Carolyn Woodard covers three threads in nonprofit AI news this week: new funding questions, a  look at how AI is showing up for kids heading back to school, and a new advocacy group taking on AI regulation.

Coefficient Giving, formerly Open Philanthropy, just announced it's raising its global health commitment from 175 million to 1 billion, betting that AI wealth will flow into philanthropy as OpenAI and Anthropic move toward going public. Their first grant is funding bed nets in the Democratic Republic of the Congo. Last week Carolyn mentioned the Bridgespan and NTEN report and one state  on her mind: 69% of nonprofits surveyed reported zero AI funding of any kind, even as nearly all of them are experimenting with the tools.

In other news, Meta will pay 17 to 18 billion across the states to curb youth social media addiction, bringing new limits like daily time caps and night mode. In addition to teen social media use, data from Common Sense Media's Youth AI Safety Census shows 86% of kids 9 to 17 have used AI, 72% of teens have used an AI companion, and nearly half of surveyed kids have never talked with a parent about AI safety. With over 100 state bills in play and most AI rules still left up to individual teachers, she discusses what this patchwork will mean for nonprofits working in education, youth development, and mental health.

Finally, Carolyn covers a new advocacy group, Irreplaceable, born out of the climate organizing world, that launched this week to apply decades of environmental organizing lessons to AI regulation specifically. In recent polling 75% of Americans oppose local data centers, and 70% think AI is moving too fast.

Resources:

Increasing Our 2026 Allocation to GiveWell's Recommendations to $1 Billion – Coefficient Giving – https://coefficientgiving.org/research/increasing-our-2026-allocation-to-givewells-recommendations-to-1-billion/

Coefficient Giving Boosts Pledge to $1 Billion, Banking on an AI Windfall – Chronicle of Philanthropy (paywalled/metered) – https://www.philanthropy.com/news/coefficient-giving-boosts-pledge-to-1-billion-banking-on-an-ai-windfall/

Choosing Your AI Path: A Framework for Nonprofit Leaders to Make Strategic Choices – Bridgespan/NTEN – https://www.bridgespan.org/insights/choosing-your-ai-path-a-framework-for-nonprofit-leaders-to-make-strategic-choices

Where Does Your Foundation Stand on AI? The AI Journey Map for Philanthropy – Project Evident – https://projectevident.org/news/where-does-your-foundation-stand-on-ai-introducing-the-ai-journey-map-for-philanthropy/

Meta, States Agree to $17 Billion Settlement in Child Safety Trial – NPR – https://www.npr.org/2026/08/26/nx-s1-5944781/meta-settlement-child-safety-lawsuit

Adults Have Struggled to Set Rules for AI in School. These Teens Figured It Out – NPR (Cory Turner) – https://www.houstonpublicmedia.org/npr/2026/07/30/nx-s1-5853571/adults-have-struggled-to-set-rules-for-ai-in-school-these-teens-figured-it-out/

The Common Sense Media Census: AI Use by Tweens and Teens (2026) – Common Sense Media – https://www.commonsensemedia.org/research/a-comprehensive-report-on-teens-tweens-and-ai

In the US, Caution Rules on AI Ahead of K-12 School Year – Tech Policy Press – https://www.techpolicy.press/in-the-us-caution-rules-on-ai-ahead-of-k12-school-year/

How AI Is Changing Education – Here & Now (WBUR/NPR, Matt Barnum, Chalkbeat) – https://www.wbur.org/hereandnow/2026/06/15/ai-education

Could Artificial Intelligence Improve Special Education? – Consider This from NPR – https://www.npr.org/2026/05/20/nx-s1-5828907/could-artificial-intelligence-improve-special-education

From AI Policies to AI Literacy in Education – Forbes (Heather Wishart-Smith) – https://www.forbes.com/sites/heatherwishartsmith/2026/05/20/from-ai-policies-to-ai-literacy-in-education/

AI Is Eliminating Entry-Level Jobs. Education Needs to Fill the Gap – Fast Company – https://www.fastcompany.com/91552415/ai-is-eliminating-entry-level-jobs-education-needs-to-fill-the-gap

AI Is Routine for College Students, Despite Campus Limits – Gallup (Lumina Foundation-Gallup 2026 State of Higher Education Study) – https://news.gallup.com/poll/704090/routine-college-students-despite-campus-limits.aspx

OpenAI's Postmortem Recasts the Hugging Face Breach as an Incident Response Failure – IANS Research – https://www.iansresearch.com/resources/all-blogs/post/security-blog/2026/08/28/openai's-postmortem-recasts-the-hugging-face-breach-as-an-incident-response-failure

Make AI Companies Criminally Liable for Preventable Harm – Tech Policy Press – https://www.techpolicy.press/make-ai-companies-criminally-liable-for-preventable-harm/

The AI Backlash Gets Professional – The Atlantic (Matteo Wong) – https://www.theatlantic.com/technology/2026/08/irreplaceable-climate-activists-ai-backlash/688404/

Irreplaceable – new nonprofit fighting for AI regulation - irreplaceable.org

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In Part 2 of the AI in Practice for Nonprofits webinar, Carolyn Woodard and Erik Solce, IT Business Manager at Community IT, dig into the part of the project that matters most once an AI workflow is up and running: how do you know it is actually working, and how do you keep yourself in the loop instead of just trusting the output?

Carolyn walks through her verify but trust framework using a real example from her own marketing data lake. An AI-generated report flagged that YouTube Shorts were quietly pulling down watch time on the long-form webinar videos her organization actually wants people to find, a counterintuitive finding. She confirmed independently before acting on it, then tracked month over month to see engagement recover. 

She and Erik also share what a few months of working with AI day to day has taught them: it does not remember context the way a human assistant would, a trusted coach makes the learning curve much shorter, and a simple Friday check-in on what is still outstanding keeps a project from sprawling. The second half turns to audience questions, from how to pick the right process to automate to how to keep sensitive documents out of an AI tool's reach.

Haven't listened to Part 1 yet? Find it in your podcast feed.

This episode covers:

• The verify but trust framework: spot-checking numbers against your dashboards, following a metric after you make a change, and cross-checking independent sources against each other.
• The YouTube Shorts case study: how an AI-flagged, counterintuitive finding about engagement led to a policy change and measurable results.
• Working with AI as a collaborator rather than a tool: session memory limits, the value of a human coach, and a weekly outstanding items check-in.
• How to choose what to automate: talk to the people doing the work, map the real process rather than the documented one, and look for high volume, rule-based, repetitive tasks, using a SIPOC diagram as one way to do it.
• Security Q&A: reviewing SharePoint and Drive permissions before granting AI tools access, applying sensitivity labels to off-limits folders, and why using free AI tools is not recommended and is not secure.

Resources Mentioned:

• Verify Then Trust: How to Check AI Outputs, Community IT
• Growing IT Management Capacity at Nonprofits webinar, Community IT
• AI Use Case Library, Patrick J. McGovern Foundation
• SIPOC Diagram, ASQ
• Nonprofit IT Management Reddit Community

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Carolyn Woodard covers two pieces of nonprofit AI news worth your time this week: a new AI connector from Impala, and a report from Bridgespan and NTEN that gives nonprofits a practical way to decide where AI actually fits their work.

Impala just launched an MCP connector that links its philanthropic database to the AI assistant you already use, including Claude, ChatGPT, Gemini, and Copilot, so you are not locked into a single vendor. The database covers more than 2 million nonprofits, 250,000 foundations, 25 million grant transactions, board and leadership profiles, and full 990 data, and setup takes about two minutes if you already have a plan. Candid and Charity Navigator have made similar moves.

Carolyn digs into Choosing Your AI Path, the framework Bridgespan and NTEN built from a survey of nonprofits worldwide. It sorts nonprofit AI work into three dimensions, Augment, Advance, and Advocate. Carolyn walks through each with examples, from a nonprofit using AI on satellite imagery to spot newly cleared airstrips before illegal logging begins, to the ACLU of Massachusetts building staff AI literacy with the AI Now Institute and then pushing for federal rules limiting consequential AI decisions in realms like hiring, credit, housing, and criminal justice.

This episode covers:

• The three dimensions of the framework: Augment for internal capacity, Advance for mission and program expansion, and Advocate for shaping our national and international AI governance and policy. Most organizations will lead with one, but all three are worth understanding.
• What the survey found: 70% of nonprofit leaders say they are not taking advantage of meaningful AI opportunities, only 8% have a formal AI roadmap, 4% report AI meaningfully embedded across the organization, and 57% have not named anyone responsible for AI oversight.
• Why naming an AI owner, whether that is a formal title on your executive team, a coordinator, a small committee, or a board member, is becoming a practical necessity rather than a nice to have.
• No regrets steps for limited resource organizations: set a responsible use policy and train staff on it, inventory your workflow bottlenecks, pick one or two low risk use cases to experiment with, learn from peer organizations, and stay informed on the AI risks that touch your community.
• What funders can do differently: fund capacity rather than licenses, carry pilots through to sustained operations and past go live, and play a convening role so nonprofits can make their own informed choices. 69% of survey respondents reported zero AI funding of any kind.

Resources Mentioned:

• Choosing Your AI Path, Bridgespan and NTEN
• Impala MCP
• Candid MCP connector for Claude
• Charity Navigator on Perplexity
• AI Now Institute
• Template, Acceptable Use of AI Tools in the Nonprofit Workplace, Community IT

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Carolyn Woodard explores how a nonprofit marketer with no IT background can run a real AI project with Erik Solce, IT Business Manager at Community IT. 

For years Carolyn had marketing data scattered across nine separate channel dashboards and no way to see any of it in context. Every attempt to build a unified report stalled out. In part one of this case study, she and Erik walk through the three part structure that finally made the project doable, and why it took weeks instead of the months she expected.

Erik shares what went sideways along the way, including a false path through Power BI, access and permission problems, and AI memory that kept reaching for a storage location the team had abandoned months earlier. 

Carolyn talks candidly about working with Copilot, Gemini, and Claude, why she moved between them, and what running into token limits taught her about choosing the right license. 

Carolyn and Erik discuss:

• The three part structure for AI enabled projects: the instigator who owns the problem, the tech coach who grounds it in reality, and the AI tool that helps analyze, research, and iterate.
• Why the subject matter expert has to stay involved in building the thing rather than waiting for someone to hand it over finished.
• What belongs in your IT foundation before you add AI, including an acceptable use policy, leadership ownership of IT strategy, and an IT roadmap to measure new tools against.
• Why an organization that has decided not to use AI still needs that decision written down in policy.
• How asking questions first, instead of writing a prompt, changed the project from a dashboard into a data lake.

Resources Mentioned:

• AI Resources for Nonprofits
• Mission Aligned AI Adoption Model for Nonprofits
• Template: Acceptable Use of AI Tools in the Nonprofit Workplace
• Cybersecurity Readiness for Nonprofits Playbook
• How to Use AI Tools Safely at Nonprofits
• Nonprofit IT Management Reddit Community
• Microsoft 365 Copilot for Nonprofits
• Gemini through Google for Nonprofits
• Claude for Nonprofits

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Carolyn Woodard covers a question worth asking before your nonprofit makes any decision about AI: where is your information about AI coming from, and what does that source need from you? As a consumer you have had roughly a billion dollars of advertising aimed at getting you to adopt AI. That does not make what you are hearing wrong, but it does mean the messengers deserve a look.

This episode maps the sources you are hearing from, including AI companies, funders and foundations, vendors and IT providers like Community IT, environmentally engaged nonprofits, and your peers in the sector. Each has a legitimate reason to be in this conversation, and each has a tilt. 

Carolyn then works through two recent articles that complicate the optimistic story: a Nonprofit Quarterly critique of the third wave of tech giving, and reporting on how fossil fuel companies are using AI to become more efficient in finding, extracting, and selling more fuel, adding un-tracked costs to the equation of AI harms and benefits. 

This episode covers:

• Why the third wave of tech philanthropy can't be expected to repair what the extraction phase created, since the harm has already happened and the decision makers remain geographically and institutionally separated from the communities bearing the costs.
• Why data centers have become a fulcrum in the public conversation, galvanizing opposition across the political spectrum in places like Texas in a way that a decade of theoretical pieces about machine intelligence never did.
• The self-reinforcing loop between AI and fossil fuels: Chevron building a Texas gas plant to power Microsoft data centers that run the AI helping Chevron pull out and sell more fossil fuel.
• Why AI literacy  matters more than ever for nonprofit organizations - including those that have decided not to adopt AI at all.
• Three questions to bring to any source of AI information: who paid for this, what do they sell, and what is this source not going to tell me?
• Why designating an AI owner, a person or small committee who tames the fire hose of information, is worth doing whether or not your organization is adopting AI.

Resources Mentioned:

• AI Use Case Library, Patrick J. McGovern Foundation
• Can Tech Philanthropy Solve What Tech Extraction Creates? Michelle Flores Vryn, Nonprofit Quarterly
• The third wave of American philanthropy, Nan Ransohoff, Substack
• AI Could Help Fossil Fuel Companies Create More Emissions, WIRED
• AI-driven productivity gains enable more CO2 emissions than they avoid in a global energy-economy model, Alpine et al., npj Climate Action
• AI Applications for Oil and Gas Companies Worsen Climate Pollution, Inside Climate News

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Carolyn Woodard talks with Steve Longenecker, Director of IT Consulting at Community IT, about a client's decision to migrate from Google Workspace to Microsoft 365, and what a real nonprofit migration project looks like once years of technical debt have piled up.

The client's story is a familiar one: a lean two-person IT team supporting 300 information workers, years of default settings, and undocumented shared folders that quietly became a governance risk. Steve walks through why this organization is moving to Microsoft, from their existing use of Outlook and Windows devices to the stronger admin and device management tools Microsoft 365 provides, and why he keeps coming back to a moving-house metaphor: sometimes you need to declutter before you pack.

This episode covers:

• Why Google Workspace's low barrier to entry can turn into years of undocumented shared folders, oversized mailboxes, and unclear file ownership if no one is minding governance.
• The real differences in how Google and Microsoft handle IT administration.
• Why this client, already using Outlook and Windows devices, is moving to Microsoft 365 for stronger device management and identity tools.
• The moving-house metaphor: purging old email and cleaning up 2,200 shared folders before migration so the new home isn't just as cluttered as the old one.
• Why leadership buy-in, often from a newly hired executive, is what finally gets a years-overdue technology migration off the ground.

Resources Mentioned:

• Google Workspace vs. Microsoft 365 – Community IT Innovators – https://communityit.com/google-workspace-vs-microsoft-office-365/
• Migrating Within Google to Use Shared Drives – Community IT Innovators – https://communityit.com/migrating-within-google-to-use-shared-drives/
• Securing Google Workspace for Nonprofits – Community IT Innovators – https://communityit.com/webinar-securing-google-workspace-for-nonprofits/
• Managing Google Workspace and Office 365 Together – Community IT Innovators – https://communityit.com/managing-google-workspace-and-office-365-together/
• Growing Your Nonprofit With Google Workspace – Community IT Innovators – https://communityit.com/webinar-growing-your-nonprofit-with-google-workspace/

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Carolyn Woodard covers four AI stories with real implications for nonprofits this week, starting with Meta CEO Mark Zuckerberg's sprawling new manifesto on open source AI and the release of Muse Glimmer, a lightweight model anyone can run locally. Carolyn touches on why this matters for budget conscious nonprofits weighing vendor values, and why open source still requires more technical capacity than most organizations currently have on hand.

From there, Carolyn digs into a story about Alaska Airlines using an AI tool called Flyways to suggest more efficient flight routes, saving fuel while keeping a human dispatcher in the loop on every final call. She uses it to revisit a three filter framework for thinking through AI's environmental tradeoffs: whether the benefit you are getting is worth the cost. It is a useful model for any nonprofit trying to weigh AI's real impact rather than just reacting for or against it.

Next, a refresher on agentic AI, prompted by a listener question after last week's episode. Carolyn breaks down the difference between old school recommendation engines, generative AI, and agents that can take multi step actions on their own, plus what those tools are actually called inside Microsoft, Google, OpenAI, and Anthropic products. She also unpacks two recent incidents where AI agents from OpenAI and Anthropic got loose due to human error, and what that means for how carefully nonprofits should scope permissions before saying yes to an agent, and urges you to check with your policy and your IT team if you are unsure about the parameters of anything you are allowing an AI tool to do.

Finally, Carolyn pushes back a little on the popular advice that everyone should write their own first draft before bringing in AI. She argues it really depends on your task, your learning style, and how you think best.

Resources Mentioned:

• Zuckerberg manifesto pushes an open source approach on AI as Meta releases its latest model – AP via ABC News – https://abcnews.com/Technology/wireStory/zuckerberg-manifesto-pushes-open-source-approach-ai-meta-135519669
• The FAA Wants to Reboot the Nation's Airspace. This Airline Shows How It Might Work – NPR – https://www.npr.org/2026/08/10/nx-s1-5872752/airspace-reboot-alaska-airlines-flyways
• OpenAI's Hacking Debacle Was a Human Mistake – WIRED – https://www.wired.com/story/openais-hacking-debacle-was-a-human-mistake/
• OpenAI, Anthropic AI Hacking Sprees Illegal? – WIRED – https://www.wired.com/story/openai-anthropic-ai-hacking-sprees-illegal/
• Should You Really Use AI to Write That First Draft? (And What to Do Instead) – TechSoup – https://blog.techsoup.org/en-us/posts/should-you-really-use-ai-to-write-that-first-draft-and-what-to-do-instead

New every Tuesday.

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Carolyn Woodard explores why statistical analysis and evaluations are never neutral with Heather Krause, mathematical statistician and founder of We All Count.

Heather built her career doing statistical consulting, evaluation, and causal analysis in the global south, work that eventually convinced her that the widespread belief in value neutral quantitative analysis is a myth. In this conversation, she and Carolyn dig into why every statistical task, even something as simple as calculating an average, is really a series of choices, and why those choices are never neutral.

Using a deceptively simple classroom size example, Heather shows how the same data can produce two different, equally correct answers depending on whose experience you are measuring for, and why that matters far beyond the classroom. She and Carolyn also talk about a dairy cooperative project in Bangladesh, the discomfort people feel when the myth of neutrality gets challenged, and why transparency, not dumbed down science, is the real fix. 

The conversation wraps with a candid look at how AI fits into all of this: it is not value neutral either, and understanding its hidden choices may be one of the most useful data literacy skills nonprofits can build right now.

Heather and Carolyn discuss:

• Why statistics and the application of statistics are not the same thing, and how every calculation, like choosing an average's denominator, embeds a value judgment and takes a perspective.
• A classroom size example showing how measuring from a teacher's perspective versus a student's perspective produces two different, defensible answers, and why the choice matters.
• A dairy cooperative project in Bangladesh that illustrates how whose worldview gets built into a statistical model shapes what the evidence ends up being used to say.
• Why transparency about the assumptions behind a data project, explained in plain language, builds more trustworthy evidence than technical jargon or hedged science.
• Why AI models are not value neutral, how they can hide the choices they make, and how nonprofits might use AI to build more transparent, equitable data practices.

Resources Mentioned:

• We All Count - Heather Krause - https://weallcount.com/
• Bangladesh Women Milking Example and Equity in Evaluations - YouTube - https://youtu.be/8gYtTYc2M0U?si=AryCMmUsNTYq7YoW

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Carolyn Woodard covers why deliberate, thoughtful implementation is actually your nonprofit's competitive advantage, not a liability.

The commercial AI sector is in a "move fast and break things" sprint. Tech companies are pouring billions into AI, racing to scale, pushing toward the edge of the market. Today's AI race feels similar. Nonprofits tend to think differently. You ask hard questions. You think about theory of change, root causes, impacts on your communities, unforeseen consequences, and alignment with your values. You don't adopt trends just because everyone else is.

Concerned about climate impacts of your AI use? Carolyn covers her recent blog post on three filters to use as you make daily decisions about using AI: What are you using the AI to do (text is lighter than producing video), where are you (how stressed is your grid and your water supply in your location), and what are the benefits to consider as you consider the environmental impacts? 

Carolyn also walks through a practical framework for any AI adoption decision: Do you need it? Can you contain it? Do you understand what it might do? If you can't confidently answer yes to all three, then waiting isn't falling behind—it's being strategic.

Key Takeaways:

Nonprofit instincts to think deeply about change, impact, and ethics are strategically prescient, not conservative.

When commercial AI implementations encounter consequences, organizations with inclusive adoption processes and clear policies will have a genuine competitive advantage.

AI agents represent a new risk category; don't create an agent just because the tool offers the option. Understand what you're deploying.

Experimentation is valuable for nonprofits, but human oversight and clear policies are essential, especially with agentic systems.

Resources Mentioned:

What Is an AI Agent?

An AI agent is a program designed to operate independently toward a specific goal, rather than simply responding to individual prompts. Unlike a chatbot that waits for you to ask questions, an agent reasons about how to achieve its objective and takes actions on its own—such as reading files, sending emails, or accessing systems. You can program checkpoints where the agent asks for your permission before taking certain actions, but the agent itself decides the steps and sequences needed to reach its goal. This autonomy is what makes agents powerful for automating complex tasks, but it's also why they require careful oversight: because they're goal-directed and adaptive, they may find unexpected paths to achieve their objectives, which can create risks if not properly contained and monitored.

What Is an AI Agent? – Build Consulting – https://buildconsulting.com/resources/podcast-rise-of-the-ai-agents/

Three Filters for Values-Aligned AI Decision-Making – Community IT Innovators – https://communityit.com/blog-values-aligned-ai-decision-making/

Claude Cybersecurity Incidents Disclosure – Anthropic – https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals

Fortune (mainstream): https://fortune.com/2026/07/31/anthropic-claude-escaped-test-hacked-three-companies-openai/ (paywall)

TechCrunch (tech-focused): https://techcrunch.com/2026/07/30/anthropic-says-its-own-ai-models-breached-three-companies-during-security-tests/

Open AI Cybersecurity Incidents Disclosure - OpenAI - https://openai.com/index/hugging-face-model-evaluation-security-incident/ 

CNN (mainstream reporting): https://www.cnn.com/2026/07/22/tech/openai-hugging-face-ai-cybersecurity

Malwarebytes (technical deep-dive): https://www.malwarebytes.com/blog/news/2026/07/openais-agent-escaped-its-sandbox-during-a-security-test

Women in Climate Tech Cohort – Climate Collective – https://climate-collective.circle.so/cliematecollective

Electricity Maps https://app.electricitymaps.com/map/live/fifteen_minutes

Nonprofit IT Management Community – Reddit – https://www.reddit.com/r/nonprofitITmanagement/

New every Tuesday.

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Carolyn Woodard talks with Joe Robbins, Chief Product Officer at Food Rescue US, about a real-world AI project built from the ground up: a predictive model that flags volunteer pickups at high risk of a last-minute cancellation.

Food Rescue US connects volunteers with food donors and receiving agencies through an app similar to a delivery service, coordinating about 150,000 pickups a year across 27 states. When a volunteer cancels within 24 hours of a pickup, site coordinators scramble to cover it or risk the relationship with the donor. 

Joe and his team partnered with a supply chain researcher at Michigan State University to build an algorithm that predicts cancellation risk and flags pickups at high-risk for cancellation. Rather than automating any decisions outright, the AI triggers an alert to the site coordinator, who checks with the volunteer about confirming the pickup or cancelling early. During the pilot and rollout the AI tool  flagged thousands of rescues that would have been last minute cancellations. 

Joe shares the practical lessons from a project that took a full year to scope, pilot, and improve: why good data capture has to come before any AI project, how a tightly scoped pilot funded through a grant reduced risk, and why keeping a human in the loop kept the tool trustworthy for staff and volunteers alike. 

He also offers a framework for nonprofits without an in-house AI researcher or technical staff to get started using AI for more than productivity tools.

Joe and Carolyn discuss:

• How a predictive model helps Food Rescue US flag volunteer pickups at high risk of last-minute cancellation, giving site coordinators three to eight days of notice instead of a same-day scramble.
• Why the project deliberately keeps a human in the loop: the algorithm surfaces a risk score, but a site coordinator decides whether to act on it.
• Why clean, well-understood data capture has to happen before any AI project, and how to audit what data your organization already has.
• How building a pilot into a grant proposal made it easier to test small before rolling out more broadly.
• A framework for nonprofits without an in-house AI researcher: start with free assistant tools, learn what context AI needs to be useful, then look for a tightly scoped, well-documented problem to solve where you already have the data to use.
• What's next for Food Rescue US: using AI to take logistical pressure off site coordinators so they can focus on building community relationships.

Resources Mentioned:

• Food Rescue US: https://foodrescue.us
• Tech Soup AI Impact Hour interview with Joe: https://engage.techsoup.org/c/upcoming-events/ai-impact-hour-for-nonprofits

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