David Shaywitz

I listen to a lot of audio, spoken word content that edifies (or at least distracts) me during daily workouts and when traveling.  Traditionally in December, I like to share with readers my annual podcast recommendations. But in reflecting on my listening habits of the last year, I realized that I’ve probably spent at least as much time listening to audiobooks, so I’m going to include some of those recommendations as well.

For HealthTech Entrepreneurs

Podcasts

Let’s get the two most obvious, and most obviously conflicted, recommendations out of the way first: Tech Tonics and The Long Run.

Tech Tonics: Since 2015, every other week, Lisa Suennen and I have shared the stories of “the people and passion at the intersection of technology and health.”  Our remarkable guests this year have included Susan Desmond-Hellmann, CEO of the Gates Foundation; Jerry Harrison, keyboardist and guitarist for the Talking Heads and now co-founder of the healthcare crowdfunding platform Red Crow.; impassioned physician-scientists Kari Nadeau (food allergy expert at Stanford), David Altshuler (human genetics, Vertex), Glenn Pierce (hemophilia entrepreneur, Third Rock), Allison Kurian (clinical cancer genetics, Stanford), and Calum MacRae (reinventing medicine from within, at Harvard); health care innovators Shami Feinglass (Danaher, when not BMX bike racing); Tanisha Carino (GSK, FasterCures, and now Chief Corporate Affairs, Alexion); Megan Callahan (head of healthcare at Lyft), Rebecca Kaul (chief innovation officer at MD Anderson), and Andy Coravos (CEO/co-founder of Elektra Labs) and many others – including Recursion Pharma’s engaging CEO Chris Gibson, who by this point is likely well-known to readers of this column (see here, here).  We also spoke with the brilliant and remarkably grounded data scientist Imran Haque, who I wrote about here; he subsequently joined Recursion.

The Long Run: In this biotech version of “Inside the Actor’s Studio,” Luke Timmerman, our own James Lipton, sits down with a number of different top biomedical innovators for an extended interview.  I loved this podcast from the first episode in September 2017 (featuring Alnylam’s John Maraganore). It remains a personal favorite.  This year, I especially enjoyed the episodes featuring strategist Janelle Anderson (as I discussed here); MIT’s incomparable biomedical engineer Bob Langer; and President of Novartis Institutes for Biomedical Research (NIBR), Jay Bradner.

A podcast that I discovered this year (though it’s not new) carries the dubious title, Invest Like The Best.  Don’t be put off.  The interviews, by seasoned investor Patrick O’Shaughnessy, are outstanding – thoughtful, nuanced, and engaging.  I have particularly enjoyed his conversations with Benchmark’s Bill Gurley, Lux’s Josh Wolfe, and Blue Mountain’s Michael Maubaussian – who I think of collectively as the “SFI Cabal,” because outside of their (busy) day jobs, they all seem to be connected to the Sante Fe Institute, an organization focused on the study of complexity and of complex adaptive systems. (Maubaussian is also a talented writer; I discuss one of his books, The Success Equation, here.)

I’ve continued to enjoy many episodes of A Healthy Dose podcast, hosted by Steve Kraus of Bessemer Ventures, and Trevor Price of Oxeon Partners; I would especially recommend their discussions with physician and serial entrepreneur Tom X Lee (co-founder, ePocrates; founder, One Medical Group; now CEO of the stealth newco, Galileo), and with Kate Ryder, founder and CEO of Maven.

Venrock’s venerable dynamic duo, Bryan Roberts and Bob Kocher, consistently find outstanding health policy guests for the Running Through Walls podcast. This year, I enjoyed their discussions with former Secretary of Health and Human Services and former Kansas governor Kathleen Sebelius, as well as the two-part interview with the brilliant Park brothers (here, here).

Two limited-run podcast series also merit consideration:

Moonrise, a fascinating series from the Washington Post’s Lillian Cunningham discussing the history of the space program, and the role of narrative in driving technology adoption, as I wrote about here.

StartUp – This was the first podcast series from Gimlet, co-founded by Alex Blumberg and Matthew Lieber. The first season was about the process of creating Gimlet; subsequent seasons were about other startups.  The latest, and last season (start here) is especially interesting because it describes what was going on at Gimlet when it was approached, and ultimately acquired, by Spotify. 

Audiobooks

Range, by David Epstein – my favorite book of the year (and like several other audiobook favorites, I re-read it after I listened to it), Range focuses on the underappreciated value of integrative, lateral thinking (versus the hyperspecialization that seems especially cherished today).  I’ve discussed this important and captivating book (for parents as well as entrepreneurs) here.

The Second Machine Age – by Erik Brynjolfsson and Andrew McAfee, this engaging book is widely regarded as the bible of the current digital revolution. The authors explore how new technologies are adopted, and what may be the impacts – mostly, but not entirely, positive – of the technologies on work, culture, and society.  (McAfee’s book explores how some of these themes relate to sustainability, in his conspicuously optimistic recent book, More From Less – my WSJ review here).

Elephant In The Brain – by Kevin Simler and Robin Hanson, this is an enjoyable exploration of social signaling, which turns out not only to be pervasive, but also, critical to resolving many behavioral paradoxes in the world around us.

Charlie Munger – Munger is one of American’s most admired, and most quoted value investors. In this book, Tren Griffin, a business strategist perhaps best known for his thoughtful, intellectually engaging writing about leading investors, seeks to help readers better understand Munger by offering explanations of Munger’s greatest hits, in a fashion reminiscent of the way ancient Talmudic scholars provide learned reflections on the Bible.  You may not emerge from Griffin’s book spiritually enlightened, but you will certainly have a deeper sense of the primary text, and the author behind it.

Nature of Technology – I discovered this Brian Arthur book by following a citation in The Second Machine Age. While initially somewhat academic and slow going, it gains momentum over time, as he shows how technology arises and evolves, and highlights the role of combinatorial innovation.  

Additional Audio Recommendations (Not Health-Related)

Podcasts

My regular podcasts (i.e. I listen to most every episode) include:

The Sub-Beacon — Squarely in the “middle-aged dudes chatting” genre (hard to understand the appeal, I know), this enjoyable weekly podcast featuring DC-area dads Jonathan V. Last, Sonny Bunch, and Victorino Matus is ostensibly focused on film reviews, but with long, delightful, detours into parenting, bourbon, steak, and The Gout.

The Bulwark – A centrist daily podcast offering thoughtful political discussion, with guests from across political spectrum; hosted by Charlie Sykes. 

The Secret Podcast – This is an irregular, supplementary podcast associated with The Bulwark, available with a minimal payment; similar in content but slightly rawer and more intimate, co-hosted by Jonathan V. Last and Sarah Longwell.

Commentary — A center/right, twice weekly podcast offering thoughtful cultural and political discussion led by Commentary editor John Podhoretz, who instills the program with a bit of a McLaughlin-Group vibe.

I’d also recommend these podcasts:

While I’m not quite interested in detailed legal analysis to sustain an interest in The Lawfare Podcast in general, I was captivated by a special multi-part series they put together, called “The Report” – essentially, a surprisingly successful effort to make Mueller Report more accessible and digestible.  You’ll need to dig the individual episodes out from within the Lawfare feed (start here), but it’s worth it.

Unorthodox – eclectic, hamish, Jewish-themed podcasts with excellent and often unexpected guests, some Jewish (like writers Bari Weiss and Andrew Marantz, and reporter Jodi Kantor), others not (eg pastor Henry Brinton, chef Edward Lee, reporter Clare Malone, and writer Sarah Blake).

99% Invisible – consistently delightful, engaging podcast about underappreciated aspects of design impacting the world around us; the show is a long-time favorite of mine, and more recently, my favorite podcast to listen to with my kids.

Audiobooks:

Age of Wonder – Richard Holmes takes us on a highly enjoyable romp through British science of the late 1700s, sharing the excitement of botanist Joseph Banks’ voyage with Captain Cook to Tahiti, astronomers William and Caroline Hershel’s revealing exploration of the heavens, and chemist Humphrey Davy’s discovery of laughing gas, among many other highlights. 

The Compatibility Gene – This is a story (really, a collection of stories) about the history of the discovery of the major histocompatibility complex, or MHC, a critical concept in immunology. It’s told in an engaging and scientifically rigorous fashion by British immunologist Daniel J. Davis.  Davis is also the author of The Beautiful Cure, about immunotherapy, which I reviewed for the Wall Street Journal last year).

Deep Work – A worthwhile self-help book by Cal Newport about the value of avoiding distractions, it was a significant influence in successfully extracting me from Twitter, as I discussed in Timmerman Report, here.

Dreamland – This account by California journalist Sam Quinones is the best book about the opioid crisis I’ve read; it’s moving, captivating, horrifying, astonishing – yet also (in a welcome contrast) nuanced, and not overly reductive in describing the historical context and potential solutions.

Factfulness – When you look at the data, it turns out that everything doesn’t actually suck.  That’s the point of this Hans Rosling book, which joins an expanding “positivist” subgenre also featuring Steven Pinker (The Better Angels of Our Nature), Andrew MacAfee (More From Less – see above), and others.

How To Fight Anti-Semitism (read by the author) – Motivated to write this heartfelt and brilliant book by the horrific October 2018 terror attack at the Tree of Life synagogue in Squirrel Hill, PA, where she was bat mitzvahed, New York Times columnist Bari Weiss characterizes the different sources of contemporary anti-semitism, and emphasizes the importance of recognizing and repelling such odious sentiment.

Antisocial (also read by the author) – This captivating account by New Yorker writer Andrew Marantz focuses on one of the sources of hate Weiss identifies: far right extremism, and takes readers insiders this movement, highlighting along the way the enabling role technology has played (see this excerpt).

American Carnage – This comprehensive, engaging, and deeply disturbing book by Politico’s chief political correspondent Tim Alberta takes us inside the 2016 campaign and documents the astonishing transformation of one of America’s major political parties.

The Secret Life of the American Theater – My guilty secret might well be my lifelong affection for musical theater, despite finding myself so bereft of talent that I failed to land even a chorus role in a high school production of Grease, where everyone was ostensibly guaranteed a part.  Growing up, I was fortunate enough to see a number of classic shows on Broadway, including The King and I, Camelot, Annie, City of Angels, and many others.  Similarly, the many Saturday mornings I spent working in the lab during my training were tolerable, if not enjoyable, due to the weekly airing of “Standing Room Only” on Emerson Radio’s WERS. About a year ago, I was delighted to hear about producer Jack Viertel’s book on American theater, and was even more delighted to listen to it (expertly read by noted actor David Pittu). In The Secret Life of the American Theater, Viertel breaks down the canonical musical into its core elements, and discusses how classic shows fulfill these elements (sometimes with remarkable creativity), or fail to (sometimes disastrously).  You can see how fixing a single, critical element can save a show – like the last-minute introduction of the “Comedy Tonight” number to establish the perfect tone for A Funny Thing Happened On The Way To The Forum.  The entire book is a treat, from start to finish – encore!

Astounding – The title of my column is a tribute to the pioneering science fiction magazine, Astounding Stories.  The real-life stories of the editor (John W. Campbell) and writers for Astounding (like Isaac Asimov, L. Ron Hubbard, Robert Heinlein, and others) turn out to be nearly as fantastic as the fiction they published, as Alec Nevala-Lee reveals in this deeply-reported account. You’ll never be able to think of Asimov or Hubbard the same way again.

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David Shaywitz

Biopharmaceutical and healthcare executives increasingly find themselves attending conferences and presentations featuring the evangelistic selling of AI by self-assured VCs, energetic entrepreneurs, and earnest consultants.  The promise is that AI will change everything.

Then the executives return to work, face the quotidian reality of their operation, and wonder whether AI will change anything.

Enter Jim Manzi.

Manzi, depending on your view, is a business operations guy with an affinity for math, or a math whiz who’s taken his talents to business operations. Manzi, as Lisa Suennen and I found out on a recently-recorded Tech Tonics podcast, scheduled to air later this year, enrolled at MIT at the age of 16 because he was bored with high school. Soon, he discovered he loved everything about the place, ultimately pursuing advanced math and physics. 

After MIT, he started a prestigious graduate program at Wharton, but then decided against academic life and dropped out after a year. He wanted to get to work, and soon found that he loved thinking his way through practical business problems.

At first, he pursued this through a quantitative management consulting firm. Later he started his own company, called Applied Predictive Technologies (APT), which enabled consumer companies to set up randomized controlled studies to empirically address real-world choices, like how best to position candy on a convenience store shelf. The company was acquired by MasterCard in 2015, for $600 million.

Jim Manzi

Manzi’s experiences at APT led him in 2012 to write a fantastic book, Uncontrolled, about both the utility of such experiments but also about the dangers of overgeneralizing from these sorts of studies to draw unfounded policy conclusions. As he told Lisa and me, “Knowing the confidence interval is as important as knowing the estimate.“

More recently, Manzi has started a new company, Foundry.ai, which essentially seems to offer “AI business solutions as a service.” The company seeks out pressing business problems that could potentially be solved by AI, and designs a fit-for-purpose solution. Projects that gain traction can potentially evolve into stand-alone businesses that might be offered by Foundry.ai to new customers with similar problems. Foundry.ai benefits by working closely with an initial customer to develop and refine a solution to a known high-value business problem, and the original customer benefits from having a pressing business problem effectively solved in a customized fashion.

As Manzi increasingly finds himself meeting with life science and healthcare delivery companies, I thought his perspective on AI might be especially relevant.

For starters, Manzi views AI as “a new name for an old thing” – as well as “the most overhyped term in the history of the world” (the irony of this description isn’t lost on him).

As Manzi sees it, “AI on television may be robots playing Jeopardy” but when “applied in real business settings to improve performance,” AI is basically “data plus math used to create statistical improvement in some repeated business decision process.” He tends to frame problems as “Is there a real opportunity to drive performance improvement?”

Consequently, Manzi tends to be on the lookout for “repeated processes.” This is pretty much the opposite of asking AI to conjure up the creativity required to make a new drug from scratch. But Manzi notes there are “business processes that happen repeatedly within drug discovery, within financial operations of a company, within the execution of randomized trials – solving those sub-problems is actually a much more appropriate use of AI.” 

As Manzi puts it, “The idea that AI is going to solve a problem that a group of extremely smart expert people cannot solve is generally a mirage; where that’s happened is in very defined problem spaces like Chess or Go.”

He adds, “When you get into an extremely unstructured problem space, doing something that no human can do, to me is a really heavy lift for an AI system.” What seems more suitable, he suggests, are tasks that “an expert human can do with reasonably good reliability” but with AI you can do “at way lower cost, way faster, and with greater reliability and lower error…. You have a more narrowly defined problem space.”

Manzi worries about the way AI is currently portrayed by some enthusiasts. “When I look at current AI technologies that I’ve seen up close and write code around, I think that there are extravagant promises being made and assertions being made about what it can do. It can do really valuable things – I build companies around it – that are enormously economically important and actually can improve clinical health outcomes.” But these solutions, Manzi says, do not involve wicked problems. 

Still, by improving repeatable processes and focusing on important sub-problems that end up consuming considerable amounts of people’s time and energy, you can free up some human ingenuity to work on the more wicked aspects of drug discovery. In this way, AI “may make it feasible to get you to a drug you might not have gotten to otherwise – a lot faster and a lot cheaper.” 

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David Shaywitz

Chris Gibson, the CEO and co-founder of Recursion Pharmaceuticals, may have captured the essence of the difference in mindset between tech VCs and life science VCs, writing in a superb recent article (a response to my previous TR post):

“The most consistent thread that differentiates tech and life science VCs is their willingness to project new data onto an industry and imagine resultant changes happening quickly.”

The operative word there is “quickly.” More on that in a minute.

To be sure, the issue isn’t whether or not VCs rooted in healthcare and life science can envision profound, revolutionary change, driven by technology. They see monumental changes coming to drug discovery, driven largely by widely available technologies (CRISPR gene editing, next-gen DNA sequencing, improved viral vectors, induced pluripotent stem cells, etc.)

In my summary of the 2018 JP Morgan healthcare conference I wrote:

“This feels like an unbelievable, almost magical time in biopharma – a colleague described it (in a good way) as science fiction coming to life. Biological technologies, approaches, and ambitions that might have been dismissed as fantasies only a few years ago now are part of the fabric of the industry.”

Century Therapeutics is just one example of a startup embracing several new technologies at once, with an eye toward creating new cell therapies. Century’s chief strategy officer Janelle Anderson discussed the confluence of factors on The Long Run podcast.

Janelle Anderson, chief strategy officer, Century Therapeutics

I reflected on the bold approach below:

“What’s apparent from Anderson’s description of Century, and from the work of other companies in the cell therapy space, is how incredibly audacious – and routinely audacious — biological engineering has become. Even the first-generation CAR-T approaches are astonishing, in that they introduce a genetically engineered fragment into a patient’s own cells – arguably an example of gene therapy, or at least a gene therapy-style technique. Then consider the approaches that Anderson describes – such as precise gene editing using CRISPR or similar techniques, as well as the use iPSCs; and it’s not just Century — aspects of these approaches are typical features of many company proposals my team and I evaluate. Any one of these elements would have been considered beyond fanciful back when I was training, or perhaps at best the sort of zany thing a radical biotech might contemplate. Today, these are the techniques and therapeutic approaches that most large pharmas are pursuing — aggressively.“

In short, life science VCs have no trouble envisioning truly transformational biological technologies. They routinely invest in companies they hope will radically improve the way disease is treated.

But of course, this isn’t what Gibson is saying.  He’s arguing that the revolutionary changes in computation that have radically changed so many other industries are about to radically change the ways drugs are discovered and developed. Recursion, specifically, is wagering on the usefulness of artificial intelligence to navigate some of the classic stumbling blocks in drug discovery. Given the enthusiasm so many life science VCs have for the types of advances listed above, Gibson wonders why so many life science VCs insist on remaining willfully oblivious to the coming AI wave.

At one level, I share much of Gibson’s long-term techno-optimism. New technologies have always driven the way science is done, providing powerful new approaches to investigating and better understanding nature, and revealing important new questions.   (See my recent discussion of the life cycle of technology revolutions here.)

But what sits less comfortably is the idea that VCs and their AI-wielding engineers have showed up to biopharma like the pros from Dover, expecting to “quickly” (to use Gibson’s word) transform drug discovery, then, presumably, move on to the next benighted industry.  (To be sure not to shoot the messenger here, let me emphasize Gibson has always impressed me as approaching his own work at Recursion with exceptional humility, and has consistently embraced a deliberate and long-term vision – listen to his interview this summer with Lisa Suennen and me on our Tech Tonics podcast, for example.)

The problem with thinking computation is poised to quickly change drug discovery and development rests partly in your definition of quickly. Does that mean next week, six months from now, or 5 years from now?  (My guess: much longer.)

There’s also the nagging question of where to begin on a problem as knotty as drug discovery. Many life science veterans would suggest that tech startups are focusing on relatively small aspects of relatively large and often quite wicked (as described by Hogarth, and later Epstein) problems. The engineers, while well-intentioned, often seem to lack a basic humility about the complexity of biology, the messiness of disease, and the extent to which complex human issues of health and illness often resist reduction to convenient bits and bytes.

Healthcare veterans, like many in biopharma, also feel like they’ve endured the promises of disruption from confident software entrepreneurs who are coming off of incredible successes in other domains, and, wanting to do good as well as do well, have decided to take their talents to healthcare. 

As former Facebook executive and newly-minted VC Chamath Palihapitiya explained in 2013: “Every interaction in every area related to health is just so sh*tty.  The software is crap, the services are crap, the people are crap. So there is a lot of value that people like us [i.e. engineers] can add because you have a very different perspective on how the system should work.” 

Unfortunately, while Palihapitiya is no longer a VC, healthcare has continued to persist pretty much unchanged, largely impervious to the disruptive efforts of Palihapitya and others.

Bob Kocher, a physician and VC at Venrock, puts it like this:

Bob Kocher

“Many entrepreneurs and non-healthcare investors look at healthcare and see a $3.3T sector growing faster than the rest of the economy using fax machines for communication, legacy software, and lots of expensive labor and can’t imagine that it cannot be disrupted quickly.  The thing they often fail to understand is that the economic incentives in healthcare may not reward the disruption or disruptor and that the bar for quality and reliability is super high since people’s lives are risk.”

Even so, and despite (and in a sense, because) of the many complexities associated with both drug discovery and with healthcare, I believe there’s a tremendous opportunity for emerging technologies to deliver profound impact.  Perhaps if some of these novel tech-driven approaches were introduced with a tad less triumphalism (the tidy manifestos, the “software is eating the world and now it’s time to eat healthcare” proclamations, the “prepare to welcome your new tech overlords” sensibility permeating so many of these endeavors) and a greater sense of authentic inquisitive engagement, it would be so much more constructive, and more effective.

I’m not sure the way technology changes everything is by arriving and saying “it’s time to change everything.” 

An enduring challenge – as I’ve discussed here and elsewhere – involves implementation, figuring out how to thoughtfully leverage technology (like improved computation) to make things better, and really improve productivity. 

Consider the classic example of the factory, described by Brynjolfsson and McAfee in The Second Machine Age (an example originally described by Stanford Professor Paul David in his 1989 paper, “Computer and Dynamo,” and expanded in a useful 1990 discussion, here).  

In the days before electricity, a steam engine-driven plant was built around the main (and only) power source, exploiting all three dimensions given the need for all equipment to run off of this.  With the arrival of electricity, Brynjolfsson and McAfee explain, it replaced the steam engine, but the architecture of the factory initially changed very little.  Thus, in one sense, the factory had been “modernized” by electricity, but had actually realized very little gain in productivity as a result of upgrading from steam power. 

What really changed things, it turned out, was when, over time, innovators figured out a radical new layout for the factory — the one we know today — where the equipment is spread out along a single floor, and each machine is individually powered, facilitating what Brynjolfsson and McAfee call “the natural workflow of materials.”  The impact of these so-called “complementary innovations” resulted in huge gains in productivity, which doubled or even tripled as a result. 

In a similar vein, perhaps, the arrival of cloud computing was hardly noticed, much less embraced, by healthcare and biopharma.  Yet consider the journey of one of the most successful and least widely appreciated tech companies that has made a fortune in biopharma: Veeva, a cloud software company founded in 2007.  It sports a market valuation of over $20 billion today. 

Veeva was created by former Salesforce engineers who approached biopharma companies with a relatively modest proposition: improve the efficiency of your sales team.  The idea was that  (a) the cloud platform would be especially helpful, given the geographic dispersion of sales reps; (b) the users, and designers, would benefit from almost instant feedback – salespeople can feel and can quantify the impact of improved information right away, and can help further improve the technology.  (In contrast, even if you improved the way drugs are discovered, it could take years to demonstrate this impact, and it could be challenging to attribute this directly to one factor or another. This is a key part of the rub when “quickly” trying to evaluate a new technology like AI for drug discovery.) 

Over time, Veeva has moved from sales into additional corporate functions, now offering a vast array of core services to many top tier pharmas, while managing to stay largely under the media radar. 

To me, this stepwise problem-solving model seems more relevant than the “this changes everything instantly” mindset.  Veeva’s approach is more similar to the way many transformative technologies actually find their way onto the scene. Success, in this case, was not born from some notion of Veeva as a self-declared disruptor of industry. Rather, it was an innovative young company initially offering something specific and useful, and then expanding opportunistically. 

Google, readers will recall, started off as simply an improved approach to search.  It didn’t start with much of a business model – certainly without the cost-per-click advertising model that has driven the company since. While Google has disrupted many industries, such as newspapers, this was hardly the original intention, which was simply to improve the way search is done.  

Biopharma and healthcare are positively replete with opportunities to do a great many things much, much better. Emerging technologies should be able to help. Ultimately, I suspect these technologies will come to permeate our organizations, and become as much a part of the firmament as PCR and Excel spreadsheets are today.

My guess is that companies that ultimately manage to deliver these transformative technologies to healthcare and life science will arrive upon the scene not with the pomp and circumstance we often see today from well-heeled startups and high-flying venture backers so adept at fomenting FOMO (fear of missing out). Instead, I would anticipate the future winners will approach the space more like Veeva, with little messianic bluster, and laser-focused on solving a critical, high-value problem. They will also be poised to progressively broaden their scope over time as they come to better understand the problems to be solved and are increasingly trusted by existing stakeholders and partners. More and more, they will be called upon to deliver the solutions life science and healthcare requires, and that patients both demand and deserve.

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[Editor’s Note: this is a new column called “Astounding HealthTech” by TR contributor David Shaywitz.]

David Shaywitz

If you want to know the difference between tech venture capitalists and biotech VCs, look at their respective views on AI applications for drug discovery.

Many of the most prominent AI drug discovery startups boast exceptionally rich valuations, driven by the enthusiastic participation of tech VCs. 

While many life science VCs view these opportunities as intriguing, they struggle to make sense of the often-stratospheric valuations.

For example, Salt Lake City-based Recursion Pharmaceuticals was valued this July at $699M, according to Bloomberg, while UK-based BenevolentAI was recently said to be valued at over $1B – a staggering figure that actually represents a haircut of about 50% from its April 2018 valuation, according to the Financial Times.

The thesis of tech VCs seems to be that these sorts of approaches will revolutionize biopharma, and anyone seeking to be competitive will need to acquire such a platform or risk becoming obsolete. 

Life science VCs, of course, recognize the limitations of contemporary biopharma drug discovery.  Successful drugs are so rare, and so hard to predict in early R&D, they are considered “a miracle,” according to the Novartis CEO and the head of research at Merck. Most life sciences VCs and research executives are wary that AI, however useful, may not represent the silver bullet for what they’re up against.

As David Weitz, the head of Takeda’s La Jolla site, who has thought deeply about this issue, recently told me in the context of this piece (disclosure: I work at Takeda’s corporate VC arm):

“The value of AI is demonstrated when its predictions are validated and the combined AI prediction / validation effort is catalytic. Do AI predictions provide insights not otherwise attainable by other methods?  Are the predictions actionable and have a reasonable probability of success to merit experimental follow-up? Taken as a whole, was a meaningful roadblock more efficiently overcome than traditional methods?

David Weitz

Even if AI supports a step in the drug discovery workstream, success only comes when the remainder of the drug discovery efforts are executed effectively. That may be outside an AI solution provider’s core expertise.  The ultimate measure is the efficient delivery of high-quality discovery candidates. The ‘how’ is secondary.

So build vs buy?  Neither.  Collaborate smartly where the partners synergize their respective capabilities.  Measure the program’s holistic success and secondarily the extent to which AI provided a breakthrough or an efficiency.”

This view seems to align with cautions raised by Derek Lowe in his recent critique of the breathless excitement around AI purportedly discovering a drug class via virtual screening. Lowe asserts this effort was not much better, if better at all, than traditional approaches. Even if the claimed AI breakthrough is found to be a legitimate reproducible advance over time, “the costs at this point are but tiny little roundoff errors in the total cost of a real drug development project,” he wrote.

This perspective seems generally echoed by noted life science investor and commentator David Grainger (as I recently quoted here).

“When you get under the hood, there isn’t really anything special [about the AI approach]… I think more data and better data science can revolutionize some human endeavors, but drug discovery is not one of them – it can help make better the parts of the process we do adequately now, but it doesn’t address the real rate-limiting step on innovation, which is understanding biology. Here, what is needed is not data and data science, but education about complex systems (a la the Santa Fe Institute). Current ‘AI’ approaches are so linear and reductionist, it makes me shake my head in disbelief at all the hype.“

As another well-regarded life science VC recently pointed out to me about one prominent AI-for-drug-discovery company, “What’s the business model?  I don’t think it’s there.”

What’s been demonstrated thus far hasn’t persuaded most of the “Where’s the Beef?” crowd that tends to populate life sciences VC firms. The tendency of life science investors is to especially prize assets that have generated some interesting data from experiments (i.e., they look like potential drugs). It isn’t unusual for even a so-called “platform” biopharma to be valued essentially based on the asset furthest along in clinical testing. Often, little, if any, value is placed on earlier-stage assets or the platform itself (see this relevant discussion of Nimbus model by Bruce Booth of Atlas, who describes a potential solution).

Tech VCs, by contrast, tend to break out the checkbook for exciting ideas that have seemingly boundless potential – especially ones that address large total addressable markets. AI for drug discovery can be put in that category.

Of course, extreme skepticism and extreme optimism aren’t the only ways to look at emerging technologies such as AI for drug discovery.

There’s always the hope – realized infrequently but not never – that a platform will truly prove to be the “gift that keeps on giving,” that a new approach will generate a series of attractive assets that will provide sustained value and ultimately revenue over time.

Unfortunately, this rarely materializes. A few disappointing examples I remember vividly, because they occurred at around the time I was there, was Merck’s acquisition of the GlycoFi platform (which was going to leverage a yeast-based platform to accelerate the development of biologics including “biobetters”) and Merck’s acquisition of Sirna Therapeutics, an RNAi company, which it acquired in 2006 for $1.1 billion and subsequently unloaded, eight years later, for $175 million to Alnylam Pharmaceuticals, with little if anything to show for either of these efforts.

Pharma executives have long memories for episodes like this. Battle scars from those disappointing acquisitions tend to remain sensitive for many years after the fact. They reinforce a sense of skepticism that the next shiny object will blow open biology or radically change drug discovery and development.

Tech investors, perhaps embolded by their experiences seeing tech deliver results in industries where this was said to be impossible, see in biopharma a high value problem that technology could help solve. 

My sense is that many tech investors view AI-enabled drug discovery as a “missing link” in drug development, and, tactically, believe pharma companies will be so smitten by the results of the approach that someone will acquire the underlying platform at an attractive premium. Meanwhile, at least right now, biopharmas seem to believe (as apparent in the comments earlier) that AI can perhaps slightly improve small aspects of drug development, but are unlikely to really move the needle.  Hence, as I discussed in a recent HBS podcast (listen here, read my summary here), pharma companies would much rather pay market value for individual assets (early-stage drugs), which the pharma feels comfortable assessing, versus paying for a platform of uncertain significance.

This creates both an opportunity and a challenge for AI-driven drug development companies.  The opportunity is that if the AI startup actually can create 10 (or even two) promising early stage drugs in the time it would typically take to create one, they could in theory sell each molecule and pocket a huge amount of revenue.  But the challenge is that many of these companies, while adept at AI, often have far less experience in making drugs, which requires an enormous amount of tacit knowledge and on-the-job experience. 

Once you’ve created a molecule, putting it through rigorous preclinical and clinical trials is also expensive. It’s non-trivial for an AI company to legitimately advance and adequately validate a number of candidate molecules. The pot of gold at the end of the rainbow, of course, is that if an AI-driven company could actually do this, and generate several promising candidates, then I imagine (as I said on the HBS podcast) either they’d make a lot of money supplying compounds to biopharmas, or they’d be in a position to acquire a biopharma for its late-phase clinical development capabilities, plus sales and marketing teams. I imagine this is the dream towards which many AI-driven drug developers are driving.

Presently, it’s difficult to know how the story will end; I am a huge fan of the phenotypic screening which is a key aspect of the efforts for a number of AI drug discovery companies – it was actually what led me to pursue my PhD in yeast genetics, as I was captivated by the power of screens and the opportunity they presented to discovery new biology.  (I suspect a similar excitement is behind the massive CRISPR screens that are driving both Maze and the GSK collaboration with UCSF and UC Berkeley that GSK’s research chief Hal Barron has set up; notably, both efforts involve pioneering UCSF biologist Jonathan Weissman, a former yeast biologist [disclosure: we attended high school together, he was a year ahead of me]); UC Berkeley’s Jennifer Doudna is co-leading the GSK partnership from the Berkeley side.

While it’s easy to see how the slew of AI-for-drug discovery companies might flounder and then fade, I can also envision – very easily – an upside scenario where one biopharma company takes the plunge, and acquires one of the high-profile AI-for-drug discovery startups. It’s exactly the sort of dramatic investment in innovation that CEOs love to do. 

If and when this happens, then other pharmas are likely to follow; as reluctant as so many are to be first movers in an uncertain new area, there’s a strong fast-follower reflex. Executives want to demonstrate involvement in this area before their boards start to press them on why they’re behind. 

If this occurs, the tech VCs investing in these AI-driven drug discovery companies are going to make out like bandits, even as many life science VCs continue to shake their heads, wondering what the fuss is all about.

[Author’s Note: I am thrilled to join the contributor roster of Timmerman Report with my Astounding HealthTech column. The title is a tribute to the iconic, mind-expanding, occasionally prescient (as I discuss here) science fiction magazine of the 1930s and 1940s. I expect to focus on the transformational opportunities and implementation challenges presented by emerging technologies in healthcare, biomedical discovery, and biopharmaceutical development, from next-gen sequencing and cell therapies to digital biomarkers and the meaningful application of AI. As part of this transition, I have now stepped away from Forbes, though my legacy posts will of course remain available on that platform. I have admired Luke for years, and have always appreciated his insight, instincts, and integrity, and it’s especially exciting and meaningful for me to have the opportunity to contribute to what he’s creating. I hope over time to deliver the insight and occasional delight that subscribers have come to expect from Timmerman Report.—David Shaywitz]

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