What Can I Spend? · June 16, 2026. Watch the recording ↓ Watch the recording ↓
Watch Justin Fitzpatrick walk through the spending conversation that replaces a probability-of-success score: a concrete monthly number a client can act on, and guardrails agreed in advance so everyone knows what happens before the markets move. Worked through live in Income Lab.
Recorded live on June 16, 2026 · 61 minutes · The last several minutes are live Q&A.
A probability-of-success score tells a client the odds their plan survives. It doesn't tell them what they can spend this month, or what to do when the score drops. Justin walked through the conversation that replaces it, live in Income Lab.
What a score actually measures, why clients hear something different, and why a falling number creates anxiety without giving anyone an action to take.
A specific monthly spending number, built from the plan, that answers the question clients actually ask. How the retirement paycheck is derived and how it updates as conditions change.
How the risk-based guardrails methodology sets the adjustment rules up front: what triggers a change, how big it is, in dollars. Your client sees the playbook before markets move.
The last several minutes are Q&A. This page is the permanent recording, open to share with colleagues.
Most retirement plans still lead with a single number: the probability that a plan succeeds. It looks precise, and it photographs well in a review meeting. The problem Justin opened with is that the number answers a question clients are not actually asking. A retiree wants to know what they can spend this month, whether they can take the trip, and what happens if the market falls. A success score answers none of that. It compresses a lifetime of decisions into one figure whose meaning is genuinely hard to explain, and whose movement, up or down, rarely comes with an instruction.
Worse, the number tends to do emotional damage in both directions. A high score invites a client to spend less than they safely could, trading years of good experiences for a cushion they will never use. A falling score creates anxiety without offering an action, so the client calls worried and the advisor has nothing concrete to hand back. The session made the case that the score is not a planning tool. It is a test result, and a confusing one.
Justin spent time on what the number is under the hood, because the critique only lands once you see it. A Monte Carlo success rate is the share of simulated market paths in which the portfolio does not run out before the plan ends. That makes it a statement about tail risk across hundreds of hypothetical futures, not a statement about the single future the client is going to live in. Two plans with the same score can imply wildly different spending, and the same plan can swing ten or fifteen points on a normal market quarter without anything in the household actually changing. When the input that moves the headline number the most is short-term market noise, the headline number is not a steering wheel.
The alternative Income Lab is built around is a concrete spending figure, the retirement paycheck. Rather than reporting the odds, the software derives the amount a household can spend now, given everything in the plan, and shows how that amount is expected to change over time. It is the answer to the question clients ask in plain language. Because it is a dollar figure rather than a percentage, it is something the client can hold onto, compare against their actual budget, and adjust around. And because it is recalculated as conditions change, it stays current instead of becoming a slide that ages the moment the meeting ends.
A spending number alone is not enough, because markets move and the number has to move with them. This is where the risk-based guardrails methodology comes in. Guardrails set the adjustment rules at the start of the relationship, in writing: how far the portfolio has to drift before spending is dialed back, how far before it can be raised, and exactly how large each change is in dollars. The client sees the playbook before anything happens, so a downturn becomes a pre-agreed adjustment rather than a panicked phone call. Justin's framing is that this turns the advisor from a forecaster, who is asked to predict, into a navigator, who has already shown the client the map and the rules for changing course.
The guardrails are also where the methodology earns its credibility. The defaults are deliberate and conservative, and the rules are deterministic, so an advisor can show a client precisely what would trigger a cut and how much it would be. There is no hand-waving about probabilities. There is a specific number and a specific rule.
The back half of the session moved into the software, where Justin built the conversation a client would actually see. He showed the spending number derived from a plan, the guardrails that bracket it, and how a change in markets flows through to a concrete, dollar-denominated adjustment rather than a moving percentage. The point of working it live, rather than on slides, was to show that the math is inspectable: an advisor can trace where the spending figure comes from and what would move it, which is exactly what is missing when a plan leads with a success score. For advisors who are new to the platform, the section below has the fastest way to get up to speed, and a walkthrough on your own client numbers is one click away.
From the live session, with a little more detail than there was time for on the call.
A probability-of-success score, usually from a Monte Carlo simulation, is the share of simulated market paths in which a plan does not run out of money before it ends. It is a statement about tail risk across hundreds of hypothetical futures, not a statement about what a specific client can spend. Clients hear "the odds my plan works," which is not quite what the number means, and a score that moves ten or fifteen points on ordinary market noise gives no one an action to take. It tends to push clients to underspend when it is high and to panic when it falls.
A concrete spending number: the retirement paycheck. Income Lab derives the amount a household can spend now, given everything in the plan, and shows how that figure is expected to change over time. It is a dollar amount a client can compare against their real budget, paired with guardrails that define in advance how and when it adjusts. You can read the full explanation in the retirement paycheck guide.
Guardrails are pre-agreed rules for adjusting spending as markets move. At the start, you set how far the portfolio has to drift before spending is dialed back, how far before it can be raised, and exactly how large each change is in dollars. Because the rules are deterministic and written down up front, a downturn becomes a planned adjustment the client already understood, rather than a surprise. The full framework is in the risk-based guardrails methodology.
Yes. The spending question anchors the plan in retirement, but the same plan carries a client through the working years and the run-up to retirement, where the questions are how much to save and what a realistic future paycheck looks like. You can model working households and show them, concretely, how today's decisions change the spending they can expect later. If you want to see that end to end, the Full Life-Cycle Planning replay walks through all three phases.
You build the expense directly into the plan rather than leaving it to a probability. A long-term care event, for example, can be modeled as a defined cost over a set period, such as a higher spending figure across the last few years of life, so its effect on the retirement paycheck and the guardrails is visible up front. That lets you stress test the plan against the expense and decide, with the client, how to prepare for it.
You are watching it. This page is the permanent recording of the June 16, 2026 session, and registrants also received an email link after the webinar. You are welcome to share this page with colleagues; the recording is open, with no second registration required.
A few people on the live call were brand new to the platform. If that's you, this is the fastest path from "I have a login" to "I can run the spending conversation."
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Justin co-founded Income Lab in 2018 to close a gap he saw across the industry: advisors lacked a way to give retirees a concrete, trustworthy answer to "how much can I spend?" He built the risk-based guardrails methodology now used by thousands of advisors, replacing probability-of-success scores with a specific spending number and dynamic adjustment rules that update automatically as conditions change.
Before Income Lab, Justin spent a decade at Jackson leading advanced planning teams and developing financial technology. He also spent seven years in academia, teaching at MIT, Harvard, Queen Mary University of London, and UCLA. He holds a PhD in Linguistics from MIT, a CFA Charter, and CFP certification.
His research and writing have appeared in Kitces.com, ThinkAdvisor, AdvisorPerspectives, and Financial Planning Magazine, and he speaks regularly at the CFP Board Research Colloquium and at NAPFA and FPA conferences.
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Practitioner-led. Real scenarios and working math, not a product tour.
Read the playbooks Justin referenced during the session.
The risk-based framework behind every spending number. The reason the math holds up through a real market.
→ FrameworkHow much a client can actually spend, and how that number updates as the world moves.
→ The CritiqueThe full case against leading a plan with a success score, and what to do instead.
→ AI ParaplannerThe Income Lab Pro paraplanner that surfaces tax-positioning moves at every stage.
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