In this episode of Innovators Inside Podcast, Ian Bergman speaks with Madhavan Ramanujam, co-founder and general partner at 49 Palms Ventures and co-author of Monetizing Innovation and Scaling Innovation, about one of the most overlooked parts of building a company: pricing.

The core idea is simple. A product is not validated just because customers like it. Founders also need to understand what customers value, what they will pay for, and how the business will capture that value.

Here are the five key takeaways from their conversation:

1. Product-market fit is not enough

Founders often ask customers whether they like a product or would use it.

That is not the same as asking whether they will pay for it.

Madhavan argues that pricing should be part of product validation from the beginning. Asking about willingness to pay can uncover problems that positive product feedback will not.

The goal is what he calls product-market-price fit.

Takeaway: Do not wait until launch to figure out pricing. Test willingness to pay while you are still deciding what to build.

2. A POC should prove business value, not just prove the technology works

For AI companies in particular, a successful proof of concept should do more than demonstrate that the technology functions.

That is only the starting point.

The stronger approach is to use the POC to build a business case with the customer. Identify the KPIs that matter, measure the impact, and quantify the value being created.

That might mean increased revenue, reduced costs, improved conversion, or hours of work saved.

Takeaway: Use pilots to answer the question, “What is this worth to the customer?” not just, “Does the technology work?”

3. AI is changing how companies should think about pricing

Traditional software pricing often focused on seats or licenses.

AI products can work differently because they may perform tasks autonomously and directly influence business outcomes.

Madhavan suggests thinking about AI pricing through two factors:

• Autonomy: How much of the work can the product complete without human involvement?

• Attribution: How clearly can the product connect its work to measurable business results?

Products with lower autonomy and attribution may still fit seat-based pricing. As autonomy and measurable impact increase, usage-based, credit-based, or outcome-based pricing can become more appropriate.

Takeaway: The right AI pricing model depends on the value the product actually delivers, not on how SaaS companies traditionally charged.

4. Good pricing should be easy for customers to understand

One simple test for a pricing strategy is to ask a customer to explain it back to you.

If they cannot, the pricing may be too complicated.

Madhavan calls this “beautifully simple pricing.” The price should connect directly to the value story.
Customers should understand not only what they are paying, but why the price makes sense relative to the value they receive.

Takeaway: Your pricing should help communicate your value proposition, not make customers work harder to understand it.

5. Your MVP may be giving away your most valuable features

The traditional minimum viable product approach encourages founders to build the smallest useful version of a product, launch quickly, and learn.

But there is a risk.

Madhavan says that, in many companies, roughly 20% of what gets built can drive most of the willingness to pay. That 20% is often also the easiest part to build first.

If founders automatically give those features away, they may be training customers to expect the most valuable part of the product for free.

His alternative framing is to think about the MVP as the most valuable product, not only the minimum viable product.

Takeaway: Before deciding what goes into your MVP, understand which features customers value most and what they are actually willing to pay for.

Pricing is part of product strategy

The bigger lesson from the conversation is that pricing should not be treated as a final decision made after the product is finished.

Pricing affects what you build, who you build it for, how you position it, and how you scale.

For founders building AI products in particular, understanding willingness to pay, measurable value, autonomy, attribution, and the right monetization model can make the difference between building something people like and building a durable business.

Watch or listen to the full episode of Innovators Inside to hear Madhavan Ramanujam and Ian Bergman go deeper into product-market-price fit, AI monetization, pricing strategy, and profitable growth.

Madhavan’s Books:
Monetizing Innovation: How Smart Companies Design the Product Around the Price
Scaling Innovation: How Smart Companies Architect Profitable Growth

Have a question for a future guest? Email us at innovators@alchemistaccelerator.com to get in touch! 

Timestamps

00:00 Pricing Before Product
06:20 Why Products Fail to Monetize
11:30 Product-Market-Price Fit
13:14 How to Test Willingness to Pay
16:09 POCs Should Prove Business Value
18:34 Pricing AI Around Labor Value
22:48 Why How You Charge Matters
24:20 AI Pricing Models Explained
34:27 Beautifully Simple Pricing
36:15 How to Raise Prices
40:32 Rethinking the MVP
43:00 Four Monetization Failure Modes
44:40 Building 49 Palms Ventures
50:17 Scaling Innovation in the AI Age