The State Capacity Ecosystem Hub is live.

• Search (in Beta) curates a personalized view of who’s working on your specific areas of interest

• Directory lists >300 orgs focused on government effectiveness and what they’re working on

• Connect lets you add what you’re working on, seek and share opportunities, and find collaborators

• Affinity helps you find surprise relationships between organizations

Two key things to do right now:

• Submit yourself to Connect — tell us what you’re working on and what you need; we’ll curate a living database of challenges, funding opportunities, and practitioner partners

• Sign up for our first event, a CivicTech AI Hackathon — June 24 5:30-9p

Credit to , my co-author on this post and builder of the Hub’s website.

Why we’re building the Ecosystem

Good policy fails because the practitioners, technologists, researchers, and funders working to fix government are operating in silos, without a clear view of each other and with few collaboration pathways. Great talent is left out of the conversation not by intent, but because it simply cannot find its way in.

We believe visibility is infrastructure. A clear map of who is doing what is the first step toward State Capacity acting like an ecosystem and not just a collection of isolated players.

Our goal is to help create that well-functioning ecosystem: one where every participant knows what the others are doing, sees them as collaborators rather than competitors, and can access shared knowledge and resources when they need them. Great theory gets operationalized. Hard-won practitioner knowledge travels. Capital flows toward the gaps, not just the familiar names.

Overview of the Tools

Search (Beta), located on the Hub, returns results relevant to you based on a mad libs-style interface that allows you to select your role, what you’re seeking, a problem area, and a geography. This feature can only show what currently exists and will improve as we add more organizations and individual connections.

Directory offers you a view of all the organizations in the Ecosystem. Use it to understand who is working on what. Filter based on segment, problem statements, geographic focus, and more. It offers a more holistic view versus Search’s curation.

Connect is the layer we’re most excited about. Find real people and organizations working on problems you care about. More importantly, submit what you’re working on so that others can find you. Together, we’ll curate a living, breathing picture of who’s working on what in the Ecosystem.

Affinity helps you find surprise relationships between organizations based on shared problem areas, overlapping funding sources, and description similarity. Use that knowledge to reenvision who you can work with and on what.

About the Event

On June 24, we’re hosting a one evening hackathon for civic technologists, government practitioners, students, and anyone else who wants to build tools that help strengthen our government’s ability to deliver.

The goal is connection, not perfection. Find collaborators interested in the same topics. Build quick, working prototypes that take ideas from -1 to 0 and show whether an idea is worth pursuing. See if someone in attendance can help connect you to the right folks to turn that idea into something real.

Check out the event page for more information.

How you can help

When we delivered the first version of the Ecosystem, we effectively shared a list of orgs; and an incomplete one at that. Folks appreciated the clarity it brought to who is working on State Capacity, but there was no way to act on that clarity. Dozens of follow-on conversations informed how to make our map more useful: living problem statements, the ability to find collaborators, and hosting live events move us from theory to action.

Chances are this version of the Ecosystem isn’t perfect either. You can keep helping us improve it. Add your organization and your problem statement. Join our events and host your own. Reach out with feedback.

Our hope is that our work echoes the lessons of State Capacity: agile delivery, test and learn methodologies, shared evidence, scalable results.

The closing message

Delivering State Capacity may be hard, but the roadblocks aren’t mysterious: they’re structural. Knowledge doesn’t travel because there’s no infrastructure for it to travel on. Capital concentrates because funders fund what they can see. Practitioners work in isolation because isolation is the default.

This is why we’ve built the Ecosystem, but it is only as helpful as the community behind it. And there’s a million other ways folks can build out the infrastructure further in their own ways.

The State Capacity Ecosystem already exists. It just needs to act like one. Let’s build it together.

For years, Jen Pahlka has written on how to improve Government’s ability to deliver. She uses State Capacity to define what good government looks like: hiring and firing the right people, updating old procedure, investing in technology, and instituting test and learn systems to drive outcomes.

I love her work. It has informed and inspired me. But operationalizing her theories has proved difficult (and Pahlka has delivered a massive step forward here with her new Recoding America Fund).

There are many thought pieces that detail why State Capacity is tough to deliver; very few help interested parties understand: what can I help with, how can I get involved, and who else is thinking about the same things?

My Contribution

There is a real ecosystem of people and organizations — some inside government, most outside it — already working on exactly the problems Pahlka describes. Founders building tools agencies actually need. Fellowships placing technologists where they’re missing. Researchers documenting and benchmarking results. Funders backing the work.

But this ecosystem has never been treated like one. My research revealed targeted resources, but no shared directory; no common language; no helpful landscape overview for newcomers.

The capital is there. The talent is there. The connective tissue is not.

So I built it.

The State Capacity Ecosystem v1 (225 orgs)

Civil Society

• Research (42) — Think tanks, university centers, and independent research institutions producing knowledge on government performance, digital governance, and state capacity.

• Fellowships (22) — Programs that place a person inside a government team, spanning early-career pipelines, mid-career rotations, and legislative placements.

• Community (21) — Volunteer civic tech groups, practitioner peer networks, and member-driven communities connecting people working at the intersection of technology and government.

• Advocacy (17) — Organizations pushing for government modernization, tech talent pipeline development, and stronger public interest technology policy.

• Training (6) — Programs that upskill government staff or practitioners already in the field.

Government

• Government (38) — Federal, state, and city digital services offices, innovation labs, and technology transformation teams operating inside government.

Capital

• Philanthropy (32) — Foundations, funder collaboratives, and pooled vehicles whose primary activity is grantmaking in support of government effectiveness and public interest technology.

• Investor (14) — VCs, PE firms, family offices, and impact funds with an explicit and demonstrated focus on government technology that builds capacity.

Industry

• GovTech (18) — Mission-oriented technology companies whose primary customer is government and whose product increases operating capacity rather than substituting for it.

• Digital Services & Consulting (15) — Mission-locked organizations whose output lands inside government: human-centered delivery, technical assistance, and process redesign.

What the map is for

This is a multi-modal information marketplace that acts as State Capacity’s connective tissue. A few examples.

You work inside government and you’ve identified a real problem. You need to figure out which startups sell into your tier of government, which digital services firms can help you implement, whether a fellowship might embed someone on your team for a year to help, and who has already tried a version of this.

You’re a founder building for government. You need the investors who actually understand government sales cycles, the communities where your future champions hang out, the state and city teams most likely to pilot something new, and the fellowships that might become your talent pipeline.

You’re a technologist looking for your next role in this space. You need to identify digital services firms that are hiring, state and city innovation teams that are growing, fellowships for experienced operators, and advocacy organizations that need your expertise.

All in one place for the first time.

The call to action

Browse the full database: Review it. Filter it. Find your organization. Find one to partner with.

Improve the map: If an organization should be on the map and isn’t, request to add it. If I made a mistake or you have further thoughts, post a comment.

Where this could go

This is version one.

The next layers are individual contacts, live problem statements, and profiles of people looking for their next role.

Perhaps in tandem with events to unlock information flow: VC + Advocacy sponsored AI-hackathon nights, with a Training partner facilitating and a Fellowship organization on the hunt for new talent.

A full-fledged community-driven resource focused on unlocking State Capacity delivery. But it only happens if the community helps deliver.

Let me know your thoughts.

This is part admonishment of how we think about AI adoption and part guide to selling AI better.

AI adoption isn’t technical, it’s human

The prevailing sentiment in Tech is that adoption is a technical problem: map the process, redesign it, automate it, deploy. Maybe that works at the bleeding edge. At most companies, that leads to a perfect solution with no uptake.

Why?

If you’re deploying AI, you’re inevitably going to run into mission-critical people who simply do not use, or even resist, AI. Until you win them over, your project is dead.

Tech focuses on rigorous discovery, thorough testing, and complex training. But non-users aren’t convinced by process. Or it indexes towards a “sink-or-swim” model. But that’s not great for morale anywhere, and I’m not convinced it actually leads to meaningful productivity gains - certainly not in government.

What does work? Getting non-users to “aha” moments that demystify technology. But until people trust a tool, they won’t use it. Technical capability is irrelevant.

If you’re selling AI, it’s your responsibility to get people over that hump; to teach them to trust not just your tool, but AI in general. Trust is AI’s currency. It doesn’t sell without it. You have a fiduciary duty to build trust.

Trust is all about communication

Whether CxO or an analyst, building trust in AI requires answering four questions.

1. How does AI work?

2. Is it good?

3. Should I be worried?

4. What do I do?

AI users and non-users don’t speak the same language, and answers are fraught with mistakes. So, I’ve demystified the conversation for you.

Mistake #1: You answer the question directly instead of understanding what folks are really asking.

Q1: How does AI work?

Insight: Folks are not asking about AI’s fundamental building blocks, they want to know if they can trust it. Frame AI in a situation that is compelling and relatable.

My answer: Think about AI as if it were a new employee just out of college. They’re probably pretty smart. They have baseline capabilities but lack expertise. You ask them to build a model. How do they do that? You give them access to Excel. Maybe you share model templates and firm best practices. You give them detailed instructions covering exactly what you need. And you let them iterate and learn, getting better over time.

AI is the same. Your AI model has (1) some amount of reasoning and language processing capabilities, which we call intelligence. And (2) it can access tools and follow instructions, which we call context. It can even iterate and learn. The more you learn to use the tool - better understanding of underlying capabilities, more specific phrasing, wider context offered - the better the output.

Mistake #2: You use technical jargon when simplicity is more effective.

Q2: Is it good?

Insight: They’re pushing for a line in the sand to define AI capabilities in the context of their work. You must balance theory with pragmatism and craft an answer that feels real.

My answer: AI has a bunch of basic capabilities that might apply to any process. It might be good at research, or information synthesis, or data analysis, or coding. How “good” it is depends on how well those capabilities map to the problem you’re solving. Now if you want to define whether AI intelligence is closer to analyst work or VP work, you’re going to have to play with those capabilities yourself. But what I think is [insert whatever you think - after all, you do have to answer their question directly!].

Mistake #3: You lack empathy.

Q3: Should I be worried?

Insight: This is a very human moment; a mixture of fear of the unknown and fear of falling behind. Approach with empathy, not theory.

My answer: I don’t know. Realistically, in terms of intelligence, benchmarks show that LLMs are already pretty darn good. Then it’s about context. People are building massive libraries of prompts to describe work, scripts to give AI standardized tools, and even armies of pre-built AI tools (you might have heard them called agents) that can complete large tasks. Over time, chances are we’ll have smart enough models, enough prewritten context, and enough prebuilt agents to complete a lot of work as we know it today. But, does that mean humans will be replaced? Maybe? We still need people reviewing AI output. And just because work is more efficient doesn’t mean there’s less of it - in fact, historically it has meant the opposite. I guess we’ll see.

Mistake #4: You misread the moment and sell too soon.

Q4: What do I do?

Insight: Note that the question is “I”, not we. AI continues to feel deeply personal even in a professional context. This is an opportunity to pitch, but not a detailed deployment roadmap. It is just conversation 1.

My answer: I feel like the key is just getting familiar with AI. I know you’re quite busy, but could we find some time to sit and play with a few tools? Are there use cases you think are really painful and repetitive and AI might be valuable? Or maybe we can work with folks who are excited about AI and problem solve with them?

The Final Word

The cultural zeitgeist is AI replacement. My practical experience is that humans make AI effective. If you want to sell AI, start by building trust with humans.

Because we’re in the age of AI, instead of an article, I’m writing a prompt.

Who You Are: An expert at large enterprise AI deployment; familiar with all the complexities of people, process, and systems that slow deployment, but with unrelenting optimism that AI offers a chance to skip past decades of sluggish technical adoption and upkeep.

Your Mission: Help me address real state capacity issues by suggesting an internal AI deployment strategy for a hypothetical government agency.

• In Scope:

  • Internal productivity improvement

• Out Of Scope:

  • Policy generation

  • Constituent services (chatbots can be done well and not so well – but not my focus)

  • Non-tech considerations (procurement and hiring can be tough – but let’s paint the skyline before designing the bricks)

Capabilities: We have simplified AI capability to three non-exclusive stages of adoption maturity; feel free to tell me there are better approaches or a more expansive set of options.

• Basic Large Language Model: Source a standard Enterprise LLM with suite of GenAI capabilities and offer basic training on how to use it **Hypothetical org may or may not be here

• Specialized AI Tools: Deploy new tools that augment specific business processes without significant system customization (e.g., Cursor for coding, Glean for file search)

• Custom AI Tools: Explore opportunities for customized, multi-step automation and AI-driven decision support across systems and workflows.

Considerations: As with most large organizations, our hypothetical org has meaningful barriers to AI adoption; some may be unique to government.

• Lots of data stored across many systems, both on-prem and cloud

• Complex file storage system spanning multiple storage locales

• Incentivizing employees to learn is appropriate; demanding uptake without support is not

• Regulatory and privacy concerns are of the utmost importance

Questions: We think these are the key questions, but feel free to tell me differently – or push for more information to inform answers.

• How have other organizations approached AI deployment?

  • What is the right way to think about AI capabilities?

  • What are common pitfalls and lessons learned in AI rollout?

• How does one prioritize top AI use cases?

  • Cross-organizational vs. function-specific use cases

  • Prioritization scorecard e.g., attractiveness (workload intensity, repeatable process, mission importance) vs. ease of implementation (staff interest, uniqueness of work)

• What are common models for tackling AI deployment?

  • Standing up deployment team(s) for intensive process-mapping and AI solutioning

  • Self-service enablement through curated playbooks, templates, and guidance materials

  • Train-the-trainer or internal champion programs

  • External consultants support intensive deployments

• What resourcing (staffing, skill sets, budget, timelines) are appropriate to support AI deployment?

  • How does this differ by deployment model?

• What other work must the org do in parallel to enable AI?

And the most important question

How do we make AI deployment scalable? Is intensive workflow and process mapping charted to AI capabilities the only way to maximize productivity (and is that really the existential AI question)?

Closing thoughts

• My commitment to you: Share a follow-on article summarizing my learnings.

• Please note: None of my words or views represent any official government or agency position.