OpenAI Discoverability: New York’s 2026 Challenge

That recent AI hearing in New York City, the one at the old Surrogate’s Courthouse, put a fine point on a problem my clients are all facing: how do you get found inside an LLM from a company like OpenAI? These AI systems are the new gatekeepers to information, and if you don’t understand how they find, chew up, and spit out your content, you’re invisible. So how does your best work actually get through all that algorithmic processing to find the people who are looking for it?

The Problem: Content Lost in the AI Black Box

For years, everyone in digital marketing lived and died by SEO for Google. We all did the same things: chase keywords, build backlinks, and optimize the user experience. But generative AI, especially with what OpenAI is doing, has thrown a wrench in the works. I see it constantly with clients now, they’re producing great content and doing all the old SEO right, but it’s having zero effect on their visibility inside these new AI answer engines.

The real issue is that we can’t see how LLMs are trained or how they pull information. It’s a black box. A search engine gives you a list of links, but an LLM synthesizes everything. It doesn’t always point back to you, and when it does, the credit is often buried. This means your best blog post or a deeply researched white paper gets vacuumed up by an LLM, its ideas rephrased and served up to a user, and you never get the traffic. This isn’t some theoretical problem. It’s a real threat to your lead gen, brand, and bottom line. It’s just frustrating to watch clients pour money into content only to see it disappear into the AI ether.

What Went Wrong First: Misguided Approaches

In the beginning, everyone tried to brute-force the old SEO playbook onto this new AI problem, and it just didn’t work. The most common mistake was keyword stuffing, thinking you could somehow trick the LLM into noticing you. Search engines have punished that for a decade, and LLMs are way past needing keyword density to figure out what a page is about. It just made content sound robotic and unreadable for actual humans.

The next bad idea was the content farm approach: churning out a huge volume of short, thin articles. The theory was that if you threw enough mud at the wall, some of it would stick and an LLM might pick up a piece. But these models are being trained on quality signals. They want authoritative, complete sources. A flood of shallow content just tanks a brand’s authority and buries the few good pieces you might have. I’ve seen clients burn through huge budgets this way, ending up with hundreds of useless articles that gave them zero AI visibility because they had no depth.

Then there was the experiment of creating special “AI-friendly” pages, which were usually stripped-down, text-only versions of a page. This was based on a complete misunderstanding of how LLMs see the web. They don’t just scrape raw text. They’re parsing the entire page context, including its structure and the site’s overall authority. Giving up a good user experience for a supposed AI advantage was a terrible trade that just led to high bounce rates with no benefit in AI discovery.

The Solution: Strategic Content Engineering for AI

The discussions at the NYC hearing, especially around data sources and content attribution, actually point to a clear path forward. Getting discovered by OpenAI means you need a mix of smart technical work, high-quality content, and a real strategy for building authority.

Step 1: Implement Advanced Structured Data Markup

This is probably the single most important technical fix you can make. LLMs understand language, sure, but they get a massive boost from explicit signals about what your content is. Using Schema.org markup is that signal. It’s a direct line of communication to the AI. You need to focus on a few key types:

• Article Markup: For any post or article, use Article schema. You have to be precise with fields like headline, author, datePublished, and especially the mainEntityOfPage to point back to the original URL. It tells the LLM what it’s looking at and who’s behind it.
• FAQPage Schema: If you have an FAQ, wrap it in FAQPage schema. This is gold. It lets an LLM pull your questions and answers directly, making your content a prime target for getting used in conversational replies. I’ve personally seen this one change make a huge difference in getting content cited in AI summaries.
• Product Schema: For e-commerce, detailed Product schema is a must. Include the name, description, offers (with price and availability), and aggregateRating. This gives the LLM structured data it can use to answer specific product questions.
• Review and Rating Schema: If you’re a service or have product reviews, use Review and AggregateRating schema. This lets the LLM pull in social proof and user opinions, which can sway how it presents information.

The whole game here is being accurate and complete. Messy or incomplete schema is worse than having none at all. Make sure you’re running your pages through tools like Google’s Rich Results Test to check your work.

Step 2: Embed Unique, Verifiable Data and Original Research

LLMs are trained on the existing internet, but they’re constantly looking for fresh, authoritative, and fact-checked info to improve. If you want to stand out, you have to give them something they can’t get elsewhere. This means:

• Conduct Original Research: Publish your own surveys, studies, or data analysis. A financial services client of mine could, for example, release its own annual report on economic trends in a specific region. This produces completely unique data points that an LLM will favor because it’s genuinely new knowledge.
• Cite Primary Sources: When you talk about regulations or scientific data, link to the source, the official government document on the Federal Register, the actual academic paper, the historical archive. An LLM gives far more weight to a primary source than to another blog post that’s just summarizing it.
• Present Data Clearly: Don’t bury your data in long paragraphs. Use tables, bullet points, and simple, declarative sentences that are easy for a machine to parse. For instance, list out “Benefit 1: [Description], Benefit 2: [Description],” instead of writing a dense paragraph.

In my experience, content that’s packed with unique data, even for a small niche, gets a ton more AI visibility. You’re effectively helping the LLM “learn” something new, which makes your content a valuable asset to its knowledge base.

Step 3: Build a Strong Authority Profile (Digital PR and Backlinks)

Backlinks still matter, a lot. Even though LLMs don’t “crawl” the web for links like Googlebot does, those links are a powerful signal of authority and trust that gets fed into their data pipelines. The people at the NYC hearing kept talking about credible sources, and high-quality backlinks are how you prove you’re one of them.

• Target Authoritative Publications: Put your PR energy into getting mentioned by industry journals, real news outlets (think Reuters or AP, not state-run propaganda), and universities.
• Internal Linking Strategy: Good internal linking is also key. A logical structure of links helps LLMs and regular crawlers map out your site and understand how your content is related. This is how you spread authority around your own site, from your big foundation pages to newer posts, which shows the depth of your expertise.
• Brand Mentions: Even unlinked mentions of your brand on other good sites help build your overall authority. LLMs can pick up on these patterns and factor them into how trustworthy they think your content is.

It’s simple logic: if a bunch of reputable sites are pointing to you or talking about you, an LLM is far more likely to treat your information as authoritative. You can’t just exist in a vacuum.

Step 4: Focus on Complete, User-Intent Driven Content

Because LLMs are built to synthesize information and answer complicated questions, content that fully answers a user’s question from all angles will always win. The game has moved from matching keywords to matching intent.

• Long-Form Content: This isn’t just about word count for its own sake, but longer articles on complex topics (think 1,500 to 3,000 words) tend to have the depth that LLMs are looking for. They provide more context, different examples, and the kind of nuance an AI needs to build a solid, complete answer.
• Answer “People Also Ask” Questions: Go look at the questions people are asking about your topic. Weave those questions and their answers directly into your content with clear headings. You’re basically spoon-feeding the Q&A part of the LLM.
• Address the “Why” and “How”: It’s not enough to just state facts. You need to explain the implications, the step-by-step processes, and the real-world benefits. LLMs are getting much better at understanding cause-and-effect and procedural instructions, so if you’re talking about a software feature, explain why it matters and exactly how to set it up, right down to the specific configuration settings.

Your goal should be to create the single best, most definitive resource on a topic. When your content is the most complete and accurate page on the web for a certain query, an LLM will naturally be drawn to it as a source.

Step 5: Prioritize Content Freshness and Accuracy

Another point from the NYC hearing was that AI models need to serve up-to-date information. Stale or wrong content gets deprioritized fast, especially for fast-moving topics like tech or finance. This means you have to commit to content maintenance.

• Regular Content Audits: Set a schedule, maybe quarterly or twice a year, to audit your most important content. Find what’s getting old and make a plan to update it.
• Update with New Information: When something changes in your industry, a new regulation, new data, go back and update your existing articles right away. Don’t just write a new post. Improving an existing, authoritative post is better. Make sure to add a “Last Updated” date to signal that freshness.
• Fact-Checking Protocols: You need a serious internal process for fact-checking. LLMs are getting good at spotting when your information contradicts more authoritative sources across their training data. If your content says one thing and a government site says another, your page is going to be ignored.

Keeping your content library fresh and accurate is a real investment, but it’s absolutely necessary for long-term AI discoverability. Your website has to be a living knowledge base, not a dusty, static archive.

Measurable Results: The Impact of AI-Focused Content

Putting these strategies into practice does more than just help your old-school SEO. It gets you real, tangible results in AI visibility. For one of my clients, a SaaS company in the project management space, we went all-in on using FAQPage schema on their support docs and feature pages. Within six months, their content started showing up as the direct answer in AI search interfaces for 30% of their target long-tail queries. While it didn’t always generate a click, it massively boosted their brand exposure and set them up as an authority in the AI’s “mind.”

I saw something similar with an environmental consulting firm that invested in publishing its own research on local water quality in specific parts of Georgia, like the Chattahoochee River basin. By getting that unique data out there and getting it cited by local government and university researchers, their site became a go-to source for LLMs answering questions about environmental issues in the region. Direct traffic didn’t explode, but their brand name was consistently part of the AI-generated summaries for relevant questions, which led to a 15% jump in inbound leads from potential clients who found them through an AI. The effect was both qualitative and quantitative, making them a true thought leader.

The whole mindset is different now. We’re moving away from just chasing clicks and toward optimizing to become part of the AI’s core knowledge. Doing that means you get more brand visibility, more authority, and a much stronger digital presence that goes beyond simple search rankings. The future is about being the source, not just appearing in a list of links.

Conclusion

The big takeaway from the NYC hearing is that getting found on OpenAI and other platforms requires a proactive strategy that moves beyond basic SEO. By using structured data, publishing unique and verifiable info, building real authority, creating truly complete content, and keeping it all accurate, you can make sure your best work gets integrated into the AI-powered information world.