Why one CIO is pumping the brakes on the AI trade-and shifting to Europe

Why this strategist is getting cautious on the AI trade

AI has driven a big chunk of equity returns over the past year. Yet Hirtle Callaghan CIO Brad Conger is tapping the brakes. His stance: keep exposure, but scale it back. The reason is simple-too many open questions, too much perfection baked into prices.

The case for caution

AI is still sorting out its winning business models. Who captures the economics-model providers, hyperscalers, chipmakers, or software firms that bundle AI as a feature? Where the compute happens (centralized vs. edge) changes the margin structure and who keeps the dollars.

At current prices, the market feels like it's underwriting multi-trillion revenue runways with very little friction. That can work-until it doesn't. If the next wave of models delivers less uplift than expected, or if open-source breakthroughs (think DeepSeek-style leaps) reset costs and pricing, momentum can stall fast.

What could crack the story

Model progress slows: future iterations (e.g., a hypothetical "GPT-6") don't deliver step-change gains.
Open-source compression: community models improve, costs fall, and premium pricing gets harder.
Capital intensity: capex outpaces monetization, dragging returns on invested capital.
Unit economics: inference costs stay high while customers treat AI as a bundled feature, not a budget line.
Data constraints or regulation increase friction and timelines.

Positioning: trim, don't torch

Conger's team is underweight the AI complex and mega-caps. If AI-related names plus the Magnificent Seven sit near 40% of the market, they run closer to 27-28%. That's a clear tilt without taking career-risk extremes. You keep core exposure and cut the concentration risk.

Set position caps and pre-plan trims into strength.
Favor cash flow yield and balance-sheet quality over pure narrative beta.
Use options or pair trades to hedge the fattest tails.

Where to be overweight: Europe on valuation

His offset is Europe. The valuation gap vs. the U.S. is wide by historical standards. Europe doesn't need U.S.-style multiples; it just needs "less bad" plus stable earnings. Recent prints have been solid, which helps sentiment and the multiple.

Focus on quality cyclicals, select financials with capital return, and industrials with backlog visibility.
Mind FX: decide whether to hedge EUR/GBP based on your base currency and policy.
Simple vehicles: the MSCI Europe Index or a STOXX 600 tracker; layer active sleeves for idiosyncratic alpha.

What to watch next quarter

Hyperscaler capex guides vs. AI service revenue attribution.
Unit economics: inference cost per query, gross margin trend on AI workloads, and payback periods.
GPU supply/demand, lead times, and pricing.
Model efficiency benchmarks and any plateauing in real-world uplift.
Earnings breadth and revisions in Europe; any narrowing of the U.S.-EU multiple gap.

Practical notes for finance teams

Treat AI like a product investment. Define the job to be done, run time-boxed pilots, measure cash impact, and sunset what doesn't clear hurdle rates. Beware vendor lock-in; keep optionality across providers.

Set a simple ROI scorecard: adoption, time saved, error rates, gross margin effect.
Standardize data access and prompts; audit outputs before scaling.
Keep contracts short and modular until the economics are proven.

If you're evaluating tools for FP&A, audit, or research workflows, this curated list can save time: AI tools for finance.

Bottom line

The AI story is real, but expectations are doing a lot of heavy lifting. Conger's playbook-dial down concentration, add Europe on valuation, and demand proof on unit economics-is a sensible way to keep upside while respecting risk. In markets like this, sizing beats heroics.