Strategic Acquisition Brief · Confidential
Ed Murphy framed the Milliman acquisition precisely: retirement security now depends on wealth accumulation, healthcare preparedness, and reliable income throughout retirement. The first two are administration problems, and Empower is buying its way to scale in them. The third is a mathematics problem, and the industry has never actually solved it. MaxiFi does: for a household’s facts and assumptions it solves, not guesses, how much can be spent, from which account, in what order, with every tax and benefit consequence carried forward and computed under current law.
The $340 million agreement for Milliman’s retirement business, announced at the end of June, is the latest step in a decade of deliberate consolidation. Ed Murphy’s framing of it is the important part: retirement security today requires more than savings alone — it depends on accumulation, healthcare preparedness, and reliable income throughout retirement.
Two of those three are problems of scale, administration and integration, and Empower has proven it can buy them. The third is different in kind.
How much can this household spend, drawn from which account, in what order, given its Social Security claiming options, its tax brackets across thirty years, its Medicare surcharges and its state of residence? That question has exactly one right answer for a given set of facts, and it changes if the order of the decisions changes.
Nothing in the workplace stack computes it. Goals-based tools return a probability that a plan survives — useful, and not the same thing. Empower already owns the participant, the plan and the assets. It does not yet own the answer.
MaxiFi is a computation service the existing stack calls. Nothing in the participant experience or the advisor workflow has to be rebuilt; what changes is the provenance of the number that appears in both.
The Empower workplace portal and app. Same enrollment, same education, same interface.
The advisor-led experience keeps its tooling. The computed plan simply arrives inside it.
The rules, the solver, the audit trail. Same inputs, same answer, every time, traceable to the law tables in force on the plan date.
One engine serves both sides of the flywheel — the participant deciding whether to roll over, and the advisor building the plan that keeps them.
Personal Capital was acquired for $825 million plus a $175 million earnout. Empower has already run the experiment of buying a planning capability outright and folding it into a workplace flywheel — and knows better than anyone what it did to conversion.
This is the same move at the layer below. Personal Capital brought an interface and a client base. This brings the computation the interface has been missing.
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 — architect, spokesperson, advisor.
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 by the engineering team as provisions are released, on an annual law-update cycle. 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. Inside a recordkeeper the inputs largely exist already — balances, contributions, wages, ages, employer plan design. That is most of what a lifetime optimization needs, which is why this integrates as a service rather than a rollout, and why it can reach 20 million investors rather than the fraction who would sit through a planning interview.
Caution about acquiring custom-built technology while AI reshapes the category is well founded. It also points the other way once the two halves of the asset are separated.
What generative AI is rapidly commoditizing is interface, workflow, reporting and integration glue — everything 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 when a participant should claim Social Security will answer fluently, confidently and unverifiably. It has no correct reference point, so no error in it is decidable. MaxiFi’s is: rerun the engine and check.
Consider Intuit. Its enduring competitive advantage is not TurboTax’s interface or its AI features. Its moat is the tax-calculation engine. Large language models can generate plausible explanations, but they cannot reliably compute taxes, optimize outcomes, or produce audit-ready answers. Intuit can confidently deploy AI because every conversational interaction ultimately resolves against a deterministic rules engine designed to produce correct and defensible results.
The same principle applies to retirement and financial planning. Advisors and consumers will interact through increasingly sophisticated AI interfaces, but the value will reside in the analytical infrastructure beneath them. The AI asks the questions. The rules engine produces the correctly computed answer.
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.
And there is exactly one of these. A build arrives in years; the income mandate, the Milliman integration and the competitive window run in quarters.
The report identifies, as explicit risks of agentic AI: auditability and transparency — 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. FINRA and the U.S. Treasury have since published an AI Lexicon and a Financial Services AI Risk Management Framework.
For a recordkeeper the exposure is compounded by fiduciary context: guidance delivered to participants at scale, inside plans governed by ERISA, is examined years after the fact. Being “AI-generated” is not a shield.
A correct-by-construction engine produces an answer that can be reconstructed and defended under the law in force on the plan date. And because the engine is deterministic, the assurance can be underwritten — a bounded accuracy guarantee no probabilistic rival can offer.
It also starts from the defensible number: the most a household can safely spend with what it has, sustainable by construction — not the aspirational figure that manufactures the wrong, litigable number.
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, including the Social Security claiming decisions your participants face. 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 income question the Milliman deal was framed around. 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. In retirement the planning engine is the one layer still un-owned — every recordkeeper and every wealth manager licenses or approximates it.
Twenty million investors and 93,000 plans are the largest captive audience in the country for an answer they can act on. The moment that matters is the rollover decision, and a computed plan is the only thing that names the dollar consequence of getting it wrong. That is conversion, not education.
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, so the warranty prices at a rounding error and is insurable. Fidelity, Vanguard and Schwab cannot offer it, because a goals-based output has no correct reference point to warrant.
A correct-by-construction engine retires the largest overhang on advice delivered at ERISA scale. We are not selling an insurance policy; the insurance is included. And there is exactly one MaxiFi.
Sponsor searches are won on things that can be checked. “Our participants receive a computed, auditable lifetime plan, and we stand behind the arithmetic” is a statement a consultant can test — and that no competing bidder can match.
Administration, healthcare and income were the three pillars. Two are being solved by acquisition of capacity. The third cannot be — there is no scale solution to an unsolved computation. There is one engine that already solved it.
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.