Between 30 and 40 percent of Americans rely on Medicaid or SNAP at some point in their lives with combined federal spending approaching $1.7 trillion annually. This is foundational infrastructure, and it deserves to be run with the precision that scale demands. Every state administering SNAP and Medicaid is being asked to move faster while also making fewer errors — with the same staff, the same systems, and no new policy. That is a structural tension that cannot be resolved by adding headcount, and it is not a management problem.
Solving this high-stakes problem requires a new and different model for how states vet people as they apply for benefits — one that protects program integrity while meeting the needs of administrators and citizens alike. The technology to build that efficient model finally exists. This kind of trust-forward model will give administrators confidence that programs are being run with integrity, while giving applicants a faster, fairer path to the benefits they're entitled to. The will to use it is what we owe the people these programs were built to serve.
How we got here
Tens of millions of Americans rely on SNAP, Medicaid, or both to put food on the table and access critical medical care. The vast majority of them are genuine and eligible, but are waiting much longer for food or medicine than they need to be.
The friction that slows everything down is that our system cannot distinguish the inputs to trust as people apply for help. Today’s system was engineered around a small cohort of bad actors, and unfortunately, every genuine applicant pays the cost.
We've built an enormous apparatus of verification around every applicant. Documentation requirements at intake. Interviews. Cross-checks against state and federal databases. Quality reviews on the back end. Each of these is a defensible response to a real risk that someone might misuse the program. Each of these also represents a delay and cost to a family that qualifies for much needed benefits.
The federal posture has now hardened. The Government Accountability Office (GAO) reported $186 billion in improper federal payments for fiscal year 2025, an increase of $24 billion over the prior year. Cumulatively, the federal government has paid out roughly $3 trillion in improper payments since 2003. Recent federal legislation introduces direct state financial exposure tied to SNAP payment error rates beginning in fiscal year 2028. States are now on the hook for accuracy in an unprecedented way.
My career has run through both sides of this challenge. Seven years as an active-duty naval officer at fleet commands and the Pentagon, followed by a decade at a top-10 U.S. insurance company leading a product innovation team. While the constraints may look different within each sector, the underlying challenge is the same.
The pressure to reduce payment error rates is up, while application volume and the workforce administering benefits remain unchanged.
In my work with state eligibility leaders, I do not hear requests for fewer responsibilities. I hear requests for tools that match the demands of the job.
A non-zero-sum approach
Speed and accuracy have been treated as opposing forces in benefits administration because the technology to simultaneously improve both did not exist until now.
The model is changing: sort at the front door. Most applicants move through fast, because most people are genuine. The small share whose self-disclosure runs the risk of being inaccurate or incomplete gets the deeper review by a caseworker. Verification effort is focused only where the risk of errors or misrepresentation is concentrated.
Clearspeed CEO, Alex Martin, made a related case in a recent piece about the GAO improper payments report. The shorthand is "clear the hay." Most of the applicant population is hay that clears quickly. The cases that need attention contain the “needles” that get found in the small share that remains.
Clearspeed uses an applicant's yes/no voice responses to preset eligibility questions and produces a real-time, unbiased risk signal for caseworkers. No personally identifiable information is used, and caseworkers see the signal before they commit time to digging deeper. Low-risk applicants clear faster, while high-risk cases get the review they actually need.
This is a new model that respects the people the program is built to serve.
Stewardship by putting trust at the front
Asking eligible families to navigate red tape designed for the small share of bad actors made sense when there was no better way to sort. But now there is.
Leading with trust is a disciplined approach: it acknowledges that most people seeking assistance accurately present their circumstances.
This approach is non-zero-sum. Eligible families get faster access to the help they need, caseworkers focus their time more effectively, and states protect their budgets. Taxpayers see public dollars deployed properly, and the programs remain credible to the people who depend on them and the people who fund them.
The genuine majority is waiting. The technology to build the system they deserve finally exists.
