AI will make the biggest companies bigger and the smallest more powerful. Hotels show who gets squeezed

In 1988, a group of eight individuals founded an investment firm in New York, recognizing that data and technology could provide investors with a clearer view of risk. This firm, which would later become BlackRock, started with an internal risk tool and evolved into a platform connecting various aspects of portfolio management. BlackRock's Aladdin system now manages $14 trillion in assets, largely due to attracting $698 billion in net inflows in a single year.

Interestingly, the average SEC-registered investment adviser focusing on individual clients employs eight people and manages $424 million in assets. Despite this, the number of small advisers has reached a record 16,544, with more than two-thirds managing assets of less than $1 billion. The gap in power lies between these two extremes. Large firms have become significantly larger, while smaller firms are easier to establish.

DeVoe & Company witnessed a record 322 wealth management transactions in 2025, up from 272 the previous year. However, the number of buyers decreased by 19%, with first-time buyers accounting for just 8% of deals, a record low. This pattern, characterized by a technology barbell, signifies the distribution of power across the market. Large platforms spread their data, expertise, and infrastructure across vast volumes, while small specialists rent capabilities they could never afford to build independently.

Artificial intelligence (AI) is poised to further accentuate this trend across various industries. AI reduces the coordination burden for large companies, while simultaneously lowering the threshold for small firms to access similar capabilities. However, size still holds advantages, such as purchasing power, insurance pooling, capital access, distribution, and institutional credibility.

This leaves the middle ground with little to no advantage, forcing firms to either build their own systems, join a larger organization, or specialize enough to attract niche customers.

Hotels serve as an exemplar of this AI-driven shift, despite not typically being associated with the technology. Hotel brands are increasingly operating asset-light, fee-based businesses, with owners bearing the burden of real estate, debt, capital expenditures, and interest-rate risk. Across the 2,216 hotels surveyed by CBRE, revenue grew by 2.6% in 2025, while total expenses increased by 3.1%. Gross operating profit margins declined from 35.1% to 34.8%.

Insurance costs remained roughly twice their 2019 levels, a trend that concerns both hotel brands and managers as much as hotel owners. The future of fee streams depends on the risk-reward balance of investing millions in building, acquiring, converting, or renovating hotels. When the expected return no longer justifies the risk, hotels remain vacant.

A 50-room hotel must forecast demand, set prices, distribute rooms, manage guest communication, coordinate maintenance, reconcile payments, and analyze results. A global chain can handle these tasks across thousands of properties, but an independent owner cannot.

AI can transform this scenario, but only if hotels adopt an AI-native operating model. This involves integrating systems for reservations, pricing, distribution, guest messaging, housekeeping, maintenance, accounting, and reputation management into a connected operating layer. With routine decisions happening continuously, people can focus on exceptions, judgment, and hospitality.

The buildings remain unchanged; the operating layer that connects data and actions across the business has evolved. The result? Direct bookings have increased more than sixfold as a share of business, third-party commissions have decreased, average Google review scores have risen, and same-store revenue per available room has increased by 19.2% year-over-year.