AI-driven tax tool from startup sparks broad sell-off at brokerages

A newly announced AI tax‑planning feature from a wealth‑management startup accelerated a sharp decline in publicly traded brokerages as investors quickly repriced the odds that automation could replace parts of traditional advisory work. The feature combines automated document parsing with client‑specific tax‑scenario generation to produce rapid, personalized recommendations without extensive human intervention. Traders moved decisively: multiple broker‑dealers saw double‑digit intraday swings as the market digested the prospect of fee and asset outflows to lower‑cost, AI‑enabled platforms. That reaction dovetailed with a broader decline in technology and software names — the S&P U.S. software and services index fell sharply across a multi‑session window as investors reweighted expectations about AI substitution and execution risk. Bank research teams including JPMorgan and Morgan Stanley have argued the rout has priced in extreme disruption scenarios unlikely to materialize in the near term, suggesting some of the moves are sentiment‑driven rather than driven by immediate cash‑flow deterioration. Market participants also pointed to international follow‑through: Asian software and IT equities slid as traders applied the same labor‑replacement narrative to exporters and cloud services vendors. Credit desks and bond investors have begun penalizing issuers perceived as exposed to margin erosion or heavy AI capex needs, widening spreads on some software issuers and tightening private‑market deal assumptions. For legacy brokerages the near‑term move is primarily about positioning, headline sensitivity and potential algorithmic selling, but repeated product launches by nimble competitors make the competitive threat more credible. Key variables that will determine ultimate impact include client migration speed, regulatory scrutiny of automated tax advice, incumbent responses (build, partner or buy) and whether trust or complexity limits blunt automation’s market penetration. Supply‑side considerations — compute costs, data work and vendor relationships — present execution and financing demands that can favor larger platforms with scale. Strategic responses range from accelerating internal engineering and strategic partnerships to emphasizing bespoke advisory services that remain difficult to automate. Over time, the industry may bifurcate between scaled, low‑fee automated platforms and boutique advisory practices focused on complex, relationship‑driven work. For investors and managers the immediate priorities are mapping AI capability to addressable revenue at risk, assessing balance‑sheet implications if higher capex or financing costs are required, and monitoring credit and private‑market signals that could constrain strategic options.