Claire Vo Tackles Monetization Strategy for AI Businesses and Protecting the Super ICs in Product

This week, Unsolicited Feedback is thrilled to welcome Claire Vo to the virtual table. Claire, the CPO at Color and former CPO at Optimizely, is known for her prolific Product HotTakes on X! Join us as we dive into an engaging conversation with Claire and get ready for some hot takes as:

1️⃣ This week’s trio analyze the real cost and pricing challenges of new AI products (starts at 10:37)

2️⃣ Brian & Fareed deliver some Unsolicited Feedback on Claire’s views on managing efficient product teams (starts at 48:14)

AI’s Monetization Challenges

Today’s discussion starts with a Wall Street Journal article detailing how GitHub's Copilot is in the red due to the costs of AI. The debate is fascinating, but ultimately we walk away with three big takeaways:

🏢 AI favors the incumbents: 🏢

Why? High start-up costs, high incremental costs of delivery, and competitive advantages in terms of user base and data richness.

Further, large companies like Replit can afford to offer their AI for free as a loss leader, bearing the expense until the cost of delivering AI decreases.

💰 Value-based pricing appears to be the early winner: 💰

Seat-based pricing will eventually decline to zero and may not have an attractive total addressable market (TAM).

Brian highlights that cost-plus pricing, where pricing is determined by applying some margin to the cost of goods, presents a few challenges:

1. Is it a strong enough counter-positioning move from a business model perspective to compete against incumbents with more cash, data, and users?
2. How do users perceive value when tokens are introduced? Do tokens inadvertently create friction?

If you can develop a solution that delivers tangible and measurable value, and customers can easily recognize this value is lower than their best alternative, the purchase is an easy yes.

🤔 It's not about real value. It's about perceived value. 🤔

Claire says perceived value is often a function of two things.

Firstly, it considers the cost of having a human or a group of humans with limited expertise perform a certain task.

Second, it considers the amount of enterprise value you know the product will produce.

💡 "I think both of those things are very quickly going to shift in folks' minds over the next couple of years. I think the perceived value of human expertise is going to collapse." 💡- Claire

In fact, Claire envisions a future where a department head, instead of thinking about headcount, thinks about budget. They can then decide what portion of that budget to allocate to humans versus AI!

Managing Efficiency in Product

❗️The problem: ❗️

"Almost everything about growing the size and scale of your team has an immediate and detrimental impact on the thing that matters most: building awesome products.

Unless you actively fight against these effects, you'll wake up one day with a very effective bureaucracy, but a very poor product." - Claire

💪 How do we battle against this? 💪

1) 😎 Smaller teams to achieve big things: 😎

Actually ingraining this mentality in an organization that needs to grow is difficult because all of the industry incentives are just fundamentally flipped in the opposite direction.

2) 🔑 Protect and Incentivize Senior ICs: 🔑

Teams are best served by more star players on the field, not by more coaches on the sidelines.

How to fix it? Eliminate financial incentives to becoming a manager by making the IC path equally or even more attractive.

This means that folks who actually choose the coach path do it for the right reasons, and not the wrong reasons.

3) 🚀 Lead by example if you are a product leader: 🚀

Emphasize that "there's no pride in size and scope. There's only pride in output and impact." - Claire