but the collective output of entire firms was "the bottleneck to a frontier lab automating everything that people do." His company has acquired a start-up that builds simulated versions of the software — Slack, Salesforce, and the like — that professionals toggle between all day, so AI can learn what people actually do at a firm like Goldman Sachs.
Job boards for Mercor now include finance-specific project categories that ask contractors to evaluate an AI's investment thesis, calibrate a retirement-income model for suitability, and check whether a simulated advisory conversation would hold up to a compliance review — the same judgments a junior advisor or paraplanner spends years learning on the job.
The industry's working assumption has been that AI mainly threatens back-office and administrative work, while advisors — who hold the client relationships — "are not going anywhere, at least not in the near future." That assumption looks less certain once you account for a labor market where advisors and CFPs are themselves being paid to help build tools meant to replicate their judgment.
It's also worth reading against survey data on next-generation advisors, which found that roughly three-quarters of senior industry leaders expect AI-driven restructuring to hit junior roles hardest — nearly twice the rate they expect at the mid- or senior level. Junior roles are traditionally how an advisor learns to run client meetings, build a plan, and handle objections. If a model can absorb that same knowledge by paying an experienced advisor for a few hours a week, firms have less reason to keep offering a 24-year-old associate the traditional apprenticeship path.
Even advisors who see real upside in AI — and 70% of advisors surveyed this spring said they use at least one AI tool today — are describing something close to unpaid training-data generation in their own client-facing work. Michael Mignosi, senior director of organic growth at Perigon Wealth Management, expects the value of advisor thought leadership to erode now that AI lets anyone "sound like an expert" instantly. Every prompt an advisor runs through a tool, every note an AI assistant takes in a client meeting, is a data point about how the job actually gets done — data that, unlike a Mercor contract, nobody is paying the advisor directly to supply.
Danielle Li, a management professor at MIT Sloan, has argued in the Financial Times that this pattern deserves scrutiny across white-collar work generally, wealth management included. Her point isn't that AI assistance hurts productivity — call-center research she cites found AI access helped newer agents the most, by encoding the techniques of top performers into software available to everyone. But once that expertise is encoded into a model, she argues, it effectively stops belonging to the worker who supplied it, and that worker typically isn't paid any more for having contributed it. In the most extreme version of this pattern, she warns, highly skilled workers can end up training the very systems that let a firm later replace them with less experienced, lower-cost staff.
Applied to financial planning, Li's advice comes down to two things. First, think carefully about how much of your process — client scripts, planning templates, objection-handling playbooks — you hand over to an employer or a training vendor, and push for direct compensation when that knowledge gets converted into a reusable asset. Second, remember this isn't just an individual calculation: an advisor who sells detailed process knowledge cheaply to a training platform affects the leverage of every advisor doing similar work, since a model trained on one contractor's expertise gets better at the job for everyone, everywhere.
Workers contracting for these platforms in other professions describe a consistent arc. Amanda Brown, a biology professor who took data-training gigs on Mercor and Handshake last year, told the Times the experience soured quickly as deadlines tightened and pay structures shifted from hourly rates to flat fees for work that took far longer than expected. Carolina Perez Sands, a speech and language consultant who contracted for Mercor, described on The Journal podcast watching an AI model absorb her corrections so quickly that within weeks there was nothing left to correct — improving the product and ending her assignment in roughly the same motion. Anton Korinek, an economist on leave from the University of Virginia to work at Anthropic, told the Times he expects demand for this kind of human training to shrink over time, and pushed back on the idea that most white-collar workers will eventually spend their careers training AI, comparing that framing to assuming everyone will end up working as a professor.
It's entirely possible the market for advisor-supplied training data turns out to be a temporary bridge: well paid while models are still learning the nuances of suitability, compliance language, and client psychology, and considerably less lucrative once they've absorbed enough of it. Contractors in other fields have already lived through that transition, sometimes within a single year of contract work.
None of this is a reason for an advisor or planner to turn down one of these gigs outright — extra income, a chance to build AI fluency, genuine curiosity about the technology are all fair reasons to take the work. But it's a reason to read the contract's IP and confidentiality terms closely, negotiate on that basis, and go in understanding what's actually being asked: not just to answer questions about financial planning, but to hand over, hour by hour, the judgment a career in advice is built on.
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