Two civic tech nonprofits launch $10M fund to help states reduce dependence on large government technology vendors

Two nonprofits are putting $10 million toward helping states overhaul benefits tech and reduce dependence on large vendors. Applications open for nonprofits, startups, and agencies working on Medicaid, SNAP, and similar programs.

Categorized in: AI News Government
Published on: Apr 15, 2026
Two civic tech nonprofits launch $10M fund to help states reduce dependence on large government technology vendors

Two nonprofits commit $10M to help states rethink how they buy technology

The Center for Civic Futures and the Recoding America Fund announced a $10 million funding round Monday to help state governments upgrade their benefits systems and reduce their dependence on large technology vendors.

The five-week application window targets nonprofits, early-stage companies, academic institutions, and government agencies working on solutions to persistent problems in Medicaid, SNAP, and similar programs: administrative burden, enrollment gaps, and poor user experience for both applicants and caseworkers.

New federal reporting requirements included in last year's budget bill are pushing states to make IT upgrades. But civic tech leaders worry states will follow a familiar pattern: funneling money to established government technology contractors rather than exploring better options.

The vendor problem

States typically treat technology procurement like infrastructure projects. They write thousands of requirements, bid them out through lengthy processes, and expect vendors to handle everything from fixing bugs to measuring system performance.

That model leaves states dependent on vendors for basic information about their own systems-including what they should be paying. "That is the strategy that has left states disempowered relative to vendors," said Robert Gordon, executive vice president for state initiatives at the Recoding America Fund.

Artificial intelligence tools could change this dynamic. Better-informed states could become more capable builders and better managers of vendor relationships, according to an editorial by Jennifer Pahlka, who founded Code for America, and Cassandra Madison, executive director of the Center for Civic Futures.

But they cautioned that government procurement processes aren't designed to build state capacity. States may lack access to code they paid for, and internal review processes aren't built for the rapid testing that AI tools enable.

Three priorities for funding

Recent listening sessions with caseworkers, chief AI officers, and IT leaders identified three areas needing work.

Back-end transformation: Most benefits programs face significant backlogs and manual processes. Caseworkers spend substantial time on paperwork rather than helping people. Testing emerging generative AI tools could reduce those workloads.

Data sharing: If Medicaid and labor agencies can more easily share data, fewer eligible recipients would lose coverage due to bureaucratic errors.

Enabling infrastructure: Parts of government remain undigitized. Legacy systems block states from experimenting with new technologies. The Recoding America Fund will focus on identifying and removing those barriers.

How the money works

This round differs from last year's $8.5 million funding in two ways. Early-concept projects can receive less than $500,000 to test prototypes for up to one year. Projects ready for real-world testing can receive up to $2 million over two years.

Funded projects receive technical assistance, expert coaching, access to government leaders and end users, and visibility with funders and policymakers. Awards will be announced in September.

The Center for Civic Futures will focus on AI tools for caseworkers and applicants. The Recoding America Fund will tackle institutional barriers to adoption. The interest is real: the Center received more than 400 applications in last year's round.

Learn more about AI for Government and how generative AI and LLM tools are being deployed in the public sector.


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