Medical Research Is Hopelessly Caught in Red Tape
To have AI breakthroughs in medicine, you first need to fix bureaucratic bottlenecks.
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A story about Paul Conyngham, an AI entrepreneur from Sydney who treated his dog Rosie’s cancer with a personalized mRNA vaccine, has been circulating on X this week. What makes the story inspiring is the initiative the owner showed: he used AI to teach himself about how a personalized vaccine could work, designed much of the process himself, and approached top researchers to take it forward.
Whether the treatment itself was curative and how much of an improvement it represents over the current state of the art is not the point here. What interests me instead is the bureaucratic absurdity Conyngham encountered while trying to pursue the treatment. In The Australian he described the long and frustrating process required simply to test the drug in his dog: “The red tape was actually harder than the vaccine creation, and I was trying to get an Australian ethics approval and run a dog trial on Rosie. It took me three months, putting two hours aside every single night, just typing the 100 page document.” Even in a small and urgent case, where the owner was fully willing to fund the treatment himself, the effort was slowed by layers of procedure.
Of course, this kind of red tape is not confined to Australia, nor to veterinary medicine. In fact, in the United States, the red tape is even worse, at least for human trials. GitLab co-founder and billionaire Sid Sijbrandij found himself in a similar position after the relapse of his osteosarcoma. When the ordinary doors of medicine closed, he entered what he called “founder mode on his cancer.” Thinking like an entrepreneur confronted with a difficult problem, he began trying to build his own path forward by self-funding his exploration of experimental therapies.
Even then, he ran into the familiar maze of regulatory and institutional barriers, which not only delayed him—making it difficult for him even to access his own tissues and to order diagnostics—but also made the price of experimental therapies all but prohibitively expensive. These are obstacles that only someone with extraordinary resources could hope to navigate—in Sijbrandij’s case by assembling an entire “SWAT team” to deal with them and work through the opacity. In the end, Sijbrandij prevailed: he orchestrated an intensive radiotherapy for himself that worked. He has been relapse-free since 2025, long after doctors had told him he was at the end of his options.
Around the same time, writer Jake Seliger faced a similar situation while battling advanced throat cancer. Like Sijbrandij, he was willing to try anything that might help. The difference was that Seliger was not a billionaire. He could not hire a team to navigate the system on his behalf, and he struggled even to enroll in the clinical trials that might have offered him a chance. Seliger died in 2024, commenting “Having my life cut short by cancer is horrible”—and with his wife, a Mayo Clinic physician, livid at a “paternalist” government system limiting access to experimental therapies even for patients who have no other option.
A system originally conceived to safeguard patients has gradually produced a strange and troubling outcome: the mere chance of survival is effectively reserved for the very few who possess the means to assemble an army of experts capable of wending through its labyrinthine procedures.
What makes these stories particularly frustrating is that we already know clinical trials—especially small, early-stage ones like the ones Sijbrandij enrolled in for himself—can be conducted far more cheaply and with far less bureaucracy than is currently required. Ironically, while we started with a story of red tape in Australia, clinical trials for humans there are conducted 2.5–3 times cheaper and faster than in the United States without any increase in adverse safety events over a 30-year span, and American lawmakers have belatedly tried to introduce pilot programs following Australia’s model.
Removing unnecessary barriers has long been important, and the issue has only become more urgent with the rise of AI. One of the central promises of the AI revolution is that it will accelerate medical progress. Organizations such as the OpenAI Foundation list curing disease as a core goal, and figures like Dario Amodei of Anthropic have argued that AI could dramatically speed up biomedical innovation. But, even in the most optimistic scenario, AI will not automatically accelerate a key bottleneck in making these dreams a reality: clinical trials. Conyngham’s observation that navigating the red tape to start a trial for his dog took longer than designing the drug itself only underscores the point.
Drug development is often described as a funnel: many ideas enter at the top, but only a few become approved treatments. Early human studies, known as Phase I trials, sit at the entrance of this process. They involve small numbers of patients and are designed to quickly test whether a new therapy is safe and shows early signs of effectiveness.
If the results look promising, the therapy moves to larger and more complex studies, including Phase III trials that enroll large numbers of patients to confirm whether the treatment truly works. Most people gain access to new therapies only after these large randomized trials are completed.
On average, moving from a promising idea to Phase III results takes around ten years and costs roughly $1.3 billion. Part of this delay is unavoidable. Observing how a drug affects the human body simply takes time. But much of it is not. Layers of unnecessary bureaucracy, regulatory opacity, and rising trial costs add years to the process without clearly improving patient safety.
Allowing a higher volume of early stage trials is a rare “win-win” for both public health and scientific progress. For patients, it transforms a terminal diagnosis from a closed door into a “chance at a cure,” providing legal, supervised access to cutting-edge medicine that currently sits idle in labs. For researchers and society, it unclogs the drug discovery funnel; by lowering the barrier to entry for new ideas, we ensure that the next generation of mRNA, peptide, and AI-driven therapies are tested in humans years sooner.
From a patient perspective, early stage trials often provide the closest practical equivalent to a right-to-try. In theory, right-to-try laws allow patients with serious illnesses to access treatments that have not yet been confirmed in large randomized Phase III trials. In practice, these pathways rarely function as intended. Pharmaceutical companies are often reluctant to provide experimental drugs outside formal trials, and treatments typically must have already passed Phase I testing. As a result, very few patients gain access through these mechanisms. Early-stage trials offer a more workable alternative. They allow experimental therapies to be tested in structured clinical environments—often in academic settings or academia-industry collaborations—where patients can be monitored and meaningful data can be collected.
Second, early-stage trials are essential for personalized medicine and the treatment of ultra-rare diseases. Many emerging therapies—such as personalized cancer vaccines, gene therapies, and other individualized interventions—do not fit easily into the traditional model of large randomized trials involving thousands of participants. By their nature, these treatments target very small patient populations and often require flexible, adaptive clinical designs.
Lastly, these trials play an important role in maintaining U.S. leadership in biotechnology and, over the long run, in safeguarding biosecurity. In recent years, China has been advancing rapidly in biotechnology, in part because it is easier to run early-stage clinical studies there.
As a result, more U.S. biotech firms are beginning to move parts of their clinical development to China. A Time magazine headline from May 2025 captures what many industry experts have been warning for years: “The US can’t afford to lose the biotech race with China.” This is no longer a hypothetical concern. U.S. early-stage funding is deteriorating, dropping from $2.6 billion in the first quarter of 2025 to just $900 million by the second quarter—the lowest level in five quarters. If this trend continues, it could gradually shift the center of gravity for biomedical innovation abroad. That poses risks not only to U.S. biotech competitiveness but also to biosecurity, much like the earlier offshoring of manufacturing supply chains created strategic vulnerabilities.
In the long run, we may need to rethink the entire sequence of requirements for drug approval, especially as personalized medicine becomes more common. For now, however, it is worth focusing on the unnecessary barriers that limit the expansion of early-stage trials to a larger group of patients.
A critical one is the Institutional Review Boards (IRBs), committees responsible for reviewing the ethical aspects of clinical studies. In practice, however, the IRB process significantly delays these efforts. As Sijbrandij, the GitLab co-founder, explained in a Century of Biology article, they can function as “a ‘vetocracy’ where one member of the board can block treatment based on even the smallest concern.”
This situation is difficult to justify: here was someone with advanced cancer who was willing to self-fund the treatment and accept the risks, yet was still prevented from proceeding. It’s as if the system would rather have you dead than risk one imperfect overly long consent form.
Another obstacle is Good Manufacturing Practice (GMP) standards, designed for drugs’ large-scale manufacture. Full GMP compliance involves validated facilities, extensive documentation, batch testing, environmental monitoring, and strict process controls. These requirements make sense for large commercial manufacturing, where thousands or millions of patients may ultimately receive the product. However, in the context of very small early-stage trials involving only a handful of patients, the same standards are often applied even though the risks and scale are entirely different.
Experience in Australia shows that a lighter approach to GMP requirements can substantially reduce costs. Clinical manufacturing conducted under Australia’s framework is roughly 2.5x cheaper for this stage of the process alone. Copying Australia’s model and reducing manufacturing costs would likely, however, only be the beginning. If regulatory reforms were combined with modern technologies for final product quality control such as standardized validation platforms, experts I interviewed suggest that manufacturing costs could plausibly fall by 5-10 times overall. Such improvements would not only lower the cost of individual trials but could also significantly expand the number of therapies that make it into clinical testing in the first place.
Lowering costs would effectively expand access to early-stage clinical trials. Today, the high cost of manufacturing and of running these trials means that only a small number of patients can participate. As an academic immuno-oncologist working in cell therapy at a prestigious U.S. academic institution—who asked to remain anonymous—told me, this often leads to heartbreaking choices. Because the size of academic grants allows treatment only for a handful of patients, he is forced to decide which patients receive the therapy and which do not.
We should also give patients greater autonomy in choosing their level of risk. In some cases, a manufacturing method might carry a slightly higher risk—say an additional 0.1% probability of an adverse event—but reduce costs by an order of magnitude. For a patient facing a terminal illness, that tradeoff may be entirely rational. A system that rigidly eliminates even small risks can inadvertently deny patients access to therapies that could meaningfully extend or improve their lives.
All of this may be in the domain of problems that everybody—at some level—knows about but doesn’t have the will to fix. There is, however, a sea change occurring in medical research. AI technology is producing genuine breakthroughs in medical cures—and those can be taken advantage of in societies like China (and, to some extent, Australia) with their smoother pathways to implementation. In the United States, though, the bureaucratic bottleneck makes AI breakthroughs all but moot. Until we fix the regulatory problem, the breakthroughs fade into insignificance.
Ruxandra Teslo is a fellow at Renaissance Philanthropy and co-founder of the Clinical Trial Abundance project. She writes about the intersection of science, culture, and policy at her Substack. She holds a PhD in Genomics from Cambridge University.
A version of this article was originally published in Ruxandra’s Substack.
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One related problem is developing and getting approval for new, usually minor, uses, of generic drugs. Although physicians may prescribe prescription drugs for off-label uses, conscientious physicians still need to see trial data that indicate that the drug will work for the new use. There is little of no incentive or government support for generic manufacturers, or the innovator company assuming the innovator is still manufacturing the drug, to develop new uses.
Just a super picayune pedantic detail: how is Australia's system 2.5x cheaper than ours? 2.5x cheaper would put them below zero dollars. I assume you meant that the US system is 2.5x more expensive, but you can't simply reverse that and say Australia is 2.5x cheaper. Similarly, when experts say we could reduce costs by 5-10 times, 5-10 times what? The math here is sloppy.