The Wealth Management Business Is Growing Fast. Here Are the Speed Bumps.

For years, the wealth management industry has been rising on a geyser of assets under management. From an estimated $27 trillion in 2018, assets are estimated at $64 trillion today and are projected by Statista Market Insights to hit $87 trillion in 2028.

But fast-growing industries face challenges. For this week’s Big Q , we asked industry professionals to identify some of them. The question: What are the biggest challenges facing the wealth management industry and why are they so important?

Alan Moore, CEO, XY Planning Network and AdvicePay: A major change is the shift from product sales to advice. It means you have to actually train on finances and advice, not just learn sales. It’s creating a spike in demand for CFP professionals—we just had the largest cycle of exam takers in the past 12 months—after the CFP has been around for 50 years. And there’s a shortage of talent who can handle being advisors.

Product organizations hired for salespeople for years and now have to adapt to hiring advice givers. It’s causing products to be reinvented with no-commission alternatives. It’s leading people to switch channels and break away—because broker-dealers are technically securities product/sales distribution platforms, and you don’t need one if you’re in the advice business.

The shift to advice has been under way for over 30 or 40 years. But the vast majority of the industry, probably 90%-plus, is still built around a product distribution business model, not around advice. You see this in recruitment efforts, where folks are being recruited into sales roles and you lose 90% to 95% of your new hires because they don’t make the cut. We don’t have 90% or 95% turnover on new hires into advice roles, but people who want to work in the advice industry are very different from the folks who want to do sales.

Ryan Parker, CEO, EP Wealth Advisors: My one-word answer as far as the biggest challenge is “people.” This has always been a talent industry. It’s about people serving people. But increasingly, the talent opportunity and challenge is getting more and more complicated and nuanced. To serve clients, it’s no longer sufficient to just have the best advisors. Plus, the best advisors are increasingly difficult to attract and to develop and to cultivate. That’s either because they’re happy with where they are, or once they get in a good situation, they probably do serve clients well by staying the course.

The talent that surrounds the client and enables the advisor, that’s where the war is really heating up. And it’s not just the financial planning or tax or estate, but it’s the technical talent—people who understand and can deploy the different technologies that are inside our industry and increasingly ubiquitous across industries. I really think that whatever your time horizon is, the ability to attract, develop, align, and then reward and retain the best talent throughout the organization is critical.

I think it starts with the front lines who are interfacing with clients every day, but it goes now to every single position. That is what’s going to separate those who are able to build something of scale and significance over time. Clients are going to go where the best talent resides. So to compete, I’m going to go for the best people.

Daniel Burke, founding partner, investment management, Callan Family Office: One of the biggest challenges facing the industry is managing the complexity and volume of all clients’ personal data.

An ultrahigh net worth family or family office often has data everywhere—siloed at multiple providers and custodians, old tax returns, et cetera. As advisors, we have to help them manage this data in order to provide good advice and execute across their full balance sheet. We’re investing more and more in data processes, data quality, and technology to help families make decisions across a clean, comprehensive set of data.

The challenge is that even if we invest to collect all of that data, the systems downstream, the third-party applications, CRMs, trading systems, reporting systems, and financial planning software, this whole ecosystem of apps and fintech investments, can’t necessarily work with all that data. The fact that so many of the technology players are focused on solving for the mass affluent leaves a real gap in the process for the ultrahigh net worth. And that’s a challenge for the industry to try to solve.

Mitch Avnet, CEO and managing partner, Compliance Risk Concepts: We provide outsourced, ongoing compliance support to asset management firms and independent investment advisors, and to the institutional folks and to broker dealers as well. To me the challenge is figuring out how to embrace new technology concepts out there, whether it’s AI, crypto, or anything else coming on the horizon Advisors have to really be thoughtful, careful and pragmatic in terms of how they leg into this stuff.

One of the biggest concerns I think any regulator or compliance officer would have when a firm gets into the world of what I call one-offs, like AI or crypto, is having the operational infrastructure expertise or capabilities to support it in place.

It’s great to say that you’re going to embrace AI. But how is that built into your overall model, specific to portfolio management? Are you just turning over the keys to a machine, or are you using it as a tool in how your team does their overall analysis and how they implement strategies? I think early entrants can get caught with their pants down when there’s a flight to a new, shiny object, if they haven’t really thought about what the potential ramifications are if something goes wrong.

A sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.

The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.

The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.

The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.

This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.

The debt behind the AI build-out

Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.

Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.

JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.

The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.

That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.

A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.

When interest rates rise, those assumptions become less forgiving.

Inflation changes the calculation

September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.

The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.

CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.

Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.

Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.

The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.

China adds another source of pressure

Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.

Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.

The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.

That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.

A reset rather than the end of the boom

The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.

What has changed is the market’s willingness to overlook the price and financing structure of that growth.

The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.

More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.

For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.

Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.

The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.

Fact box

Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026

Philadelphia Semiconductor Index: Down approximately 11.4 per cent

Micron Technology: Down approximately 14.6 per cent

Samsung Electronics: Down approximately 18 per cent

SK Hynix: Down approximately 24 per cent

US September inflation: 3.7 per cent annually

Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion