AI and the missing first rung of the investment career ladder

By Paul Moody

Artificial intelligence is already reshaping the start of an investment career with new entrants using it to summarize filings, interrogate data, produce first drafts of research and test investment scenarios in a fraction of the time these tasks once required.

Clearly AI offers a significant opportunity, but it also creates a less visible risk.

For generations, junior professionals developed judgement through the work itself: building models, checking assumptions, reading footnotes and revising investment notes after challenge from more experienced colleagues. Some of that work was repetitive, but it revealed how analysis is constructed, where errors arise and when a persuasive conclusion rests on weak foundations.

As AI absorbs more of these foundational tasks, the profession may face a hidden learning gap; greater productivity, but fewer opportunities for new entrants to develop disciplined, ethical and accountable judgement.

The danger is not simply that junior professionals will trust machines too readily. It is that output may conceal limited understanding. AI’s capabilities are irregular: it may perform exceptionally well on one task and fail on another that appears similar. Consequently an AI answer is not necessarily a reliable one and this changes what early-career professionals need to learn.

Technical knowledge remains essential but new entrants must still understand financial statements, valuation, portfolio construction, economics and risk. Indeed, those foundations become more important when professionals are expected to interrogate AI-generated work.

I expect the priority to increasingly shift from processing information to exercising judgement: knowing which data matters, what assumptions deserve challenge, where uncertainty remains and when human oversight must take the lead.

That requires early-career professionals being able to verify sources and calculations, reconstruct how a conclusion was reached, identify plausible alternative interpretations and explain when an issue should be escalated. They must also understand that accountability cannot be delegated to a system that cannot itself hold a professional duty.

Employers therefore need to redesign their approach to new recruits and I believe, every firm introducing AI into junior workflows should ask: What learning experience are we removing, and how will we replace it?

The answer may include more deliberate mentoring, scenario exercises, case discussions, decision logs and investment post-mortems. In addition, managers should assess the reasoning behind a recommendation, not only the quality of the finished presentation.

The CFA® Program provides a rigorous foundation of knowledge, ethics and professional standards. In an AI-enabled market, translating that foundation into practice becomes even more important.

AI can help the next generation contribute sooner and become more capable but trusted judgement will not emerge automatically from better tools. It must be deliberately taught, visibly supported and professionally recognized.