Recent headlines have highlighted how large language models (LLMs) perform well and quickly on the CFA exam. These attention-grabbing headlines should not be viewed as a “death sentence” for a certification renowned for its rigorous curriculum and challenging pass rates. Rather, they serve as another illustration of artificial intelligence’s (AI’s) expanding capabilities and offer an opportunity to reflect on competency standards within the financial industry.
When AI Passes the CFA Exam
First, AI proponents should breathe a sigh of relief. This scenario is precisely where AI is expected to excel: a well-defined body of knowledge, abundant homogeneous training data, and a test format standardized across participants globally and through time. This outcome should not be surprising given how LLMs have demonstrated impressive capabilities in other standardized examinations beyond finance.
These tests are designed to assess baseline competencies, and AI’s success in these areas underscores its ability to process and synthesize vast amounts of information efficiently, especially where passing thresholds do not demand perfect accuracy. If AI didn’t perform well in this scenario, it would certainly contribute to the ongoing debate about the outsized investments in its advancement.
Technology Has Always Raised the Bar
Second, as Mark Twain reportedly said, “History doesn’t repeat itself, but it often rhymes.” The progress of AI echoes broader trends in the financial industry and underscores that this progress isn’t necessarily linear, but can occur in leaps and bounds. The financial sector has embraced many technological advancements, moving from pen and paper to calculators, then to computers, Excel spreadsheets, Python programming, and more. None of these transitions turned out to be an existential threat to the profession; rather, they enhanced efficiency and analytical capabilities, freeing up professionals from routine tasks and allowing them to focus on higher-value activities.
This historical perspective is exemplified by Benjamin Graham, father of value investing and driving force behind the CFA designation. Graham wrote optimistically about “The Future of Financial Analysis” in the Financial Analysts Journal in 1963, when the computer made its entry in the investing world.
Competence Keeps Evolving
Third, AI serves as a reminder that the bar for what constitutes basic competency is a continuously evolving standard, and that success in this industry, as in many others, requires an ongoing commitment to upskilling. CFA Institute has long promoted this approach, adapting its curriculum to integrate topics such as AI and big data. The breed of financial analyst still exclusively using pen and paper, not having basic computing skills, being apprehensive of Excel spreadsheets, or having no appreciation for the potential of programming has largely become obsolete.
Not using AI is no longer an option and leveraging it where it’s value-adding, and with the appropriate guardrails, can become a significant advantage. The time saved through AI-driven analysis can be redirected toward more strategic thinking, complex problem-solving, and client engagement. To further this goal, CFA Institute has launched data science certificates and practical skills modules focusing on Python, data science, and AI to equip professionals with forward-looking skills.
Why Human Judgment Still Matters
Finally, AI will not be a replacement for distinguishing yourself as an investment professional anytime soon. Success in the field demands more than rehashing common and easily accessible knowledge. Landing that first job requires more than tapping into a broad corpus of knowledge; it demands demonstrating the ability to apply knowledge in ever-changing market circumstances, critically analyze information, and innovate — a challenge that goes well beyond merely passing Levels I, II, and III.
In that vein, hiring managers will more likely ask, “What aspects of the CFA curriculum will you leverage to assess how uncertainty around tariffs may impact the supply chain in your industry?” They will less likely ask, “Do these investments look suitable given this hypothetical client’s investment profile?”
Similarly, investment performance is driven by finding outliers and identifying information that the market may be missing. This requires not only a deep understanding of foundational knowledge, but also the ability to contextualize it and express nuanced judgment grounded in subject matter expertise. While AI tools can serve as powerful assistants in this endeavor, the ability to uncover differentiated insights in a timely manner necessitates skills that extend far beyond surfacing consensus views that pass an exam threshold.
As CFA Institute has been emphasizing for years, the future belongs to those who master the AI + HI (human intelligence) model, where investment professionals achieve superior outcomes through the synergy of machines and humans. The parting words of Graham’s 1963 FAJ article still ring true: “Be all as it may, of one thing I am certain. Financial analysis in the future, as in the past, offers numerous different roads to success.”
I acknowledge the contributions of LLMs in reviewing and refining my outline and draft.
