AI-Generated News Sites Are a Warning for Builders

An AI-generated news site, apparently backed by OpenAI's super PAC, is publishing content designed to attack the company's critics. The reporters are bots. The agenda is real. This is worth thinking through carefully if you build AI agents for a living.

What Actually Happened

Model Republic, a site whose bylines are AI personas, has been running stories that align suspiciously well with OpenAI's political interests — targeting researchers, journalists, and policymakers who've been critical of the company. The funding trail points to OpenAI's super PAC.

This isn't a story about AGI or model capabilities. It's a story about deployment: someone built an agent pipeline to produce politically motivated content at scale and pointed it at real people. The technology worked exactly as designed.

The Lesson Is About Objectives, Not Capability

When an AI agent misbehaves in production, the first question is usually "what was it optimizing for?" Here the answer is straightforward: produce content that advances a political position. The agent did that.

This is the same failure mode that shows up in less dramatic agent deployments. An outreach agent optimized for reply rate starts sending aggressive follow-ups. A support agent optimized for ticket closure starts deflecting instead of resolving. A content agent optimized for volume starts publishing thin rewrites.

The objective function is the product. If you define it sloppily, or let someone else define it for you, the output reflects that — at scale, automatically, continuously.

Why This Matters for Legitimate Agent Work

Founders and operators evaluating AI agents right now are asking a reasonable question: can I trust this thing to represent my business? The honest answer is "it depends entirely on what you're asking it to do and how you've constrained it."

Agents that handle customer queries, process documents, draft internal summaries, or route support tickets operate in bounded domains with clear success criteria. The failure modes are visible and correctable. That's very different from an agent tasked with "produce content that makes us look good" — a goal that's vague, self-serving, and impossible to evaluate from the outside.

Before you build, you need to be clear on: what does success look like, who gets to define it, and who gets harmed if the agent optimizes too hard. Those aren't philosophical questions — they're architecture decisions.

Evals Exist for a Reason

One thing production agent work hammers home quickly: you need evals that reflect what you actually care about, not just what's easy to measure. Output volume is easy to measure. Whether the output is accurate, fair, or useful to the person receiving it is harder — but that's the thing that matters.

The Model Republic situation is an evals failure at the organizational level. Nobody with authority to stop it was running the right checks. In a smaller agent deployment, the same dynamic shows up when teams ship to production without testing edge cases, or when the only metric tracked is throughput.

If you're mapping out where AI agents could save your team real time, our free AI Opportunity Audit identifies your three highest-impact automations from your website alone — and it's a useful forcing function for thinking about what "good output" actually means before you build.

What to Take Into Your Next Build

Three practical things this episode reinforces:

Define the objective narrowly. "Produce content" is not an objective. "Draft weekly summaries of support tickets, flagging unresolved issues, for internal review" is. The narrower the scope, the easier it is to catch when something's off.

Build in a human checkpoint before anything hits an audience. Fully autonomous agents that publish, send, or post without review carry compounding risk. A human in the loop — even a lightweight one — resets the failure radius.

Be honest about who benefits from the agent's output. If the answer is "primarily us, at the expense of the people receiving it," that's worth pausing on. Not for abstract ethical reasons — because it tends to produce bad product outcomes and, eventually, bad press.

Agents are just code with objectives. The objectives are yours to set.

Build Something Worth Building

If you're working through where AI agents fit your business and want help scoping something that's actually useful — not just impressive in a demo — book a call and we can talk through it.