Three Elements, All Satisfied by Accident
Investment adviser status is not a licence you apply for when you decide to be an adviser. It is a status that attaches when three elements are true, and a product team can satisfy all three in a sprint without anyone using the word "advice" in a planning document. The fiduciary duty arrives at the same moment.
The framing that causes the problem. Most fintech teams check adviser status once, at incorporation, against the product as it was then described — and never again. But the elements are evaluated against conduct, not against a business plan, and conduct changes every release. The feature that moves you across the line is almost never the one labelled "advisor" in the roadmap. It is a helpful assistant answering a question a user typed, or a personalisation change that quietly put account balances into a prompt that used to be generic.
Element by Element, and How Each One Gets Satisfied Without a Decision
Each element below has a statutory shape, a way product teams misread it, and a hard case worth arguing about internally before a regulator argues it for you.
Advice about securities
The statutory shape: Advising others, either directly or through publications or writings, as to the value of securities or the advisability of investing in, purchasing or selling securities.
How it is satisfied by accident: Teams assume this means naming a ticker. It does not. Advice about the advisability of a securities transaction includes advice about asset allocation among categories of securities, advice about whether to hold or liquidate, and advice about the selection of a manager or fund. A retirement projection tool that concludes the user is under-allocated to equities has advised on the advisability of purchasing securities without ever printing a symbol.
Hard case: A general-purpose assistant answering "should I sell my company stock before the vest?" for an identified user with a known position.
As part of a regular business
The statutory shape: Engaging in the activity with some regularity, as a component of the business, rather than on a rare and isolated basis.
How it is satisfied by accident: Product teams read "business" as "our main business" and conclude that a side feature cannot trigger it. The test is regularity, not centrality. A feature shipped to every user that answers investment questions thousands of times a day is the paradigm case of regularity, even if it produces no revenue line of its own and even if the company describes itself as a software company.
Hard case: A budgeting app whose assistant fields allocation questions in perhaps two percent of sessions — which is still tens of thousands of instances a quarter.
For compensation
The statutory shape: Any economic benefit, which need not be a separate fee, need not be paid by the person advised, and need not be labelled as an advisory fee.
How it is satisfied by accident: This is the element teams most confidently believe they fail, and the one they most often satisfy. Compensation includes a subscription fee for a bundle that contains the advice, revenue share from a broker or custodian the advice routes to, payment for order flow downstream of a recommendation, and referral fees from a product the assistant surfaces. Free-to-the-user is not free-of-compensation.
Hard case: A free tier monetised entirely by affiliate revenue on the products the assistant recommends — which is compensation and a conflict simultaneously.
The Exclusions People Reach For, and What Breaks Them
Three real exclusions exist and one imaginary one does most of the work in internal conversations. Each real one is narrower than its reputation, and each has a specific engineering decision that destroys it.
The publisher exclusion
What it covers: Bona fide publications of general and regular circulation that do not offer advice tailored to individual client characteristics.
What breaks it: Personalisation. The exclusion has always turned on whether the content is impersonal. A newsletter that discusses market conditions is excluded; the same content generated with knowledge of this user's holdings, income, age and stated goals is not. A model with retrieval over the user's own account data has by construction defeated the impersonal requirement, and the fact that the underlying prose is templated does not restore it.
The solely-incidental exclusion
What it covers: Advice that is incidental to another business and for which no special compensation is received.
What breaks it: Two ways. First, special compensation — a premium tier priced on access to the advisory feature is special compensation for the advice, which is the exact fact the exclusion was drafted to exclude. Second, prominence: advice that is marketed as a headline capability is difficult to characterise as incidental to anything.
The broker exclusion
What it covers: Advice by a broker-dealer that is solely incidental to the conduct of its business as a broker and for which no special compensation is received.
What breaks it: Scope creep from recommendation into ongoing monitoring. A broker recommending a transaction sits under a best-interest standard; a broker whose assistant continuously monitors and prompts on a portfolio starts to look like it is exercising the kind of ongoing discretion that carries adviser status instead.
"It's just software"
What it covers: Nothing. This is not an exclusion.
What breaks it: It has never mattered whether advice was delivered by a person, a spreadsheet, a decision tree or a model. The statute reaches advice, however produced. Automated advisers have been treated as advisers for well over a decade; the model changes the generation method and nothing about the analysis.
Seven Surfaces, Graded
The line does not run between products. It runs through them, surface by surface, and a single product routinely has surfaces on both sides.
The publisher posture survives only while the content is identical for every reader. The moment it is assembled with the reader's positions in context, it leaves.
A tool that reports what a portfolio would have returned is arithmetic. A tool that reports the user should therefore shift allocation has concluded.
Grading a specific portfolio communicates advisability. The grade is a conclusion about securities the user holds, delivered to that user.
Cohort framing does not make a recommendation impersonal — the cohort was selected using this user's attributes, which is the definition of tailoring.
This is where non-deterministic output meets a fiduciary standard. The compliance review covered the prompt; the liability attaches to the answer.
Discretionary authority raises custody, suitability and best-execution questions on top of registration, and it removes the argument that the user made the decision.
Rollover recommendations carry their own overlay of duties and disclosure expectations, and are a standing enforcement priority precisely because the conflict is structural.
The Conflict That Lives in the Weights
The duty of loyalty is the part of this that has no analogue in ordinary software compliance. A conflict of interest in a traditional advisory business is a fee, a relationship or an ownership stake — a fact you can write down and disclose. In a recommendation system it is a gradient. If the ranking objective includes conversion, revenue, retention or any proxy correlated with them, the system has a financial interest in the recommendation, and it expresses that interest in every output without ever recording a decision to do so.
Generic disclosure does not reach this. A statement that the firm "may receive compensation in connection with certain products" describes a possibility; the ranking function describes a systematic tilt. Full and fair disclosure of a systematic tilt has to say that it is systematic. The alternative — and the cleaner engineering answer — is to remove revenue-correlated signals from the objective entirely and be able to prove it, which turns a disclosure problem into a documented model-governance artefact.
Four questions to ask about the ranking objective
- What is in the objective function, written out in full, including every auxiliary loss and every post-ranking business rule applied after the model returns?
- For a given output, can we produce a cheaper or lower-fee equivalent that ranked lower, and state why it ranked lower in terms that do not mention our revenue?
- Who approved the last change to that objective, and does the approval record show that the conflict question was asked rather than assumed?
- If the training data is behavioural, does it encode past sales pressure — so that the model has learned a conflict that no longer exists in the current fee schedule?
The Records Problem Nobody Budgets For
Advisory recordkeeping assumes reproducibility. A recommendation letter can be re-read; a model output cannot be re-derived, because temperature, model version, retrieval freshness and system-prompt revisions all move underneath it. That single property turns recordkeeping from a storage question into a capture question, and capture has to be designed in at the point the output is generated — retrofitting it produces a corpus that starts on the day someone noticed.
The minimum viable record for a client-facing advisory output is the verbatim exchange, the retrieval context in scope at generation time, the model and version identifier, the system-prompt or policy version, the user attributes that drove personalisation, and the identity of the reviewer if one was in the loop. Anything less answers a different question than the one an examiner asks.
Frequently Asked Questions
Does an AI feature that answers investment questions make us an investment adviser?
It can, and the test does not care that the advice came from a model. Status turns on three elements: advising others as to the value of securities or the advisability of investing in, purchasing or selling securities; doing so as part of a regular business; and receiving compensation. Teams usually accept the first, argue about the second and confidently deny the third — and the third is where they are most often wrong, because compensation means any economic benefit, need not be a separate advisory fee, and need not be paid by the person advised. A bundled subscription, revenue share from a custodian, or affiliate payments on surfaced products all satisfy it. Registration then falls to the SEC or to individual states depending on assets under management and business model.
Doesn't the publisher exclusion cover a general-purpose assistant?
It protects bona fide publications of general and regular circulation offering impersonal advice — not advice tailored to individual client characteristics. That is precisely the property a modern assistant is built to destroy. If the model retrieves the user's holdings, balances, age, income or goals, the output is personalised by construction, and hedged phrasing does not restore impersonality. The exclusion still works for a genuinely one-to-many artefact that renders identically for every reader. It stops working the moment the same pipeline assembles that artefact with the reader's own positions in context — usually a product decision made for engagement reasons by someone who never saw this analysis.
What does fiduciary duty actually require of a model?
Care and loyalty, both of which translate into engineering obligations. Care requires a reasonable basis for the advice given this client's objectives, which means the retrieval context must actually contain the client's circumstances and the system must decline when it does not. Loyalty requires eliminating or fully and fairly disclosing conflicts, and AI systems fail here distinctively: a ranking model tuned on revenue-correlated outcomes is a conflict expressed in weights rather than in a fee schedule, and a generic statement that the firm may receive compensation does not disclose it. The practical test is whether you can explain why a given product appeared ahead of a cheaper equivalent, and whether the explanation would embarrass you.
Is calling our product AI-powered a legal risk on its own?
Yes, independently of everything else. Overstating AI capability in marketing or filings is a straightforward misrepresentation theory, and it has been an active enforcement line against advisers describing capabilities they did not have. It is favourable to a regulator because it needs no investor loss and no complicated causation — the claim either matched the system or it did not. Exposure is heightened when the claim appears in a brochure, a pitch deck used to raise capital, or a website page, and when the implementation is a rules engine, a vendor API called on a subset of requests, or a feature that shipped after the claim. Keep a dated file mapping each public capability claim to the component that implements it.
How do marketing rules apply to AI-generated performance content?
Advertising by a registered adviser is subject to detailed conditions, and generated content is advertising when it promotes services. Hypothetical performance — including backtests and projections — may only be presented to audiences with the resources to understand it and requires specific supporting disclosure, which is hard to reconcile with an assistant generating projections on demand for retail users. Testimonials and endorsements require disclosure of compensation and conflicts, so generated social proof is a compliance event rather than a growth tactic. And performance figures carry prescribed net-of-fee and time-period requirements that a model ignores when it summarises. The safest posture: the model never originates a performance number, it only renders one computed by a reviewed pipeline.
What records do we have to keep of what the model told a client?
Communications relating to recommendations and advice given must be retained and retrievable for examination, with recent years easily accessible. A non-deterministic system creates a problem no earlier advisory technology had: the recommendation cannot be reconstructed by re-running the input, because temperature, model version, retrieval freshness and prompt revisions all move underneath it. Contemporaneous capture is therefore the only compliant design — verbatim exchange, retrieval context in scope, model and version identifier, system-prompt or policy version, personalisation attributes, and reviewer identity if any. Storing only the question and a summary of the answer is the common shortcut, and it fails the one question that matters: show us what you told this client on this date.
We only serve clients in one state. Does that help?
It changes which regulator, not whether you are regulated. Advisers below the federal assets-under-management thresholds generally register with states rather than the SEC, and state regimes bring their own examination programmes, net-capital or bonding requirements, individual representative licensing and advertising rules — several of which are stricter than the federal analogues. Single-state operation also erodes fast for a software product: an internet-delivered assistant reaches whoever signs up, and a de-minimis client threshold in a neighbouring state is crossed by a marketing campaign nobody flagged to compliance. Geographic scope is a control you have to actually enforce at signup, not an attribute you observe after the fact.
The Screenshot Test
Take a screenshot of one real assistant answer to a real user who has a real balance with you. Remove your logo. Show it to someone who has never seen your product and ask them one question: did this tell the person what to do with their money?
If the answer is yes, the three elements are a formality — you are arguing about compensation, and compensation is the element you are most likely to lose. Decide that deliberately, with counsel, before a user decides it for you in a complaint.
Related Reading
- AI quoting bots and insurance producer licensing — the same acts-not-titles analysis in the adjacent financial-services licence.
- AI elder financial exploitation and diminished capacity — what a financial firm owes when the model notices something about the client.
- Circular 230 and AI-drafted tax returns — practice standards attaching to a second regulated financial deliverable.