Pricing in the AI Era: From Inputs to Outcomes, with Paid CEO Manny Medina

TL;DR

Manny Medina discusses AI pricing models and his company Paid.

Transcript

your customer will always default to some like the easiest way to buy which is either you know some kind of fixed price or a consumption price for the first year to see if it works but if it does work it is up to the AI agent builder and creator to go back to the same customer and say let's align on things that are important to you and charge for i... Read More

Key Insights

• AI companies are transitioning from simple activity-based pricing to more sophisticated value-based approaches to capture their fair share of the value they create.
• Focused AI applications targeting narrow problems are proving more successful than broad platforms, as they can deliver more specialized and effective solutions.
• AI pricing models are evolving, with four main approaches: activity-based, workflow-based, outcome-based, and agent-based pricing.
• AI companies need to move away from charging purely by activity to avoid competition based solely on price, and instead align pricing with value delivered.
• The cost of AI services is not solely determined by LLM costs but also includes cloud costs and third-party API costs, which can drive up expenses.
• Understanding and managing margins is crucial for AI companies to ensure they are capturing value and not just providing it to customers.
• AI companies are currently experiencing 'vibe revenue,' where initial sales are made based on hype, but true value and stickiness will be tested during renewals.
• The ability to offer bespoke contracts and pricing tailored to customer-specific outcomes can be a significant competitive advantage for AI companies.

Questions & Answers

Q: What is the main focus of Manny Medina's new company, Paid?

Paid focuses on providing billing, pricing, and margin management tools for AI companies. It aims to help these businesses transition from traditional activity-based pricing models to more sophisticated value-based approaches. By doing so, Paid enables AI companies to better understand their unit economics and capture a fair share of the value they create for their customers.

Q: Why does Manny Medina believe focused AI applications are more successful?

Manny Medina argues that focused AI applications targeting narrow problems are more successful because they can deliver specialized and effective solutions. These applications address specific workflows and problems that lack clear software solutions, allowing them to capture value more effectively than broad platforms. By becoming the best at solving a particular issue, focused AI applications can achieve significant success and profitability.

Q: What are the four main pricing approaches discussed by Manny Medina?

Manny Medina discusses four main pricing approaches for AI companies: activity-based pricing, workflow-based pricing, outcome-based pricing, and agent-based pricing. Activity-based pricing involves charging based on the volume of activity, while workflow-based pricing charges for a series of activities. Outcome-based pricing involves charging based on the achievement of specific outcomes, and agent-based pricing involves charging based on the deployment of AI agents that replace certain human roles.

Q: How does Manny Medina suggest AI companies should handle pricing to avoid competition based solely on price?

Manny Medina suggests that AI companies should move away from charging purely by activity, as this approach can lead to competition based solely on price. Instead, companies should align their pricing with the value they deliver to customers. This involves transitioning to value-based pricing models, such as workflow-based or outcome-based pricing, which focus on the value provided rather than just the activity performed. This approach helps differentiate the company and avoid price-based competition.

Q: What challenges do AI companies face on the cost side of the equation?

AI companies face challenges on the cost side due to the combination of cloud costs, LLM costs, and third-party API costs. These factors can drive up expenses, making it essential for companies to understand and manage their margins effectively. Additionally, as AI models become more advanced and require more computation, the cost per token may not decrease as expected. Companies need to carefully evaluate their cost structures to ensure profitability and value capture.

Q: What is 'vibe revenue,' and how does it affect AI companies?

'Vibe revenue' refers to the initial sales AI companies make based on the hype and excitement surrounding AI technologies. While this can lead to early sales and customer acquisition, the true value and stickiness of the product are tested during renewals. AI companies need to demonstrate real value and outcomes to retain customers and transition from vibe revenue to sustainable, value-based revenue models. This requires a focus on delivering tangible results and aligning pricing with customer value.

Q: How can bespoke contracts benefit AI companies?

Bespoke contracts can benefit AI companies by allowing them to tailor pricing and terms to specific customer needs and outcomes. This approach enables companies to align their pricing with the unique value they provide to each customer, creating a competitive advantage. By understanding the customer's business and delivering customized solutions, AI companies can build stronger relationships and increase customer loyalty. Bespoke contracts also help differentiate the company from competitors and protect against commoditization.

Q: What inspired Manny Medina to start Paid?

Manny Medina was inspired to start Paid after experiencing the challenges of managing pricing and margins for AI agents at Outreach. He realized that traditional SaaS tools were not built for the evolving needs of AI businesses, which require more dynamic and adaptable pricing models. By starting Paid, Medina aims to address these challenges and provide AI companies with the infrastructure needed to manage their business operations effectively. His goal is to help AI companies capture more value and succeed in the competitive AI landscape.

Summary & Key Takeaways

• Manny Medina discusses the challenges AI companies face with traditional SaaS pricing models and introduces new approaches like outcome-based and agent-based pricing. He emphasizes the importance of understanding unit economics to capture more value.

• Medina highlights the success of focused AI applications targeting specific workflows, which are printing money by addressing narrow problems. He contrasts this with broader platforms that face more competition and difficulty in delivering clear value.

• Paid, Medina's new company, provides infrastructure for AI companies to manage billing, pricing, and margin management. It aims to help AI businesses transition to value-based pricing models and better understand their economics.