For gene editing to achieve its potential, companies must solve an economic problem, not just a scientific one. The key is developing a manufacturing system that dramatically lowers costs, making one-time cures for the "long tail" of rare mutations financially viable and accessible.

The traditional drug-centric trial model is failing. The next evolution is trials designed to validate the *decision-making process* itself, using platforms to assign the best therapy to heterogeneous patient groups, rather than testing one drug on a narrow population.

The fear of toxicity pushes many companies to pursue the same few well-validated targets, leading to an average of nine assets per target. This hyper-competition not only crowds the market but, more importantly, leaves vast patient populations without effective options because their diseases lack these "popular" targets.

The Orphan Drug Act successfully incentivized R&D for rare diseases. A similar policy framework is needed for common, age-related diseases. Despite their massive potential markets, these indications suffer from extremely high failure rates and costs. A new incentive structure could de-risk development and align commercial goals with the enormous societal need for longevity.

Despite scientific breakthroughs and better technology, the cost per approved drug has steadily increased over the last 60 years. This phenomenon, the reverse of Moore's Law, is called Eroom's Law and highlights a fundamental productivity problem in the biopharma industry, with costs approaching $1B+ per successful drug.

True innovation in getting drugs to patients is not about pharma creating pricing models alone. It requires a multi-stakeholder partnership where payers, physicians, and manufacturers work together to solve problems for specific patient subgroups. This collaborative effort, not a unilateral one, is what truly saves lives and reduces costs.

With over 5,000 oncology drugs in development and a 9-out-of-10 failure rate, the current model of running large, sequential clinical trials is not viable. New diagnostic platforms are essential to select drugs and patient populations more intelligently and much earlier in the process.

Despite billions invested over 20 years in targeted and genome-based therapies, the real-world benefit to cancer patients has been minimal, helping only a small fraction of the population. This highlights a profound gap and the urgent need for new paradigms like functional precision oncology.

The fastest, cheapest path to drug approval involves showing a small survival benefit in terminally ill patients. This economic reality disincentivizes the longer, more complex trials required for early-stage treatments that could offer a cure.