After watching Glitch wind down and spending three months discovering I’m definitely not a “growth team” person at Handshake, I found myself back where I belong: civic tech. There’s something clarifying about trying to make venture-backed products work and realizing that what you actually want is to build things that help people navigate government services. So when I heard about an AI Residency at Propel focused on the social safety net, it felt like coming home – just with better funding and actual infrastructure this time.
For folks who haven’t needed to interact with SNAP benefits, Propel is an app that helps people check their EBT balance, find stores that accept benefits, and get updates about their state’s programs. About 5 million Americans use it – roughly 1 in 4 SNAP beneficiaries. The team there has built something genuinely helpful, and they’ve earned deep trust with their users by actually giving a damn about making government benefits easier to navigate.
But I didn’t join to work on the main app 😲. I joined a new residency program specifically focused on HR1 and its impact on SNAP and Medicaid. We’re exploring how AI might help states and beneficiaries deal with the massive changes coming down the pipeline.
What HR1 Actually Does (abridged)
Here’s what HR1 does: it adds new purchase restrictions and work requirements for SNAP beneficiaries while increasing administrative costs for states. And – this is the part that has states panicking – it penalizes states financially based on their error rates. The higher your error rate in administering benefits, the more you have to pay to run the program. States are scrambling to understand their current error rates and figure out how to reduce them before these penalties kick in.
The Structure of the Residency
Our residency pairs three engineers with three Subject Matter Experts who’ve actually run these programs. These folks have been state administrators, they’ve implemented SNAP and Medicaid, they’ve sat in the rooms where these decisions get made. Our role as engineers is to support their vision and use their connections to build what they know states need.
The goal isn’t just to build tools – it’s to demonstrate the right ways to use AI for social safety net programs. We want states to embrace what we believe is responsible, helpful AI, not the kind that makes bold promises about “solving poverty with machine learning”.
Real Work During the Shutdown
During the recent government shutdown, I got to see what this looks like in practice. We built crawlers to pull information directly from state websites and get it in front of folks through Propel. Usage went up dramatically – people were checking constantly for any updates about their benefits. The food stamps subreddit had screenshots from Propel all over it, with folks sharing what they’d learned with each other.
I used Jina.ai to build what we believe is the most comprehensive database of active food pantries and banks. This tool turns messy web pages into clean markdown with preserved links – work that would have taken weeks of manual data entry five years ago now takes hours. I’m still skeptical about LLMs broadly, but for specific tasks like “parse these 500 county websites and extract food pantry hours,” they’re incredibly effective.
What We’re Building (maybe!)
States are being forced to move quickly, which creates both opportunity and risk. There’s real energy and appetite for new approaches, but y’all know how this goes – when government moves fast, vendors appear with promises of AI magic that’ll solve everything.
We’re building practical tools:
- Systems that help caseworkers identify error patterns before they become expensive penalties
- Crawlers that monitor policy implementations across states so they can learn from each other
- Tools that translate federal requirements into clear implementation guides
- Verification systems that actually work for people with complicated lives
This isn’t the AI that gets venture funding or conference keynotes. It’s the kind that might keep someone’s benefits from being wrongly terminated because a system finally understood that their gig work counts as employment. We need engineers who understand that “reduce error rates” translates directly to “keep families fed.”
We’re moving quickly, and throwing away what doesn’t work as quickly as possible. This list could be different in two weeks!
Why This Matters Now
What makes this moment different is the combination of pressure and possibility. States have to act – HR1 isn’t optional. But we have a chance to guide that action toward tools that actually help rather than systems that just add complexity.
Having Propel’s reach matters here. We’re not guessing what might help – we can see how policy changes impact real folks, often before states fully understand what’s happening. That feedback loop between users, states, and our tools is what makes this approach different from typical government modernization efforts.
If you’re working on AI and HR1 responses from any direction – state agencies, cities trying to fill gaps, contractors who care about outcomes – please reach out. The next year is going to shape how these policy changes actually play out for millions of Americans. States are moving fast because they have to. Let’s make sure they’re building things that help.