Pricing in the AI Era: From Inputs to Outcomes, with Paid CEO Manny Medina
Summary
In this episode, Manny Medina, former CEO of Outreach and founder of Paid, discusses the transformation of pricing strategies in AI companies amid the shift from traditional SaaS models to AI agent-driven business models. Initially, customers prefer simple pricing schemes such as fixed or consumption-based models to trial AI solutions, but as value proves itself, AI companies must engage customers to negotiate pricing aligned with meaningful outcomes. Medina highlights how Paid's platform supports AI businesses in managing this transition, offering tools to measure unit economics, implement margin management, and adopt sophisticated pricing models such as outcome-based and agent-based pricing. The discussion underscores a key industry trend favoring narrow, specialized AI agents that focus on specific workflows, which are currently more profitable than broad AI platforms that struggle with scope and differentiation. Medina challenges the assumption that AI will first replace high-paid, creative jobs; instead, he argues AI adoption will start by augmenting such roles but will ultimately displace less desirable and harder-to-fill jobs, such as insurance adjusters. Collaborative workflows and AI co-pilots are spotlighted as successful paradigms, enabling human-AI partnership and enhancing productivity, with examples including legal and medical AI applications. The episode details the pricing maturity curve from activity-based to outcome-based and agent-based pricing, explaining the advantages and operational implications of each. Pricing complexities unique to AI, such as fluctuating compute costs, contract flexibility, and risk mitigation through outcome pricing, are explored, revealing the inadequacy of SKU-based pricing for AI solutions. The competitive landscape is described as prone to commoditization and 'swirl,' pushing companies to differentiate through vertical specialization and deep workflow integration. Lastly, Medina shares insights on the cultural excitement among AI founders, challenges in cost visibility, and references foundational AI literature to support better understanding by AI entrepreneurs. Overall, the episode presents a nuanced view of how AI companies must reimagine pricing, product focus, and customer engagement to thrive in the evolving AI era.
Key Takeaways
- 1AI companies initially adopt simple pricing models, such as fixed or consumption-based pricing, to lower customer friction during early adoption phases. As the AI solution demonstrates value, providers must proactively renegotiate pricing to align with outcomes and dimensions that matter most to the customer, facilitating better value capture.
- 2Paid, founded by Manny Medina, provides essential infrastructure for AI companies to manage pricing and margins as they scale from experimentation to production. The platform enables a transition from basic activity-based pricing to more nuanced value-based and outcome-driven pricing models, helping companies capture a fair share of economic value generated for customers.
- 3Specialized AI agents that focus tightly on narrow workflows or problems are outperforming broad AI platforms, which are still struggling to gain traction. These focused agents serve specific niches with clearly defined use cases, replacing manual or BPO (Business Process Outsourcing) tasks and delivering superior value.
- 4AI’s impact on the labor market will not immediately displace the highest paying creative jobs but will more likely automate less desirable, harder-to-backfill roles after a period of augmentation in high-paid roles. AI adoption in high-value jobs may be intermittent and auxiliary rather than outright replacement initially.
- 5Collaborative workflows featuring AI co-pilots that augment human users rather than replace them are proving highly successful and sticky in real-world deployments. AI tools integrated deeply into human workflows enhance productivity and create strong user dependence, reducing churn risks.
- 6Traditional SaaS pricing models based on fixed seats or feature-based SKUs are inadequate for AI products, which require pricing approaches reflecting workflow complexity, activity variability, or outcome delivery. Emerging models include activity-based, workflow-based, outcome-based, and agent-based pricing to align charges more closely with business value.
- 7Outcome-based pricing models mitigate buyer risk and improve vendor alignment by charging customers according to measurable business results enabled by AI solutions. This model fosters trust, encourages deeper collaboration, and supports bespoke contracts tailored to customer-specific outcomes.
- 8Pay-by-agent pricing models are emerging as an effective billing approach, where customers pay based on the number of AI agents deployed, priced analogously to the human roles they replicate, with additional outcome-based bonuses. This makes AI pricing intuitive, budgetable, and aligned with labor cost savings and productivity gains.
- 9The AI pricing maturity curve progresses from charging per activity or seat, through workflow-based pricing, to outcome-based and ultimately agent-based pricing models. Each stage requires deeper customer engagement, greater pricing flexibility, and enhanced unit economics visibility to achieve sustainable differentiation and profitability.
Notable Quotes
"Your customer will always default to the easiest way to buy, which is either 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 it."
"We are right now in, if you were to take the analogy of hedgehog versus fox, we're in hedgehog land right now. So like if you take up a very narrow problem and then you hedgehog into it and you become the best at that one thing, that is printing money right now. Like everyone that I'm seeing."
"I actually disagree with that hypothesis. I think AI is the highest paid people will buy AI as a side thing and ditch it with the same regularity that they ditch other things in their lives. I think AI is going to stick the landing where it actually takes over our role fully that nobody else wants to do."
"I think the co-pilot approach, and I don't mean Microsoft co-pilot. I mean the approach of AI that is giving people superpowers. Seems to really be working, you know, Harvey and legal or open evidence in medicine."
"You don’t debug Vibecode, you throw it away and you start. And you put it in production and when it breaks, you start. It’s wonderful. You can always start. This is very comforting to your customers."
"So like moving to a workflow allows you to move out of the, the treadmill of charging for, you know, for pure work to charging for actually that is worth something to somebody. And then eventually you will get to some kind of outcome."
"I think AI changes completely, completely changes that. Because in the past, we wanted to put everyone in these little boxes, you know, called SKUs. And then we wanted to count SKUs and HQ will have a discretionary amount of discounting and whatnot. Um, that's how, that was a world of rows and columns and you don't need that anymore."
"And in the agentic world, you wouldn't do that all the time with the customers that you want to go big with. And there is no, you know, you can always put a chat interface to say, interpret this contract for me and give me the annualized value. You can inquire, you know, the, all the, the body of contractors we have done and get a sense for like your unit economics and your growth and like what this looks like."
"So you get X many calls, X many meetings. You have a book of accounts. You have, you know, contacts within each of that account. And each of those contacts get an activity, right? So that's where you get the, you know, 100 calls a day type of SDRs. You can do the same thing with an AI SDR."
"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 it."
"So what I'm seeing right now is that for those who are targeting BPO budgets, to win that business, they go at the BPO price and lower it. And they say, I'm going to do the same as a BPO, cheaper 24 seven, and I'm going to accumulate all the data that the BPO used to do."
"Like, you have all sorts of, like, you know, bad activity happening all through. And like, there's no amount of evil that is going to like rescue you out of that. So like the better way to do it is to have the model do most of the work."
"I think in the very short term, as we're trying to figure this out, I just don't, I just don't know is sort of the short of it. Yeah."
"They come in once a year and then they, they, they peace out. You know, you're left holding the bag. Whereas you actually need guidance on this stuff every time you talk to your customer to get sure that you're getting, you're getting your, your price is worth."
"When we were rolling out agents at Outreach and I wanted to understand the business, the business fundamentals of the agents, my margins, the value that I'm adding and how am I adding value. So the underpinning software supporting me was not helpful, was not built for a world in which pricing needs to evolve, where the agents are doing, they are delivering more than just, you know, bits and bytes are delivering this full outcomes."
Episode questions
Why is it important for AI companies to move from simple consumption-based pricing to outcome-based pricing?
Initial consumption or fixed pricing makes it easier for customers to try new AI solutions, reducing friction. However, as these AI agents prove their effectiveness, simple pricing models fail to capture the full value delivered. Outcome-based pricing aligns the business’s revenue with the real impact on customers, incentivizing AI creators to optimize for meaningful results rather than mere usage. This results in fairer value capture on both sides and better understanding of unit economics for AI businesses, which supports scaling and improved profitability. This transition is crucial because traditional SaaS pricing models often don’t translate well to AI’s value dynamics.
What advantages do specialized AI agents have compared to broad AI platforms according to Manny Medina?
Specialized AI agents focus deeply on solving narrow, well-defined problems, which allows them to deliver significantly better performance and value in those niches. This specialization makes them attractive to customers with unmet needs that no traditional software addresses. Manny cites companies such as Quandry (policy renewals) and Happy Robot (calling truckers) as examples of agents capturing value in specific domains, replacing BPOs and tedious manual work. Broad platforms, by contrast, face challenges of scope and complexity, making it harder to gain traction currently. These focused agents can ‘print money’ by being the best at their task, illustrating a current market trend favoring vertical specialization.
How does Paid help AI companies as they transition from experimentation to production?
Paid provides AI companies with tools to manage their pricing, costs, and margins as they move from experimental projects to fully operational AI products. It facilitates the shift from simple activity-based or consumption pricing to more sophisticated, value-based pricing models. This helps AI companies accurately understand their unit economics and capture the value they create for customers more effectively. By doing so, Paid addresses a growing pain point in AI businesses where pricing complexity increases with scale and maturity. This kind of infrastructure is critical for AI startups to optimize financial performance and scalability in a rapidly evolving market.
What is the significance of collaborative workflows and AI co-pilots in the current AI landscape?
Collaborative workflows, where AI tools work alongside humans rather than replacing them, are emerging as a dominant and successful approach in AI deployment. AI co-pilots amplify human capabilities by automating routine aspects and providing enhanced insights, effectively granting users 'superpowers.' Manny points to examples like Harvey in legal and OpenEvidence in medicine demonstrating the power of such collaboration. These integrations tend to be highly sticky because they embed deeply into customer processes, making switching costly and difficult. This trend reflects a preference for augmentation over replacement, helping AI companies achieve sustainable adoption and growth.