The Theranos strategy lesson
At its pinnacle, her company, Theranos, boasted a valuation exceeding $9 billion, drawing investments from prominent figures such as Rupert Murdoch and Henry Kissinger. But the tide quickly turned when a Wall Street Journal article exposed Theranos technology as highly flawed, resulting in numerous misdiagnoses.
Yet Holmes had adhered to the playbook of successful Silicon Valley tech startups to the letter. Following in the footsteps of Steve Jobs, Bill Gates, and Mark Zuckerberg, she dropped out of college to pursue her vision for a portable blood-testing device that would revolutionize healthcare. She conceived her blood-analysis device as the “iPod of healthcare” and, after visiting an Apple Store, was inspired to collaborate with retail chains such as Walgreens to bring it to the public. Importantly, she adopted a strategy that had proven very effective for several tech giants, including Microsoft, Oracle and Apple: promoting an imperfect product, at times with excessive hype, to secure funding and feedback for improvement.
Why, then, did Holmes fail where Jobs and others succeeded? After all, as her defense attorney asserted at her trial, “Failure is not a crime; trying your hardest and coming up short is not a crime.”
Holmes’s blood-testing device was based on a technology that did not yet exist. In this sense, her vision was grounded in deep-tech innovation, the practice of harnessing the most recent advancements in scientific understanding to create technologies that were previously inconceivable. But the blueprint she followed to develop her company was tailored for recombining and refining pre-existing technologies.
Ultimately, Holmes’s fundamental error was in conflating technology startups with deep-tech entrepreneurship. The two pursuits demand fundamentally different mindsets, and her failure to grasp this crucial distinction proved to be her ultimate undoing.
De-risking a novel technology
Launching an imperfect “minimum viable product” to collect early market feedback, while demonstrating the potential of this new product, is a method that has been formalized by the lean startup movement. Holmes implemented this strategy by launching a prototype in retail chains. However, this trial-and-error approach to reducing market risk was never meant to de-risk a science-based technology that did not yet exist. Translating scientific breakthroughs from the research labs into practical applications can open unforeseen and, as Theranos shows, disastrous consequences.
Unlike their low-tech counterparts, deep-tech entrepreneurs must decouple the launch and development processes of their product, first maturing their technologies before deploying them in the real world. This decoupling extends time to market and precludes early revenue from deep-tech ventures. For example, new drugs and quantum computing technologies can take years of development, facing an extended period of generating no revenues. In fact, deep-tech investors frequently discourage entrepreneurs from seeking alternative revenue sources, emphasizing the sole focus on technology development. Instead of relying on early revenue streams as proof of concept, they must conduct controlled experiments and generate scientific data to convince investors.
Hence, the inherent risks associated with deep-tech innovation significantly increase the development time and financial uncertainties. Yet, over the last two decades, a quiet revolution has been reshaping the advancement of science-based technologies, holding the promise of substantially diminishing these very risks.
The deep-tech revolution
The scientific theories driving deep-tech innovation guide the imagination of innovators to the most viable solutions. Science empowers them to conduct insightful thought experiments, exploring scenarios where various elements of reality are altered.
Integrating AI and computer-assisted approaches significantly accelerates this discovery process, expands the range of potential solutions that can be imagined, and reduces the costs of experimentation. Because simulations and tests are conducted digitally rather than directly in the real world, the approach considerably mitigates the huge technological and financial risks of deep-tech innovation.
Terrapower, founded in 2006, sought to innovate in nuclear energy by repurposing spent nuclear reactor fuel, potentially providing a sustainable power solution for generations. Initially, the astronomical costs and perceived risks of building a reactor for testing were prohibitive. Using supercomputers for simulation, however, Terrapower’s engineers could assess the viability of their technology and make cost-effective iterations to gain confidence before constructing a physical nuclear power plant. This breakthrough revealed the startup’s potential and attracted early-stage investors, including Bill Gates.
The rise of computers and AI is prompting innovators to rethink their approach to deep-tech innovation. Innovators no longer simply ask what might work to solve a problem, but how to represent the problem in a way that enables computers to find a solution.
Perhaps more importantly, the rise of the machine allows entrepreneurs to start a deep-tech venture without engaging in costly scientific research.
Consider PhagoMed, a biotech firm founded by two former BCG consultants in 2017. Remarkably, they secured over $700K in seed funding without conducting a single lab experiment. Their groundbreaking discovery was primarily computational, leveraging publicly accessible databases to analyze viral DNA and proteins. They also efficiently outsourced the synthesis of their newly identified molecules for less than $20 thousand. In 2021, BioNTech acquired PhagoMed for an impressive sum of approximately $130 million.
A new imperative for leaders
The boundary between visionary leadership and overambitious risk in the realm of deep tech cannot be understated. As executives, there is a profound lesson to be drawn from the Theranos case: the importance of aligning innovation strategy with the technology being developed. The narrative serves as a critical reminder that while iterative development and rapid market entry are valuable strategies, they must be appropriately tailored to the technological maturity and inherent risks of the endeavor.
Leaders must foster environments where scientific rigor and robust validation precede market hype. Executives can draw from the Theranos lesson the courage to embrace the slow, often arduous journey of deep-tech innovation, while ensuring responsible stewardship over the technologies that promise to redefine our future. This balance of ambitious vision and prudent management is what defines true thought leadership in an era increasingly dominated by groundbreaking scientific advances.