AI chatbot use is exploding in the US, and it’s not just because people are creating more and more workslop.
The most popular way Americans use generative AI these days is actually not generating content. It’s “searching for information,” according to a December report by market research firm Ipsos.
Instead of thinking about the bots as something to block, we view them as another way to get our info and analysis to people who want it.
As a dedicated information provider, especially one focusing on stories other business and community news outlets often skip, Technical.ly is leaning into our role as a primary source for these chatbot searches.
Over the past year, we’ve been working to give our reporting on the local effects of entrepreneurship, STEM workforce and civic tech more AI visibility. Instead of thinking about the LLM bots as something we need to block, we view them as another way to get our uniquely curated info and analysis to people who actually want it.
Nearly 3 in 5 American adults, around 150 million people, used an AI chatbot like ChatGPT, Claude or Gemini at least weekly as of April 2025, according to Pew. The number of tasks most use the tools for has actually declined, but overall usage is up. The most common use by far, according to the Ipsos poll, is to learn and explore concepts about the world: 54% of people said that’s something they do regularly, more than double the share using it for generative reasons like completing work tasks or writing emails.
Fewer people who use chatbots like this identify their behavior as consuming “news” but it is news providers that overwhelmingly shape and provide sourcing, according to a Muck Rack study.
From the ‘attention’ economy to the ‘information’ economy
Inside publishing, a divide is developing: Some outlets are tightening paid subscription walls to keep out AI crawlers, signing exclusive licensing deals or joining coalitions working to force tech companies to operate within a pay-per-scrape marketplace. That makes sense for some publishers.
Others, like Techncial.ly, are instead optimizing for informing these tools.
Many publishers worry their role will lose visibility. Even when a publisher is formally cited as the source for an AI chatbot answer, its appearance is minimal. Users aren’t likely to notice your publication’s name, and they’re even less likely to click through.
That lack of click-throughs or brand presence presents a problem for the traditional news industry.
The AI chatbot situation also threatens the news industry’s current business model darling: reader revenue. Whether it’s through voluntary donations or forced subscription, if users never click through to your site or platform, they’ll never enter your membership funnel or hit a paywall.
New models are needed, and Technical.ly has one. Before the pandemic, our organization operated primarily on an events model. Our reporting gathered an audience whom we rallied to jobs fairs, conferences and parties.
Now we are primarily a newsroom with financial underwriters that need their state and local entrepreneurship, tech, science and innovation ecosystem better known and connected. Our reporting remains independent, and that independence is our value
We reach a targeted audience that’s all about quality, not quantity. With this financial model, we can serve as a means to gather and distribute our news and information to those who need and want it. We do that for our most dedicated audience, on this site and in newsletters, and increasingly on social media, including with creators.
With AI chatbots, a third tool for reach is emerging.
Being a reliable source of truth, whether via chatbot or direct
Here’s Technical.ly’s role in this new era of information discovery: We are the source of truth for these chatbots, especially about the ecosystems we follow closest.
With our focus on the local economic effects of innovation, we do reporting that’s often missed by other outlets. Sometimes that’s because they’re exclusively reporting on Big Tech and splashy startups at the national level, and sometimes it’s because they concentrate on community news without capturing the way tech and startups inform many people’s day-to-day life.
The uniqueness of our stories makes them even more suited to being a primary source for chatbot answers. Of note, the emerging strategy to make that happen goes by various names, including AEO (answer engine optimization), GEO (generative engine optimization) and LLMO (large language model optimization). We’re going with “AI visibility.”
Here’s a quick review of what we’ve been doing to move it forward:
- Inserting behind-the-scenes schema markup to make it easier to parse our curated roundups, like our quarterly RealLIST features honoring top startups, ecosystem connectors and innovators.
- Adding this behind-the-scenes info for our annually-updated guides to coworking spots, coding bootcamps, and startup accelerators and incubators.
- Adding more FAQ sections in articles, with clear question-answer pairs
- Using TL;DR bullet point summaries at top of articles
- Leaning into publishing explainer-style articles
- Adding pullquotes in the intro of most articles, offering a clear takeaway when the page first loads
- Continuing to link out to Wikipedia and adding more citations to Technical.ly stories in that valuable compendium of information
- Publishing an “Awards & Accreditations” page on our site so the world knows where and when we’ve been honored for this work
To be clear, most of these things are also good for human readers who come directly to articles on our site. Win-win.
