Financial Advisors are teaching AI to do your job
Even advisors who are genuinely enthusiastic about AI in their own practice are, in a sense, feeding a version of the same pipeline. Plenty are using tools like the Canadian-built Focal AI to cut down on after-meeting admin, and every meeting note an AI assistant summarizes, every prompt an advisor runs, is a data point about how the job actually gets done. The difference between that and a Mercor contract is nobody’s cutting them a cheque for it.
The same push-pull is playing out at the institutional level, just with bigger numbers. RBC, TD, and BMO are betting heavily on AI even as their own federal regulator warns them the technology is shrinking the window they have to catch and patch security flaws in their own systems. TD expects roughly $1 billion in annual value from AI by 2028. RBC is pointing to $700 million to $1 billion in enterprise value by 2027, and told Davos in January that $2 billion of its $6 billion annual tech budget now goes to modernization and AI. That same technology, per OSFI’s warning, is compressing the timeline banks have to fix the vulnerabilities it can also be used to find. It’s the same tension advisors are facing one client meeting at a time, just playing out at bank-balance-sheet scale. Embracing the tool and staying ahead of what it makes possible are two different projects, and doing one doesn’t mean you’ve done the other.
Why this should actually bother you
Danielle Li, a management professor at MIT Sloan, has made the case in the Financial Times that this deserves more scrutiny across white-collar work generally, and wealth management is squarely included. Her point isn’t that AI assistance is bad for productivity: call-centre research she cites found AI access helped newer agents the most, by encoding what top performers do into software everyone can use. But once that expertise gets baked into a model, she argues, it stops belonging to the person who supplied it, and that person usually isn’t paid any more for having handed it over. In the worst version of this, she warns, highly skilled workers end up training the systems that let a firm swap them out for cheaper, less experienced staff down the road.
For financial planning, that means thinking hard about how much of your process you’re willing to hand to an employer or a training vendor: client scripts, planning templates, the way you handle objections. Push for real compensation if that knowledge is getting turned into something reusable. And it’s not only a personal calculation. An advisor who sells detailed process knowledge cheap to a training platform chips away at the leverage of every other advisor doing similar work, because a model trained on one contractor’s expertise gets better at the job everywhere it gets deployed. It’s a shared professional asset getting sold off retail, one hourly contract at a time, and it’s fair to be annoyed about that even if you’d never take one of these gigs yourself.
The pattern elsewhere isn’t encouraging
People contracting for these platforms in other lines of work describe a pretty consistent arc. Amanda Brown, a biology professor who took data-training gigs on Mercor and Handshake last year, told the Times the work soured fast once deadlines tightened and pay shifted from hourly rates to flat fees for jobs that took way longer than expected. Carolina Perez Sands, a speech and language consultant who contracted for Mercor, told The Journal podcast she watched an AI model absorb her corrections so quickly that within weeks there was nothing left to correct. That improved the product and ended her gig at roughly the same time. Anton Korinek, an economist currently 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. He pushed back on the notion that most white-collar workers will spend their careers training AI, likening it to assuming everyone will eventually end up a professor.
