Many B2B companies touting themselves as "AI-powered" are finding their growth stalled. This isn't due to faulty technology, but rather a fundamental misunderstanding of how to truly monetize artificial intelligence in the current market. The era of simply being "AI-enhanced" is over; real revenue generation in B2B AI hinges on pursuing one of three distinct and impactful strategies.
The Framework: Three Paths to AI Revenue
According to industry experts Jason and Rory, there are only three viable paths for B2B AI companies to generate significant revenue:
- Path 1: Attach to Compute Infrastructure
- Path 2: Replace Human Headcount
- Path 3: Massively Displace an Incumbent
Anything outside these three frameworks is largely considered "noise" in the B2B AI landscape. Let's delve into why each path works, how to determine if your company is genuinely pursuing it, and common pitfalls to avoid.
Path 1: Attach to Compute Infrastructure — The DataDog Playbook
DataDog's 23% stock surge following its earnings report wasn't mere luck; it was the direct result of a simple, powerful principle: sell essential services to the companies building AI. As these AI leaders grow, your business grows automatically.
"The AI leaders, the hyperscalers — they're starting to buy like classic B2B companies," Jason explains. "They're recycling the same people in procurement. So if you're attached to AI budget and you're a DataDog era, you're actually going to have a great 2026."
Rory adds critical context, highlighting that companies like DataDog provide core compute infrastructure. "These hyperscalers are the most compute-intensive companies that have ever been known. If you're selling compute stuff, you should be having a great quarter. If you're selling routers, switches, interconnects, whatever it takes to stand up Stargate — you're going to be golden."
What This Actually Means
Hyperscalers such as OpenAI, Anthropic, Google, Microsoft, and Meta are deploying unprecedented amounts of computing power. They aren't just buying different things; they're buying significantly more of everything:
- More observability (e.g., DataDog)
- More networking equipment (e.g., Broadcom)
- More security infrastructure
- More data pipeline tools
- More developer tooling for machine learning workflows
The key insight here is that these companies procure like traditional B2B enterprises, but at 10 times the scale and speed. OpenAI, for instance, isn't a mystical entity when it comes to procurement; it needs observability, security, and data tools—all the same infrastructure any rapidly scaling company requires, just in far greater quantities due to its massive compute deployments.
How to Know If You're Actually Playing This Game
Ask yourself these critical questions:
- Is my revenue directly correlated to compute deployment? If OpenAI spins up 1
