Strategic Acquisition Brief · Confidential
The second quarter brought $148.1 billion of net new assets, more than half of it tied to IPOs — and with it a cohort whose planning problem is unlike anything else on the book: concentrated stock, staged liquidity across several years, and a forty-year tax and benefit sequence in which the order of the decisions is worth more than the product selection. That is arithmetic rather than a projection, and it has exactly one right answer for a given set of facts. MaxiFi produces it: computationally exact, economics-based planning — for a household’s facts and assumptions, it solves, not guesses, the lifetime plan, every dollar of taxes and benefits computed under current law. Deterministic, reproducible, auditable. Built over 30 years by BU economist Laurence Kotlikoff.
The second quarter brought $148.1 billion of net new assets into Wealth Management, a record, with more than half tied to IPOs, and total client assets across Wealth and Investment Management reaching $10 trillion. On the call, CFO Sharon Yeshaya put roughly 70% of the top 100 unicorns by market cap inside the workplace pipeline, and described the deliberate work of servicing those companies from the earliest stages.
The strategy is well understood and it is working: administer the stock plan, convert the employee as the wealth arrives. Solium in 2019, E*TRADE in 2020, EquityZen under Ted Pick. More than a trillion dollars has already migrated from workplace and E*TRADE channels into adviser-led relationships.
A newly liquid founder or early employee presents concentrated stock, staged liquidity across multiple years, and a forty-year sequence of tax, benefit and withdrawal decisions in which the order of the decisions is worth more than the product selection. Which lot, which year, which account, in which sequence, against which bracket.
That is not a conversation. It is a computation, and it has exactly one correct answer for a given set of facts. Ted Pick has publicly named advisor tools and tax optimization as acquisition territory. This is the engine underneath both.
MaxiFi is not an application Morgan Stanley would operate, and it does not displace the Goals Planning System. It is a computation service GPS calls. The advisor opens the same screen; the client sees the same report format. What changes is the provenance of the numbers on it — and, consequently, what the firm is able to say about them.
GPS, Morgan Stanley Online, the advisor workstation. No migration, no retraining, no new login.
Shareworks and the workplace channel keep doing what they already do well: putting the firm in front of employees at the moment wealth arrives.
The rules, the solver, the audit trail. Same inputs, same answer, every time, traceable to the law tables in force on the plan date.
A computed, auditable output is a different legal object from a hypothetical projection. The disclosure follows the engine, not the other way round.
Morgan Stanley became the first major bank to open its wealth funnel to external AI agents. That is the right direction and it raises the stakes on exactly one question: what does the agent do when a client asks a dollar question with a forty-year consequence? A general-purpose model will answer fluently, confidently and unverifiably. A deterministic engine underneath it answers correctly, and can prove it.
MaxiFi is the financial-planning platform of Economic Security Planning, Inc., built over more than three decades by Professor Laurence Kotlikoff of Boston University. It uses consumption smoothing and dynamic programming to compute the single, mathematically optimal lifetime plan — solving simultaneously across Social Security strategy, federal and state taxes, Roth-conversion sequencing, withdrawal order, life-insurance need, estate planning, and upside investing.
Goals-based tools and rule-of-thumb calculators answer “What is the chance you hit your number?” MaxiFi answers “What is the optimal path, and how much can I spend today without jeopardizing tomorrow?” It is not a better simulator. It is a different class of engine.
Prof. Laurence Kotlikoff — William Fairfield Warren Professor at Boston University; Harvard Ph.D.; former Senior Economist on the President’s Council of Economic Advisers; named by The Economist among the 25 most influential economists. He intends to stay on with the acquirer in whatever capacity best serves the product. The more important fact for an acquirer is that the engine’s currency does not rest on it: rule maintenance is routine engineering, not founder work, and runs without his involvement.
MaxiFi’s economics build on Nobel-laureate work, and Nobel laureate Robert Merton teaches with MaxiFi at MIT Sloan as an “outstanding science-based lifecycle and retirement management platform.” Featured in Bankrate’s “Best financial planning software of 2025” roundup, cited as best for near- and long-term tax planning and the decumulation phase.
The moat is the rulebase as much as the solver: thirty years of encoded, continuously maintained federal and state tax, Social Security and benefit rules, carried under a regression suite re-run against every law change, plus patent-winning optimization algorithms built from economic theory rather than scraped text. The maintained surface is concrete: federal, Social Security, Medicare Part B and 42 state income tax codes, updated as provisions are released. Stated plainly, because it will be checked: the solver is the replicable half — the mathematics is published, much of it by Kotlikoff himself. The rulebase is not, because encoding thirty years of law correctly is the decade.
Planning tools die on data entry. In the workplace channel the inputs problem largely solves itself: the firm already holds the equity award data, the vesting schedule and the liquidity calendar. Those are precisely the inputs a lifetime optimization needs, and precisely what makes the newly liquid cohort tractable at scale rather than one bespoke case at a time.
Caution about acquiring custom-built technology in this environment is well founded. On inspection it is also the argument for this asset.
What generative AI is rapidly commoditizing is interface, workflow, reporting, document generation and integration glue — the entire category of thing that makes a software platform expensive to own and quick to date. None of that is what is on offer here.
What AI does not produce is a validated rulebase or the evidentiary history that makes an output defensible. A model asked to sequence a concentrated-stock disposition against a Roth conversion will generate a fluent, confident, unverifiable answer. It has no correct reference point, so no error in it is decidable. MaxiFi’s is: rerun the engine and check.
The incumbent planning vendors have each attached generative AI to goals-based engines over the past year. The result is a language model in front of arithmetic that was never deterministic to begin with. As models improve they converge on one another, and the industry mistakes that agreement for accuracy.
The part you would be buying is the part AI has made scarcer. MaxiFi does not approximate. It computes — iteratively, multivariately and simultaneously across taxes, benefits, longevity and cash flow, year by year for a whole life. It is provable, not merely confident: the answer that holds up when someone with an adverse interest checks the math. That claim is about the computation — the optimization and the tax and benefit math are exact and inspectable — not about predicting markets.
And the clock is real. A build arrives in years; the cohort, the agents and the competitive window run in quarters. The engine — and its economist — exist now, once.
The report identifies, as explicit risks of agentic AI: auditability and transparency — complicated, multi-step agent reasoning can make outcomes difficult to trace or explain; domain knowledge — general-purpose agents may lack what complex, industry-specific tasks require; and autonomy — agents acting without human validation or approval. FINRA and the U.S. Treasury have since published an AI Lexicon and a Financial Services AI Risk Management Framework.
For a firm that has just opened its wealth funnel to external agents, auditability is not an abstract concern — it is the control that makes the strategy supervisable. The substance of a recommendation is governed regardless of the interface delivering it, and being “AI-generated” is not a liability shield.
A correct-by-construction engine addresses the exposure directly: if the math is right, reproducible and auditable, the answer holds up to scrutiny on its own terms. And because the engine is deterministic, the assurance can be underwritten — a bounded accuracy guarantee no probabilistic rival can offer, because their output has no correct reference point to warrant.
CBS MoneyWatch (May 7, 2026) ran an identical retirement question — a 50-year-old single woman retiring at 65 — through two leading AI models. The verdicts diverged. MIT’s Andrew Lo was quoted on the underlying structural point: today’s consumer AI carries no best-interest duty. Kotlikoff was quoted describing the risk that AI “may do more harm than good” when it mishandles claims like Social Security timing or substitutes an average for a maximum life expectancy.
A concrete, checkable example: AI engines trained before the One Big Beautiful Bill Act (enacted July 2025) told users the federal estate-tax exemption would “sunset” on January 1, 2026 — reverting to roughly half its level. In fact, the Act permanently raised the exemption to $15 million per person starting in 2026.
A model repeating pre-2025 training data would confidently tell a household to rush an irrevocable estate move it no longer needs — a costly, hard-to-reverse error delivered with total confidence. A computed engine, fed current law, does not carry stale assumptions forward as fact.
Neither example is about any single company’s brand. It is the same structural point twice: confidence is not correctness, and an answer’s value depends on the currency and correctness of the computation behind it — not the fluency of the sentence delivering it.
Larry’s Economics Matters Substack — 137,000+ subscribers — has run a six-post sequence testing named frontier engines against MaxiFi on dollar-specific household problems. The variance across engines on identical, checkable prompts is the proof: the correctness cannot come from the model layer.
Acquiring MaxiFi acquires the megaphone these pieces ship from — pointed, with credibility no one in the category can match, at exactly the questions a newly liquid client asks first. The CBS finding is the named, neutral proof; the Substack series is the dated, dollar-specific record behind it.
Durable value accrues to whoever owns the deterministic engine under the trusted interface — not to the interface, and not to the model. In wealth management the planning engine is the one layer still un-owned. Every firm in the category licenses or approximates it. There is another option.
More than half of a record quarter came in through IPOs, and the workplace pipeline holds most of the top unicorns. The binding constraint on converting that cohort into multi-decade advisory relationships is not distribution — you have it — it is whether the first planning conversation produces an answer good enough to be acted on. A computed plan is that answer.
MaxiFi’s determinism makes a planning-side accuracy guarantee offerable for the first time: a computational error is objectively decidable, so the warranty prices at a rounding error and is insurable. A goals-based competitor cannot offer it at any price, because the warranted event cannot be defined. Merrill, UBS and Wells cannot answer it.
You have opened the funnel to external agents. A correct-by-construction engine retires the largest overhang on that strategy — being confidently wrong with client money at scale. We are not selling an insurance policy; the insurance is included. And there is exactly one MaxiFi. It will sit somewhere.
The market pays for defensible, low-risk earnings. A substantiated correctness claim, backed by a guarantee, makes the wealth growth story proprietary while removing a tail risk in the same motion — and no rival can copy the claim without inviting a substantiation challenge they cannot survive.
Today the firm discloses that GPS reports are not financial plans and that projections are hypothetical. That is candid, and it is a ceiling. With a computed engine underneath, the same output becomes something the firm can stand behind rather than qualify — which is a different competitive position, not a better paragraph.
MaxiFi is being offered through a focused strategic process — the engine, its IP, and thirty years of R&D. The preference is an acquisition; that is where the strategic value sits. Continuity de-risks it: Larry Kotlikoff intends to stay on with the acquirer in whatever capacity best serves the product — architect, spokesperson, advisor. The next step is a 30-minute live demonstration: MaxiFi solves a real household’s plan while the leading models are asked to match it. The gap is the thesis. Evidence deepens with commitment — nothing is deployed, nothing left behind, and the full case is provable in an acquirer’s first quarter of ownership.