Strategic Acquisition Brief · Confidential
Roll-ins reached $1.7 billion in the quarter and more than $7 billion over the trailing twelve months, both up nearly 20%, while transfer deposits rose 30% year over year. Money arriving is the easy part. What Principal is then asked — how much can this household actually spend, from which account, in what order — is a computation, and it has exactly one correct answer. MaxiFi produces it: for a household’s facts and assumptions it solves, not guesses, the lifetime plan, every dollar of taxes and benefits computed under current law.
On June 30 Principal expanded its income suite with savings vehicles designed to generate lifetime income inside defined-contribution lineups, including LifeTime Income Builder Index target date funds. Days later, Nuveen, TIAA and Principal announced expanded access to guaranteed lifetime income. The strategic direction could hardly be clearer.
The second quarter delivered against it: Retirement and Income Solutions pre-tax operating earnings up 8%, transfer deposits up 30% year over year, and roll-ins of $1.7 billion in the quarter against more than $7 billion over twelve months — both up nearly 20%. Enterprise earnings grew 13% and adjusted earnings per share rose 17%.
An income product answers where the money can come from. The household is asking something harder: how much, starting when, drawn from which account first, with what consequence for taxes, Social Security timing, Medicare surcharges and the surviving spouse — for the next thirty or forty years.
That is an optimization with one correct answer for a given set of facts, and it changes when the order of the decisions changes. No goals-based planner computes it; they report the probability that a plan survives a simulation.
MaxiFi is a computation service the existing stack calls. It does not replace the recordkeeping platform, the participant portal or the advisor tooling; it supplies the number that appears inside them.
The portal, the enrollment path, the education. No migration and no new interface to adopt.
The tools stay where they are. The plan they render simply becomes a computed one.
The rules, the solver, the audit trail. Same inputs, same answer, every time, traceable to the law tables in force on the plan date.
At the roll-in decision, at the income-product recommendation, and in every sponsor search where the differentiator has to be verifiable.
Federal, Social Security, Medicare Part B and 42 state income tax codes, updated as provisions are released, on an annual law-update cycle. A 146-year-old insurer already owns and maintains validated computational models whose outputs must be defensible years after they are produced. This is that kind of asset, not a consumer software business.
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, plan design. The same computed plan then serves three audiences that normally require three different stories: the participant deciding whether to roll in, the advisor building the recommendation, and the consultant scoring the RFP.
Caution about acquiring custom-built technology in this environment is well founded, and it 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. 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 how to sequence withdrawals across a 401(k), an IRA and a taxable account 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.
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 the clock is real. A build arrives in years; the income mandate and the sponsor search calendar 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.
In a retirement business the exposure is compounded by fiduciary context: recommendations made to participants inside ERISA plans are examined years after the fact, under the law as it stood at the time. 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 — which is exactly what an examiner asks for. 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.
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 — Social Security timing, Roth sequencing, withdrawal order. 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 June expansion was built around.
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 licenses or approximates it. Corp dev is demonstrably open: Beam Benefits is under agreement, and the balance sheet supports in-domain capability purchases.
Searches are won on differentiators a consultant can verify. “Participants receive a computed, auditable lifetime plan and we stand behind the arithmetic” is testable, and no competing bidder can match it. That is a win-rate argument, not a marketing one.
Roll-ins are already up nearly 20%. The decision to consolidate is made when a household sees the dollar cost of not doing it — which requires computing both paths. A probability of success cannot make that case; a computed number can.
A correct-by-construction engine retires the largest overhang on guidance delivered at ERISA scale, just as scrutiny of unguarded AI advice rises. We are not selling an insurance policy; the insurance is included. And there is exactly one MaxiFi.
A lifetime income product is easier to recommend when the engine can show precisely what it fixes, when the shortfall arises, and how much of it the product closes. The computation makes the product concrete rather than conceptual.
The income suite answers where retirement money can come from. The household is asking how much, and when, and in what order. That answer exists, it is computable, and today it belongs to a vendor rather than to Principal.
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.