Value-Based Pricing and AI: A Recipe for Healthcare Inequality?

Greetings from Berlin,

As I write this, I’m reflecting on the growing influence of value-based pricing in AI-driven healthcare and how it could reshape accessibility and affordability. Drawing troubling parallels with the pharmaceutical industry’s monopolistic practices, I’m exploring how regulatory barriers and corporate dominance are threatening to turn healthcare into an overpriced intangible asset. More importantly, I’m advocating for an open-source, commons-based approach to ensure AI fulfills its promise as a tool to reduce inequalities, lower costs, and address the urgent global healthcare challenges we face today.

This weekend, I came across an insightful interview with Sarah Friar, OpenAI's CFO, where she discussed the company's pricing strategy. She highlighted that OpenAI boasts 250 million weekly active users, with 5-6% being paying customers. Friar questioned whether the current pricing aligns with the value provided to customers, citing an example where para-legals, who typically charge $1,000 to $2,000 per hour, are now being replaced by ChatGPT services at $60 per month. She emphasized that the value-to-price ratio will eventually align. This suggests that, in the absence of open-source alternatives, the cost of accessing their AI services could escalate significantly.

For those familiar with my work, this perspective resonates with my longstanding concerns. Transforming health data into financial assets allows financial markets, rather than the market, to dictate pricing. Consequently, the benefits derived from patient-contributed data in building AI systems lead to financial extraction, with minimal social value return. Drawing from my experience as a former student of Michael Porter at Harvard Business School, I recognize this as value-based pricing, a strategy extensively employed by the pharmaceutical and biotech industries to set prices, often resulting in exorbitantly priced therapies.

Understanding Value-Based Pricing

Value-based pricing involves setting prices based on the perceived value a product or service offers to customers, rather than on production costs or competitor pricing. In OpenAI's case, their AI services provide substantial value by potentially replacing high-cost human labor, such as paralegals, while charging a fraction of the traditional cost. Imagine if Uber adopted value-based pricing based on your time savings. Your 2-mile ride now costs $500 because walking would’ve taken an hour, and your hourly rate is clearly $500, right? Uber justifies it by pointing out the immense value they created by saving you from exhaustion—and maybe some sore feet. Or value-based pricing for shoes? Sure, they protect your feet from harm and make walking infinitely more comfortable, so let’s price them at $10,000 per pair. After all, what’s a little price inflation when compared to the priceless value of avoiding stepping on a sharp rock?

Beneficiaries of This Model

In this framework, OpenAI stands to gain significantly. While customers benefit from reduced costs compared to traditional services, OpenAI captures a considerable portion of the value by leveraging user-contributed data to enhance their AI models. This approach enables the company to monetize the collective knowledge and data inputs of millions of users, potentially leading to substantial financial gains. In this framework, healthcare organizations and technology companies deploying AI stand to gain significantly. While patients and providers may benefit from reduced costs compared to traditional healthcare services, these organizations often capture a disproportionate share of the value by leveraging user-contributed health data to enhance their AI models.

However, AI has the potential to be a powerful equalizer in healthcare, addressing the deep-rooted inequalities that plague health systems globally. By enabling access to affordable, high-quality care, an open sourced approach for AI could lower costs, expand reach, and help resolve critical shortages of financial and human resources, ensuring that everyone, regardless of their geographic or economic status, receives the care they need.

This transformative potential lies in rethinking how we build and deploy AI. A model grounded in open-source collaboration and equitable value distribution can ensure that AI not only meets the immediate needs of underserved populations but also paves the way for a more inclusive and sustainable healthcare system.

Critiques of Value-Based Pricing

Critics argue that value-based pricing can lead to:

1. Exploitation of Citizens & Patients: Especially when alternatives are limited, leading to higher prices.
2. Lack of Transparency: In pricing decisions, making it difficult for consumers to understand cost structures.
3. Price Discrimination: Charging different prices to different customers without clear justification.
4. Short-Term Focus: Prioritizing immediate profits over long-term customer relationships.

Monopolistic Practices and Pricing

Monopolistic behaviors can exacerbate the drawbacks of value-based pricing. The pharmaceutical industry's handling of insulin prices serves as a stark example. Despite being a life-saving medication discovered nearly a century ago, insulin's price has surged dramatically in recent years due to limited competition and inelastic demand. Insulin is a medicine that can be produced for just a few dollars, but manufacturers Eli Lilly, Sanofi, and Novo Nordisk mark up the price as much as 5,000 percent and there are seven million Americans with diabetes that have no choice but to pay. The price of insulin increased by over 1,500% between 1999 and 2017, leading to thousands of diabetic patients in the United States facing life-threatening situations due to unaffordable medication costs. The price in the US grew so high that people were doing desperate things to get by, like using expired insulin, relying on crowdfunding to pay their bills, or taking less insulin than they need in an effort to ration their supplies. The exploitation of patients' willingness to pay is evident in the pharmaceutical industry's pricing strategies. For instance, the price of insulin increased by over 1,200% between 1999 and 2017, leading to thousands of diabetic patients in the United States facing life-threatening situations due to unaffordable medication costs.

This scenario illustrates how monopolistic control over essential products can lead to extreme price inflation, disconnected from production costs or reasonable profit margins.

Imagine a world where our healthcare needs are entirely dependent on a handful of global AI corporations headquartered on the West Coast of California. These companies could impose value-based pricing models that transform a simple 30-cent transaction into a staggering $1,000 expense, prioritizing profit over accessibility and fairness. Do we want this to be the foundation of the greatest technology human kind will invent in this century? To those who argue, incorrectly, that such exorbitant prices are necessary to drive innovation, I challenge this deeply flawed and misleading narrative. It’s nothing more than misinformation dressed as justification, and it deserves to be sent straight into oblivion.

Monopolies in AI are often facilitated by regulatory barriers that are set so high that only large corporations have the resources to comply with these regulations and liabilities. This creates a significant entry barrier for smaller, innovative companies or open-source initiatives, further consolidating control in the hands of a few major players. It is no coincidence that lobbying efforts in Brussels likely influenced aspects of the EU's AI Act, creating a framework that inadvertently favors established corporate giants.

With the shift in political dynamics and the new government in the United States, it’s likely that this approach will come under review. For AI to truly democratize healthcare and address global inequalities, we must advocate for regulations that ensure safety and accountability without stifling innovation or disproportionately benefiting the largest companies. Open Source based standards, transparent compliance pathways, and equitable regulatory frameworks can empower diverse stakeholders to contribute to and benefit from AI, fostering a more inclusive and impactful healthcare system.

Advocating for a Commons-Based Approach

Transitioning from exclusive ownership of medical knowledge and private intangible assets to a commons-based model could offer significant benefits:

1. Increased Accessibility: Ensuring vital information and treatments are available to all.
2. Fostering Collaboration: Encouraging joint research and development efforts.
3. Reducing Artificial Scarcity: Making life-saving technologies more widely available.
4. Promoting Innovation: Facilitating open knowledge sharing to drive advancements.

Advantages of a Commons-Based Model

I have been advocating for an open source or digital commons based approach, as this will foster a more equitable distribution of value, promote innovation, and ensure that advancements in AI and medical knowledge benefit society as a whole, rather than concentrating wealth among a few powerful entities.

Adopting a commons-based or open-source approach in AI and medical fields leads to a commodification of medical knowledge and market transformation:

1. Cost Reduction: Companies competing by lowering costs through automation and economies of scale, resulting in more competitive pricing models.
2. Differentiation: Creating intuitive interfaces, seamless multi-platform experiences, and excellent patient support to stand out.
3. Continuous Improvement: Investing in research and development to introduce novel products to the market more quickly.
4. Building Trust: Focusing on establishing trusted brands, thought leadership, and cultivating customer loyalty.

In discussions with colleagues from organizations such as the World Economic Forum and the World Health Organization, I frequently encounter strong support for the value-based care model. This raises a critical question: why do institutions tasked with promoting universal access to care often adopt policies that achieve the opposite, reinforcing barriers rather than breaking them down?

A shift towards more open, collaborative, and equitable models is essential for meaningful progress.

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Bart