The New Definition of Independence

I was on a panel a few weeks ago discussing where the wealth management industry is headed when someone in the audience asked a question I’d been waiting for all day: Isn’t this just trading one dependency for another?

You leave the wirehouse, you leave the old platform, and now you’re tied to a new one. What’s actually different?

It’s a fair question, but it begins with an outdated idea of what independence means. We tend to talk about independence as though it were binary: either you’re independent or you’re not. But the industry has changed, and the definition needs to change with it. Today, independence has at least three different dimensions, even though most advisors have spent their careers focused almost entirely on one.

The first is the one everyone knows: Whose registration are you under? It’s the question that helped drive the independent movement in the first place. Whose name is on the ADV? Who is the regulatory principal? Who ultimately determines what you can and cannot do for a client? Advisors understand this form of independence because, for the better part of two decades, it has been the industry’s primary measure of whether you truly control your business.

But registration is no longer the only place where control matters.

The second question is, whose stack are you running on? This has less to do with who owns the client assets than with who controls the technology you rely on to operate the business every day: the CRM, the portfolio management system, the compliance workflow, the marketing engine and all the systems connecting them. You may own the firm, but if another company’s roadmap determines the capabilities available to you and their pricing determines what those capabilities cost, you have gained one form of independence while remaining dependent in another.

Until recently, those two questions captured most of the debate. AI introduces a third, and over time I think it may prove to be the most consequential: Whose intelligence is it?

New AI-driven options are being introduced daily, impacting how advisors choose to develop their practice. There are agentic AI options that help prepare for meetings, draft communications, surface recommendations, identify opportunities and coordinate workflows across the firm.

As they become more capable, they are creating something that didn’t really exist as an asset under the old technology stack: an accumulated understanding of how a firm thinks, how its advisors work, how decisions get made and what it knows about its clients.

That changes the ownership question, considerably. If the ecosystem spends years connecting information, remembering decisions, recognizing patterns and learning from the daily activity of a practice, who owns what it has learned? Does that intelligence become an asset of the firm, or does it remain embedded in the platform that generated it?

Looking at the options available to advisors through all three of these lenses makes the tradeoffs much easier to see.

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In the traditional employee model, the answer is straightforward. The institution controls the registration and technology stack, and increasingly it will control the intelligence layer as well. That isn’t a criticism; it’s the bargain. You gain the resources, infrastructure and scale of the institution in exchange for operating within its environment. For many advisors, that is a perfectly reasonable trade. For others, the desire for greater control is precisely what led them toward independence in the first place.

The independent broker-dealer model shifted that balance by giving advisors greater freedom over how they built their businesses and served their clients. But while operating freedom expanded, advisors continued to affiliate under someone else’s broker-dealer, meaning the underlying registration still belonged to someone else.

Turnkey platforms took the model a step further by making it easier for advisors to establish their own RIAs, changing the answer to the registration question while potentially introducing a different kind of dependency. When technology, compliance, capital, marketing and other critical functions are bundled into one or two commercial relationships, you may own the RIA while relying heavily on a small number of vendors to operate it. If one of those partners changes its pricing, technology roadmap or strategic priorities, the impact can extend across multiple parts of your business at once.

The newest model is perhaps the most interesting: AI-native firms built from the ground up around proprietary technology, often paired with compelling economics and meaningful equity participation for advisors. There is a lot to like about that proposition. A technology environment designed as one ecosystem can achieve a level of integration that a collection of applications often cannot, while giving advisors a genuine stake in the value they help create.

But that, too, comes with a tradeoff. Advisors typically affiliate under the platform’s registration and operate within its proprietary technology environment. As AI becomes more deeply embedded in that environment, the third question becomes particularly important because the intelligence generated through the advisor’s activity lives there as well.

If a platform has spent years learning how you serve clients, how you make decisions and how those relationships have evolved, what happens to that accumulated knowledge if you eventually decide to leave?

There isn’t one universally correct answer to any of these choices, and suggesting otherwise misses the point. Advisors will make different decisions depending on what they want to build, how much control they want to retain, and what they are willing to trade in exchange for infrastructure, economics, technology or scale.

What matters is understanding the trade you are actually making — not just today, but as the value of the intelligence being created grows over time.

That thinking has shaped how we’ve built Intellebox over the last year.

Our architecture grew out of the experience of having already operated an independent RIA and realizing that owning the firm doesn’t necessarily mean owning everything that makes the firm valuable. Once you’ve operated in an environment where critical parts of the business live inside someone else’s platform, you become much more deliberate about which forms of control you are willing to give up.

That led us to make some fundamental architectural choices. The firm’s registration remains its own, while Intellebox sits above the CRM, custodian and other systems and vendors the firm already uses rather than requiring everything to move into a proprietary ecosystem. The idea is to connect and add intelligence to the infrastructure a firm has already chosen, not ask the firm to surrender that infrastructure in order to benefit from ours.

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More important, however, is what happens to the knowledge being created.

Every meeting, communication, workflow and decision adds another layer of context to what a firm knows about its clients and how it serves them. Over time, those individual pieces become something far more valuable: institutional intelligence.

We believe that intelligence should remain with the firm that created it, so that changing a CRM, switching technology partners or vendors, or eventually being acquired doesn’t mean leaving years of accumulated knowledge behind in a vendor’s ecosystem.

That principle becomes much more consequential as AI evolves and improves. Under the old technology model, changing vendors primarily meant migrating records, rebuilding integrations and retraining employees. Those things could be painful, but the asset being moved was relatively clear.

In an agentic world, the asset is no longer just the data. It is the context surrounding it: the connections the system has made, the history it understands, the patterns it recognizes, and the institutional knowledge accumulated across thousands of interactions. The longer these systems are in place, the more valuable that context becomes. At some point, it stops looking like a feature of the software and starts looking like an asset of the firm itself.

Which brings me back to the original question. Moving from one model to another can absolutely mean exchanging one form of dependency for another. The point isn’t that one model is independent and another isn’t, or that Intellebox is somehow the only “truly independent” option. Independence has become more nuanced than that.

What matters is understanding where you want control, what you are willing to outsource and where you are comfortable accepting dependency.

For advisors evaluating any platform today, that means asking three questions rather than one:

Whose registration is it?

Whose stack is it?

And whose intelligence is it?

Different advisors will answer those questions differently, and they should. The right model is the one whose tradeoffs align with the business an advisor is trying to build. But as AI becomes more deeply embedded in the way firms operate, the third question will become increasingly difficult to ignore.

Ten years from now, I suspect the firms that feel they truly own what they built will be the ones that recognized early on that independence was no longer just about owning the business. It was also about owning the intelligence the business created.

In an industry being inundated with AI options, that distinction matters more every day.

Because the next question isn’t simply whether you own the firm.

It’s whether you own what the firm knows.