I Went to an AI Meetup. The Internet Was Not Invited.

It was freezing when I parked in the garage and climbed the icy steps into a hotel lobby in New Hampshire. By the time I reached the third floor conference room, the cold gave way to the smell of fresh pizza, low laughter, and quiet chatter.

A handful of people stood around talking. A few others were already seated, laptops open, half-working, half-waiting. I slid in, grabbed a bottle of water, and found a seat.

Nick Pope, the organizer, welcomed me, checked me in, and explained the goal of AI in New Hampshire: to make New Hampshire the most AI-literate state in America. A bold ambition, backed by bi-monthly meetings and a full AI week launching this spring.

There were about fifteen of us in the room. Mostly men at first. A few women arrived later, likely juggling dinner or family. The room felt relaxed, curious, and honest.

I had come straight from work and would be leaving early for another commitment. Still, I wanted to be there. Not online. Not reading a summary later.

In the actual room.

I wanted to see what happens when AI leaves the feed and enters shared space.

That context mattered, because the conversations that followed were not about hype. They were about power, architecture, and responsibility.

Sometimes the most important AI insights do not come from announcements, but from how people react to them in real rooms.

An animated Nick Pope moderating the discussion

A Quiet Room When Power Shifts Are Announced

When news of OpenAI 5.2 and its partnership with Disney came up, the room went quiet.

Not excited. Not alarmed. Just still.

Disney opening its IP to Sora and allowing user-generated AI content to surface on Disney+ is not a small move. Yet the discussion did not center on creativity or fandom. It centered on data.

• Who sees what users are prompting?

• How often are Disney characters mentioned?

• Is that information shaping future content decisions?

The curiosity was cautious. The skepticism was healthy. People were less interested in the headline and more interested in the economics underneath it.

AI Is Expanding Past Text, Even as Limits Appear

The conversation moved easily beyond text. Voice, sound, visuals, texture. That future felt assumed.

At the same time, physical constraints surfaced. Transistor limits. Token-based architectures. Real bottlenecks that slow scale, even as ambition grows.

The takeaway was subtle but grounding. AI is accelerating, but it is not frictionless. Physics, infrastructure, and policy still shape what is possible.

Dirty Data Is the Problem Everyone Feels

Unstructured data came up repeatedly. Not always by name, but by frustration.

Messy documents. Disconnected systems. Information scattered across tools with no clear ownership.

The most practical advice was simple: prune data as you go. Do not wait to clean it later.

Many AI performance issues are not model failures. They are data governance failures that show up downstream.

RAG as Architecture, Not an Add-On

Retrieval-Augmented Generation sparked interest precisely because it was unfamiliar to many in the room.

The discussion stayed theoretical, but one framing stood out. RAG is not a bolt-on feature. It is an architectural choice.

Start with a trust ecosystem. If you are on the Microsoft stack, begin with Copilot. Then extend outward with RAG systems that reflect your internal knowledge, not the entire internet.

Agentic RAG came up as a way to move from static retrieval to contextual action.

The signal was clear. Organizations want AI that understands their world.

Hallucinations Are a Governance Issue Now

A brief reference to a Deloitte case involving government refunds in Australia, caused by hallucinated AI outputs landed quietly, but firmly.

No one debated the technology. They did not need to.

When AI outputs affect money, policy, or public trust, hallucinations stop being an academic concern. They become a governance failure.

Oversight and accountability are no longer optional.

Content Repurposing Is Becoming an AI Workflow

The group also touched on content workflows. This was one of the real-life problems a small business owner came to the forum with. Tools like Riverside and Descript were discussed for turning long-form video into short-form clips.

Cost surfaced quickly, which led to interest in RAG-based approaches that make existing content searchable and reusable rather than constantly re-edited.

I loved the way we brainstormed the audience-member's usecase to propose low cost alternatives using AI. This is what its all about!

AI Tools Are Starting to Look Like Roles

Mentions of tools positioned as AI CMOs or operational partners surfaced quietly. Lindy. Honeybook. Others.

This reflects a shift. AI is no longer framed only as a tool. It is increasingly framed as a functional role.

That raises an unresolved question. If AI occupies roles, where does responsibility sit?

The Real Value Was Being There

Yes, I learned new things. News I had missed. Concepts I had not fully explored.

But the deeper value was realizing others were also trying to keep up. Others were surprised. Others were behind on some threads and ahead on others.

I slid in late and left early. Still, the time mattered.

Meaning-making is still happening socially.

ReclaimingAI Takeaway

AI is accelerating. Partnerships are consolidating power. Architectures are maturing. Governance gaps are becoming visible.

But none of that replaces the need for human sense-making.

If we want to reclaim AI as something that serves people rather than overwhelms them, we need more rooms like this. Spaces where curiosity is allowed, skepticism is welcomed, and no one has to pretend they are fully caught up.

Much thanks to the organizers of AI in New Hampshire for creating that kind of space. If you are local, or even just curious, it is worth checking out what they are building at aiinnh.org.

And more broadly, if there is an AI meetup, roundtable, or working group near you, go. Show up. Sit in the room. Listen more than you talk.

The feed will always be there.

But the real work of understanding what AI means for our lives, our work, and our communities is still happening in rooms where people gather, share a meal, and think out loud together.

Sometimes the most important thing you can do is simply choose to be in the room where it happens.