AI is the biggest technology story of the decade. It’s also becoming one of the most boring.

Browse any news site and the AI coverage blurs together: announcements of new models, breathless features about capabilities, worried analyses about jobs, interviews with the same handful of executives saying the same things.

Most AI journalism is interchangeable. One outlet’s coverage could swap with another’s and readers wouldn’t notice.

This is a problem. AI is genuinely important—it deserves coverage that’s distinctive, skeptical, and useful. Here’s how to do better.

The Problems with Current Coverage

Let me be specific about what’s wrong:

Too much access journalism. Coverage built around announcements, press releases, and executive interviews inevitably reflects company messaging. OpenAI, Google, and Anthropic get to set the agenda. Reporters react rather than investigate.

Not enough skepticism. Claims about AI capabilities are often repeated without verification. When a company says their model can do X, reporters often write “the model can do X” rather than testing whether it actually can.

Hyped framing dominates. “Revolutionary,” “groundbreaking,” “could change everything”—this language makes coverage feel like marketing rather than journalism.

Missing context. Individual stories appear without framework. Readers don’t understand how pieces fit together, what actually matters versus what’s noise, or how to evaluate competing claims.

Same sources, same voices. A small group of executives, researchers, and pundits appear in story after story. Important perspectives—workers affected by AI, researchers outside elite institutions, people in other countries—are underrepresented.

Technical complexity avoided. When technical details matter, coverage often skips them. Readers are left unable to evaluate claims because they don’t understand basic concepts.

Finding Distinctive Angles

Better AI coverage requires finding angles others miss. Here’s where to look:

Follow the money. Who’s funding AI development? Who benefits? Who loses? Financial investigation reveals dynamics that capability coverage misses.

Go local. How does AI affect your specific community? The impact on a factory in Geelong is more meaningful to local readers than abstract discussions of automation.

Talk to workers. The people actually using AI in their jobs—or worried about losing them—have perspectives that executives don’t. Ground-level reporting beats executive interviews.

Investigate failures. When AI goes wrong, what happens? Algorithmic harm, bias incidents, deployment failures—these stories matter and are underreported.

Examine the supply chain. AI requires data labeling, often by workers in developing countries under poor conditions. It requires massive energy and hardware resources. These stories get minimal attention.

Cover the rest of the world. Most AI coverage focuses on US companies. What’s happening in Europe, Asia, Africa? Different regulatory environments, different use cases, different impacts.

Question assumptions. “AI will transform X” is often stated as given. Is it true? What evidence exists? Skeptical examination of conventional wisdom is always valuable.

Technical Reporting Done Right

The best AI journalists understand the technology well enough to explain it—and to know when claims don’t hold up.

This doesn’t mean you need a computer science degree. It means developing enough literacy to:

• Understand what different types of AI actually do (and don’t do)
• Recognize when capability claims are inflated
• Ask specific questions that reveal limitations
• Translate technical concepts for general audiences

The investment is worth it. Reporters who can read a technical paper—or at least understand its claims—produce different coverage than those who can only summarize press releases.

Building this knowledge takes time. Start with accessible resources: books like “You Look Like a Thing and I Love You” or the Practical AI podcast. Follow researchers who explain their work clearly. Take online courses if that’s your learning style.

The goal isn’t expertise—it’s literacy. Enough to know what questions to ask and when you’re being misled.

Practical Approaches

Here are specific approaches for better AI stories:

Test claims yourself. When a company announces a capability, try it. Document what actually works versus what’s claimed. This simple step often reveals significant gaps between marketing and reality.

Seek disconfirming evidence. If you’re writing about AI benefits, talk to people experiencing harm. If you’re writing about harms, find legitimate benefits. The best stories acknowledge complexity.

Develop independent sources. Researchers outside major companies, policy experts, workers in affected industries—build a source network that isn’t dependent on company PR.

Follow up on old stories. What happened to the AI deployment announced a year ago? Did it work? What changed? Follow-up coverage often reveals what announcements miss.

Make it concrete. Abstract discussions of AI impact are less powerful than specific examples. One detailed case study beats ten paragraphs of generalization.

Show your work. When you evaluate an AI system, explain your methodology. Transparency about how you reached conclusions builds reader trust.

What Readers Actually Need

The purpose of AI coverage is helping readers make sense of a complex, fast-moving story. Ask what readers actually need:

• Understanding: What is this technology? What does it actually do?
• Context: Why does this matter? How does it connect to other developments?
• Evaluation: Is this claim true? Is this deployment working?
• Guidance: What should I do? How does this affect my life, job, decisions?
• Accountability: Who’s responsible? Are powerful actors being held accountable?

Coverage that serves these needs is valuable. Coverage that generates pageviews without serving them is just noise.

The Opportunity

Here’s the good news: because most AI coverage is mediocre, there’s enormous opportunity for journalists who do it well.

Distinctive, skeptical, well-sourced AI journalism stands out. It builds audience loyalty. It establishes credibility that lasts.

The investment required—technical literacy, independent sources, time for investigation rather than reaction—is significant. But the return is a body of work that matters and readers who trust you.

AI will remain a dominant story for years to come. The journalists who cover it best will have meaningful careers. The ones who cover it like everyone else will be indistinguishable from the noise.

Choose to be distinctive.