By now you have probably seen the Medvi story. Jack Gallagher, a self-taught entrepreneur in Los Angeles, built a GLP-1 telehealth company with $20,000 and his brother as his only hire. In its first full year, Medvi generated $401 million in revenue, 250,000 customers, and a 16.2% net profit margin that nearly triples what Hims and Hers produces with 2,442 employees. It is tracking toward $1.8 billion in 2026. Sam Altman has been predicting something like this since 2024, when he told Reddit co-founder Alexis Ohanian that he and his tech CEO peers had a betting pool for the year the first one-person billion-dollar company would emerge. Medvi is as close as anyone has gotten.
The conversation that followed is predictable. If one person can build a billion-dollar company with AI, imagine what a large enterprise with thousands of employees and billions in resources can do. Most commentators stopped there. I want to take it further, because I think the more interesting question is not whether enterprises can become Medvi, but whether there are pockets inside every large enterprise where the conditions that made Medvi possible already exist.
Renting the infrastructure
I would argue that Gallagher did not build anything new. He rented existing telehealth infrastructure that the rest of the industry spent a decade and hundreds of millions constructing, pharmacy networks, regulatory frameworks, payment rails, medical review systems, and fulfillment infrastructure. He then used AI to compress the customer-facing layer above it into something two people could operate. The product itself is a single repeatable transaction: a patient fills out a form, an AI-assisted triage process routes it to a doctor, a medication ships. No competing internal stakeholders, no legacy technology systems, no inconsistent data definitions, no workforce that needs to fundamentally change how it operates before any of this can work.
What made Medvi possible was not AI alone. It was the combination of AI and a set of conditions that eliminated the organizational complexity that makes scaling AI so hard everywhere else. It was built in a greenfield environment with a single homogenous transaction at scale using existing infrastructure to build on top of, rather than rebuild from scratch.
The enterprise version already exists
The conditions under which Medvi exists are not unique to startups. They are present inside almost every large organization, hiding in the segments that the core business has always considered too small or too fragmented to invest in properly.
The long tail of any large business, the thousands of small accounts, small merchants, small customers that generate individually modest revenue but collectively represent significant scale, almost always meets Medvi’s conditions. The transactions are homogenous and repeatable, and the infrastructure already exists. The problem has never been the complexity of serving these customers. It has been the unit economics of staffing human coverage at that volume. I have seen this work firsthand and wrote about it in a previous edition of this newsletter, which describes how I build and ran an AI agent to double revenue.
The takeaway from Medvi is not that large organizations should try to operate with fewer people. It is that the Medvi playbook, finding a homogenous high-volume process, building AI on top of existing infrastructure rather than rebuilding everything, and eliminating the organizational complexity that slows everything else down, is replicable inside the walls of any large enterprise if you know where to look.
The places to look share a few characteristics. They are typically high volume and low variability, meaning the same interaction happens thousands of times with minor differences each time. They sit on infrastructure that already exists and already works. And they have been chronically underserved not because the business does not want to serve them, but because the economics of human coverage never made sense at that scale.
One filter matters above all others before committing to any of these segments: the AI intervention has to connect directly to a revenue or margin outcome, not just an efficiency gain. This is where most enterprises make a costly detour. They apply the same Medvi-style logic to their customer service function, their internal helpdesk, their HR processes, and they generate real productivity improvements that never show up in the P&L. Those are legitimate uses of AI but they are not the Medvi play. Gallagher built a revenue machine, not a cost reduction exercise. The enterprise equivalent is finding the segment where AI does not just reduce the cost of serving customers but actively unlocks revenue that was previously out of reach because of the cost of scale. The small business segment I wrote about doubled not because it became cheaper to operate but because it finally became possible to reach customers the business had always wanted to serve but never could at that volume and margin.
PwC’s CEO Survey Snapshot, published this month and drawing on responses from CEOs across 95 countries, found that just 20% of companies are capturing 74% of all AI-driven value. That concentration is not random. The companies capturing disproportionate returns found their ‘Medvi’, the pockets of their business where the conditions were already right and deployed AI there first, rather than attempting enterprise-wide transformation simultaneously.