Before ChatGPT, there was Google.
I was in my mid-forties, a graduate student in bioinformatics at Michigan, and I had been reading about metformin. It is an old, cheap diabetes drug, and a group of researchers was trying to get the FDA to approve it as a treatment for aging. A faculty member told me he had been taking it off-label. At the time, approved use required a diagnosis of diabetes.
I made an appointment with my primary care physician and went in prepared to ask for a prescription. I understood the science well. While I waited in the exam room, I searched a little more to sharpen my arguments.
The doctor came in. I told him what I wanted. He said no. It wasn’t indicated.
I made my case. He wasn’t convinced. He turned to his computer, and I assumed he was consulting a clinical database or some specialized reference I had no access to. Then he read me a possible risk and a possible benefit.
They were the same search results I had pulled up minutes earlier.
I was paying a $50 copay for a man to read Google back to me. On this question, I knew more than he did. I didn’t get the prescription from him. I switched doctors to get it.
What he controlled was not knowledge. It was the gate.
The Gate Is Not the Safety
A prescription requirement is presented as a safety mechanism, and part of it is one. But look at what stands behind the physician’s signature. A licensed pharmacy vouches that the drug is what the label says. A medical board can revoke the license of the person who signed. A reporting system collects adverse events. A party exists who can be held responsible if something goes wrong. Provenance, verification, safety reporting, reputation, dispute resolution. Five separate functions, and the signature is the single gate into all of them.
They were bundled largely because bundling was the cheapest way to produce trust at scale. A century ago, no individual could verify a pharmaceutical batch, track adverse events across a population, or audit a prescriber. An institution could do most of it at once, so the institution became the product.
Trust is a function. Institutions are one implementation of it. The people asking for trust are the demand side. The people supplying credibility are the supply side. The machinery that connects them is infrastructure, and for most of the twentieth century medicine had essentially one design.
The gate in that exam room did not examine my case. It enforced the bundle.
The absurdity runs all the way up. In 2015, at a Gordon Research Conference on the biology of aging in Maine, I sat in the audience while the research team behind the metformin effort laid out the goal: get the FDA to accept aging as something a drug could be approved to treat. Once aging counted as a target, the funding doors for longevity science would open. The regulatory word for it was “indication.” Everyone understood what it meant. Aging was to be treated as a disease. On the record, the team’s lead investigator said the following year that neither the FDA nor his group wanted to say aging is a disease. The researchers were not being evasive. They were speaking the only language the gate reads, a language in which the plain word carries regulatory risk and the euphemism carries none. That vocabulary is part of the infrastructure we inherited, and it is one of the parts worth leaving behind.
The Bundle Is Coming Apart
The components of institutional legitimacy are separating, and they are separating because each one is becoming cheap on its own.
An independent analytical lab will now run identity and purity on a single vial of a peptide for a few hundred dollars and return a report anyone can check against the lab’s own records. A crowdsourced testing platform has tested more than six thousand samples from roughly 185 vendors, anonymizing each sample before it reaches the lab so the vendor cannot pick its best batch. Direct-to-consumer laboratories sell the bloodwork that once required a referral. Wearables measure continuously what clinics measure once a year. AI can work through a body of literature in an afternoon that no physician would have time to read.
Demand is moving too. The people turning to these tools are not the information-poor skeptics of popular imagination. Edelman’s 2026 health survey found that respondents who believe three or more contested health claims consult an AI platform for health answers at more than three times the rate of those who believe none: 61 percent monthly or more, against 19 percent. Asked what makes someone a legitimate health expert, 71 percent of the first group said the person had helped them before. Among those holding no contested beliefs, 56 percent said the same.
That is not a rejection of evidence. It is a demand for different evidence: track record, demonstrated performance, something earned rather than conferred. Institutional medicine has been slow to notice that this population is a market. What it is shopping for is trust.
Trust With No Authority Behind It
The strongest test of whether trust can exist without institutions is a market where institutions are entirely absent.
AlphaBay operated on the dark web from December 2014 until an international police operation shut it down in July 2017. No court would enforce a contract made on it. No regulator inspected anything. Every participant was anonymous, and a large share were criminals. What it did have was a platform: rules, a review system, and escrow, all run by administrators with no legal standing whatsoever. By the logic that justifies the institutional bundle, it should have been a swamp of fraud.
It wasn’t. Filippo Andrei and Giuseppe Veltri analyzed 114,385 listings from 6,033 sellers and found that 97.7 percent of buyer reviews were positive and fraud reports were rare. Fraud here means what buyers said in their reviews, counted by searching for words like “scammer,” not incidents anyone independently verified. More importantly, they found which signals went with fraud and which went with honesty. Costly signals, the ones that require real investment and are hard to fake, were associated with fewer fraud reports. Transactions run through escrow carried roughly 21 percent fewer than those paid up front. Sellers with stronger reputations and longer tenure on the market drew fewer fraud reports too. Cheap signals ran the other way. Listings written with richer vocabulary and a more upbeat tone were associated with more fraud. These are associations drawn from a single snapshot of one market, not proof of cause. But the pattern is exactly what signaling theory predicts.
The fraudsters wrote better copy.
No infrastructure produces truth. What infrastructure can do is raise the cost of sustaining a false claim and lower the cost of demonstrating a true one.
The study carries an inconvenient detail. Escrow mattered because the platform held the money in a central wallet, and that wallet was also the market’s greatest vulnerability. Administrators of other markets disappeared with escrow balances. When police took AlphaBay down, they seized the market and millions in cryptocurrency held by its creator. The strongest trust signal in the most lawless market available still leaned on an intermediary. Decentralization does not remove the need for trust machinery. It changes who builds it, and who can be held to it.
Routing Around the Chokepoint
Trust is not the only function that has been pulled out of a centralized system. Distribution was pulled out first, and the mechanism is worth watching closely.
Napster made music sharing effortless in 1999, but every search passed through an index the company controlled. When the lawsuits came, they had one target. Napster shut down in 2001. BitTorrent, released the same year, did not route through a central index, and by 2005 clients had dropped the need for a central tracker altogether by distributing the lookup across the users themselves. There was no single company whose servers could be switched off to end it. The pressure that killed Napster had nothing to aim at.
Software development went through the same transition from a different direction. From 2002, the Linux kernel, among the most important pieces of software on earth, was maintained with a proprietary version control tool licensed free to its developers. In 2005, that arrangement collapsed. In about ten days Linus Torvalds had written Git, in which every developer holds a complete copy of the full history. No license could ever again be withdrawn from the project, because there was no longer a central copy to withdraw.
Neither case is about trust. Distribution and version control are mechanical problems. But both show the pattern this article cares about. When a single point of control becomes a liability, the architecture moves around it, and the old chokepoint stops mattering without anyone defeating it. Napster was not out-competed by a better Napster. It was made irrelevant by a design that had no Napster in it.
Who Pays for Verification
Petroleum was sold as medicine before it was sold as fuel. In the 1850s, Samuel Kier bottled Pennsylvania rock oil and marketed it as a cure for nearly everything, backed by testimonials. Testimonials are the cheapest signal there is.
A few years later, investors who wanted to know whether the same oil could be refined into a lamp fuel paid Benjamin Silliman Jr., a Yale chemist, to analyze it. Silliman’s report put a scientific reputation behind a commercial claim, and it helped the venture raise the money that led to Edwin Drake’s well in 1859. The industry that became Standard Oil began with a paid, costly signal displacing a cheap one.
The same logic built product safety in the United States. In 1893, a young electrical inspector named William Henry Merrill was sent to assess fire risk at the World’s Columbian Exposition in Chicago. The insurers who would pay for the fires funded his idea for a testing laboratory. With $350 of equipment, he opened it above a fire insurance patrol station and signed its first test report in March 1894. It became Underwriters Laboratories. Its certificates mattered because insurers required them, and once the mark was required, manufacturers started paying for the testing and the insurers’ subsidy fell away.
Hartford Steam Boiler, founded in 1866, worked the same way: the company that insured boilers inspected them, because it held the loss when they exploded.
Demand for verification comes from the party holding the loss. Who ultimately pays can shift once that demand hardens into a requirement, as it did at UL. That may be the most useful pattern in the history of trust infrastructure, and it points directly at the hard part. Some verification has an obvious buyer. Some has none.
Andrew Carnegie offers the model for the second kind. He financed public libraries across the English-speaking world, and the terms mattered as much as the money. He paid for the building. The town supplied the site and committed to ongoing funding. He did not own the libraries, did not meter their use, and did not decide what people read in them. They outlived him by generations. His foundation also paid for the Flexner audit that reshaped American medical education, so his money has already built medical infrastructure once. The weakness of the library model is that it financed access, not evaluation. A library gives you the books. It does not tell you which ones are right.
The Live Case
The market for research peptides is where all of this is happening now, in public, without permission.
Testing exists, and it is cheap. A single-compound identity and purity test costs a few hundred dollars. Some platforms test for free. A buyer can, for the first time, check what is actually in the vial.
But look at what the public record is made of. At the most widely cited lab, results are private by default, and publishing one to the public database costs an extra fee. That structure invites selective disclosure. A vendor has every reason to pay to publish a pass and little reason to pay to publish a failure, so the public archive shows what vendors chose to show.
The labs themselves are another gap. An independent result is only as good as the lab that produced it, and someone has to check the checkers. In my research, I could find no proficiency testing scheme that checks peptide labs against each other, the blind-sample programs federal law requires of clinical laboratories, and that hemp potency labs already buy for a few hundred dollars a round.
And a verified batch is not a verified outcome. Knowing what is in the vial says nothing about whether the compound is safe or effective in the person who takes it. I found no public registry that links independently tested batches to what happened to the people who used them. The closest thing is a buyer satisfaction score attached to a lab result, which is a cheap signal riding on a costly one.
The peptide market has built the assay. It has not built the system that makes the assay trustworthy.
What Gets Built
Almost nothing in the stack is missing as a capability. What is missing is the connection between capabilities.
Labs need proficiency assurance: blind samples, sent by someone with no stake in the result, scored and published. Failures need to be published as readily as passes, which means the cost of publication has to fall on someone other than the vendor who failed. Verified batches need to be linked to outcomes, so that “this vial was 99 percent pure” eventually becomes “and here is what happened to the four hundred people who took it.” That record would establish traceability and give investigators something to work with. It would not establish cause. Proving that a compound works still takes appropriate methods, controls, and analysis. But no well-designed comparison can run on data that was never collected. And somewhere, someone needs to pay the critic who turned out to be right.
The first two already have plausible buyers. Vendors who test well want the signal to mean something. Buyers want labs they can rely on. The third should find buyers once the data exists. The fourth has no buyer at all. The loss from an unchallenged bad claim is real, but it is scattered across everyone who believed it, and scattered losses do not produce a buyer. This is the piece for capital that does not need a return, and it is where a Carnegie belongs.
One principle should govern all of it. The infrastructure must never decide which questions are legitimate to ask. It records how well a question was investigated. That is the line institutional medicine crossed, and it is the line any replacement has to hold.
This is the opportunity. For the entrepreneur, the missing connections are the market, and the market is large: everyone who has stopped trusting the incumbent route but has not stopped needing trust. For the person who doubts mainstream medicine’s claims, this infrastructure is a way to demonstrate rather than assert, to turn a conviction into a record. For the biohacker at every level, from the person buying a first vial to the person running a crossover trial on themselves, it is the difference between hoping the input was right and knowing it.
The Concerns Are Design Requirements
None of this is safe by default.
Reputation systems can be gamed. A seller can build a clean record over years and then cash it in on a single large fraud, which is what an exit scam is. Fake identities can flood any system that counts votes. Commercial standards are not biomedical standards: a pure vial says nothing about whether the compound in it works, or works for you. Outcome registries built from people who chose to take unregulated compounds will be shaped by who those people are, and naive analysis will mistake their differences for the drug’s effects. And coordination is hard. A thousand small testing efforts do not add up to a surveillance system unless someone builds the connective layer, and builds it well.
Every one of these is a reason to build carefully. None of them is a reason not to build. The institutional system has failure modes of its own. They are simply familiar, and familiarity is easy to mistake for safety.
Demonstrate, Don’t Plead
The doctor in that exam room had the same search results I had. What he had that I didn’t was the signature, and the signature stood in for an entire trust apparatus that neither of us could see. I couldn’t prove anything to him. All I could do was argue.
What I needed was not a better argument. It was a way to show my case: a verified source, a measured response, a record that someone other than me could check. That infrastructure did not exist then. Its components exist now, scattered across independent labs, testing platforms, and communities that do not need anyone’s permission to keep building.
The next person who walks into that exam room should not have to plead. And the capability to build the system that spares them keeps arriving faster than anyone planned for.