This episode unpacks the bold prediction that a consumer health tech company will become the world's largest, exploring how technology, particularly AI, is poised to revolutionize a historically complex and inefficient healthcare system.
The Trillion-Dollar Consumer Health Tech Prediction
- BJ and Daisy revisit their 2022 prediction that the world's largest company will be a consumer health tech entity.
- Daisy notes the significant influx of excitement and company formation around this thesis, acknowledging it's still early days.
- Strategic Insight: The conviction behind this prediction signals a massive untapped market, urging Crypto AI investors to identify early movers in AI-driven consumer health solutions.
Defining "Consumer Health Tech" Beyond the Obvious
- Daisy clarifies that their definition of a "consumer health tech company" extends beyond direct-to-consumer (DTC) models like Ro or Hims, which primarily prescribe drugs or offer telemedicine.
- A consumer health company, in their view:
- Doesn't have to be DTC; it can be distributed B2B.
- Doesn't have to be cash-pay; it can be fully insurance-covered and free for consumers.
- Doesn't have to be a services company; it can be pure technology.
- The defining characteristic is an "amazing consumer experience," a stark contrast to traditional healthcare's often poor user engagement.
- Speaker Perspective (Daisy): Daisy emphasizes a broad, experience-centric definition, opening the field to diverse business models, including those leveraging AI for superior user interaction and personalization.
Why Tech is Positioned to Tackle Healthcare's Complexity
- Daisy addresses common perceptions of healthcare:
- Intimidating (Big Market): U.S. healthcare is five times the size of the global advertising industry. For venture capitalists, large markets are attractive.
- Highly Regulated: Many large companies (Airbnb, Lyft, Google, Facebook) operate in or eventually face regulation; it's a marker of success.
- Complex: All industries are complex beneath the surface.
- Technologists are needed because:
- Healthcare is fundamentally a logistics, data, and operations problem, areas where tech excels.
- Healthcare is a consumer engagement problem. Daisy states, "88% of Americans are metabolically unhealthy. Half of people don't go to the PCP. Half of medications that are prescribed are never taken."
- The best tech companies excel at changing human behavior, a critical need in healthcare.
- Actionable Insight: AI's ability to process vast datasets, optimize logistics, and personalize engagement makes it a prime candidate for solving these core healthcare challenges. Investors should look for AI applications targeting these specific pain points.
The "Why Now?" for Tech Founders Entering Healthcare
- BJ identifies key drivers for tech talent (from Instacart, Spotify, Coinbase) moving into healthcare:
- Engineering Discipline: Healthcare and life sciences are increasingly becoming engineering disciplines, attracting tech mindsets. Software is an easily engineered medium, and as other fields become more engineerable, they attract similar talent.
- Massive TAM (Total Addressable Market): The sheer size of the healthcare market is a significant draw.
- Mission-Driven Appeal: The potential to "change the world's healthcare" is a powerful motivator.
- Strategic Implication: The influx of experienced tech founders signals a maturation of the health tech space. Crypto AI researchers can collaborate with these founders to integrate decentralized AI solutions, potentially addressing data privacy and model integrity.
Public Reaction: Controversy and Resonance
- Daisy discusses the strong, mixed reactions to their article, "Hey tech, it's time to build in healthcare."
- Controversy: Many venture capital firms have historically written off healthcare due to past failures. Daisy notes, "of the hundred largest public software companies one is a healthcare and life science company."
- Resonance: Everyone experiences the healthcare system's shortcomings. The U.S. spends ~20% of GDP on healthcare (double other developed countries) yet has worse outcomes.
- Speaker Perspective (Daisy): Daisy views the controversy as a sign they're challenging entrenched views, while the resonance underscores a universal desire for better healthcare solutions.
Addressing Pushback: Entrenched Systems and Incumbents
- BJ responds to concerns about entrenched vendors, integration issues, and funding challenges.
- Two approaches for new entrants:
- Full-Stack Rebuild: Companies like Devoted Health (a Medicare Advantage company offering both insurance and care) build everything from the ground up, avoiding reliance on existing problematic systems. This is hard but offers direct control.
- Medicare Advantage: These are health plans offered by private companies that contract with Medicare to provide Part A and Part B benefits, often with additional coverage.
- Working Within the System: This requires deep knowledge of both tech (e.g., AI) and the healthcare system. The number of people with this dual expertise is growing.
- Actionable Insight: AI startups can choose to disrupt by building new, AI-native full-stack solutions or by creating AI tools that integrate with and improve existing infrastructure. The latter may offer faster market entry but requires navigating complex stakeholder relationships.
Addressing Pushback: U.S. System Peculiarities and the Necessity of Technology
- BJ tackles criticisms regarding the U.S. as an outlier in health spending versus outcomes, and questions about why technology is necessary.
- Current "Tech" in Healthcare: BJ argues that much existing "tech" like MRIs with computer displays or Electronic Medical Records (EMRs) are not transformative in the way modern AI is.
- Electronic Medical Records (EMRs): Digital versions of paper charts in clinician offices, containing a patient's medical history from one practice.
- BJ: "MRI is really the same MRI from like the 50s just now with on a computer display. And so that's not really tech."
- Why AI is Different Now: The AI revolution enables data utilization in previously unimaginable ways.
- U.S. vs. Other Countries: Cultural differences (diet, lifestyle) impact U.S. health outcomes independently of the healthcare system structure.
- Behavioral Change: Technology, especially AI, is powerful in its ability to affect behavioral change, which is crucial for addressing lifestyle-related health issues prevalent in the U.S.
- Strategic Implication: AI's capacity for personalized behavioral nudges and interventions is a key differentiator. Crypto AI researchers could explore how decentralized identity and data ownership can empower users in AI-driven behavioral change programs.
The "Why Now" Continued: Market Shifts Favoring Consumer Empowerment
- Daisy highlights additional factors creating a favorable environment:
- Rise of High-Deductible Health Plans (HDHPs): These plans require patients to pay a significant amount out-of-pocket before insurance coverage begins. While potentially unfortunate, this trend introduces free-market dynamics, as consumers become more cost- and quality-conscious.
- Post-COVID Consumer Demand: A wave of consumers, not just biohackers, now actively seek to understand their bodies, driven by experiences with chronic issues or a heightened awareness of mortality post-COVID. This includes interest in comprehensive blood tests and full-body MRIs.
- Daisy: "It just feels like we're seeing this real consumer shift."
- Market Trend: The shift towards consumer-driven health decisions creates demand for transparent, AI-powered diagnostic and health management tools.
Improving the Consumer Experience: Frictionless, Actionable, Delightful
- BJ outlines the ideal consumer healthcare experience:
- Frictionless: Eliminating excessive clicks, long waits, and cumbersome processes.
- Actionable: Providing clear information that empowers users to take control of their health (agency).
- Great Overall Experience: Shifting healthcare interactions from a burden (like a DMV visit) to a streamlined, user-centric process, similar to Apple or Google product experiences.
- Actionable Insight for AI Developers: Focus on AI interfaces that are intuitive, provide clear, actionable insights from complex health data, and reduce administrative friction for users.
A 30-Year Vision for Healthcare: Continuous Monitoring and AI-Driven Care
- Daisy and BJ paint a picture of future healthcare:
- Ubiquitous Wearables: Devices (some subcutaneous) continuously monitor health, predicting illness before symptoms appear (e.g., Oura Ring detecting COVID onset).
- Unified Data: All health records and wearable data consolidated in one place.
- AI-Powered, Home-Centric Care: 90% of healthcare delivered via phone/home, featuring AI doctors for initial consultations, escalating to human doctors if needed. At-home blood collection and medication delivery will be common. Hospitals will primarily be for major surgeries.
- BJ adds that this vision reframes healthcare as a logistics problem: delivering the right care, at the right place, at the right time, akin to Amazon's model.
- Future Outlook: This vision heavily relies on AI for predictive analytics, diagnostics, and personalized care delivery. Crypto AI investors should consider infrastructure for secure data aggregation and AI model deployment in decentralized or privacy-preserving ways.
Payment Models and the Evolution of "Consumer-Driven"
- The discussion touches on whether future health tracking devices (like Oura Ring, Eight Sleep) will remain consumer-purchased or become insurance-covered.
- BJ distinguishes types of care:
- Sick Care (Extreme): Emergency surgery (e.g., brain hemorrhage).
- Sick Care (Treatment): Managing an illness.
- Preventative Care: Avoiding illness, where technology has a natural and significant role.
- Daisy believes insurance will increasingly cover wearables and other "consumer" products, especially as companies build solutions that improve the traditionally poor consumer experience of incumbent healthcare systems.
- Strategic Consideration: As AI demonstrates ROI in preventative care, payment models will likely shift. Crypto AI solutions could facilitate novel payment and incentive mechanisms tied to health outcomes or data contributions.
Incentives, Consumer Choice, and the UX Gap
- The poor user experience in healthcare is linked to misaligned incentives: providers optimize for insurance companies (the payers) rather than patients.
- Daisy argues that a superior patient experience can drive behavior change (e.g., adherence to appointments and medication).
- BJ suggests that as out-of-pocket payments increase, consumer choice will drive demand for better experiences, forcing incumbents to adapt or compete with new, consumer-centric entrants.
- BJ: "It takes someone that that has a very different mindset... often these are companies that come from the outside that work with the inside. So like in streaming music it's something like Spotify."
- Market Dynamics: The "Spotify of Healthcare" analogy suggests a significant opportunity for AI-driven platforms that prioritize user experience and choice, potentially disrupting established players.
Applying Proven Tech Engagement Strategies to Healthcare
- The conversation explores leveraging tech's expertise in consumer engagement (retention, gamification) for healthcare.
- BJ notes that when consumers have choices and pay directly, providers must focus on retention, which feeds back into product development.
- Daisy emphasizes that the world's best consumer talent resides in tech, skilled at capturing attention and changing behavior. This talent is largely absent from traditional healthcare.
- Marrying healthcare understanding with consumer tech expertise can create "really magical things."
- Opportunity for AI: AI can power sophisticated gamification, personalized retention strategies, and adaptive user journeys, making health management more engaging and effective than ever before.
Two Paths to Becoming the World's Largest Health Tech Company
- Daisy outlines two primary strategies for a consumer health tech company to achieve massive scale:
- The "Payvider" Model (Vertical Integration): Becoming both the insurance company and the provider of care.
- Example: UnitedHealth Group (UnitedHealthcare + Optum). Daisy notes their low NPS (Net Promoter Score) of 4, suggesting a huge opportunity for a payvider with an Apple-like consumer experience.
- Net Promoter Score (NPS): A metric measuring customer loyalty and satisfaction, based on how likely customers are to recommend a company/product/service.
- Obstacle: Requires immense capital and time to build out both payer and provider capabilities and scale geographically, as BJ points out.
- The Horizontal Strategy:
- Consumer Marketplace (Amazon of Healthcare): A central platform for finding providers, choosing insurance, and sourcing affordable drugs. A typical 20% marketplace take rate on the massive healthcare spend could lead to huge revenues.
- Financial Infrastructure Layer (Visa of Healthcare): Modernizing healthcare payments, which Daisy describes as "stuck in the 1980s" with faxes and paper checks.
- Obstacle: Requires deep integration with the traditional healthcare system—onboarding all providers, insurance companies, Pharmacy Benefit Managers (PBMs), etc., which BJ notes is a significant infiltration challenge.
- Pharmacy Benefit Managers (PBMs): Companies that manage prescription drug benefits on behalf of health insurers, Medicare Part D drug plans, large employers, and other payers.
- Investment Thesis: Both paths offer enormous potential. AI is critical for optimizing operations in a payvider model and for powering discovery, personalization, and efficiency in a marketplace or financial infrastructure play.
Could Incumbent Tech Giants (FAANG) Lead the Charge?
- The speakers discuss whether large tech companies like Apple, Amazon, or Google could dominate healthcare.
- BJ invokes the Innovator's Dilemma: It's hard for incumbents to disrupt themselves, especially in a field as technically difficult and specialized as healthcare.
- Innovator's Dilemma: A concept by Clayton Christensen where successful companies can fail by overlooking disruptive innovations because they focus on sustaining innovations for their current, most profitable customers.
- Daisy notes that while these companies see the dollar signs in healthcare, they are not "healthcare native." Their efforts (e.g., Amazon buying One Medical, Apple's health features) have been more like "dabbling" than deep, transformative investment.
- Daisy: "It's like, you know, will the electric car come from Detroit? Maybe not at first."
- Similarly, it's unlikely that traditional healthcare giants like UnitedHealthcare will suddenly excel at consumer experience.
- Competitive Landscape: Startups with AI at their core and a healthcare-native focus may have an advantage over large, diversified tech companies trying to enter healthcare as a side business.
The Critical Role of AI in Healthcare's Future
- The conversation pivots to why AI, specifically, is a game-changer.
- Healthcare Needs AI (BJ):
- Rising costs, vast data volumes, and the potential to convert services into compute.
- AI can start with subclinical applications (AI for nurses, therapists) to bend the cost curve.
- Technology as a democratizer: AI can provide everyone access to the "best" virtual nurse or therapist.
- AI Needs Healthcare (BJ):
- Healthcare's high costs can justify the significant expense of developing and deploying advanced AI models.
- It's a data-rich environment where AI can have a massive impact, unlike less critical applications.
- BJ: "To replace that with you know cat pictures is nice but like... that's not where we're spending huge fraction of GDP."
- Leapfrogging Traditional Software (Daisy):
- Healthcare has largely missed the traditional software revolution.
- It may leapfrog directly from human services to AI, similar to developing countries going from cash to mobile payments, bypassing credit cards.
- AI "humans" (powered by Large Language Models (LLMs) and multimodal models) can perform tasks currently done by large call centers and administrative staff in healthcare.
- Large Language Models (LLMs): AI models trained on vast amounts of text data to understand, generate, and manipulate human language.
- Multimodal Models: AI models that can process and understand information from multiple types of data, such as text, images, and audio, simultaneously.
- A reported "20% of AI dollars have gone toward healthcare," underscoring the perceived fit.
- Crypto AI Relevance: The need for robust, trustworthy AI in high-stakes healthcare applications opens doors for verifiable computation (like zkML) and decentralized AI governance models to ensure fairness, transparency, and data privacy.
AI's Impact on Real-World Outcomes and Behavioral Change
- The discussion highlights that even curing all cancer would only extend U.S. lifespans by about three years, emphasizing the importance of addressing social determinants of health and behavior.
- Social Determinants of Health (SDOH): The non-medical factors that influence health outcomes, such as economic stability, education access and quality, healthcare access and quality, neighborhood and built environment, and social and community context.
- AI's potential for driving behavioral change is seen as a key lever.
- Examples of tech-driven behavioral change:
- Oura Ring users drinking less alcohol due to sleep score impact (BJ and Daisy).
- Eight Sleep users discussing optimal sleep temperatures at social events.
- Function Health users comparing "biological ages" derived from comprehensive blood tests.
- BJ notes a post-COVID trend towards healthier lifestyles, amplified by social dynamics and tech-enabled tracking.
- Opportunity for Crypto AI: AI-powered platforms can create personalized health journeys. Crypto incentives could further motivate positive behaviors and data sharing within secure, user-controlled ecosystems.
A Confluence of Factors: The "Why Now" for a Health Tech Revolution
- The speakers agree that a convergence of factors creates a "why now" moment:
- Growing availability of health data (wearables, diagnostics).
- Increased consumer demand for health agency and information.
- Evolving incentives in the healthcare market.
- Maturation of AI capabilities.
- This confluence suggests the system is ripe for disruption by technologists.
Final Call to Action: For Doubters and Technologists
- For those skeptical of technologists leading healthcare transformation, BJ emphasizes that many successful new founders are not complete outsiders but possess deep medical system understanding alongside tech expertise – a combination that "just didn't exist before."
- Daisy encourages technologists on the fence:
- Daisy: "Wouldn't it be amazing to be working on something that really matters? And you don't have to cure cancer. You don't need a PhD to save lives."
- She extends an invitation: "The water's warm. Come on in."
- The analogy of early internet companies (like Amazon starting as an online bookstore) suggests that today's nascent health tech ventures could evolve into transformative giants.
- Strategic Takeaway for Researchers & Investors: The intersection of deep healthcare knowledge and cutting-edge AI (potentially with crypto-based enhancements for trust and data sovereignty) represents a frontier with profound societal impact and investment potential.
Conclusion
This episode argues that AI-driven, consumer-centric models are set to fundamentally reshape healthcare, creating unprecedented market opportunities. Crypto AI investors and researchers should focus on solutions that leverage AI for superior user experiences, data-driven insights, and behavioral change, particularly those addressing data privacy and system interoperability.
