The Traditional “Triple, Triple, Double, Double, Double” Rule is Dead for AI Startups
If you’ve been in SaaS for a while, you know the classic growth rule of thumb: “Triple, Triple, Double, Double, Double.” It was the gold standard for B2B software companies scaling from $1M to $100M ARR.
But here’s the thing – AI startups are breaking this model entirely.
Take Glean and Harvey. They’re not just beating the old model; they’re shattering it. Glean grew 20x in a single year. Harvey hit 10x. The bar isn’t just higher – it’s in a different stratosphere.
A deep dive with two leaders at the forefront of AI startup scaling and investment.
Meet Our Experts
Rajan Sheth
General Partner at Hypergrowth Partners and interim CMO at Together AI, Rajan brings a unique perspective from both the operational and investment sides of AI scaling. Previously leading growth at Cohere and Heroku, he’s been instrumental in scaling multiple category-defining companies in the AI space.
Emily Zhao
Principal at Salesforce Ventures, Emily leads AI investments for one of tech’s most active venture funds, with over $6B deployed across 400+ investments. Her portfolio includes some of the most promising companies in AI, including Cohere, Runway, and Together AI.
Together, they bring insights from having seen and shaped the playbooks of dozens of AI companies going from $0 to $100M+ ARR.
What’s Really Different? The Rise of “Experimental Revenue”
Here’s what’s fascinating: we’re seeing something entirely new in the AI space – what I’ll call “Experimental Revenue.” It’s a phenomenon where customers are paying real money, but not in the way we’re used to in traditional SaaS.
Why? Three key factors:
- Application Novelty: We’re seeing an explosion of new use cases and solutions
- Technology Novelty: The underlying tech is evolving at breakneck speed
- Customer Behavior Novelty: Even enterprise customers are running multiple concurrent pilots
The result? Companies are seeing massive growth numbers, but with a catch – high churn rates and unpredictable usage patterns.
The Real Signs of “Experimental” vs “Real” ARR
Want to spot the difference? Here are the tell-tale signs:
Real ARR Signals:
- Increasing contract lengths (beyond 6 months)
- External-facing production use cases
- Stable usage patterns post-feature releases
- Single-vendor decisions for critical use cases
Experimental Revenue Red Flags:
- High growth coupled with high churn
- Usage spikes only around new feature releases
- Multiple vendor testing for the same use case
- Short contract lengths with no progression
The New GTM Playbook: Why Marketing-Led is Winning
Here’s what’s really interesting – the next generation of successful AI companies will be marketing-led, not product or sales-led. Why? Because the market is moving too fast for traditional enterprise sales cycles.
Successful companies are running three motions simultaneously:
- Bottom-up developer adoption
- Top-down enterprise engagement
- Strategic partnerships
And they’re doing it from Day 1, not sequentially like in traditional SaaS.
5 Actionable Strategies for AI Startup Growth
1. Create a Movement, Not Just a Product
Cohere didn’t just build an enterprise LLM – they created a movement around enterprise AI transformation. They systematically inserted themselves into policy discussions and changed market narratives.
2. Leverage “Zero-Cost” Brand Building
Build around thought leaders and technical experts. Cohere and Together AI have shown you can build massive brand equity without traditional brand marketing spend.
3. Charge for POCs
Make them refundable, but charge. It creates urgency and investment from potential customers. One AI infrastructure company saw 80% higher conversion rates after implementing this strategy.
4. Focus on Production Use Cases
The key metric isn’t initial adoption – it’s production deployment. External-facing use cases are particularly valuable as they’re harder to rip out once implemented.
5. Build Multiple Growth Engines
Don’t wait to layer in enterprise sales on top of PLG, or partnerships on top of direct. Run all three simultaneously from the start.
The 5 Key Metrics That Matter Now
- Production Use Case Ratio: What percentage of your revenue comes from production vs. experimental use cases?
- Contract Length Progression: Are you seeing movement from 3 to 6 to 12-month contracts?
- Usage Stability: How stable is usage 30 days after new feature releases?
- Vendor Consolidation: Are you becoming the primary vendor in your category for customers?
- External Use Case Ratio: What percentage of your use cases are customer-facing vs. internal?
What This Means for 2025
The AI startup playbook is being rewritten in real-time. The winners will be the companies that:
- Build marketing-led organizations from Day 1
- Focus on converting experimental to production revenue
- Run multiple growth motions simultaneously
- Create movements, not just products
- Invest in brand and narrative alongside product
The old rules of SaaS growth are dead. Welcome to the new world of AI startup scaling.
