Strategic Acquisition Brief · Confidential
MassMutual has been assembling a planning capability in earnest — a Private Wealth division stood up at the end of 2025, an integrated wealth platform across the MML network, and sustained attention to the hardest question in the book: how a household should actually draw down what it has saved. Every layer of that capability is owned or licensed except one. The number on the plan still belongs to an outside vendor. MaxiFi is the engine that answers 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.
MassMutual Private Wealth was stood up at the end of 2025 and opened to advisors this year, built — in Daken Vanderburg’s framing — to meet demographic shifts in what clients need. The wealth business runs on Advisor360° with the WMS-Orion platform alongside it, and the firm has been actively working the decumulation and retirement-distribution question — the part of planning where the arithmetic is hardest and the consequences of getting it wrong are largest.
That is a firm systematically assembling a planning capability. Every layer of it has been bought or licensed — except the one that produces the number.
MML advisors deliver plans today through eMoney, MoneyGuide and RightCapital. All three are goals-based engines: they simulate outcomes against assumptions and report a probability. That is a legitimate and useful thing to do, and it is not the same thing as computing an answer.
The consequence is that MassMutual does not own the number on the page — three outside vendors do, and none of them can tell you whether the number is right, because none of them has a correct reference point to check it against.
The integration question is the one that matters, and it has a bounded answer. MaxiFi is not an application MassMutual would operate. It is a computation service the existing stack calls. An advisor never leaves Advisor360° or the Orion workflow; the client sees the same report format. What changes is the provenance of the numbers on it.
Advisor360° and the WMS-Orion platform stay exactly as they are. No migration, no retraining, no new login.
eMoney, MoneyGuide and RightCapital continue to render the plan — or are replaced over time, at your election. The presentation layer is not the asset.
The rules, the solver, the audit trail. Called as a service. Same inputs, same answer, every time, traceable to the law tables in force on the plan date.
Nothing above the line is ripped out, so integration is an engineering question with a bounded answer rather than an open-ended platform program.
Lifetime planning is exactly such a function, and getting it wrong is its own kind of disaster: the retiree who runs out of money at 82, the family under-insured by a million dollars. It is also precisely where a model, left alone, fails — because it reaches for the same rules of thumb the planning incumbents use, and in this domain approximation is not “close enough”; it is wrong, in ways that compound every year to the household’s detriment.
The maintenance surface after integration is a rulebase on an annual law-table cycle — closer to an actuarial model than to a software product line. Your firm already owns and maintains validated computational models under regulatory scrutiny. That is a core competence, not a distraction.
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 and on advisor adoption. MaxiFi already serves an advisor-channel professional base today, and the output is a plan a financial professional can defend line by line in front of a client — the only kind that survives contact with a career agency force. The auditable trail is what makes the advice supervisable at scale.
Caution about acquiring platform-centric, 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 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 industry has now run this experiment in public. Over the past year the incumbent planning vendors have each attached generative AI to goals-based engines. The result is a language model in front of arithmetic that was never deterministic to begin with — a more articulate approximation. As models improve they converge on one another, and the industry mistakes that agreement for accuracy. A perfect mimic of an approximation is still an approximation, and the error compounds every year to the household’s detriment.
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 evaluation on your desk, the liability, 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.
The substance of a financial recommendation is governed regardless of the interface that delivers it, and being “AI-generated” is not a liability shield. The exposure scales with the size of the advised population — which, for a career agency force plus an independent broker-dealer, is considerable.
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.
It also starts from the defensible number: the most a household can safely spend with what it has, sustainable by construction — not the aspirational “how much will you need” that manufactures the wrong, litigable figure.
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 decumulation question the firm has been working. 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 it from someone. There is another option.
Every MassMutual financial professional carries into every recruiting conversation and every client meeting the only advice in the industry that can be stood behind with a stated accuracy guarantee — and Northwestern Mutual, New York Life and Guardian cannot say the same. A computed plan also identifies the precise shortfall a protection product solves, at the precise date it arises. That is a materially different conversation from a probability of success, and it is the one Private Wealth was built to have.
The claim persuades; the guarantee closes. MaxiFi’s determinism makes a planning-side accuracy guarantee offerable for the first time: a computational error is objectively decidable — rerun the engine and check — so the warranty prices at a rounding error and is insurable, with a stated exclusive remedy. A Monte Carlo or rule-of-thumb competitor cannot offer it at any price, because the warranted event cannot even be defined.
A correct-by-construction engine retires the largest overhang on advice at scale — being confidently wrong with people’s money — just as regulatory scrutiny of unguarded AI advice rises. We are not selling an insurance policy; the insurance is included. And there is exactly one MaxiFi. It will sit somewhere.
A mutual is not managing to a quarterly multiple. It can buy an asset whose value is measured in decades of compounding advisor productivity and policyholder outcomes, and hold it through the period in which every stock-company competitor is still deciding whether to rent. That is a structural advantage in this particular acquisition, and it is not available to most of the field.
Goals-based planning tools answer a probability question. The question the household asked is an arithmetic one. Whichever vendor renders the plan, the number on the page belongs to that vendor — and cannot be checked, because there is nothing to check it against. This is the option that ends that.
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.