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Welcome to the 13 new subscribers who have joined Future Human since our last edition! Join 353 other leaders learning about the future of human health by subscribing here:

Hi friend,

Welcome back to Future Human! For those who live pretty much anywhere in the continental U.S. besides maybe the west coast, we hope you all stayed safe and warm during the incredible storm over the weekend. Here in Manhattan, we doubled our yearly snowfall from 9 to 20 inches. That was crazy for us, but a weak performance comparatively. My brother was in northern New Jersey, no more than 90 minutes from here, and got 39 inches. He had quite the ski day out on the slopes. I, unfortunately, was studying. That said, tons of exciting news came out over the previous week that we felt you should know as it will impact patients and the broader healthcare industry, so let’s get into it.

To more lives saved,

Andrew, Isabelle, and Nicholas

This is one of the few “AI in healthcare” announcements that feels less like a shiny product launch and more like a systems move. Gates is tying the partnership directly to the reality of shrinking international aid and the risk of losing hard-won gains in preventable deaths. And he is leveraging AI as the lever to keep care quality from collapsing under workforce constraints. 

If Horizon1000 is serious about reaching ~1,000 primary care clinics by 2028, starting in Rwanda, that’s not a pilot  — that is a real attempt at frontline deployment under messy, real-world conditions. And the stated focus (e.g., pregnant women and HIV care) is exactly where “better protocol adherence + less admin load” can translate into outcomes quickly, if the tool is designed for the people actually doing the work.

Optimistic read: Horizon1000 could become a blueprint for “AI as health infrastructure” by real-life testing a protocol co-pilot that helps stretched teams triage, document, and follow guidelines more reliably, without replacing clinicians. 

I’m watching three things: Does it actually fit the frontline workflow (low connectivity, local language, minimal clicks)? Do they prove safety and bias performance at scale (not just in demos). Is governance clear when the model is wrong? If they get that right, it’s one of the cleanest real-world tests yet of whether AI can expand quality care quickly and responsibly, which is very exciting and hopeful.

I remember in the application cycle for my venture capital fellowship, I was asked what a reasonable revenue multiple was in the healthtech space. I wasn’t too confident, but I guessed 8x. I wasn’t too far, it’s a bit lower around 4-6x. That said, it appears I could have guessed anything and been marginally right as OpenEvidence just threw tradition to the wind.

With only $100M ARR, OpenEvidence just raised $250M at a—wait for it—$12B valuation. We are talking about a 120x multiple. Now this is not new for them, they have raised aggressively with low (but growing) revenue. Just 11 months ago in February, they became a unicorn and achieved a $1B valuation with single digit million revenue. As someone who uses the product daily, I am not criticizing but rather wondering where investors think they will discover new revenue streams. Founder and CEO, Daniel Nadler, and his team have developed an incredible product that now 40% of physicians are using. The usage is skyrocketing, so they do not need to prove their worth there. We will be watching closely to see how they justify this rich valuation over the coming months. The ad-based monetization of these AI chatbots is still in its early stages. If they can execute like search engines did 20 years ago, they will be well on their way to making this valuation look cheap. Congrats to them!

Amazon is taking an internal approach to delivering AI to patients for care efficiency and no doubt cost savings long term. As healthcare grapples with AI capabilities moving forward, large incumbents will naturally fall into two categories: those that purchase tools and features from innovative startups to offer customers and those that develop internally to keep close tabs on what they integrate. It appears Amazon has chosen the later. Their new Health AI assistant provides 24/7 personalized health guidance grounded in each patient's medical history. When patients have more clinical questions, the assistant links patients to their care team via messaging or by booking a same or next day appointment.

From a patient’s standpoint, this is a terrific service. No more dialing providers furiously and being left on hold waiting to get a better understanding of clinical findings and next steps. For the providers, given that the system is knowledgeable about the patient’s full history, this could be incredibly valuable. The danger with asking medical questions to general platforms is that it cannot make the connections between test findings, medication rationale, and your broader past medical history. That journey is the crux of why certain treatments or procedures are indicated, so without that knowledge, AI platforms provide generic and frequently incorrect responses. Good on Amazon for getting ahead of that and providing their patients with a service that integrates their unique characteristics in each interaction.

We wrote an intense deep dive about Isomorphic Labs last summer, so we here at Future Human are big fans. That said, it is important to review their work honestly and tell you about the reality checks that seem to be happening across their teams. In Davos last weekend at the World Economic Forum, Isomorphic founder and CEO Demis Hassabis said they expect to have first clinical trials of a drug developed with their AI platform by the end of 2026. Now that is a rapid timeline from right now, but it is a delay from the end of 2025 plan they set a while back. This may be no more than any old corporate pushback, but it is important to reflect on it and accept that, as of now, AI is far from the magic bullet in healthcare when biology and science remain more complex than even the most powerful computers can register. Operationalizing AI predictions into wet‑lab studies, preclinical models, assay validation, and toxicology are still slow and costly — and key reasons why clinical entry timelines can slip. This isn’t unique to Isomorphic, across the industry, AI drug companies have delayed late‑stage proof points. Many have strong predictions but few have yet moved candidates into mid‑late clinical testing. It is all part of the innovation path, so not much to be concerned about, but when engineers under Google are pushing timelines back, it is good to step back and appreciate the complexity of therapeutic development.

Telehealth providers and startup teams are getting closer to the legislative security they have been waiting for for so long. If you have not been in the loop, COVID-era telehealth flexibility which allows clinicians to see patients over video call across state lines has been debated in Congress for the last few years. Thus far, legislators have just kicked the can down the road with small 6-12 month extensions. Now they are more seriously looking at safeguarding this terrific policy.

The proposal to extend Medicare’s flexibilities for two more years and hospital-at-home waivers for five years would provide much-needed stability after years of stopgap renewals, giving providers and patients more certainty around virtual care and home-based acute care models. To state the obvious, these services have filled critical gaps in access — especially for rural and mobility-limited patients — and have shown how alternative care delivery can improve engagement and capacity without eroding quality. Now, to make this work, Congress must not only pass the bill in the Senate and reconcile potential differences between chambers, but also ensure that CMS develops clear, evidence-based guardrails for quality, safety, reimbursement, and data reporting. Providers will need guidance on compliance, workforce training, and integration with traditional care pathways, while lawmakers should consider steps toward more permanent legislative reform rather than temporary extensions so health systems can confidently invest in long-term virtual and home-based care infrastructure. Robust evaluation of outcomes and cost impacts will also be vital to justify future policy support. Let’s see what happens!

We hope you enjoyed this edition of Vitals!

We always appreciate feedback, questions, and conversation, so feel free to reach out on LinkedIn or by replying to this email.

To more lives saved,,

Andrew, Nicholas, and Isabelle