If you’ve looked at other retirement planning tools, you’ve probably seen the words “AI-powered” on their websites.  Here’s what that usually means: they’ve connected a chatbot — the same technology behind ChatGPT — to their financial calculator.  You ask it a question in plain English, it runs a scenario, and it explains the result back to you in plain English.  The math hasn’t changed.  The strategies haven’t improved.  You just have a more conversational way to poke around.

Some tools go a step further and offer a Roth conversion “optimizer.”  Under the hood, these typically use a greedy algorithm: they try filling each tax bracket one at a time, check whether a single number went up or down, and pick the bracket that looked best.  They optimize one thing at a time, and they don’t consider how that choice interacts with your other strategies.

Rock My Retirement does something fundamentally different.

The problem it solves is this: your retirement strategies don’t operate in a vacuum.  When you take Social Security, how you handle Roth conversions, which accounts you draw from, and where you reinvest surplus cash all interact in ways that are nearly impossible to reason about by hand.  And there are so many possible combinations that you can’t just try them all out.

For example, in Rock My Retirement, you can choose strategies like:

* When you and your spouse will take Social Security.
* Whether to do a Roth conversion for your 401ks and IRAs.
* When to start your Roth conversion.
* Over how many years to handle the Roth conversion.
* Which accounts you will draw down to pay bills.
* Which accounts you will reinvest in when you have extra funds.  

For a typical retiree, Rock My Retirement allows more than twenty nonillion possible combinations for these choices.  That’s a 2 followed by 31 zeros.  To try them all out, it would take a high-end desktop computer thousands of times longer than the universe has existed.  There’s no practical way to find the best one.

And that’s when Rock My Retirement’s AI Optimization Engine says, “Hold my beer.”

To build the AI Optimization Engine, I created a genetic algorithm — a search technique inspired by natural selection.

Here’s the key: it doesn’t just try a massive number of random combinations and hope for the best.  That would be brute force, and it wouldn’t get very far with twenty nonillion possibilities.  Instead, the algorithm starts with a small population of random strategies, evaluates how well each one accomplishes your objectives, and then breeds a new generation by mixing and mutating the top performers.  The next generation is smarter than the last.  Strategies that worked well pass their traits forward.  Strategies that didn’t are discarded.  And with each generation, the algorithm gets better at predicting which combinations are worth exploring — for your specific circumstances, plans, and goals.

After hundreds of generations, it has learned an extraordinary amount about what works for you.  It’s not following a script or filling in a tax bracket.  It’s discovering strategies that emerge from the interactions between all of your choices — strategies you would almost certainly never stumble onto by hand.

One challenge is how to find a “better” solution when you have multiple competing goals.  Of course, we all have multiple goals and priorities for our retirements, and the Engine will search for a solution that best matches yours under your unique circumstances.  But here’s where things get complicated:  If you have more than three competing goals, most genetic algorithms stall out.

So, I built an advanced variant called NSGA-III — a Non-dominated Sorting Genetic Algorithm III.  NSGA-III was developed for exactly this class of problem: optimizing many competing objectives simultaneously.  It’s the same family of algorithms used to design aircraft engines, plan satellite constellations, optimize military mission planning, and manage drug discovery pipelines — problems where there are many conflicting goals, enormous search spaces, and no single “right answer,” only trade-offs.  When the stakes are that high, you don’t ask a chatbot.

Retirement planning is that kind of problem.  You want to minimize risk, maximize what you leave behind, keep a cushion for emergencies, and maybe swing for the fences on upside — and these goals pull in different directions.  NSGA-III is built for exactly that tension.

The Engine surfaces combinations of strategies you would almost certainly never find on your own — tailored to your priorities, tested against every overlapping window of real economic history, and ready for you to explore, compare, and adopt with a single click.