I. Setting the stage
Healthcare is a rare bird. We can neither disregard it nor make it cheap, simple, and safe (Fernandez et al., 2012). In fact, we globally spend more than 10% of the GDP in healthcare and about $1,000 per capita every year (World Health Organization, 2018). But this is not the whole story: approved drugs halve every 9 years (i.e., Eroom’s Law); population aged over 60 years is expected to grow by 56% over the next ten years (TM Capital, 2017); the workforce to meet patients demand is declining; and the need of having a higher quality of care and more control and transparency over individual healthcare are affecting the sector.
All this is creating the perfect storm for biohacking to emerge as a new healthcare paradigm that could finally provide a solution to the so-called healthcare “iron triangle” (access, affordability, effectiveness).
But there is another major trend that is suggesting that do-it-yourself medicine may be a substantial part of the future healthcare landscape. The convergence of artificial intelligence, cheap IoT devices, and blockchain is already improving both preventive and post-treatment healthcare in such a way that patients are not required to leave their houses to look for treatments. It is not hard to then imagine our homes as the hospitals of the future.
So biohacking, which today includes self-experimentation medicine, becoming a “grinder” (augmenting the body with DIY cybernetic devices), and measuring various biomarkers and behaviors toward a quest for personal optimization, may in fact represent the medicine of the future.
II. The opportunity and market dynamics
It is easier to estimate the size of an opportunity when trying to win or expanding a market, but creating new ones is way more complicated and this is the case for biohacking. This is in part a wellness business ($3.7 trillion according to Bosshart et al., 2018) where premium services are easier to charge (“wellness tourist” spends over 60% more than an “average international traveler”). It is in part related to the anti-aging sector, a $42.5 billion opportunity that will grow up to $55 billion by 2023 according to Orbis. But it is also a data business (a $14 billion), a smart drug business ($4 billion), and a synthetic biology one ($3 billion).
Hence, regardless of the precise estimates, it is a huge untouched fast-growing market, where every sub-segment is estimated to individually grow of about 20% by 2023.
Furthermore, there are two other dynamics emerging which should be considered: first, Goldman et al. (2013) proved that if the longevity industry actually increases life expectancy of about two years (a fair estimate of its effect), the economic benefit would amount to $7.1 trillion over the next 50 years (only partially due to reduced costs).
Second, we might encounter unforeseen spillovers, as for instance the growth of the “wellness real estate” market (i.e., homes designed to support the residents’ holistic health) or the use of this new data in several adjacent sectors (e.g., insurance, e-commerce, HR, personal finance, etc.).
III. The role of tech giants
So why the “Quantified Self” approach has not already taken over? Well, there are a series of technical, commercial, and ethical challenges that need to be overcome first.
Technically speaking, the major problems concern the scientific validity of certain DIY practices, as well as the lack of a shared general curated dataset. Artificial intelligence can help on the first aspect (especially because most of the biohacking processes can be simulated and A/B tested) while blockchain can be useful to address the second point, creating master patient indices and a single longitudinal dataset of patient records.
From a commercial point of view instead, most of the R&D requires a huge initial capital injection, which is historically a barrier that is splitting the sector in two (i.e., biotech vs pharma). Furthermore, both the hardware and software development require talent that is usually not easily accessible to the healthcare industry, and the continuous fight with the regulators make the whole picture even more complex.
Finally, the ethics behind it is cumbersome: there are concerns on privacy, security, and liability overall, as well as a general reluctance among practitioners to holistically consider biohacking.
Some of those challenges suggest why tech giants may be uniquely positioned to take on this industry. They own tons of data, and not only healthcare one, but information that may exponentially complement the profile of a person. They have talent, and many of them know how to deal with hardware R&D. They have access to health data coming from non-health products. They have a lot of money, but coming from innovative revenue streams that therefore create the right incentive to pursue longer-term goals and to change the traditional healthcare research paradigm. They already have an ecosystem of other devices, software, and services they offer, which makes the deployment incredibly easier.
Tech giants will then be no longer simple enabler, providing platforms or infrastructures to healthcare providers, but they will likely become healthcare operators themselves.
IV. What about technology?
But technology — i.e., software and apps, analytics, and hardware — can do much more.
The software side is the easiest to be embraced. There exist several apps to track sleep (e.g., Sleep Cycle), periods (e.g., Clue), to meditate (e.g., Calm), etc., which are user-friendly but also require a certain degree of self-discipline to implement any behavioral change.
The analytics side is getting easier to be managed because of the new wave of artificial intelligence technologies. Even though the idea of “citizen data scientists” (people with some sort of background that makes them able to run their own analysis) is spreading around, most of laypersons will need help figuring out what to track, what to do with their own data, and how to implement the changes and measure the effects (and this is what AI is here for).
Finally, the hardware side is likely the hardest to deal with. Devices like the FitBit, Apple Watch or the Oura ring are available to a premium consumer class, but they are far to be either perfect or cheap. The benefits of specialized hardware are clearly that automatically gathers data for you, and possibly provides real-time recommendations on the edge.
V. Unbundling the biohacking market
Even if today biohacking offer mainly includes self-help apps, the market and possibilities are wider. It can span indeed from genomics (e.g., 23andMe, Color, Day Two, Promethease, FoundMyFitness Genetics, The ODIN), to mental health (Ginger, Tao Connect, Touchkin); from synthetic biology (e.g., uBiome, Viome, Ginkgo Bioworks) to neuroengineering and brain-machine interfaces (e.g., Neuralink, Kernel, Paradromics, Neurable, BrainCo, SenseLabs, Muse); from biomarkers tracking (e.g., Chronometer, One Drop, Rise, Kurbo, Cardiogram, PhysIQ, Thriva) to supplements products and analytics (e.g., Examine, Labdoor, Sun Genomics, Care/of, Nutrigene, Multiply Labs); from food (Impossible Foods, NotCo, Beyond Meat) to anti-aging (Calico Labs, Unity Biotechnology, Life Biosciences).
But above all, this is nowadays a niche hardware market, with only a few specialized players operating in the consumer space (e.g., Lumen, Amina, Spire, Sano, Naked Labs) and even fewer on a larger scale (e.g., Bulletproof Labs).
What these players are introducing, in addition to pure product innovation, is new business models. We can already notice the use of traditional retainer fee models (e.g., per member per month; through licensing; per transaction), but also emerging pay-as-you-live models that reward healthier lifestyle or longevity-as-a-service marketplaces.
VI. Final thoughts
Barbi et al. (2018) recently showed that humans are nowhere near their maximum lifespan, if such a limit exists at all. This implies that biohacking is a huge market with endless opportunities for players that can today invest in analytics and hardware capabilities, support hyperconnectivity, foster disintermediation, and facilitate human-tech entanglement.
In order to get there, those pioneers have to be able to deal with the ethical implications of this discipline, as well as the deriving cybersecurity challenges and privacy issues. They also have to reconceive the healthcare paradigm from reactive to proactive, and get rid of traditional data dashboard in favor of an actionable automated analytical approach.
Barbi, E., Lagona, F., Marsilli, M., Vaupel, J. W., Watcher, K. W. (2018). “The plateau of human mortality: Demography of longevity pioneers”. Science, 360 (6396): 1459–1461.
Bosshart, D., Frick, K., Kwiatkowski, M., Thalmann, L. (2018). “Wellness 2030: The new techniques of happiness”. GDI Study №45.
Fernandez, J. M., Stein, R. M., Lo, A. W. (2012). “Commercializing Biomedical Research Through Securitization Techniques.” Nature Biotechnology 30: 964–75.
Goldman, D. P., Cutler, D. M., Rowe, J. R., Michaud, P., Sullivan, J., Olshansky, J. S., Peneva, D. (2013). “Substantial Health and Economic Returns From Delayed Aging May Warrant a New Focus For Medical Research.” Health Affairs, 32 (10): 1698–1705.
TM Capital (2017). “The Next Generation of Medicine: Artificial Intelligence and Machine Learning”. White Paper.
World Health Organization (2018). “Public Spending on Health: A Closer Look at Global Trends”. White Paper.
This article first appeared on Forbes.