90% of AI company blogs say the same thing: 'AI is transforming everything. We're at the forefront. Here's a vague promise.' Nobody reads this. Nobody shares it. Nobody buys because of it.
Here's what actually works — based on what we've seen drive real conversations and pipeline in the AI services space.
The Content That Builds Trust
1. Show Your Work
The highest-performing content for AI companies isn't thought leadership — it's build logs. How you built something, what went wrong, what you learned. Technical readers (your buyers) can smell generic content from a mile away. They trust specificity.
- ▸Case studies with real metrics (not 'improved efficiency')
- ▸Architecture deep-dives with actual design decisions explained
- ▸Failure stories — what broke, why, and how you fixed it
- ▸Comparison posts with honest assessments (not 'we're the best')
2. Have an Opinion
The safest content strategy is also the least effective. 'AI is powerful but has limitations' says nothing. 'AI agents handle 70% of consulting work — the other 30% still needs humans' says something specific and arguable. Opinions attract the right audience and repel the wrong one. Both are valuable.
3. Be Honest About Limitations
Every AI company claims their product works for everything. The ones that say 'here's where we're great, here's where we're not, and here's what we're working on' build disproportionate trust. Honesty is a competitive advantage when everyone else is overselling.
Content Types That Generate Pipeline
| Content Type | Trust Impact | Pipeline Impact | Effort |
|---|---|---|---|
| Technical case studies | Very High | High | High (need real projects) |
| Honest comparison posts | High | High | Medium |
| Architecture deep-dives | Very High | Medium | High |
| Tool reviews (honest) | Medium | Medium | Low |
| Industry analysis | Medium | Low | Medium |
| Founder stories | High | Low | Low |
| Generic thought leadership | Low | None | Low |
What We Write and Why
Our content strategy at Proxie follows three rules:
- ▸Every post includes something you can verify — a metric, a code snippet, an architecture decision. No abstract claims without evidence.
- ▸Every post acknowledges a limitation — what we can't do, what doesn't work yet, what requires human judgment. This builds more trust than 100 testimonials.
- ▸Every post teaches something useful even if you never hire us — if a reader learns something valuable, they remember us when they need help.
The Topics That Work in 2026
- ▸Model comparisons with production data (GPT-5.3-Codex vs. Claude Opus 4.6 — what works where)
- ▸AI agent ROI analysis by department (marketing, sales, finance, legal)
- ▸Technical tutorials with real code (RAG systems, multi-agent orchestration, evaluation pipelines)
- ▸Honest assessments of new tools (Claude Cowork, OpenAI Codex app, Cursor, etc.)
- ▸Founder stories with real lessons (failures, pivots, honest metrics)
- ▸Cost comparison content (traditional consulting vs. AI-native approaches)
This post is meta — we're writing about what to write about. But it's also genuine advice. The AI content landscape in 2026 is noisy. The companies that cut through the noise are the ones that say real things, backed by real work, with real honesty. That's what we're trying to do here.
Need Help With Your Content Strategy?
Our 15-agent swarm handles content production at scale — with human review on every piece. If you need consulting-grade content that builds trust instead of generic AI hype, let's talk.