To map the pitch, TacticAI uses graph neural networks - treating all 22 players as individual nodes and their physical interactions as connections. This allows the club's data science department to virtually drag and drop players to test different defensive setups in real time.

Maybe the most important slide from my chat @MadhavanSF A 2x2 for determining your ideal pricing model: "There's attribution and autonomy. The bottom left, that is the quadrant where your attribution is low, and your autonomy is low. In that situation, the best pricing archetype is seat-based or a subscription model, because you're not able to attribute a lot of value to what you bring. The bottom right quadrant, those are companies that can prove more attribution, and show what they actually bring to the table. But they're still not in a fully autonomous mode, there are still humans in the loop. A hybrid pricing model is the best option for them. If you look at the top left quadrant, those are products that are very autonomous, but are not strong on attribution. These tend to be mostly backend or infrastructure kind of products. In that situation, you need to be on a pay-for-what-you-consume model. The quadrant that you really want to be in is the top right one. That's the outcome-based pricing model. This is where you have great autonomy and great attribution. You are not only charging for work delivered, but you're charging for work delivered that was delivered by AI with no humans in the loop. If you want to win in AI, figure out a way to get to that quadrant because that's a magic quadrant.

Madhavan Ramanujam (@MadhavanSF) has helped 30 unicorns architect their pricing strategies. He also wrote the definitive book on pricing strategy, Monetizing Innovation, and next week, he's releasing his newest book, Scaling Innovation: How Smart Companies Architect Profitable Growth, which will instantly become the new go-to guide for pricing your AI product. In our conversation, Madhavan shares: 🔸 Why popular AI coding tools may have already doomed themselves with underpricing 🔸 His 2x2 framework for determining your ideal pricing model 🔸 Why AI companies can capture 50% of value (vs 10% for SaaS) 🔸 Why AI companies must get monetization right from day one—not “figure it out later” 🔸 The negotiation trick that helped one founder 4x their deal size overnight 🔸 How to price your POCs for maximum leverage 🔸 How companies like Intercom’s Fin and Sierra pioneered outcome-based pricing (charging $0.99 per AI resolution) 🔸 The single question that reveals if your pricing is too complex Listen now 👇 • YouTube: youtu.be/NR85H55eYkM • Spotify: open.spotify.com/episode/1YK… • Apple: podcasts.apple.com/us/podcas… Thank you to our wonderful sponsors for supporting the podcast: 🏆 @Enterpret_ai—Transform customer feedback into product growth: enterpret.com/lenny 🏆 @DeveloperXM—A platform for measuring and improving developer productivity: getdx.com/lenny 🏆 @withpersona—A global leader in digital identity verification: withpersona.com/lenny Pre-order the book here: amazon.com/Scaling-Innovatio… If you purchase more than five copies, send a screenshot of your receipt to promo@49palmsvc.com to enter to win a free pricing strategy conversation with Madhavan, a signed copy of the book, an invite to the book launch, and more.

Tickets just dropped for Interrupt: The AI Agent Conference by LangChain 🚀 🌒 Join us this May 13-14 in San Francisco for Interrupt, LangChain’s first-ever conference — a space for anyone building or shaping the future of AI agents. Learn more: bit.ly/langchain-interrupt-a…