Business Insider surveyed its latest class of "Wall Street Rising Stars" — young professionals at firms including Goldman Sachs, Blackstone, KKR, Evercore, Apollo, JPMorgan, and Morgan Stanley — on what skills matter in an AI-saturated finance industry. The consensus is striking in its uniformity: the skills that mattered before AI still matter, but they matter sooner. The mechanical grunt work that once defined a junior analyst's first two years — building pitch decks, formatting reports, running preliminary models — is increasingly handled by LLMs. Jim Tilson, a 35-year-old senior managing director at Evercore, frames the divide bluntly: "The highest performers use AI as a tool to make themselves smarter, while the lowest performers let AI do the work and get replaced." Diana Liu at Blue Owl Capital reports hearing complaints that junior staff now default to AI for problem-solving rather than building their own frameworks first. The recurrent theme is judgment as the new entry ticket. Daniel Borkenstein at Apollo uses AI as a "thought partner" to stress-test investment theses but insists the point of view must come from the human. "Nobody is going to sell their company to Claude," he says. "You can't go to the investment committee and say 'Claude said so.'" David Oppenheim at Evercore notes that the best juniors already think beyond assigned tasks to the bigger picture — a skill that used to develop over years, not months. Relationship skills are being revalued upward. Kate Casey at KKR calls interpersonal collaboration "the differentiator." Fiona Ismail at JPMorgan emphasizes deep listening — understanding what a client actually needs, not just what they ask for. "AI can answer what you ask it, but it can't get to the heart of what someone is actually trying to say," she notes. Ryan Febles at Capstone Investment Advisors frames it as a leadership filter: executives want to know who they can confidently put in front of management teams. Several respondents push back against premature automation. Jake Epstein at Blackstone tells first-year analysts they have two jobs: doing the work and learning. "It's always worth doing something yourself at least once to learn how it's done" before letting AI accelerate the process. Maya Venkatraman at Morgan Stanley, who oversees leveraged finance for U.S. transportation infrastructure, warns that technical understanding remains essential: "You still need the technical understanding to spot which assumptions are weak and which answers don't hold up." The most forward-leaning advice comes from Goldman Sachs. Mike Washington, a 32-year-old managing director, recommends junior staff learn LLMs and experiment with vibe coding to automate routine tasks, noting that some top analysts are gaining senior access specifically because they're "proficient" in AI workflows. He estimates 5-6 hours per week of self-directed AI study yields outsized returns. Bridget Chaudhry, also at Goldman, frames self-teaching ability as the single best predictor of junior success. The implicit message across all interviews is that AI compresses the apprenticeship timeline. Skills that once developed gradually through years of repetitive work — pattern recognition, judgment under ambiguity, the ability to hold a point of view — are now table stakes within the first year. Matthew Caltabiano at Millennium Management advises students to publish their work online. Rex Johnson at Walleye Capital counsels comfort with ambiguity and rapid failure. The floor has risen, and the ceiling hasn't moved.
