Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Today’s guest on The Long Run is Christian Henry.
Christian is the CEO of Menlo Park, Calif.-based PacBio. It makes DNA sequencing instruments that are used by scientists around the world.
Christian Henry, CEO, PacBio
The company has long toiled in the shadow of the market leader – some would say monopolist in DNA Sequencing – Illumina.
PacBio was way overhyped in its early days, and crashed hard. It almost went out of business. But it always had a hard core band of loyal customers that allowed it to hang in there. I wrote a column in 2014 calling PacBio the “post hype sleeper” of genomics, predicting a comeback.
It has taken a long time, but that’s what happened. PacBio found a way to hang in there so it could continue to improve its technology on key parameters that matter for its scientific customers – cost, throughput, and accuracy.
PacBio’s most compelling advantage is that it offers “long read” sequencing. That means that its instrument can read long segments of DNA from a sample, before they get assembled back together into a whole genome. The longer read technology enables PacBio to scan especially tricky parts of the genome – long repeats and structural variations especially — and piece them together with a high degree of accuracy. Illumina, in contast, built its empire with “short read” technology, which had important advantages in terms of cost and speed.
A couple years ago, Illumina sought to acquire PacBio for $1.2 billion. That deal was scrapped after antitrust regulators in the US and Europe raised monopoly concerns.
This is where the story gets really interesting. Henry is a former Illumina executive, one of the key players in its rise to dominance. He competed against PacBio for years. He had retired from Illumina, and was sailing around the world with family. When the Illumina acquisition fell through, he took on a new challenge as CEO of PacBio. If PacBio was going to be independent, it would need to find a way to compete.
Over the past year, under new leadership, PacBio has executed on a bold growth strategy. Henry brought in some key recruits from his former employer. PacBio has raised a ton of cash, and now has more than $1 billion in the bank. It acquired San Diego-based Omniome – a company with short-read sequencing technology that’s supposed to deliver a higher degree of accuracy. That could help fill a void in PacBio’s product lineup that it offers to customers who want short-read technology, as well as long read technology.
PacBio is now worth more than $5 billion.
It looks like a much stronger competitor now than it did a year ago.
It’s a classic story for The Long Run.
Now, before we dive in, a word from the sponsor of The Long Run – Answerthink.
Answerthink has been consistently recognized by SAP, one of the largest enterprise software companies, as a top business partner for delivering and implementing SAP solutions for small and midsized life science companies. Their SAP certified solutions designed for the Life Science Industry are preconfigured, rapidly deployable and address fundamental business and IT challenges such as:
Explore how Answerthink can streamline your business processes to ensure growth.
Visit Answerthink.com/timmerman and get a copy of the e-book- “Top Three Barriers to Growth for Life Science Organizations.”
That’s Answerthink.com/timmerman
‘Art of Cells’ is the latest installation of Gibco’s campaign which explores the wonderful relationship between research scientists and their cells. We’ve searched far and wide for talented artists that express creativity in their own, incredibly unique style, and paired them with one of our research scientists.
Each artist was then tasked to create a piece of artwork that portrays the true beauty of cells. From poetry to embroidery, 3D animation to music, the ‘Art of Cells’ project promises to explore cell science in a way that has never been done before.
Discover the ‘Art of Cells’ at thermofisher.com/GibcoLoveYourCells.
Now, please join me and Christian Henry on The Long Run.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
David Shaywitz
When it comes to emerging digital and data technologies, most pharma CEOs today are singing from the same hymnal.
They all emphasize their commitment to digital transformation, and assert that the adoption of digital processes are key to their companies’ success, and vital for the industry’s future.
A recent Lazard survey of healthcare leaders echoes this message. The investment bank found that most senior executives believe “advances in digital technologies, data analytics AI and ML” represent the force that will “most transform the healthcare industry over the next 5-10 years.”
Outside of the C-suite, however, the view of emerging digital technologies seems noticeably more pessimistic. As one physician managing a large team at a big pharma wrote me this week, reacting to a STAT article on a disappointing clinical sepsis-prediction algorithm:
“Hard for me to mentally reconcile real world experience like this with all the hype around how we probably won’t need doctors in the near future because ‘AI will do the same work, but better,’ and we won’t really need drug hunters either because AI is going to solve that too.”
This operator candidly captures the current sentiments of most practicing physicians and drug developers, who’ve noticed that the promise of emerging digital technologies doesn’t yet appear to have found consistent, meaningful, impactful expression in their work.
If digital technologies are going to transform the industry, where is this change? And when are we going to start to see palpable and persuasive examples, rather than continuing to learn on theoretical use cases and rosy consultant forecasts?
For starters, we should consider the life cycle of innovation (a topic I’ve discussed at length here), based on the model of economist Carlota Perez.
Short version: it characteristically takes a long time — a really long time, on the order of decades — to go from a raw discovery to implementation at scale. It’s hard for people to get their heads around something that’s new. Often, a series of incremental tweaks are required before a novel concept can achieve widespread use.
This phase – trying to get our hands around emerging digital and data technologies – is exactly where we are now in healthcare. Figuring out how to apply these technologies effectively is both our abiding challenge and our remarkable opportunity.
Often, we seem to have a magical view, of technology in general and of AI in particular. There’s even a phrase – “enchanted determinism” – that’s been used to lampoon the nearly-religious view that some have of AI, the view that AI will somehow solve what ails us.
But AI is a tool not a deity, despite the reverential way it is spoken of by some impassioned advocates, entrepreneurs, and investors. It’s a useful tool for solving certain types of problems.
The next question is how do we begin to unlock and access the exceptional potential of emerging digital and data technologies in biopharma?
Here, we seem to be trapped between two extremes – magic and nihilism:
Magic in the sense of the belief that a thorny problem can be resolved simply by applying a powerful technology, generally AI. It can often seem like AI is pitched as the solution for every problem, including those that haven’t yet been identified. The tendency to exaggerate the potential of a new technology to promote adoption only adds to the challenge. The almost inevitable failures here tend to reinforce a sense of…
Nihilism, a belief that emerging digital and data technologies are so overhyped as to be functionally useless at best, and a waste of time and resources at worst. Busy drug developers often find themselves figuring out how to avoid getting dragged into what they see at the latest innovation initiative so that they can instead use the time to get their actual work done.
So why am I still so optimistic about the potential for emerging digital and data technologies to radically improve the way new medicines are discovered, developed, and delivered?
Short answer: because we’re learning.
To see how these trends are starting to play out, we can consider the example of Lilly, a company that, for the first time, was ranked top in innovation in Idea Pharma’s annual Pharmaceutical Innovation Index, released in April 2021.
Dave Ricks, CEO, Eli Lilly
Last week, at the “Future of Health Data Summit” in Washington, DC organized by Datavant, Lilly’s CEO Dave Ricks was interviewed onstage by Martin Chavez about Lilly’s approach to digital and data technologies. Ricks took the reins at Lilly in January 2017; Chavez is the former chief financial officer and chief information officer of Goldman Sachs and currently partner and vice chair of the global investment firm Sixth Street Partners. (Note: I have no relevant disclosures related to either Datavant or Lilly.)
Chavez’s interview of Ricks covered two general areas:
– Examples of impact/examples of continued challenge
– Approach to organization and talent
In 2019, about a year after Vas Narasimhan became CEO of Novartis and told the Wall Street Journal he aimed to “become a focused medicines company that’s powered by data science and digital technologies,” Narasimhan reflected on his early learnings.
On the positive side, he cited AI’s contribution to the company’s finance and clinical trial operations. But he also acknowledged that the “The Holy Grail of having unstructured machine learning go into big clinical data lakes and then suddenly finding new insights” remained elusive.
What was perhaps most striking about Chavez’s recent discussion with Ricks was how similar many of the themes were. Ricks similarly emphasized the use of data on the commercialization side – “pricing problems…spend optimization problems” while also pointing out this use case is “pretty well-trodden in pharma.”
Also like Narasimhan, Ricks highlighted the value of data in improving clinical trial operations, calling out in particular a clinical trial optimization capability, which he described as “a virtual tool” used by Lilly to run simulations seeking to optimize trial protocols by examining the effect of tweaking a range of parameters on outputs such as projected recruitment speed and screen failure rate.
Ricks seemed far more cautious about the contribution of AI to the discovery phase of their work, although he acknowledged the apparent success of BenevolentAI’s approach in identifying Lilly’s marketed rheumatoid arthritis drug baricitinib (a JAK kinase inhibitor marketed as Olumiant) as having potential application in the treatment of COVID-19.
Subsequent clinical studies have documented the utility of the medication in a subset of hospitalized patients, as MGH infectious disease physician Rajesh Gandhi concisely summarizes here. Gandhi tells me the data suggest “baricitinib has an important role in hospitalized patients who are rapidly worsening and have evidence of inflammation.”
More generally, however, Ricks remained guarded about the application of AI to discovery. “The idea that you could just give a biology problem to a computer and it will tell you what a drug design is for it is not a reality, and it’s not going to be a reality for maybe a decade or more,” he said.
Asked by Chavez to identify the key obstacles in leveraging data, Ricks explained that “in life sciences, in general, the problems…are the big ones, which is we don’t have enough data or enough understanding about how to organize it to simulate the human organism. I think that’s a big problem.”
In contrast, Ricks said, “The easier problems are from the market working in.” In Ricks’s view, many of the commercialization challenges in pharma are not fundamentally different from other sectors, “there’s just different data.”
Manufacturing challenges in pharma, Ricks suggested, are also similar to other industries. As Siebel mentions in his book, Ricks pointed to the application of data tools to optimize predictive maintenance.
While clinical trials are obviously not a component of most other industries, they also represent, Ricks says, an “operational problem” and thus are also ripe for data-driven optimization that doesn’t rely on deep biological understanding. “We don’t need to envision how liver cells interact with heart cells to understand this,” he explains. “We just need to know our own operation and then work on ways to optimize that.”
Ricks argues Lilly’s efforts here over the last decade have reduced drug development cycle time (the time between IND filing and FDA approval) significantly, from over 12 years to under six years on average). He said Lilly,and the industry more generally, could get even faster in drug development, saying five years from IND to NDA is an appropriate goal.
In contrast, on the discovery side, Ricks doesn’t expect similar holistic, data-enabled improvements in the overall process, but believes “the discovery side will be more about tools that become platforms that eventually become solutions.” He adds, “we have to start somewhere.” In other words, AI might not comprise the whole “toolbox,” but might offer “a tool or two…that can help the human medical chemist be better at their job, speed up certain processes, and allow you to find success.”
Several specific data tools have yielded promising results in discovery, Ricks says. For example, the company has had some success “applying ML tools to very specific organic chemistry problems.”
He also highlighted the company’s positive experience using computers to profile protein structures, and predict which chemical entities in a known library are likely to have the best fit – a useful shortcut, he says.
One pleasant surprise: the utility of algorithms in monoclonal antibody development. Although biologics tend to be large molecules, he says, the relevant interface space is much smaller and essentially more manageable and predictable.
“It seems paradoxical,” he acknowledges, “because biologics seem more complicated, but in this way they’re less complicated.” He asserts this sort of approach saves “maybe 15, 20% of the time on monoclonal antibody discovery,” adding “we use it routinely.”
Since becoming CEO in 2017, Ricks says, he has focused on the company’s digital and data strategy and associated company organization. He’s placed someone specifically in charge of data and data strategy, and established a centralized group that’s in charge of all enterprise data management and analytics and yet does not operate through a “centralized model.”
Instead, Ricks describes the group as working through something more like a “consulting model,” where data scientist careers belong to the Center (for data and analytics), but their job reports to the function they’re in (such as clinical pharmacology or new product planning). They roll back to the data center when a project is complete, he explains, and then wait to go out to work on the next suitable problem.
Ricks notes that the market for data scientists is “hotter than anything,” and “so competitive,” and notes “10% of our [data scientist] roles are open any given time.”
One of the most interesting observations shared by Ricks was the apparent success of a program that enabled interested, traditionally trained scientists to “go back to school while they work,” receiving additional training (on Lilly’s dime) as data scientists. When they return, Ricks says, “they become the data scientist partner of their former lab mates.”
According to Ricks, this represents “a much better approach than just dropping in theoretically trained data scientists who then have to learn everything about chemistry and biology, which is complicated.” Ricks says because the model has been so successful, it’s been extended from discovery scientists to include clinical development researchers as well.
In other words, according to Ricks, it seems to work out better, at least at Lilly, to teach traditional (but motivated) pharma researchers about data science than trying to teach traditional data scientists about pharma, suggesting the industry domain expertise is what’s most difficult to learn.
It’s not clear this choice will even be necessary in the future.
As Chavez, the former Goldman Sachs executive, pointed out, data science first came to finance several decades ago. Today, he says, “people who are traders are also data scientists. So rather than having people in different roles with different experiences collaborating together, you wanted to get it all in the same body.”
Ricks emphasized that he didn’t think everyone at Lilly needed to become a data scientist, though he said data are part of everyone’s job. Consequently, over the last three years, Lilly has “run a pretty comprehensive retraining of the entire workface on basic tools,” with an emphasis on teaching employees how to “self-serve your own data queries and how to access the data you need to access in this enterprise dataset – and know when you’re at your edge and when you need to call in an expert.”
Between the breathless promises and ensuing disappointments, it’s easy for clinicians and researchers to become disillusioned, and write off the application of digital and data technologies as just the latest innovation trend.
This would be a mistake.
Emerging digital and data technologies are following a familiar innovation journey, and we are incredibly fortunate to be at one of medical history’s most exciting inflection points. Clinical providers and medical researchers in universities and industry – those on healthcare’s front line — have the remarkable opportunity – and arguably also the obligation — to figure out how to leverage powerful but still relatively raw technologies, and come up with a way to apply or adapt them to our most pressing health challenges.
Bridging the gap between technology and application absolutely requires the insight, experience, and expertise of those in the trenches, actively wrestling with problems and intensively searching for more effective solutions. Both the engineers developing new technology, and the lead users in healthcare and biopharma who seek to apply it, increasingly recognize the urgent need for partnership and creativity if the potential associated with emerging technologies is to be translated into durable improvements in human health.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
Today’s guest on The Long Run is Keolu Fox.
Keolu Fox, assistant professor, UCSD
Keolu is an assistant professor at UCSD with a fascinating mix of interests. He’s a Native Hawaiian, and got his PhD in genome sciences from the University of Washington. He’s now taking that mix of life experience and scientific training, and putting it to work on projects that sequence the DNA of traditionally overlooked indigenous populations.
Like many next-generation scientists, Keolu is thinking about impact, and not only in the traditional sense through academic publications. Keolu is committed to advancing our scientific knowledge of genomics and medicine through sequencing indigenous populations around the world, and doing it in a way that isn’t exploitive, that is mutually beneficial, and that builds trust.
I’ve known Keolu since he was a graduate student, and it’s been fun to see his career take off. He’s a fun guy to talk to, and we had some unvarnished, especially candid moments in this conversation.
Before we get started, a word from the sponsors of The Long Run.
Answerthink has been consistently recognized by SAP, one of the largest enterprise software companies, as a top business partner for delivering and implementing SAP solutions for small and midsized life science companies. Their SAP certified solutions designed for the Life Science Industry are preconfigured, rapidly deployable and address fundamental business and IT challenges such as:
Integrating your business applications
Delivering validated reporting
Increasing your speed to market
Support for global rollouts
Delivering a fully compliant solutions that meets FDA’s strict standards.
Explore how Answerthink can streamline your business processes to ensure growth.
Visit Answerthink.com/timmerman and get a copy of their e-book- “Top Three Barriers to Growth for Life Science Organizations.“
That’s Answerthink.com/timmerman
“I had seen you several times before in textbooks, but seeing you with my own eyes was a whole different story. You were breathtaking.”
This is an excerpt from a love letter written by Marietta to her cells. One of several incredible love letters written by amazing research scientists to give us a glimpse into the wonderment, the beauty, and the challenges of cell research.
Join us as we continue this exploration of a connection like no other, part of the Love Your Cells campaign.
Watch Marietta read her incredible love letter at thermofisher.com/GibcoLoveYourCells
Now, please join me and Keolu Fox on The Long Run.
Please subscribe and tell your friends why it’s worthwhile. Quality journalism costs money. When you subscribe to Timmerman Report at $169 per year, you reward quality independent biotech reporting, and encourage more.
The Timmerman Traverse for Life Science Cares was everything I dreamed it would be.
Here’s a quick recap:
For those unfamiliar, the Timmerman Traverse for Life Science Cares brought together a team of 20 biotech executives and investors to hike the Presidential Traverse in the White Mountains, Sept. 13-15, 2021.
This 20-mile trail goes up and across the most famous peaks in New England – Mt. Washington, Jefferson, Adams, and more. It’s demanding, as it involves 8,800 feet of net elevation gain. Our group was composed of some experienced hikers, and some enthusiastic beginners. We spent 2.5 days together on the rugged trails, with a couple nights in Appalachian Mountain Club huts.
This team felt some brisk wind in their faces. When the clouds passed on the high ridgeline, they got to bask in the sun, marveling at Mother Nature – at long distance and close range.
They pushed themselves physically. There were a couple long, 9 to 10-hour days on the trails. There were some tired legs. There were a couple sore ankles and knees, and a few moments when people needed to extend a hand to a teammate. That was never a question. This team was imbued with a spirit of kindness, generosity, and good humor from start to finish.
Most importantly, people in this group got to know some amazing biotech entrepreneurs and investors as people on a deep, human level. Friendships were formed that will last a lifetime.
See what a few members of the team had to say:
Vineeta Agarwala, general partner, Andreesen Horowitz
“It was amazing to see leaders across our biotech industry come together to raise funds to help bridge the unfortunately real gap between the medicines we develop, and the patients and communities who need access to them. I’m so grateful to be part of this dream team, and hopeful that the funds we are raising for Life Science Cares will help impact and support vulnerable communities across the country.” – Vineeta Agarwala, general partner, Andreesen Horowitz
“This team displayed innate motivation to help each other at those inevitable tough moments that arise for each of us along our journey. The same is true of our life science industry as we work together through Life Science Cares to help those in our community through their tough moments.” – Samantha Truex, venture partner, Atlas Venture
Art Krieg, founder and CSO, Checkmate Pharmaceuticals
“I’ve traversed the Presidential Range many times in all kinds of conditions over the years, but the Timmerman Traverse was the most fun yet: an incredible group of people raising money for an amazing cause in Life Science Cares – hope I can join you again next year!” – Art Krieg, founder and chief scientific officer, Checkmate Pharmaceuticals
“One unique thing about this group was the combination of discussing some aspect of new biology, cutting-edge drug development or drama in boardrooms while gingerly navigating the murderous talus of Mt. Adams et al.” – Jens Eckstein, managing partner, Apollo Health Ventures
“The industry is at its best when we come together to solve challenges: whether that is navigating boulders on Mt. Clay, or raising money and awareness for charities like Life Science Cares; a helping hand, teamwork, and support for one another are the hallmarks of success, and this team had them in spades.” — Jeb Keiper, CEO, Nimbus Therapeutics
Katherine Andersen, head of life sciences, SVB
“During dinner on Day 1, a lone hiker sat with us for dinner at the AMC hut and shared that he was on a solo trek to Georgia with hopes of arriving by Christmas. Being about 300 miles in, we asked him what his biggest learning was so far. His response was this: ‘It has reminded me what really matters, you know? Family, a roof over your head, and food on the table.’ His comments became a drum beat that helped us put one foot in front of the other the rest of the week…True leadership is grounded in service and I count myself as fortunate to have been able to be part of the Timmerman Traverse team – a group that has really set the standard of excellence for giving back to the community.” — Katherine Andersen, head of life sciences, SVB
Reid Huber, partner, Third Rock Ventures
“We started as industry colleagues with an aligned philanthropic goal. We finished as friends, deeply connected through an experience none of us will ever forget and all of us will work to rekindle in our lives.” — Reid Huber, partner, Third Rock Ventures
“On the top of Mt Clay, there are no hierarchies, job titles, favored sons, or favored daughters. You are driven by your personal grit, your fellow climbers, and the beauty and challenges in front of you. We climbed for ourselves, we climbed for each other, but most importantly we climbed for the community being served by Life Science Cares. It was a bond we will never lose.” — Dave Melville, founder and CEO, The Bowdoin Group
Alice Pomponio, managing director, BrightEdge, American Cancer Society
“Not all who wander are lost. Together this team forged common ground to lift up those less fortunate, climbing new heights and supporting each other every step of the way.” — Alice Pomponio, managing director, BrightEdge, American Cancer Society
This isn’t a one-off thing. Since 2017, with the Everest Climb to Fight Cancer for Fred Hutch, I’ve been serving as a volunteer, mobilizing the biotech community to give back. Sometimes that involves giving back to the scientific community and sometimes that involves giving back to the wider community in which we all live and work.
Life Science Cares is a phenomenal organization that helps the biotech community leverage its resources – financial and human – to make a bigger impact in the fight against poverty.
I’m excited to see where this goes.
If you supported this campaign already, THANK YOU. If you are curious about getting involved in future trips, let’s talk.
There’s tremendous capacity for good in this industry. Let’s tap into it. luke@timmermanreport.com.
David Shaywitz
Although we are all impossibly busy, most people I know in the biotech industry still make time to read books.
Books may sometimes have a hard time competing with flashier forms of media in the online attention economy, but they aren’t going away anytime soon. This seems especially true now that so many titles are available in audiobook form, enabling us to appreciate them while traveling or exercising (my favorite time to listen).
Perhaps because of my occasional reviews for the Wall Street Journal, I’m often asked by colleagues (and family members) for book recommendations.
Here are a few suggestions, in three different categories: Tech and AI; Psychology and Wellness; and COVID.
As AI has traversed through the hype cycle (hopefully we’re beginning to slowly climb the Slope of Enlightenment), we’ve been inundated by books about how we got here, what’s happening now, and where AI might be headed.
Initially, many authors focused on what might be called the technology transformation imperative — some version of “tech is taking over the world; the potential is enormous.” Some focused on the opportunity: leverage data and emerging technology and you can do incredible, previously unimaginable things, argued Erik Brynjolfsson and Andrew McAfee in Second Machine Age (2014), see here.
Others emphasized the urgency of action: ignore technology and your business will go extinct, Tom Siebel argued in Digital Transformation (2019), which I discuss here. Some experts, like Jim Manzi, have offered more balance.
Over time, the tone of the discussion seemed to perceptibly shift. The limitations and risks of the technology increasingly attracted the attention of authors, reflecting a similar concern by many in the field (the so-called “Tech-lash”- see here).
Brian Christian, author, “The Alignment Problem.” Photo credit: Eileen Meny
The challenge of getting AI do you what you want it to do was discussed with particular eloquence by Brian Christian in The Alignment Problem (2020), which I reviewed for the WSJ, and would highly recommend. In 2021, I reviewed a raft of AI-related books that shared the sense the technology was not an unalloyed good, and needed to be considered and evaluated with nuance.
Of these, the best overview for the general reader is probably Cade Metz’s Genius Makers: The Mavericks Who Brought AI to Google, Facebook and the World. It’s a captivating and approachable history of Deep Learning and the researchers behind it.
As TR readers will recall, I’ve become increasingly interested in positive psychology, a preoccupation that has clearly puzzled some colleagues. One physician-scientist in pharma affectionately refers to it as “crystals in Sedona,” and tells me “You’re in Boston now, not California. Time to get real.”
Positive psychology, it turns out, is an academic discipline that was largely developed, and then deliberately reified, through the remarkable determination of one indominable scientist: University of Pennsylvania psychologist Martin Seligman. Today, many are familiar with aspects of this field through the wildly popular Happiness Lab podcast, hosted by Yale Professor Laurie Santos (and which I highly recommend), and through her similarly popular Coursera course, “The Science of Well-Being” (it’s also fantastic). I’ve been told Seligman’s Coursera offering on Positive Psychology is superb as well.
This summer, I completed a virtual, week-long course offered jointly by professor Laura Kubzansky and colleagues at the Harvard School of Public Health, and by professor Andrew Steptoe and colleagues at University College London, entitled “Exploring the Linkages Between Mental Well-being & Physical Health Outcomes.” Crystals weren’t on the syllabus, which focused instead on a critical review of the data and methodology in published, peer-reviewed studies.
I came away from the class with a renewed appreciation for both the extraordinary difficulty of durably driving healthy behavior change, and the value of positive psychology interventions – PPIs — as a promising tool in our arsenal (as I’ve recently discussed). Examples of PPIs include “savoring” (taking time to deliberately appreciate an experience); unexpected acts of kindness; expressions of gratitude; enhancing social connections; also exercise and sleep. The potential of PPIs as distinct tools hasn’t been lost on digital health companies focused on mental health and behavior change; one – Happify – is explicitly based on such interventions, and a suite of PPIs are central components of many other offerings as well.
Seligman has written several books on positive psychology, including Authentic Happiness (2002), a title he says he dislikes but which was forced on him by his publisher. In 2018, he published The Hope Circuit, his autobiography.
Both books are unbearably, and almost unimaginably, narcissistic, on the one hand, but also, in their own way, engaging (at least at times), and provide a remarkable window into the internecine world of academic politics, and what it takes to navigate them successfully. (Readers may also confront the question discussed in my last TR piece, and that Shira Ovide has written about so eloquently in The New York Times: can you have the positive qualities of transformative entrepreneurs – including entrepreneurs of ideas – without some of the more difficult attributes?)
Several other books I enjoyed after learning about them from the Santos podcast, and found particularly worthwhile, include Good Habits, Bad Habits (2019) by USC professor Wendy Wood, and Stumbling on Happiness (2006), by Harvard’s Daniel Gilbert. Through my children’s school, I also discovered Think Again, by Wharton Professor Adam Grant, about the value of continuously rethinking ideas.
Grant’s work connects not only with a central theme of Reid Hoffman’s Masters of Scale (discussed in WSJ here, and in TR here), and of Pixar’s former CEO Ed Catmull (I discuss here and here), but also with legendary scientists. In Horace Freeland Judson’s epic history of molecular biology, Eighth Day of Creation, Francis Crick tells the author “Just as important as having ideas is getting rid of them.” (Nerd alert: this was one of the quotes I used in my high school yearbook.)
While Grant’s message is critically important, at times his argument feels a bit tidy. While he’s unquestionably right that not getting unduly wed to ideas is important, it’s not clear that there’s any way to discern reliably when you need to persist in an uphill battle and when you should drop an idea and move on.
As I wrote in my WSJ review of Safi Bahcall’s Loonshots, a book about cultivating innovation in organizations, and discussed in Forbes:
“The uncomfortable truth is that doing something new is risky, and it is often impossible for an organization to determine, in advance, whether an innovative team is working on a hit or a flop. As the late Harvard researcher Judah Folkman used to tell his students: ‘If your idea succeeds, everybody says you’re persistent. If it doesn’t, you’re obstinate.’”
In short: it’s relatively easy to craft narratives around failures to re-evaluate, or successes associated with thinking again, but there are compelling, equal, and opposite narratives as well, and it may not be possible, in the moment, to know which path will be best. At times, rethinking could save your company, at other times, you’d have been better off brashly forging ahead, and it simply may not be possible to know in advance which approach is best.
COVID, visibly, has disrupted the world and consumed our attention. The pandemic – both the virus and our response to it – has threatened our physical and mental health, our economic health, and the health of our relationships with one another.
Our explanatory models for COVID have become intertwined with our identities, calling to mind Miles’ Law: where you stand depends upon where you sit.
We’re desperate to understand “what went wrong,” ostensibly to prevent future pandemics, but also to satisfy our fundamental human need for some explanation of how this terrible thing could happen. Several books have already been published on the pandemic, and hundreds more are sure to follow.
Two that are particularly worthwhile: Michael Lewis’s The Premonition (published May 2021), and Scott Gottlieb’s Uncontrolled Spread, out this week (note: Scott, a colleague at the AEI, had shared a completed copy of his manuscript with me ahead of publication).
Lewis has a remarkable gift for accessing complex and unwieldy subjects, like sabermetrics (Moneyball) and the financial crisis (The Big Short) by focusing the story on seemingly peripheral characters, typically unheralded individuals who emerge as protagonists. In The Premonition, he introduces us to an eclectic crew of physicians and scientists who both individually and collectively were striving to help the nation recognize the pandemic and respond to the threat. Through their efforts, we appreciate the dimension of the challenge and the difficulties of developing an effective response.
Scott Gottlieb, author, “Uncontrolled Spread.”
Gottlieb’s approach seems an ideal complement to Lewis’s. As FDA commissioner from 2017-2019, and a familiar presence on television and social media, Gottlieb seems to have almost Bob Woodward-level access to all the key players in the federal government and industry.
He is able to present a remarkably comprehensive and insightful description of the evolution of the pandemic, and our national struggle to develop a strategy and execute a plan. Uncontrolled Spread is as compelling as it is concerning, and represents an ideal starting point for readers hoping to understand how we got here.
Both Lewis and Gottlieb agree on several fundamental points. Perhaps most importantly, the authors seem to emphatically conclude that the CDC is intrinsically a fusty, academic, highly bureaucratic organization not culturally suited to respond with real-time actions to prevent suffering and death in a fast-moving crisis like COVID-19.
One of Lewis’s protagonists, Dr. Charity Dean, a state public health official in California at the time, writes, in response to a proposed COVID response strategy, “The single most important part of this plan is IT IS NOT RUN BY THE CDC. It is run and overseen by an entity with actual experience in front line warfare in outbreaks.” In both books, the CDC is portrayed as an agency that might write a superb New England Journal of Medicine paper characterizing an epidemic — well after the acute danger has passed.
But another take-away that emerges from both books is that a key contributor to the challenge of responding to situations like COVID is the complex and often conflicting motivations driving human behavior.
For example, as Gottlieb discusses, China’s suppression of information clearly delayed the global appreciation of the danger posed by the virus, and contributed to its worldwide spread. But as he also reveals, the suppression seems to have occurred at every level – not only did Beijing punish physicians who tried to share knowledge of the outbreak in China, but even within China, Gottlieb writes that “information was being held by provincial officials” who were evidently concerned about alarming the central government.
Both Gottlieb and Lewis also highlight a number of examples where the “villain” seems to be a human being with feet of clay operating within the rigid constraints and often perverse incentives of bureaucratic structures and processes.
For instance, Lewis describes the challenge faced by researchers from the Chan-Zuckerberg Initiative attempting to provide free COVID testing – in this case to the Zuckerberg San Francisco General Hospital:
“How much is it going to cost?” asked the woman at Zuckerberg General, after the team at Chan Zuckerberg had explained their new COVID-19 testing lab.
“It’s free,” said Chan Zuckerberg.
“There was this super-long pause,” said Joe (DeRisi, the scientist who led the development of the test and) who was on the line.
“We don’t know how to do no-cost,” said Zuckerberg.
“What do you mean?” asked Chan Zuckerberg.
“It shows up as an error in the hospital computer if we put in zero cost,” the hospital official clarified. “It won’t accept zero.”
“Can’t you put like one-tenth of a cent?” asked Joe.
They couldn’t.
And here we are.
David Shaywitz
In today’s Wall Street Journal, I discuss Reid Hoffman’s Masters of Scale, a new book by the renowned Silicon Valley entrepreneur (PayPal, LinkedIn), investor (Facebook, Airbnb), and podcast host that proposes to distill the secrets of successful business and social entrepreneurs.
The review speaks for itself, of course, but Hoffman’s account, and others like it (there are many – for example, Guy Raz’s delightful How I Built This) present a seductive portrait of entrepreneurship that TR readers should approach with caution.
Hoffman appears to earnestly believe that building a company represents, in his words, a “hero’s journey.” Often, he presents their adventures through this narrative template, offering carefully crafted dramas that, like an Aesop Fable or a tale from Greek mythology, tidily convey a handy lesson and an appealing moral at the end.
Many of these founder stories have in their own way approached the level of mythology, serving as familiar touchstones for the startup community, and inspiration for would-be heroes seeking their own ascent to Olympus.
Reid Hoffman
Much of Hoffman’s advice seems authentically insightful, the result of hard-won experience. One example: his suggestion that successful entrepreneurs need to “learn to unlearn,” since the strategies and approaches that take the company to a certain point might not take them to the next inflection point.
Here, Hoffman echoes one of the most powerful points former Pixar CEO Ed Catmull makes in Creativity Inc., (see here, here) and also shared with me in conversation: success, especially in any creative domain, isn’t formulaic – and intrinsically, can’t be. Just because you’ve managed to come up with one exceptional movie, say, doesn’t mean that now you know how to create the next imaginative film.
Catmull writes,
“People want to hang on to things that work—stories that work, methods that work, strategies that work. You figure something out, it works, so you keep doing it—this is what an organization that is committed to learning does. And as we become successful, our approaches are reinforced, and we become even more resistant to change.”
He goes on to argue that change is essential, and that creative teams have to reinvent themselves constantly. This is also a point University of Pennsylvania psychologist Adam Grant emphasizes repeatedly in his popular new book, Think Again.
I’ve previously contrasted this to the tendency of many pharma companies to fixate on a successful new drug and look for the magic, repeatable formula for success, a golden methodology or durable template that might serve as a model for future drug development. I’ve seen many examples of this ending badly.
As instructive as Hoffman’s advice can be, his founder tales can also be misleading. Many of these stories – oft-repeated legends by now – are so compelling it can be easy to forget just how sculpted and sanitized most are. For example, in a chapter on the importance of establishing a good company culture, Hoffman extols the approach taken by a Boston marketing startup, where the executives emphasize all the right things. Yet this feel-good adulation contrasts sharply with journalist Dan Lyons’s lived experience at the same company, savagely documented in Disrupted.
Entrepreneurs seeking balance would do well to read Ali Tamaseb’s SuperFounders, discussed here, which offers a relatively quantitative look at factors that are (and are not) associated with founder success, defined in this context as achieving a billion-dollar valuation. The book, with its more quantitative perspective, is especially valuable as a counterpoint to narrative-driven stories that are so digestible, and hence, popular.
For example, while heartwarming accounts of founders doggedly pursuing, and fulfilling their life passion attract journalists and engage readers, Tamaseb points out that many successful founders discovered their purpose only after they landed on something they toiled at for years happened to take off (see here).
Tamaseb very deliberately seeks to tease conventional wisdom away from established fact; for example, many investors (including, explicitly, Hoffman) confidently advise individual would-be founders to find a partner.
However, as Tamaseb explains, his data reveal “no correlation between number of cofounders and success.” While he notes that “most successful solo founders either had a successful exit before or were big-name executives at a large company,” he still is reluctant to advise first-time founders without this background to reflexively partner up.
“I would say to a first-time founder not to force finding a co-founder just so that they have a co-founder,” he tells me. “Chances are their company will fail because of conflict with a person they don’t know. But if there’s someone that adds to their company something they can’t bring and they know how to work together, it’s probably beneficial to them to pair up.”
Aspiring founders seeking pragmatic advice also might discover an essential resource in Jeffrey Pfeffer’s unvarnished studies of leadership. A professor at Stanford’s Business School, Pfeffer has spent his entire academic career rigorously studying how leaders actually acquire power; for around four decades, he’s taught a popular course on the topic “Paths to Power.” He’s also written several books on this, including Power, and Leadership B.S. – both of which are required reading; his forthcoming Seven Rules of Power promises to be similarly relevant.
Jeffrey Pfeffer, Thomas D. Dee II Professor of Organizational Behavior, Stanford Graduate School of Business
(Note for readers in universities: there a classic paper, “How To Swim With Sharks,” written by the late John Hopkins professor of bioengineering Richard Johns under the pseudonym Voltaire Cousteau, that offers similarly concise, and equally essential, advice tailored to aspiring academic leaders.)
Pfeffer’s basic point is that the qualities that leaders use to acquire power are often (and nearly always) different that the noble attributes leaders tend to extol and pay homage to after power is acquired.
(Here Pfeffer echoes William S. Gilbert’s legendary Pirate King, who first observed over a century ago, “But many a king on a first-class throne/If he wants to call his crown his own/Must manage somehow to get through/More dirty work than e’er I do.”)
With example after example, Pfeffer points out that powerful leaders very deliberately craft their own history after the fact. “Once you become successful and powerful,” he writes, how you got where you are will be mostly forgiven, forgotten, or both.” Consequently, he is especially suspicious of tidy success narratives – especially when meticulously crafted by those who’ve already achieved power.
The animating conceit of Hoffman’s Masters of Scale is that entrepreneurs can realistically hope to pretty much have it all. They can possess the aggressive, expansionist mindset and indomitable determination to enables their startup to achieve explosive growth, yet, through diligent stewardship of culture and leadership, they also can accomplish this while avoiding nearly all the toxicities that have given so many rapidly-growing tech companies a bad name. Examples here include Facebook’s deliberate prioritization of growth to pursue “domination,” as I’ve discussed here, or the notorious bad behavior of founders like Uber’s Travis Kalanick.
Hoffman, explicitly, and to his credit, acknowledges that entrepreneurs often need to make tough choices, and occasionally navigate through transiently uncomfortable situations. He describes executives in the early days of PayPal ignoring clamoring customers so the team could rapidly build the next iteration of the platform. “You need to think about future customers as well as your current ones,” he says, adding he’d make the same decision again.
At the same time, he emphatically condemns the behavior of Kalanick, for example, and suggests startups avoid these outcomes by placing an early emphasis on culture, and demanding a different sort of leader.
It’s easy to read Hoffman and nod along with his comforting assurances that a startup can rise to prominence without using especially objectionable means to get there.
But then, we think of Pfeffer’s many uncomfortable examples of how leaders actually acquired power. We’re compelled to ask ourselves whether we should really expect a startup’s path to power is likely to be that different. It seems more than possible that the traits required to grow a fragile, nascent startup into a global powerhouse may be conspicuously different, and considerably less congenial, than the traits the company, its management, and its investors typically choose to highlight after the fact.
In a particularly eloquent meditation on founders, Shira Ovide of the New York Times writes that while she’s angry at founders who behave badly, “deep down, I also wonder whether unscrupulous, boundary-pushing executives are an inescapable part of innovation — rather than an aberration.”
She’s also not sure “if curtailing the excesses would also curb the ambition that we want. Sometimes the zeal to imagine ridiculously grand visions of the future brings us Theranos. And sometimes it brings us Google. Are these two sides of the same coin?”
Ovide sees Tesla CEO Elon Musk as an example of both extremes. He is an outrageously audacious entrepreneur, whose vision for electric vehicles has delivered “potentially planet-transforming change.” At the same time, Ovide writes, “Musk has endangered people’s lives by overhyping driver-assistance technology, has repeatedly over-promised technology that hasn’t panned out and has skirted both the law and human decency.”
As Ovide’s colleague Cade Metz highlights in Genius Makers (my WSJ review here), such hype is not unusual among the Silicon Valley elite. These visionaries, Metz writes, recognize that “ideas might fail. Predictions might not be met. But the next idea wouldn’t succeed unless they, and everybody around them, believed that it could.”
Thus the wrenching question Ovide ponders: “maybe it’s impossible to separate the reckless carnival barker who deludes himself and others from the bold ideas that really are helping to change the world for the better.”
Hoffman, fundamentally, wants to assure entrepreneurs it’s possible for startups to rapidly soar to extraordinary heights, and behave relatively admirably while doing it. I want to believe this – of young companies, visionary founders, powerful corporate executives, prominent academic leaders, ambitious politicians. We all do. But, like Pfeffer, I struggle to get there, and find myself in the same boat as Ovide, who concludes, ruefully, “I just don’t know if we can separate the wonderful from the awful.”
Today’s guest on The Long Run is Mara Aspinall.
Mara is a veteran diagnostics executive, investor, and educator. She’s a venture investor with Bluestone Venture Partners, the CEO of Health Catalysts (a consulting firm), and the co-founder and professor of the practice of biomedical diagnostics at Arizona State University. It’s the only program of its kind in the country, designed to provide graduate level education at the intersection of technology and business in diagnostics.
Mara Aspinall, managing director, BlueStone Venture Partners; professor of the practice, biomedical diagnostics, Arizona State University
Before all that, she cut her teeth in diagnostics as the president of Genzyme Genetics before its sale to LabCorp, and as CEO of Roche’s Ventana Medical Systems – when it was in the early days of developing a companion diagnostic strategy to pair up with targeted therapies.
Mara is simply one of the smartest and most experienced people I know in the diagnostics business. I have sought her perspective on the crucial – and undercovered – diagnostic testing aspect of the pandemic response. She has written a few excellent guest editorials on Timmerman Report that I’ve been privileged to edit. I’ll post links to those articles on the show summary at Timmerman Report.
This episode was recorded Friday, Sept. 3. Regular listeners know this isn’t a news-oriented podcast, but Mara is a COVID-19 diagnostics expert and this is an undercovered aspect of the pandemic response. I did notice in the days after this recording that President Biden and others have started talking more about increasing efforts to expand testing. So if this show feels a little dated by the time you listen, just recognize that everything moves fast in the pandemic. I think quite a lot of what Mara had to say will still be relevant throughout the fall, and far into the future.
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Now, please join me and Mara Aspinall on The Long Run.
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