Will AI Displace Financial Advisors? (EP. 455)

Episode Summary

Executive Summary: This live Animal Spirits episode centered on two big debates: whether AI will replace financial advisors and whether private credit is in a bubble. Michael Kitsis argued AI will raise efficiency but mostly change the work mix—not eliminate advisors—while Phil Huber said private credit headlines overstate risk and confuse normal credit-cycle losses with systemic failure. The episode also included a roast segment and a launch announcement for Exhibit A’s new monthly white-label report.

Main Topics: AI and the future of financial advisors (Priority: 5/5): The hosts read a listener email arguing AI could replace advisors by ingesting financial documents, personal preferences, and goals to generate tailored advice instantly. Michael Kitsis responded that DIY users were never likely clients, and technology historically has not erased advisory demand; it has mostly shifted tasks and raised service expectations. Historical resilience of the advisory business model (Priority: 5/5): Kitsis used industry history to show that over decades, fees, margins, and overhead have stayed remarkably stable despite major technology changes. He argued tech has reduced lower-level administrative work but has also led advisors to serve fewer households more deeply, rather than simply scaling client counts. Private credit under media scrutiny (Priority: 5/5): Phil Huber pushed back on narratives that private credit is 'in a bubble,' arguing the criticism conflates normal defaults, sector rotation, and public-market mark-to-market pressure with actual structural weakness. He emphasized that the asset class has survived multiple cycles and that many headlines ignore loss history and lender dispersion. Liquidity, redemptions, and fund structure in private credit (Priority: 4/5): Huber explained that semi-liquid private credit vehicles are designed with liquidity management tools such as revolvers and liquidity programs to avoid forced selling. He said redemptions and prorations are often portrayed more negatively than they function in practice, and that these protections exist to benefit remaining shareholders. The state of market volatility at the live event (Priority: 3/5): In the intro and outro, the hosts discussed a dramatic overnight move in crude oil and equity futures, noting markets appeared calmer by the time of recording. They framed the market as unusually resilient and vulnerable at the same time, with buyers repeatedly stepping in on down days. Roast segment and show announcement (Priority: 3/5): The live episode included a planned comedic roast between Michael and Ben, as well as jokes written by Ben’s children. The show ended with an announcement that Exhibit A will publish a monthly white-labeled commentary report alongside its chart products.

Key Arguments: AI is making advisors more efficient, but the people most attracted to AI-based DIY advice are usually not the same people who become advisory clients. Technology has historically changed the composition of advisory work, not the economics: fees, overhead, and margins have stayed near-constant, while client loads fell and service depth increased. Advisor tech improves client experience and internal operations, but firms often use freed-up time to go deeper with existing clients rather than add many more households. Private credit is experiencing a normal credit cycle, not a bubble; rising headlines reflect attention and fear more than deteriorating fundamentals. A single default or mark-down in private credit is not evidence of systemic collapse because the asset class contains thousands of borrowers and diversified exposure across lenders. Public-market valuations of private credit-related firms may already be pricing in severe downside scenarios, which complicates the claim that the market is ignoring risk. Liquidity issues in semi-liquid private credit products should be managed through design and credit facilities, not assumed to require fire sales of loans. The real impact of AI on software borrowers may be uneven: some legacy companies will be disrupted, others will adapt, and lenders are senior creditors rather than equity holders capturing upside. The advisory profession is likely to expand its service set further into tax, estate, and broader life-planning work rather than disappear. Comedians and human hosts were jokingly framed as safer from AI than advisors because AI still struggles with humor and nuance.

Data Points: Listener email cited AI capabilities: Import every financial document and tailor an investment instantly - Email read on stage arguing AI can replace human advisors Advisory firm growth at Ritholtz Wealth: From 7 people to almost 90 - Michael described firm growth as a reason AI will not eliminate people management Typical advisor fee then and now: 1% - Kitsis said median advisory fees have remained roughly unchanged over decades Typical overhead expense ratio: About 40% - Kitsis compared historical and current advisory industry benchmarking Typical profit margin: About 30% - Kitsis said median margins have stayed roughly stable over time Client load change: Down massively from 200-300+ clients to far fewer per advisor - Kitsis argued technology reduced client counts while deepening service Historic advisory office staffing example: 3 advisors with 8 support staff - Kitsis’ anecdote from an early-career independent brokerage Private credit portfolio size at Cliffwater: About $40 billion across two credit funds - Phil Huber introduced Cliffwater’s scale in the space Private credit history tracked by index: Back to 2004 - Huber referenced Cliffwater’s index history Number of unique middle-market borrowers: Over 10,000 - Huber cited the breadth of the middle-market borrower universe Additional broadly syndicated loan borrowers: About 1,400 - Huber added these to explain the scale of the overall credit universe Average historical default rate: About 2% - Huber used this as a base-rate expectation for annual defaults Expected annual defaults at that base rate: Over 200 - Huber explained how a 2% rate maps to borrower count Software exposure in the index: A little over 20% - Huber said technology/software is the largest sector but not one-third Effective loan maturity: 3 to 4 years - Huber said this creates natural organic liquidity Annual repayment rate: About one-third of the portfolio - Huber explained typical loan turnover in private credit Index performance in 2008: Down about 6.5% - Huber used the GFC as a stress-test benchmark Credit losses in 2008: About 60 bps - Huber contrasted losses with market drawdown Credit losses in 2009: About 7% - Huber discussed realized losses after the crisis Credit losses in 2010: About 3% - Huber continued the post-crisis loss history Index performance in 2009 and 2010: Up meaningful double digits in both years - Huber argued income offset losses over time Prior market reaction to private credit yields: 12%+ returns/yields in 2022 - Huber noted this attracted tourist capital into the asset class Long-run expected return for private credit: 8% to 10% - Huber framed realistic return expectations

Pivotal Quotes: "I think the power planner role is basically done." — Michael Kitsis: Kitsis on AI’s impact on entry-level advisory jobs "Betty's job is Orion or Black Diamond or one of the other portfolio management software platforms." — Michael Kitsis: Historical example showing how technology replaced admin work without changing firm economics "Private credit is not in a bubble." — Phil Huber: Huber’s core rebuttal to media narratives

Implications: The discussion suggests AI will reshape advisor workflows and hiring, but not erase human advice; meanwhile, private credit remains a cycle-sensitive but still functioning asset class. Investors should focus on base rates, fund design, and long-term behavior—not headlines.