10 Key Takeaways from Bessemer Venture Partners 'State of Health AI 2026' Report
- Nelson Advisors
- 2 days ago
- 4 min read
Health AI has moved from hype to execution, with a new cohort of AI native businesses scaling faster, with better unit economics and attracting both IPO and private-market capital; Bessemer’s thesis is that this “Health Tech 2.0” wave is real, not another ZIRP-style bubble.
1. Health Tech 2.0 vs 1.0
Health Tech 1.0 IPOs (Telehealth, virtual care, broad “digital health”) rode COVID/ZIRP tailwinds with weak unit economics and subsequently destroyed public-market trust, closing the IPO window through 2023.
Health Tech 2.0 IPOs (Waystar, Tempus, Hinge, Omada, Caris, HeartFlow) came public 2024–25 with profitable or near-profitable models, strong net retention and clear ROI and now represent ~30% of the $121 Billion BVP Health Tech Index market cap.
2. Public market comeback, but with a trust gap
New Health Tech 2.0 stocks rose ~18% in 2025, roughly in line with NASDAQ and S&P 500, while the broader health tech index was up 4% and cloud software (EMCLOUD) fell 7%.
Despite 2x the revenue growth and FCF margins of high‑growth software peers, health tech still trades at a 10–20% valuation discount due to perceived complexity, regulatory risk, and memories of 2020–21, a gap Bessemer expects to narrow over 12–24 months.
3. Private-market proof: M&A, funding, valuations
Global health tech M&A hit ~400 deals in 2025 (vs 350 in 2024), with acquirers using AI to drive both revenue growth and margin expansion (e.g., SmarterTechnologies–Access Healthcare–SmarterDx roll‑up,
Waystar–Iodine, R1–Phare Health).
Venture deal volume (~527 health tech VC deals, ~$14B deployed) and a 42% jump in average round size to $29.3M show deal-making back at pandemic levels; 55% of all health tech funding now goes to AI and late stage valuations (especially Series D+) are rising fastest, led by mega‑rounds such as Abridge, Ambience, Function Health and OpenEvidence.
4. Health AI X Factor and “Supernovas”
A small set of Health AI “supernovas” (e.g., SmarterDx, Abridge, OpenEvidence) are growing 6–10x annually, hitting $100–200M ARR in under five years and compressing the time to $100M ARR from a decade to as little as 18–36 months.
The Health AI X Factor is the justification for premium NTM multiples, arguing some $30M ARR businesses can be fairly valued at $1B+ because growth curves are structurally steeper than traditional SaaS.
5. Four pillars of X Factor companies
Velocity: Continuous 6x+ growth supported by visible pipeline, strong customer references, and no “lumpy” resets; future growth must already be embedded in implementations and expansions.
Durability & defensibility: High NRR, recurring revenue, deep workflow integration, proprietary data moats, clinical validation, and pricing power; companies competing mainly on price or with sub‑100% NRR are flagged as fragile.
AI productivity & margins: Truly AI‑native firms show ARR/FTE of $500k–$1M+ (vs $100–200k in services, $200–400k in pre‑AI SaaS) and can reach 70–80% gross margins by automating human work rather than merely wrapping services with “AI”.
Platform expansion: The strongest players land with a high‑ROI wedge and expand to “systems of action” across workflows (e.g., Zingage moving from AI care navigation into rev‑ops, claims, recruiting), rather than trying to build a full platform from day one.
6. Prediction: Payers race to catch up
Provider‑side AI in RCM has improved documentation, appeals and revenue capture, putting pressure on payers via higher medical loss ratios, more complex claims, and rising administrative load.
Bessemer expects 2026 to be an inflection point where payers accelerate AI adoption across payment integrity, prior authorisation/utilisation review, and member engagement, with founders advised to pick high‑ROI, high‑complexity wedges.
7. Prediction: Clinical AI in triage and risk, not autonomy (yet)
Clinical AI is shifting from concept to scale in triage and risk assessment use cases that keep clinicians in the loop: pre‑visit risk stratification, inpatient deterioration prediction, triage optimisation and smarter specialty referral.
Regulatory, liability and reimbursement constraints still limit fully autonomous diagnosis, so founders are urged to start from front‑office/admin entry points that naturally extend into decision support, risk scoring, and coordination.
8. Prediction: CMS pilots AI-first reimbursement
The key bottleneck for clinical AI is payment, not tech; most providers are still paid for time and procedures, not AI‑enabled prevention or monitoring.
CMS is expected in 2026 to test new CPT codes and models for AI‑assisted diagnosis, preventive care and AI‑enhanced remote monitoring, with the logic that successful CMS pilots will pull commercial payers into similar reimbursement within 12–24 months.
9. Prediction: Consumer cash-pay drives fastest clinical AI uptake
Consumers are already paying out of pocket for AI‑enhanced care, exemplified by RadNet’s study where 36% of 747k+ women chose $40 AI mammography, yielding 43% higher cancer detection and ~93% accuracy when radiologists used AI assistance.
Direct‑to‑consumer models (AI‑first primary/urgent care, AI second opinions, AI health coaches) offer a faster route to revenue and validation than waiting for reimbursement, and are framed as laying the groundwork for “AI doctors” over the next decade.
10. Prediction: Infrastructure, VBC and Digital CROs as next wave
A new health AI data infrastructure layer is emerging to serve model labs and AI apps, but founders must differentiate from Snowflake/AWS/Databricks, design recurring “AI‑services‑as‑software” revenue, and eventually capture some application‑layer value.
AI is expected to revive value‑based care by radically lowering the marginal cost of engagement and monitoring, and to enable “digital CROs” that replace large swathes of wet‑lab and animal testing with in silico models, robotic labs, and AI‑optimised trial design, attacking drug R&D’s cost–speed–competition “trilemma”.
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