Two essays hit my inbox in the last few days. One warned that AI concentrates power. The other argued libraries already have the skills to govern it. Together they made me wonder why so many libraries are still standing on the sidelines.
The first article, Tressie McMillan Cottom’s piece, “A Reading List for Our Age of A.I.” in the New York Times, will really strike a chord with the AI resistance minded. Cottom argues that AI isn’t a product: it’s politics. It’s part of the system where money, data, and power are consolidating in the hands of a few unelected executives. That’s certainly not hyperbole.
It’s an important reminder why libraries should not ‘opt out’ of AI literacy programs.
If you really believe that AI is politics, then whoever explains it to the public is doing vital work for democracy, whether they admit it or not. Without an authentic, public service voice in the conversation, messaging falls to the vendors, the influencers hyping the service, and consultants cashing in on chaos. None of them answer to the public. None of them take an oath to intellectual freedom, privacy, or equitable access.
Both priorities are long-standing, well-defined missions of the public library. That’s not branding; it’s the vital distinction between an institution and a sales funnel.
If Cottom is right that power is concentrating, the answer isn’t fewer trusted, disinterested guides in the room. Our communities need more voices advocating for the public interest. Libraries have exactly the kind of public trust this moment requires. Too many are choosing to spend it on “Avoid AI” workshops instead.
I know the objection, because I have made the point myself myself: engaging with these tools feeds the power machine Cottom is warning us about. Every library patron steered toward ChatGPT or Gemini is a data point handed to the companies concentrating that power.
Fair, but many of our patrons are already there. We’re not steering anyone at this point. Consider what the alternative actually buys. Opting out doesn’t slow the machine down; it just means nobody in the room is asking the hard questions on your patrons’ behalf.
My career in libraries was not spent helping people avoid technology. The job was always helping patrons master it.
Which is exactly why the second piece I read this weekend matters more than the first: “How Library Science Principles Power Community AI Literacy” from PublicLibrariesonline.org by Jose Ruiz-Vazquez, assistant manager at the Milwood Branch of the Austin Public Library.
Ruiz-Vazquez put it better than I can: “We do governance all the time. We just don’t call it that.”
Controlled vocabulary, provenance, bias evaluation, and information stewardship are not a side-quest to AI governance. We’re applying the same set of skills to a different challenge. Librarians have been examining the provenance and reliability of information for a long time. That experience doesn’t fade when the data is generated by a mathematical language model.
The key point here, and it’s the one that should end this debate: Ruiz-Vazquez, offers a direct institutional challenge: libraries are already positioned to be the “verification layer for democracy” but only if they claim the role.
What does “getting in the game” actually require? Every major technology transition has been figured out by people willing to experiment in public. Consider applying the skills our libraries already possess:
· Provenance: Where did this information come from?
· Authority: Who produced it, and who owns the system?
· Privacy: What happens to the data a patron enters?
· Evaluation: What are the tool’s limitations, biases, and failure modes?
· Access: Who can use it, and who is locked out due to cost, language or disability?
· Context: What does this output ignore?
· Human judgment: When should a person, not a model, make the decision?
A practical approach begins modestly:
· Create a small, public AI-tool registry. Evaluate the five tools patrons ask about most. Document privacy policies, costs, proper uses and limitations identified.
· Integrate AI literacy into existing programs. Help job seekers use AI without losing their own voice. Help patrons examine vital records without treating a chatbot as ‘the expert’. Help students understand research and develop critical thinking skills.
· Build standards together. Libraries should collaborate on evaluation frameworks, documentation practices, and preservation methods. We do not need every branch to reinvent the wheel, (or, in this case, the hallucinated zoning ordinance).
Solutions like this are starting to take shape. Frisco Public Library is building AI programming with community partners rather than a single subject workshops. That’s a launch point, not a finished model. Nobody’s built the whole thing. Could it be that the infrastructure doesn’t exist yet because too many libraries decided the safer, more virtuous lane was standing outside it?
The library’s distinctive contribution is not technical novelty. It is public-interest information stewardship. You don’t need a six-figure budget. You only need staff willing to try the tools, document where problems and shortfalls, and refuse to accept denial as a valid position.
Caution has a place. I’m less sure about “resistance”. Refusal for the wrong tool, at the right moment, might indeed be the correct response. But refusal as the whole strategy is outsourcing your professional judgment to whoever’s willing to stand up before you.
Libraries have spent decades helping people navigate every major shift in how information is created, distributed, and trusted. AI isn’t the first revolution we’ve seen. It’s simply the first one that too many librarians have decided someone else should lead.

If we think our role in a public library is to teach people to avoid technology, we’ve misunderstood the assignment.

That’s what my librarian colleague called GPT chatbot makers during a recent conversation about GenAI tools. Despite being tech-savvy, a voracious reader, and (I think) a sci-fi enthusiast, she remains unwaveringly critical of all things GenAI.I understand why. Socially conscious information professionals strongly object to technologies that steal the livelihoods of the creative class. Knowledge professionals despise stolen art and are equally, if not more passionate about data authenticity, factual accuracy and sourcing. When you factor in power consumption and other environmental concerns, the GenAI conversation ends before it ever begins.Silicon Valley Technocrats, fighting to reach trillion dollar valuations, perhaps driven by some notion of “effective altruism”, argue GenAI is a vital first step toward the singularity. “Move fast and break things” remains a core value. AI advancement is positioned as a moral imperative—social justice concerns notwithstanding.
As a technologist, who’s been disrupted out of more than one career and is still dedicated to helping people leverage technology, I believe both perspectives have merit. The hype and money surrounding AI today are indeed intoxicating. And those who fear it’s all moving too fast have equally valid concerns. Examples: the proliferation of self-checkouts, ATMs, and now chatbots prepared to replace artists, copywriters, coders and front line service workers of all types.
We’re going to witness entirely new industries and dramatic new careers based on developing technology. We’ll also lose some jobs and even entire industries. Both statements are true.
Isn’t it really just two sides of the same coin?
I wish that, somehow, we could get the GenAI technocrats to re-read Orwell’s “1984” and not treat it like a “How To” manual. Not all technical advancements will lead to the “Star Trek” utopian universe.
And if you need to expand your own reading list…
Chat with a professional, neighborhood librarian instead of a GPT.
I’d also like to see the GPT resistance movement types, the ‘Clockwork Scorners’ embedded in the profession, apply simple lessons of ‘high-aim steering’ to life in 2025. The worst possible thing we can do right now is focus solely on what is directly in front of us. The road ahead is long, but we’re moving at speeds unachievable until now.
We’re all a little ‘tomorrow blind’. We can’t predict what’s ahead or lurking off to the side. Yet, a closed mind will miss important signs and wonderful opportunities almost every time.
See the “BIG Picture”.
GenAI is not perfect, but it is the early social proof, a conceptual demo, like a rocket, driving gigantic investments toward some pretty amazing human achievements.
Is AI the moon shot of this generation or
is it just a flock vultures laughing all the way to the bank?