Strategic Acquisition Brief · Confidential
Sasan Goodarzi has said you cannot run a business on a large language model, because accuracy and compliance matter. He was talking about books. It is twice as true of a household’s lifetime plan, where a mis-sequenced Roth conversion or a mis-computed claiming date is a real, measurable loss that surfaces years later. MaxiFi is the engine that removes the guess: 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.
Intuit is expanding an agentic platform across its consumer and business surfaces, on consumption pricing, with the accuracy of the underlying computation as the thing that makes the agents safe to ship. That architecture is already proven in tax: the natural-language query resolves against a validated rules engine, and the answer is auditable.
The same customers are asking a second class of question — when to claim Social Security, whether to convert to a Roth this year, how much they can safely spend in retirement, which account to draw from first. Those questions have correct answers. Intuit does not currently own an engine that computes them.
The data is already there: income, filing status, state of residence, dependents, retirement account activity, self-employment income. That is most of what a lifetime optimization needs, and it is collected annually, at scale, with the customer’s consent, in the one interaction where they are already thinking about their financial life.
A tax return is a one-year computation. A lifetime plan is the same rules extended across forty years of interacting decisions — a harder problem, and a far larger one, because it converts an annual transaction into a continuous relationship.
MaxiFi is not an application Intuit would operate alongside its own. It is a computation service the existing agents call when the question is about a lifetime rather than a filing year.
TurboTax, Credit Karma, the assistant surface. Same interface, same conversational layer, same product velocity.
The orchestration and the models keep doing what they do well. They simply gain a second deterministic engine to resolve against.
The rules, the solver, the audit trail. Same inputs, same answer, every time, traceable to the law tables in force on the plan date.
The done-for-you promise extended from a filing year to a lifetime — and a guarantee that can cover it.
Federal tax law, Social Security provisions, Medicare Part B and 42 state income tax codes, updated as provisions are released, on an annual law-update cycle, with a regression suite re-run against every legislative change. That is the same discipline the tax engine already operates under — the same calendar, the same kind of team, the same definition of done.
No other acquirer in the market can absorb this asset with less friction, because no other acquirer already runs a rule-maintenance function of exactly this shape.
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 Intuit the inputs largely already exist, refreshed annually and verified against filings. That is what makes a computed lifetime plan deliverable to a hundred million consumers rather than to the fraction who would sit through a planning interview — and it is the difference between a feature and a franchise.
Caution about acquiring custom-built technology while AI reshapes software 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.
The solver is the replicable half: the mathematics of lifecycle consumption smoothing is published, much of it by Kotlikoff himself, and the patent has expired. The rulebase is not — thirty years of encoded federal, state, Social Security and Medicare provisions, versioned and re-run against every legislative change. Encoding them correctly and keeping them correct across three decades is the decade.
Intuit’s durable advantage was never TurboTax’s interface. It is the tax-calculation engine underneath — which is why Intuit can put AI in front of consumers and still produce audit-ready answers, and why the accuracy guarantee was offerable in the first place. A warranty is only possible where an error is objectively decidable, and an error is only decidable where something was computed rather than generated.
Retirement and lifetime financial planning have exactly the same architecture: high stakes, dense and constantly changing rules, an answer that must be defensible years later, and consumers who cannot check the work themselves. It is the largest remaining category with that shape.
What it lacks is the engine. Every planning incumbent has attached generative AI to a goals-based tool this year — a language model in front of arithmetic that was never deterministic. The move Intuit made in tax has not been made in planning, by anyone.
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.
A build arrives in years. The agentic launch, the competitive window and the frontier assistants arriving in personal finance all run in quarters. The engine — and its economist — exist now, once.
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.
Retirement guidance delivered at consumer scale is examined years after the fact, under the law as it stood at the time — a standard Intuit already meets in tax and no one currently meets in planning.
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 — which is to say the accuracy guarantee Intuit invented for tax becomes offerable in planning for the first time.
It also starts from the defensible number: the most a household can safely spend with what it has, sustainable by construction — not an aspirational target that manufactures the wrong, litigable figure.
The gap between a confident answer and a correct one is no longer a matter of opinion. It has been measured by independent researchers, published in a peer-reviewed journal, and reported by CNBC, Newsweek, Money and Quartz.
The Journal of Financial Planning (June 2026) put identical, detailed household scenarios to seven widely used AI tools — ChatGPT, Claude, Gemini, Copilot, DeepSeek, Meta AI and Perplexity — and asked two questions: do they give consistent recommendations to the same prompt, and are those recommendations consistent regardless of the user’s gender and ethnicity?
On the first, no. For one identical family, emergency-fund recommendations ranged from $19,500 to $37,500 — a statistically significant spread. Portfolio allocations differed significantly in equities, cash and alternative assets.
Nicolini, Cude & Chatterjee · Journal of Financial Planning 39(6) →
Holding every financial fact constant and changing only the described race or gender of the household head, some tools returned identical recommendations and others did not. One assigned a 75 percent bond allocation to an African American–led household while giving otherwise identical White-led households materially higher equity.
The retirement question is the sharpest case. Nearly every recommendation was the traditional 4 percent rate — and the only variation that appeared came from changing the household’s described race or gender.
For a regulated institution deploying guidance at scale, that is differential output from a process that cannot be traced. A deterministic engine is examinable by construction: every input that affects the answer is explicit, so when a variable moves the output you can see which one, and by how much. That makes fairness testable rather than asserted.
The authors measured consistency and fairness, and call for future work across larger sets of financial scenarios. Whether a recommendation is the economically optimal one for a particular household was outside their design.
That question has a published answer, and it predates the AI debate by years. Writing in Forbes in June 2018, Kotlikoff ran a 66-year-old couple through MaxiFi and computed their correct spend-down rate at 6.2 percent. Change their asset mix and it becomes 5.3 percent. Change it again — no regular assets, smaller retirement accounts — and it becomes 10.5 percent. A companion column found the correct replacement rate for a single couple ranging from 62.3 percent to 135.1 percent across eight variations in their circumstances.
Across every household computed, the correct rate was never the rule of thumb. That is what it looks like when the answer responds to the facts — and it is the difference between a number retrieved and a number solved.
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.
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 Roth sequencing and bracket-filling.
Acquiring MaxiFi acquires the megaphone these pieces ship from — pointed, with credibility no one in the category can match, at exactly the questions your consumers ask the moment the return is filed.
Durable value accrues to whoever owns the deterministic engine under the trusted interface. Intuit is the proof of that proposition in tax. Planning is the same proposition, in a larger category, and the engine is unowned.
A tax return is an annual transaction. A computed lifetime plan is a continuous relationship, and it names the specific product the customer needs next — with a date and a dollar figure, derived rather than pitched. That is the highest-intent cross-sell surface in consumer finance.
The claim persuades; the guarantee closes. Intuit knows this better than anyone. 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. H&R Block, NerdWallet and the frontier assistants cannot answer it.
A correct-by-construction engine retires the largest overhang on giving money guidance to a hundred million consumers through an agent. We are not selling an insurance policy; the insurance is included. And there is exactly one MaxiFi — unowned, it reaches every competitor through the same API anyone can rent.
One substantiated-computation moat is a good business. Two — tax and lifetime planning, sharing a rule-maintenance function and a single agentic front end — is a category position no one else in consumer finance can assemble, because no one else already owns the first one.
Intuit built an engine that made a promise warrantable, then put AI in front of it. Nobody has done that in retirement and lifetime planning. The engine that would let you exists, it has been running for thirty years, and it is available once.
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.