TL;DR - Direct Answer
Market your AI startup by selling capabilities (what users can DO) not features (what you built). Focus on: (1) Specific accuracy metrics with benchmarks, (2) Latency at scale, (3) Cost comparisons, (4) Integration effort. Avoid: "AI-powered", "proprietary algorithms", "state-of-the-art" without data.
Engineers don't buy "we use GPT-4." They buy "96.3% F1 on GLUE vs 94.1% GPT-4 baseline."
The AI Wrapper Discourse
Every ML founder's nightmare:
Someone comments on your Hacker News launch: "This is just a GPT wrapper."
The fear is real because:
- Many AI products ARE just wrappers (OpenAI API + UI)
- Buyers are skeptical (been burned by overhyped AI before)
- Differentiation is hard (everyone says "AI-powered")
The overcorrection:
You load your landing page with technical jargon:
- "Proprietary transformer architecture"
- "Advanced multi-modal embedding space"
- "State-of-the-art performance on industry benchmarks"
Result: Engineers roll eyes. Non-technical buyers are confused. Nobody clicks "Start Trial."
The real question: "What can users DO with your AI that they can't do now?"
Everything else is noise.
What ML Buyers Actually Evaluate
Criterion 1: Accuracy (with proof)
❌ Bad marketing: "State-of-the-art performance on industry benchmarks"
✅ Good marketing: "96.3% F1 score on GLUE benchmark vs 94.1% GPT-4"
Why this works:
- Specific number: 96.3% (not "high accuracy")
- Comparable: vs GPT-4 baseline (they can verify this)
- Verifiable: GLUE is public benchmark (you can't fake it)
Criterion 2: Latency (with scale)
❌ Bad marketing: "Fast inference times for real-time applications"
✅ Good marketing: "p95 latency <100ms at 10K requests/second"
Criterion 3: Cost (with math)
❌ Bad marketing: "Cost-effective AI solution for enterprises"
✅ Good marketing: "$0.003 per 1K tokens vs $0.03 OpenAI (10x cheaper at scale)"
Criterion 4: Integration Effort
❌ Bad marketing: "Easy to integrate with existing workflows"
✅ Good marketing: "Add 3 lines to your Python script. No infrastructure changes."
The Capability Bridge for AI Products
Formula:
- ❌ Feature: "We use GPT-4"
- ✅ Capability: "Process 10K documents in 2 minutes"
- ✅✅ Benefit: "Ship your doc analysis feature this week, not next quarter"
Examples by AI category:
- Vector DB: "Query 10M embeddings in <100ms" (not "fast vector search")
- LLM API: "96.3% F1 vs 94.1% GPT-4" (not "state-of-the-art")
- ML Ops: "Deploy to 50 regions in parallel" (not "multi-region support")
- Data Pipeline: "Process 1TB in 15 min" (not "fast data processing")
Key Takeaways
- Avoid "AI wrapper" by showing specific differentiation. Not "we use AI" but "96.3% accuracy vs 94.1% baseline."
- Quantify everything. Accuracy with benchmarks, latency at scale, cost per 1K tokens, integration in X lines of code.
- Use Capability Bridge. Sell what users can DO, not what you built.
- Show honest trade-offs. "Works great for X, not ideal for Y" builds more trust than "perfect for everyone."
- Engineers trust specifics. "p95 latency <100ms" beats "fast" every time.
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Theo Popov is the co-founder of GTM Stacker. Former COO who bootstrapped a restaurant franchise to $4.1M revenue and 11 locations. 8+ years in operations, now running the full B2B marketing engine—content strategy, LinkedIn and X growth, and outreach systems across email and social at scale.