In 1926, when Carter G. Woodson launched Negro History Week, he was not asking America to celebrate Black people. He was building infrastructure for a community to know itself — its past, its contributions, its intellectual inheritance — so that it could not be defined by those with a vested interest in its erasure.
That was a literacy project.
Not literacy in the narrow sense — not phonics, not grammar, not the mechanical ability to decode words on a page. Literacy in the structural sense: the capacity of a community to read the systems that govern its life, and to write itself into those systems on its own terms. Woodson understood that a people who cannot narrate their own history will be narrated by others. And the narration of others, when it comes to Black people in America, has never been neutral. It has always been an instrument of control.
One hundred years later, the systems that govern our lives are being rewritten in a language most people cannot read. That language is artificial intelligence. And the question facing Black communities in 2026 is not whether AI will affect us. It already has. The question is whether we will be fluent enough to shape what comes next — or whether we will be shaped by it, again, without our consent.
This is not a technology essay. This is a literacy essay. And the argument is simple: if the last century of Black organizing centered access to education, capital, and civic power, the next century must include access to AI fluency. Not as a luxury. Not as a niche specialization for the technically inclined. As a baseline capacity for community self-determination in an automated world.
AI literacy is the new financial literacy. And the communities that treat it that way will be the ones that lead.
Why the Financial Literacy Analogy Matters
The comparison is not casual. It is structural.
For decades, Black communities fought for access to capital — to banking, to credit, to homeownership, to the basic financial infrastructure that the rest of America took for granted. That fight produced institutions: the Freedman’s Bank, Black-owned insurance companies, mutual aid societies, credit unions, and eventually the policy frameworks — the Community Reinvestment Act, fair lending laws, and financial literacy programs — that tried to close the gap between how the system was supposed to work and how it actually worked for Black people.
The financial literacy movement emerged because access alone was not enough. You could open the bank doors, but if people did not understand how interest rates worked, how credit scores were calculated, how predatory lending operated, how wealth accumulated across generations, then access became another trap. Financial literacy was not about making individuals smarter. It was about giving communities the collective knowledge to navigate systems designed without them in mind and, in many cases, designed against them.
AI is the same architecture with new wiring.
The systems that now determine who gets a job interview, who qualifies for a loan, who receives adequate healthcare, who gets surveilled, and who gets a second chance — these systems increasingly run on artificial intelligence. They are opaque, powerful, and consequential. And the communities most affected by them are, by design and by history, the least likely to understand how they work.
This is not because Black people lack interest. The opposite is true. Research from Jobs for the Future found that Black workers and learners are leading AI adoption — experimenting with tools, exploring applications, showing the kind of early-mover initiative that has always characterized communities that survive by being resourceful. The gap is not interest. It is infrastructure. It is training. It is institutional support. Black communities are adopting AI faster than the systems around them are preparing them to use it well, govern it wisely, or protect themselves from its harms.
Sound familiar? It should. It is the same pattern that played out with financial products — early adoption without adequate structural support, leading to exploitation. Subprime mortgages were not marketed to Black homeowners because those homeowners were unsophisticated. They were marketed because the infrastructure of protection — regulation, literacy, institutional advocacy — had not kept pace with the infrastructure of extraction.
AI without literacy is the same setup. The tools arrive first. The understanding arrives later. And in the gap between those two things, value flows upward and harm flows down.
What AI Literacy Actually Means
Let us be precise, because the phrase “AI literacy” risks becoming as vague and diluted as “digital skills” became in the 2010s — a buzzword that sounds urgent but means nothing specific enough to build on.
AI literacy, for the purposes of this essay and SOLE’s framework, means three things:
Functional fluency. The ability to use AI tools effectively in the contexts that matter to your life — work, education, health, finances, creative expression, and civic participation. This is the most visible layer, and it is where most training programs begin and end. It is necessary. It is not sufficient. Teaching someone to use ChatGPT without explaining how large language models work, where their data goes, what their limitations are, or who profits from their use is like teaching someone to sign a mortgage without explaining the interest rate. It produces users, not citizens.
Critical literacy. The ability to evaluate AI systems — to ask who built them, what data trained them, what assumptions they encode, who they serve, and who they harm. This is where literacy becomes power. A person with critical AI literacy can read a job rejection and ask whether an algorithm was involved. They can encounter a health recommendation and ask whether the system that produced it performs equitably across racial demographics. They can hear a school administrator praise a new “behavioral analytics” platform and ask what data it collects, where it goes, and whether it has been audited for disparate impact. Critical literacy turns passive consumers of AI into active interrogators of the systems that shape their lives.
Structural literacy. The ability to understand AI as an economic and political system — not just a tool — and to engage in its governance. This is the layer that separates individual skill from collective power. Structural AI literacy means understanding that the companies building these systems are making design choices that are also policy choices. It means understanding how regulation works, where the enforcement gaps are, what community advocacy looks like in the context of algorithmic governance, and why having Black people in the rooms where AI policy is written is not a diversity initiative — it is a survival strategy.
Financial literacy followed the same architecture. First: can you balance a checkbook? Then: can you evaluate a loan offer? Then, can you understand the policy environment that shapes whether you build wealth or lose it? Each layer was necessary. None was sufficient alone. And the communities that developed all three layers — functional, critical, structural — were the ones that moved from surviving the financial system to shaping it.
AI literacy must follow the same path. And it must do so on a timeline measured in years, not decades, because the systems are already deployed and the decisions they make cannot be deemed unjust in retrospect. The harm is happening now.
The Blueprint: Five Pillars for Community AI Literacy
SOLE does not believe in frameworks that live on whiteboards. The following blueprint is designed for the institutions that anchor Black community life — churches, schools, HBCUs, nonprofits, professional associations, civic organizations, barbershops and beauty salons, fraternity and sorority chapters, and community health centers — and for the leaders who run them. It is not a curriculum. It is an infrastructure plan.
Pillar One: Normalize AI Fluency at the Community Level
The single most important thing Black institutions can do right now is make AI a regular part of community conversation — not as a threat, not as a magic solution, but as a reality that requires understanding.
This starts where all cultural shifts start: with the people who already have trust. Pastors, coaches, school counselors, union stewards, chapter presidents, program directors. These are the people whose authority comes not from credentials but from presence — from being in the room, knowing the community, and being accountable to it.
Operation HOPE understood this when it built its financial literacy infrastructure through community-embedded coaches rather than remote online courses. The HOPE AI initiative, launched in December 2025 with UNCF, 100 Black Men of America, Big Brothers Big Sisters, and other institutional partners, takes the same approach to AI — embedding literacy in the organizations people already trust, rather than asking them to seek it from institutions that have historically excluded them.
SOLE’s position: every Black civic institution should have at least one AI-literate leader by the end of 2027. Not an expert. Not a developer. A person who understands enough to ask the right questions, evaluate the tools the institution is being sold, and translate what is happening in the AI economy to the people they serve. This is not a moonshot. It is a decision.
Pillar Two: Invest in HBCUs as AI Infrastructure
HBCUs educate nearly 20 percent of all Black college graduates while representing just 3 percent of the nation’s colleges and universities. They are, and have always been, the infrastructure of Black intellectual development. They must become the infrastructure of Black AI development.
This is already happening. North Carolina Central University launched a pioneering AI research institute in late 2025. Morehouse College has built an immersive Metaversity program. Morgan State is leading digital innovation. Huston-Tillotson University is hosting the second annual HBCU AI Conference in March 2026, convening more than 600 participants — students, faculty, industry leaders, policymakers — to shape AI through an HBCU-informed lens. A consortium of five HBCUs is working with Georgia Tech’s AI Institute to build degree programs in AI and data science.
But these efforts are underfunded and unevenly distributed. Not every HBCU has the resources to build an AI lab or hire data science faculty. The investments required — in compute infrastructure, faculty development, curriculum design, and student support — must come from the same coalition of public, private, and philanthropic funders that has historically sustained higher education. And those funders must understand that investing in HBCU AI capacity is not charity. It is an economic strategy. The communities these institutions serve are the communities most affected by AI deployment. Training those communities to build, govern, and audit AI systems is the most efficient path to equitable outcomes.
Pillar Three: Build AI Literacy into K-12 and Youth Development
The pipeline does not start in college. It starts wherever young people are.
Changing Expectations, an NSF-funded initiative, is already bringing AI literacy to Black high school students through churches and community organizations — teaching them not just to use AI tools but to build AI voice chatbots for social justice projects. Programs like SeedAI’s Hack the Future and Black Tech Street in Tulsa are delivering AI training directly to underserved neighborhoods.
But these are islands of excellence in an ocean of absence. AI literacy must be integrated into the systems that serve Black youth at scale — public schools, after-school programs, summer enrichment, and mentoring organizations. The 100 Black Men of America’s partnership with HOPE AI is a promising model: embedding AI readiness into an existing mentoring infrastructure that already reaches thousands of young people and families.
For youth development organizations, the opportunity is to weave AI fluency into the same fabric of leadership, identity, and purpose that defines their educational mission. AI literacy for young Black men is not a departure from character development. It is an extension of it. Understanding the systems that will evaluate your resume, assess your creditworthiness, and determine your risk score before you ever walk into a room — that is self-knowledge. That is power. That is exactly the kind of preparation these institutions exist to provide.
Pillar Four: Train for Governance, Not Just Use
Here is where the financial literacy analogy breaks most clearly — and most importantly.
Financial literacy, for all its value, remained largely a consumer-facing project. It taught people to navigate the financial system. It did not, in most cases, teach people to change it. The structural reform — the regulatory fights, the policy campaigns, the shareholder activism — was left to a different class of advocates.
AI literacy cannot make the same mistake. The governance questions are too urgent, the deployment is too fast, and the window for shaping the rules is too narrow.
Every AI literacy initiative — from HBCU programs to community workshops — should include a governance component. Not just: how do you use this tool? But: who decides how this tool is built? What are your rights when this tool decides for you? How do you file a complaint? How does regulation work? Where are the enforcement gaps? How do you show up at a city council meeting and ask the right questions about the predictive policing tool your police department just purchased?
This is the difference between producing AI consumers and producing AI citizens. Communities that learn only to use AI will be served by it — on terms set by others. Communities that learn to govern AI will have a say in those terms. SOLE’s mission is to ensure Black communities are in the second category.
Pillar Five: Measure What Matters
You cannot close a gap you cannot see.
The HOPE AI Equity Index, announced as part of the HOPE AI initiative, represents the first national attempt to measure inclusive AI readiness at the community level. SOLE supports this effort and calls for it to be expanded, refined, and made publicly accessible — so that communities, institutions, and policymakers can track whether the AI literacy gap is narrowing or widening, and where investment is most urgently needed.
Measurement must go beyond adoption rates. The metrics that matter are: Can people in this community identify when AI is involved in decisions that affect them? Can they evaluate whether those systems are fair? Do they know their rights? Are there local institutions equipped to support them when algorithmic decisions go wrong? Is there a pipeline from AI awareness to AI careers in this community?
These are the metrics that distinguish access from agency. And agency — not access — is the goal.
From February to the Rest of the Year
This essay closes SOLE’s February 2026 package because it points forward.
Everything else in this series — the history, the data, the policy analysis, the cultural critique, the sector-by-sector briefing — was designed to establish the landscape. To name what has happened, what is happening, and what is at stake. This essay is different. This essay is about what we build.
Black History Month has always risked becoming a retrospective. A month of looking backward. Woodson designed it as something else — a provocation, a curriculum, a demand that the present reckon with the past to shape the future. One hundred years in, the best way to honor that design is to use it.
The AI economy is not coming. It is here. The tools are deployed. The decisions are being made. The wealth is being distributed. The governance frameworks are being written — or, in too many cases, deliberately left unwritten. Every month that passes without Black community fluency in these systems is a month of decisions made without our input, value extracted without our consent, and futures shaped without our voice.
SOLE’s commitment does not end in February. This package — the essays, the data, the frameworks, the blueprints — is the foundation for year-round programming. Workshops for institutional leaders. Governance toolkits for community organizations. Curriculum partnerships with schools and youth development programs. Policy briefings that translate the regulatory landscape into actionable intelligence for the people most affected by it.
We are not asking Black communities to become Silicon Valley. We are asking them to become what they have always been: literate, organized, strategic, and unwilling to let anyone else write their story.
One hundred years ago, Woodson built the infrastructure for a community to remember itself. The next hundred years require the infrastructure for a community to govern itself — in an age where the systems of governance are increasingly automated, opaque, and consequential.
AI literacy is that infrastructure. And SOLE is committed to building it — not as a publication project, but as a permanent practice.
The commemoration is over. The work begins now.
