Making AI ventures successful

Building an AI Company? Did you know your startup economics are already working against you?

Everyone’s talking about building AI companies.

VCs are pouring billions. Founders are pivoting to AI. Seems like an AI gold rush

But, having built an AI product ground-up, and through multiple conversations with AI founders, I’ve noticed one critical blind spot.

Your SaaS experience may not be enough to prepare you for AI economics.

Here’s why:

Traditional SaaS companies celebrate 80%+ margins. They deploy once and scale infinitely. Their infrastructure costs decrease with scale. But AI companies? We’re looking at a different reality:

• 40-60% margins (vs 80%+ for SaaS)
• Rising costs with scale (Linear token consumption)
• Continuous investment in computing power
• Unpredictable unit economics
• Very expensive skilled talent

Wondering why this happens? Let me break it down further:

Every client interaction has a cost. Every model improvement needs training. Every scale-up increases cloud costs. While your SaaS friends watch their unit economics improve with scale, you’ll be watching your AWS bills grow.

Your pitch deck may show full automation. But here’s what you’ll actually need:

• Engineers constantly fine-tuning models,
• Teams handling endless edge cases,
• Experts validating training data,

Each of these start adding to your operational costs in ways you didn’t expect.

Remember how your SaaS product scaled? Build once, sell many times. Be warned, that may not happen with your AI solution. Every customer brings unique scenarios. Every industry has its edge cases. Every implementation needs adjustment.

To sum up, if you are a first time AI founder, brace yourself for:

• Higher funding needs
• Higher scaling costs
• Delayed ROI
• Thinner margins

Also Read: AI is a very active monkey: Should we tame it or let it run wild?

So what does it take to build a successful AI venture?

The answer lies in turning these challenges into your competitive advantage. While others chase the AI gold rush with SaaS economics in mind, you can:

Build flexible and adjustable pricing models to calculate the fixed and variable costs

Design efficient architectures that optimize computing usage and easily integrate new tools

Create hybrid teams with a dynamic mix of seasoned experts and fast learners

Every technological revolution had its economic learning curve. The winners weren’t always the first movers. But they were the first to figure out sustainable economics.