Latest / The Capitalism and Freedom in the Twenty-First Century Podcast / Jay Bhattacharya on the NIH as An Innovation Accelerator | Hoover Institution

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Jay Bhattacharya on the NIH as An Innovation Accelerator | Hoover Institution · The Capitalism and Freedom in the Twenty-First Century Podcast

• 0:08
  - This is The Capitalism
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  and Freedom in the 21st Century podcast, an official podcast
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  of the Hoover Institution Economic Policy Working Group,
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  where we talk about economics, markets, and public policy.
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  I'm Jon Hartley, host today, my guest is Jay Bhattacharya,
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  who is the current director of the National Institutes
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  of Health, the NIH,
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  and previously was Professor of Medicine, economics
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  and Health research policy at Stanford University
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  until March, 2025 when he went emeritus
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  to join the National Institutes of Health.
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  Welcome, Jay. Welcome back.
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  - Thanks, Jon. I still remember your help in packing up my
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  office in the last days.
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  Those were, those were kind of sad
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  and bittersweet in some ways.
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  It was very special.
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  - Very special. And missing you walking
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  around in your Sanford sweatshirt.
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  So it's, I gotta
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  - Put a tie and on for this job.
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  - Well, well, great. Well, I know you bought a bunch
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  of new suits and ties, is my understanding from,
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  from your interview with, with Peter Robinson.
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  It maybe it's a new, a new look or, or a new permit look,
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  but super excited, you know, to, to talk with you
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  and you've since moved across the country
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  to, to Washington, DC and,
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  and I know NIH is is based in, in Bethesda and,
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  and you're living in the DC area.
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  I'm just curious, one, like how's this transition been?
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  Can you explain to us a little bit about what NIH does?
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  You know, I think a lot of people are familiar with the,
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  how it funds a lot of scientific research,
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  but can, can you explain to us sort of at,
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  at a more granular level what NIH is doing,
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  what's going on in the halls of NIH on on any given day?
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  - Sure. So how first, how all,
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  all these questions all at once, Jon.
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  So let's start with the first one. I mean, the,
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  the transition's been shocking.
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  It's been, it's a very different job
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  to be a professor than it is to be the director of the NIH
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  and, you know, just personally very dislocating.
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  I'd been a professor at Stanford for 25 years,
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  almost 40 years at Stanford,
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  if you include all the time i on my education.
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  So it's, it was, it, it's been,
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  it's been quite the transition,
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  but it's also been, I don't,
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  I mean the NIH is a incredible organization.
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  It's more than a century old.
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  And it's, it,
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  if you look at almost every single advance in biomedicine
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  that we take for granted today, well,
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  almost every single medicine that we use, all the sort
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  of know knowledge we have in biomedicine, I mean,
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  there's some role the NH has played in that.
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  And that is just to, to lead an institution that with
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  that incredible history is really quite humbling.
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  And, you know, it's a, it's, it's faced a lot
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  of challenges in the past few years.
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  And in, in some ways it's funny that I'm,
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  I am the NIH director.
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  'cause you know, the previous, one of the previous directors
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  of the NIHI mean, you sort
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  of didn't like me very much, I guess at the time.
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  We, we've since broken bread together.
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  I, I don't, Francis Collins. Francis Collins, yeah.
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  And I, I think, you know, I was a big critic of the NIH
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  during the pandemic, but at the same time,
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  I have a great deal of admiration for the work of the NIH.
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  I've been, NIH funded my entire career as a re I was a
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  reviewer for NIH grants for almost two decades.
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  I I love the NIH
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  and so the, the, the ability to like have it,
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  have the ability to lead it and, and,
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  and help it address the problems that it sort of created
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  for itself during the pandemic to,
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  to restore public trust in it.
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  That's, that's, that, that's, and and to,
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  and to have it actually lead into the next
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  century of innovation biomedicine.
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  That's, that's my job.
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  - That that's terrific.
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  So I, I'm just curious, so, so one,
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  you've laid out a vision for NIH
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  Tell us, what is your vision for NIH
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  and tell us where did the Biden NIH go wrong.
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  - Okay. So, you know, I was thinking about how
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  to do this job and I, as I said, my, I view my job as a sort
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  of addressing like a crisis public trust in the NIH.
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  There's a pew survey that's, I think 2024 found
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  that one in four Americans think that scientists do not do,
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  do not do things that are in the interest the, the,
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  for the wellbeing of the people.
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  I mean, just think about that.
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  Like the NIH relies on essentially broad public
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  support for forever.
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  It's been, it's, it's enjoyed pretty bipartisan support.
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  Both Democrats and Republicans have supported it pretty
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  substantially.
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  In fact, I just saw a paper that suggests
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  that Republicans have had, have supported it more than,
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  than Democrats have in terms of like votes
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  for funding increases and all
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  that over the last two decades.
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  And so what you have here is a, is an institution
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  that's broadly loved, and yet the pandemic had ruptured
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  trust in terms of like creating a vision for it.
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  I, my view is that I, I want the, I want,
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  I want the reforms rather than focusing on what went wrong.
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  I want the, the reforms of the NIH to restore the NIH
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  to its path toward its mission, which is that, which is
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  to do research, to support research that advances the health
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  and longevity of the American people.
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  Now if you look at the state of American health,
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  it's not good, Jon.
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  Right? In a dozen years,
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  we've had no improvement in life expectancy in this country.
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  That is absolutely shocking.
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  I mean, this is something, when I was a grad student, I was,
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  I remember reading about the, the rise in life expectancy
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  in the United States and, and elsewhere
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  and just being, just marveled at it.
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  When, when I was a, when I was a, when I was born, I was in,
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  I mean, Indian, I grew a,
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  a naturalized citizen came to the US when I was three.
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  I was born in India. I think the,
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  the average life expectancy
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  of an Indian boy born in 1968 was year I was born is
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  something like 48 or 50.
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  Wow. And now I'm like 57.
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  And so you, you and
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  what you saw is a steady rise in life expectancy in the
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  developing world and the developed world.
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  And I thought that would go on forever.
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  Now that we're on the other end of a dozen years
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  of no increase in life expectancy in the richest one
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  of the richest countries under earth, the United States,
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  you know, we're in a crisis, right?
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  The, and, and in a way the NIH has not done its job.
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  It's, it's, the job is to advance, to support research
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  that advances life expectancy and, and health.
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  We have chronic disease crises all over the place that,
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  that are, you know, just, I just, I just look at the, the,
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  the rates of chronic, of obesity of dia type two diabetes
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  of, of ca of cancer incidents of, of, you know,
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  sy psychological maladies including depression
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  and anxiety at scale.
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  You have a huge
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  problems in the health status of our country.
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  We are not a healthy country, Jon.
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  And, and so that's the first element of my vision.
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  I want the NIH to focus on research
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  and to do research that reverses those trends.
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  I, I think the American America Health, again,
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  movement is an amazing opportunity for us
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  to refocus the, the, the, the activities,
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  the NIH toward things
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  that actually do advance the health of the public.
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  Now, you know, we've seen in the past dozen years huge
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  advances in knowledge about genetics and, and a few other,
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  and other, other items by medicine.
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  But if those don't translate over to better health, then
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  what purpose do they serve?
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  Knowledge for knowledge sake is not worth funding.
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  And it wouldn't, it will not continue
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  to gain garnish support
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  of all the American people, if that's all it does.
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  So item one, we have to make America healthy again.
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  And the NIH is going to play an
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  enormously important part of that.
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  Okay. Got questions more?
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  'cause I can go, like, I have five items.
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  - No, I keep going. Keep going. Okay,
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  - Item two.
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  Item two. We have to solve a, a, a,
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  a longstanding problem in biomedicine.
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  That that's been, that's been known.
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  My, my colleague Jonny and Edes, a CO's Stanford, who's a,
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  I think the most highly cited living
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  scientist on earth, right?
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  Just an incredible man.
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  And he, in 2005 wrote a paper with a title
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  that goes something like, you know,
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  why most published biomedical scientific papers are wrong?
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  Absolutely Mindblowing title for a paper.
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  It's five pages long.
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  Anyone, any young graduate student looking
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  to make A-A-A-A-A shock should emulate this paper.
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  Although you can't take this
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  'cause he, he already thought of it.
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  The, the the, and it's very convincing.
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  Basically, the, the, the assertion is that science is hard
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  and the standards that we have
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  for publishing in science do not guarantee that the things
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  that are published are true.
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  Worse. It, we all have these like publishing standards
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  that guarantees that a large part of what we know
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  to be true, generally novel findings will not get published.
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  And so you have, you have both problems.
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  The false positive and false negatives problem in,
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  in biomedical, in biomedical publishing where large chunks
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  of what's published, when independent teams finally ever get
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  along to looking at it, don't find the same thing.
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  You know, I had a professor in medical school told me, Jay,
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  half of what you're learning is just false.
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  Jon, half of what you're learning is just false.
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  It, it's true for econ as much as it is, I think
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  for, for biomedicine.
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  And the reason why is
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  because economics is hard and medicine is hard.
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  Science is hard. You can, as a scientist,
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  you can convince yourself that you know the truth.
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  You, you do your analyses and you can, you know,
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  but very often you make hidden assumptions
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  that you didn't realize you were making there.
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  There's some, like, see, there's, there's like things you,
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  you do to like that where you,
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  where there's unexamined assumptions that you, you have made
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  and you result results in.
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  You're convincing yourself, you're right.
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  And you're, you're a good writer.
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  You, you can be very convincing even to peer reviewers.
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  And the statistical standards we use are not strong enough
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  to guarantee that there won't be good, you know,
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  false ideas published
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  with statistical evidence to suggest that they're
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  - True.
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  - So, and then,
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  and so what you have is then is a replication crisis caused
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  by the fact that science is hard.
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  You build on top of that a reward structure in science
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  that says, if I can publish my papers in a top journal,
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  then I am an excellent scientist
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  who says two things about the world, I'll make,
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  I'll climb the social status ladder.
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  And, and so that creates incentives to, to write papers
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  that are, that are, I mean, and sometimes fraudulent, right?
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  Because all that matters is you publish a hot
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  paper and a top journal.
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  If you can get away with it.
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  You might, you might, you might cut some corners here.
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  Cut some corners there. Or,
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  or PA, I mean, you know, I, I think, yeah, I mean
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  - There's, there are the Francesca Gino and,
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  and Dana, a sort of cases
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  where it's like, you know, outright fraud.
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  Somebody actually like, actually totally made up their data.
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  But I, I guess there's the more pernicious thing, which is,
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  you know, just running, you know, a thousand regressions
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  and only reporting the one that worked and,
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  and then, you know, so, so it's a p hacking thing.
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  But in reality we know that, you know,
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  that there's some just naturally, you know,
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  some hypotheses are, are gonna occasionally be, you know, a,
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  a false positive from time to time.
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  And so if, if we end up just only publishing these sorts of,
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  you know, these PA studies, you know,
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  we end up being in a place where, you know,
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  we publish things that sort
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  of just accidentally happened to be true.
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  And, and we just found those, those, those small,
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  those small instances and, and we hunted them all down.
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  And even though they're not really that robust.
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  - Yeah, well, so think about that.
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  So now I have to say, when I look at the, the scope of of,
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  of, of the Jon's allegation, which is essentially the most
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  of what's published, much of what's published is not true.
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  And also the, the fraud, the, the potential for fraud.
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  I actually think the, the first is much more important.
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  I mean, much more pers pervasive,
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  but most scientists aren't committing fraud.
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  The vast majority are not committing fraud.
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  We're just telling ourselves that we,
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  we think the world works a certain way
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  and we can convince ourselves we publish it,
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  but the world doesn't work that way.
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  The science is just hard.
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  Now, they're linked problems
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  because you can, you have a system that rewards
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  publication in top journals as a measure of,
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  of social status and, and, and advance in science.
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  You're gonna get the fraud. But the harder,
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  the harder problem is sifting between true and false.
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  When you publish publish.
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  And the replication crisis is a symptom of that.
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  When, I mean, like, I think there were, like,
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  there've been now dozens and dozens
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  and dozens of, of, of demonstrations
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  of this replication crisis.
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  You know, in, in field after field,
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  after field psychology probably was the, the first big,
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  big field to have these crises.
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  But now you're seeing these crises in field, after field,
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  after field biomedicine, certainly.
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  And in fact there's, when developers,
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  when drug developers sort of decide whether
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  to invest in research in a certain area, they'll,
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  they'll conduct their own private replication efforts
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  to check if the papers they're relying on are true
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  before they make the, you know, millions of dollars
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  of investment in, in subsequent research.
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  Right? Re in in a sense, it's a violation of the basic idea
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  of how science operated once upon a time, which is on trust.
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  If you have a paper that's published, I, I would trust you
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  that you've reported accurately what you've,
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  what you've done, you've you.
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  But if, if I can't rely on that,
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  then it makes the whole advance in science difficult.
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  I mean, there's enormous consequences
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  for having a biomedical literature published
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  that is not reliable.
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  I, and I, I want to solve that problem.
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  I wanna solve that problem. And I think the NIH has it in
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  its capacity to do so.
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  In my view, there's sort of two related things.
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  One is there's no re there's very little return
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  to doing replication work.
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  It's often something you hand off to a grad student to do,
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  graduate student to do, to, to, like, you know, as a prac,
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  as a practice exercise.
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  You can't get it published anywhere.
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  'cause it's, it's seen as non-original.
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  So that's one problem, right?
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  No, almost no returns to, to actually doing replication and,
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  and, and publicizing it.
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  And then the second problem is that the, the,
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  the re the, you never get any return for doing the kinds
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  of pro-social things as a scientist
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  that allow replication to happen.
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  In fact, it's the, it's the other way.
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  If someone approaches me
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  and says, Jay, I wanna replicate your paper from 1987
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  or whatever, I actually 1996
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  or whatever that mean my first paper.
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  Well, I'm gonna, I'm gonna say, well,
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  why do you hate me, Jon?
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  Why? What's, what's wrong with, with, with me?
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  And rather than what I should be thinking,
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  which is it's a great honor that you're revisiting my
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  not very seminal work from 1996.
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  Like, it gives it more importance, right?
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  And in fact, that act of be a, a pro-social action,
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  like I make my, my data publicly available.
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  I make my code publicly available.
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  I describe my sort of protocols in a way so
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  that you don't even have to approach me.
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  You can just try to replicate it yourself
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  just based on what I've written.
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  Those are all measurable things that are good for science,
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  even if you don't do replication, right?
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  They're activities of science
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  that allow replication to happen.
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  And yet we don't reward it.
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  We measure, I mean, I'm sure you know, you've, you've gone
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  to Google Scholar, you can look
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  and see what Google Scholar, they, they,
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  they have measures like the number of citations
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  that you have or the number of papers that you have
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  or the, the H index, God forbid you, you know what,
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  as an aside, the H index is a terrible measure of
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  of, of scientific product.
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  It's like, it's, you know, you know how it works, right?
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  It's, yeah. You gotta have an H index of, of, of, of,
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  of K means you have at least k papers
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  with K citations each, right?
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  So imagine Francis and Watson
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  and Crick, Fran Watson, Francis Crick and,
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  and James Watson, they published their one big one paper in
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  their entire life, which is the, the,
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  the double helix structure of DNA.
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  It gets 1 million citations, fundamentally transforms
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  how you think about, everyone thinks about biology.
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  It's a, it's a, if just had that one paper in your life,
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  you think you had a successful scientific career. Right?
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  - Right. But an each index of one, I guess in, in this case,
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  - Yes.
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  'cause in this hypothetical, they don't have a second paper
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  with at least two citations.
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  So they at, but they do have one paper
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  with at least one citation, a million citations, mind you.
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  But one, their H index is one.
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  And if same thing with like, they,
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  they'd have the same H index as someone
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  with a million papers each with one citation in the Journal
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  of Irreproducible results or something.
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  And that, I mean, that also would have an H index as one.
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  It's a terrible measure. But you'll notice what's what
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  that those measures, what they're actually measuring.
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  They're measuring volume, how many papers you have
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  and influence volume and influence.
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  Are those the only real measures of, of
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  productivity for scientists?
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  We want, it's like measuring a baseball player by, you know,
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  steel stolen bases,
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  but you don't also have caught stealings.
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  Yeah. Or home runs, but you don't also have strikeouts.
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  Right. You need to have a more complete measure of set
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  of things that we want.
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  And you, if you measure it as, as, as economists all know,
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  if we measure it, we'll reward it, we can reward it.
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  You can change cultures around measurement. Right.
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  Let's stick with ba Oh, sorry Jon, go ahead.
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  - No, I was just saying, you know, and it,
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  I wonder if this sort of shifted, I dunno,
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  maybe there's a reason for it in the sense that like,
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  you know, there, there are just fewer
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  low hanging fruit scientific ideas nowadays, you know,
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  that there's this whole sort of idea that, you know,
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  productivity is slowing down
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  'cause we're running out of good ideas.
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  And, and maybe at some level in, in academia,
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  people are sort of running out of ideas.
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  So it's, you know, the last fewer Nobel prizes given out
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  for, you know, these incredible, you know,
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  singular achievements, whether it's, you know, double helix,
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  DNA or Black Shoals, you know, the,
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  there's just like concrete single papers right now.
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  It's, you know, a career and it's a literature
• 17:59
  or changing a literature.
• 18:01
  It's the, you know, the AMO glue Jonson Robinson paper.
• 18:03
  You know, a lot of people are like Wally and the papers
• 18:05
  aren't really wrong, really rep replicable replicable.
• 18:08
  But, but it changed the direction of the literature
• 18:11
  and that it got people using, it was the first paper to use,
• 18:14
  you know, applied tools
• 18:15
  with understanding whether institutions matter for growth
• 18:17
  and, and economic growth is a big question.
• 18:19
  And, and I, I think, you know, drone brilliant.
• 18:21
  I think, you know, it's a brilliant research team,
• 18:23
  but it's interesting how it's maybe this, this is all part
• 18:26
  of a trend that people are trying
• 18:28
  to really promote themselves when, you know, there,
• 18:32
  there really aren't as many very concrete findings
• 18:35
  as maybe there were say a couple generations ago.
• 18:38
  - I just don't agree with that, frankly.
• 18:39
  I don't, I actually, it's part of a third part of my vision.
• 18:42
  I I don't believe that we there, that the world is bereft
• 18:45
  of opportunities for ideas that advance fundamentally
• 18:48
  and our understanding of the way the world works.
• 18:51
  I mean, if I look at the way our knowledge
• 18:53
  of the way the world works, the, the physical world works,
• 18:56
  I, I mean I think we're still in our infancy
• 18:57
  of our understanding of it, right?
• 18:59
  There's vast fields of knowledge still to to, to to, to be,
• 19:02
  to be gained vast ways of thinking
• 19:04
  that we just haven't discovered yet.
• 19:07
  What we have is a incentives problem,
• 19:09
  not an opportunities problem in my view.
• 19:12
  That, that we haven't given people enough incentives,
• 19:15
  by the way, that's my third part of my vision to like make,
• 19:17
  get to transform biomedicine in a way so that people want
• 19:20
  to find those ideas, those new ideas.
• 19:23
  We, we've become stuck in a rut in ways
• 19:25
  and sta some sort of the Peter Thiel
• 19:27
  stagnation kind of idea.
• 19:29
  And I wanna fix that
• 19:32
  before we move off replication though, just,
• 19:34
  so I just wanna, I wanna close the loop on replication.
• 19:38
  If you solve those two, bare those problems,
• 19:41
  you reward people who want to do replication,
• 19:44
  give them a place to publish it,
• 19:45
  and you measure pro-social activities that,
• 19:48
  that encourage replication by scientists, you're going,
• 19:51
  you're gonna have replication essentially become the
• 19:54
  standard of truth in science.
• 19:57
  It's a epistemological revolution.
• 20:00
  Rather than saying, is it published in the QJE
• 20:03
  or the a ER
• 20:05
  or is it published in New England Journal of Medicine,
• 20:07
  biomedicine or, or cell
• 20:08
  or Nature as the standard
• 20:10
  of whether some idea is true or false.
• 20:13
  Instead, what become will is
• 20:15
  what will become the standard of truth.
• 20:16
  Is, is is the idea replicated?
• 20:21
  Are other people when they look at the same thing,
• 20:23
  do they find the same thing as you do?
• 20:25
  And is it, is it important, obviously will remain an
• 20:27
  important part of this, but like is if you have important
• 20:30
  ideas that are replicated, those ideas then have the, have,
• 20:34
  have people are more c that are true.
• 20:36
  It won't just be that you're published in a top journal
• 20:39
  right now what we have is authority based measures of truth.
• 20:44
  - An - Epistemological revolution with rep
• 20:45
  with replication says we it should be rep replication.
• 20:48
  That is, that's the standard of truth.
• 20:50
  Imagine you search in Biomedicines PubMed,
• 20:52
  you search PubMed, you get a paper of of where that comes up
• 20:56
  and you have a replication button.
• 20:57
  You click the replication button
• 20:59
  and all the relevant replication efforts
• 21:01
  for the paper pop up alongside it with some AI summary of
• 21:03
  what, what it says and what's links
• 21:05
  to each paper of replication effort.
• 21:07
  So you can assess yourself,
• 21:10
  it won't matter then if it's published in a top journal,
• 21:12
  even top journal papers won't necessarily replicate
• 21:14
  - Abso Well I'm, I'm totally with you that,
• 21:17
  that we need replication and,
• 21:18
  and I've written some papers on
• 21:21
  how much p hacking there are in certain RCTs.
• 21:24
  That, and,
• 21:25
  and to what degree pre-registration, for example, improves
• 21:29
  or, or, or reduces the amount of p hacking.
• 21:32
  But I, I'm just curious like what are the tools
• 21:35
  that we can use to properly create these incentives?
• 21:38
  Like are we gonna have like, I don't know,
• 21:40
  a more replication journals
• 21:43
  or are we gonna have, I dunno, more, more meta science,
• 21:47
  you know, type studies being rewarded
• 21:50
  or like, I I'm just curious in, in the sense that
• 21:54
  I think there's still this problem, which is, you know,
• 21:55
  we have this like tenure year system
• 21:57
  that generally is rewarding people for these, you know,
• 22:01
  papers that, you know, it takes time to write a paper.
• 22:04
  It takes time to replicate a paper that at, at some level,
• 22:07
  like it, it's still the, you know, you have these tyrannies
• 22:10
  of whatever the metrics are in economics, it's top five,
• 22:14
  how many top five publications you have.
• 22:16
  You, maybe you need five
• 22:17
  or so to get tenure in a, in a top department, similar
• 22:20
  with, you know, finance.
• 22:21
  There's top three clear top three journals.
• 22:24
  Like how do you break the tyranny of the top five
• 22:26
  or how do you get the top five to actually care about
• 22:30
  replication and how do you get people to be incentivized
• 22:34
  to do replications to the point where
• 22:36
  that could actually help someone get tenure.
• 22:38
  I'm, I'm curious.
• 22:39
  - Let me, let me, lemme just stick to biomedicine
• 22:41
  'cause that's that as the job at the IHII fixed economics is
• 22:44
  another, is another question probably beyond me, Jon ot,
• 22:49
  frankly, the, the in, in, in biomedicine,
• 22:53
  the NIH has tremendous power to solve this problem.
• 22:56
  So first you, you, we, we, we, we can
• 23:00
  and will start awarding researchers, large grants
• 23:04
  to do independent replication.
• 23:07
  That'll be their job. We'll find the the buddy the, the,
• 23:09
  the, the, the Jon the other, the, the, the,
• 23:12
  the the new the new Johnny Andes of the world and,
• 23:15
  and have them compete for grants
• 23:16
  and give them grants, large grants.
• 23:19
  Those are signals to their institutions
• 23:22
  that these are excellent scientists
• 23:23
  and they'll start to gain promotion
• 23:25
  and tenure where that, whereas now they don't get it just
• 23:27
  because simply the NIH awarding a large grant is a strong
• 23:30
  signal of an, of this is an excellent scientist, Right?
• 23:35
  Second, the NIH can stand up a replication journal.
• 23:38
  In fact, we'll do that. We already have the
• 23:40
  journal of the National Cancer Institute.
• 23:41
  It's not something that we're not used to doing.
• 23:43
  And essentially have a a a a repository
• 23:46
  where you can put your replication work
• 23:48
  and we can link it to PubMed, which is another NI H product
• 23:51
  where, where, where the, where you can do the, the, the kind
• 23:54
  of replication button in the search results
• 23:57
  and say, does this paper replicate?
• 24:00
  And get that, get that summary.
• 24:01
  That's, those are all just, I think I can just add like,
• 24:04
  you know, this is something I can just have the NIH do
• 24:07
  as director and in fact I'm going to have the NIH do
• 24:09
  as director, we'll have to stand up.
• 24:11
  We're working with standing up this new application office
• 24:13
  that we're in inside the office of director,
• 24:15
  where you'll coordinate those replication activities across
• 24:18
  all the N nih and then
• 24:19
  - Third, this, this is the office
• 24:21
  of research Economics Playing and analysis or arepa, is
• 24:24
  - That Yeah, that's, well that's the new, that's the, well
• 24:26
  that's the current acronym for the, for the, for the
• 24:27
  or repa for the, for the, for the, for the office.
• 24:30
  But that, but that would be its activity to, to sort of
• 24:33
  coordinate the kind of replication activities with the aim
• 24:37
  of inducing a culture change within all, all of,
• 24:40
  and you know, essentially an epistemological revolution is
• 24:43
  what it, what it's really aiming at.
• 24:45
  And then the third thing is
• 24:46
  that we can start to develop metrics.
• 24:48
  There's a whole field of science and science.
• 24:50
  My, my, my colleague and, and former student ko Packin
• 24:54
  and I have written a whole bunch of papers together using
• 24:56
  the method of e of, of methods
• 24:58
  of economic econometric methods applied to science.
• 25:02
  And you know, the people like Pierre Ale, Bruce Weinberg,
• 25:06
  Donna Ginther whole host
• 25:07
  of like fantastic economists who've devoted their attention
• 25:10
  to this, to this field.
• 25:12
  We can, we can develop metrics,
• 25:13
  those pro-social, those metrics.
• 25:15
  We can expand the metrics so that we,
• 25:17
  right now we are right are in a kind of, okay,
• 25:20
  you're too young for this Jon,
• 25:21
  but like once upon a time there was a,
• 25:23
  a fantastic saber nutrition.
• 25:25
  Saber metrics is like the science of baseball research.
• 25:29
  And there was a guy named Bill James.
• 25:31
  - Bill James, I, I know Bill James.
• 25:32
  I, I met, I I used to work, I spent a year doing
• 25:36
  football analyst for Dallas Cowboys. Long story
• 25:37
  - Short, are you serious? Okay, that's fantastic.
• 25:39
  - He's still going. I think - He is he still,
• 25:41
  I think he works with Red
• 25:42
  Sox, I'm not sure. Anyways, he Yes,
• 25:43
  - I think that's right.
• 25:44
  Or or was for a long time. Long time. Yeah.
• 25:45
  He might have part of ways in in
• 25:46
  recent years I think, but yeah,
• 25:47
  - Well he, I, in the 1980s, I used to b get his
• 25:51
  baseball abstracts.
• 25:53
  Apparently he was like a night watchman.
• 25:54
  He would sit there writing books, cranky books about
• 25:57
  how no one understood baseball as,
• 25:59
  because they didn't have the right set of statistics.
• 26:02
  And I would read him go like, man, he's right.
• 26:04
  So like he had this like, he had this like point,
• 26:06
  which anyone who knows baseball realizes very quickly
• 26:09
  that a baseball hitter, their main job is to not get out.
• 26:14
  And yet the baseball, the standard baseball statistics
• 26:18
  back then didn't value walks.
• 26:21
  Well, if you, if you're walk, if you get a walk,
• 26:23
  you draw four balls and, and you, you get to first base
• 26:26
  or whatever that is, that means you didn't get out.
• 26:29
  That's a good baseball player, right?
• 26:32
  That, that has the ability to draw walks.
• 26:33
  So, so they, to these like statistics
• 26:35
  where he would like rank baseball,
• 26:36
  re-rank them based on a broader set
• 26:39
  of pro productivity metrics
• 26:40
  and very different players ended up being the top player.
• 26:42
  Like Wade Boggs turned out to be a big, a big hero
• 26:44
  of his and, and mine.
• 26:46
  'cause he was a Red Sox fan. I'm a Red Sox fan.
• 26:48
  So, so we went from that
• 26:51
  to the Moneyball, remember that movie?
• 26:54
  It was about the Oakland A's who grabbed all
• 26:56
  of these statistics, transformed
• 26:58
  how they made this personnel decisions
• 26:59
  and became a small market team that would get, you know,
• 27:03
  that would win a lot of games every year as a result of the,
• 27:05
  of taking advantage of these like new statistical methods.
• 27:08
  We are right now in science, we are, we are,
• 27:11
  we are in the Bill James age.
• 27:12
  There are all these science of science kind
• 27:14
  of ideas floating around with metrics that are, that are ing
• 27:18
  around ling up.
• 27:20
  We are not in the Moneyball age.
• 27:21
  We have to start measuring productivity using the,
• 27:26
  the, the kind of of things we actually want scientists
• 27:29
  to do, right?
• 27:31
  Pro-social behavior by scientists.
• 27:33
  Like do you share what fraction
• 27:35
  of your papers have you shared your data?
• 27:37
  What fraction of your papers have you have
• 27:39
  you shared your code?
• 27:41
  That would be in economics, in what?
• 27:43
  Frac In, in, in biomedicine it might be tissue samples.
• 27:45
  It might be a, it might be like how con how
• 27:49
  how well do you write your method sections so
• 27:52
  that people can don't have to come
• 27:54
  and ask you for how did you do it?
• 27:57
  What, what your secret sauce was.
• 27:58
  Can they can just replicate it directly
• 27:59
  just based on the description.
• 28:02
  I mean, if you have those metrics,
• 28:04
  people will start to do it.
• 28:06
  And then if you have this epistemological revolution where,
• 28:08
  what matters is are, you know,
• 28:10
  are are your ideas replicable?
• 28:11
  Are do you have you, are you right?
• 28:14
  Are do you have important ideas
• 28:15
  that actually are replicated?
• 28:18
  Then what will happen is all the incentives
• 28:20
  for fraud will drop away.
• 28:22
  It won't matter how many top five
• 28:23
  papers you have, it'll matter.
• 28:24
  Is, are the ideas you have,
• 28:26
  whether they're published in the top five papers
• 28:27
  important and are they replicated?
• 28:31
  Right? It just changes the whole
• 28:32
  incentive structure of science.
• 28:34
  And I think the NIH can for at least
• 28:35
  for biomedicine, we can accomplish this.
• 28:38
  - Yeah, absolutely. Well, I guess one, one thing, you know,
• 28:41
  for example, I I think that would be super interesting
• 28:43
  to look at and, and maybe something that you're already,
• 28:45
  you're doing is, you know, we, we don't,
• 28:48
  or the public at least hasn't really,
• 28:50
  and researchers outside the NIH haven't really had the data
• 28:54
  that we need, for example, to, to measure like
• 28:56
  how effective NIH grants are
• 28:58
  and like to do that,
• 29:00
  I think you need at least I guess from an econom from an
• 29:04
  econometric sort of approach and,
• 29:05
  and a methodological approach, you know, you'd need,
• 29:09
  you know, the applications, you know, for the NIH grants,
• 29:11
  you know, not just the successful ones
• 29:12
  but the, those that have been turned away and
• 29:15
  and are rejected and,
• 29:16
  and so, you know, you could probably do, you know, some sort
• 29:19
  of analysis of, you know, if there was some ranking,
• 29:22
  you know, you could sort of look at the marginal ones
• 29:24
  that were, you know, just accepted the ones that weren't.
• 29:27
  And, and, you
• 29:28
  - Know, we're gonna, we're gonna, we're gonna have that,
• 29:29
  that, that's what, that's what this office,
• 29:30
  this new office I'm developing is gonna do, is gonna have,
• 29:34
  it's gonna have, we'll,
• 29:35
  we'll have great econometricians in there.
• 29:37
  We'll make the data.
• 29:38
  I mean, there's some restrictions 'cause you don't want
• 29:41
  scientists to send their proposals in
• 29:44
  and fear that they're gonna, the idea will get stolen.
• 29:46
  So, so there'll be some protections,
• 29:47
  but we'll make the, the, to the extent that that's feasible,
• 29:51
  those data and raw data available.
• 29:54
  So a a broader set of scientists can start
• 29:56
  to make their own metrics as well.
• 29:58
  I, I mean I want this field to just blossom.
• 30:00
  'cause it's the key to solving all, all the,
• 30:03
  all the crazy instances of scientific fraud.
• 30:05
  You ask yourself, how, how come it's happening?
• 30:08
  Like why are these like prominent people being f
• 30:10
  found of committed fraud?
• 30:12
  It's not fundamentally a, a moral issue.
• 30:15
  It's not, and how do I put this? I I said that wrong.
• 30:18
  It's not fundamentally an issue of individual moral failure,
• 30:22
  repeated individual moral failure.
• 30:23
  It's a, it's a system failure.
• 30:25
  We have created the wrong incentives in science.
• 30:29
  If we create a different set of incentives
• 30:31
  around replication, all the incentives
• 30:34
  to commit fraud will go away.
• 30:35
  'cause why would you commit fraud if you're
• 30:36
  not gonna need credit for it?
• 30:37
  Who cares if you're top publishing, top journal, if,
• 30:40
  if you committed fraud, then no one's gonna
• 30:41
  be able to replicate your work. You're need credit for it.
• 30:44
  - Exactly. All but getting back to truth, I mean, that's,
• 30:47
  - Yeah, it's an epistemological revolution
• 30:49
  - That has to be, you know,
• 30:51
  the truth isn't, you know, centric.
• 30:54
  You know, the, there has to be, you know, truth
• 30:56
  before justice or,
• 30:59
  or you know, other concept, you know,
• 31:00
  for whatever reason why people get into doing research and,
• 31:05
  and you know, maybe people wanna help or,
• 31:08
  or, you know, maybe, you know, there's a lot of researchers
• 31:11
  that maybe have some sort of activist in intentions,
• 31:16
  you know, to, to shape policy
• 31:18
  or to shape, I don't know, you know, their field or,
• 31:21
  or some sort of policy or,
• 31:23
  or some set of, I don't know, commercializing some sort
• 31:26
  of set of drugs or,
• 31:27
  or whatever have you, whatever those incentives are,
• 31:30
  you know, truth has to come before
• 31:32
  - Well, that, that's the, the thing, like once you,
• 31:33
  once you, once you establish that some idea is,
• 31:37
  is has some likelihood of being true, then you can start
• 31:41
  to discuss is it important for public policy?
• 31:45
  Is it important for of development of drugs?
• 31:48
  Is it important for changing how patients should behave or,
• 31:51
  or, or what, you know, advice
• 31:52
  of the advice we give pa whatever, right?
• 31:55
  That truth is a necessary condition
• 31:57
  for the effectiveness of science.
• 32:00
  And it's, I mean, that's, that's why, that's the second part
• 32:02
  of my vision is this rep, I wanna
• 32:04
  solve this replication crisis.
• 32:05
  It's 'cause it's fundamental if, if we think of science
• 32:09
  as a hugely productive that thing,
• 32:10
  but it's only productive to the extent that it,
• 32:12
  it actually encourages people to have incentives
• 32:15
  to find true things,
• 32:18
  true things about the way the physical world works.
• 32:20
  Absolutely. Okay, let's go to my third part. Third point.
• 32:25
  We spent a long time on this one.
• 32:26
  This was, this is, but this was important, right? So
• 32:28
  - Replication crisis is a big one.
• 32:29
  - Okay? So you've already set up the third one, which is
• 32:32
  the stagnation problem, right?
• 32:34
  So a few years back, Chad Jones
• 32:37
  and some of his colleagues, a fantastic economi, Stanford
• 32:40
  of course wrote a, wrote, wrote a paper where he calculated
• 32:45
  the number of papers in, in, in,
• 32:49
  in cancer per in, in breast cancer,
• 32:53
  breast cancer per advance in, in, in,
• 32:57
  in survival for breast cancer patients, right?
• 33:01
  And what you find is like, you know, if you go back
• 33:03
  to the fifties, sixties, the, the, the number
• 33:07
  of papers written per improvement in survival
• 33:10
  of breast cancer patients was actually pretty low.
• 33:13
  And then, and then there's like this flattening of the curve
• 33:15
  where now, now every single sort of month
• 33:20
  of life expectancy increase for cancer,
• 33:22
  for breast cancer patients involves literally, you know,
• 33:25
  thousands and thousands of papers.
• 33:27
  The per dollar we spend on biomedicine,
• 33:30
  we're getting less advanced than
• 33:32
  we, than than we used to get.
• 33:34
  Right. And you, you laid it out.
• 33:36
  Well actually, you said, well, what if there aren't enough?
• 33:39
  What if we've run through all the easy ideas
• 33:42
  and we're just, we're just now on the flat
• 33:44
  of the curve into, into perpetuity all.
• 33:46
  We're never gonna make, make, make any new advances
• 33:49
  because it, everything is just really,
• 33:51
  really hard to make any advances.
• 33:52
  You know, I I was, I was reading the other day, the, the,
• 33:56
  a physicist, very famous physicist,
• 33:58
  empirical physicist from the turn of the 19th century
• 34:02
  who said exactly that about physics.
• 34:04
  You know, he'd seen a enormous generation
• 34:06
  of tremendous advance in physics
• 34:07
  where electromagnetism had been sort of worked out,
• 34:11
  you know, of course there was Isaac Newton
• 34:12
  and mechanics had been worked out huge,
• 34:15
  huge advances in, in physics.
• 34:17
  And he said, well, what if we're done with physics
• 34:19
  and all the rest is just footnotes, you know,
• 34:21
  the the finding the, the 500th
• 34:25
  decimal in planks constant or something.
• 34:26
  Right? And you know, of course, just a few years
• 34:30
  after that, there were enormous revolutions in physics.
• 34:33
  You know, I i I, you know, the, the, I understand relativity
• 34:37
  and the quantum theory and like, just, just huge advances.
• 34:42
  You don't know that you, you, you don't know
• 34:45
  that those advances around the, around the bend
• 34:47
  unless you keep knocking at the door.
• 34:49
  You keep trying new ideas out you, in you.
• 34:52
  And, and let's just bring it back to economics.
• 34:54
  You need, you create a incentive structure
• 34:57
  where those new ideas are allowed to be tried out.
• 35:01
  - I, I think to, you know, to complete your analogy, I,
• 35:03
  you know, it's, I I think when I,
• 35:05
  I believe George Bernard Shaw
• 35:07
  and Einstein won the Nobel Prize in the same year,
• 35:12
  or, or were at the same Nobel Prize gala,
• 35:14
  and I believe George Bernard Shaw, you know,
• 35:15
  was congratulating Einstein in overturning Newton.
• 35:18
  And he said, you know, he, he looked forward
• 35:20
  to the day when, you know, someone overturns what, you know,
• 35:22
  what Einstein's, you know, thought of the day was.
• 35:24
  So, I mean, you know, it's, I guess it's
• 35:26
  knocking at, at that door.
• 35:27
  - Yeah. Again, let's leave aside physics
• 35:29
  because I dunno anything about physics, but like,
• 35:31
  but let's just stay with this, stay with biomedicine.
• 35:35
  I, I did some work with Miko Plin a few years back
• 35:38
  where we looked at
• 35:40
  how old are the ideas in the published biomedical
• 35:43
  literature, how old are the newest ideas pub
• 35:45
  supported by the NIH?
• 35:47
  Okay, so first of all, how do you measure how old ideas are?
• 35:50
  It's actually weirdly simple.
• 35:51
  So you just, you take PubMed, you take all
• 35:54
  of the papers in PubMed, which is all
• 35:56
  of them in biomedicine in that were published in 1940.
• 36:00
  You then do the same thing in 1941, get rid
• 36:03
  of all the synonyms and then subtract off all the
• 36:05
  1940 ideas.
• 36:07
  And what you're left with are the new ideas that are,
• 36:09
  that were introduced into medicine in 1941.
• 36:12
  Just come straight out
• 36:14
  of some complica lar large scale
• 36:15
  computer analysis, easy to buy.
• 36:18
  And then you do it in 42, 43, 44, 45.
• 36:21
  You have, what you have is a history of biomedicine.
• 36:23
  Every year you see all the new ideas
• 36:25
  that were introduced in that year.
• 36:27
  You go back to the papers
• 36:28
  and ask how old are the newest ideas in each paper?
• 36:32
  How old are the newest ideas in each paper, right?
• 36:35
  So paper that's on the cutting edge will be working on ideas
• 36:37
  w that are zero year old at the time
• 36:40
  that the paper is published, papers
• 36:43
  that are working on older ideas
• 36:44
  or will have, you know, 10, 15 years old is the newest idea
• 36:48
  right Now, if you go back
• 36:50
  and ask what, how old were the ideas for papers
• 36:55
  that were supported by the NIH in the 1980s?
• 36:59
  Well, they were working, we were working, they were,
• 37:01
  those papers were for working on ideas
• 37:03
  that were 0 1 2 years old.
• 37:04
  Like really the bleeding edge, if you look at the 2000 tens
• 37:07
  and two, you know, what you see is papers
• 37:10
  that are working on ideas that are seven, eight years old.
• 37:14
  We become way too small.
• 37:15
  See conservative in biomedicine, like too, way too afraid
• 37:18
  to like try new ideas out.
• 37:21
  In a sense, we punish failure too much.
• 37:24
  It's very different than Silicon Valley, right?
• 37:25
  So in Silicon Valley you have like
• 37:28
  a port your portfolio manager, you have 50 projects.
• 37:31
  You, you've, you've read, you've, you've like done all your,
• 37:35
  you've, you've, you went, you
• 37:36
  want, you got your Stanford MBA.
• 37:37
  So, you know, you just need to diversify the portfolio.
• 37:40
  And the key thing is, it doesn't matter if the 49
• 37:43
  of those projects fail, if the 50th is is like Google
• 37:45
  or something, that's a very successful portfolio
• 37:49
  In biomedicine and in, in
• 37:51
  and at the IH in particular,
• 37:53
  it's become much more conservative in the sense of,
• 37:57
  you know, if if an institute
• 38:00
  of director at the NIH funds a portfolio of 50 projects
• 38:03
  and 49 of them fail and the 50th solves type two diabetes
• 38:08
  or something, that's the often you're gonna get,
• 38:11
  people are gonna complain
• 38:12
  that why do they fund 49 projects that failed?
• 38:17
  That's a problem. Like it's, it's, it's so, so the result of
• 38:21
  that kind of reward structure,
• 38:23
  and I remember like being told this very early on in my
• 38:26
  research career is, Jay, be sure to hit the bunt singles.
• 38:29
  You can swing for the fences every once in a while,
• 38:31
  but you gotta hit the bunt singles,
• 38:32
  get, get a lot of papers out.
• 38:35
  And, and the the problem is that, is
• 38:39
  that we need people to be willing to s try new ideas out,
• 38:44
  to invest in them, to take risks on them.
• 38:49
  I mean, Silicon Valley doesn't punish people who fail all
• 38:51
  that all that much, right?
• 38:53
  If they've failed productively, they,
• 38:55
  they'll get another chance.
• 38:57
  We have limited liability
• 38:58
  and all these sorts of things that give you this.
• 39:00
  Yeah, yeah. But we buy, it's the other way, Jon, we,
• 39:03
  we look, if you've, if you, your, your postdoc,
• 39:07
  if you don't get a paper and sell, you're done.
• 39:09
  You're not gonna get that next postdoc, right?
• 39:12
  It's, it's, we punish failure too much in biomedicine
• 39:15
  and we, we essentially don't allow new research,
• 39:18
  new early career researchers who are the font of new ideas,
• 39:22
  EE enough ability
• 39:23
  to try their new ideas out when they're still young.
• 39:26
  I mean that's, that's new. By the way, in bio in 19,
• 39:27
  in the 1980s, I funded researchers with large grants.
• 39:31
  Their first large grant would be in their mid, mid thirties
• 39:34
  and now it's in their mid forties.
• 39:35
  Yeah. We, we basically make it too difficult
• 39:39
  to establish yourself as a career in biomedicine
• 39:41
  nowadays than, than than before.
• 39:43
  And I think that's the root of the stagnation problem that,
• 39:46
  that that, that that, that you saw that Chad Jones sort
• 39:49
  of documented so well Chad
• 39:50
  and his colleague documented so well, I think who else?
• 39:55
  There's another Stanford economist
• 39:57
  who, who was, who was on the paper.
• 39:58
  But anyways, but the point is that that stagnation problem,
• 40:03
  that stagnation problem is again an incentive problem.
• 40:07
  And we have to change the incentives in how we fund research
• 40:11
  to allow that kind of, of innovation to actually
• 40:15
  - Happen.
• 40:17
  So it's, it's more like how do you make NIH kind
• 40:20
  of like a venture capital or VC accelerator in a sense.
• 40:23
  - Yeah, exactly. Exactly.
• 40:24
  And so, okay, so this is something I just recently been
• 40:26
  working on and I think this is this, this,
• 40:28
  I dunno if this is the only innovation we need to do this,
• 40:30
  but it, I think it's part of it.
• 40:32
  We have these at the NIH the way it's structured,
• 40:35
  there are 27 institutes and centers
• 40:37
  and offices, each of which are like focused, many
• 40:40
  of which are focused on disease areas or whatnot.
• 40:42
  They, and they're the head of it is an institute director,
• 40:44
  is a world class scientist,
• 40:46
  and they're responsible for the portfolio
• 40:48
  of investments in their area that the NIH makes
• 40:54
  the, the, the emphasis has in many of those institutes is
• 40:57
  to, you have, we have a great peer review system
• 40:59
  where you send in a proposal, we'll get a scored,
• 41:02
  and they'll do is they'll, they'll look at the top n
• 41:04
  percentile, you know, 10% of scored grants
• 41:08
  and then fund those grants.
• 41:10
  'cause we have a great, these, this peer review system that
• 41:12
  that's with, again,
• 41:13
  with world class scientists doing the peer review,
• 41:15
  evaluating each grant.
• 41:19
  The problem is, and I've I've been a peer reviewer,
• 41:21
  I can tell you the emphasis is on methods.
• 41:24
  It's so easy to say
• 41:25
  to a new idea, well, no one's tried that.
• 41:27
  It can't work really, really easy to do that.
• 41:30
  Kill, kill a new idea.
• 41:32
  They're supposed to score innovation and they do,
• 41:34
  but they don't really emphasize innovation in deciding
• 41:37
  what gets the best scores.
• 41:39
  And also the peer review will tend to focus on areas
• 41:42
  that are currently hot rather than things
• 41:43
  that are promising but not yet hot.
• 41:46
  Right. The, the institutes have their own strategic plans
• 41:50
  that they put up every, a few, few years of like where they,
• 41:53
  where the most promising ideas there are in their fields.
• 41:56
  So what I've done is a, in a is a change in
• 41:58
  how the institutes are going to choose their grants.
• 42:02
  They'll still have peer review, you'll still have the,
• 42:04
  the scientific scores and the peer review.
• 42:05
  Absolutely the fundamental bedrock of how the science,
• 42:08
  the proposal are gonna be evaluated.
• 42:10
  But suppose there's 10 grants
• 42:12
  that are basically looking at the same idea and,
• 42:14
  and that are part of the strategic plan
• 42:16
  and all, you know, basically one
• 42:18
  or two grants that didn't score quite as well
• 42:20
  because the peer review reviewers didn't really know about
• 42:22
  the idea didn't because the new idea that that the, the,
• 42:27
  the, the, the,
• 42:28
  the institute directors will have the capacity
• 42:30
  to pick their portfolio to match the strategic plan.
• 42:33
  Maybe they'll take one or two grants from the, the 10 that
• 42:35
  that's in the hot area and one
• 42:37
  or two grants from the, the less hot area,
• 42:39
  but like very promising area.
• 42:41
  And they're gonna be evaluated not based on
• 42:44
  at a very in single individual grant working,
• 42:46
  but rather the portfolio as a whole.
• 42:49
  Does it actually advance knowledge in ways
• 42:51
  that improve health of the population?
• 42:53
  Right? Does it make America healthy again?
• 42:55
  Does it change biomedicine?
• 42:57
  Does it, does it reduce fundamental changes in basic science
• 42:59
  that, that advance biomedical knowledge, things like that
• 43:02
  for the portfolio as a whole, rather than sort of grant
• 43:06
  by grant, we gotta punish failure less.
• 43:08
  We also have to think of ways to like advance the careers
• 43:10
  of early care researchers much more.
• 43:13
  And that's, that's stuff also I think we can
• 43:15
  do much better than we have.
• 43:17
  - Absolutely. Well, it's funny, you know, I I think you and
• 43:21
  and Peter Thiel were in the same class
• 43:23
  or maybe a year apart from each other at Stanford.
• 43:24
  So I think gi given how much time you spent at Stanford,
• 43:29
  both in, I think all your schooling in many years as a,
• 43:32
  as a professor, I think you probably understand the,
• 43:35
  the VC mentality probably better than anyone else. So
• 43:39
  - Peter and I were friends.
• 43:41
  I mean, I think I met him when was 19.
• 43:43
  He, he was, you know, he founded the Stanford Review.
• 43:45
  He was my dorm. He was poor then.
• 43:47
  So at one point I think i, I lent to pizza money.
• 43:50
  I think he still owes me. Peter,
• 43:51
  if you're listening, you don't have to pay me back.
• 43:53
  I forgive the debt. He, he, he
• 43:57
  - Gotta think about how much interest there could be
• 43:58
  there, you know?
• 44:00
  But the return is on that.
• 44:01
  - No, we, we, we were friends.
• 44:03
  We are friends now still, but he,
• 44:05
  but yeah, I mean his, he's, he I think has been the,
• 44:08
  probably the most prominent person articulating this idea
• 44:12
  about stagnation, scientific stagnation and, and, and,
• 44:15
  and decrying it calling for changes
• 44:17
  and how we think about science so that we can,
• 44:19
  we can get more real advances.
• 44:22
  And I think he's right, I think,
• 44:23
  but I, I don't, I don't think the problem is any lack of
• 44:26
  o opportunities in science.
• 44:28
  I think the ideas are there.
• 44:29
  We just need to set the incentives for people to find them.
• 44:32
  - Hmm. That's, it's fascinating.
• 44:33
  It's been a, it's amazing, you know, for many years until,
• 44:38
  I think just recently, maybe the past couple years since
• 44:40
  sort of the dawn of all these generative AI tools going
• 44:43
  online, you know, that the, there was kind of a refrain
• 44:46
  for like 10 years during the 2010s
• 44:48
  and that, that period, you know, we had these books like
• 44:51
  by Robert Gordon in economics, you know, the rise
• 44:53
  and fall of economic growth and the rise
• 44:57
  and fall of American Economic Growth, I think is the title.
• 44:59
  And, and you know, this just this idea, you know,
• 45:02
  that we're running out of ideas
• 45:03
  and that's why we're, you know, becoming less productive
• 45:07
  and, and you know, we had these great ideas in the early
• 45:09
  20th century, whether it was commercial air travel,
• 45:13
  you know, air conditioning, you know, which a lot
• 45:15
  of people work in much warmer climates,
• 45:18
  huge developing world and,
• 45:20
  and you know, the southern United States, you know,
• 45:22
  you had all these, you know,
• 45:24
  you had all these household appliances, you know,
• 45:27
  much later, or I mean,
• 45:28
  you had the automobile in the early 20th century and,
• 45:31
  and the internet at the, at the close
• 45:32
  of the 20th century and, and all these things.
• 45:34
  It's kind of like, I guess what a lot of people look back on
• 45:37
  and say, you know, in the first maybe couple decades
• 45:40
  of the 20th, of the 21st century is, you know, oh, you know,
• 45:43
  compared to all those incredible innovations all we got
• 45:46
  were, you know, social media apps.
• 45:48
  And I mean, those, those might be, you know, some sort
• 45:50
  of a negative productivity innovation perhaps.
• 45:53
  But, you know, where where are all these incredible,
• 45:56
  you know, innovations that are gonna increase productivity.
• 45:59
  And it, it's interesting now, you know, we,
• 46:02
  we obviously had, you know, the, the mobile phones
• 46:05
  and the smartphones and so forth,
• 46:08
  but, you know, which allows to do certain things.
• 46:10
  But I think, you know, the, the release
• 46:11
  of generative AI tools, I think the conversation around
• 46:14
  that is, has changed at least.
• 46:16
  And it's more sort of, I, I guess people are open
• 46:18
  to this idea that, you know, generative AI tools and,
• 46:21
  and sort of their successors
• 46:22
  or innovative successors could potentially, you know,
• 46:25
  be hugely transformative.
• 46:27
  I'm curious if you have any thoughts on, I guess,
• 46:29
  generative AI and how it intersects with, with NIH,
• 46:33
  what NIH is funding and, and thinking about,
• 46:36
  - I mean, I think AI tools are,
• 46:37
  are probably a counter example to the,
• 46:39
  the assertion you're making about the, the, the, the,
• 46:41
  the assertion, the, the, the hypothesis you were putting
• 46:43
  forward about the slow down in ideas.
• 46:45
  I mean, that's a just unexpected leap in the ability of,
• 46:49
  of machines to, to look like they're reasoning,
• 46:52
  although I don't think you're actually reasoning,
• 46:53
  but that's, but, but they are really a big step forward.
• 46:57
  And I, I, I think that in health it's, it, it can,
• 47:01
  it's gonna transform the way we do.
• 47:04
  Drug discovery already has like, you know, alpha fold
• 47:06
  and its ability to predict protein structures has,
• 47:10
  has just sort of pushed drug development, you know,
• 47:13
  leaps and bounds forward.
• 47:15
  It's gonna change the way that, I mean, just something
• 47:17
  as mundane is like, you know, you go to a doctor
• 47:21
  and the doctor's staring at the screen the whole time rather
• 47:23
  than looking at you because they're writing in all the, the,
• 47:27
  the, the, the billing codes that need to bill, bill,
• 47:30
  imagine AI sitting there listening
• 47:31
  and then filling in all the, the, the, the, the, the records
• 47:35
  for the, for for, for the billing.
• 47:36
  And then of course the doctor can look
• 47:37
  and just very quickly do that afterwards.
• 47:39
  Instead, they spend their time looking at you. Right?
• 47:42
  Imagine a tool that helps the doctor make sure
• 47:46
  that they thought through every single diagnosis, get
• 47:49
  that they give suggestions to radiologists
• 47:52
  or cardiologists for where,
• 47:55
  where they compare against other
• 47:56
  patients that are similar to you.
• 47:58
  You know, you're still gonna need doctors,
• 47:59
  you still need the human touch when people are sick,
• 48:02
  they really do need human beings to like be there caring
• 48:05
  for them, especially smart human beings
• 48:06
  that are like thinking about what their problems are in,
• 48:08
  in, in concrete ways.
• 48:10
  But AI has the, the possibility of transforming
• 48:13
  so much in biomedicine
• 48:15
  and we are absolutely the niche investing in that.
• 48:17
  It's, we, I, you know, maybe I'm wrong, if could,
• 48:20
  maybe it won't be as productive as I think it will be,
• 48:21
  but I, I mean, my view, it's already proved
• 48:24
  to be quite productive and it will be.
• 48:26
  I, I think if we invest more, we'll be, we'll think
• 48:28
  of better ways to make sure that it, that, that,
• 48:30
  but to make that productivity happen,
• 48:31
  but also to make sure that it doesn't hallucinate
• 48:33
  and hurt patients and things like that,
• 48:36
  - That, that's fascinating.
• 48:37
  Okay. I think that was number four. Is that right?
• 48:39
  - That was three actually.
• 48:40
  That's still, that's still under three. The stagnation.
• 48:43
  Let me do four. I'm almost done. I promise Jon.
• 48:46
  Four is, four is we have
• 48:49
  to make sure that we don't harm people
• 48:54
  or take existential risks on behalf
• 48:56
  of the human populations in the research we do.
• 48:59
  Okay, lemme motivate this, this is gonna be controversial
• 49:04
  to some people, not controversial to others.
• 49:06
  It is quite possible that COVID was caused
• 49:09
  by scientific research that we did, that we supported
• 49:13
  that thes, the Chinese did, conducted
• 49:15
  that we, that we were part of.
• 49:17
  There was a big debate in the,
• 49:20
  in the 2000 teens
• 49:23
  over a research paradigm called gain of function research,
• 49:27
  dangerous gain of function research.
• 49:28
  Lemme lemme tell you, the PE research paradigm, the idea was
• 49:30
  that we could prevent all pandemics.
• 49:33
  It's a very utopian idea.
• 49:34
  What you do is, here's what you do.
• 49:35
  You fund research to go out into the wild places,
• 49:39
  find the viruses, find the pathogens, bring them back
• 49:43
  to city centers, manipulate them so
• 49:47
  that they're more transmissible among humans
• 49:50
  only in the Petri dish.
• 49:52
  The, the reason you do that is that if you can distinguish
• 49:55
  between pathogens, you bring back from the wild
• 49:57
  that are very easily manipulable so
• 49:59
  that they become only a few evolutionary steps
• 50:00
  to make the leap in human populations versus other pathogens
• 50:04
  that, that are very evolutionary far away
• 50:06
  from being able to make that leap.
• 50:07
  Well then you can focus on the pathogens that are likely
• 50:09
  to make the leap that are close in evolutionary space,
• 50:12
  prepare vaccines in advance,
• 50:14
  prepare all this other stuff in advance so
• 50:18
  that when it makes the leap, you're ready.
• 50:20
  That's the paradigm. The first time someone actually
• 50:24
  accomplished this of taking avian flu in 2010
• 50:27
  and 11, making it more transmissible.
• 50:30
  This is NIH funded research.
• 50:31
  The scientific community said, this is ridiculous.
• 50:33
  This is too dangerous.
• 50:36
  You have the chance, if there's a lab leak
• 50:37
  of causing a massive pandemic that will kill millions
• 50:41
  of people, we shouldn't be doing this kind of research.
• 50:43
  Right? Th let's go back to physics
• 50:45
  and Rico Fermi, when he launched the nuclear age, that that,
• 50:49
  what he did is he did a calculation, the first nuclear
• 50:53
  chain reaction on the, on the squash court at
• 50:56
  the University of Chicago.
• 50:57
  Of all places. He did a calculation
• 50:59
  of co will this chain reaction consumed the earth,
• 51:03
  burned the earth, or, or can we make it stop if we want to?
• 51:08
  And the calculation
• 51:09
  before he did this experiment was the, the probability
• 51:12
  of consuming the earth was zero.
• 51:15
  And so then he started, then he did the experiment
• 51:17
  that would launch the nuclear age.
• 51:19
  We need to do that with biomedicine.
• 51:22
  We need, and President Trump's executive order on danger
• 51:25
  gain function actually allows us to have that kind
• 51:27
  of risk-based paradigm
• 51:28
  for thinking about regulating biomedicine.
• 51:31
  Like the vast by the majority
• 51:32
  of biomedical research has no risk
• 51:33
  of causing this kind of pandemic.
• 51:35
  Right? So, but we need a regulatory structure
• 51:37
  that gives incentives to scientists, institutions, to,
• 51:40
  to subject any, any experiment that does have the potential
• 51:44
  for catastrophic risk to be subject to this regulation
• 51:47
  where other independent eyes can decide,
• 51:49
  should we do this work?
• 51:52
  A scientist alone
• 51:54
  or small, an institutional alone should not be independently
• 51:57
  be able to make decisions that risk vast harm
• 52:02
  to VV mil, literally billions of people simply
• 52:06
  for scientific curiosity or other reasons.
• 52:09
  And so that the, the, the, the gain of function,
• 52:12
  executive order by President Trump essentially allows us
• 52:14
  to have a regulatory framework that will, that will reduce
• 52:18
  that risk to near zero.
• 52:19
  And we're, the White House is still working on that.
• 52:21
  We're still, when we're helping,
• 52:23
  this is something I think is tremendously important.
• 52:26
  It's important to me because the way you restore trust is
• 52:28
  by telling people, we're, we're working on your behalf.
• 52:30
  We're not trying to do crazy things that,
• 52:32
  that will risk your family's health and wellbeing.
• 52:35
  Quite the opposite. And so to me, this kind of sort of
• 52:40
  attention to biosecurity, to to buy, to, to buy sort of, to,
• 52:44
  to, to, to attention to like the, the, the, the sort of,
• 52:46
  the sort of the riskiness
• 52:47
  of Biore biomedical research is very, very important.
• 52:50
  It's a necessary condition for restoring trust.
• 52:53
  - Are are there any efforts to ensure that that's happening,
• 52:56
  say in China, you know, obviously this, you know,
• 53:01
  they may not be as, you know, as careful as, as the US
• 53:04
  with these sorts of things.
• 53:05
  I mean, obviously there's only
• 53:06
  so much you can do from like a diplomatic standpoint and,
• 53:10
  and you know, China that's, you know,
• 53:13
  has broken many agreements
• 53:14
  and they, you know, they don't respect
• 53:17
  us intellectual property.
• 53:18
  I mean, there's, there's all sorts of issues,
• 53:20
  but I guess is that, obviously human rights is kind of,
• 53:23
  I think has been front
• 53:24
  and center with, with a lot of these conversations
• 53:26
  with China's, along with sort of defense issues,
• 53:30
  Taiwan and so forth.
• 53:32
  But ha have these sorts
• 53:33
  of conversations come up in any of those sorts of discussions?
• 53:36
  I also know, like ai, often one of the criticisms
• 53:38
  that I hear about sort of unregulated AI is that while,
• 53:42
  you know, there's some possibility that, you know,
• 53:45
  someone could use a, a chatbot tool in, say,
• 53:49
  a random foreign country to help build some sort
• 53:52
  of a bio weapon that they could use against the
• 53:54
  United States or peaceful people.
• 53:57
  Do either of those things concern you or
• 54:00
  - They do - Actually justify these things?
• 54:02
  - Absolute concern. Me, I mean, I think, I think
• 54:04
  President Trump has set a, a huge example
• 54:07
  for the whole world by taking this kind of,
• 54:10
  of dangerous gain of function seriously
• 54:12
  and regulating it so that we essentially make
• 54:14
  a commitment not to do it.
• 54:15
  We're sending, telling the whole world,
• 54:17
  you shouldn't be doing it either.
• 54:19
  You shouldn't be taking risks that ri risk, the, the,
• 54:22
  the harm to every human being on the planet just
• 54:24
  for scientific curiosity
• 54:26
  or whatever gains you think you're gonna get from it.
• 54:28
  Even if it's, let's say it's a bio,
• 54:30
  you think you're doing bio weapons
• 54:31
  research with it or whatever, right?
• 54:32
  It's, that's banned by the 1973 Bio Weapons Convention.
• 54:36
  But say, say you're doing a really, there's no way
• 54:39
  to guarantee that you won't harm your own population in the
• 54:42
  context of that research.
• 54:44
  We kind of need a, a, a strengthened bio weapons convention,
• 54:47
  something that a, a gain of functions convention,
• 54:49
  essentially a dangerous gain of functions convention
• 54:53
  that's international in scope that says,
• 54:55
  this is not research that's worth doing.
• 54:57
  That's not going to help anybody.
• 54:59
  It's not in any country's interest to do it
• 55:01
  because it, it's, it's research that if you,
• 55:05
  if you cause catastrophic harm to human populations, you,
• 55:08
  you will also harm your own populations in doing it.
• 55:11
  There's no nationalistic interest in doing it.
• 55:12
  This is the kind of thing that really should be
• 55:14
  subject to international treaties.
• 55:17
  But I think I'm, I'm, it's really, it's really amazing
• 55:19
  that President Trump is the one that actually took the step
• 55:22
  that said, look, we're going to unilaterally not do this
• 55:25
  because it's not worth,
• 55:27
  it's not an American interest to do it.
• 55:28
  It's frankly not in anybody's interest
• 55:30
  to do this kind of dangerous research.
• 55:33
  - Absolutely. No, it's, it's, yeah,
• 55:35
  being five years out from COVID, it's still slums large in,
• 55:39
  in, in, in the memories of, of many to the least.
• 55:44
  Okay, so number five. Have we
• 55:45
  - Got last one?
• 55:46
  And I, I pro I, I promise I'll let you go, Jon.
• 55:48
  You probably, you probably had no
• 55:49
  idea it keeps you so long, but No,
• 55:50
  - No.
• 55:51
  - The number five is, is free speech and academic freedom.
• 55:56
  And Jon, you probably know this, actually,
• 55:58
  we talked about this in the, in our previous podcast.
• 56:00
  So we did, did together COVID was a very difficult time
• 56:04
  for scientific scientists to dis express their ideas,
• 56:07
  especially if they disagreed with sort of the pre dominant,
• 56:10
  pro lockdown, pro mask mandate,
• 56:12
  provax mandate kind of, kind of ideas.
• 56:16
  And it made scientific progress during the COVID era much,
• 56:20
  much harder than it should have been.
• 56:22
  Science depends on free speech just to thrive, right?
• 56:25
  If you disagree with me, Jon, you absolutely should be able
• 56:28
  to say that you disagree with me and, and you explain why.
• 56:30
  And you know, I've had many situations I've taught at
• 56:32
  Stanford for 25 years.
• 56:34
  I had so many situations.
• 56:35
  My, some of my proudest moments when a student would tell me
• 56:38
  I'm wrong, and then they would be right.
• 56:40
  And we write a paper together, you know, hiding the fact
• 56:43
  that I was wrong before, of course, except from the world.
• 56:46
  But that kind of of open correction
• 56:50
  of each other is the heart and soul of science
• 56:53
  and restrictions on free speech
• 56:55
  and academic freedom are anathema
• 56:57
  to scientific progress there.
• 56:59
  They're absolutely necessary step re required
• 57:03
  for progress in science.
• 57:05
  And the NIH needs to be a, a, a catalyst for this kind
• 57:09
  of free speech throughout the country.
• 57:12
  At the NIH itself.
• 57:13
  I, when I came in, I found out
• 57:15
  that they had a policy for that.
• 57:17
  We have a whole, a whole bunch of intramural scientists,
• 57:20
  ama amazing biologists who, who do a lot of, a whole host
• 57:24
  of different kind of research internally inside the NIH
• 57:27
  publish it in scientific literature.
• 57:29
  I found out there was a policy that many of these institutes
• 57:32
  where the scientists themselves had to seek permission,
• 57:35
  substantive permission from their supervisors
• 57:37
  before they were allowed to send their papers out for review
• 57:40
  by other, by, you know, journals.
• 57:43
  That's not academic freedom.
• 57:44
  That, I mean, that you can't make science advances advance
• 57:47
  if you have that, those kinds of restrictions
• 57:49
  where you're worried about what your supervisors
• 57:50
  think about your science.
• 57:52
  I mean, obviously you want good science,
• 57:54
  but you don't, I mean, you don't want your supervisors
• 57:56
  to tell you, well, I don't like this result.
• 57:58
  You can't send it out. So I put in a policy now
• 58:01
  where any intramural researcher can just send their paper
• 58:04
  out for scientific review without asking any permission.
• 58:08
  I fully expect there'll be papers published at the NIH
• 58:10
  that I don't agree with.
• 58:12
  I don't even like, but those researchers should,
• 58:14
  that the NIH should absolutely have the cha the,
• 58:17
  the opportunity to publish those papers.
• 58:22
  I wanna lead by example, but I also wanna encourage,
• 58:24
  and this is something I think the Trump administration,
• 58:26
  it's been, it's caused a lot of angst in the,
• 58:29
  in the scientific community, in the,
• 58:30
  in the in scientific community,
• 58:31
  the academic community at large.
• 58:32
  But I think it ultimately in the long run is a good thing.
• 58:36
  The, the sort of like pushing universities
• 58:41
  to, to adopt policies consonant with this idea
• 58:45
  of scientific freedom, of academic freedom as essentially
• 58:48
  as a way to say, look, if if you don't do this,
• 58:51
  then you're not really a great research partner with us.
• 58:55
  Right? If you don't have these policies that allow,
• 58:58
  that the, the scientists at your institutions, the to,
• 59:01
  to have their ideas
• 59:02
  and not suppress them to allow free speech at universities,
• 59:07
  really, the universities that don't have those kinds of
• 59:11
  free speech environments are not good
• 59:13
  environments to do science.
• 59:15
  You're always looking over your shoulder wondering if the
• 59:18
  paper you're writing is gonna get you canceled.
• 59:20
  Well, you're not gonna write that paper, right?
• 59:24
  And you, you, you, you cannot have this sort of like, sort
• 59:27
  of political apparat overseeing hiring decisions.
• 59:31
  And, and now it's caused a lot of angst.
• 59:33
  I know among researchers that will look, my,
• 59:37
  my field is fine, how come you're blaming me for
• 59:40
  problems in these other fields?
• 59:43
  But the university as a whole needs to have a culture
• 59:47
  that advances science that that, so that
• 59:51
  that has free speech at its core.
• 59:53
  Because if you don't, then you can't actually trust
• 59:56
  that the scientific work out
• 59:58
  of those universities is really true.
• 1:00:01
  Right. It's, it's only in that free speech culture
• 1:00:04
  that you get excellent science.
• 1:00:06
  - Absolutely. No, I, I couldn't agree with you more.
• 1:00:08
  And it's, it's very interesting to think about, yeah.
• 1:00:11
  I mean culture at universities right now and,
• 1:00:14
  and how it's, how, how it's shifting or, or,
• 1:00:17
  or maybe not shifting,
• 1:00:18
  but you know, certainly, you know,
• 1:00:22
  president Trump's done a lot to,
• 1:00:25
  to change some of that focus.
• 1:00:27
  - Well, his very first, one of his very first executive
• 1:00:29
  orders was to reestablish free speech in this country.
• 1:00:32
  I was part of a, a lawsuit against the Biden administration.
• 1:00:36
  'cause the Biden administration, they had a systematic
• 1:00:38
  policy, Jon, to suppress free speech, right?
• 1:00:41
  They would go to social media companies,
• 1:00:43
  order them essentially to take down vast pages
• 1:00:46
  of even true scientific ideas.
• 1:00:49
  Like, you know, if you, if you're vaccine injured, they went
• 1:00:51
  to Facebook and told people to, told Facebook to take down p
• 1:00:55
  private pages
• 1:00:57
  where vaccine injured people would just talk to each other,
• 1:01:00
  - Right?
• 1:01:01
  - They, they, they ordered Twitter a whole, I mean,
• 1:01:03
  and you could, the, this lawsuit,
• 1:01:05
  this Missouri versus Biden lawsuit uncovered vast evidence
• 1:01:08
  of a, about all of government approach
• 1:01:10
  to suppress speech contrary on scientific matters.
• 1:01:14
  Contrary what the si the, the, the,
• 1:01:16
  the Biden administration thought was, was good or true
• 1:01:18
  or just, and they were often wrong.
• 1:01:20
  They SS they pressured
• 1:01:24
  social media companies to suppress.
• 1:01:26
  I was, I was actually b blacklisted at Twitter the day I
• 1:01:30
  joined in 2021.
• 1:01:32
  Wow. And you know, it, the,
• 1:01:34
  the Supreme Court ultimately ruled that I, I I
• 1:01:36
  and my colleagues didn't have standing to sue
• 1:01:39
  because we didn't have an email that said from Biden's folks
• 1:01:42
  to the Facebook or something that said censor J
• 1:01:45
  but we did have is emails that said censor the kinds
• 1:01:48
  of ideas that, that, that, that,
• 1:01:50
  that I was espousing, right?
• 1:01:52
  That that masking toddlers was a bad idea.
• 1:01:53
  That opening schools is a good idea.
• 1:01:55
  That, you know, that that mask va vaccine mandates made no
• 1:01:59
  sense given that the vaccine didn't
• 1:02:00
  stop the spread of COVID.
• 1:02:02
  They suppressed ideas at scale.
• 1:02:04
  And the Supreme Court said that's fine,
• 1:02:07
  the government can do that as, as long
• 1:02:10
  as they don't name a single individual.
• 1:02:12
  - Yeah. - Right.
• 1:02:14
  Now the main thing protecting free speech in this country is
• 1:02:17
  President Trump's executive order.
• 1:02:19
  That, that, that the government's not going to do that.
• 1:02:24
  I've, I've, I've been watching like this bruhaha over,
• 1:02:27
  over some, some late night comedian, what's his name?
• 1:02:30
  - Jimmy Kimmel. - Jimmy Kimmel, right
• 1:02:32
  - Over Charlie. The Charlie Kirk.
• 1:02:34
  - Yeah. What you had there was like,
• 1:02:35
  you could see it like the guy was like, he was gone
• 1:02:37
  for three or four days
• 1:02:38
  because he's, he essentially made, you are right,
• 1:02:41
  he made some, some basically a false statement about how
• 1:02:44
  what Charlie, Charlie Kirk actually believes in the,
• 1:02:45
  in the wake of his death.
• 1:02:47
  And a lot of people got upset
• 1:02:49
  and they were like writing to,
• 1:02:51
  to their local TV station saying,
• 1:02:52
  why are you having this guy lying about Charlie Kirk,
• 1:02:55
  the guy who's like, you know, a who's who's, who's,
• 1:02:57
  who's just been assassinated
• 1:02:58
  for his political beliefs and free speech beliefs.
• 1:03:01
  And he's pulled off the air for,
• 1:03:03
  for like four days by a B, C.
• 1:03:06
  And somehow that's a free speech issue.
• 1:03:08
  It's not the government telling a b, C
• 1:03:11
  to pull him off the air that did that.
• 1:03:12
  It was the, it was the, it was the individual
• 1:03:16
  people writing their local, local, local TV stations.
• 1:03:21
  You can see this because after he was restored, you know,
• 1:03:23
  dozens and dozens of the,
• 1:03:25
  of the local channels are not gonna show him again.
• 1:03:29
  Right. I think that's, that's consistent with free speech.
• 1:03:31
  Like that's, that's speech based on, based on like what,
• 1:03:35
  what people are willing to interested to hear.
• 1:03:37
  Like I, I don't have a right when I put a tweet up on
• 1:03:39
  Twitter to have millions of people look at it,
• 1:03:42
  but I do have a right to put now a right
• 1:03:44
  to put whatever I want up on Twitter.
• 1:03:46
  Absolutely shouldn't have the government
• 1:03:49
  essentially tell people.
• 1:03:51
  And you know, if you think about the difference
• 1:03:53
  between Jimmy Kimmel and, and what happened
• 1:03:54
  during the pandemic and the Biden administration,
• 1:03:57
  let's say the worst case, let's say you believe,
• 1:03:59
  and you're listening to this, you say, oh, well look,
• 1:04:01
  the government did say that A, B, C take Kimmel off.
• 1:04:04
  Well, didn't,
• 1:04:06
  but like, let's say you believe that at least there, A, B,
• 1:04:08
  C can can say, well, Kimmel, you,
• 1:04:10
  you're, you're being taken off.
• 1:04:11
  You're the government's telling you to take us off.
• 1:04:13
  But the, what the government did during the Biden
• 1:04:14
  administration was say, take, get, take all these ideas off,
• 1:04:18
  and I wouldn't even know they could do it anonymously.
• 1:04:23
  It's vast power to suppress speech is what, what
• 1:04:26
  what the Biden administration did
• 1:04:28
  and got away with it, frankly.
• 1:04:29
  And as a result, millions of people are worse off.
• 1:04:33
  Their kids didn't get years
• 1:04:34
  of schooling that they should have had.
• 1:04:36
  They were, they lost their jobs over ma over
• 1:04:38
  vaccine mandates.
• 1:04:39
  They, they, they couldn't visit their
• 1:04:42
  loved ones in hospital as they were dying.
• 1:04:44
  All this because the, the honest scientific debate
• 1:04:46
  that should have happened during the pandemic did not happen
• 1:04:49
  thanks to these re speech restrictions.
• 1:04:52
  We can't have, we can't have that for lots of reasons.
• 1:04:55
  But certainly we can't have scientific progress
• 1:04:57
  unless we establish an environment where speech is
• 1:05:00
  absolutely free in the sense that, I mean
• 1:05:02
  that I've been talking it about it. Absolutely.
• 1:05:05
  - I have one last question for you, Jay.
• 1:05:06
  And that's, it's about animal testing.
• 1:05:09
  So animal testing, my understanding has actually lessened,
• 1:05:13
  or, or, or I know, you know, there's this terrible,
• 1:05:17
  you know, instances of animal testing on,
• 1:05:19
  on beagles that was going on.
• 1:05:21
  I think under the NIH purview, some of this has stopped and,
• 1:05:25
  and my sense is that you have a position that, you know,
• 1:05:28
  we should avoid animal testing
• 1:05:31
  whenever, you know, wherever possible
• 1:05:34
  unless there's no alternative sort of viable model.
• 1:05:37
  I mean, tell us a little bit about
• 1:05:39
  that in terms of what's going on.
• 1:05:40
  I I think there's some people out there that will be very
• 1:05:42
  interested and excited to hear that
• 1:05:45
  that animal testing's been r reduced
• 1:05:47
  under, under your tenure.
• 1:05:49
  - Yeah, I mean its, it's actually interesting.
• 1:05:50
  Like peta you know, the people
• 1:05:53
  for the ethical treatment animals had sent me flowers
• 1:05:56
  when I, when I put this, the
• 1:05:57
  policy I'm about to tell you in place.
• 1:06:00
  Although there's some groups that, that,
• 1:06:01
  that still don't like me 'cause they want zero animal use.
• 1:06:04
  So first lemme just set the stage.
• 1:06:06
  So animal use and research for, for in is,
• 1:06:09
  has been a mainstay for biomedical research for many, many,
• 1:06:12
  many for essentially centuries.
• 1:06:15
  And you can understand why,
• 1:06:16
  like imagine you're trying a new drug
• 1:06:17
  out for the very first time.
• 1:06:19
  You, you can't give it to humans to start.
• 1:06:21
  You just cannot because if you do, you don't know the dose,
• 1:06:24
  you don't know what if it's lethal.
• 1:06:25
  You have, you, you need to have some sense of is it, is it,
• 1:06:29
  is it, you know, it does will what, what what the safety is
• 1:06:33
  before you f give it to a first human, you have
• 1:06:36
  to know a lot about the c the new, the new new drug or
• 1:06:38
  or molecule before you give it to a human, right?
• 1:06:41
  And then there's like animal systems that indicate
• 1:06:45
  something about the human physiology, right?
• 1:06:48
  So like the first knowledge of
• 1:06:50
  how our circulatory system came from animals, an analysis,
• 1:06:54
  analysis of animals, you know, William Harvey.
• 1:06:58
  And, and so like you can understand why animal use is, is,
• 1:07:01
  is, is, is, is part of biomedical research.
• 1:07:04
  At the same time there's a lot of animal use.
• 1:07:07
  The animal models are just thoughtlessly used thoughtlessly
• 1:07:10
  in a, in a very, very sim simple sense.
• 1:07:12
  Mean clear si single sense meaning
• 1:07:16
  the question is just people are used
• 1:07:17
  to using some animal model
• 1:07:19
  and they don't ask whether the thing you learn from the
• 1:07:22
  animal model can be translated over to humans, to,
• 1:07:24
  to something that's useful for humans.
• 1:07:27
  And in fact, occasionally you'll have animal models
• 1:07:29
  that mislead you, right?
• 1:07:31
  So like you, you know, you have a rat model
• 1:07:33
  or mouse model of Alzheimer's disease.
• 1:07:34
  You can cure the Alzheimer's disease and the mouse,
• 1:07:36
  but that treatment
• 1:07:39
  that works in the mouse does not translate over to humans.
• 1:07:42
  Well then why are you even working with that model if,
• 1:07:44
  if the knowledge you gain from
• 1:07:45
  that mouse model doesn't translate over to humans?
• 1:07:48
  So that's the new policy i I put in place thanks to,
• 1:07:51
  there's a woman who'd been working at this in the NA
• 1:07:53
  for more than a decade named Nicole Klein story who,
• 1:07:56
  who sort of, who, who, who taught me about this.
• 1:07:59
  There're now huge advances in alternatives
• 1:08:04
  to animal models
• 1:08:06
  for in many cases you can use these things called organoids,
• 1:08:08
  which like essentially tissue cells on a chip.
• 1:08:11
  You can have theil, you can have just a, you these sort
• 1:08:16
  of ins meaning like AI methods, a whole host
• 1:08:19
  of other methods that sometimes replace the animal models
• 1:08:23
  that people traditionally use
• 1:08:24
  and do better at predicting what
• 1:08:26
  what will happen in humans when you give a new drug to it
• 1:08:28
  or something, right?
• 1:08:30
  And so that, that's the new policy,
• 1:08:33
  the new policy first you have,
• 1:08:34
  you can't have unethical treatment of animals.
• 1:08:36
  You can't have beagles that are tortured.
• 1:08:38
  That's, that's just a given. That's never gonna happen.
• 1:08:41
  And if it does happen, we're gonna go,
• 1:08:42
  we're gonna put the hammer down on you.
• 1:08:45
  And if you do use animals for research, you have
• 1:08:48
  to justify it by saying, by showing that the research
• 1:08:50
  that you're doing, the knowledge you're gaining,
• 1:08:53
  can translate over to human activities, human human,
• 1:08:56
  human human applications.
• 1:08:57
  And also that there aren't other alternative methods that,
• 1:09:01
  that could do it better than the animal model itself.
• 1:09:05
  If the, the goal is the mission,
• 1:09:07
  which is advanced human health through research,
• 1:09:10
  advanced longevity through research.
• 1:09:12
  That's the goal of that, of that.
• 1:09:14
  And, and the happy byproduct of that is that we're not
• 1:09:18
  doing research on, on using animal models that don't, that,
• 1:09:21
  that, that are, that, that aren't necessary
• 1:09:23
  for advancing human health.
• 1:09:25
  - Well that's, that's fantastic.
• 1:09:27
  I'm sure there's many beagles animals and,
• 1:09:30
  and people who, who thank you for it.
• 1:09:33
  Jay, I I really wanna thank you for coming on.
• 1:09:34
  This has been an amazing conversation.
• 1:09:36
  Really enjoyed it and it's so great to see you
• 1:09:37
  and hear how you're doing at NIH
• 1:09:40
  and to hear about your new vision for it.
• 1:09:42
  So it's a real honor to have you on.
• 1:09:44
  - Thank you, Jon. I'm looking forward to coming back
• 1:09:46
  and visiting Stanford someday when I,
• 1:09:47
  when I finally get, you know, outta here.
• 1:09:50
  - We'll, we'll look forward to having you. All right.
• 1:09:53
  This is the Capitalism and Freedom in
• 1:09:54
  the 21st Century podcast, an official podcast
• 1:09:56
  of the Hoover Institution Economic Policy Working Group,
• 1:09:58
  where we talk about economics, markets, public policy.
• 1:10:00
  I'm Jon Hartley, your host. Thanks for, for joining us.