But retirees are not passive beneficiaries since they face a mirror-image version of the same risk: less time to recover if the AI trade reverses. A sequence-of-returns shock in a tech-heavy portfolio at age 67 is not the same as a drawdown at 32. The upside and the fragility are two sides of the same coin.
Jonson said although this applies across the board, it’s particularly apparent for tech workers. “Depending on when they joined, some employees are sitting on pretty remarkable paper gains, in some cases 20x or more,” Jonson told Fortune. “Liquidity events provide them opportunities to secure financial independence, pay off the house, pay for college, diversify, and still have some upside.”
A young employee at a major AI company may have a salary, bonus, career path, and stock compensation all tied to the same market narrative. If that worker also has a large amount of company stock, the risk is not just that the market sours, but that their job and their portfolio fall at the same time.
But for a lucky few, he said, “it’s truly a have your cake and eat it too moment.”
However, not everyone is in that position. Employees who joined later may be facing a much more emotional calculation. “We’ve seen the narrative shift over the past few months with OpenAI and Anthropic, and employees are worried it could be a winner-take-all situation,” Jonson said. “That can make equity feel less like guaranteed upside and more like a concentrated emotional bet, which carries a lot of psychological weight.”
That’s where Jonson says financial planning matters, saying it’s not whether AI will keep winning, but rather, how much of someone’s financial life is tied to that outcome.
“One way to potentially spin it is: You have a million dollars of your company stock. Should I just sell and put it in a high-yield savings account?” Jonson said.
For Jonson, he said investors should answer that question and look at it at an angle less of risk tolerance and more of what he calls “risk ability.”
“Risk tolerance, I think, gets a bit overused in the industry, but I think it’s not married enough to risk ability. Like, what is your ability to take risk?”
Risk tolerance is how much volatility someone thinks they can handle. Risk ability is whether their actual life can absorb the hit. A 28-year-old with a stable job, emergency savings, and decades before retirement may be able to ride out a market selloff. A 28-year-old whose paycheck, stock grants, and career prospects are all tied to one AI-heavy employer may not have the same cushion, even if they feel comfortable taking risk.
“I think risk tolerance is so much confined to, like, I don’t understand risk. And once you can understand risk, then I think your risk tolerance gets closer to your risk ability, because you understand why,” Jonson said. “I think that’s where we as an industry can do a better job of helping people think about risk ability more at the forefront.”
Take previously considered “safe bets” as a prime example. Bonds, money-market funds, and high-yield savings accounts no longer look as irrelevant as they did during the zero-rate era. For a tech worker sitting on concentrated company stock, moving some money into safer assets may not be a bearish call on AI, but diversification.
Retirees seem to face the opposite problem: Though they may not face the same job risk from AI—and may actually benefit if AI keeps lifting corporate profits and stock prices—they also have less time to recover if the AI trade reverses.
AI is also changing how his own clients approach financial advice. “We’re seeing clients, including employees at AI companies, use AI more and more as a first pass for major financial decisions,” Jonson said. “This can naturally help inform them, but they still come to us as advisors to help contextualize that information into their situation and implement the right strategies.”
In one case, Jonson pointed to a client who used AI to evaluate a tender offer and initially felt confident in the answer. After advisors added a broader planning lens, including taxes, concentration risk, liquidity needs, and long-term goals, the client brought that framing back to the AI tool, which recognized that the added context changed its initial analysis.
“AI is a good validator,” Jonson said. “But the quality of the answer depends on the quality of the framing.”
