From BlackRock to boutique advisers
In 1988 eight entrepreneurs gathered in a New York office to launch an investment firm built on the belief that data and technology could clarify risk. That venture grew into BlackRock, whose Aladdin platform evolved from an internal risk-management tool into a full-scale system linking portfolio construction, trading, operations and accounting. By the end of 2025 the firm reported $14 trillion in assets under management after attracting $698 billion of net inflows in a single year.
The striking parallel is the number eight. The average adviser registered with the SEC that focuses on individual clients also employs eight staff members and oversees about $424 million in assets. Small advisers are not vanishing; their count hit a record 16,544 in 2025, with more than two-thirds managing less than $1 billion.
While the giant expanded dramatically, the boutique model became easier to launch, creating pressure on the middle tier of the industry.
The emerging AI barbell
Data from DeVoe & Company shows 322 wealth-management transactions in 2025, a record rise from 272 the previous year. However, the market is consolidating: sellers increased by 18 percent, buyers fell by 19 percent, and first-time buyers accounted for only 8 percent of deals, the lowest share on record. Acquisitions are concentrating in a shrinking set of private-equity-backed platforms.
This pattern resembles a technology barbell. Large platforms spread data, expertise and infrastructure across massive volumes, while small specialists rent capabilities they could never afford to build. Firms in the middle carry enough overhead to need scale but lack sufficient scale to fund a differentiated platform.
Artificial intelligence is likely to accelerate this dynamic. AI reduces the fixed costs of revenue-management teams, 24-hour service centres and enterprise technology stacks, making them affordable for the smallest players. At the same time, it does not erase the advantages of purchasing power, insurance pooling, capital access, distribution networks and institutional credibility that large firms enjoy.
Hospitality shows the split
The hotel sector provides a concrete illustration. At a recent industry conference, brand executives discussed growing fee revenue while owners focused on insurance, labour costs, property taxes, renovation mandates and looming loan repayments. According to the latest CBRE Trends survey, revenue across 2,216 hotels grew 2.6 percent in 2025, but total expenses rose 3.1 percent, pushing gross operating profit margins down from 35.1 percent to 34.8 percent. Insurance costs remain roughly double their 2019 level.
These pressures affect both brands and owners because every future fee stream depends on investors committing millions to build, acquire, convert or renovate hotels. When expected returns no longer justify the risk, rooms stay closed.
A 50-room property must forecast demand, set prices, distribute rooms, answer guest enquiries around the clock, schedule housekeeping, coordinate maintenance, reconcile payments and interpret results. A global chain can spread these functions across thousands of rooms; an independent owner cannot.
Artificial intelligence can change the equation, but only if it is integrated into a connected operating layer rather than added as a separate subscription for each function. When pricing, distribution, guest communication, property operations and accounting share data, routine decisions happen continuously and staff can focus on exceptions, judgement and hospitality. Learning from one property can be applied to the next, removing repetitive coordination.
What the future may hold
In January, Kasa acquired Mint House, an apartment-hotel brand with nearly 1,000 units across 22 properties. After integrating the portfolio onto Kasa's platform, direct bookings rose more than sixfold as a share of business, third-party commissions fell, Google review scores improved from 4.24 to 4.56 and same-store revenue per available room increased 19.2 percent year-over-year. The buildings themselves did not change; the operating layer did.
The layer evaluates booking pace, competitor rates, local events and channel costs throughout the day, handles routine guest queries, links negative reviews to the underlying maintenance issue and provides owners with clear explanations of changes in revenue, labour and cash flow. The goal is to free people's time for judgement, creativity and genuine care.
When the same barbell appears in hospitality, the largest hotel companies and management platforms will use AI to make their infrastructure more powerful, while independent hotels and small brands gain access to capabilities once reserved for global chains. The squeeze will be strongest for regional managers and mid-size brands that operate between 15 and 50 hotels, large enough to bear corporate overhead but too small to spread the cost of a differentiated technology platform.
Recent consolidation in the sector, such as Stonebridge's acquisition of Real Hospitality, Nautic Partners' purchase of Davidson Hospitality, Griffin's merger into Meyer Jabara and PM Hotel Group's absorption of Sightline Hospitality, already reflects this pressure.
Beyond hotels, similar dynamics are expected in property management, healthcare services, accounting, insurance and logistics, where fragmented software and repetitive coordination are common. The next generation of AI companies may resemble operators more than pure software vendors, combining industry expertise, people and technology into a single operating model that can be offered to businesses of any size.
In summary, artificial intelligence will strengthen the biggest platforms and empower the smallest specialists, leaving firms in the middle with a stark strategic choice.