Strategic Acquisition Brief · Confidential
That is Intuit’s own doctrine — a deterministic tax knowledge engine is the authoritative source for every tax calculation, and the LLMs never do the math. It is the reference architecture for high-stakes AI. But it exists for tax only. The Consumer Platform now promises wealth optimization, personalized wealth-building and debt plans — dollar answers with consequences, and no deterministic engine underneath them. MaxiFi is that engine: the provably correct lifetime plan, built over 30 years by BU economist Laurence Kotlikoff.
In November 2025, Intuit fused Credit Karma and TurboTax into the all-in-one Consumer Platform — “done-for-you daily finance management and wealth optimization” across credit, debt, money management, wealth building, and tax. Its named agents already act on dollars: a Debt Assistant that crafts personalized pay-down plans, a Refund Assistant that recommends how to “crush debt… or invest for the future.” Investor Day made doubling household savings rates a mission metric. And with agent experiences now rolling out through partnerships with both frontier labs, every one of ~100 million consumer relationships can ask a lifetime-money question — and get a generated answer.
For tax, Intuit refuses to let a model compute a number with legal and financial consequences: the deterministic tax knowledge engine computes; the LLM only converses. For wealth building, debt strategy, retirement, and lifetime spending — numbers with larger and longer consequences — there is no equivalent engine underneath. The fastest-growing surface of the platform runs on exactly the architecture the tax side forbids.
MaxiFi closes that gap — a validated, deterministic engine that computes the mathematically correct lifetime plan, rather than approximating one. The tax-knowledge-engine architecture, extended from the April form to the whole of a household’s financial life.
A large language model is a horizontal capability. In any function where a wrong answer is catastrophic, no serious operator ships the raw model to the user — a purpose-built application layer sits on top of it, encoding the domain’s rules and holding the model to them, turning raw generation into an action that is correct, defensible, and safe to deploy. Intuit wrote the industry’s reference case: a deterministic tax engine under TurboTax’s AI, because a hallucinated tax number has serious legal and financial consequences. Patient-facing healthcare AI runs inside a safety layer, not on a raw model; no one boards a plane flown by an unverified black box. The doctrine is settled — where the stakes are real, the engine computes and the model converses.
Lifetime financial 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.
Built from the ground up over thirty years, MaxiFi is the proprietary workflow that computes the correct, auditable answer under the actual tax and benefit rules — the function customers actually want performed. The model doesn’t have it. The customer doesn’t have it. The planning incumbents approximate it — and, in this domain, approximating it means getting it wrong, to the household’s cost. Value accrues to whoever owns the trusted workflow: Intuit itself is the proof, a company built on a deterministic engine under a trusted interface. The planning engine is the one still un-owned layer in consumer finance.
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 — the computed answer the Consumer Platform can incorporate and stand behind.
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. Larry Kotlikoff 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.
Patent-winning algorithms refined over 30+ years, built from economic theory rather than scraped text — exactly the kind of intellectual property a probabilistic model cannot reverse-engineer by sampling tokens.
Planning tools die on data entry. Inside Intuit, the inputs problem disappears: a filed TurboTax return plus Credit Karma data pre-populate a MaxiFi plan. Add the AI+HI network — 13,000 experts delivering the same auditable plan the agent shows the customer — and the advisor-channel Pro base MaxiFi already serves today.
Every planning tool — and every AI model trained on them — answers a lifetime-money question by approximation: rules of thumb, replacement ratios, withdrawal heuristics, simplifying assumptions. As models improve, they converge on one another and the industry mistakes that agreement for accuracy. But a perfect mimic of an approximation is still an approximation — still wrong — and the error compounds a little further every year, to the household’s detriment.
MaxiFi does not approximate. It computes — iteratively, multivariately, and simultaneously across taxes, benefits, longevity, and cash flow, year by year for a whole life — the one optimal plan. The same inputs produce the same answer, every time, with a full audit trail from data to result. It is provable, not merely confident: the only 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.
The path mirrors the tax stack: MaxiFi as the computation service GenOS agents call for any planning number — the model never does the math, exactly as with tax. And because MaxiFi can generate billions of provably correct planning cases (perturbing Survey of Consumer Finances observations and computing each one), the capability can also be trained into the models Intuit already partners with, under Intuit’s control — the correctness lives in the weights, with the engine as the authoritative check. How the incorporation is engineered is Intuit’s call; what MaxiFi brings is the one input that is provably correct going in, so what comes out can be, too.
The substance of a financial recommendation is governed regardless of the interface that delivers it. A system that tells a household how to allocate a refund, pay down debt, or build wealth is making a recommendation in substance, whatever it is called in the product. The rules do not pause for new technology, and being “AI-generated” is not a liability shield — regulators put the industry on notice in 2024 and have kept generative AI on the examination agenda since.
At the sector level, AI-generated money advice offered without a fiduciary safeguard is beginning to draw its first suits across the industry. That is a category-wide dynamic, not a claim about any one company’s litigation history. The direction of travel is toward accountability for the substance of the answer, not the label on the product that delivered it — and the exposure scales with the size of the advised population.
A correct-by-construction engine addresses the underlying exposure directly: if the math is right, reproducible, and auditable, the answer holds up to scrutiny on its own terms. It is the same assurance Intuit already gives on every tax return — extended to every planning answer the Consumer Platform gives. And it 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 — including both of the labs Intuit now partners with. The variance across engines on identical, checkable prompts is the proof: the correctness cannot come from the model layer.
“The AI said John and Jane can spend approximately $52,000 per year in discretionary spending. MaxiFi’s demonstrably correct answer — verifiable by inspecting its reports — is $63,382.”
Read the head-to-head →“AI’s best hope of providing accurate economics-based planning is by pairing a conversational front end with MaxiFi’s computed results — precisely correct, not clearly pretend.”
Read the structural argument →Estate-planning head-to-head naming a frontier model’s output against MaxiFi’s computed result — the same structural gap, applied to estate and gifting strategy.
Read the estate test →“The median household leaves $182,370 of lifetime Social Security on the table. AI tells Jane a job change adds at most $35K in lifetime benefits when the right answer is $168K.”
Read the Social Security test →Head-to-head against two frontier models on the shape of lifetime spending — the “retirement smile” — comparing generated narrative against MaxiFi’s computed trajectory.
Read the retirement-smile test →A frontier model’s Roth-conversion sequencing tested against MaxiFi’s optimized path — MaxiFi’s computed strategy came out 72.7% better on the same household facts.
Read the Roth-conversion test →Acquiring MaxiFi acquires the megaphone these pieces ship from — pointed, with credibility no one in the category can match, at the consumer-finance surface Intuit’s platform now owns. Larry Kotlikoff intends to stay on with the acquirer in whatever capacity best serves the product, turning a category critic into the correctness narrator for the platform that incorporates MaxiFi. The CBS finding is the named, neutral proof; the Substack series is the dated, dollar-specific record behind it.
Intuit’s own history is the argument: durable value accrues to whoever owns the deterministic engine under the trusted interface — not to the interface, and not to the model. The model layer is commoditizing in real time; Intuit partnered with both frontier labs precisely because neither differentiates it. The one un-owned layer left in consumer finance is the deterministic planning engine. It runs as one funnel: attractant → adhesive → loyalty → continuity — correctness draws the customer in on the highest-stakes question they will ever ask, keeps them because a right answer on money is not replaceable, and turns tax-season customers into year-round planning relationships.
The Consumer Platform’s declared aim is year-round money outcomes — the escape from the April spike. Lifetime planning is the highest-intent, highest-trust surface there is, and the natural bridge from a filed return to a twelve-month relationship: every TurboTax return is a pre-populated MaxiFi plan.
A correct-by-construction engine retires the largest overhang on agentic money advice — being confidently wrong with people’s money at platform scale, just as sector-wide scrutiny of unguarded AI advice rises. And there is exactly one MaxiFi — and it will sit somewhere. In the model layer, it reaches every customer of those models through the same API you rent, competitors included. Inside Intuit, it is yours: deployed under your brand, denied to some, licensed to others on your terms.
Intuit trades on trust — “more money, no work, complete confidence.” The market pays a premium multiple for defensible, low-risk earnings; a provable planning engine makes the Consumer Platform’s growth story proprietary and un-copyable while removing a tail risk in the same motion.
“Double household savings rates” is a per-household math problem: what should this household save, given everything? Rules of thumb cannot answer it; MaxiFi computes it — the only auditable basis on which the mission metric can be claimed, measured, and defended.
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.