5
Mar
2025

Sluggish Corporate AI Adoption Has Motivated Entrepreneurs To Pick Their Spots 

David Shaywitz

As economic historian Carlota Perez has described, there is typically a significant time lag between when the promise of novel technology begins to emerge and the productive deployment of this technology at scale; TR readers will recall the discussion here from June 2023.

Today, we are seeing this with generative AI, an emerging technology that everyone is still trying to get their arms around (see this TR discussion).  The inevitable uncertainty associated with these early days has hardly discouraged large consulting firms, who have all developed “AI playbooks” and are busy persuading potential corporate clients that they are lagging their peers in AI and risk impending extinction. 

A Cautionary Tale From Big Pharma

Nevertheless, meaningful (vs performative) adoption of generative AI with most large enterprises has been predictably sluggish (see this TR discussion from August 2024).

Ziv Bar-Joseph

Ziv Bar-Joseph, the former VP-Head of R&D Data and Computational Sciences at Sanofi, candidly and generously shared on LinkedIn lessons learned from his recent experience at the large French pharma. These takeaways include (emphasis added):

– Pharma moves slowly. It takes over 10 years to develop a drug. So its not surprising that planning and decision making at big pharma can look very long and frustrate potential partners (and our own employees). Some of it is just bureaucracy. But much of it is intentional. These are often decisions that will have long lasting impact and its important to get them right.

– Adoption remains a major challenge. Not because of any principled objection to AI or new technology. But rather because it is very hard to change the way people work. Other issues affecting adoption of new technology are changing needs, people leaving and change in priorities and focus.

–  Sanofi is not an AI company. While it continues to develop cutting edge AI tools, Sanofi would prefer to purchase or partner rather than build AI products internally.  This makes economic and business sense. But it can be frustrating for our internal teams, especially when the decision comes after internal work already started on a similar product (usually because at the time we started the external solution was not available).

These findings are neither unique to Sanofi nor likely to shock regular TR readers. Yet Bar-Joseph’s insights emphasize a real challenge faced by the field: how to most effectively leverage AI when it’s brutally difficult to meaningfully implement AI in large corporations at a non-glacial time scale.

Don’t Make The Tool – Implement It

One answer that seems to be emerging – at least among impassioned AI investors – is to identify focused and more manageable opportunities (vs transforming a giant pharma corporation) and drive the AI mediated change yourself.

Cass Mao, a Silicon Valley tech entrepreneur, wrote recently on LinkedIn:

I know 10 different people leaving venture investing right now to do a PE [private equity] play with AI.
The basic thesis being: more opportunity than ever right now to drive value in SMB [small and medium sized businesses] using technology. But adoption is slow, super fragmented market, a ton of competition with 100s of new tools launching every week.
“Easier” to buy a small company, drive adoption, and reap rewards via direct ownership of the bottom line – a few million in personal upside within a few years.
vs. be deploying capital in the fast moving froth, high vals, high churn, high competition. so many tools funded and fighting the distribution war, competing with similar tools to seek adoption.
you’re seeing tools that cost $20/month eliminate **tens of thousands of dollars of cost**.
better to be the SMB with 50 different ways to save $30,000, than one of 50 SaaS cos charging $240/year.

In short, she’s suggesting that:

     (a) Cheap AI tools can save a significant amount of money if deployed effectively and in the right context;

     (b) If you can identify the right opportunity (basically a job amenable to your tool, and a small organization where you can actually implement the technology), you can do better implementing the AI yourself and pocketing the revenue from the efficiency gains vs trying to sell a particular AI tool in a very crowded market.

The question, of course, is how (or whether) this can be applied to biopharma.

Consider the following approach, suitably anonymized, but inspired by a real techbio example (there are probably a number of startups trying something similar).

Let’s say you’ve identified a specific, valuable aspect of drug development where you believe your technology (AI or something else) gives you an economically valuable competitive advantage. You might believe, for example, you have a more efficient way of running clinical trials. Such a company might then seek to in-license clinical-stage assets from pharmas, execute efficient clinical development past a value inflection point, and then out-license the further de-risked assets back to pharma at a higher valuation. 

The basic strategy itself isn’t particularly original, and startups, with conviction around one asset or another, in-license molecules all the time. The difference in this case is that because you believe your technology allows you to prosecute assets more efficiently, you focus on leveraging this perceived advantage, ideally by raising sufficient money (from entranced tech VCs, say) to fund enough shots on goal. 

Through your superior efficiencies of clinical development, you hope, you have a high chance of clinical, and hence financial success. (You, and especially your tech investors, may also hope your technology allows you to identify more promising assets than skilled drug developers alone, though I’m still very skeptical about this part.)

Beware of Pyrrhic Technological Victories

The success of this type of approach will depend, of course, both on how well the technology works and how much of competitive advantage it actually delivers – does it really move the needle for the tech-enabled company? 

Consider this example from genetics: metabolism of the blood thinner warfarin is strongly influenced by two genes, CYP2C9 and VKORC1. Genetic testing can determine whether you are likely to be a “fast” or “slow” metabolizer. Yet, utilizing genetic testing turns out to offer at best minimal advantages to the traditional clinical approach of “going low and going slow,” to arrive empirically at a therapeutic dose. Thus, while it might seem in theory that genotyping technology could improve care, in practice, most doctors haven’t embraced it because the impact seems less than the aggravation.

Similarly, techbio companies need to be sure both that their technology really works and that it imparts a meaningful advantage. It’s a tough ask, but one that’s clearly attracted the interest of both investors and entrepreneurs who are convinced about the promise of AI in biopharma and are intensively pursuing the highest-leverage place in which to deploy it.

4
Mar
2025

Flashback: Q&A on Founding TR

[Dear Readers: This Q&A from the Knight Science Journalism at MIT blog on Feb. 3, 2015 captures my thoughts on founding TR and where biotech was going. Thank you for your support. — Luke ]

By Wade Roush

For writers, part of the fuss about the Web explosion of the late 1990s was that it was finally possible to cut out the middlemen. Blogs meant you could reach readers immediately and directly, without all the usual apparatus of the commercial media—editors, publishers, advertisers, circulation managers—getting in the way.

Reprinted with permission of Knight Science Journalism Fellowships at MIT.

3
Mar
2025

A Woman in the TechBio Arena: Najat Khan on The Long Run

Najat Khan is today’s guest on The Long Run.

She is the chief R&D and chief commercial officer of Salt Lake City-based Recursion. The company is one of the first-generation companies that have sought to reinvent drug R&D from the ground up with automated technologies that collect a lot of biological data and then analyze that data with AI algorithms. Some call this approach TechBio or industrialized drug discovery or engineered biology. The basic idea is to make drug discovery faster, cheaper and to improve the probability of success.

Najat Khan, chief R&D officer, chief commercial officer, Recursion

Longtime listeners may want to go back and listen to Recursion co-founder and CEO Chris Gibson on The Long Run in May 2022, as he talked about the company’s origin story.

Najat joins Recursion now that it has a much bigger pipeline of drug candidates to test its founding vision. She has a variety of experiences in healthcare and drug discovery. She previously worked to integrate AI applications into the R&D operations at Johnson & Johnson, where she was chief data science officer and a senior vice president overseeing portfolio strategy. She has a wide range of experiences across the R&D enterprise, at both large and small companies.

This is an extremely hard challenge. Despite some of the breathtaking advances in AI of late, we will not be getting magic bullet cancer drugs popping off the AI assembly line anytime soon. A lot of thoughtful work needs to be done first to set up the platforms right, and to chip away with incremental advances at various bottlenecks along the R&D continuum.

Najat is one of the people in the arena, so to speak, who understands the range of challenges and is humble about what it will take to get the job done.

Now before we get started, a word from the sponsor of The Long Run. 

This is a message to drug hunters who are up for a challenge. Are you ready? Here it is: there’s a new prize competition to spur discovery of drugs targeting TBXT. It’s a transcription factor involved in a number of cancers. The competition is offering more than $500,000 in prizes to investigators or companies who identify potent TBXT binders. The great part is that you only need to come up with the compounds; the competition organizers handle biophysical evaluation of submitted compounds. All the resulting data is returned to you confidentially. And you keep all IP rights.

TBXTchallenge.org

And, some of you have seen that I’m celebrating the 10th anniversary of Timmerman Report.

Reaching this milestone has gotten me thinking about my various activities, and that includes The Long Run. My friends at The Linus Group helped craft a survey so you can provide me some feedback on what you like, what you don’t, and what you’d like to see more or less of from this podcast. It only takes 5 minutes, and if you complete the survey, you will be entered with a chance to win one of three complimentary annual subscriptions to Timmerman Report (a $199 value). 

Complete the survey

Now, please join me and Najat Khan on The Long Run.

24
Feb
2025

Rewiring, Not Retiring: Health and Innovation for the Vanguard Generation

David Shaywitz

Most of my columns tend to focus on the importance and difficulty of applying emerging technologies to biopharma R&D, aiming to accelerate the delivery of impactful medicines to patients. 

Yet, as long-time readers know, I have an enduring interest in how to sustain health and ward off illness, a discipline that’s sometimes called Preventive Medicine, Preemptive Health, or, the term I prefer, Functional Longevity. 

Over the years, I’ve examined what it takes to make behavior change last, explored the role of technology and health coaches in tackling obesity, shared my own weight loss and maintenance experience, and looked at the discovery and impact of the GLP-1RA’s.  I’ve discussed the limitations of existing digital fitness platforms, which seem disproportionately focused on helping the young and fit stay young and get fitter.

I’ve examined how corporate wellness programs have struggled to evolve, often failing when interventions are implemented in isolation rather than part of a holistic strategy (as British scholars Michael Kelly and Mary Barker have thoughtfully discussed).  Similarly, I’ve reviewed the disappointing results of an ambitious behavior change study, while continuing to emphasize that we still must proceed with hope rather than cynicism. 

I’ve also talked about the often elusive promise wearables offer to provide deeper health insights, and the challenges encountered by “Quantified Selfers.” 

At the heart of all these explorations is agency – your confidence in your ability to influence the world and positively shape your health and environment.  Most recently, I’ve considered how AI might enhance personal agency and improve health. 

The Vanguard

Of the many opportunities within functional longevity, one stands out: the inadequately served needs of a demographic I think of as the “Vanguard.”  These are folks (like me) who are over fifty and grew up in an era of personal technology affording unprecedented agency.  They’ve torn up in disgust the AARP membership card they were sent when they turned 50. For them, 50 isn’t an inflection point — it’s simply a continuation of the dynamic, engaged life they’ve always lived and intend to keep living. 

They are tech-savvy and highly engaged but also face unique challenges. Reading tiny text on a screen and interacting with clunky interfaces isn’t always seamless, and their expectations for fitness and social spaces often differ substantially from twentysomethings. But they aren’t retreating to the sidelines — they expect products and services designed for their reality and are ready to engage with those who take them seriously.

I see in this demographic a profound opportunity to enhance health, not just manage decline — focusing on movement, connection, purpose, and agency to extend healthspan without the hype of extreme longevity fads, miracle pills, or fringe biohacking. It’s about real, evidence-based innovations that help the Vanguard move better, think sharper, stay engaged, and live with purpose.

Venture investors, particularly those in the Valley, are famously obsessed with youth. Many tech VCs adore and pursue young founders, despite the considerable success that more experienced founders have achieved, as Ali Tamaseb has so thoughtfully discussed in Super Founders, and as Ben Cohen highlights in a recent WSJ column. 

The result? A flood of products built by young founders, for young users, leaving those older than fifty overlooked, underserved, naively caricatured – and often all three. It isn’t so much age bias as it is market blindness. This disconnect has created a massive gap in innovation — and a profound opportunity for those willing to meet the Vanguard where they are.

The Vanguard isn’t aging out — they’re leveling up; they’re rewiring, not retiring.  They need entrepreneurs who see them, meet them, and take them where they want to go.

This is one of the great unmet needs of our time — and an extraordinary opportunity for entrepreneurs, investors, and innovators ready to design, build, and deliver the future the Vanguard expects – and deserves.

24
Feb
2025

Defend Young Scientists

Luke Timmerman, founder & editor, Timmerman Report

Biotech thrives on the creative dynamism of young scientists. Always has.

Young scientists are under pressure. NIH grants are on hold. If the NIH budget is gutted, and a generation is forced to find other ways to earn a living, then the biotech industry will lose.

It might not be clear for the next couple of quarters or next couple of years. But inevitably, if fewer people pursue careers in science, biotech companies will find it harder and harder to find qualified people to do the work.

Biotech should stand up now to defend the next generation of scientists.

Young scientists have been feeling the squeeze for some time. They earn starvation wages for years as graduate students and postdocs. Grants from the National Institutes of Health are so competitive, so hard to get, that the average age of a first-time R01 grant winner is past the age of 40. This makes it difficult for students, postdocs, and early career scientists to stay in the game long enough to someday pursue the dream of an independent research lab with a stable source of funding.

Nobody said science should be cushy. Scientists should expect to work hard on worthy problems to earn support from taxpayer-funded grants. But these barriers to entry are too high. It discourages people from entering science, or sticking with science when times get tough as they invariably do.

People in their 30s are often at the most creative, most productive stage of their scientific careers. This is the time to be optimistic and ambitious. It’s the time for making some of the big choices in life — what career to pursue, where to live, who to marry, how to save up to someday purchase a home.

The thrill of discovery will always be a draw. But our society chooses the extent to which we support it. We can choose to whack government science budgets and systematically devalue work at the National Institutes of Health, National Science Foundation, and other agencies. If we do, it would be familiar to what we have already done with humanities fields like visual arts, literature, theater, and journalism. Our culture largely steers young people away from these career paths, knowing that these jobs don’t pay well.

We shouldn’t allow this to happen to science, especially at such an auspicious moment in time.

Consider the opportunities in advanced biologics. We are in the early phase of a cell and gene therapy revolution.  It’s a source of competitive R&D advantage of the United States. That ought to translate into a competitive advantage for manufacturing. Jobs in this industry come with good wages, good benefits, and good career growth potential. They can also be a source of pride and dignity – no small thing.

There’s reason to think there could be a lot of these jobs in the next 10-20 years. More than 1,000 Investigational New Drug (IND) applications for cell and gene therapies are on file with the FDA. There were 1,975 clinical trials for cell and gene therapies at the end of 2024, according to the Alliance for Regenerative Medicine. Most of the product candidates in those trials won’t turn into new FDA-approved products, but some will. Some will be big.

Jobs will have to be created in specialized, customized manufacturing plants for cell and gene therapies. Advanced biologics like bispecific antibodies, antibody-drug conjugates, and targeted radiopharmaceuticals are here today, and bound for growth. Many of these medicines could be Made in the USA. There are national security reasons, national competitive advantage reasons, logistical shipping reasons, and workforce development reasons to keep most of this work on US soil.

There are large swaths of the map outside of San Francisco, New York and Boston where this work can be done relatively inexpensively.

Biotech – at least its more established and profitable players — could choose to invest more in internship programs, apprenticeships, and public/private partnerships with states to develop the workforce needed to make these things.  

Cultivating people over the long term will require a shift in mindset. Like any profit-driven industry, biotech isn’t in the business of creating jobs for the sake of creating jobs. It pays to be prudent when making new hires. It’s logical to want people to be ready to hit the ground running. In a harsh, risk-averse investing climate like we’ve had the past 3-4 years, it’s essential for many companies to hunker down and think about how to get through just the next few quarters.

For those able and willing to think a little more long-term, however, there’s an opportunity to reconnect biotech with the public. By standing up loud and proud for young scientists, biotech can become a symbol of individual career advancement, national economic prosperity and a healthier future for all.

Biotech can be the source of hope and optimism. It can be a symbol of the American Dream, much like how heavy manufacturing played that role in the Midwest during the 20th century.

It will require extending a steady hand to young people. It will require taking chances on people who might not otherwise get one.

We should invest more in science education. We should invest more in cutting-edge research at our universities. We should invest more in workforce development for an array of biotech jobs.

Investing in people for the future of biotech could even help rebuild some of the trust that has eroded.

Biotech should stand up for the next generation.

18
Feb
2025

Defend the FDA

Luke Timmerman, founder & editor, Timmerman Report

We need a competent, well-supported, tough and independent food and drug regulator in this country.

We need that independent cop on the beat so we can have confidence that the medicines, vaccines, diagnostics and devices at the core of modern healthcare have passed the scrutiny of an uncorrupted, scientifically grounded and ethical group of people working in the public interest.  

If the new Trump Administration wants to focus on cutting waste and the thicket of senseless bureaucratic rules and craft attitudes that slow down innovation, OK. If it thinks regulation might work more smoothly if drugs and food were under separate management, I’m listening. If it wants to ditch industry user fees, as long as the FDA budget is fully supported by citizen-taxpayers, that’s not a bad idea.

But if this Administration cuts so deep that the FDA can’t do its job, because it thinks only a fig leaf of regulation helps business interests, then we have a problem. 

We’ll have a Wild West of medicines being shilled online much like vitamin supplements based on unsubstantiated or false claims. If the budget slashers go too far, we’ll turn back the clock to the snake oil days that would make impossible medicine’s “first do no harm” credo.

That would be a terrible mistake. Biotech and pharmaceuticals are brimming with potential.

The FDA has always had a complex relationship with industry. It has had, and will always have, clashes with industry over how much evidence is necessary to approve a medicine, what the right safety–efficacy balance is for a given situation, or what a valid clinical trial design ought to look like to prove the benefits of a medicine outweigh its risks.

The FDA has always had critics. Always will. It will make mistakes because no one, and no agency, is perfect. But at a high level of budgetary policy, the problems at FDA stem more from being understaffed than from being overstaffed.

The agency has been underfunded and neglected through its history. Members of Congress and Presidents don’t win elections on abstract ideas like maintaining a safe and effective supply of medicines, vaccines, diagnostics and medical devices. The agency is mostly invisible, operating in the background.

Big budget increases haven’t come from members of Congress. In the 1990s, AIDS activists were furious over the bureaucratic bottleneck that caused new drug reviews to drag on for more than two years on average. Those patient advocates banged on the door and demanded the FDA review new treatments for AIDS as if their lives depended on them.

That activism paid dividends. It led to the Prescription Drug User Fee Act of 1992, which has been renewed every five years since. That law created the user-fee structure that speeds up drug reviews, and which now delivers about half of the FDA’s $7.2 billion annual budget.

Questions

In the midst of reassessing the consistently underfunded FDA budget, we should be asking some hard questions:

  • How much of the agency’s budget should come from user fees? How much is too much?
  • Does the FDA have enough people to regulate the current and coming tidal waves of biomedical innovation?
  • Can it efficiently handle hundreds of new applications for cell and gene therapies, which are more complicated to evaluate than typical small molecule pills?
  • Does it have the facilities and updated high-tech equipment it needs?
  • Does it have credible leadership capable of hiring great people and inspiring them to fulfill the agency’s mission?
  • Will the new commissioner be capable of communicating the nuanced value of the FDA to Congress, the President, and the public?

In the spirit of bold, ambitious thinking about re-imagining government that’s more efficient and accountable to the citizens, I’d like to propose a few ideas to reimagine and improve the FDA.

Move out of Washington, DC. The FDA doesn’t need to be in Washington. The FDA has almost 20,000 employees. Not all are in Washington, but many are. Scientific review teams can do their jobs anywhere, and it might be best to do the job in a quiet place free from excessive influence from Congressmen, the White House, or industry lobbyists.

By moving the FDA out of Washington, the federal government could find some budget savings. The FDA has valuable land that could be redeveloped by private developers of mixed-use business and residential properties. FDA could take the proceeds and build an integrated campus in the middle of the country on cheaper land.

How about Wichita, Kansas (pop. 390,000) or Tulsa, Oklahoma (pop. 411,000) or Milwaukee, Wisconsin (pop. 561,000). These cities offer relatively lower living costs and high quality of life. They have diverse demographics with White, Black, Hispanic and Native American populations that closely reflect the population of the country. The FDA, operating down the street from healthcare providers in a place like this, could test promising  initiatives in a “real world” healthcare setting.

Done right, the FDA could gain valuable insights into how to operationalize things like telemedicine for clinical trials, remote patient monitoring, and more efficient ways to gather a range of data types from genotype to phenotype. These initiatives could modernize the clinical trial enterprise.

By doing what it does differently, and better, the FDA could speed up drug R&D and lower the cost of what it takes to develop a new medicine. It could do that without undermining safety, and in a transparent way that builds back public trust in biopharmaceutical R&D.

FDA workers could put down roots and be good scientific citizens in their communities. They would be parents of kids in local schools, and neighbors to ordinary people.

Double the budget over five years while reducing user fees. The FDA has always had to struggle for resources. When it succeeds, no one notices. When it screws up, it’s headline news. The FDA doesn’t get to bask in the glory of discovery like the NIH. Its public health mission sometimes puts it crosswise with powerful constituents, like drugmakers or tobacco companies or Big Agriculture. The FDA doesn’t have a lot of natural allies in Congress who understand or enthusiastically support its multi-faceted work. Its budget, at $7.2 billion for 2025, is small potatoes for an agency that regulates one-fifth of the US economy. How is it supposed to monitor manufacturing facilities and supply chains around the world to keep us safe from contaminated or counterfeit medicines?

For more than 30 years, the agency has long been kept on a tight leash in Congress and has been forced to rely increasingly on industry user fees. Almost half of the FDA budget—$3.5 billion a year—comes from these fees that companies pay for the agency to review applications.

That’s one way to save a few taxpayer dollars. But it also creates a financial dependence on industry, and a closeness that can sometimes get a little too close in ways that are subtle and hard to quantify. I think there’s a place for user fees in the FDA budget, much like we pay user fees to visit National Parks. But the lion’s share of the agency’s budget ought to come from us, the taxpayers. Robert F. Kennedy Jr. isn’t entirely wrong to question this arrangement, although he goes too far in making broad-brushstroke claims of corruption.

With so many groundbreaking biopharma products coming down the pike, the FDA needs double the budget just to keep with the anticipated demands on this overstretched agency.

Pay Attention to the New Commissioner. The FDA acting commissioner since Jan. 24 is Dr. Sara Brenner. She’s an agency veteran, a physician and a public health advocate. She has deep experience in the diagnostics group at FDA and worked on the COVID-19 response. That might make her a short-timer in the role, as there are still plenty of hard feelings in the public about the way the pandemic was managed.

At a moment when the agency needs to restore public trust and break out of some of limited thinking of the past, it needs a commissioner with excellent communication skills and a vision for a 21st century FDA. The next FDA commissioner needs to communicate to the public and advocate passionately with leaders on Capitol Hill and the executive branch. Scott Gottlieb was skilled at this part of the job and understood how to strike the balance of protecting public health while facilitating quality development of new products.

Marty Makary

I’m thinking of someone to lead the FDA who has public health experience, who believes in the FDA mission to the bone, who can communicate scientific risk / benefit equations to a Nobel laureate or your grandma, and who doesn’t have too many industry conflicts.

Dr. Marty Makary of Johns Hopkins University is the nominee from President Trump. He’s a physician and a health policy expert. He has written guest editorials in major news outlets and appeared on FOX. He’s written bestselling books. He was critical of government policies during COVID. I haven’t interviewed Dr. Makary, but his qualifications for the job appear solid. I would like to hear him outline his vision for the FDA in front of the Senate.

Get Ahead of the Trends with PDUFA VIII. The Prescription Drug User Fee Act, the governing compact that has set the terms of engagement between industry and FDA since 1992, is renewed and updated every five years. The current iteration of PDUFA is due to expire in September 2027. It may seem far off, but behind-the-scenes negotiations between industry and the agency often take more than a year. The industry and agency will have to discuss fundamentals like fee rates, support for new initiatives, and allocating resources to keep up with the agency’s mission. It’s not too early to think about a new bargain.

Besides the need for more staff, there’s always a need to stay current with information technology, and lab technology tools, to keep up with an industry that is well-funded and moving faster than ever. The agency also needs resources for staffing up far-flung field offices so it can adequately do facility inspections, especially with the vast array of generic drug facilities around the world and the boom in biologic manufacturing here and abroad.

If we don’t make this investment, we can expect a slowdown in new product approvals—an abundance of innovations that can’t get all the way to people. It would be an “innovation pile-up” reminiscent of a Third World country.

If we’re smart, we’ll invest now to get ahead of the curve.

A visionary and well-funded FDA could consider master protocol study designs that could cut down on some of the inefficiency that bogs down many clinical trials today, like legalese in informed consent forms or slow Institutional Review Boards that are inconsistent from site to site. There are too many small, single-site, investigator-sponsored studies—small and crappy trials that never yield clear-cut answers. The FDA is the one agency that can take the lead on demanding new standards.

It’s easy to overlook the FDA. It’s easy for companies, investors, journalists and former FDA people to criticize. Often, we’re right when we do. It needs our scrutiny, our tough, independent and fair-minded questioning.

But the FDA also needs our support. It deserves our most creative, constructive ideas on how to fulfill its mission. We shouldn’t take it for granted. The pharmaceutical industry wouldn’t be worth much if the FDA were to wither on the vine.

We can’t allow that to happen. I don’t think it will. Let’s revitalize the FDA, and put it in position to be great at what it does for the next 100 years.

17
Feb
2025

Zero-Toll Medicine: How Individuals Can Use Crypto and AI to Fix US Health Insurance

D.A. Wallach, general partner, Time Bioventures

The U.S. health insurance system is stupid, immoral, and infuriating.

It is time to get rid of it altogether and replace it with an intelligent, modern, and efficient infrastructure befitting the American people and the 21st century.

Incrementalism is not the answer. Solutions that add further fragmentation and complexity (including Medicare Advantage, Accountable Care Organizations, delegated managed care, and integrated managed care) ultimately obscure the need to burn it down and start afresh.

I propose a different model which I’m calling “Zero-Toll Medicine.” It starts by leveraging advances in AI and blockchain technology to deliver health insurance free of toll-taking intermediaries like private insurance companies and Pharmacy Benefit Managers.

Zero-Toll Medicine aims to put patients in control of their own healthcare spending, eliminate vast amounts of administrative cost and complexity, and drive far more efficient market-based competitive pricing. It is rooted in libertarian and socialist idealism alike, exploiting the virtues of decentralized decision making and markets while realizing the civilized goal of healthcare as a fundamental right of American citizenship.

Why Do We Have Health Insurance?

Insurance exists primarily to pool risk. We do not know at birth who will require substantial medical care over his or her lifetime, but we can be certain that many of us will. And as the philosopher John Rawls argued, we should design our society such that we would be OK being born into any particular lot in life, since none of us chooses his or her circumstances at birth. Pooling our resources to guarantee basic medical care to everyone no matter where we’re born or how our lives unfold should be uncontroversial given our enormous societal wealth.

Limits on this commitment must arise from the fact that medical care can be very costly. We are unlikely to agree as a nation to cover unlimited care for every individual without regard to how much longevity or happiness it might buy. Therefore, rationing or “managing” the amount of care is inevitable. Furthermore, we cannot be held hostage to medical care at any price, and so should desire that medicine exist within the market economy, subject to competitive forces that optimize supply via price signals.

These three goals: 1) risk pooling 2) rationing care and 3) competitive pricing, are in theory the functions of our existing insurance system. But it fails at all three.

  1. Risk Pooling: The current US healthcare system fails to pool risk efficiently because, unlike in other insurance markets, we (rightly) prohibit insurers from discriminating on the basis of pre-existing conditions, or pricing policies on the basis of individual risk (apart from generic characteristics of a patient like geographic location and age). In principle, if health insurance companies had the ability to discriminate, they could generate profits from careful underwriting and risk prediction. But in our system, they effectively must cover any patient who signs up. Furthermore, if we seek to maximally spread out costs, we should have the largest risk pool possible, one that includes everyone – people who are healthy and people who are ill and need more costly healthcare services. Our current system, by contrast, splits the population into thousands of smaller risk pools, among them: employees of individual companies (self-insured employer plans), poor people in each state (Medicaid plans), retirees (Medicare), and so forth.
  2. Rationing Care: US healthcare fails to ration care ethically because this rationing is largely done at the insurance plan level. Pre-authorizations, claim denials, and benefit design are managed by plans, which amounts to their “playing doctor.” This is not a job that insurers are qualified to do, and they do not do it well. Moreover, evidence-based medicine demands that a “standard of care” dictate what care is appropriate and medically necessary, and this standard should be universal vs. varying arbitrarily across different insurers. The care that is appropriate for a patient has nothing to do with who insures them.
  3. Competitive Pricing: Our system fails to drive optimal competitive market pricing and supply because it aggregates both supply and demand into oligopolistic blocs and removes decision-making power from the actual providers and users of care. On the demand side, each insurer effectively becomes a representative of tens of thousands of patients (and their employers, who hire plans and bear costs). On the supply side, large health systems and private-equity-financed provider roll ups are increasingly the counterparties in price negotiations. In other words, large insurance companies negotiate against large providers to set prices. None of these prices is transparent to the patient, making it impossible to shop around for the best deal.Unfortunately, this means relatively little true competition exists on either side. In many geographic areas, there are only one or two dominant healthcare providers where all the patients have to go. These providers therefore have tremendous leverage when negotiating with insurers. If a plan wants to operate in that geographic market, it has little choice but to ultimately contract with these health systems and take the prices on offer.

The dominant healthcare provider networks often negotiate opaque “omnibus” pricing schedules with insurers. Specifically, an insurer will contract with a health system for the full range of its services – surgeries, inpatient stays, hospital-administered drugs, etc. The insurer will be optimizing to minimize its overall expected costs, and the provider will be optimizing to maximize its overall expected profits. This means that different payers negotiating with the same provider may end up with very different prices for the same services. In other words, the same product (say, a joint replacement) will have a different price depending on the patient’s insurance company.

This excessive complexity begets excessive administrative operations. Many people are employed to do these jobs, managing the billing process. Every time a patient comes in for a service, providers have to figure out who is insuring the patient and what the negotiated price will be for that insurer. Then the provider will need to bill that payer for the service and manage collections. In several states, including California, if you go to a hospital, you will discover that the anesthesiologists, physicians, and hospital itself are all different commercial entities, each with its own contracts with your insurance company, tripling the administrative billing apparatus required to get paid for your care. If this seems like it doesn’t make sense, that is because it doesn’t.

Subsidizing Subsidies

What I have described so far are failures of our employer-based private insurance system, which covers just over half of Americans. The other side of our system is public insurance, which covers 36% of the population in total. Among those publicly insured are 93% of those 65 and older (Medicare), 36% of children (Medicaid), and 16% of working-age adults (Medicaid). Others are covered by the Veterans Administration and state programs.

The public system is itself a patchwork of government and private entities. For example, Medicaid is run by the states, but outsources substantially to private insurance companies in what are known as MCO arrangements (managed care organizations). Similarly, Medicare is run by the federal government, but a majority of beneficiaries now choose Medicare Advantage plans, which are also outsourced plans run by private insurers. Even Traditional Medicare (the original plan that is managed by CMS and not outsourced) outsources its claims processing and assorted functions to 12 “MAC”s (Medicare Administrative Contractors), privately-owned and somewhat mysterious vendors that also engage in care rationing across Medicare patients (for advanced diagnostics, as one example).

% of US Population Insured by Insurance Type. Source: U.S. Census Bureau, Current Population Survey, 2023 and 2024 Annual Social and Economic Supplements (CPS ASEC).

 

While there are imaginable efficiencies derived from these outsourcing arrangements, you can be sure that there is also a lot of graft and toll taking. If the government lacks discipline in spending taxpayer money, private companies one further layer removed are often even less scrupulous.

This is particularly true if there is little competition for these contracts. To take one well-publicized example, Medicare Advantage plans have pressured physicians they control to overdiagnose patients with chronic diseases in order to receive larger payments without delivering correspondingly valuable incremental care. The entire premise of Medicare Advantage is in fact questionable insofar as the government pays plans a premium beyond typical Medicare costs to manage each patient. The last government MedPac estimate is that we are spending $83 billion more than we would in Traditional Medicare to cover Medicare Advantage members.

In spite of this waste, Medicare and Medicaid still pay artificially low prices to care providers. And the majority of hospitals, including all non-profit hospitals, must accept these prices in order to qualify for their tax exemptions. The result is that many providers serve public-insurance patients at a financial loss and therefore need to make up for it (i.e. overcharge) their private insurance patients. Private-insured patients provide an enormous subsidy for public-insured patients. In fact, private payer prices are oftentimes negotiated explicitly as multiples of Medicare rates. This also drives a perverse incentive for hospitals to manage their “payer mix” in favor of patients with private insurance. While many hospitals take seriously their obligation to the common good, it is simply a fact that their financial health is imperiled if they take care of too many poor people.

The subsidies run in the other direction, as well. Since employer-based insurance is a tax-deductible expense, all taxpayers are subsidizing private insurance. And as economist Uwe Reinhardt pointed out, since employer-based insurance is a form of compensation, the deductibility of these premiums amounts to a regressive tax policy because the tax avoided by high wage earners (on “insurance compensation”) would have been at a higher rate than the tax avoided by low wage earners.

In short, employers subsidize public-insured patients, taxpayers subsidize employer-insured patients, and we redistribute wealth from lower to higher income workers in the process. These convoluted cash flows make it exceedingly difficult to calculate precisely who is getting screwed most.

The short answer: it’s all of us.

Radical Simplification

A smart and modern healthcare system would achieve the following goals:

  1. Enable every American citizen to afford a satisfactory level of medical care
  2. Ensure that medical care is evidence-based and transparent
  3. Create robust competition among healthcare providers and pharmaceutical companies to win the business of patients, driving efficiency, patient experience, and innovation

Decentralized ledgers and smart contracts, technologies originated in the cryptocurrency industry, enable a new model of American healthcare capable of meeting these goals with a radical new open insurance and payments system, “Zero Toll Medicine” (ZTM).

Here are the building blocks of this system:

  1. A new, open blockchain protocol, ZTM, on which every US citizen and healthcare provider would have a wallet. This wallet would function as a bank account specifically for government-funded healthcare spending and receipts.
  2. A minimal federal government health insurance agency, which could replace CMS. This “agency” would primarily comprise a computer codebase governing its interactions with the ZTM protocol and participants but would inevitably require some number of staffers to manage activities in which human judgment is unavoidable.
  3. A ZTM stablecoin pegged to the US Dollar.
  4. A library of composable smart contracts enabling programmable payments in the stablecoin.
  5. A computer-readable standard of care.

The aim of this system would be to directly fund patient wallets when they require basic medical care, and to enable them to shop and directly pay providers and product manufacturers for this care with no insurance companies, PBMs, or other intermediaries. The system would also allow patients to further fund their wallets with their own money (or supplementary insurance payouts) in order to spend it on services and products beyond basic care.

Basic Care

The term “basic care” is of course ambiguous and loaded. There is no alternative but for the country to determine what level of care it deems worth covering for every person, and this is a political and ethical decision, not a technical matter.

“Basic care,” then, amounts to whatever care can be paid for with the country’s total public health insurance budget divided by the number of its citizens, with the caveat that spending would end up unevenly allocated according to individual patient needs (as with payouts for all insurance).

In 2023, our country as a whole spent an estimated $4.9 trillion on healthcare. And at that year’s level of taxation, the federal government took in about $4.5 trillion of total revenues, of which it spent roughly $2 trillion on Medicare and Medicaid. In other words, the government currently covers about 40% of our total healthcare budget and employers/employees cover about 60%. That latter 60% is misleading since, again, taxpayers subsidize healthcare costs through the tax deductibility of employer-sponsored premiums and subsidies for exchange plans (Obamacare), to the tune of $300 billion per year.

In other words, we currently socialize about 50% of total healthcare spending while employees bear the remaining 50%. I say that employees bear that share because the premiums their employers pay on their behalf would otherwise be paid to them as wages.

If we were to eliminate employer-sponsored insurance, then, 1) wages would go up, 2) government revenue would go up since these incremental wages would be taxed, and 3) employees would be left without insurance, but with more income available to pay for their healthcare needs out-of-pocket.

Assuming healthcare spending remained constant, and all incremental tax revenue went to public healthcare spending, the 50/50 split between socialized and privatized healthcare cost-bearing would remain the same.

With the government covering 50% of costs but expanding coverage to all Americans (not just those currently covered by Medicare and Medicaid), we could only afford to cover a level of care substantially lower than that supported by Medicare today. It would be fairly simple primary care services for all. But as Amy Finkelstein and Liran Einav point out in their excellent book, Medicare today is “Cadillac” health insurance and represents a dubious choice society is presently making to give the elderly sterling coverage instead of providing everyone with basic coverage.

The latter should be our goal, and the level of this basic coverage can be increased should we wish to socialize more than 50% of health spending. I personally would advocate something more like 70-80% of cost coverage for everyone (members of Congress enjoy 72%), but the redistributive implications of such a choice would depend on the tax strategy funding it and are difficult to project. Ultimately, society as a whole pays for 100% of healthcare costs, so the share borne by the public sector is simply a matter of how much we choose to insure each other vs roll the dice on our individual future costs.

As Finkelstein and Einav also argue, basic care should begin with foundational primary care for every citizen. Today, we spend a mere 5% of our healthcare budget on primary care. Spending more on primary care for everyone could deliver cost savings by helping prevent costly chronic illnesses. This is one area in which higher spending is likely warranted and could bring down overall costs. The paradigm of preventive medicine requires investing in the health of patients before they are sick or injured, and like education, pays off over long periods of time and in diffuse, difficult-to-calculate ways.

In today’s private-insurance model, the frequent movement of patients between insurers (typically when they change jobs) disincentivizes investment in primary and preventive care since its payoffs are assumed to be too far in the future to generate an ROI. Moving to a public insurance model eliminates this irrational short-termism.

Zero-Toll Medicine would engage each citizen as a stakeholder in the healthcare costs of the entire society, and could provide direct incentives for individuals to proactively invest in their health. For example, each year, every citizen could receive a payment to cover annual primary care costs. When the individual spent this on a wellness exam, labs, etc., he or she could receive an automated tax credit of an amount sized to drive maximum compliance. Various other “gamification” mechanisms could be considered to drive this sort of virtuous behavior at scale.

Bundled Stablecoin Payments

Beyond primary care, patients would receive so-called “bundled payments” sized to cover the projected reasonable costs of basic care for most conditions or needs. In a non-acute context, patients would receive a diagnosis from their primary care physician, and this diagnosis, entered into the ZTM protocol, would instantly trigger a bundled payment to their wallet corresponding to the “episode of care” associated with it.

For those unfamiliar, an episode of care is a concept utilized in today’s so-called “value-based” healthcare models and describes a set of medical services and products required to treat a condition over a pre-determined period of time. For example, if a patient needs a joint replacement, an episode of care covering this could encompass pre-surgery appointments, the surgery itself, the medical devices used in the surgery, post-surgical doctors’ appointments, and physical therapy services for a couple of months. A “prospective bundled payment” would be a payment made prior to all of this care that is sufficient to pay for it.

Bundled payments are today utilized in a variety of insurance models with the goal of aligning incentives between those who pay for healthcare and those who deliver it. The majority of insurance arrangements today remain “fee for service,” wherein providers bill insurance for each individual service and product they deliver. Many have concluded that this incentivizes over-delivery of care by rewarding providers when they rack up as many charges as they can, and bundled payments arose as a means of setting a fixed budget for an episode of care.

These episode of care models typically allow providers to profit if they are able to treat patients for less than the budget, generally by letting providers pocket the difference between their costs of care and the bundle amount. This can, unsurprisingly, create the opposite incentive, to penny-pinch on care and risk under-treatment. Compensatory payment structures are meant to overcome this by, for example, tying final “bonus” payments to measurable patient outcomes, so that docs maximize their profits only when they have rendered verifiably high-quality care.

In today’s value-based care models, this kind of incentive structure tends to flow downward from the ultimate source of insurance funding. Medicare Advantage plans, as the most prominent example, receive per-patient fixed annual payments from the government to manage the care of individuals who enroll in them. This amounts to their taking on the “risk” of those patients’ annual costs, since if a patient costs more to care for, the insurer will need to cover it, but will do so at a loss. They may then enter into agreements with physician groups or health systems to delegate this risk.

The aspiration of any of these models is that, ultimately, someone – a physician, a group of physicians, or an insurance company – will have a financial motive to be cost-conscious while taking good care of patients. Zero-Toll Medicine embodies the same logic but puts the power of value-based medicine in the hands of patients themselves!

Power To The Patients

Concurrent with receiving a bundled payment, the patient and their physician would also receive an automated report summarizing the diagnosis and the standard of care for treating it.

This report on the standard of care would clearly lay out for the patient the evidence supporting the recommended course of treatment and the projected itemized costs for obtaining this treatment, which would add up to the total bundle amount. Based on the location of the patient, the report would also generate a list of providers in the area, along with the prices they charge for the recommended services and the outcomes their patients have achieved in the past.

Based on this outcomes data, the patient could also receive a prediction of the range of outcomes they might expect, and the probabilities of these outcomes. Importantly, this report would also include information on complication rates and malpractice incidents associated with providers, empowering patients with visibility into the risk profile of the providers they consider.

The same framework would apply to pharmaceutical products. There would be no insurance plan drug formulary, no PBM, no rebates, and no convoluted discount networks. The patient would be free to choose among the drugs approved by the FDA and indicated by the standard of care for their condition, and could purchase these directly from a pharmacy or pharmaceutical manufacturer.

This would put patients in a position of power to decide where they want to spend their healthcare dollars, and would dramatically invert today’s hierarchy, forcing physicians and pharma companies to appeal directly to patients to win their business.

Furthermore, this model would lead to “one price for one product,” since each provider or product manufacturer would be competing in an open market for the business of millions of individual patients. Real price discovery would occur as patients determined where they could get the best value for their money, and attempts at predatory pricing would simply result in low sales.

Open Information

The model I’ve described can only work if information that is currently secret and siloed becomes public and open. This includes the prices that providers charge, which they have in the past brazenly refused to publish even when the federal government mandated it. More controversial than prices are data on the number of procedures doctors have done, complication rates, malpractice, and patient outcomes.

But Zero-Toll Medicine can drive this information sharing simply by requiring it of any network participant. For a hospital, physician, device company, diagnostic lab, or pharma company to receive payments through the ZTM system, an ongoing real-time “credentialing” process could be mandatory. Specifically, to be approved on the network, a medical provider would need to verify its qualifications to deliver specific types of care and then would need to feed into the network real-time data regarding patient outcomes to remain approved. This credentialing could likely be almost entirely automated.

Infinitely Programmable Healthcare

The most innovative aspect of Zero-Toll Medicine is programmability, which is uniquely possible with a crypto protocol. By programmability, I mean that smart contracts can be composed to endow payments with limitless custom logic. Some examples:

  • If a patient receives a bundled payment for a particular episode, those stablecoins could be made spendable only for a set duration of time, and on providers specifically credentialed for the associated services. This would prevent, for example, fraud in which a patient uses a payment intended for a surgery and spends it on cosmetic Botox injections over several years.
  • A provider like a hospital could take on an entire episode of care from the patient and commit to certain outcomes. The patient’s payment for this could be held in escrow until the patient or a 3rd party oracle on the network validated that the outcome was achieved.
  • Similarly, a pharmaceutical company could offer a drug on a pay-for-performance basis, giving patients the choice between a lower up-front price and a higher performance-based price which they only pay if particular outcomes are achieved.

It is worth mentioning that the volume of transactions and speed required to support ZTM may be infeasible with today’s incumbent “L1” blockchains. There are ongoing efforts, including Improbable’s Somnia, to create “gigachains” with sub-second finality and low enough gas costs to unlock this kind of highly scaled use-case.

Private Funding

These ideas merely scratch the surface of automated logic that could be deployed over the ZTM network. Practically, there would be two types of funds flowing through it: government insurance-funded stablecoins and patient-funded stablecoins. The latter would arise to the extent that patients desired to spend more on their healthcare than the government’s basic care budget would allow.

Say a patient required an inpatient surgery and the government bundle for this was only sufficient to cover a shared hospital room, the patient could put additional funds into her wallet to pay for a private room. This would result in a wallet balance comprising some amount of “public stablecoins” and some amount of “private stablecoins.”

Many benefits of this network, and most obviously comprehensive visibility into healthcare spending, result from having all healthcare spending take place on the network. And so a major question would be how to incentivize all private spending to be done on network vs off network.

One possible solution would be to make private spending tax advantaged. In the surgery example I used, a patient could fund private stablecoins into their wallet, and upon spending them on the surgery, would receive a tax credit or deduction. Again, programmable logic could determine what healthcare spending would benefit from this treatment: perhaps a private hospital room purchase would not but paying for a higher priced surgeon with better expected outcomes would.

There is precedent for this sort of tax-advantaged healthcare spending in today’s HSAs. But rather than deduct contributions to an account (saving), ZTM would only provide a tax benefit upon spending on qualified products and services.

From the standpoint of providers and manufacturers, payments received through the ZTM network would seamlessly integrate public and private funded stablecoins. And recipients could cash-out into USD at any time.

Finally, supplementary insurance could easily coexist with this model. Patients desiring additional coverage beyond the basic care government coverage could purchase plans independently. But these plans would pay out benefits directly to patients or into their ZTM wallets. The goal again would be to ultimately push all spending into the network, irrespective of its funding source.

Summary

Zero-Toll Medicine proposes eliminating America’s existing public and private insurance systems and replacing them with a programmable digital infrastructure for public insurance to cover all citizens, yet compatible with private spending, as well. This infrastructure would enable Americans to freely shop for their medical care with no intermediaries constraining the choices they could make.

By putting patients in control of all healthcare spending, ZTM would lead to maximally competitive market price setting and replace the oligopolistic and opaque pricing schemas that hamstring medicine’s affordability today. By allowing patients to decide what they value, prices would converge with patient demand, not the demands of today’s corporate intermediaries like insurers and PBMs who imperfectly represent patient interests due to their own financial incentives.

ZTM’s programmability would create a dynamic infrastructure for democratically-determined priorities and regulations constraining publicly-funded healthcare spending and allow instantaneous enactment of these rules. It would create total transparency around usage of healthcare products and services, the quality and cost-efficiency of this care, and its safety.

While such an ambitious revolution in our system is difficult to imagine, business as usual will bankrupt our country and continue eroding the public’s confidence in government and medicine. The sacred cow of healthcare reform efforts has unfortunately been our for-profit insurance industry, which extracts rather than adds value. It is high time to dissolve it for good and reclaim America’s leadership among advanced nations in the innovative provision of public health.

Postscript: Q&A w Peter Kolchinsky

My friend Peter Kolchinsky, founder of RA Capital, raised helpful questions in response to this essay. I share them below with my responses.

Peter Kolchinsky, managing partner, RA Capital

  • PK: What if the algorithm doesn’t allocate enough credits to allow someone to afford the nuanced approach that escaped codification in the algorithm?
    • DAW: The approach I’ve proposed would leverage existing ontologies for estimating the services and costs appropriate for managing a diagnosis. For example, DRGs are today widely utilized for inpatient care. In the value-based care industry, Optum Symmetry Groups and Prometheus Episodes could be useful starting points. Any attempt to regularize complex care paths will be imperfect, but it is likely that we could cover 80-90% of patient needs to start, and then build out more customized episode definitions or even bespoke episodes for less straightforward situations. The ZTM protocol could be programmed to give diagnosing physicians more or less discretion in charting a patient’s prescribed episode. Less time-bound chronic condition management could similarly be approached in a more bespoke manner, or broken up into renewable episodes of a year at a time, for instance.
  • PK: What if there are complications?
    • DAW: To receive payments on the ZTM protocol, physicians would need to report outcomes into the network, which would make them fully transparent. Patients, regulators, and others could directly view any provider’s complete history of services provided and corresponding outcomes. This would make it straightforward to generate comparative performance analytics on providers and products in real-time. Critically, complications and patient harm would become matters of public record. There could also be Amazon-like patient ratings for providers or individual services rendered.
  • PK: What if physicians judgement results in upcoding and issuance of additional credits that enrich the doctor?
    • DAW: As I mentioned in the essay, this behavior is currently a scourge on the Medicare Advantage market. While dishonesty will always be impossible to eliminate wholesale, ZTM would put physician diagnosis patterns into the open. Whereas today, this sort of fraud has been visible only to individual insurance plans or CMS, with ZTM it would all be publicly visible. De-identified patient characteristics (demographics, etc) could be viewable for a provider’s patient population and the provider’s diagnostic and prescribing behavior could be compared to that of other providers. Bots on the network could even be programmed by independent citizens to constantly look for fraud on the network, and win bounties when they found it.I have separately proposed the creation of a new category of labor that I call the “medical accountant,” which would in my “Streaming Medicine” model become the primary point of contact for patients. This individual would charge patients a flat annual fee like an accountant to be their sherpa through the healthcare system, and would use AI software to accomplish many of the functions of a primary care physician today. Any sort of complex or specialty care recommended by this individual would not redound to their financial benefit. Furthermore, patients could choose to cut this professional in on any benefits or bonuses achieved through the efficient management of their health.Any untoward behavior emerging in ZTM could be rapidly disincentivized and extinguished through the programmability of the network and the ability to modify the smart contracts conditioning any stakeholder’s payments.
  • PK: What use is the credit to a patient if they don’t spend it? Can they pocket the money?
    • DAW: The goal would be for patients to spend as little of their bundled payments as they can to achieve the targeted outcomes of the care the bundle covers. To incentivize spending as little as possible, any bundled payments that are not spent should benefit the patient somehow. I have proposed as one possibility that this could result in tax credits, but other incentives could be considered. As with capitated payments in today’s value-based models, this runs the risk of patients not taking care of themselves in order to get financial benefits. These incentives therefore would need to be contingent on patients actually purchasing the care they had been recommended, and achieving solid, measurable outcomes.This is a tough needle to thread, but all things considered, I believe that putting patients in charge of these decisions will work better than putting intermediaries in charge of them. Patients have the most at stake when it comes to their own health, so while they may behave irrationality, so too do physicians, hospitals, and insurance companies, to whom we entrust these sorts of choices today.
  • PK: What if patients don’t spend credits on prevention and get sicker and have to be issued more credits? Will we regret letting the patient make a poor choice that sticks us all with the cost? Maybe better to have just said “You should get this preventative treatment and we’ll pay for it…. if you don’t get it, then you lose out, you don’t get to keep any credits, and we’ll all be worse for it… in fact, can we pay you to go through with the preventative treatment?”
    • DAW: As with the previous question, tuning incentives to drive the behavior that is best for patients and society at large will be a constant challenge and opportunity. ZTM provides a framework for programmatically optimizing and evolving these incentives over time.

 

D.A. Wallach is general partner with Time Bioventures. See more of his writing and speaking on SubStack.

13
Feb
2025

Defend the NIH

Luke Timmerman, founder & editor, Timmerman Report

Too many people don’t believe anymore in the American Dream.

But if you can’t dream big, you can’t accomplish big things.

Today, the National Institutes of Health — biomedical science itself — is under attack. It needs us to stand up in its defense. The NIH is an engine of the American Dream.

The NIH, with a $47.7 billion a year budget, is a force for human health and the economy. It has catalyzed the economy for decades. It attracts the best and brightest young scientists from around the world to come do their work here, to build their lives here.

Think about just one NIH project, the Human Genome Project. The budget hawks of the 1990s called it a costly boondoggle, a giant waste of money. It became a seed investment for the ages. It cost $3.8 billion between 1990 and 2003. What did we get? Through 2010, the estimated economic impact was $796 billion, according to a Battelle study.

That’s $141 of economic activity for every $1 invested by the US government. That’s right — a 141X return on investment!

The dividends don’t stop there. That calculation was made in 2010. Biotech would be in the dark ages today without that DNA code on every desktop, and the ability to sequence DNA at high speed and low cost. You can draw a straight line between that catalytic investment by the US government (and to a lesser extent the UK, France, Japan and others), and biopharma’s invention of dozens of cancer drugs, life-changing treatments for rare diseases, and mRNA vaccines for COVID-19.

The Human Genome Project is only one success story. There are many others. One 2024 study found that for every $1 we taxpayers spend on the NIH, we get $2.64 in economic activity. This is the best return on investment we get from federal government spending.

The NIH, composed of 27 institutes, supports the best work in cancer, infectious disease, neuroscience, mental health and other fields.

Ordinary citizens in the US have no idea that our taxpayer dollars can have this kind of impact. They have no idea that when we send our $47.7 billion to Washington for the NIH, 85 percent of that money gets circulated right back to the states that are home to so many vibrant universities.

People have no idea that the NIH money is divvied out on a competitive, merit-based grant review system. Or that the institutes themselves are led and staffed by brilliant hard-working people from around the world.

People have no idea that you and me — the US taxpayer — are by far the most powerful investors in biomedicine. No country in Europe invests at this magnitude. China? It has ambition. But it envies the NIH system, seeks to copy it, and knows it still has a long way to go.

Citizens in the US tend to have an exaggerated view of what billionaires like Bill Gates and Warren Buffett can do. Drinking from the poisoned chalice of political entertainment media for far too long, we’ve internalized the idea that government is full of screw-ups, and business titans like Gates and Buffett are the true wellsprings of science and innovation.

Generous as they are, their impact pales by comparison to the US taxpayers. The Bill & Melinda Gates Foundation, the world’s largest philanthropy, invests $5 billion a year across all its programs that include education, global development and global health. The NIH pumps $47.7 billion into biomedical research alone for the American people. The Gates Foundation knows this, and it’s why they are always talking about partnerships. They know the NIH is the beating heart of biomedical research.

We as a country just don’t fully appreciate what a gem we have in the NIH.

In my state of Washington, the late Sen. Warren Magnuson brought home the bacon that built the University of Washington Health Sciences Center and the Fred Hutchinson Cancer Center. These are sparkling stars in my community, and in the global scientific enterprise.

Sen. Magnuson operated in the optimistic post-WWII era, guided by FDR’s science advisor, Vannevar Bush. It’s worth re-reading Bush’s seminal document “Science, the Endless Frontier.” 

When Vannevar Bush wrote that visionary document in 1945, we hadn’t even discovered the double helix structure of DNA.

Imagine how much further we could go today with a reinvigorated NIH.

Instead of sabotaging our country’s global advantage in biomedical research with indiscriminate budget cuts, we should triple down on our investment. Here are a few ideas that bolster the Midwest, address increasing diseases, and build on our existing biomedical leadership:

  1. A new NIH intramural campus, located West of the Mississippi. The NIH campus in Bethesda, Maryland currently has about 6,000 research scientists. I’m thinking of a new 5,000 or 6,000-scientist campus in a Midwestern or Western city. It could be an attractive place for young families, a place with affordable housing, cultural amenities, existing biomedical building blocks, and good transportation bones to handle an influx of newcomers. St. Louis would work. It’s the home of Washington University in St. Louis, it’s the Gateway to the West, and a major American city that has lost 65 percent of its population since its peak in 1950.

Why do this? Planting a major research stake in the ground, outside of Washington DC, New York, San Francisco or Boston could have multiple benefits. We would get good bang for our taxpayer investment, with lower operating and capital expenses. A new NIH campus would spur surrounding economic development with housing, transportation, small business. It would send a powerful cultural message that this 21st century industry creates shared prosperity, not just prosperity for a few coastal cities.

Putting an NIH campus in St. Louis would give young scientists a place to spread their wings and get established – where they could make the same wage as in a place like Bethesda but where their salary would support a higher standard of living. Not only that, but a fresh new campus would create some healthy competition for Bethesda and help bust out of moldy Groupthink patterns of thought.

A St. Louis NIH campus could attract fresh new thinkers. I’m reminded of Nobel Laureate Mario Capecchi, who left Harvard University to go to the University of Utah to get away from some of the suffocating aspects. At Utah, free to do his own thing, he invented knockout mouse technology. (Capecchi, by the way, is a Holocaust survivor and orphan who found refuge in the US. He’s a poster boy for immigration reform, but that’s another column.)

  1. Build up four new cornerstone institutes of the NIH. We could support areas that need more basic research. I’m thinking of a reborn National Institute of Infectious Disease, a beefed-up National Institute of Mental Health, and a more generously funded National Institute of Neurological Disorders and Stroke. 

Why do this? These institutes are traditionally underfunded, especially compared with the big dog at NIH – the National Cancer Institute ($7.2 billion annual budget). But look at the future of disease burden. There’s SARS-CoV-2 and flu and malaria, TB and HIV. Then depression and anxiety and schizophrenia and bipolar disorder. Then Alzheimer’s and Parkinson’s and multiple sclerosis.

We’re talking about diseases affecting tens of millions of people worldwide.

Biopharma invests little in these areas because these fields are less mature than cancer, and because there’s less government-funded basic research to build on. There simply are not as many promising, short-term angles for industry R&D to attack. That was true for cancer in 1970. But then President Nixon and leaders in Congress (including Sen. Magnuson) made a big bet on the National Cancer Institute.

That investment is paying dividends today. The death rate from cancer has fallen 33 percent since 1991, according to the American Cancer Society.

We can achieve gains like this for many more patients. We are in a biology renaissance. The needs for human health are enormous and growing. The US government is by far the most powerful force in the world for this kind of catalytic investment. The NIH is capable of creating entirely new industries.

With NIH as the bedrock, along with a vibrant biotech industry, the US should be able to create hundreds of new drugs and diagnostics and vaccines over the next 100 years. We have a generation of talented young people that are yearning for purpose and meaning in work.

Let’s stand up against the cynicism and pessimism that is infecting our country.

Let’s defend the NIH budget. Let’s continue to lead the world in biomedical research.

Let’s continue to pursue this variation of the American Dream.

11
Feb
2025

Building a Fully Integrated Biopharma For the Muscle: Robert Blum on The Long Run

Robert Blum is today’s guest on The Long Run.

Robert is the CEO of South San Francisco-based Cytokinetics. He joined the company back in the beginning in 1998 and became CEO in 2007.

Robert Blum, CEO, Cytokinetics

This company, and Robert’s career, are emblematic of what this show is about — The Long Run that it takes to develop new medicines. The company has been in business more than 25 years and still doesn’t yet have any of its own products for sale on the US market. It has burned through more than $2.5 billion since the beginning, according to its most recent filing with the Securities and Exchange Commission.

The narrative of a relentless, but perennial development-stage company is likely to change this year. Investors expect it to win FDA clearance to start selling its first product. It is aficamten for hypertrophic cardiomyopathy or HCM. It’s a thickening of the heart muscle that makes it more difficult to efficiently pump blood. The drug is a cardiac myosin inhibitor that has been shown to improve exercise capacity and clinical outcomes for patients whose lives are constrained by HCM.

Robert is a fascinating character. He grew up in a Jewish family and went to Catholic school in Western North Carolina. He idolized biotech entrepreneur Bob Swanson as a kid. He has spent his adult life trying to follow the playbook of the full, vertically integrated biopharma company that discovers, develops, and commercializes its own medicines – the original biotech model envisioned by the Genentech co-founder. This is just about the hardest thing imaginable in business.

There’s a tenacity and grit to Robert that I think you’ll hear clearly. It was especially tested in the low moments, like when the FDA declined to approve omecantiv mecarbil, a cardiac myosin activator for heart failure.

This episode was recorded at the JP Morgan Healthcare Conference in San Francisco. We could have gone for another hour or two talking about some of the key events in the company’s history. But listeners will certainly get a sense for the man’s philosophy, how it got here on the cusp of building its own commercial team, and why Cytokinetics has had the staying power to remain independent and in position to compete with Bristol Myers Squibb in a high-stakes therapeutic category.

Now before we get started, a word from the sponsor of The Long Run:

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Have you listened to a handful of episodes of The Long Run? My friends at The Linus Group helped craft a survey so you can provide me some feedback on what you like, what you don’t, and what you’d like to see more or less of from this podcast. It only takes 5 minutes, and if you complete the survey, you will be entered with a chance to win one of three complimentary annual subscriptions to Timmerman Report (a $199 value).

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Now, please join me and Robert Blum on The Long Run.

10
Feb
2025

Rethinking Risk-Benefit in Sickle Cell Disease Therapy

Alan Anderson, MD

Pfizer’s decision last fall to withdraw voxelotor (Oxbryta) from the worldwide market is an example of how companies make risk-benefit calculations about medicines, and how those decisions vary widely from one category to another.

Cancer drugs have their own set of standards.

The risk-benefit calculus routinely accommodates uncertainty and severe side effects, if the medicine offers a modest and temporary clinical benefit, given the life-threatening nature of many cancers.

Sickle cell disease is a different story. Decisions to advance or shelve a drug candidate often appear to be driven by a more risk-averse model.

This is odd, because sickle cell disease dramatically reduces quality of life and results in a 30-year reduction in life expectancy. This different calculus for cancer and sickle cell disease patients is especially concerning when considering that sickle cell disease research and development suffers from chronic underinvestment and historical stigmatization.

Patients with sickle cell disease have never been given the R&D resources or clinical support they deserve. These patients have a debilitating, chronic illness. Their treatment options are few.

Pfizer released a statement on Sept. 25 that concerns over Oxbryta’s long-term safety profile and questions about its overall clinical benefit were key reasons for its market removal. However, these explanations seem at odds with the evidence from the HOPE clinical trials, which showed a significant, sustained improvement in hemoglobin levels, reduction in hemolysis, and a reassuring safety profile (NEJM article, June 2019).

Moreover, since FDA approval in November 2019, many U.S. physicians have reported tangible clinical benefits and manageable side effects in patients treated with Oxbryta.

This disconnect between Pfizer’s stated rationale and both the trial data and real-world experience raises questions about whether factors other than the established clinical performance are driving the decision to pull the medication from the market. It was also surprising that no alternatives were made available to clinicians, including the addition of warnings to the label or the development of a compassionate use program for patients with no other options.

When a medication like Oxbryta is removed from the market with little warning or input from the community it serves, the consequences are far-reaching. This decision, and others like it, limit the ability of clinicians to engage in detailed, patient-specific discussions about the trade-offs inherent in therapy choices. These conversations are standard practice in oncology.

Cancer patients are routinely informed that aggressive treatments come with high risks, yet the potential benefits may justify these risks. By applying a balanced risk-benefit framework to sickle cell disease, pharmaceutical companies could empower physicians to provide tailored, nuanced care, fostering informed decision-making even amid uncertainty.

Furthermore, the rapid removal of an effective drug like Oxbryta undermines trust among patients, clinicians, and advocacy groups. A more inclusive process that incorporates these voices can ensure that decisions are made with a full understanding of the lived realities of patients, whose needs and perspectives are often marginalized. Such an approach not only aligns with ethical imperatives but also has the potential to drive innovation by fostering a more collaborative environment among industry, regulators, and the community.

The case of Oxbryta should serve as a wake-up call: pharmaceutical companies must recognize that a one-size-fits-all approach to risk evaluation is inadequate.

Instead, embracing a more balanced and inclusive strategy will enable physicians to have the kind of comprehensive, informed bedside discussions that are the hallmark of oncology care. By adopting a nuanced risk-benefit framework for sickle cell disease treatment, we would honor the principle that every patient deserves the best possible chance at improved quality of life.

 

Alan R. Anderson, MD, Associate Professor of Pediatrics, University of South Carolina School of Medicine; Medical Director, Lifespan Sickle Cell Center of Excellence, Prisma Health, Greenville, SC

4
Feb
2025

DeepSeek Shocked Silicon Valley, but It’s Not Earth Shaking for Biotech

Simon Barnett, partner and head of research, Dimension

DeepSeek, the artificial intelligence (AI) research group owned by Chinese hedge fund High-Flyer, dominated last week’s news cycle—at least for 24-48 hours. The group launched R1, the latest in a series of cutting-edge large language models (LLMs). Investors panicked, erasing over $1 trillion of U.S. equity market cap in a single day.

Nvidia (NVDA), the maker of high-powered AI chips, shed $500 billion alone. Speculators feared the demand for Nvidia’s chip might dry up since DeepSeek found an ostensible workaround delivering stellar LLM performance on vastly less compute. The news sent shockwaves throughout Silicon Valley.

Could there be a similar DeepSeek moment for the burgeoning intersection of machine learning (ML) and the life sciences?

No—I don’t think so. The conditions leading to the market frenzy in the natural language processing (NLP) space are quite different from those in biotech—setting these domains on divergent courses.

THE STATE OF THE ML UNION

The frontier of NLP research is governed by so-called scaling laws. Chief amongst these is that training large ML algorithms using vast datasets and enormous gobs of compute will result in monotonically improving model performance. 

Big tech is betting these models will steadily breach performance thresholds wherein they can extract inexorably more economic value—first by displacing low-level tasks and eventually complex knowledge work. 

In the NLP realm, complex ML model architectures and Internet data are easy to come by. Compute is the scarce resource gating the progress of LLMs. Training large algorithms requires expensive, specialized hardware—principally Nvidia (NVDA) GPUs

Frontier NLP model development is thus a pay-to-play endeavor. Companies like OpenAI and Anthropic have contributed enormously to ML research. They’ve also leveraged the compute scaling narrative to corral the capital necessary to build veritable armamentariums of GPUs. The size and scope of these compute investments – in sheer dollar terms – functions like a competitive moat.

These conditions have shaped the industry into an oligopoly of well-capitalized, closed-source groups exchanging dollars for compute with the hope that scaled ML models will power a growing set of applications—forking the history of life on Earth.  

DEEPSEEK R1 CONSTITUTES A NARRATIVE VIOLATION

DeepSeek R1 seemingly violated the closed-source scaling narrative, casting uncertainty over big tech’s ML primacy.

R1 isn’t the most performant model, but it’s good enough to power most downstream tasks. Far more relevant are the facts that—(a) DeepSeek made R1 open-source under the MIT license and (b) DeepSeek claims R1 cost ~10x less to train and ~90% less to use than other contemporary LLMs.* 

The emergence of an inexpensive, open-source (ish) LLM has driven the value capture conversation marginally away from the close-source cabinet and more towards the application layer—to groups solving UI/UX issues and product/market fit questions. Understandably, big LLM providers aren’t static. They will rebut with their own competitive salvos. Perhaps they already have. 

BIOLOGY IS A SEPARATE BEAST

Biotech companies and academics alike are releasing scaled biomolecular ML models at a fever pitch, whether to predict protein-ligand complex structures, engineer therapeutic proteins, or surface developable small molecule hits. 

However, the dynamics governing the evolution of ML in the life sciences is unique. Market participants should be cautious about reapplying the parables of the current moment in natural language processing into the biotechnology field. 

While GPUs cost the same across ML domains, they aren’t the rate limiter (yet) in the life sciences. Throwing disproportionately large compute at sparse data results in overfitting, a dangerous phenomenon where models overstate their performance and struggle to generalize.

Data is the scarce asset in the life sciences. Biological data doesn’t expand ambiently like the Internet, with several billion users contributing to the information commons every day. Open-source life sciences datasets are relatively small and contain experimental artifacts, challenging ML model training. Even so, this public data has been enough to power innovation to date.

ML model innovation is perennially useful in biology. For example, AlphaFold2’s architectural novelties burst open the field of computational protein structure prediction in 2021 despite the underlying dataset—the Protein Data Bank—being available for years. 

The reason there’s unlikely to be a singular DeepSeek moment at the intersection of ML and biology is because we’re still inundated in DeepSeek moments. Acts of algorithmic clairvoyance have spiked the field in new, exciting directions.

As the open-source data wellspring dries up, however, ML in the life sciences may move in the opposite direction to NLP. The field may instead shift towards closed-source walled gardens that house high-throughput, experimental data foundries—the scarce asset that will imbue scaled biological models with economically valuable capabilities. 

 

 

*DeepSeek likely distilled R1 from other LLMs. They also did not include all the R&D costs they drafted off of to build R1, casting significant doubt on the stated training costs. The inference costs have replicated in users’ hands, making them much more viable. 

2
Feb
2025

Timmerman Report Turns 10

Timmerman Report is 10 years old today.

On Feb. 2, 2015, I rode my bike to the office on a wet Seattle morning and turned on the lights. I thought there was a need for clear, probing, contextual — and fiercely independent — biotech journalism.

The past 10 years of biotech have been remarkable. I’ve had a front-row seat.

It’s been a privilege to report on lifesaving CAR-T cell therapies for cancer and autoimmunity, a CRISPR gene editing cure for sickle cell disease, transformative medicines for hepatitis C, HIV and cystic fibrosis and more. A wide range of emerging treatment modalities are here today and arriving by the bushel tomorrow. I’m talking about antibody-drug conjugates for cancer, targeted radiopharmaceuticals, bispecific and multi-specific antibodies, targeted small molecule protein degraders, mRNA vaccines and therapies, small molecules that bind at allosteric sites, and RNA-based medicines. The underlying biology is being better elucidated, and the tools to intervene with disease targets are getting better.

The core challenge remains: How to bring these medical marvels closer to more patients, at a price that we can afford and that rewards the scientists and entrepreneurs who create them.

Thank you to all of the loyal subscribers who have made it possible for me to cover these seismic events. Special thanks to my advisory board: Vicki Sato, Bob More, Julie Sunderland, Rob Perez, Katrine Bosley and Roger Longman.

I’m grateful to have this platform for independent biotech journalism, and to steward it as a trusted source. TR seeks to understand, to inform, to enlighten, and to challenge readers to think. It’s about exploring the frontiers of science and drug discovery and thinking through the implications of it for society.

That’s not necessarily the formula for going viral. Yet the business is thriving, with more than 1,300 active, paid subscribers. The Long Run podcast has received 1.5 million plays since it began in 2017.

Timmerman Report is a platform for covering biotech without fear or favor. It is the beating heart of everything I do. TR has opened up avenues for me to give back, mobilizing the biotech community around worthy causes. Since 2017, my mountaineering campaigns have catalyzed the biotech community to give back more than $12 million to alleviate suffering from cancer, poverty, and sickle cell disease.

Those trips have been deeply meaningful. Friendships have been formed. Companies and collaborations have been born. I’m proud that we, together, have catalyzed so much good work. This will continue. The impact will grow.

Sometimes we all need to stop and think about what we’re doing, where we’ve come from, and where we’re going. I’m not taking anything for granted.

Thank you. For reading. For listening. For supporting quality independent journalism. And for doing the day-to-day, bit-by-bit work of improving human health and creating a better world.

Come celebrate the TR 10th anniversary with fellow readers and listeners.

TR10 East Coast Party / Cambridge, Mass. / Mar. 6

TR10 West Coast Party / Seattle / Mar. 13

Interested in joining HSBC Innovation Banking as a sponsor of these free and fun community events? Write to luke@timmermanreport.com.

Let’s imagine an even more vibrant biotech future for the next 10.

 

2
Feb
2025

The Future of AI and Health, Part III: Improving Health By Enhancing Agency

David Shaywitz

Part II of this series is here

“Agency,” Harvard’s Zak Kohane and I agree, is the word of 2025.   

Kohane’s reasoning: “Patients understand how to increase their agency in their disease journey with often correct and thoughtful instant second opinions from AI.”   

This perspective aligns with the opportunities described by A16z VCs Vijay Pande and Marc Andreessen, as discussed in Part II.

Agentic AI

While I’ll focus on human agency, it’s important to acknowledge that agency is also showing up everywhere in connection to generative AI technologies (although Kohane says “the agency of computational entities is less of a quantum leap by comparison”).

What’s excited the AI community are “AI agents” or “agentic AI” — AI-enabled tools that can perform a series of tasks with far greater flexibility than traditional, rule-based automation approaches. 

As data scientist Sahin Ahmed explains in a January 2025 Medium post, AI agents are:

“emerging as a significant force driving the next wave of technological innovation. AI agents are autonomous systems capable of perceiving their environment, making decisions, and taking actions to achieve specific goals. This burgeoning field is experiencing rapid growth, with the AI agents market projected to soar from $5.1 billion in 2024 to $47.1 billion by 2030.”

AI agents may also be useful in health and science. Kohane’s (and my) colleague in Harvard’s Department of Biomedical Informatics, Marinka Zitnik, along with her co-authors, published a visionary Perspective in Cell late last year offering a bold framework for “Empowering biomedical discovery with AI agents.” Such agents, the authors suggest, would work alongside human scientists, enhancing their abilities.  

This is where I get particularly interested – the opportunity, in the broadest sense, for technologies to enhance human agency. 

Human Agency

The idea that technology, particularly AI, can expand our agency is the central thesis of Reid Hoffman’s latest book, Superagency, which I recently reviewed in the WSJ (here).  

Agency – including the power of individuals (with a lot of luck) to shape the course of history, and our obligation, in the face of ambiguity, to try our best rather than shrug our shoulders – is also an important theme of several upcoming books I’ve read for review (stay tuned).

Martin Seligman, Zellerbach Family Professor of Psychology, University of Pennsylvania; Founding Director, Positive Psychology Center

Perhaps the most comprehensive perspective of human agency has been developed by University of Pennsylvania professor Martin Seligman, the father of positive psychology, whose work I’ve discussed for TR readers here in May 2021 and here in December 2021.

As Seligman explained in a 2021 talk at the MIT Media Lab (video here), his entire career has focused on the importance and plasticity of agency, which he says boils down the belief, “I can make a positive difference in the world.”

In an ambitious 2021 “psycho-historical” paper making the audacious argument that agency is responsible for human progress, he writes,

“When do we try hard? When do we break out of our sloth and overcome barriers that seem insurmountable? When do we reach for goals that seem unobtainable? When do we persist against the odds? When do we make new, creative departures? These all require Agency, an individual’s belief that he or she can influence the world.”

Agency has three components, he says:

  • Efficacy – I can achieve a specific goal in here and now;
  • Optimism – I can achieve this specific goal now just now but also far into the future;
  • Imagination – I can achieve many goals far into the future.

Historically, Seligman says, the field of psychology focused on misery. 

“Freud and Schopenhauer told us that the best you could ever do in life was not to be miserable.  So a successful life held suffering as close to zero as possible.  There was no theory of well-being.” 

His own initial work focused on depression, and he is perhaps best known for the “learned helplessness” model of this condition.

Over time, as he recognized resilience even in people facing difficult situations, he decided that he was wrong about the learned helplessness model. Helplessness is our default reaction, he concluded, and what can be learned is hopefulness and optimism.

He subsequently founded the field of positive psychology which he describes as “the generalization of optimism.” He introduced the “PERMA” framework, composed of five different, buildable elements, as discussed in TR here in May 2021. 

Critically, he says, “having PERMA is causal.” It’s not that being successful makes you happier. Citing longitudinal studies, and alluding to evidence-based interventions, he says “happy people are healthier, they live longer, they have better social relationships. They’re more productive at work. They’re better citizens. They’re more creative. And they’re more resilient.”

Referencing what he describes as an extensive literature, Seligman argues that “people who have high agency are physically healthier,” adding “If you look at people with high agency holding constant the traditional risk factors, they live on average 6-8 years longer,” compared to people with low agency.   

He continues, “Optimistic people — people who believe that they can control things far into the future, bounce back. They resist depression.  They succeed.  They try harder and give up less at school.”   They do better in college than their SATs predict, he says, and do better at work.  In contrast, “pessimistic people are less productive than their talents predict.” 

From a health perspective, he says “about 20 well-done studies” have shown that after accounting for “the usual risk factors,” it turns out that “being in the bottom quartile of pessimism is roughly equivalent to smoking 2-3 packs of cigarettes a day for longevity. “

Critically, he believes agency is teachable. Positive psychology, he argues, tells us “how to build efficacy, how to build optimism, and a little bit about how to build imagination – although if someone tells you they know how to build creativity, hold on to your wallet; that’s unknown.”

Seligman contends the way to build agency has been “well-defined,” adding “there are a couple of hundred articles on exercises that build agency and well-being.” 

He explains one approach, often referred to as “cognitive restructuring,” involving recognizing “the most catastrophic things you say to yourself when things go badly and then to dispute them realistically.” 

He continues,

“So if you’re rejected by your girlfriend, you have to be conscious that you’re saying, I’m unlovable, you’ll never find love again. And then we teach people to realistically dispute their most catastrophic beliefs, treat them as if they were said by a third person whose mission in life was to distort reality and to make you as miserable as possible.”

So far, this all sounds really promising. 

However, in my reading, data demonstrating that positive psychology (“pro-agentic”) interventions meaningfully improve health are, most generously, a work in progress. 

The field has been dogged by methodological issues like consistency in both approaches to interventions and approaches to measurement, as discussed by Feig et al in 2022, here, and by Kubzansky et al in 2023, here

“The existing literature is limited by small sample size, low study quality and inconsistent intervention content and outcome measurement,” Feig writes.

So where does this leave us?

Foundationally, I suspect Seligman is right about the centrality of agency, about its power and importance, and its role in enabling us, as individuals and collectively, to live better, healthier, more meaningful lives.

I am also excited by the promise of technology to help us build and enhance our sense of the agency Seligman describes, while recognizing the need for more persuasive data linking agency-enhancing interventions to meaningfully improved health. 

I’m particularly drawn to the health-promoting opportunities I see at the intersection of interventions seeking to enhance agency and interventions that seek to encourage movement (a priority discussed in TR here in November 2021). Imagine the promise of a fitness platform (like Peloton or Tonal), device (Whoop, Oura, Apple Watch), or app (Strava, All Trails) if supercharged by deliberate, PERMA-informed efforts to enhance our sense of agency. (The alignment of the Timmerman Traverse with these goals has not escaped TR’s notice.)

Data Agency

One important caveat here is to the ensure we’ve learned a critical lesson from the tribulations of the early quantified-self movement (see my TR discussion here from April 2021): simply measuring or tracking data doesn’t necessarily lead to insights or improvements.

As former quantified-self enthusiast and Wired editor Chris Anderson famously tweeted in 2016, “After many years of self-tracking everything (activity, work, sleep) I’ve decided it’s ~pointless. No non-obvious lessons or incentives 🙁”

The point here is that, generally speaking, simply delivering us lots of data may not translate to meaningful improvements in either agency or health.

On the other hand, I strongly share the optimism of Goldberg, Kohane, and others that generative AI tools are already helping to empower patients and caregivers, a trend that (as Andreessen suggests, see Part II) will only continue. 

One example highlighting both the potential and need was recently described in The Free Press: a writer’s mom, initially diagnosed with Alzheimer’s Disease, was subsequently discovered to have a reversible condition. It turned out her symptoms were caused by an occult leak of spinal fluid, which was identified and corrected. Her symptoms disappeared, her “life restored,” as the author put it.

While AI played no role in this story, it is the exactly the sort of unusual but transformative diagnosis that in the future, AI is likely to suggest. 

Harried physicians – especially in the case of patients like in the example above, who are evaluated for a spectrum of symptoms by a range of doctors – tend to focus on the most common conditions, aligned with their specialty.  If you see a pulmonologist for insomnia, she’s likely to suspect obstructive sleep apnea, obtain a sleep study, and recommend CPAP.  In contrast, a psychiatrist may consider anxiety or depression and suggest cognitive behavioral therapy or a medication. 

If you are a patient or a caregiver, it’s easy to imagine that an AI tool, uniquely, may have the patience to receive as much information as you are willing to offer; the ability to engage with you around the data you’re providing; and the insight (if that’s the right word) to integrate the totality of information and suggest potential diagnoses or at least next steps in a comprehensive manner, not biased by the specialty (or relative ignorance) of a given physician.

Finally, speaking of cognition and agency, an encouraging paper (originally flagged by Daniel Drucker) from Australia was recently published in Nature Medicine.  This study demonstrated that the delivery of four on-line personalized interventions targeting established dementia risk factors (“physical activity, nutrition, cognitive activity, and depression/anxiety) to older people “resulted in significantly better cognition in older adults over 3 years.”

While an animated discussion around potential methodological issues flared in response to Eric Topol’s LinkedIn post about the study, the possibility of reducing dementia risk through online interventions is tremendous exciting, especially if you consider this is likely the beginning of an optimization process. 

In the same way traditional combination medical therapies for conditions from HIV to cancer improved significantly through incremental advances (as I’ve discussed here), the establishment of a foothold for digital therapies might provide a meaningful starting point for future progress.

Bottom Line

We are living through a remarkably exciting time to pursue the promise of technology, especially AI, in health – even if no one really knows what this future will look like.  The use of technology to enhance personal agency represents a significant opportunity, and through the consumer-facing tools of generative AI, is already reframing the patient/doctor relationship.  The application of technology to develop and support our sense of efficacy and optimism – as well as to encourage us to move more – represents an area of exceptional interest and significant potential impact.

2
Feb
2025

The Future of AI and Health, Part II: Andreessen and Colleagues Weigh In

David Shaywitz

Part I of this series is here.

Over its 15 years of existence, the Andreessen Horowitz (“A16z”) venture capital firm has evolved from a media-savvy disruptive upstart to an exceedingly well-heeled incumbent and powerful force in Silicon Valley and the country. 

In April 2024, the firm announced a gargantuan $7.2B (with a “B”) fund.  For a typical VC firm with a “2 and 20” compensation structure that pays the VC a 2 percent annual fee for assets under management and a 20 percent share of the returns on investment, this would translate to $144 million annually in management fees alone, even if the firm didn’t generate any positive returns (which it apparently does).

That’s a lot of kombucha. As Nate Silver points out in On the Edge (a book I discussed in TR in August, 2024), a16z’s reputation helps it recruit top founders, leading to a positive feedback loop, which Andreessen tells Silver is “sort of a self-fulfilling prophesy.”

A16z made its first real splash with the Aug. 20, 2011 publication of Marc Andreessen’s essay “Why software is eating the world,” advancing the thesis that most every business was going to become a software business. This thesis proved both resonant and prescient. It positioned A16z as the VC firm that seemed to understand and could explain where technology was headed. 

On Apr. 18, 2020, in the midst of the pandemic, Andreessen’s effort to follow this up with a new call to action, “It’s time to build,” seemed to fall comparatively flat. 

Three years later, Oct. 16, 2023, Andreessen attracted considerable attention with his publication of the “Techno-optimist manifesto,” advancing his perspective that technology makes life better for humanity, so let’s not impede this progress with ignorant regulation (my reaction in TR in October of 2023 is here).

Marc Andreesen

In July 2024 Andreessen and firm co-founder Ben Horowitz, “both longtime Democratic givers…stunned the tech world” by endorsing Trump, the New Yorker’s Susan Glasser reported, adding that Horowitz subsequently announced “a significant donation” to the Harris campaign. In recent months, Andreessen has spent time with the President, seeking to influence national priorities on a range of technology and economic issues.

What does this have to do with biotech and healthcare? A16z has an abiding interest in biomedicine, represented by their “Bio+Health” vertical, which seeks to introduce tech innovation into these sectors, based on the firm’s tacit (and at times, not so tacit) conviction that they understand tech, and the future of tech, better than anyone. 

In early January 2025, Eli Lilly and A16z announced the establishment of a $500M “Biotech Ecosystem Venture Fund,” paid for by Lilly (thank you, Zepbound…), co-managed by both organizations, and focused on what A16z Bio & Health partner Vineeta Agarwala describes as “bleeding-edge technology,” Stat’s Matthew Herper reports.

Vineeta Agarwala, general partner, A16Z Bio & Health

In keeping with A16z’s pursuit of thought leadership, the firm offers many (many) podcast episodes focused on the intersection of technology and health.  Occasionally, these can feel like efforts to improve healthcare through the earnest application of tech analogies – the term “full stack” makes an appearance in nearly every episode.  Most of the time, however, these discussions are engaging and informative – and never more so than when Andreessen himself is featured.

One of the more thoughtful conversations I’ve heard about AI and health emerged from recent A16z podcast (imperfect transcript here) featuring Andreesen, who was joined by Bio+Health general partners Vijay Pande and Julie Yoo. Their discussion topic – “Can Tech Finally Fix Healthcare?” – could have been cribbed from the title of the 2013 book Lisa Suennen and I wrote, “Tech Tonics: Can passionate entrepreneurs heal healthcare with technology?”

To be sure, listening to the three tech VCs discuss solutions to healthcare woes inevitably evokes the famous SNL “Bill Swerski’s Super Fans” sketches (“Coach Ditka versus a hurricane: who would win?” “Ditka! Ditka!”) except that instead of Ditka, the response is invariably “AI!” 

Reflexive embrace of AI aside, the discussion touches upon several themes of likely interest to TR readers:

How AI enters health: The route “might not be tools,” Andreessen suggests, but rather “almost like the equivalent of hiring workers, full stack.” (Buzz!)

Yoo agrees. “The allocation of revenue within health care enterprises that goes towards technology or IT, as they would call it, has been an order of magnitude smaller in health care than other peer industries,” she points out, adding that AI is increasingly “packaged” or conceptualized as “labor units,” raising the possibility of tapping into the much larger HR budgets at healthcare organizations, rather than the IT budgets.

In health systems, Pande suggests, AI might start with “subclinical nursing and primary care. Then inching into clinical primary care, I think, would not be that crazy to imagine. Essentially, AI could be a great router.”

Vijay Pande, general partner, A16z Bio & Health

He continues, “especially if you think about what a primary care physician has to do, they have to ingest all this data and make a diagnosis and then send to a specialist. That’s increasingly literally becoming a data science problem.”

Where AI might struggle: Andreessen highlights “Morevac’s paradox,” which I sometimes think of as the technology version of the classic Gilligan’s Island joke: “What’s the deal with the professor – he can figure out how to turn a coconut into a radio but he can’t fix a hole in a boat?”

Look at technologies like ChatGPT, Andreessen says.

“They will paint art for you. They will compose music for you. They will debate, like, abstract philosophy for you. They will explain quantum physics to you, but they can’t pack your suitcase. They can’t clean your toilet, they can’t cook you lunch. They can’t do anything that a normal person operating in the world can do.”

He adds, “The more abstract, intellectual, knowledge-driven, data-driven, the task, the easier it is to get the AI to do it. The more applied, the physical, messy, unpredictable, the sort of all the human elements are probably the hardest thing.” 

(This may be an opportune time to point out that that the most challenging – and rewarding — parts of medicine involve these pesky “human elements,” and incidentally, nowhere is this more true than in primary care.)

Pande notes that a challenge for AI in the physical world is that “it has nothing to learn from,” in contrast to the digital world. 

Andreesen offers that the “Tesla self-driving car might be the reason for optimism here.” He points out that Tesla:

“tried different approaches to AI, and it turned out the thing that worked was basically put a neural network in the wild, embodied in the form of a car, and in fact, embodied in the form of a million cars, and then let those cars drive around and collect the data and learn about reality.”

He adds, “The more you take the rules out of the system, and the more you just expose it to the real world and let it gather the data and let it build up the neural network, the better it gets at self-driving.”

Big picture considerations: Andreessen places healthcare in a select group of industries, that also include education, housing, and law/government, that he says exhibit flat or negative productivity growth – we’re doing less with more, rather than more is less. 

In healthcare, specifically, he asks how is it that “we have some of the best doctors in the world.  We have the best technology in the world.  We pay the most and we have the worst outcomes. How is that possible?” (I’ve recently discussed this disconcerting paradox in The Bulwark, see here.) 

The fundamental culprit in all four problematic domains, Andreessen believes, is the combination of constrained supply (due in large part to excessive regulation, he says) and subsidized demand. 

“The political system is totally unable to deal” with this challenge, Andreesen argues, concluding (wait for it…), the answer “has to come from the private sector, and it has to come from the introduction of disruptive technologies.”

He points to television sets as a “case study in the opposite direction,” an example of what can happen when the markets are allowed to work, and technology is liberated. 

“The price of television sets has crashed as the quality has exploded,” he says. “You’re going to have a flat panel TV and 32K resolution that’s going to cover your whole wall. It’s going to cost $100, but yet it’s going to cost you a million dollars to send your kid to college.”

Enhancing the agency of healthcare consumers: Andreessen argues that “in healthcare from here on out … the existing system, left to its own devices, is going to degrade further and further. It’s going to give worse and worse results, at higher and higher cost over time. As a consequence, some percentage of people are going to try to break out of that.” 

This creates an opportunity, he believes, for companies that meaningfully help patients take control of their health and give them enhanced agency.

(As I will discuss in Part III, I couldn’t agree more strongly with the emphasis on agency, the contributions of agency to health, and the opportunities for technologies to enhance agency and sustain or improve health.)

Yoo points to the emergence of agency-enhancing technologies that are “totally dislocating the price curve.”

Pande then offers what I found to be the most resonant observation of the entire discussion:

“What we’re really providing patients is agency … this sense of agency is starting to really grow. Where people have this ability to monitor their health and then do something about it and then repeat. And that cycle, actually, I think, works for a lot of people. And being in control of your health feels very different than being at the mercy of a system.”

Thinking About Change. Citing Amara’s Law, Andreesen explains that “changes in technology take a lot longer to happen than you think they will, but when they happen, they have much more consequence than you think. They’re much more dramatic than you think.” (I discussed the arc of technology adoption in TR, here in June 2023.)

Andreesen notes that doctors have been famously frustrated by patients coming in with printouts from Google searches, and says this phenomenon is only going to grow, as patients have more sources of information. Not everything they dig up will be valid, he acknowledges, but notes “there are going to be things that actually work,” and these will spread.

(These sentiments, aligned with Pande’s focus on technology’s ability to enhance patient agency, have been strongly echoed by such leading lights as Carey Goldberg and Zak Kohane, who have emphasized (as discussed in TR in August, 2023) the opportunities generative AI has created for patient empowerment. We will return to the theme of agency in Part III of this series.)

Julie Yoo, general partner, A16Z Bio & Health

Yoo notes that physicians are also leveraging emerging technologies, citing social media as one example. (OpenEvidence, an AI tool I discussed in TR in October, 2024) stands out as well.)

The bottom line, Andreesen argues, is that while the talking heads may debate whether “a new technology is good or bad, and should it be adopted or not, those are all beside the point. Because the fact is, young people are just going to use what’s helpful and useful and they’re not going to have the emotional reaction. And I think that that will apply to many of the things that we’ve been talking about.”

Andreessen’s point about the adoption of technology is correct, though it’s hardly confined to the young (an enduring bias of some of Silicon Valley’s most experienced leaders; hopefully more will consider the data presented in books such as Ali Tamaseb’s Super Founders, as discussed in TR here in April 2021). 

Practitioners of all ages are astutely attuned to new approaches that offer palpable benefit, particularly once they see colleagues productively utilizing these tools. The rapid adoption of non-invasive prenatal testing is one example; the recent embrace of OpenEvidence is another (I’ve reviewed both in TR, here in October 2024).

Continue to: The Future of AI and Health, Part III: Improving Health By Enhancing Agency

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