I'm not sure healthcare needs a store, but it does need simple pricing. When you're sick, I bet the last thing you want to do is comparison shop, because what you want is to understand what's going on, what to do about it, and how much it'll cost before you start. @Doctronic already made this simple: 1. Our AI is free 2. AI prescription renewals (if your state allows it) are free 3. A licensed physician text prescription renewal (if your state allows it) is $19 4. A licensed physician video visit for most other needs, in all 50 states, is $39 Use insurance if you want, and it may be even cheaper. If you're not happy, we'll make it right, and we'll even refund your out-of-pocket payment. I'm not sure you need a catalog, a cart, or a price lookup when it's that simple, but I love seeing others enter the space to help make healthcare better for Americans.

How AI is changing the way companies like @Doctronic scale: Anyone who has been through hypergrowth knows the pattern. At seed stage, everyone does everything. There's too much work and too few people, so generalists carry the company. Then, as the company grows, that stops working. The problems get deeper, the stakes get higher, and you move faster when people have real depth. @ReidHoffman and @ChrisYeh called the shift from generalists to specialists one of the core transitions of a scaling company. I think AI adds another step to that progression: Generalist → Specialist → Owner. I'm using "owner" for lack of a better term, but it may be the right one. An owner has deep expertise in something important but doesn't stop at the boundary of their specialty. They own the outcome. They spot what needs to be done, make good calls, use AI aggressively, and only pull in other people when their expertise genuinely matters. A backend owner may be exceptional at backend engineering, but they also understand and own the product problem. With AI, they can build enough of the frontend, test the feature, and get it shipped instead of handing it through three different people. The same thing is happening in finance, marketing, operations, product, and almost every other function. And we're not the only ones seeing it. @LinkedIn replaced its Associate Product Manager program with an Associate Product Builder program and introduced a formal Full-Stack Builder path. @Wix merged its frontend, backend, and mobile engineering guilds into one, and its new "xEngineer" role keeps deep expertise in a core domain while using AI to work across stacks and own outcomes end to end. BTW: Wix explicitly says this is not a traditional full-stack generalist. It's a "specialist amplified by AI." @zoink at @Figma said, "We're all product builders, and some of us are specialized in our particular area." And @Tobi made "reflexive AI usage" a baseline expectation at @Shopify. @PwC is calling the larger phenomenon "role convergence." Companies historically were constrained by the fact that execution was expensive and expertise was scarce. AI obliterated both those constraints. Work that once required coordination across several specialists can be owned by one person, and this makes judgment and genuine expertise more valuable. We need more owners. Organizations must move away from roles defined by inputs and toward roles defined by outcomes and accountability. This is not a move back to generalists. It's a move to people owning the outcomes. Fewer handoffs → smaller teams → more judgment → more accountability → much more individual leverage. The people who thrive in this new paradigm will combine depth, ownership, judgment, and speed, and use AI to massively increase what they can accomplish. This is how we have organized Doctronic. If you've got what it takes to be an owner, and want to help build the first AI doctor, we're growing and always hiring. Talk to us: doctronic.ai/careers/

OpenAI published six reports yesterday documenting misaligned model behavior observed during training or evaluation. The full reports are worth reading. There's a lot of talk of "slowing down the frontier" because of things like this, and from that document there are four examples I feel are particularly telling: 1) A model inserted instructions to disregard its constraints into summaries passed to its future self. 2) A model wrote itself reminders to conceal mistakes from users and fabricate data. 3) A model found an exposed credential on GitHub and used it without authorization. When it still couldn't retrieve the requested data, it fabricated the numbers and presented them as sourced facts. 4) A model uploaded retrieved data to the public internet without permission, so it would have something it could cite. I don't view this as proof that bigger models are turning sinister. These incidents point to problems involving training incentives, system design, and enforced boundaries. And restricting tools, network access, and autonomy can prevent specific unauthorized actions. And this is one reason I'm focused on small models doing narrowly scoped jobs, especially in medicine. To be clear, I don't think small models are more virtuous. Specification gaming predates frontier language models, and distillation can transmit unwanted behavior from teacher to student. But I think narrowly scoped models can be safer for clinical work. We can specify what they must do, enforce limits in software outside the model rather than trust it to obey instructions, and hard-code when a clinician must take over instead of leaving that judgment to the model. A bounded job with clear requirements is easier to test rigorously than an open-ended one. Narrow scope is what makes a model testable. Small size is what makes it portable. Small models also open a path toward greater privacy and lower resource use. At @Doctronic, we already have our models running on laptops. I expect our models to run on your phone in the next 1-2 years. That means your patient input can remain on your device during inference, without using datacenters. This creates an opportunity to reduce both inference costs and energy use. Maybe we need to slow down the frontier, maybe not. But we don't need to wait for the frontier to advance to build useful, rigorously tested AI for healthcare. openai.com/index/model-misal…

Love to see @FDA open public comment on a regulatory framework for GenAI medical devices. Getting this right matters. @Doctronic plans to submit comments. Our view: AI in clinical care is safest when patients can quickly and easily reach a licensed physician as needed. fda.gov/media/194242/downloa…

Where healthcare is going in the next 2-3 years: - Private clinical AI 24/7, anonymous by default (always free) - Login for private clinical AI w/ HIPAA, personalized with your medical history, wearables, labs (also free) - AI management of simple things: prescriptions, labs, chronic conditions (also free) - AI guides you to care and providers when needed - AI guidance shared with providers makes every provider 10x more effective Some of this is live. The rest is being built. Let's fix healthcare in America.

1. 92% of American adults have delayed or skipped medical care because of cost. 2. The average doctor's visit without insurance costs $171. 3. One in five say their health got worse because they waited. Americans hold $220 billion in medical debt and the industry thinks the answer is to finance the cost with payment plans, medical credit cards, or debt. Why not just fix the system? A @Doctronic medical AI visit is free, and our video visits with licensed doctors are $39 cash, 24/7, in all 50 states. yahoo.com/news/us/articles/s…

Every message a patient sends @Doctronic is checked for emergencies: heart attack, stroke, difficulty breathing, almost 50 distinct scenarios. The AIs that do these checks we call Guardians. They're independent, they run outside other AI models, they are not guardrails baked into it. This independence means that the system watching for danger is not the system doing the talking. When we first implemented these, every check meant a call to many large AI models, which works, but was slow and expensive. So we taught small, fast models to do the first screen. They learned from millions of our own data points, with the large models as the teacher. Many cases now clear in under a millisecond. They are also roughly 750x cheaper and we've saved over a second per message. We plan to share these models in order to make clinical AI safer for everyone.

Thrilled to announce @Doctronic has acquired Summer Health! Getting pediatric help in this country is harder than it should be. Nearly 4 million children live in counties without a single pediatrician. More than half of rural pediatric hospital units have closed, and the share of hospitals with minimal pediatric capability has more than doubled over the past two decades. So parents wait for appointments, drive hours, or end up in the ER at 2am for questions a pediatric clinician could answer in minutes. We felt this from every direction at Doctronic, as did our partners: the health systems, health plans, and pharmacies we work with. Summer Health spent years building on-demand pediatric care, trusted by tens of thousands of families, with more than 100,000 real-world pediatric encounters. That expertise and data is the foundation for our new pediatric AI and pediatric on-demand care. @ellenjdasilva saw before almost anyone that parents want answers by text at 2am, not a waiting room at 2pm. I'm so glad she and her team are building the next chapter with us. Pediatrics on @doctronic rolling out nationwide soon.

Medicare just changed millions of lives: eligible seniors can now get brand-name GLP-1s (Wegovy, Zepbound) for $50 a month. KFF estimates 3.8 million people qualify. Nearly 40% of Americans over 65 have obesity, along with the heart failure, kidney disease, and strokes that follow it. Now, the most effective treatment we've ever had is affordable for the people who need it. Of course, prescriptions require a physician evaluation and prior authorization. For seniors in rural counties, seniors without a primary care doctor, seniors who can't spend a day sitting in a waiting room, that visit is the last barrier standing between them and care. @Doctronic was built to remove barriers like that. Our AI-driven evaluation plus licensed physicians can help today, no waiting room required. And we're working on a GLP-1 specific pathway at a price you'll assume is a typo. Fewer heart attacks. Fewer strokes. Millions of healthier years. medicare.gov/publications/12… kff.org/medicare/nearly-four…

Getting tested used to take weeks, but @SHealthKit collapsed that wait. Getting treated can still take weeks, but in partnership with Simple HealthKit, @Doctronic now collapses that wait too. When a person needs help, testing, AI clinical assessments, and licensed physicians are ready. Screening then treatment, in one easy flow. Proud to build this with the team at @SHealthKit.

I LOVE this opinion in @WashingtonPost from @ZekeEmanuel and Miriam J. Paramore. When a generic drug costs nothing, patients abandon it less than 5% of the time. But as the costs go up so does the abandonment. Makes sense, right? Past $125 and abandonment hits 45%. Past $500, it's 60%. So a $0 co-pay on cheap generics could save many lives and billions of dollars. But that's not the only place where there should be a $0 cost. As we're doing in Utah with @Doctronic, the doctor writing a renewal should also be $0. But wait, there's more! Per the article, the patient discussed "must visit her physician for annual prescription renewals." That visit is ANOTHER bottleneck beyond the price of the doctor visit. Let's start stacking more zeros! $0 co-pay. $0 prescription renewal. 0 unnecessary doctor visits, 0 minute wait time to get a renewal, can we even get $0 medication itself for some of these cheap generics? Get the f (friction) out! washingtonpost.com/opinions/…

So @Doctronic fixed primary care pricing. A doctor visit that runs $150-300 uninsured? Ours is $39. No insurance needed (though we accept it if you want to use it), no surprise bill, no games, and a money back guarantee! Oh and prescription renewals in Utah are $0 and are $19 nationwide. But then our doctors order labs and it gets expensive again. E.g. CBC + CMP + lipid + A1c: ~$260 retail at the big labs, ~$130 at the cheapest shop I can find. Many Americans simply can't afford labs after a visit. What startup is working on fixing this? America needs a Basic Wellness Panel for $40. If you're solving this problem, DM me. I can send you 2,000 lab orders a week immediately to kickstart your business.

Today at the AWS Summit NYC I'm on a panel on building autonomous workforce systems. The questions are difficult and deserve meaningful answers: where does the agent live, when must it defer to a human, and what's the metric that proves it works? @Doctronic is putting agents in a clinical seat, where the wrong answer has a patient on the other end. Come find me at the Javits Center and hear how we're handling this. aws.amazon.com/events/summit…