In Science We Trust? Understanding Americans’ Confidence In Science, Scientists, And Scientific Institutions

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- Welcome and thank you for joining us
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for today's webinar hosted by
the Hoover Institution Center
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for Revitalizing American
Institutions in partnership
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with the Hoover Technology
Poly Accelerator.
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My name is Erin Tillman and I serve
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as an associate director
at the Hoover Institution,
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and I'll be the webinar
host for today's session.
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Before we begin, let's review
a few housekeeping items
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following brief opening remarks.
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Today's session will consist
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of a 40 minute discussion followed
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by a 30 minute question and answer period.
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To submit a question, please use the q
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and a feature located at the
bottom of your zoom screen.
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While we may not have time
to address all questions,
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we'll do our best to respond
to as many as possible.
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A recording of the webinar
will be available on the RAI
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event webpage of the Hoover
website within the next three
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to four business days, the Center
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for Revitalizing American
Institutions, also known
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as RAI was established
to study the reasons
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behind crisis in trust
facing American institutions.
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Analyze how they're operating in practice
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and consider policy
recommendations to rebuild, trust
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and increase their effectiveness.
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RII Opera Op RII operates
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as the Hoover Institution's
first ever center is a testament
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to one of our founding principles,
ideas, advancing freedom.
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Today's webinar is co-hosted
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by the Tech Hoover Technology
Policy Accelerator,
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which delivers research and insights
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to help leaders understand
emerging technologies
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and their geopolitical impacts
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so they can seize
opportunities, manage risks,
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and advance American interest.
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The discussion examines how Americans view
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and trust science amid
rapid technological change,
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political polarization and misinformation.
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Our panelists will present
data-driven insights on public
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confidence in scientists
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and scientific institutions,
as well as the social
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and cultural factors
influencing these perceptions.
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It gives me great pleasure
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to introduce today's moderator, Amy Zart.
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Amy is the Morris Arnold
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and Nor Nona Jean Cox,
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senior fellow at the Hoover Institution,
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where she leads the
Technology Policy accelerator
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and the Oscar National Security
Fellows Affairs Program
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specializing in US intelligence,
emerging technologies
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and national security.
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She's also an associate director
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and senior fellow at
the Stanford Institute
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for Human Centered ai
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and a senior fellow at the
f Friedman Bogley Institute.
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We are honored to have three
guest panelists, Russ Altman,
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mark Horowitz, and author Skip Lucia.
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Russ is the Kenneth Fung professor
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of bioengineering genetics, medicine
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and biomedical data science at Stanford.
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His research applies AI data science
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and in info informatics
to advance medicine
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with focus on drug addiction
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and how genetic variation
shapes drug response.
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He's also the founder and
editor of the annual Review
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of biomedical Data science
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and host of the podcast,
the Future of Everything.
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Mark is the fort founder chair
of electrical engineering
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and professor of computer
science at Sanford,
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a pioneering engineer and entrepreneur.
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His work has shaped modern
digital systems from RISC
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microprocessors to high
speed memory interfaces.
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Through his co-founding of Rambus,
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his current research spans
electrical engineering
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and computer science with
applications in life sciences.
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And finally, we're joined
by Skip Leia, vice President
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for research and Innovation
at the University of Michigan
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and professor of Political Science
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and research professor at the
Institute of Social Research.
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He previously served as
the assistant director
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of the National Science Foundation
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and co-chaired the White House
subcommittee on open science.
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His work focuses on increasing
the public value of research
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and strengthening trust in sciences.
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More complete biographies
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of our speakers are available
on the webinar webpage.
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Thank you, Russ, mark, and
Skip for joining us today,
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and I'll hand it off to Amy
to start today's conversation.
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- Thanks so much, Erin. Excuse me.
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I wanna thank Mark Russ
and Skip for joining us.
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I'm sure it's gonna be
a great conversation.
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What I wanna do is begin by
asking each of our guests
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to do it, commit an unnatural
act for a faculty member,
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which is to share insights
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and thoughts about the subject in three
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to five minutes only.
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So, but I have trust, I
have trust in science,
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I have trust in my colleagues.
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I know they're gonna do a great job
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of putting on the table sort of data
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and insights so we have
a, a good scene set
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or a good basis to begin.
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So skip, let's start with you.
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Set the table for us about
how you're thinking about,
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you've been researching this issue,
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you've been involved in the policy space.
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Start by setting the scene for us
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and then I'll turn to
Mark and then to Russ.
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- Sure. Thank you so
much for the opportunity.
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I'll talk about, first
talk about universities
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and then science generally.
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So there are real questions about trust
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and universities amongst the public,
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and there's a pretty good reason why.
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So about a thousand years ago, the model
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for the modern years of
university was developed
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and the idea was you take
these little clusters
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of subject matter experts
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and you put them in close
physical proximity with them
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and other groups and
they inspire each other
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and they challenge each other and they
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bounce off each other.
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You put students and community
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members and they change the world.
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And it's been a fabulous model, right?
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It, it's, it's done so much
for our lives for about 980
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of those thousand years.
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Places like universities
had a near monopoly on the
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production and distribution
of many kinds of information.
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It's one of the reasons
that people my age sat
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through terrible classes
in college, right?
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'cause where else were you
gonna go to learn this?
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But about 20 years ago, the
internet became a viable source
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for the mass production and
distribution of information.
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And so it's led to a cultural moment
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where people look at
institutions of higher education
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and they ask lots of questions,
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but typically they come down
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to why should we pay for what you do?
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Because I can get all this information
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on the internet for free.
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And the other is, why should we trust you?
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Because you guys seem a little different.
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And so let, let's talk
about those perceptions.
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I've recently led a
national academy's effort to
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do an overview of, of
all the studies on trust
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and science filtered by data quality.
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So I'm only gonna tell you
about what the, the, the,
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the studies that we can,
I, I think validate.
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So, so there's two main findings.
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One is we all know
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that there's been a decline
in trust in science,
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but I wanna put that in context.
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If you look, companies like Gallup,
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who actually have great
methodology trust in every insti
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social institution
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that they measure has fallen
over the last 20 years, right?
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And within the cluster of institutions
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that are regularly measured, science,
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scientific institutions,
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medical science are still relatively high.
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They're still in the set with the military
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and largely above everybody
else who's measured.
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But there's something else underneath the
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data when you dig in it.
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And the question comes to,
who are you guys working for?
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Are you working for yourselves?
Are you working for us?
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That's a question that society's asking.
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And I'll give you an, here's an example.
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Penn has done these,
this great extensive data
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survey collection where they
asked detailed questions about
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what people think about
various aspects of science.
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And here are some findings
that report in the NASA paper.
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So does the public believe
that we can train chemists
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and physicists and people
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who specialize in English literature?
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Yeah, we don't have to
convince anybody of that.
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They think universities can do this.
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But when you start asking
questions about do we share the
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public's values?
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Do we cut corners to do
things like publish papers
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and draw attention to ourselves?
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Are we biased when it comes
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to making claims about top
topics like climate or,
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or politically controversial topics?
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And that's where we've lost
a fair amount of the topic.
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There's an asymmetry,
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there's more on the right than the left,
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but even the left is asking questions.
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But one of the most surprising
things we found when we were
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looking at the data was
when you ask Americans in
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20 24, 20 25,
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what do you think scientists should do?
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There's actually a huge
consensus, like 90 plus percent
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of Americans agreeing on anything.
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And they agree on things
like, when we find new data,
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we should update our beliefs.
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And when we have sort of
relationships that could affect
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what we find, we should disclose them.
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And you might say, how do
Americans have opinions on
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this and what, whatever.
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American had science classes
basically through high school.
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So there is a consensus in the country on
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what people think science is.
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And so where a lot of the
trust problems are coming from
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right now is they have
a sense of what they,
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we should be doing and
questions about are we
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actually doing that?
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So again, like in a lot
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of the larger trust
literature at this moment,
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people look at institutions like ours
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and they say, are you guys working for us
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or are you working for yourselves?
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That provides both challenges
for us and opportunities,
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and I look forward to
talking about them in
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the, in the time ahead.
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- Thanks so much. I'm
gonna, we're gonna come back
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to this question of you.
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Is this just a low bar, right?
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We know that scientists
are trusted more than
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politicians, right?
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But you know what's going on
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with the declining trust in
institutions more broadly.
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Okay, mark, over to you.
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- Thank you, Amy, since you
said I should be using facts.
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I have many opinions on this subject,
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but I'll try to start off by something
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that I actually know and is factual.
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And that has to do with how the innovation
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ecosystem works in this
country, which I think is,
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is somewhat misunderstood.
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It is true that in many areas
their industrial funding
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of research is much larger than government
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and they companies do a lot
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of work in actually pushing things out.
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But I think what's not
quite as well understood is
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that those company innovations build upon
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ideas that were created
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through more fundamental
research usually done in a
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university that may lay
dormant for a number of years
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before pick being picked up.
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And, and so part of the problem,
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and it gets back to what
Skip was talking about too,
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is you have a number of
people in a university,
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if you're gonna innovate,
you have to create ideas
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that most people think are stupid.
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Because if the idea's
gonna change the world,
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that means when you tell it to
somebody, they think it's bad
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because otherwise it's not
gonna change the world.
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Everybody agrees that's
the way things are.
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So you have a bunch of crazy ish people
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who really are true believers in something
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and they work on things either
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because they're just curious about it,
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or they view the world slightly
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differently than other people.
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Now the the truth is
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that when most people say it's
a bad idea, it is a bad idea.
259
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But a few of those
things actually turn out
260
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to be very good ideas.
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Those ideas then get pushed out
262
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that goes into the startup
ecosystem of this country.
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And again, most of those ideas fail,
264
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but the ones that actually make it
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through are things like Google
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or another company was VMware,
267
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which basically changed
the whole way servers did.
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So I can go through, you
know, trillions of dollars in
269
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industry that's been spawned
270
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by people doing various kinds of research.
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And so the problem is that
people will say things are true
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because they believe them that
don't turn out to pan out,
273
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but that doesn't mean they're lying
274
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or they're not in your best interest.
275
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It's because you need some
true believers to push,
276
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push basically science for forward
277
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and to do the unconventional
thing that's necessary.
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So I'm, you know, I
under my three minutes,
279
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but let me stop there
because I, I can go on,
280
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but the rest is my opinion and
we'll get to that later on.
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- We certainly will. But,
so I wanna take a little bit
282
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of your time, mark and, and
push you a little bit on that.
283
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'cause I've heard you talk about this
284
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before for our listeners,
285
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talk about the fundamental
research connection to Google
286
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because that's what you were alluding to.
287
00:11:22,380 --> 00:11:24,720
But let's make that a
little bit more explicit.
288
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- Yeah. So I've been very lucky
289
00:11:26,430 --> 00:11:29,430
'cause I've been at
Stanford for about 40 years
290
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and through that period
291
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of time I've seen enormous
innovations in creates
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essentially Silicon Valley that came out.
293
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So in Google's case,
294
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there was an NSF funded
research on digital libraries.
295
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Now the digital library
thing was just trying
296
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to think about cataloging,
you know, information.
297
00:11:48,060 --> 00:11:50,040
And then, you know, a couple
298
00:11:50,040 --> 00:11:52,800
of very adventuresome
students started playing
299
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and thinking about, well, what's the
300
00:11:54,040 --> 00:11:55,180
biggest information we have?
301
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It's the internet. And they
started thinking about how
302
00:11:57,430 --> 00:12:00,700
to cattle, how to search
it or how to find things.
303
00:12:00,700 --> 00:12:02,260
And they came up with a much better idea,
304
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which is called page rank.
305
00:12:03,970 --> 00:12:07,390
And that then spun out to form Google.
306
00:12:07,390 --> 00:12:12,250
Now a decade earlier, I think
there were a couple crazy kids
307
00:12:13,150 --> 00:12:18,100
who were working for a faculty
member who was on sabbatical,
308
00:12:18,100 --> 00:12:20,890
and that was much earlier
in the internet age.
309
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And they started cataloging the internet
310
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that ended up being Yahoo.
311
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Then there was some work
that was done, you know,
312
00:12:28,150 --> 00:12:31,545
at Stanford on interesting
processor designs.
313
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And that led to the MIPS computer systems
314
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and silicon graphics, which
ended up pushing, you know,
315
00:12:36,730 --> 00:12:39,100
the whole computing industry.
316
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So, and all of these things happened
317
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because somebody had an idea
318
00:12:42,880 --> 00:12:44,800
that they could do
something a little better
319
00:12:44,800 --> 00:12:47,170
or they thought there was a
different way of doing it.
320
00:12:47,170 --> 00:12:50,860
That went, went against the,
you know, popular belief.
321
00:12:50,860 --> 00:12:55,480
My little foray into this was
in high performance memory.
322
00:12:56,530 --> 00:13:00,580
We thought that memory bandwidth
was gonna be very important
323
00:13:00,580 --> 00:13:02,710
and started working on
technology for that.
324
00:13:02,710 --> 00:13:04,930
And again, pushed against
conventional wisdom
325
00:13:04,930 --> 00:13:07,060
'cause we're making the
drams more complicated.
326
00:13:07,060 --> 00:13:08,980
But as you might have heard,
327
00:13:08,980 --> 00:13:11,980
there's this DRAM memory shortage today,
328
00:13:11,980 --> 00:13:15,730
and the hot ticket is
something called HBM dram,
329
00:13:15,730 --> 00:13:18,130
which is high bandwidth memory dram.
330
00:13:18,130 --> 00:13:23,130
And that all came out of
this push 30 years ago
331
00:13:23,710 --> 00:13:26,020
now, 35 years ago now, to thinking
332
00:13:26,020 --> 00:13:27,850
that memory bandwidth is
gonna be a big problem.
333
00:13:28,690 --> 00:13:30,370
- So related to trust, right?
334
00:13:30,370 --> 00:13:32,140
This, this gets to the question of
335
00:13:32,140 --> 00:13:36,700
how do we build trust
in these institutions
336
00:13:36,700 --> 00:13:38,440
and researchers who are doing work
337
00:13:38,440 --> 00:13:40,030
where the tangible connection,
338
00:13:40,030 --> 00:13:41,380
what's the return on investment
339
00:13:41,380 --> 00:13:44,920
to the American people is in
the far off future. That's
340
00:13:44,920 --> 00:13:45,920
- Challenge, right?
341
00:13:45,920 --> 00:13:47,350
And the, and the other thing
is that if you actually have a
342
00:13:47,350 --> 00:13:50,500
vibrant ecosystem, many of
the things you try will fail,
343
00:13:50,500 --> 00:13:51,500
- Will fail.
344
00:13:51,500 --> 00:13:54,190
- I mean, if you don't fail,
you're being very incremental.
345
00:13:54,190 --> 00:13:56,980
I mean, that's part of
the part of science.
346
00:13:56,980 --> 00:13:59,260
And I think that's something
that's hard for the public
347
00:13:59,260 --> 00:14:00,820
to understand 'cause they think we're,
348
00:14:00,820 --> 00:14:02,440
we're wasting their money.
349
00:14:02,440 --> 00:14:04,185
And I think that's a
fundamental challenge.
350
00:14:04,185 --> 00:14:05,555
- Yeah. Russ, over to you.
351
00:14:06,490 --> 00:14:09,430
- Yeah, so Skip and Mark have
really laid the ground on,
352
00:14:09,430 --> 00:14:11,830
so I'm just gonna throw in a few thoughts.
353
00:14:12,820 --> 00:14:14,080
The first one is, as you said,
354
00:14:14,080 --> 00:14:17,560
there is a general reduction
in trust in institutions.
355
00:14:17,560 --> 00:14:20,320
And that part of that is
just pulling down education.
356
00:14:20,320 --> 00:14:24,250
And I think that comes
from many factors, most
357
00:14:24,250 --> 00:14:25,450
of which I'm not an expert at,
358
00:14:25,450 --> 00:14:28,480
and definitely I don't have
data on, but I'm aware of that.
359
00:14:28,480 --> 00:14:30,760
And it's part of the conversation
360
00:14:30,760 --> 00:14:32,350
that academics are
having all the time about
361
00:14:32,350 --> 00:14:34,330
how can we go against that trend.
362
00:14:34,330 --> 00:14:36,010
So I'm gonna put that aside.
363
00:14:36,010 --> 00:14:38,470
I think there's an issue
about communication.
364
00:14:38,470 --> 00:14:42,610
The, the explosion of media
over the last few years
365
00:14:42,610 --> 00:14:46,570
and the lack of media
training that, that scientists
366
00:14:46,570 --> 00:14:49,960
and engineers get means
that it's extremely exciting
367
00:14:49,960 --> 00:14:53,630
to have a young reporter call
you up about your latest paper
368
00:14:53,630 --> 00:14:54,920
and ask you about it.
369
00:14:54,920 --> 00:14:57,980
And it's extremely hard for you
370
00:14:57,980 --> 00:15:00,800
to not be incredibly excited about your
371
00:15:00,800 --> 00:15:01,970
latest piece of work.
372
00:15:01,970 --> 00:15:05,630
And it's incredibly hard for
that media person not to say,
373
00:15:05,630 --> 00:15:07,550
okay, what could this mean in terms
374
00:15:07,550 --> 00:15:09,650
of a revolutionary future?
375
00:15:09,650 --> 00:15:13,730
And that unfortunate kind of
positive feedback loop during
376
00:15:13,730 --> 00:15:16,790
that interaction can lead
to reports to the public
377
00:15:16,790 --> 00:15:19,100
that are exciting for a few moments.
378
00:15:19,100 --> 00:15:22,190
But that when you look back
on the it as a scientist,
379
00:15:22,190 --> 00:15:25,400
you may really regret the level of promise
380
00:15:25,400 --> 00:15:27,530
and the level of hope that maybe
381
00:15:27,530 --> 00:15:30,620
what might have been a very
preliminary finding generated.
382
00:15:30,620 --> 00:15:32,450
So I think that we have a media problem,
383
00:15:32,450 --> 00:15:35,060
and I think there's many
players in that problem,
384
00:15:35,060 --> 00:15:36,890
and it conspires
385
00:15:36,890 --> 00:15:40,040
to create false expectations
along the lines that, that,
386
00:15:40,040 --> 00:15:42,585
that Mark and Skip have already mentioned.
387
00:15:42,585 --> 00:15:47,450
The, the next thing I
wanna mention is that
388
00:15:47,450 --> 00:15:48,860
science is complicated
389
00:15:48,860 --> 00:15:51,230
and it doesn't always map to common sense.
390
00:15:51,230 --> 00:15:54,440
And there's a, and, and as science and,
391
00:15:54,440 --> 00:15:55,850
and technology has gotten more
392
00:15:55,850 --> 00:15:58,220
and more complicated, it becomes more
393
00:15:58,220 --> 00:16:02,120
and more difficult for the,
for the public to understand
394
00:16:02,120 --> 00:16:04,760
what is being said and what
and how contingent it is.
395
00:16:04,760 --> 00:16:06,650
Is it definitely true? Maybe true.
396
00:16:06,650 --> 00:16:09,140
And that feeds back into,
into the first issue.
397
00:16:09,140 --> 00:16:11,510
And so education
398
00:16:11,510 --> 00:16:15,740
and science education has to
be a priority for our society
399
00:16:15,740 --> 00:16:18,290
because the, then you can
ask the right questions
400
00:16:18,290 --> 00:16:19,810
and then you have a sense, and, and,
401
00:16:19,810 --> 00:16:22,670
and I think Skip described it
beautifully, you don't have
402
00:16:22,670 --> 00:16:24,950
to be an expert, but if
you understand the process
403
00:16:24,950 --> 00:16:27,170
and the contingency of all knowledge
404
00:16:27,170 --> 00:16:30,320
that it all could be wrong
tomorrow if the right new
405
00:16:30,320 --> 00:16:33,370
information comes out,
that helps make a, a,
406
00:16:33,370 --> 00:16:36,440
a good public dialogue.
407
00:16:36,440 --> 00:16:38,480
And then the third thing
I wanted to say is the,
408
00:16:38,480 --> 00:16:41,090
this question of the scientific bargain.
409
00:16:41,090 --> 00:16:42,860
What is the deal? You know, van Bush
410
00:16:42,860 --> 00:16:47,750
and others created this post World War II
411
00:16:47,750 --> 00:16:50,300
structure for a bargain.
412
00:16:50,300 --> 00:16:52,490
The bargain was the government
413
00:16:52,490 --> 00:16:55,940
and the public would pay lots of academics
414
00:16:55,940 --> 00:16:57,290
to do lots of research.
415
00:16:57,290 --> 00:17:01,520
And in exchange they would do
research in, in, in support
416
00:17:01,520 --> 00:17:04,670
of public, public goals, public missions.
417
00:17:04,670 --> 00:17:06,740
And it was supposed to be
a good deal for everybody.
418
00:17:06,740 --> 00:17:09,140
And you can argue that
it has been a good deal,
419
00:17:09,140 --> 00:17:13,220
but it needs to be tended
to nurtured, curated.
420
00:17:13,220 --> 00:17:14,810
And I don't,
421
00:17:14,810 --> 00:17:17,360
I don't think universities
have done a good job.
422
00:17:17,360 --> 00:17:20,480
I think they have, we have
started to take it for granted.
423
00:17:20,480 --> 00:17:24,230
And so we need to reexamine that compact
424
00:17:24,230 --> 00:17:25,940
and make sure that it's updated
425
00:17:25,940 --> 00:17:27,230
and that it's still valuable.
426
00:17:27,230 --> 00:17:29,150
Because of all this, I
started this podcast,
427
00:17:29,150 --> 00:17:31,520
which you heard in my
introduction, the idea was
428
00:17:31,520 --> 00:17:33,260
to have a public forum
429
00:17:33,260 --> 00:17:36,830
where scientists can talk
like real people about their
430
00:17:36,830 --> 00:17:40,160
passion for their work,
why they do what they do,
431
00:17:40,160 --> 00:17:41,780
what the hopes are for the future,
432
00:17:41,780 --> 00:17:44,600
and what the challenges are
in getting the work done.
433
00:17:44,600 --> 00:17:45,890
- Okay, so I'm gonna channel,
434
00:17:45,890 --> 00:17:47,930
I'm the national security
person of the bunch,
435
00:17:47,930 --> 00:17:52,290
and normally I look at the
downside risk of science,
436
00:17:52,290 --> 00:17:53,670
engineering technology.
437
00:17:53,670 --> 00:17:55,950
And so Russ, you know,
438
00:17:55,950 --> 00:17:58,350
I often get teased in the
AI institute that Amy,
439
00:17:58,350 --> 00:18:00,600
you think every technology is a weapon.
440
00:18:00,600 --> 00:18:03,150
And I say that's because
every technology is a weapon.
441
00:18:03,150 --> 00:18:04,710
But I'm gonna flip the script
442
00:18:04,710 --> 00:18:08,520
and I wanna present some data
about the optimistic case
443
00:18:08,520 --> 00:18:12,090
of trust in science and ask
why are you still so worried?
444
00:18:12,090 --> 00:18:13,830
So this is from I'll,
445
00:18:13,830 --> 00:18:16,350
I'll skip mentioned a
couple points I wanna add to
446
00:18:16,350 --> 00:18:21,120
that from Pew and Gallup,
57% of Americans in October
447
00:18:21,120 --> 00:18:24,150
of this year, including
a majority of Republicans
448
00:18:24,150 --> 00:18:25,890
and a majority of Democrats,
449
00:18:26,730 --> 00:18:31,470
see the US being a leader in
science as important, right?
450
00:18:31,470 --> 00:18:33,450
A majority of Americans
think it's important the US
451
00:18:33,450 --> 00:18:34,680
as a leader in science.
452
00:18:34,680 --> 00:18:37,620
And that's gone up over
the past three years.
453
00:18:38,670 --> 00:18:42,660
Large majorities and both
parties, large majorities say
454
00:18:42,660 --> 00:18:45,990
that government investments in
scientific research aimed at
455
00:18:45,990 --> 00:18:48,090
advancing knowledge is worthwhile.
456
00:18:49,080 --> 00:18:50,670
Those numbers are going up too.
457
00:18:50,670 --> 00:18:53,370
So the trend is going up, not down.
458
00:18:55,260 --> 00:18:58,440
Large majorities of Americans
say they have at least a fair
459
00:18:58,440 --> 00:19:00,150
amount of confidence in scientists
460
00:19:00,150 --> 00:19:01,680
to act in the public interest.
461
00:19:01,680 --> 00:19:05,040
Back to the, what's the
bargain that Russ questioned?
462
00:19:05,040 --> 00:19:07,200
Skip made the point that trust in
463
00:19:09,540 --> 00:19:13,380
scientists as a group is actually
higher than trust in other
464
00:19:13,380 --> 00:19:14,460
institutions.
465
00:19:14,460 --> 00:19:16,380
Second only to trust in the military,
466
00:19:16,380 --> 00:19:18,210
but all those numbers are going down.
467
00:19:18,210 --> 00:19:20,100
And then last but not least,
468
00:19:20,100 --> 00:19:22,980
a rare bipartisan moment in Congress
469
00:19:22,980 --> 00:19:25,470
to reject deep spending cuts both
470
00:19:25,470 --> 00:19:27,210
to the National Science Foundation
471
00:19:27,210 --> 00:19:29,490
and the National Institutes of Health.
472
00:19:29,490 --> 00:19:34,200
Veto proof majorities in
both houses of Congress said,
473
00:19:34,200 --> 00:19:37,050
we need to keep funding these
institutions at least at the
474
00:19:37,050 --> 00:19:38,880
level that they currently are.
475
00:19:38,880 --> 00:19:40,080
So a rare bit
476
00:19:40,080 --> 00:19:43,020
of bipartisan good news
coming out of Washington.
477
00:19:43,020 --> 00:19:45,150
So with all of this good news about trust
478
00:19:45,150 --> 00:19:48,990
and science is actually pretty
good, why are we so worried,
479
00:19:48,990 --> 00:19:52,890
number one and number two,
what are the indicators
480
00:19:52,890 --> 00:19:56,700
that you're really focused on to gauge
481
00:19:56,700 --> 00:19:58,350
how concerned you should be?
482
00:19:58,350 --> 00:19:59,430
Skip, why don't we start with you?
483
00:20:00,750 --> 00:20:05,130
- Yeah. So the read on
the politics is spot on.
484
00:20:05,130 --> 00:20:08,850
There's strong bipartisan
support for research and science.
485
00:20:08,850 --> 00:20:10,140
One thing that is changing
486
00:20:10,140 --> 00:20:12,360
and that people like us at
universities should think about
487
00:20:12,360 --> 00:20:14,880
is, is what are we being asked to do?
488
00:20:14,880 --> 00:20:18,870
So in the Vanderberg
Bush era, there was a,
489
00:20:18,870 --> 00:20:20,130
Congress had a willingness to write a
490
00:20:20,130 --> 00:20:21,150
blank check, if you will.
491
00:20:21,150 --> 00:20:23,070
Like we know the science is important,
492
00:20:23,070 --> 00:20:24,570
you guys figure it out.
493
00:20:24,570 --> 00:20:25,710
Over time,
494
00:20:25,710 --> 00:20:28,020
the directives from government have become
495
00:20:28,020 --> 00:20:29,370
much more detailed.
496
00:20:29,370 --> 00:20:31,680
And, and again, I think there's
a reason for that, right?
497
00:20:31,680 --> 00:20:34,830
There's a sense that maybe
we can get information
498
00:20:34,830 --> 00:20:36,090
from other places.
499
00:20:36,090 --> 00:20:38,370
And so one thing to be aware of is,
500
00:20:38,370 --> 00:20:39,810
is over the last 20 years,
501
00:20:39,810 --> 00:20:41,250
and particularly the last 10,
502
00:20:41,250 --> 00:20:42,930
I dunno if I'm gonna do a graph here,
503
00:20:42,930 --> 00:20:46,470
but the growth in the type of grants
504
00:20:46,470 --> 00:20:49,930
that many people my age got,
which were discipline specific,
505
00:20:49,930 --> 00:20:52,480
you know, individual
investigator, the growth rate
506
00:20:52,480 --> 00:20:53,830
of those has been very slow
507
00:20:53,830 --> 00:20:55,780
and slowing over the last 20 years.
508
00:20:55,780 --> 00:20:58,540
And where the growth has really
been in things like arpa,
509
00:20:58,540 --> 00:21:01,720
you have darpa, defense
Advanced Projects Agency,
510
00:21:01,720 --> 00:21:04,810
now you have arpa, e arpa,
arpa, I, the TIP directorate.
511
00:21:04,810 --> 00:21:09,100
They're more applied program
based, cluster based.
512
00:21:09,100 --> 00:21:12,070
And part part of that is
a bipartisan philosophy
513
00:21:12,070 --> 00:21:14,740
that we want innovation faster.
514
00:21:14,740 --> 00:21:18,970
We don't really wanna interrogate
how basic research works.
515
00:21:18,970 --> 00:21:20,140
And you know, the other,
516
00:21:20,140 --> 00:21:22,630
the last thing I'll say just
from my time in government is,
517
00:21:22,630 --> 00:21:24,370
you know, when I meet
members of Congress, most
518
00:21:24,370 --> 00:21:25,930
of whom are, are very thoughtful.
519
00:21:25,930 --> 00:21:29,830
Like the, you know, they have
to go in front of constituents
520
00:21:29,830 --> 00:21:31,450
and they have to answer
the following question.
521
00:21:31,450 --> 00:21:32,560
Like, so when I was at NSF,
522
00:21:32,560 --> 00:21:35,920
the average engineering grant
was $130,000 at the time,
523
00:21:35,920 --> 00:21:39,010
that was two times median
household family income.
524
00:21:39,010 --> 00:21:41,950
And so they were in the
position to go out in front
525
00:21:41,950 --> 00:21:44,770
of people and say, well, here's
a study from engineering.
526
00:21:44,770 --> 00:21:46,810
Why is this so important
527
00:21:46,810 --> 00:21:49,330
that you'll take the entire
year's earnings of two media
528
00:21:49,330 --> 00:21:51,640
and American households
and do it for that?
529
00:21:51,640 --> 00:21:53,830
And so some of the pressure that members
530
00:21:53,830 --> 00:21:57,100
of Congress are in are answering
that question in an era
531
00:21:57,100 --> 00:21:58,570
where for, you know, better
532
00:21:58,570 --> 00:22:00,190
or worse, people look
at the internet and say,
533
00:22:00,190 --> 00:22:02,470
but I can just get a lot of
my information from Google.
534
00:22:02,470 --> 00:22:04,390
So that's, that's part of the pressure.
535
00:22:04,390 --> 00:22:07,360
And I think going forward,
you know, making the case
536
00:22:07,360 --> 00:22:09,580
for a workflow that includes basic science
537
00:22:09,580 --> 00:22:11,890
and applied science as
part of an ecosystem
538
00:22:11,890 --> 00:22:14,620
that drives progress, saves
lives, improve lives, is,
539
00:22:14,620 --> 00:22:17,620
is like arguably the best way
that we've ever done this kind
540
00:22:17,620 --> 00:22:21,075
of at scale in the history
of, of, of humanity thinks a,
541
00:22:21,075 --> 00:22:22,120
a powerful argument to make.
542
00:22:22,120 --> 00:22:23,470
And we just need to
figure out how to make it
543
00:22:23,470 --> 00:22:25,210
in a new circumstance.
544
00:22:25,210 --> 00:22:27,280
- Ross and Mark, you wanna jump in here?
545
00:22:27,280 --> 00:22:29,560
- Yeah, I just wanna add a couple points.
546
00:22:29,560 --> 00:22:31,960
I completely agree with Skip the ratio
547
00:22:31,960 --> 00:22:33,250
of fundamental research
548
00:22:33,250 --> 00:22:35,530
to basically more directed
research has been changing
549
00:22:35,530 --> 00:22:37,750
and there's an issue in funding of
550
00:22:37,750 --> 00:22:39,250
that more fundamental research.
551
00:22:39,250 --> 00:22:42,460
And as I said, those are
the seed corn, you know,
552
00:22:42,460 --> 00:22:43,900
if you wanna use that analogy
553
00:22:43,900 --> 00:22:47,290
for all the more applied research.
554
00:22:47,290 --> 00:22:49,300
And if we don't have people sort
555
00:22:49,300 --> 00:22:52,720
of pushing at the
frontier, it's a problem.
556
00:22:52,720 --> 00:22:56,410
Like I, I personally have
an issue with the quote,
557
00:22:56,410 --> 00:22:59,110
curiosity based research, which is
558
00:22:59,110 --> 00:23:01,030
how fundamentals often said.
559
00:23:01,030 --> 00:23:02,860
And it's, it's like, it sounds like, oh,
560
00:23:02,860 --> 00:23:04,870
we're just like thinking, oh, a butterfly,
561
00:23:04,870 --> 00:23:06,220
that would be a nice thing to study.
562
00:23:06,220 --> 00:23:08,380
We'll just, you know, or,
or something, you know,
563
00:23:08,380 --> 00:23:10,150
- This, it sounds like
we're sipping lattes
564
00:23:10,150 --> 00:23:11,775
and not doing something serious,
565
00:23:11,775 --> 00:23:12,775
- Right?
566
00:23:12,775 --> 00:23:15,850
And, and the, and the point
is curiosity based research is
567
00:23:15,850 --> 00:23:19,480
basically somebody who has
deep expertise in some area
568
00:23:19,480 --> 00:23:23,980
has an idea of an area that
has not yet been explored
569
00:23:23,980 --> 00:23:26,560
and they're curious
about what happens there.
570
00:23:26,560 --> 00:23:28,840
Or it seems like, you know,
571
00:23:28,840 --> 00:23:30,940
there could be some
interesting relationship
572
00:23:32,080 --> 00:23:34,570
and they're just
interested in exploring the
573
00:23:34,570 --> 00:23:36,820
frontier of knowledge.
574
00:23:36,820 --> 00:23:39,010
Like, so I would prefer exploring frontier
575
00:23:39,010 --> 00:23:41,350
of knowledge than
curiosity based research.
576
00:23:41,350 --> 00:23:43,720
'cause one seems way more
serious than the other,
577
00:23:43,720 --> 00:23:46,190
but to me they're basically the same.
578
00:23:46,190 --> 00:23:48,440
That they're meaning the same thing. Okay.
579
00:23:48,440 --> 00:23:50,330
So that's one point I wanna make.
580
00:23:50,330 --> 00:23:52,970
And the second point I
wanna make is that part
581
00:23:52,970 --> 00:23:54,890
of the problem we have today is
582
00:23:54,890 --> 00:23:57,890
that the society is very
polarized in information flows.
583
00:23:58,820 --> 00:24:02,210
And if you're in a very
polarized, you know,
584
00:24:03,230 --> 00:24:04,970
information space
585
00:24:04,970 --> 00:24:09,020
and you think that
innovation requires failures,
586
00:24:11,000 --> 00:24:14,450
okay, you're in a very
difficult situation politically
587
00:24:14,450 --> 00:24:17,330
because whatever you do that doesn't work
588
00:24:17,330 --> 00:24:20,510
and you expect most of the
stuff not to work will be taken
589
00:24:20,510 --> 00:24:24,260
by the polar, you know,
opposite, you know, the,
590
00:24:24,260 --> 00:24:27,170
the other side as stupidity on the
591
00:24:27,170 --> 00:24:29,090
side that actually did it.
592
00:24:29,090 --> 00:24:33,590
And you know, this goes in,
in all different domains
593
00:24:33,590 --> 00:24:36,260
and probably mostly in, in climate is sort
594
00:24:36,260 --> 00:24:38,270
of the most polarized right now.
595
00:24:38,270 --> 00:24:40,460
And, and it's just, it's
one of those situations
596
00:24:40,460 --> 00:24:43,820
where science fundamentally
has to have disagreements.
597
00:24:43,820 --> 00:24:47,390
It is almost never that
everybody agrees on a topic.
598
00:24:47,390 --> 00:24:52,070
You always have people thinking,
no, that's not true. Right?
599
00:24:52,070 --> 00:24:56,030
And that makes muds,
you know, that's normal.
600
00:24:56,030 --> 00:24:59,780
You know, we basically, if
90 some odd percent say X
601
00:24:59,780 --> 00:25:03,260
and some smaller percentage say
why, we basically say, okay,
602
00:25:03,260 --> 00:25:05,150
well this is, you know,
the plan of record,
603
00:25:06,260 --> 00:25:09,050
but it's very hard in a
very polarized society
604
00:25:09,050 --> 00:25:12,170
because they will claim
each other's are idiots
605
00:25:12,170 --> 00:25:13,820
and we can't trust science.
606
00:25:13,820 --> 00:25:15,110
So I I'm still worried.
607
00:25:16,220 --> 00:25:18,260
- Yeah, IIII am too,
608
00:25:18,260 --> 00:25:20,450
and I'd like to have a a
bad news and a good news.
609
00:25:20,450 --> 00:25:21,980
A bad news is COVID,
610
00:25:23,210 --> 00:25:26,060
COVID was e extremes in both directions.
611
00:25:26,060 --> 00:25:29,360
As a technologist, I'm a
doctor and a researcher.
612
00:25:29,360 --> 00:25:33,230
And as one of those, watching
them create diagnostics
613
00:25:33,230 --> 00:25:35,090
and vaccines at the scale
614
00:25:35,090 --> 00:25:37,520
and speed that it
happened was breathtaking
615
00:25:37,520 --> 00:25:39,050
and made me proud to be a human.
616
00:25:40,010 --> 00:25:45,010
At the same time, there
was a big problem in
617
00:25:45,230 --> 00:25:48,260
the public understanding
of the loss of freedom,
618
00:25:48,260 --> 00:25:50,240
the taking away of people's freedom,
619
00:25:50,240 --> 00:25:52,730
school decisions, other decisions.
620
00:25:52,730 --> 00:25:55,700
And that all got confounded where for one
621
00:25:55,700 --> 00:25:58,610
of the first times ever
biomedical research,
622
00:25:58,610 --> 00:26:00,560
which was almost pristine
623
00:26:00,560 --> 00:26:04,970
and above reproach, was
drawn down into the fray.
624
00:26:04,970 --> 00:26:06,290
And many of us in, in,
625
00:26:06,290 --> 00:26:08,660
in my field were like, what's going on?
626
00:26:08,660 --> 00:26:11,180
People used to always trust us and, but
627
00:26:11,180 --> 00:26:14,390
because of our involvement
in, in a big mess, let's,
628
00:26:14,390 --> 00:26:17,630
and I think it's fair to call
the COVID response a big mess.
629
00:26:17,630 --> 00:26:21,080
There was a definite hit to
biomedical research and, and
630
00:26:21,080 --> 00:26:22,580
and the value of it.
631
00:26:22,580 --> 00:26:25,190
And it's, and there was a
questioning about the role of it
632
00:26:25,190 --> 00:26:26,720
and who's, who are you working for?
633
00:26:26,720 --> 00:26:30,020
The question that came up
earlier, so that's the bad news,
634
00:26:30,020 --> 00:26:31,820
is I think there was a big hit there.
635
00:26:31,820 --> 00:26:33,500
And I think it's, it's
a lesson to all of us
636
00:26:33,500 --> 00:26:37,130
that when big national crises happen,
637
00:26:37,130 --> 00:26:38,780
scientists need to stay in their lane.
638
00:26:38,780 --> 00:26:41,060
They need to make sure they
do the work that's needed,
639
00:26:41,060 --> 00:26:42,680
generate the technologies,
640
00:26:42,680 --> 00:26:45,600
but they need to also understand
that there are other people
641
00:26:45,600 --> 00:26:48,090
who are going to then take those results
642
00:26:48,090 --> 00:26:51,270
and figure out how to turn
that into policy in action.
643
00:26:51,270 --> 00:26:52,890
The, the good news is
644
00:26:52,890 --> 00:26:55,350
after the end of the
Cold War, let's say 1990,
645
00:26:55,350 --> 00:26:57,990
we had a period of 20 years,
and this is not my expertise,
646
00:26:57,990 --> 00:26:59,850
but I'm just, I did, I was alive
647
00:26:59,850 --> 00:27:02,310
and I was watching it where there wasn't a
648
00:27:02,310 --> 00:27:03,750
focused adversary.
649
00:27:03,750 --> 00:27:06,870
And that led to a certain amount of larges
650
00:27:06,870 --> 00:27:11,220
and maybe a little bit of
laxity and lack of discipline.
651
00:27:11,220 --> 00:27:13,710
And in some sense, the good news,
652
00:27:13,710 --> 00:27:16,620
and I say this, you know, with quotes, is
653
00:27:16,620 --> 00:27:18,030
that we now have a clear adversary.
654
00:27:18,030 --> 00:27:21,090
There's a competitor on Earth China
655
00:27:21,090 --> 00:27:23,640
that is working very
hard in all of my areas
656
00:27:23,640 --> 00:27:25,260
and probably all of Mark's areas
657
00:27:25,260 --> 00:27:27,690
and probably all of sc skip's areas.
658
00:27:27,690 --> 00:27:31,710
And that focuses the mind in a
way that makes me optimistic.
659
00:27:32,610 --> 00:27:34,950
Of course lots of things have to happen,
660
00:27:34,950 --> 00:27:37,830
but part of what you're
seeing in the, the actions
661
00:27:37,830 --> 00:27:41,970
of Congress and the other good
news is we now know exactly
662
00:27:41,970 --> 00:27:46,560
why we're doing this in
ways that from 1990 to 2010
663
00:27:46,560 --> 00:27:49,050
or so, we might not
have been very clear on.
664
00:27:49,050 --> 00:27:51,690
And that was my generation
being a little bit,
665
00:27:51,690 --> 00:27:52,980
getting a little fat.
666
00:27:52,980 --> 00:27:56,010
And I use that in the sense of
like not staying in, in, in,
667
00:27:56,010 --> 00:27:57,240
in good physical shape.
668
00:27:58,440 --> 00:28:02,040
So that's a, that's the second
thought that comes to mind.
669
00:28:02,040 --> 00:28:05,880
- So I wanna, I wanna drill
down a little bit on the, what,
670
00:28:05,880 --> 00:28:08,010
what is the race we have against China
671
00:28:08,010 --> 00:28:10,410
and what are we talking about
when it comes to funding
672
00:28:10,410 --> 00:28:11,880
for research?
673
00:28:11,880 --> 00:28:14,700
Right? So all funding
is not created equal.
674
00:28:14,700 --> 00:28:17,160
What we've seen is actually
federal investment.
675
00:28:17,160 --> 00:28:20,160
That patient long-term investment
676
00:28:20,160 --> 00:28:22,740
and fundamental research has
actually declined dramatically
677
00:28:22,740 --> 00:28:24,030
since the 1960s.
678
00:28:24,030 --> 00:28:27,960
It's a third lower than
it was in the 1960s.
679
00:28:27,960 --> 00:28:30,000
Meanwhile, China is copying that model
680
00:28:30,000 --> 00:28:32,100
and investing six times faster.
681
00:28:32,100 --> 00:28:34,140
And fundamental research
in the United States is
682
00:28:34,140 --> 00:28:38,370
so doubling down on the van of
our Bush model in China, sort
683
00:28:38,370 --> 00:28:39,990
of moving away from the van
684
00:28:39,990 --> 00:28:42,000
of our Bush model in the United States.
685
00:28:42,000 --> 00:28:43,950
One of the arguments we often hear is,
686
00:28:43,950 --> 00:28:46,290
well there's venture capital,
687
00:28:46,290 --> 00:28:48,390
there's this thing called the free market.
688
00:28:48,390 --> 00:28:52,920
And so there's lots of private
sector investment in r and d,
689
00:28:52,920 --> 00:28:56,040
but not the same kind of r and d.
690
00:28:56,040 --> 00:28:57,990
So for our listeners,
691
00:28:57,990 --> 00:29:01,320
talk about what's different about
692
00:29:01,320 --> 00:29:04,020
what can only the federal government do,
693
00:29:04,020 --> 00:29:06,390
where is private investment going
694
00:29:06,390 --> 00:29:08,340
to have the biggest impact,
695
00:29:08,340 --> 00:29:11,430
and how do we get to a new model?
696
00:29:11,430 --> 00:29:14,250
Russ, you mentioned
sort of a new compact so
697
00:29:14,250 --> 00:29:17,880
that we actually as a nation are investing
698
00:29:17,880 --> 00:29:21,390
and by the way, building
trust in those investments in
699
00:29:21,390 --> 00:29:22,500
research and development.
700
00:29:23,610 --> 00:29:26,820
- Well I, you know, I, I'm
really technology focused,
701
00:29:26,820 --> 00:29:29,430
but I have done some
work in, in, in industry.
702
00:29:29,430 --> 00:29:31,200
And the thing that I notice
when I'm in industry,
703
00:29:31,200 --> 00:29:32,550
and I certainly have played
704
00:29:32,550 --> 00:29:35,520
with venture capitalists
is they care about money.
705
00:29:35,520 --> 00:29:38,760
I mean, venture capitalists
don't invest in technology
706
00:29:38,760 --> 00:29:40,410
'cause they want to grow technology
707
00:29:40,410 --> 00:29:44,380
or they want to create some
great future venture capitalists
708
00:29:44,380 --> 00:29:46,720
are capitalists, they invest in technology
709
00:29:46,720 --> 00:29:49,000
because they think it's
gonna make them money.
710
00:29:49,930 --> 00:29:54,340
Okay? In a similar way, when
an industry is doing research
711
00:29:54,340 --> 00:29:56,470
and development, they're
doing it to make money.
712
00:29:58,360 --> 00:30:00,790
The problem is that in
the fundamental research
713
00:30:00,790 --> 00:30:03,190
and in that, you know,
the things, the research
714
00:30:03,190 --> 00:30:06,250
that changes the world,
generally speaking, the people
715
00:30:06,250 --> 00:30:07,660
that do the research
716
00:30:07,660 --> 00:30:10,630
or the institution that funded
the research does not capture
717
00:30:10,630 --> 00:30:13,030
the value, right?
718
00:30:13,030 --> 00:30:15,850
Google is worth a bazillion dollars,
719
00:30:15,850 --> 00:30:18,310
but Stanford didn't get that money
720
00:30:18,310 --> 00:30:21,040
and you know, so it
was a greater good kind
721
00:30:21,040 --> 00:30:22,840
of thing that happens.
722
00:30:22,840 --> 00:30:25,420
And so I think, again, the
concept that's important
723
00:30:25,420 --> 00:30:30,250
to realize is that we need for society
724
00:30:30,250 --> 00:30:32,440
to do this fundamental research,
725
00:30:32,440 --> 00:30:33,850
but the institutions who do
726
00:30:33,850 --> 00:30:37,180
that research generally
don't capture the value
727
00:30:37,180 --> 00:30:40,480
because in a, you know,
a change the world kind
728
00:30:40,480 --> 00:30:43,540
of situation, it's not what
the business of the company is.
729
00:30:43,540 --> 00:30:46,420
And so generally people leave
the company who've done this
730
00:30:46,420 --> 00:30:47,740
and they start a new company,
731
00:30:48,940 --> 00:30:52,090
students graduate from Stanford
and they start companies.
732
00:30:52,090 --> 00:30:54,490
There's tremendous value creation.
733
00:30:54,490 --> 00:30:56,260
But the, the institution that paid
734
00:30:56,260 --> 00:30:58,300
for the research doesn't
generally capture the value
735
00:30:58,300 --> 00:30:59,320
in these kinds of things.
736
00:31:00,700 --> 00:31:02,170
And if you look at that,
737
00:31:02,170 --> 00:31:04,900
venture capital's not
gonna fund it, right?
738
00:31:06,220 --> 00:31:08,440
Industry's not gonna fund it.
739
00:31:08,440 --> 00:31:10,660
The only institution
740
00:31:10,660 --> 00:31:14,140
that will fund it is basically
a national institution
741
00:31:14,140 --> 00:31:18,820
because the nation does
capture the benefit, right?
742
00:31:18,820 --> 00:31:21,880
And in fact, if we have a
good ecosystem to do that,
743
00:31:21,880 --> 00:31:24,070
and I think the United States had
744
00:31:24,070 --> 00:31:26,920
and still has one of the
best, it attracts people from
745
00:31:26,920 --> 00:31:29,410
around the world who
have ideas to come here
746
00:31:29,410 --> 00:31:32,320
and the nation benefits from that as well.
747
00:31:32,320 --> 00:31:36,040
So, you know, I I think we
need to keep that in mind.
748
00:31:36,040 --> 00:31:38,770
And when people say, well,
well there's way more money in,
749
00:31:38,770 --> 00:31:42,310
you know, industry funding, correct?
750
00:31:42,310 --> 00:31:44,260
But that money is directed
to make more money
751
00:31:45,400 --> 00:31:49,300
and it's not directed to
basically change the world.
752
00:31:49,300 --> 00:31:52,480
- And if I can just add to
that, I, I think a corollary to
753
00:31:52,480 --> 00:31:56,290
that is basic science tends to
be cheaper than the scaling.
754
00:31:56,290 --> 00:31:59,620
So you want to scale an industry
when once the technology is
755
00:31:59,620 --> 00:32:02,920
proven or the proof of concept
is there, you don't want
756
00:32:02,920 --> 00:32:04,120
to do that in academia.
757
00:32:04,120 --> 00:32:05,410
Academia should not be doing that.
758
00:32:05,410 --> 00:32:07,990
If I develop a drug, I
should develop the drug,
759
00:32:07,990 --> 00:32:11,200
but then I need to get it out
of Stanford into a company
760
00:32:11,200 --> 00:32:14,440
that knows how to make kilogram
quantities of that drug.
761
00:32:14,440 --> 00:32:16,900
So there, there is a clear boundary about
762
00:32:16,900 --> 00:32:20,380
what is appropriately done
in an, in a university
763
00:32:20,380 --> 00:32:22,900
and when the scaling laws are just beyond
764
00:32:22,900 --> 00:32:24,310
what the university can do.
765
00:32:24,310 --> 00:32:26,140
The other thing I wanted to
address, which is related
766
00:32:26,140 --> 00:32:27,940
to this, is I think
you, Amy said, you know,
767
00:32:27,940 --> 00:32:28,990
who are you working for?
768
00:32:28,990 --> 00:32:31,420
This becomes an issue. Who are
you scientists working for?
769
00:32:31,420 --> 00:32:34,330
Are you working for us,
the public or yourself?
770
00:32:34,330 --> 00:32:37,390
And the answer very clearly is both
771
00:32:37,390 --> 00:32:40,370
because as an academic,
let's be honest, I want
772
00:32:40,370 --> 00:32:41,600
to do good science
773
00:32:41,600 --> 00:32:44,630
and I want to write papers
that my colleagues say, boy,
774
00:32:44,630 --> 00:32:48,170
I wish I wrote that paper,
but rusted, I'm jealous
775
00:32:48,170 --> 00:32:49,910
and I want to get, and
I wanna change the way
776
00:32:49,910 --> 00:32:51,020
they think about our field.
777
00:32:51,020 --> 00:32:53,120
So that's all about me, me, me, me.
778
00:32:53,120 --> 00:32:55,130
But when I make a discovery
779
00:32:55,130 --> 00:32:59,840
that actually has legs I wanna
bring that, I wanna make sure
780
00:32:59,840 --> 00:33:02,930
that that gets licensed to a
company that already exists
781
00:33:02,930 --> 00:33:06,590
or that I start that company
to make sure it can scale.
782
00:33:06,590 --> 00:33:09,920
And so really it, it would,
it's not a fair question
783
00:33:09,920 --> 00:33:11,000
who you're working for because
784
00:33:11,000 --> 00:33:14,120
of course we have all the
same personal ambitions
785
00:33:14,120 --> 00:33:15,500
that everybody has,
786
00:33:15,500 --> 00:33:19,280
but also as part of our
job description is that, is
787
00:33:19,280 --> 00:33:22,940
that requirement that we take
the stuff that really works
788
00:33:22,940 --> 00:33:25,040
and push it out into the world.
789
00:33:25,040 --> 00:33:27,080
- Like just one point on this,
790
00:33:27,080 --> 00:33:29,660
like when we think about
the role of government,
791
00:33:29,660 --> 00:33:31,460
it's important to take
a minute to think about
792
00:33:31,460 --> 00:33:32,930
how large the US government is.
793
00:33:32,930 --> 00:33:36,920
Like if you, if you just think
about the, the NIH budget
794
00:33:38,120 --> 00:33:40,610
in a single year, there it is between 50
795
00:33:40,610 --> 00:33:45,200
and 55 million, you'd need
a $2 trillion fund to kind
796
00:33:45,200 --> 00:33:46,730
of roll that off in.
797
00:33:46,730 --> 00:33:49,310
So, so it it's, it's very large.
798
00:33:49,310 --> 00:33:53,150
And the, the other
thing again, as, as Russ
799
00:33:53,150 --> 00:33:55,160
and Mark had mentioned
is the risk tolerance
800
00:33:55,160 --> 00:33:56,210
of a portfolio like
801
00:33:56,210 --> 00:33:58,640
that you actually can
take some risks over time,
802
00:33:58,640 --> 00:34:00,560
build a portfolio and so forth.
803
00:34:00,560 --> 00:34:03,890
So one of the, one of the
conversations I was in both in DC
804
00:34:03,890 --> 00:34:05,660
and here is if you thought about like,
805
00:34:05,660 --> 00:34:07,700
let's say you were just trying
to build a national research
806
00:34:07,700 --> 00:34:09,410
portfolio, there's a way
807
00:34:09,410 --> 00:34:11,240
to think about the role
of government, right?
808
00:34:11,240 --> 00:34:13,940
And it funds this high
risk early stage kind
809
00:34:13,940 --> 00:34:16,490
of intellectual stuff with
the long time horizons.
810
00:34:16,490 --> 00:34:18,980
If you are, again, thinking
about portfolio management,
811
00:34:18,980 --> 00:34:20,660
you'd put that in the federal government,
812
00:34:20,660 --> 00:34:22,910
you would then take like
the mainline philanthropies
813
00:34:22,910 --> 00:34:26,060
and some industry to do
like the translational stuff
814
00:34:26,060 --> 00:34:28,700
where there's a limited and,
and they would fund that.
815
00:34:28,700 --> 00:34:30,710
And then for the venture cat, the sort
816
00:34:30,710 --> 00:34:32,150
of new venture philanthropy
817
00:34:32,150 --> 00:34:34,130
and things like that, there's another set
818
00:34:34,130 --> 00:34:37,070
of like low probability
risks that you can't justify
819
00:34:37,070 --> 00:34:39,530
to the taxpayer but
actually could pay off.
820
00:34:39,530 --> 00:34:40,970
You know, they, they can do that.
821
00:34:40,970 --> 00:34:42,740
And I think for the nation as a whole,
822
00:34:42,740 --> 00:34:44,930
if those three entities
were more coordinated,
823
00:34:44,930 --> 00:34:47,660
had more visibility towards
one another, the benefits
824
00:34:47,660 --> 00:34:49,430
to the ecosystem could be great.
825
00:34:49,430 --> 00:34:52,160
And I feel like things like
the genesis mission now at, at,
826
00:34:52,160 --> 00:34:54,410
at Department of Energy
are an attempt to do this.
827
00:34:54,410 --> 00:34:56,030
Like, to rethink how,
828
00:34:56,030 --> 00:34:58,640
how we think about the
whole mega science workflow
829
00:34:58,640 --> 00:34:59,870
and all the component parts
830
00:34:59,870 --> 00:35:02,120
and how they could work
together more effectively.
831
00:35:02,120 --> 00:35:05,000
- Just to add a, a sort
of a point of color to,
832
00:35:05,000 --> 00:35:07,340
to make it very clear the scale difference
833
00:35:07,340 --> 00:35:08,780
that you're talking about, skip.
834
00:35:08,780 --> 00:35:10,520
'cause I think it's so important,
835
00:35:10,520 --> 00:35:12,620
if you think about Eric
Schmidt's, you know,
836
00:35:12,620 --> 00:35:17,450
moonshot philanthropy to fund science
837
00:35:17,450 --> 00:35:19,670
and engineering and he's
talking about, you know,
838
00:35:19,670 --> 00:35:23,060
giving away a hundred
million dollars a year,
839
00:35:23,060 --> 00:35:27,680
that's the cost of one F 35, right?
840
00:35:27,680 --> 00:35:30,320
So government has scale in a way
841
00:35:30,320 --> 00:35:32,960
that the private sector does not, right?
842
00:35:32,960 --> 00:35:34,340
And we often forget that.
843
00:35:34,340 --> 00:35:36,770
I wanna turn right,
skip, you've talked a lot
844
00:35:36,770 --> 00:35:37,830
and you've written a lot
845
00:35:37,830 --> 00:35:41,760
and researched this question
of where does trust come from?
846
00:35:41,760 --> 00:35:45,060
So let's dig into that a little bit more
847
00:35:45,060 --> 00:35:48,210
and is there something different about
848
00:35:48,210 --> 00:35:52,950
what drives trust in science versus trust
849
00:35:52,950 --> 00:35:54,360
in other fields?
850
00:35:54,360 --> 00:35:57,120
- Yeah, so there, there are
great literature on trust both
851
00:35:57,120 --> 00:35:58,830
with interpersonal trust and trust
852
00:35:58,830 --> 00:36:01,050
and like entities like organizations or,
853
00:36:01,050 --> 00:36:04,445
or society trust is, is always a, a,
854
00:36:04,445 --> 00:36:08,090
a a present moment perception of,
855
00:36:08,090 --> 00:36:09,690
of future relationships, right?
856
00:36:09,690 --> 00:36:12,930
Trust really matters when
you're engaging with someone
857
00:36:12,930 --> 00:36:16,320
or something and there's
risk in the future, right?
858
00:36:16,320 --> 00:36:18,090
And so the thing about trust is you can't,
859
00:36:18,090 --> 00:36:19,950
like you can ask someone to trust you,
860
00:36:19,950 --> 00:36:21,750
but that's irrelevant.
861
00:36:21,750 --> 00:36:24,510
People have decide,
decide them themselves.
862
00:36:24,510 --> 00:36:25,710
And so there are different levels
863
00:36:25,710 --> 00:36:28,230
of trust at the lowest
level, it's transactional,
864
00:36:28,230 --> 00:36:31,800
which is at this moment for
this purpose, I have enough
865
00:36:31,800 --> 00:36:33,690
of an understanding about
you that I trust you
866
00:36:33,690 --> 00:36:35,070
to do this thing.
867
00:36:35,070 --> 00:36:37,350
But that's actually of limited
usefulness when you're trying
868
00:36:37,350 --> 00:36:39,840
to take risks in, in science.
869
00:36:39,840 --> 00:36:41,520
So there needs to be a different level
870
00:36:41,520 --> 00:36:44,310
where I basically need to
understand your source code.
871
00:36:44,310 --> 00:36:47,160
I have to have a fundamental
kind of understanding of,
872
00:36:47,160 --> 00:36:49,830
in a wide range of situations,
there's something like a,
873
00:36:49,830 --> 00:36:54,210
a mantra or a small list of
kind of axioms, if you will,
874
00:36:54,210 --> 00:36:56,070
that drive the decisions you make.
875
00:36:56,070 --> 00:36:58,170
And if I feel like I understand that
876
00:36:58,170 --> 00:37:01,380
and I look at you as a person
or someone as an organization,
877
00:37:01,380 --> 00:37:03,660
and I see consistent behavior with that,
878
00:37:03,660 --> 00:37:05,820
that's actually the
manifestation of trust.
879
00:37:05,820 --> 00:37:08,700
So now in new circumstances
emerge if I feel like I
880
00:37:08,700 --> 00:37:11,790
understand your algorithm, it,
it, it's, it's really great.
881
00:37:11,790 --> 00:37:13,350
One of the more pernicious things about
882
00:37:13,350 --> 00:37:14,640
social media now, right?
883
00:37:14,640 --> 00:37:18,210
Is, is, is how much it eats
away at something like that.
884
00:37:18,210 --> 00:37:21,480
Because as Mark was saying,
like I could make what you,
885
00:37:21,480 --> 00:37:23,160
you might call a, a mistake
886
00:37:23,160 --> 00:37:25,650
or I could do a study that doesn't work
887
00:37:25,650 --> 00:37:27,330
and you could say, well it doesn't work,
888
00:37:27,330 --> 00:37:29,310
it skip is really stupid, right?
889
00:37:29,310 --> 00:37:32,130
And the, you know, the under, if, if part
890
00:37:32,130 --> 00:37:33,930
of why people trusted me is they thought
891
00:37:33,930 --> 00:37:36,270
that I I was applying
intelligence to problems,
892
00:37:36,270 --> 00:37:37,980
it really eats away at that.
893
00:37:37,980 --> 00:37:40,890
Whereas if we're in a
community where it's like, no,
894
00:37:40,890 --> 00:37:42,330
you know, skip was trying to do a thing
895
00:37:42,330 --> 00:37:43,920
and there was an underlying theory
896
00:37:43,920 --> 00:37:46,320
and we all knew there was
a chance it wouldn't work.
897
00:37:46,320 --> 00:37:49,410
But the reason to do this
work is if, if if we learn
898
00:37:49,410 --> 00:37:52,170
that it doesn't, then a
thousand other people never have
899
00:37:52,170 --> 00:37:53,820
to make that mistake again.
900
00:37:53,820 --> 00:37:55,710
And if it does work, you know,
901
00:37:55,710 --> 00:37:58,770
we're gonna save a thousand
lives with, with the same amount
902
00:37:58,770 --> 00:38:01,260
of effort that we would've
saved one today, right?
903
00:38:01,260 --> 00:38:03,420
So that's like, that's the
type of narratives that,
904
00:38:03,420 --> 00:38:04,560
that facilitate trust.
905
00:38:04,560 --> 00:38:06,600
But I will say that social
media makes that kind
906
00:38:06,600 --> 00:38:09,480
of trust really harder to, to maintain
907
00:38:09,480 --> 00:38:12,480
because it's so easy to
take a thing that I do
908
00:38:12,480 --> 00:38:15,780
and turn it into a, a, a
stereotype that just undermines,
909
00:38:15,780 --> 00:38:17,850
you know, the ability to have confidence.
910
00:38:17,850 --> 00:38:21,030
- So if we, if we sort of broaden
the aperture a little bit,
911
00:38:21,030 --> 00:38:22,920
we've talked a lot about trust,
912
00:38:22,920 --> 00:38:26,460
let's disaggregate trust in science,
913
00:38:26,460 --> 00:38:30,630
trust in universities, or is
there something else going on?
914
00:38:30,630 --> 00:38:33,630
You all have talked about
social media, the, the sort
915
00:38:33,630 --> 00:38:35,980
of media environment we're
in, the environment we're in.
916
00:38:35,980 --> 00:38:39,310
I'm, I, I keep thinking about
research that our colleagues,
917
00:38:39,310 --> 00:38:41,620
Doug Rivers and David Brady did, you know,
918
00:38:41,620 --> 00:38:44,680
they do this election, you
know, longitudinal polling.
919
00:38:44,680 --> 00:38:47,230
And I remember in the
last cycle, Brady said
920
00:38:47,230 --> 00:38:49,690
for the first time, typically
you think people vote
921
00:38:49,690 --> 00:38:50,800
for president based on
922
00:38:50,800 --> 00:38:52,840
how they feel about the economy, right?
923
00:38:52,840 --> 00:38:54,580
The economy is a big driver, a
924
00:38:54,580 --> 00:38:56,440
because about who your selection
925
00:38:56,440 --> 00:38:59,620
for president this last
cycle, they saw the opposite
926
00:38:59,620 --> 00:39:01,060
for the first time.
927
00:39:01,060 --> 00:39:05,050
How you felt about your political
party actually influenced
928
00:39:05,050 --> 00:39:07,480
your view of the economy, right?
929
00:39:07,480 --> 00:39:10,060
Whether it was do whether
the economy was doing better
930
00:39:10,060 --> 00:39:12,040
or doing worse was a factor
931
00:39:12,040 --> 00:39:14,830
of your politics not the other way around.
932
00:39:14,830 --> 00:39:16,750
They hadn't seen that before.
933
00:39:16,750 --> 00:39:20,110
So I'm curious to get your
perspectives about do you think
934
00:39:20,110 --> 00:39:24,280
polarized politics are influencing
how people view science
935
00:39:24,280 --> 00:39:26,770
and trust, number one
936
00:39:26,770 --> 00:39:28,000
and number two,
937
00:39:28,000 --> 00:39:30,400
do you think there's something
different going on about
938
00:39:30,400 --> 00:39:34,450
trust in science versus
universities versus this expression
939
00:39:34,450 --> 00:39:37,900
of identity and culture
and where you belong?
940
00:39:37,900 --> 00:39:40,900
I know I've thrown a lot at you
all, you can take your pick.
941
00:39:40,900 --> 00:39:42,940
- So I, I want to be
careful to talk about stuff
942
00:39:42,940 --> 00:39:44,020
that I actually know about.
943
00:39:44,020 --> 00:39:47,530
So, because this is good stuff
that I, I'm interested in,
944
00:39:47,530 --> 00:39:51,250
but I, I do think that there
is something going on here
945
00:39:51,250 --> 00:39:54,670
where in the old days, let's
say the old days, let's pretend
946
00:39:54,670 --> 00:39:56,080
that they were good old days.
947
00:39:57,070 --> 00:40:00,310
- That - Would be yesterday
if, if you, if if it was hard
948
00:40:00,310 --> 00:40:04,240
to propagate, if you had
a view about something
949
00:40:04,240 --> 00:40:05,500
that you wanted to be true
950
00:40:05,500 --> 00:40:07,870
that would help you in
the world get, get a,
951
00:40:07,870 --> 00:40:10,300
achieve your goals, we, you
know, you wanna make money,
952
00:40:10,300 --> 00:40:12,460
you wanna be elected to office.
953
00:40:12,460 --> 00:40:15,040
And if there was, if there
were scientific facts
954
00:40:15,040 --> 00:40:17,530
or scientific theories
that were problematic,
955
00:40:17,530 --> 00:40:18,610
that was a big problem
956
00:40:18,610 --> 00:40:20,980
because you would have all
of these people saying,
957
00:40:20,980 --> 00:40:23,800
this is a science is not,
is kind of going against
958
00:40:23,800 --> 00:40:25,690
what you're saying and we can't be a
959
00:40:25,690 --> 00:40:26,890
hundred percent positive.
960
00:40:26,890 --> 00:40:28,660
But what you're saying is unlikely
961
00:40:28,660 --> 00:40:30,970
to be true based on what we know.
962
00:40:30,970 --> 00:40:32,980
It would be contingent,
but it would be hard.
963
00:40:32,980 --> 00:40:34,990
Now that's not a problem.
964
00:40:34,990 --> 00:40:39,940
If you need something to be
true and it's not, it's easy.
965
00:40:39,940 --> 00:40:41,470
You, we know how to do this.
966
00:40:41,470 --> 00:40:43,780
You get on your social
media, you figure out
967
00:40:43,780 --> 00:40:46,690
what your bots are gonna
say, you flood Facebook,
968
00:40:46,690 --> 00:40:50,710
you flood the, the Twitter type platforms
969
00:40:50,710 --> 00:40:52,480
and you can create doubt so
970
00:40:52,480 --> 00:40:54,550
that you can push your agenda forward.
971
00:40:54,550 --> 00:40:58,510
So the ability to make, to make
972
00:40:58,510 --> 00:41:00,490
science almost irrelevant
973
00:41:00,490 --> 00:41:04,000
to many policy debates is
easier than it's ever been.
974
00:41:04,900 --> 00:41:08,770
And that causes me, me, as
a scientist some concern.
975
00:41:08,770 --> 00:41:12,070
And I think it changes the
nature of what people are willing
976
00:41:12,070 --> 00:41:13,270
to say in public
977
00:41:13,270 --> 00:41:16,450
because they know that there's
a toolkit for making sure
978
00:41:16,450 --> 00:41:19,150
that they won't get, they won't be held
979
00:41:19,150 --> 00:41:20,350
to the scientific standard
980
00:41:20,350 --> 00:41:22,150
because you can always
inject out in the ways
981
00:41:22,150 --> 00:41:24,430
that skip just described.
982
00:41:24,430 --> 00:41:26,530
- So let me follow up with
you, Russ on that and,
983
00:41:26,530 --> 00:41:29,470
and ask you to comment on an
area where you are an expert
984
00:41:29,470 --> 00:41:31,180
and that is ai.
985
00:41:31,180 --> 00:41:36,180
So yes, as we think about what
happens to trust in science
986
00:41:36,260 --> 00:41:39,530
when the scientist is
no longer human, right,
987
00:41:39,530 --> 00:41:41,150
when you're using your chat bot,
988
00:41:41,150 --> 00:41:43,340
and by the way, you can
have your chat bot have the
989
00:41:43,340 --> 00:41:46,010
personality you want
or the accent you want
990
00:41:46,010 --> 00:41:47,480
or the language you want.
991
00:41:47,480 --> 00:41:49,760
So you feel like you're
more, you have more affinity
992
00:41:49,760 --> 00:41:51,380
with that chat bot.
993
00:41:51,380 --> 00:41:54,620
What happens when your queries
about, I wanna believe this
994
00:41:54,620 --> 00:41:57,260
and your sycophantic ai friend
995
00:41:57,260 --> 00:41:58,260
- Right?
996
00:41:58,260 --> 00:42:00,740
- Is is not even human
and that's the scientist.
997
00:42:00,740 --> 00:42:02,780
What do you think is likely to happen?
998
00:42:02,780 --> 00:42:05,240
- So, so I'm gonna, I'm gonna
disagree with you be, and,
999
00:42:05,240 --> 00:42:07,340
and I think you probably
know I was gonna say this,
1000
00:42:08,180 --> 00:42:09,530
you are not allowed
1001
00:42:09,530 --> 00:42:13,730
to call AI a scientist right
now the working definition
1002
00:42:13,730 --> 00:42:15,440
of scientist is integral
1003
00:42:15,440 --> 00:42:18,020
to having a human being be the scientist.
1004
00:42:18,020 --> 00:42:20,660
So yes, there are amazing
power tools that I am
1005
00:42:20,660 --> 00:42:22,820
and all my students are using every day.
1006
00:42:22,820 --> 00:42:23,900
I'm guessing Mark
1007
00:42:23,900 --> 00:42:26,030
and all of his students
are using every day.
1008
00:42:26,030 --> 00:42:29,690
I'm guessing skip, but there's
a professionalism principle
1009
00:42:29,690 --> 00:42:33,440
here that until further notice,
the scientists are humans
1010
00:42:33,440 --> 00:42:36,680
who take full responsibility
for what they did
1011
00:42:36,680 --> 00:42:38,270
with the AI and a lot.
1012
00:42:38,270 --> 00:42:41,390
And, and, and, and therefore
if the, you never are allowed
1013
00:42:41,390 --> 00:42:44,150
to say the AI did bad science.
1014
00:42:44,150 --> 00:42:48,770
If you used AI and got a bad
result, you did bad science.
1015
00:42:48,770 --> 00:42:51,800
And that's a basic principle
of professionalism that we,
1016
00:42:51,800 --> 00:42:54,470
it it appears like people are
on a slippery slope there.
1017
00:42:54,470 --> 00:42:55,700
And I think we have to stop that.
1018
00:42:55,700 --> 00:42:58,940
I think that actually applies
to every single profession
1019
00:42:58,940 --> 00:43:00,680
that's using AI is that
1020
00:43:00,680 --> 00:43:03,380
unless you want absolute chaos, it has
1021
00:43:03,380 --> 00:43:07,310
to be human responsibility
for the outputs of, of ai.
1022
00:43:07,310 --> 00:43:09,290
And I know that there's many cases where
1023
00:43:09,290 --> 00:43:12,260
that's not happening and
that's eroding public trust
1024
00:43:12,260 --> 00:43:14,180
because the public is assuming
1025
00:43:14,180 --> 00:43:18,140
that scientists are using AI
in responsible ways compatible
1026
00:43:18,140 --> 00:43:20,600
with the compact that we
have with the government
1027
00:43:20,600 --> 00:43:21,650
and with our funders.
1028
00:43:21,650 --> 00:43:24,530
And the moment you say
that AI is a scientist
1029
00:43:24,530 --> 00:43:28,790
that can do its own thing
without supervision,
1030
00:43:28,790 --> 00:43:30,050
the goose is cooked.
1031
00:43:30,980 --> 00:43:33,470
So I'm sorry you gave me
the opportunity to give
1032
00:43:33,470 --> 00:43:35,810
that big speech and I
probably used up all my time,
1033
00:43:35,810 --> 00:43:37,340
but that's very important.
1034
00:43:37,340 --> 00:43:38,870
- I asked and you answered.
1035
00:43:38,870 --> 00:43:40,250
Mark, you look like you wanna jump into
1036
00:43:40,250 --> 00:43:41,420
this conversation too.
1037
00:43:41,420 --> 00:43:44,060
- Well I just wanted to say
that you asked the question,
1038
00:43:44,060 --> 00:43:45,170
is polarization a problem?
1039
00:43:45,170 --> 00:43:47,090
And I think polarization
is an enormous problem.
1040
00:43:47,960 --> 00:43:50,300
It's a problem for exactly
the reasons that Russ
1041
00:43:50,300 --> 00:43:52,310
and you pointed out is that
1042
00:43:53,390 --> 00:43:56,150
there are contrary views
generally in science.
1043
00:43:57,320 --> 00:44:01,670
It is very easy to, if you're
polarized to pull the one
1044
00:44:01,670 --> 00:44:03,170
or two studies that said something
1045
00:44:03,170 --> 00:44:05,450
that have been discredited later,
1046
00:44:05,450 --> 00:44:09,800
but use them as basically
the, the science.
1047
00:44:11,150 --> 00:44:14,630
And it's very hard if you're
not an expert in an area to,
1048
00:44:14,630 --> 00:44:17,300
to sort through or to know
what the preponderance
1049
00:44:17,300 --> 00:44:19,730
of the evidence is to use a legal term.
1050
00:44:19,730 --> 00:44:23,060
And, and so people get confused and then
1051
00:44:23,060 --> 00:44:27,470
because they hear
scientists argue both sides,
1052
00:44:27,470 --> 00:44:29,510
they just figure, well
maybe people don't know
1053
00:44:29,510 --> 00:44:31,980
and they can decide
whatever they want, right?
1054
00:44:31,980 --> 00:44:35,940
Or they, they choose the, you
know, the view that agrees
1055
00:44:35,940 --> 00:44:37,260
with what they wanna believe.
1056
00:44:38,460 --> 00:44:39,900
Where if you took a step out
1057
00:44:39,900 --> 00:44:42,900
and just looked at the, you
know, took a wider view,
1058
00:44:42,900 --> 00:44:45,450
it'd be very clear what the answer is.
1059
00:44:45,450 --> 00:44:47,070
So I think this is an enormous problem
1060
00:44:47,070 --> 00:44:48,690
and I think the politicians
1061
00:44:48,690 --> 00:44:51,390
and the people who have
a point of view play it.
1062
00:44:51,390 --> 00:44:55,680
Now again, this is made
even worse by the fact
1063
00:44:55,680 --> 00:45:00,680
that many of these issues
are now business decisions.
1064
00:45:01,230 --> 00:45:04,290
People are buying for money,
1065
00:45:04,290 --> 00:45:06,360
making money on these
different technologies.
1066
00:45:06,360 --> 00:45:08,340
And so they will spend things
1067
00:45:08,340 --> 00:45:11,490
to make their product seem
better than other products,
1068
00:45:11,490 --> 00:45:14,190
which has traditionally happened.
1069
00:45:14,190 --> 00:45:15,930
You know, it always happens in the market,
1070
00:45:15,930 --> 00:45:18,120
but now these are highly
technical projects
1071
00:45:18,120 --> 00:45:19,350
prob you know, products.
1072
00:45:19,350 --> 00:45:22,050
And so they basically say
something about science,
1073
00:45:22,050 --> 00:45:23,700
about those products
1074
00:45:23,700 --> 00:45:26,130
and that further confuses the whole thing.
1075
00:45:27,150 --> 00:45:30,900
And then if I'm just gonna pile
on the worst part, you know,
1076
00:45:30,900 --> 00:45:32,910
that makes the thing that
ha makes this all happen is
1077
00:45:32,910 --> 00:45:36,210
that we really are in an
intention economy, right?
1078
00:45:36,210 --> 00:45:40,860
That the, your, your currency
is how many views you have
1079
00:45:40,860 --> 00:45:45,780
and that causes, let's just
say the more extreme views
1080
00:45:45,780 --> 00:45:49,530
to get lots of, you
know, to be promulgated.
1081
00:45:49,530 --> 00:45:52,320
Where in general science
is usually a little boring.
1082
00:45:52,320 --> 00:45:53,760
I mean, it's not so exciting.
1083
00:45:54,600 --> 00:45:58,050
And I think all of these
things, you know, join together
1084
00:45:58,050 --> 00:46:01,950
to make people have ultimately
less faith in in science.
1085
00:46:03,630 --> 00:46:05,940
- Can I add a point just
on on, on Mark's point
1086
00:46:05,940 --> 00:46:08,610
for almost all of human history,
1087
00:46:08,610 --> 00:46:11,370
the baseline assumption we
would have about any human
1088
00:46:11,370 --> 00:46:14,880
and any topic was they
wouldn't know the answer
1089
00:46:14,880 --> 00:46:16,980
to a hard question and they had no way
1090
00:46:16,980 --> 00:46:18,750
of finding out, right?
1091
00:46:18,750 --> 00:46:20,640
I think, and you know, I
think about the first day
1092
00:46:20,640 --> 00:46:22,110
of the May 10th, 1950,
1093
00:46:22,110 --> 00:46:24,450
when the National Science
Foundation was, was created,
1094
00:46:25,350 --> 00:46:28,170
the average American household
had 12 to 15 books in it
1095
00:46:28,170 --> 00:46:29,730
of any kind, right?
1096
00:46:29,730 --> 00:46:31,800
If you lived in a city,
you could go to a library
1097
00:46:31,800 --> 00:46:33,600
and get two more, right?
1098
00:46:33,600 --> 00:46:36,060
If you wanted to remember
anything about it, you had
1099
00:46:36,060 --> 00:46:39,240
to use a notebook and, and
ballpoint pens weren't mass
1100
00:46:39,240 --> 00:46:40,830
produced until the 1950s.
1101
00:46:40,830 --> 00:46:44,160
So typically it's a quill pen
on really bad paper, right?
1102
00:46:44,160 --> 00:46:46,500
That was the, the state
of how we knew things
1103
00:46:46,500 --> 00:46:48,365
and now we're in this
different environment.
1104
00:46:48,365 --> 00:46:50,430
I, I think there, there's
a fundamental adaptation
1105
00:46:50,430 --> 00:46:52,290
that universities have to make.
1106
00:46:52,290 --> 00:46:53,880
I think there's this belief
that we're in the knowledge
1107
00:46:53,880 --> 00:46:55,470
production business and that's
1108
00:46:55,470 --> 00:46:58,200
because like at one time there
were only five articles in
1109
00:46:58,200 --> 00:47:01,320
chemistry and then someone wrote
the sixth chemistry article
1110
00:47:01,320 --> 00:47:03,240
and haa we have new knowledge,
1111
00:47:03,240 --> 00:47:05,910
but now there's just like
knowledge production everywhere
1112
00:47:05,910 --> 00:47:08,160
and, and AI is just gonna amplify that.
1113
00:47:08,160 --> 00:47:11,280
So the world isn't gonna kind of reward us
1114
00:47:11,280 --> 00:47:13,440
for producing information,
but the thing that we can do,
1115
00:47:13,440 --> 00:47:15,900
because we can convene
because we're human beings.
1116
00:47:15,900 --> 00:47:18,720
'cause we, we can legitimate
knowledge if we have the right
1117
00:47:18,720 --> 00:47:20,790
processes, if we operate
with a certain type
1118
00:47:20,790 --> 00:47:23,970
of transparency, we have
a legitimation capacity
1119
00:47:23,970 --> 00:47:26,340
that's really unrivaled
throughout the world.
1120
00:47:26,340 --> 00:47:28,710
You might be able to
create some of it in ai,
1121
00:47:28,710 --> 00:47:31,270
but even that's probably
only in certain fields
1122
00:47:31,270 --> 00:47:33,910
where you can validate certain
types of stacks with respect
1123
00:47:33,910 --> 00:47:35,560
to certain types of knowledge bases.
1124
00:47:35,560 --> 00:47:36,850
If everyone agreed to it
1125
00:47:36,850 --> 00:47:38,710
and if everybody understood the coding.
1126
00:47:38,710 --> 00:47:40,870
But since that's unlikely,
there's so many domains
1127
00:47:40,870 --> 00:47:43,750
of knowledge where the
convening power of universities
1128
00:47:43,750 --> 00:47:47,050
where the back and forth we
have to interrogate claims.
1129
00:47:47,050 --> 00:47:50,590
Like that's the, that's the
sort of secret sauce for us.
1130
00:47:50,590 --> 00:47:52,480
But I feel like just,
1131
00:47:52,480 --> 00:47:54,580
I think sometimes universities
have been slow to adapt
1132
00:47:54,580 --> 00:47:56,080
and we're hoping the world will come back
1133
00:47:56,080 --> 00:47:58,480
to just rewarding us for
producing more information
1134
00:47:58,480 --> 00:48:00,250
and that ship sailed.
1135
00:48:00,250 --> 00:48:02,410
But the but the, the lane that's available
1136
00:48:02,410 --> 00:48:04,420
to us is pretty exciting if we,
1137
00:48:04,420 --> 00:48:06,100
you know, if we look at it the right way.
1138
00:48:06,100 --> 00:48:07,660
- So let's spend a
little more time on that.
1139
00:48:07,660 --> 00:48:09,790
'cause we've talked about problems.
1140
00:48:09,790 --> 00:48:12,760
Let's talk about
directions for solutions of
1141
00:48:12,760 --> 00:48:14,410
what universities need to do.
1142
00:48:14,410 --> 00:48:17,500
I mean, I often joke that
China invented bureaucracy,
1143
00:48:17,500 --> 00:48:19,660
but universities perfected it, right?
1144
00:48:19,660 --> 00:48:23,680
We are a very slow changing
set of institutions at a time
1145
00:48:23,680 --> 00:48:26,440
where technology is disrupting so much
1146
00:48:26,440 --> 00:48:28,750
and we are training the next generation
1147
00:48:28,750 --> 00:48:31,120
and that part of it, not just
the knowledge production,
1148
00:48:31,120 --> 00:48:32,800
but the training, the next generation,
1149
00:48:32,800 --> 00:48:35,980
the human capital piece has to adapt to.
1150
00:48:35,980 --> 00:48:37,150
So Russ, I wanna start with you
1151
00:48:37,150 --> 00:48:39,640
'cause I know you're on,
you've been as penance
1152
00:48:39,640 --> 00:48:41,260
for your sins been appointed
1153
00:48:41,260 --> 00:48:45,250
to a Stanford high level
committee to think about, right?
1154
00:48:45,250 --> 00:48:48,370
How to communicate, how to
adapt the university, how
1155
00:48:48,370 --> 00:48:51,580
to reimagine the compact
with the federal government.
1156
00:48:51,580 --> 00:48:53,590
Share your thoughts on
what do you think we
1157
00:48:53,590 --> 00:48:58,030
inside the academy need to
do very differently, both
1158
00:48:58,030 --> 00:49:01,300
to adapt and back to our
question about trust,
1159
00:49:01,300 --> 00:49:05,980
to restore trust in
universities at this partic in
1160
00:49:05,980 --> 00:49:07,930
particular at this political moment.
1161
00:49:07,930 --> 00:49:09,100
- Yeah, thanks. And, and you know,
1162
00:49:09,100 --> 00:49:11,620
skip had the high level
idea exactly right.
1163
00:49:11,620 --> 00:49:13,240
He said something that trust,
1164
00:49:13,240 --> 00:49:16,360
deep trust depends on people
understanding the DNA of
1165
00:49:16,360 --> 00:49:17,590
of how you work.
1166
00:49:17,590 --> 00:49:20,650
And so I think the first
thing that universities
1167
00:49:20,650 --> 00:49:23,620
and scientists have to do is
think about the details of
1168
00:49:23,620 --> 00:49:28,180
how we work and say where has
there been like an erosion
1169
00:49:28,180 --> 00:49:31,840
of what would be the, the
ideal set of processes?
1170
00:49:31,840 --> 00:49:36,840
Processes. So for example,
are we debating ideas
1171
00:49:36,850 --> 00:49:41,380
or are we taking existing models as
1172
00:49:41,380 --> 00:49:43,780
the reigning model not to be challenged.
1173
00:49:43,780 --> 00:49:46,420
And so you have to inculcate in students
1174
00:49:46,420 --> 00:49:48,280
that when there is something
1175
00:49:48,280 --> 00:49:50,200
that we're almost positive is true,
1176
00:49:50,200 --> 00:49:51,940
that's the thing to go after.
1177
00:49:51,940 --> 00:49:54,400
Because if it's not
true, that's a big deal.
1178
00:49:54,400 --> 00:49:56,980
So all knowledge in science is contingent.
1179
00:49:56,980 --> 00:50:00,580
And what I mean by that is
everything can be disproven
1180
00:50:00,580 --> 00:50:03,670
tomorrow by a clever person coming up.
1181
00:50:03,670 --> 00:50:05,020
And that's both a strength and a weakness.
1182
00:50:05,020 --> 00:50:06,310
It's a strength because it's true
1183
00:50:06,310 --> 00:50:09,760
and it leads to a more
robust set of understandings.
1184
00:50:09,760 --> 00:50:10,780
It's a problem
1185
00:50:10,780 --> 00:50:13,780
because you, if you take
that glibly as a, as a,
1186
00:50:13,780 --> 00:50:15,400
as a non-scientist, you
can say, well that means
1187
00:50:15,400 --> 00:50:16,750
that everything could be wrong.
1188
00:50:16,750 --> 00:50:18,700
And it is true that
everything could be wrong,
1189
00:50:18,700 --> 00:50:22,240
but we're all working
extremely hard or we should be.
1190
00:50:22,240 --> 00:50:24,430
And so an analysis of the degree
1191
00:50:24,430 --> 00:50:28,550
to which we are questioning
all our foundational principles
1192
00:50:28,550 --> 00:50:31,370
constantly is something
that has to be renewed.
1193
00:50:32,420 --> 00:50:33,650
We have to make sure
1194
00:50:33,650 --> 00:50:37,700
that the financial model is justifiable
1195
00:50:37,700 --> 00:50:40,700
and that the, it's easily understood this
1196
00:50:40,700 --> 00:50:42,200
that does need to be easily understood.
1197
00:50:42,200 --> 00:50:45,170
You might not understand all
the details of plasma physics,
1198
00:50:45,170 --> 00:50:46,250
but you need to understand
1199
00:50:46,250 --> 00:50:49,880
that when the government
gives money to a, an academic,
1200
00:50:49,880 --> 00:50:51,380
how is that money used?
1201
00:50:51,380 --> 00:50:52,850
How is it stewarded
1202
00:50:52,850 --> 00:50:55,310
and is there a reporting level
1203
00:50:55,310 --> 00:50:56,900
that makes everybody comfortable?
1204
00:50:56,900 --> 00:50:59,450
Anybody in the real world,
I'm, I'm an academic,
1205
00:50:59,450 --> 00:51:00,650
I don't call that the real world.
1206
00:51:00,650 --> 00:51:02,510
I'm not that stupid, but people
1207
00:51:02,510 --> 00:51:04,490
who have real jobs in the real world know
1208
00:51:04,490 --> 00:51:07,010
that there's ledgers
and there's receivables
1209
00:51:07,010 --> 00:51:08,720
and there's payables and,
1210
00:51:08,720 --> 00:51:10,670
and we have a language that we've started
1211
00:51:10,670 --> 00:51:15,590
to use in academics that is so
foreign from that vocabulary
1212
00:51:15,590 --> 00:51:18,470
that it creates barriers and not trust.
1213
00:51:18,470 --> 00:51:21,110
And then the third thing, because
three is a biblical number
1214
00:51:21,110 --> 00:51:22,310
is reproducibility.
1215
00:51:23,930 --> 00:51:27,590
There is a reasonable
expectation that we pay attention
1216
00:51:27,590 --> 00:51:29,570
to the reproducibility of our work.
1217
00:51:29,570 --> 00:51:33,470
I think this is sometimes
used against us and fine,
1218
00:51:33,470 --> 00:51:35,930
but the fact is there's a
couple of things going on.
1219
00:51:37,130 --> 00:51:38,480
There is a laxity
1220
00:51:38,480 --> 00:51:41,540
that I've seen in my
career in peer review.
1221
00:51:41,540 --> 00:51:44,330
So peer review is the
key thing in science.
1222
00:51:44,330 --> 00:51:47,240
We get an article that, that
one of our colleagues wrote,
1223
00:51:47,240 --> 00:51:48,860
we're not conflicted with that colleague,
1224
00:51:48,860 --> 00:51:51,110
we don't really care,
but we read the paper
1225
00:51:51,110 --> 00:51:52,610
and we write a review for an editor
1226
00:51:52,610 --> 00:51:55,250
of a journal about if this
paper looks like a good
1227
00:51:55,250 --> 00:51:58,730
contribution because life is complicated
1228
00:51:58,730 --> 00:52:01,100
and life has more
distractions than it used
1229
00:52:01,100 --> 00:52:02,630
to be, I believe.
1230
00:52:02,630 --> 00:52:05,330
And I, I think there's
some data that the quality
1231
00:52:05,330 --> 00:52:08,150
of peer review and the rigor has gone down
1232
00:52:08,150 --> 00:52:10,340
and that is death for science.
1233
00:52:10,340 --> 00:52:11,930
So we, it needs to be evaluated.
1234
00:52:11,930 --> 00:52:14,420
So let's, let's separate whether
there's actually a problem.
1235
00:52:14,420 --> 00:52:17,900
It needs to be constantly
renewed and reexamined
1236
00:52:17,900 --> 00:52:21,320
because it's the, it's the
bedrock upon which scientific
1237
00:52:21,320 --> 00:52:23,330
claims are adjudicated.
1238
00:52:24,170 --> 00:52:26,270
The other issue with
reproducibility that I just want
1239
00:52:26,270 --> 00:52:29,480
to add is that sometimes
it's not a question
1240
00:52:29,480 --> 00:52:32,870
that it was a fraudulent or
they made up the results.
1241
00:52:32,870 --> 00:52:35,840
That's a very small and
terrible proportion.
1242
00:52:35,840 --> 00:52:40,220
It's just that they did an
experiment that is so specific
1243
00:52:40,220 --> 00:52:44,660
that what they found is true,
but just kind of not relevant
1244
00:52:44,660 --> 00:52:46,190
and not useful to the world.
1245
00:52:47,180 --> 00:52:50,990
We know in physics force
equals mass times acceleration.
1246
00:52:50,990 --> 00:52:55,400
And that is a, it, it is
a very robust observation
1247
00:52:55,400 --> 00:52:58,550
that has withstood tests for decades.
1248
00:52:58,550 --> 00:53:02,120
And in fact, centuries when
people publish papers in biology
1249
00:53:02,120 --> 00:53:05,450
nowadays, sometimes it's not
that it can't be reproduced,
1250
00:53:05,450 --> 00:53:08,360
it's that the level of detail
that you would have to do
1251
00:53:08,360 --> 00:53:11,750
to get exactly that same
result means that it's a house
1252
00:53:11,750 --> 00:53:13,250
of fragile cards
1253
00:53:13,250 --> 00:53:15,560
and it really is not a robust finding
1254
00:53:15,560 --> 00:53:17,750
that we should move
forward with in the future.
1255
00:53:17,750 --> 00:53:22,190
So these are again, true but
not very useful or relevant
1256
00:53:22,190 --> 00:53:25,260
and then that's a big part of
our reproducibility crisis.
1257
00:53:25,260 --> 00:53:27,780
So those three points come to mind
1258
00:53:27,780 --> 00:53:28,950
as things we need to work on.
1259
00:53:30,450 --> 00:53:31,860
- Mark, skip, wanna add
1260
00:53:31,860 --> 00:53:33,390
to the list things we need to work on.
1261
00:53:34,290 --> 00:53:37,200
- Can I, I, mark, do you
wanna go or No, go, go ahead.
1262
00:53:37,200 --> 00:53:40,710
Yeah, I mean, I think just
rest said was brilliant.
1263
00:53:40,710 --> 00:53:42,180
I, I like, I want people
1264
00:53:42,180 --> 00:53:43,890
to think about three
different concepts, right?
1265
00:53:43,890 --> 00:53:47,880
And one is the scientific
method, one is a scientist
1266
00:53:47,880 --> 00:53:49,590
and one is science, right?
1267
00:53:49,590 --> 00:53:53,220
The scientific method, I'm
gonna, I'm gonna throw out it's,
1268
00:53:53,220 --> 00:53:54,510
it's unassailable.
1269
00:53:54,510 --> 00:53:58,830
Like it is a way to evaluate
conjectures about cause
1270
00:53:58,830 --> 00:54:02,130
and effect categories,
existence and so forth.
1271
00:54:02,130 --> 00:54:04,650
And the key thing about
it is, if you follow it,
1272
00:54:04,650 --> 00:54:06,060
it's inner subjective.
1273
00:54:06,060 --> 00:54:07,890
And what I mean by that is you
don't know whether the thing
1274
00:54:07,890 --> 00:54:10,680
you find is true or false,
but if you are transparent
1275
00:54:10,680 --> 00:54:14,130
and specific about how you do
it, any person who followed
1276
00:54:14,130 --> 00:54:16,650
that method should find
the same thing you do.
1277
00:54:16,650 --> 00:54:18,510
And that's the leverage
that science has, right?
1278
00:54:18,510 --> 00:54:20,580
So that's like the scientific method.
1279
00:54:20,580 --> 00:54:24,720
A scientist is a furry, highly
flawed person who, you know,
1280
00:54:24,720 --> 00:54:26,130
spends a small part of some
1281
00:54:26,130 --> 00:54:28,620
of their waking hours
using the scientific method
1282
00:54:28,620 --> 00:54:29,910
to discover things,
1283
00:54:29,910 --> 00:54:32,220
but the rest of the time they have an ego
1284
00:54:32,220 --> 00:54:34,110
and they have needs and
things of that nature.
1285
00:54:34,110 --> 00:54:35,700
And so that's important to keep in mind.
1286
00:54:35,700 --> 00:54:38,790
And science is largely
a pop culture phenomenon
1287
00:54:38,790 --> 00:54:42,210
that involves a scientific
method and has scientists in it,
1288
00:54:42,210 --> 00:54:44,220
but people can do, people claim all kinds
1289
00:54:44,220 --> 00:54:46,050
of things in the name of science.
1290
00:54:46,050 --> 00:54:48,690
But for this endeavor in
which we're all engaged
1291
00:54:48,690 --> 00:54:50,370
to have maximum social impact
1292
00:54:50,370 --> 00:54:53,340
and to save as many lives
as possible, any time
1293
00:54:53,340 --> 00:54:56,550
that we can go back and hue to
the scientific method, right,
1294
00:54:56,550 --> 00:54:58,290
and hue to the inner subjectivity.
1295
00:54:58,290 --> 00:55:00,570
And I'll just give you an
example of where it didn't work
1296
00:55:00,570 --> 00:55:01,920
and how we can improve it.
1297
00:55:01,920 --> 00:55:04,890
Follow the science,
take the vaccine, right?
1298
00:55:04,890 --> 00:55:06,780
So there's two parts of
that sentence, right?
1299
00:55:06,780 --> 00:55:09,030
Take the vaccine, the scientific,
1300
00:55:09,030 --> 00:55:11,910
the scientists did this incredible thing
1301
00:55:11,910 --> 00:55:14,460
where they looked at the COVID vaccines
1302
00:55:14,460 --> 00:55:16,470
and the distribution results were found.
1303
00:55:16,470 --> 00:55:19,020
And for most people it had
this incredible effect.
1304
00:55:19,020 --> 00:55:21,030
And for some people it had a bad effect.
1305
00:55:21,030 --> 00:55:22,800
And then there are other
things socially involved
1306
00:55:22,800 --> 00:55:24,060
with taking vaccines.
1307
00:55:24,060 --> 00:55:26,310
That's what the scientific
method can show you.
1308
00:55:26,310 --> 00:55:28,590
But then there's this question
of, well, should you take it?
1309
00:55:28,590 --> 00:55:29,940
And then there's all kinds of moral
1310
00:55:29,940 --> 00:55:31,380
and ethical questions for which
1311
00:55:31,380 --> 00:55:34,800
that scientific method I
described is like just not well
1312
00:55:34,800 --> 00:55:36,150
equipped, right?
1313
00:55:36,150 --> 00:55:41,150
But when we fused them, it
just caused a lot of confusion.
1314
00:55:41,190 --> 00:55:44,160
And that's this time when
the scientists who like had,
1315
00:55:44,160 --> 00:55:46,050
we had our ego and we had,
and we had good intentions
1316
00:55:46,050 --> 00:55:50,790
that like we, we, we misrepresented
the scientific method
1317
00:55:50,790 --> 00:55:51,900
and then caused a problem.
1318
00:55:51,900 --> 00:55:54,270
And to Russ's point, things
like reproducibility,
1319
00:55:54,270 --> 00:55:57,420
our commitment to serving people through,
1320
00:55:57,420 --> 00:55:59,880
through the method is one way
1321
00:55:59,880 --> 00:56:01,830
to just demonstrate
what we're trying to do.
1322
00:56:01,830 --> 00:56:02,910
And it's a way to build trust.
1323
00:56:03,960 --> 00:56:06,570
- No, I, I, I don't have
too much more to add.
1324
00:56:06,570 --> 00:56:09,240
You know, the only thing I
would add in, in terms of this,
1325
00:56:09,240 --> 00:56:11,160
the scientific method and, and viruses
1326
00:56:11,160 --> 00:56:14,820
and vaccines is, you know,
you could just leave it
1327
00:56:14,820 --> 00:56:16,620
to the capital system.
1328
00:56:16,620 --> 00:56:19,620
And, you know, from a return
on investment perspective,
1329
00:56:19,620 --> 00:56:21,870
people who take vaccines
are less likely to get sick.
1330
00:56:21,870 --> 00:56:24,670
So you could just change
the insurance rates
1331
00:56:24,670 --> 00:56:26,080
or health insurance costs,
1332
00:56:26,080 --> 00:56:27,190
whether you take the vaccine or not.
1333
00:56:27,190 --> 00:56:29,230
It's your choice, you know, but Right,
1334
00:56:29,230 --> 00:56:31,210
because the way science works,
1335
00:56:31,210 --> 00:56:34,420
a scientific method would
deter, would determine that the,
1336
00:56:34,420 --> 00:56:37,810
you know, expected health costs
would be higher on average
1337
00:56:37,810 --> 00:56:40,240
for people who didn't take
vaccines than did, right?
1338
00:56:40,240 --> 00:56:43,300
I think that's still part of
the scientific method, right?
1339
00:56:43,300 --> 00:56:44,860
But what you want to do, that's,
1340
00:56:44,860 --> 00:56:46,030
that still could be your choice.
1341
00:56:46,030 --> 00:56:49,300
So there are ways that,
that, as, as Skip said,
1342
00:56:49,300 --> 00:56:50,770
we could have mediated this better
1343
00:56:50,770 --> 00:56:55,420
and disentangled that it
gets a little more difficult
1344
00:56:55,420 --> 00:56:59,170
with global phenomenon
that have externalities.
1345
00:56:59,170 --> 00:57:03,700
And that then that, that
becomes a, a more complicated.
1346
00:57:03,700 --> 00:57:06,190
- So I wanna bring into
the conversation something
1347
00:57:06,190 --> 00:57:09,010
that we haven't mentioned so far.
1348
00:57:09,010 --> 00:57:11,650
So it gets to political polarization,
1349
00:57:11,650 --> 00:57:13,300
but when we look at ourselves
1350
00:57:13,300 --> 00:57:15,160
and our institutions, we see
1351
00:57:15,160 --> 00:57:17,800
that in fact they do
not represent the views
1352
00:57:17,800 --> 00:57:19,720
of America, right?
1353
00:57:19,720 --> 00:57:22,780
That there is a dramatic skewing
1354
00:57:22,780 --> 00:57:25,060
of political perspectives in universities,
1355
00:57:25,060 --> 00:57:28,930
which we know well
heavily skewed on one side
1356
00:57:28,930 --> 00:57:32,650
of the political spectrum,
which some would argue
1357
00:57:33,520 --> 00:57:36,250
influences what you take for granted,
1358
00:57:36,250 --> 00:57:39,190
what research is considered valuable,
1359
00:57:39,190 --> 00:57:43,030
what questions are asked,
how free people are
1360
00:57:43,030 --> 00:57:46,600
to actually challenge
each other in a classroom,
1361
00:57:46,600 --> 00:57:49,180
in a seminar to say, I
don't agree with you.
1362
00:57:49,180 --> 00:57:51,250
Because if you're the only one in the room
1363
00:57:51,250 --> 00:57:55,540
who holds the views, you do,
it's a lot harder to disagree.
1364
00:57:55,540 --> 00:58:00,220
So how should we think
about the political skewing
1365
00:58:00,220 --> 00:58:01,930
of universities
1366
00:58:01,930 --> 00:58:04,270
and the current environment
1367
00:58:04,270 --> 00:58:06,310
where the administration
is saying, you know what,
1368
00:58:06,310 --> 00:58:11,230
it's not okay to have
to have this environment
1369
00:58:11,230 --> 00:58:13,510
where in particular, certain
1370
00:58:14,740 --> 00:58:16,990
groups are not considered,
1371
00:58:16,990 --> 00:58:19,420
not protected in the way that others are.
1372
00:58:19,420 --> 00:58:23,350
How, and we are sitting, this
is the elephant in the room,
1373
00:58:23,350 --> 00:58:25,900
this is a very charged
moment for universities.
1374
00:58:25,900 --> 00:58:30,310
I'd like to get your thoughts
on how this all intersects
1375
00:58:30,310 --> 00:58:33,010
with trust and science
and what we do about it.
1376
00:58:34,960 --> 00:58:36,310
- Well, let me just point out,
1377
00:58:36,310 --> 00:58:40,870
since we're talking some facts,
it turns out that the split
1378
00:58:41,925 --> 00:58:46,210
in, in view is highly skewed
by education level as well.
1379
00:58:47,080 --> 00:58:49,060
Now, I don't know if it's cause or fact,
1380
00:58:49,060 --> 00:58:52,990
but if you look at people who
have PhD degrees, degrees,
1381
00:58:52,990 --> 00:58:54,940
they're highly skewed in the same way
1382
00:58:54,940 --> 00:58:57,310
that academics are highly skewed.
1383
00:58:57,310 --> 00:59:01,300
And since most academics have
PhD degrees, I, you know,
1384
00:59:01,300 --> 00:59:03,250
that we can say that
there's a problem that
1385
00:59:03,250 --> 00:59:05,170
that skewing exists in the first place.
1386
00:59:05,170 --> 00:59:07,570
But if I'm hiring from a
pool of people that have, of
1387
00:59:07,570 --> 00:59:09,460
- Course, right PhDs, right?
1388
00:59:09,460 --> 00:59:10,900
- They're gonna be skewed
- That the,
1389
00:59:10,900 --> 00:59:13,030
that the university
environment is reflective
1390
00:59:13,030 --> 00:59:15,460
of the pool from which it's drawing. Got
1391
00:59:15,460 --> 00:59:16,460
- It.
1392
00:59:16,460 --> 00:59:16,845
Exactly. And the second thing is
1393
00:59:16,845 --> 00:59:18,790
- We, the question is, does
the, is there an effect there?
1394
00:59:18,790 --> 00:59:20,170
And if there is, how concerned are you
1395
00:59:20,170 --> 00:59:21,350
about what that effect be?
1396
00:59:22,545 --> 00:59:26,210
- You, you know, when I interview
candidates for, you know,
1397
00:59:26,210 --> 00:59:27,500
positions in electrical engineering
1398
00:59:27,500 --> 00:59:29,600
and computer science, the questions of
1399
00:59:29,600 --> 00:59:32,930
what your political
views are never come up.
1400
00:59:32,930 --> 00:59:35,630
So, and I think it would be
inappropriate if they came up.
1401
00:59:35,630 --> 00:59:40,630
'cause it's really not germane
to the part of the computer,
1402
00:59:40,730 --> 00:59:43,040
you know, in the fields
that we're interviewing.
1403
00:59:43,040 --> 00:59:47,390
So like there is this problem,
I'm not sure in the domains
1404
00:59:47,390 --> 00:59:50,780
that I work in, it's a
very serious problem.
1405
00:59:50,780 --> 00:59:55,780
Maybe other people would
disagree with me, but
1406
00:59:56,480 --> 00:59:57,480
- I can talk about it a little bit.
1407
00:59:57,480 --> 00:59:59,000
And I'm at a public university, so this,
1408
00:59:59,000 --> 01:00:00,860
this lands a little bit differently here,
1409
01:00:00,860 --> 01:00:02,420
but I, I wanna start, like there's a,
1410
01:00:02,420 --> 01:00:04,550
there's a field in psychology that studies
1411
01:00:04,550 --> 01:00:06,320
how people perceive each other.
1412
01:00:06,320 --> 01:00:08,186
And there's this notion
of ingroup outgroup.
1413
01:00:08,186 --> 01:00:11,090
And so if we're part of an
ingroup, if we're part of like,
1414
01:00:11,090 --> 01:00:12,710
let's say people are liberals
1415
01:00:12,710 --> 01:00:15,290
and most of the contact
you have are with liberals.
1416
01:00:15,290 --> 01:00:17,030
Like you can form this perception.
1417
01:00:17,030 --> 01:00:20,390
And the perception is the
liberals, like I, I know people
1418
01:00:20,390 --> 01:00:21,710
who are liberals, but they're all,
1419
01:00:21,710 --> 01:00:23,270
they come from different
parts of the world
1420
01:00:23,270 --> 01:00:25,790
and they think about liberal
ideology differently.
1421
01:00:25,790 --> 01:00:28,190
And so our group, like even
though it's liberal, it's very,
1422
01:00:28,190 --> 01:00:29,750
very, very diverse.
1423
01:00:29,750 --> 01:00:31,310
Now, if you never meet conservatives,
1424
01:00:31,310 --> 01:00:32,990
then here's the next part of it.
1425
01:00:32,990 --> 01:00:36,950
And they're all the same. And
they think very simply, right?
1426
01:00:36,950 --> 01:00:38,990
So this is a, this is a common phenomenon.
1427
01:00:38,990 --> 01:00:41,510
And so one of the things that
happens on the left is the
1428
01:00:41,510 --> 01:00:44,240
left constantly refers to
the right as stupid, not
1429
01:00:44,240 --> 01:00:46,430
because they have actual evidence of it is
1430
01:00:46,430 --> 01:00:48,110
because they actually
don't interact very much.
1431
01:00:48,110 --> 01:00:49,520
And so you can form the stereotype
1432
01:00:49,520 --> 01:00:53,150
and by the way, on the right,
similar things on the left,
1433
01:00:53,150 --> 01:00:54,830
but that's not like a new phenomenon
1434
01:00:54,830 --> 01:00:56,240
that's just like ingroup outgroup.
1435
01:00:56,240 --> 01:00:59,090
And it's been going on for
as long as human beings have,
1436
01:00:59,090 --> 01:01:00,260
have, have gone in groups.
1437
01:01:00,260 --> 01:01:03,140
And so now, like to this
point when you say, well,
1438
01:01:03,140 --> 01:01:05,480
when we're hiring at
universities in technical fields,
1439
01:01:05,480 --> 01:01:08,510
there's an, there's a ide
ideologically correlated selection
1440
01:01:08,510 --> 01:01:11,390
on who gets PhDs and that's fine.
1441
01:01:11,390 --> 01:01:14,150
But when, if, if we were
only technical institutes
1442
01:01:14,150 --> 01:01:15,770
that only gave technical advice
1443
01:01:15,770 --> 01:01:17,540
and then we walked away
from everything else,
1444
01:01:17,540 --> 01:01:19,640
maybe everything would be great.
1445
01:01:19,640 --> 01:01:21,410
But we're at these
universities where we tend
1446
01:01:21,410 --> 01:01:23,780
to give other advice about what
trade-offs you should make,
1447
01:01:23,780 --> 01:01:25,340
whether we do it as an institution
1448
01:01:25,340 --> 01:01:26,870
or whether individuals do it.
1449
01:01:26,870 --> 01:01:29,690
Now people are saying, you
know, universities are providing
1450
01:01:29,690 --> 01:01:31,490
a lot of advice about
1451
01:01:31,490 --> 01:01:33,830
what trade offs we should
make and how we should live.
1452
01:01:33,830 --> 01:01:36,080
And again, being trained
in the scientific method,
1453
01:01:36,080 --> 01:01:39,380
as awesome as it is, may not
give you any special benefits
1454
01:01:39,380 --> 01:01:41,210
when it comes to moral
and ethical knowledge,
1455
01:01:41,210 --> 01:01:41,990
which are the ways that
1456
01:01:41,990 --> 01:01:43,730
societies evolve a lot of trade-offs.
1457
01:01:43,730 --> 01:01:46,070
And I think part of our current moment
1458
01:01:46,070 --> 01:01:48,320
is sometimes will do things
like follow the science,
1459
01:01:48,320 --> 01:01:50,330
take the vaccine, and people
are like, wait a minute,
1460
01:01:50,330 --> 01:01:51,740
there's some other considerations here.
1461
01:01:51,740 --> 01:01:54,200
And we're like, we, we don't get that.
1462
01:01:54,200 --> 01:01:56,300
And so I think that's part of like how
1463
01:01:56,300 --> 01:02:00,440
to adapt again at a public
institution we have more lanes
1464
01:02:00,440 --> 01:02:02,300
of feedback where we can, you know, try
1465
01:02:02,300 --> 01:02:03,860
and hear from lots of different people
1466
01:02:03,860 --> 01:02:06,320
and you never give up your
integrity, but you try
1467
01:02:06,320 --> 01:02:08,120
and treat everybody with respect.
1468
01:02:08,120 --> 01:02:10,250
And then you talk about what
you're doing with respect
1469
01:02:10,250 --> 01:02:12,920
to the, the kind of portfolio of views
1470
01:02:12,920 --> 01:02:14,870
and that, that's sometimes hard to do.
1471
01:02:14,870 --> 01:02:16,670
But I think you have to try,
1472
01:02:16,670 --> 01:02:19,320
because the worst case
scenario is when we don't try
1473
01:02:19,320 --> 01:02:22,110
and we assume that everybody
who isn't us is stupid and,
1474
01:02:22,110 --> 01:02:24,210
and then we like, that's a disaster.
1475
01:02:24,210 --> 01:02:26,850
And I think that's part
the perception that that's
1476
01:02:26,850 --> 01:02:29,675
what we're doing, which
unfortunately is based in reality
1477
01:02:29,675 --> 01:02:31,130
to, to some extent is,
1478
01:02:31,130 --> 01:02:33,660
is is a big part of the
trust problem we have. Now,
1479
01:02:33,660 --> 01:02:35,430
- Lemme just take the
opportunity to give a shout out
1480
01:02:35,430 --> 01:02:37,415
to my Hoover colleague Josh Ober,
1481
01:02:37,415 --> 01:02:38,850
who's a political science professor.
1482
01:02:38,850 --> 01:02:42,150
He, yeah. The, the, we political
scientists know each other
1483
01:02:42,150 --> 01:02:45,510
who has spearheaded this wonderful
civics initiative here at
1484
01:02:45,510 --> 01:02:49,080
Stanford where you can
volunteer to add your syllabus
1485
01:02:49,080 --> 01:02:52,350
to the website that says,
you know, I really value the,
1486
01:02:52,350 --> 01:02:55,410
you know, the debate I
really value to Russ's point,
1487
01:02:55,410 --> 01:02:57,300
we should be interrogating
all these ideas.
1488
01:02:57,300 --> 01:02:59,010
That's the class I'm setting up.
1489
01:02:59,010 --> 01:03:01,590
That's the, that's what
the culture that we have.
1490
01:03:01,590 --> 01:03:05,250
And so students can be more
savvy shoppers of the courses
1491
01:03:05,250 --> 01:03:09,210
that they take and they can
self-select into a community
1492
01:03:09,210 --> 01:03:12,990
of students where they
want to have that exposure
1493
01:03:12,990 --> 01:03:14,700
to different ideas in the give and take.
1494
01:03:14,700 --> 01:03:16,920
So there's, there is work being done,
1495
01:03:16,920 --> 01:03:19,050
but there is work to be done.
1496
01:03:19,050 --> 01:03:20,430
- I was just going to add
1497
01:03:20,430 --> 01:03:23,640
that there are great lessons
from World War II and, and,
1498
01:03:23,640 --> 01:03:24,870
and Korea where
1499
01:03:24,870 --> 01:03:26,910
because of the large number of,
1500
01:03:26,910 --> 01:03:28,260
in this case it was mostly men,
1501
01:03:28,260 --> 01:03:30,330
but it was also women who were mixing
1502
01:03:30,330 --> 01:03:31,470
from all over the country.
1503
01:03:31,470 --> 01:03:34,800
It created a better understanding
of people's perspectives
1504
01:03:34,800 --> 01:03:36,480
and it actually led to a lot
1505
01:03:36,480 --> 01:03:39,780
of good things is my
understanding from the literature.
1506
01:03:39,780 --> 01:03:41,400
And I do think that college
1507
01:03:41,400 --> 01:03:42,930
and university should be a place
1508
01:03:42,930 --> 01:03:45,870
where you meet people
from all over the spectrum
1509
01:03:45,870 --> 01:03:48,690
and it's a failure of
the university and the,
1510
01:03:48,690 --> 01:03:51,570
or the college if it doesn't
provide that experience
1511
01:03:51,570 --> 01:03:54,660
because that's so valuable
and so important just to,
1512
01:03:54,660 --> 01:03:56,820
and so I, and I believe there's
empirical evidence for this.
1513
01:03:56,820 --> 01:04:00,480
And so to the degree that
this is too homogenized
1514
01:04:00,480 --> 01:04:02,940
or a monoculture, it's a disservice
1515
01:04:02,940 --> 01:04:05,100
to the paying customers. Right.
1516
01:04:05,100 --> 01:04:06,990
- Right. And I will just add in the end
1517
01:04:06,990 --> 01:04:09,570
that even though we're highly
skewed, we're not monolithic
1518
01:04:09,570 --> 01:04:13,020
and you know, in the faculty
there are differences
1519
01:04:13,020 --> 01:04:16,080
of opinions that come up,
maybe not as experience,
1520
01:04:16,080 --> 01:04:17,940
- Maybe experience maybe
Mark you've had the same
1521
01:04:17,940 --> 01:04:19,080
thing as in a faculty meeting.
1522
01:04:19,080 --> 01:04:21,210
If you have two faculty
members, you have five opinions.
1523
01:04:22,830 --> 01:04:26,430
Right. Okay, let's turn to
some listener questions.
1524
01:04:26,430 --> 01:04:27,750
So this is a question for you, mark.
1525
01:04:28,740 --> 01:04:31,020
It says, thank you for your
point of view. I agree with it.
1526
01:04:31,890 --> 01:04:33,510
So it must be a very good point of view.
1527
01:04:34,530 --> 01:04:37,500
Most academics engaged in
public conversations about the
1528
01:04:37,500 --> 01:04:41,520
payoff of federally funded
research come from the non-ST
1529
01:04:41,520 --> 01:04:44,850
STEM side of the enterprise
at Stanford and elsewhere.
1530
01:04:44,850 --> 01:04:48,510
Right? They're unable
to articulate your view
1531
01:04:48,510 --> 01:04:49,830
with the same persuasion.
1532
01:04:49,830 --> 01:04:52,740
So the questioner asks, why
don't the STEM departments
1533
01:04:52,740 --> 01:04:57,330
or universities like MIT take
a more active role in leading
1534
01:04:57,330 --> 01:04:59,550
the public conversation on these issues?
1535
01:04:59,550 --> 01:05:02,250
Let me add an addendum to that question
1536
01:05:02,250 --> 01:05:05,580
because you are taking an
active role in the tech policy
1537
01:05:05,580 --> 01:05:07,830
accelerator in our
emerging technology review,
1538
01:05:07,830 --> 01:05:10,380
and you are going to Washington
to make these arguments.
1539
01:05:10,380 --> 01:05:14,100
So why don't more people do
that and why do you do it?
1540
01:05:14,100 --> 01:05:17,740
- Probably the reason more
people don't do it is that
1541
01:05:17,740 --> 01:05:20,110
we tend to be pretty busy
1542
01:05:20,110 --> 01:05:23,860
and pretty enthusiastic about
the work that we're doing.
1543
01:05:25,330 --> 01:05:29,770
And so, you know, if we want
1544
01:05:29,770 --> 01:05:34,360
to change the world, we
think probably not correctly
1545
01:05:34,360 --> 01:05:37,090
that we should just focus
on our work and get it done.
1546
01:05:38,230 --> 01:05:39,550
And dealing
1547
01:05:39,550 --> 01:05:42,280
and talking with people from outside
1548
01:05:42,280 --> 01:05:45,640
of your community is an enormous reach.
1549
01:05:45,640 --> 01:05:50,080
I, Russ knows this 'cause
he's done it, I've done it.
1550
01:05:50,080 --> 01:05:52,840
But you know, I I've been working in kind
1551
01:05:52,840 --> 01:05:54,820
of interdisciplinary areas for a while
1552
01:05:54,820 --> 01:05:58,720
and you know, I would talk
to people I on two sides
1553
01:05:58,720 --> 01:06:00,700
that would like to work with each other.
1554
01:06:00,700 --> 01:06:03,070
And they both say that
they're standing out there
1555
01:06:03,070 --> 01:06:04,750
and they've made all these great offers
1556
01:06:04,750 --> 01:06:06,340
and nothing's come back.
1557
01:06:06,340 --> 01:06:07,780
And when I talk to both of them,
1558
01:06:07,780 --> 01:06:09,610
they both have like dinosaur arms.
1559
01:06:09,610 --> 01:06:11,680
Like they're not, they're, you know,
1560
01:06:11,680 --> 01:06:13,090
they think they're stretching,
1561
01:06:13,090 --> 01:06:14,830
but they have no conception
1562
01:06:14,830 --> 01:06:16,120
that the other group doesn't think
1563
01:06:16,120 --> 01:06:19,720
of research in the same way
that they think about research.
1564
01:06:19,720 --> 01:06:21,610
And it's very hard.
1565
01:06:21,610 --> 01:06:22,990
And so to do this kind
1566
01:06:22,990 --> 01:06:26,530
of communication takes not
only deep technical knowledge,
1567
01:06:26,530 --> 01:06:29,410
but a really willingness
to become a student
1568
01:06:29,410 --> 01:06:33,490
and watch people and figure
out what they're thinking
1569
01:06:33,490 --> 01:06:34,750
and how they think.
1570
01:06:34,750 --> 01:06:36,700
Because to communicate you really need
1571
01:06:36,700 --> 01:06:39,250
to bridge the different perspectives.
1572
01:06:39,250 --> 01:06:41,260
And it takes effort.
It takes a lot of time.
1573
01:06:42,940 --> 01:06:47,260
Why I'm doing it is, you
know, I I guess I think
1574
01:06:47,260 --> 01:06:50,350
that the benefit is really large
1575
01:06:50,350 --> 01:06:54,100
and it frustrates me when
people argue about things
1576
01:06:54,100 --> 01:06:56,260
that they really shouldn't
be arguing about.
1577
01:06:56,260 --> 01:06:59,290
It's just not a communi,
it's a lack of communication.
1578
01:07:00,760 --> 01:07:02,680
So I've decided to spend some of my time,
1579
01:07:02,680 --> 01:07:03,940
which I is the most precious thing.
1580
01:07:03,940 --> 01:07:05,260
I have to do this,
1581
01:07:05,260 --> 01:07:07,780
but I think many people in
the STEM areas are very busy
1582
01:07:07,780 --> 01:07:09,340
and they're very focused on their research
1583
01:07:09,340 --> 01:07:12,190
and it's, it's basically time
away from their research.
1584
01:07:12,190 --> 01:07:14,320
Yeah. I Rus do you have something
1585
01:07:14,320 --> 01:07:16,240
else that you would say there?
1586
01:07:16,240 --> 01:07:19,420
- No, I mean, part of this
question is whether the,
1587
01:07:19,420 --> 01:07:22,420
the non STEM colleagues,
1588
01:07:22,420 --> 01:07:24,460
are they making the best
case for their work?
1589
01:07:24,460 --> 01:07:27,550
And you know, I love being
at a full university.
1590
01:07:27,550 --> 01:07:30,160
I, I I, yes, I spend most
of my time in the school
1591
01:07:30,160 --> 01:07:31,930
of engineering and the school of medicine,
1592
01:07:31,930 --> 01:07:35,170
but some of my most best,
some of my best conversations
1593
01:07:35,170 --> 01:07:36,220
and my best interactions are
1594
01:07:36,220 --> 01:07:38,020
with English professors
and art professors.
1595
01:07:38,020 --> 01:07:40,990
And it enriches not only my
life, but my students' life.
1596
01:07:40,990 --> 01:07:42,430
So we need to get that across
1597
01:07:42,430 --> 01:07:46,090
because being at a full
university where every area
1598
01:07:46,090 --> 01:07:47,680
of human endeavor
1599
01:07:47,680 --> 01:07:50,710
and knowledge is represented
is a huge benefit
1600
01:07:50,710 --> 01:07:51,910
and a huge privilege.
1601
01:07:51,910 --> 01:07:54,670
And I, I think the
question asker is right.
1602
01:07:54,670 --> 01:07:56,920
We don't make that case nearly enough
1603
01:07:56,920 --> 01:08:01,420
that even us techies love
having those people on our
1604
01:08:01,420 --> 01:08:04,210
campus and they enrich the entire e
1605
01:08:04,210 --> 01:08:05,830
and they don't enrich just our lives,
1606
01:08:05,830 --> 01:08:07,270
they enrich our research,
1607
01:08:07,270 --> 01:08:08,270
- Right?
1608
01:08:08,270 --> 01:08:10,000
I mean, I I was talking in one
1609
01:08:10,000 --> 01:08:11,530
of these interdisciplinary things
1610
01:08:11,530 --> 01:08:14,440
with somebody from I think classics
1611
01:08:14,440 --> 01:08:16,340
and they were, you know,
we were just talking about
1612
01:08:17,930 --> 01:08:19,010
societal issues
1613
01:08:19,010 --> 01:08:21,110
and they just pointed out that
in every story you've ever
1614
01:08:21,110 --> 01:08:25,820
read, bigger is better, right?
1615
01:08:25,820 --> 01:08:28,730
In the story, you know,
you grow, you do something.
1616
01:08:28,730 --> 01:08:31,190
So if you're trying to
deal with sustainability,
1617
01:08:31,190 --> 01:08:34,370
you're saying, you know,
maybe not bigger is better.
1618
01:08:34,370 --> 01:08:37,100
It's very hard story to tell
1619
01:08:37,100 --> 01:08:39,770
because it's not the story
that you've ever read in any
1620
01:08:39,770 --> 01:08:41,780
of the, you know, the texts.
1621
01:08:41,780 --> 01:08:44,150
And I just thought that was wow, you know,
1622
01:08:44,150 --> 01:08:47,750
a very interesting point that
sort of communicates a lot
1623
01:08:47,750 --> 01:08:49,190
of things that I would never think about.
1624
01:08:49,190 --> 01:08:51,440
So just, I completely agree with Russ.
1625
01:08:51,440 --> 01:08:54,380
- So I, I will say, Russ, I'm
a little offended that in the,
1626
01:08:54,380 --> 01:08:56,990
in the realm of all the
types of disciplines
1627
01:08:56,990 --> 01:08:59,000
that you talked about,
you did not say at the top
1628
01:08:59,000 --> 01:09:01,640
of your list political science,
but we're gonna let that go.
1629
01:09:03,470 --> 01:09:05,150
But what I would say is,
from the perspective,
1630
01:09:05,150 --> 01:09:06,320
and I'm sure Skip, I'd love
1631
01:09:06,320 --> 01:09:08,360
to hear your your perspective on this too.
1632
01:09:08,360 --> 01:09:09,680
From my perspective,
1633
01:09:09,680 --> 01:09:13,070
as a political scientist doing
work in national security,
1634
01:09:13,070 --> 01:09:16,550
our impact is directly influenced
1635
01:09:16,550 --> 01:09:19,880
and augmented by a deeper understanding,
1636
01:09:19,880 --> 01:09:23,810
which only our technical
colleagues can provide about these
1637
01:09:23,810 --> 01:09:25,160
emerging developments
1638
01:09:25,160 --> 01:09:28,280
and how they could shape
the world, both the promise
1639
01:09:28,280 --> 01:09:30,440
of these capabilities and the pitfalls
1640
01:09:30,440 --> 01:09:32,930
or perils of these capabilities.
1641
01:09:32,930 --> 01:09:34,550
So Russ and,
1642
01:09:34,550 --> 01:09:36,530
and Mark have heard me say this a lot,
1643
01:09:36,530 --> 01:09:40,460
if it's only political scientists
like me opining about tech
1644
01:09:40,460 --> 01:09:43,100
policy, we're all in trouble, right?
1645
01:09:43,100 --> 01:09:47,420
We need to have people with a
deep technical understanding
1646
01:09:47,420 --> 01:09:51,530
of how these technologies
work to inform better policy.
1647
01:09:51,530 --> 01:09:53,150
So it's not just being part
1648
01:09:53,150 --> 01:09:54,980
of a full university benefits the techies,
1649
01:09:54,980 --> 01:09:56,360
it benefits the non-techies
1650
01:09:56,360 --> 01:09:59,750
and the policy engagement that we do.
1651
01:09:59,750 --> 01:10:01,555
Skip. Is there anything
you wanted to add to that?
1652
01:10:01,555 --> 01:10:04,490
- Well, I just, I want to
distinguish between two things.
1653
01:10:04,490 --> 01:10:07,430
'cause there's the act
1654
01:10:07,430 --> 01:10:10,610
of telling the story about
the value of research
1655
01:10:10,610 --> 01:10:11,840
and it it's great
1656
01:10:11,840 --> 01:10:16,220
and people put together,
I wanna differentiate
1657
01:10:16,220 --> 01:10:18,590
that from what someone hears when you make
1658
01:10:18,590 --> 01:10:19,790
that presentation.
1659
01:10:19,790 --> 01:10:23,060
Because ultimately the
purpose of any presentation is
1660
01:10:23,060 --> 01:10:24,170
to form a new memory
1661
01:10:24,170 --> 01:10:27,140
or alter the set of existing
memories between the set
1662
01:10:27,140 --> 01:10:29,480
of per people that you're,
you're trying to go at.
1663
01:10:29,480 --> 01:10:31,910
And those can be two
really different things.
1664
01:10:31,910 --> 01:10:34,310
So I'm, I'm just gonna cut
to an example that I have.
1665
01:10:34,310 --> 01:10:36,530
I have met hundreds of
members of congress, hundreds
1666
01:10:36,530 --> 01:10:39,800
of times, and I have a strategy going in.
1667
01:10:39,800 --> 01:10:42,020
And the strategy is I'm not gonna ask them
1668
01:10:42,020 --> 01:10:44,000
for anything, right?
1669
01:10:44,000 --> 01:10:45,830
I'm not gonna ask them for anything.
1670
01:10:45,830 --> 01:10:48,020
Instead, what I'm gonna
do is, is I'm going
1671
01:10:48,020 --> 01:10:51,170
to understand everything
they've talked about in public
1672
01:10:51,170 --> 01:10:52,310
for the last six months.
1673
01:10:52,310 --> 01:10:54,020
And you might think that's hard to do,
1674
01:10:54,020 --> 01:10:56,540
but if you're a member of the
United States Congress, you,
1675
01:10:56,540 --> 01:10:58,850
you tend to talk about
the same things a lot.
1676
01:10:58,850 --> 01:11:00,530
So you, you can figure that out.
1677
01:11:00,530 --> 01:11:01,820
So, so that's the first thing I know.
1678
01:11:01,820 --> 01:11:04,670
The other thing I know is on
a day when they're meeting me,
1679
01:11:04,670 --> 01:11:08,360
they're probably meeting at
10, 15, 25, 30 other people
1680
01:11:08,360 --> 01:11:10,385
and they're gonna be very nice to me.
1681
01:11:10,385 --> 01:11:13,470
And, and what they remember
might have to do with
1682
01:11:13,470 --> 01:11:15,810
what we spent seven,
the 30 minutes together.
1683
01:11:15,810 --> 01:11:17,910
But what's probably gonna
happen is at five 30
1684
01:11:17,910 --> 01:11:20,730
or six o'clock or six 30 when
they meet with their staff
1685
01:11:20,730 --> 01:11:23,040
and the, the member politely
asks some version of
1686
01:11:23,040 --> 01:11:24,450
what the hell happened today?
1687
01:11:24,450 --> 01:11:27,420
And then they go through the
day and try and figure it out.
1688
01:11:27,420 --> 01:11:29,880
You want to be the person who
puts something in the room,
1689
01:11:29,880 --> 01:11:31,410
so they call you back.
1690
01:11:31,410 --> 01:11:33,180
But typically it's not
gonna be talking about
1691
01:11:33,180 --> 01:11:34,560
how great your research is 'cause
1692
01:11:34,560 --> 01:11:37,620
'cause the they don't have, right?
1693
01:11:37,620 --> 01:11:41,910
And so because I know what
they care about, I can,
1694
01:11:41,910 --> 01:11:42,930
with complete sincerity
1695
01:11:42,930 --> 01:11:45,240
and integrity, say, thank
you member of congress
1696
01:11:45,240 --> 01:11:48,480
for doing A, B, and C, it's a
great service to the nation.
1697
01:11:48,480 --> 01:11:49,740
And then they might talk about B
1698
01:11:49,740 --> 01:11:51,300
and say, yeah, B is really important.
1699
01:11:51,300 --> 01:11:53,820
And then in my back pocket
I had this note like,
1700
01:11:53,820 --> 01:11:54,930
did you know that you know,
1701
01:11:54,930 --> 01:11:57,540
Stanford is actually doing this work on b
1702
01:11:57,540 --> 01:11:59,730
and in the last couple
months it's done this thing.
1703
01:11:59,730 --> 01:12:01,110
And if I say it the right way,
1704
01:12:01,110 --> 01:12:03,000
the staff leans in and
the member leans in.
1705
01:12:03,000 --> 01:12:05,430
Like, can you gimme more
information about that?
1706
01:12:05,430 --> 01:12:08,130
And if you can pull that off
two or three times in a meeting
1707
01:12:08,130 --> 01:12:10,440
because you are focused
on answering the questions
1708
01:12:10,440 --> 01:12:12,780
that they're, they're asking in public.
1709
01:12:12,780 --> 01:12:14,430
So instead of trying
to sell them something,
1710
01:12:14,430 --> 01:12:16,650
you're helping them do
the thing they say, they,
1711
01:12:16,650 --> 01:12:18,300
they call you back.
1712
01:12:18,300 --> 01:12:21,420
And when they call you back,
it's a different conversation.
1713
01:12:21,420 --> 01:12:23,160
And if you ever wanna have a conversation
1714
01:12:23,160 --> 01:12:24,870
with anybody in policy where you ask them
1715
01:12:24,870 --> 01:12:26,280
for something, it's that call.
1716
01:12:26,280 --> 01:12:28,590
It's not the first one.
But if you come in sort
1717
01:12:28,590 --> 01:12:31,530
of selling your latest paper,
people will be polite to you
1718
01:12:31,530 --> 01:12:32,760
and they'll say great things to you
1719
01:12:32,760 --> 01:12:35,100
and the next day they won't
remember any of it, right?
1720
01:12:35,100 --> 01:12:38,070
And so I just think that's
a, a skill to think through.
1721
01:12:38,070 --> 01:12:40,380
- I'll just share and then I,
I wanna make sure we, we get
1722
01:12:40,380 --> 01:12:41,760
to some other questions.
1723
01:12:41,760 --> 01:12:43,560
When a bunch of us were in Washington
1724
01:12:43,560 --> 01:12:46,950
with the Stanford Emerging
Technology Review last month,
1725
01:12:46,950 --> 01:12:49,830
it was very telling that the senators
1726
01:12:49,830 --> 01:12:51,840
and members of Congress
and their staff that we met
1727
01:12:51,840 --> 01:12:53,910
with said over and over again,
1728
01:12:53,910 --> 01:12:57,540
and I think this is true, academics tend
1729
01:12:57,540 --> 01:12:59,310
to think about the data
1730
01:12:59,310 --> 01:13:02,310
and we're really, we're
really nerdy right?
1731
01:13:02,310 --> 01:13:03,960
About what's the statistical
1732
01:13:03,960 --> 01:13:05,970
significance, et cetera, et cetera.
1733
01:13:05,970 --> 01:13:10,080
But what convinces most
people is the story.
1734
01:13:10,080 --> 01:13:12,690
And we need to talk in language
1735
01:13:12,690 --> 01:13:15,330
that communicates our value proposition
1736
01:13:15,330 --> 01:13:17,730
to our stakeholders in Congress
1737
01:13:17,730 --> 01:13:21,330
and the American people in
a way that lands with them.
1738
01:13:21,330 --> 01:13:23,820
Everybody remembers the story.
1739
01:13:23,820 --> 01:13:26,760
Nobody wants to hear about
the statistical significance
1740
01:13:26,760 --> 01:13:28,170
or the quantitative analysis.
1741
01:13:28,170 --> 01:13:31,530
As lovely as that is,
as important as that is,
1742
01:13:31,530 --> 01:13:33,120
it's not persuasive.
1743
01:13:33,120 --> 01:13:38,010
Okay? Question would be
interested in the panelists
1744
01:13:38,010 --> 01:13:39,570
views and whether part
1745
01:13:39,570 --> 01:13:44,490
of the problem is the
phrase the science says
1746
01:13:44,490 --> 01:13:46,860
when in fact, as Professor Horowitz
1747
01:13:46,860 --> 01:13:48,390
and others have touched on,
1748
01:13:48,390 --> 01:13:52,320
science is about openly
contesting ideas in an
1749
01:13:52,320 --> 01:13:54,090
evidence-based way.
1750
01:13:54,090 --> 01:13:56,790
So part of the problem
might be a misunderstanding
1751
01:13:56,790 --> 01:13:58,890
of most people, and especially in politics
1752
01:13:58,890 --> 01:14:01,710
and policy about what science is
1753
01:14:01,710 --> 01:14:05,250
and science isn't. What do y'all think
1754
01:14:06,210 --> 01:14:07,290
- My first thought is?
1755
01:14:07,290 --> 01:14:11,980
Yes. Period. New paragraph.
1756
01:14:13,960 --> 01:14:15,880
That is absolutely true.
1757
01:14:15,880 --> 01:14:18,250
And I think my COVID example showed what,
1758
01:14:18,250 --> 01:14:20,590
when people start getting confused,
1759
01:14:20,590 --> 01:14:21,910
science doesn't say things.
1760
01:14:21,910 --> 01:14:25,690
It's just like AI scientists do things
1761
01:14:25,690 --> 01:14:28,780
and scientists have
theories and they have data
1762
01:14:28,780 --> 01:14:33,130
and they have ideas
about what might be true
1763
01:14:33,130 --> 01:14:35,590
and with different levels of confidence,
1764
01:14:35,590 --> 01:14:38,800
but that never translate
directly into a policy.
1765
01:14:38,800 --> 01:14:42,550
There's a whole layer of social
debate that has to happen
1766
01:14:42,550 --> 01:14:44,650
before a scientific theory
1767
01:14:44,650 --> 01:14:47,620
or fact is used as the basis of policy.
1768
01:14:47,620 --> 01:14:50,830
- But Russ, we hear often
in the political debate,
1769
01:14:50,830 --> 01:14:52,420
follow the science.
1770
01:14:52,420 --> 01:14:54,610
- Yeah, I like that,
- That science says that
1771
01:14:54,610 --> 01:14:55,990
- I don't like follow the data,
1772
01:14:57,010 --> 01:14:59,890
follow the data is even worse
than follow the science.
1773
01:14:59,890 --> 01:15:03,280
And we can get into this, but
data, you know, is, is raw,
1774
01:15:03,280 --> 01:15:06,070
it is unprocessed, it
is filled with noise,
1775
01:15:06,070 --> 01:15:07,360
it is filled with problems.
1776
01:15:08,290 --> 01:15:11,530
The, the job of the scientist
is to, is to take a look at
1777
01:15:11,530 --> 01:15:13,240
that data, apply theories,
1778
01:15:13,240 --> 01:15:17,020
and then come up with some
contingent conclusions.
1779
01:15:17,020 --> 01:15:19,810
So I think we need, this is
kind of what I was saying
1780
01:15:19,810 --> 01:15:22,330
before, is when we're
dealing with media, when need
1781
01:15:22,330 --> 01:15:23,860
to change the vocabulary
1782
01:15:23,860 --> 01:15:26,290
that we use when we're
talking about our findings,
1783
01:15:26,290 --> 01:15:28,240
and it's gonna involve humility.
1784
01:15:28,240 --> 01:15:31,000
Another thing that is not the most common
1785
01:15:31,000 --> 01:15:34,030
resource on a campus, but
I think we need humility
1786
01:15:34,030 --> 01:15:37,180
and we need openness to
debate and, and challenge.
1787
01:15:37,180 --> 01:15:40,420
And it means that we're gonna
sound like less productive.
1788
01:15:40,420 --> 01:15:43,091
And sorry, that's how we
probably have to sound out.
1789
01:15:44,890 --> 01:15:47,560
- You know, I, I completely
agree with what Russ said.
1790
01:15:47,560 --> 01:15:50,290
I just don't want people
to walk away thinking
1791
01:15:50,290 --> 01:15:52,420
that science doesn't matter in policy.
1792
01:15:52,420 --> 01:15:54,010
I mean, I, I I think
1793
01:15:54,940 --> 01:15:59,080
science does not say
determine a policy outcome,
1794
01:16:00,250 --> 01:16:04,270
but it constrain the policy outcomes.
1795
01:16:04,270 --> 01:16:05,860
Or what it says is
1796
01:16:05,860 --> 01:16:08,770
that certain policy
outcomes have made certain
1797
01:16:08,770 --> 01:16:10,960
trade-offs, okay?
1798
01:16:10,960 --> 01:16:12,820
And it's very important to understand
1799
01:16:12,820 --> 01:16:15,700
what those trade-offs are, right?
1800
01:16:15,700 --> 01:16:19,360
And science scientific results, right?
1801
01:16:19,360 --> 01:16:22,870
Information that comes informs
1802
01:16:22,870 --> 01:16:25,600
what those trade offs will look like.
1803
01:16:25,600 --> 01:16:28,000
And there is always some disagreement.
1804
01:16:28,000 --> 01:16:32,230
I mean, the point is science
does not say X is true.
1805
01:16:32,230 --> 01:16:37,090
It just says with high
probability X is true.
1806
01:16:37,090 --> 01:16:38,830
Right? You know, may not,
1807
01:16:38,830 --> 01:16:42,160
but you know, if, if you're
placing a wager right now,
1808
01:16:42,160 --> 01:16:43,180
I place it this way
1809
01:16:43,180 --> 01:16:46,000
or you know, here are
the odds or whatever.
1810
01:16:46,000 --> 01:16:48,310
And I, I think there's,
1811
01:16:48,310 --> 01:16:52,300
because of those two
things are interrelated,
1812
01:16:52,300 --> 01:16:55,870
there is some confusion
that is generated there.
1813
01:16:55,870 --> 01:16:59,380
So as, as Amy you said in,
as a political scientist
1814
01:16:59,380 --> 01:17:03,610
or someone who's looking at
policy understanding what is
1815
01:17:03,610 --> 01:17:05,320
or is not capable,
1816
01:17:05,320 --> 01:17:08,470
what the way technology's
going is very important
1817
01:17:08,470 --> 01:17:11,210
to inform data policy.
1818
01:17:12,710 --> 01:17:14,960
Just like you shouldn't
make policy without
1819
01:17:14,960 --> 01:17:16,430
talking to the technologists.
1820
01:17:16,430 --> 01:17:19,280
You don't want technologists
to make policy without talking
1821
01:17:19,280 --> 01:17:22,820
to the, to the policy and
political science people.
1822
01:17:22,820 --> 01:17:25,460
That would be equally disastrous.
1823
01:17:25,460 --> 01:17:28,160
It is that interplay, which
is the most important.
1824
01:17:28,160 --> 01:17:32,450
And I think in those kinds of
things, it's easy for people
1825
01:17:32,450 --> 01:17:33,740
to misunderstand
1826
01:17:33,740 --> 01:17:37,700
and people like polar positions
1827
01:17:37,700 --> 01:17:40,370
that it's either yes or no.
1828
01:17:40,370 --> 01:17:44,900
And in reality, science always
says it's more complicated.
1829
01:17:44,900 --> 01:17:47,120
- There's another field
that faces exactly the same
1830
01:17:47,120 --> 01:17:48,950
challenges that you all
have just discussed.
1831
01:17:48,950 --> 01:17:52,040
And that is the world of
intelligence analysis, right?
1832
01:17:52,040 --> 01:17:55,940
This is not CSI TV show where
there's, here's the piece
1833
01:17:55,940 --> 01:17:58,340
of evidence and you know,
you know who did, who,
1834
01:17:58,340 --> 01:17:59,960
who did the deed, right?
1835
01:17:59,960 --> 01:18:02,030
This is the same piece of data.
1836
01:18:02,030 --> 01:18:05,630
To your point, Russ could
mean many different things.
1837
01:18:05,630 --> 01:18:08,510
There's a image of tanks
massing on a border,
1838
01:18:09,650 --> 01:18:12,710
is an invasion coming or is
that a bluff for negotiation?
1839
01:18:12,710 --> 01:18:16,460
The same smoking gun can have
multiple interpretations.
1840
01:18:16,460 --> 01:18:20,450
It's the same conundrum for
scientific inquiry, right?
1841
01:18:20,450 --> 01:18:22,760
Which is it to your point
about, it's about the analysis.
1842
01:18:22,760 --> 01:18:24,800
And then the same challenge is if,
1843
01:18:24,800 --> 01:18:28,010
if there's new information
that refines the analysis,
1844
01:18:28,010 --> 01:18:30,890
and in the case that one
case that's been publicly de
1845
01:18:30,890 --> 01:18:32,360
that's been declassified,
1846
01:18:32,360 --> 01:18:36,410
an estimate about Iran's
nuclear weapons capabilities.
1847
01:18:36,410 --> 01:18:40,700
In 2007, it was used
politically to say, well there,
1848
01:18:40,700 --> 01:18:42,200
there's the intelligence community again.
1849
01:18:42,200 --> 01:18:43,760
They, they failed, right?
1850
01:18:43,760 --> 01:18:46,040
They, their earlier estimates were wrong.
1851
01:18:46,040 --> 01:18:47,390
So it, what was a sign
1852
01:18:47,390 --> 01:18:49,370
of progress was interpreted politically
1853
01:18:49,370 --> 01:18:50,870
as a sign of failure.
1854
01:18:50,870 --> 01:18:52,190
That's the dynamic I think
1855
01:18:52,190 --> 01:18:54,740
that we've been talking
about in science as well.
1856
01:18:54,740 --> 01:18:57,290
You have a new finding,
it changes what you think
1857
01:18:57,290 --> 01:19:01,010
and people look at it as this
is a sign that you were stupid
1858
01:19:01,010 --> 01:19:04,550
or you failed before as opposed
to marching toward progress.
1859
01:19:04,550 --> 01:19:07,310
Okay, next question.
1860
01:19:08,630 --> 01:19:11,300
Professor Altman's point
about China being a
1861
01:19:11,300 --> 01:19:12,590
focused adversary.
1862
01:19:12,590 --> 01:19:14,510
This was true
1863
01:19:14,510 --> 01:19:19,510
and becoming more true I think
until some point last year.
1864
01:19:19,550 --> 01:19:21,680
I'm wondering whether the focus on China
1865
01:19:21,680 --> 01:19:26,450
as an adversary might be
becoming less sharp today,
1866
01:19:26,450 --> 01:19:27,830
for example, depending on
1867
01:19:27,830 --> 01:19:30,590
how people read the US
national security strategy.
1868
01:19:30,590 --> 01:19:32,870
For listeners who aren't
geeking out on the national
1869
01:19:32,870 --> 01:19:36,350
security strategy, China is
not the number one priority
1870
01:19:36,350 --> 01:19:37,460
of the administration.
1871
01:19:37,460 --> 01:19:40,370
Our own hemisphere has
now taught that list.
1872
01:19:40,370 --> 01:19:43,580
So is China as an adversary, less
1873
01:19:43,580 --> 01:19:46,850
of a focusing feature in the
political discussion today?
1874
01:19:48,380 --> 01:19:51,350
- So I'll just, since, since
my name was shouted out,
1875
01:19:51,350 --> 01:19:53,060
I will say briefly,
1876
01:19:53,060 --> 01:19:56,870
there is many fronts upon
which you could consider
1877
01:19:56,870 --> 01:19:58,100
China an adversary.
1878
01:19:58,100 --> 01:19:59,420
And so there's a military front,
1879
01:19:59,420 --> 01:20:01,670
which I know very little about,
1880
01:20:01,670 --> 01:20:04,850
but there is also the
scientific technology front,
1881
01:20:04,850 --> 01:20:06,020
which I know more about.
1882
01:20:06,020 --> 01:20:09,690
And there is no doubt that, you know, he
1883
01:20:09,690 --> 01:20:12,780
and she who invent the
technology understand how
1884
01:20:12,780 --> 01:20:14,010
to scale it best
1885
01:20:14,010 --> 01:20:16,290
and understand how to
bring it to practice best.
1886
01:20:16,290 --> 01:20:18,660
And in many ways then can
control it depending on
1887
01:20:18,660 --> 01:20:20,040
how much disclosure there is.
1888
01:20:20,040 --> 01:20:24,420
And so definitely not
in a militaristic set,
1889
01:20:24,420 --> 01:20:26,370
but in a sense that we're
having a competition
1890
01:20:26,370 --> 01:20:27,450
about technology.
1891
01:20:27,450 --> 01:20:30,270
And this includes AI and includes biology
1892
01:20:30,270 --> 01:20:31,890
and synthetic biology.
1893
01:20:31,890 --> 01:20:34,230
They are very serious.
They're doing great work.
1894
01:20:34,230 --> 01:20:35,700
Let's, let's just give credit.
1895
01:20:35,700 --> 01:20:38,100
You open up an issue of Science magazine
1896
01:20:38,100 --> 01:20:41,670
and it is filled with first
rate scientific reports
1897
01:20:41,670 --> 01:20:44,940
contingent as they are,
as, as is all science.
1898
01:20:44,940 --> 01:20:49,560
And so I think that you
don't want to create a false,
1899
01:20:49,560 --> 01:20:51,480
a false adversary,
1900
01:20:51,480 --> 01:20:53,160
but I do think that in terms
1901
01:20:53,160 --> 01:20:57,180
of competition on important
technologies, both for security
1902
01:20:57,180 --> 01:21:01,050
and for, for lack of a
better word, for lifestyle
1903
01:21:01,050 --> 01:21:04,410
and our capabilities to
support human existence,
1904
01:21:04,410 --> 01:21:06,420
China is a big player right now.
1905
01:21:06,420 --> 01:21:07,800
And I'm sure there are other frontiers
1906
01:21:07,800 --> 01:21:11,550
that I haven't even mentioned, like social
1907
01:21:11,550 --> 01:21:15,270
and soft power in developing
countries where again,
1908
01:21:15,270 --> 01:21:16,830
that this might focus our attention.
1909
01:21:16,830 --> 01:21:21,030
So I think without being
a a, a, without trying
1910
01:21:21,030 --> 01:21:22,620
to be startling or,
1911
01:21:22,620 --> 01:21:27,360
or make, make emergencies
that don't exist,
1912
01:21:27,360 --> 01:21:29,970
I do think that this
provides a useful focus
1913
01:21:29,970 --> 01:21:31,920
for why we're doing some
of the things we do.
1914
01:21:31,920 --> 01:21:33,690
It should not be the only focus,
1915
01:21:33,690 --> 01:21:36,270
but it is one of the
reasons why, for example,
1916
01:21:36,270 --> 01:21:39,630
basic science funding should
be a national priority.
1917
01:21:39,630 --> 01:21:42,270
- I think there's an aspect of
the, I mean there's an aspect
1918
01:21:42,270 --> 01:21:45,150
of the rivalry that's real
also at the same time in the
1919
01:21:45,150 --> 01:21:47,040
private sector, in the public sector
1920
01:21:47,040 --> 01:21:49,380
and at universities, China is far
1921
01:21:49,380 --> 01:21:52,710
and away our biggest collaborator, right?
1922
01:21:52,710 --> 01:21:54,450
So, you know, we're
worried about this stuff,
1923
01:21:54,450 --> 01:21:56,610
but in each of those
sectors it, it's there.
1924
01:21:56,610 --> 01:21:59,700
But here's one of the things
that's like just in play
1925
01:21:59,700 --> 01:22:01,890
for all of our lifetimes,
1926
01:22:01,890 --> 01:22:05,010
or let's say most of our
lifetimes, we could assume
1927
01:22:05,010 --> 01:22:07,650
that in all our most key areas of science
1928
01:22:07,650 --> 01:22:09,990
and technology, the United
States of America was
1929
01:22:09,990 --> 01:22:11,190
the standard setter.
1930
01:22:11,190 --> 01:22:13,380
So it wasn't just that you
invent things, you would come up
1931
01:22:13,380 --> 01:22:15,240
with the frameworks and do
all of the frameworks on it,
1932
01:22:15,240 --> 01:22:17,070
which everything else was built.
1933
01:22:17,070 --> 01:22:20,040
And it's, we, it's the US has
been the leader in that for
1934
01:22:20,040 --> 01:22:22,830
so long that I think we
could take for granted just
1935
01:22:22,830 --> 01:22:25,470
how beneficial that is
to the nation in terms
1936
01:22:25,470 --> 01:22:28,200
of like why we have great
universities, why people want
1937
01:22:28,200 --> 01:22:30,570
to come here, like what that
means in capital markets
1938
01:22:30,570 --> 01:22:32,370
and the types of risks you can take.
1939
01:22:32,370 --> 01:22:35,040
And China has really focused
on a lot of those areas
1940
01:22:35,040 --> 01:22:37,410
and is actually leading us in quite a few,
1941
01:22:37,410 --> 01:22:39,180
it's really a different situation
1942
01:22:39,180 --> 01:22:42,210
to be the standard taker
than the standard setter.
1943
01:22:42,210 --> 01:22:43,680
And I think that's, you know, if,
1944
01:22:43,680 --> 01:22:46,710
if folks in Washington were to
ask me at that level, right?
1945
01:22:46,710 --> 01:22:48,360
That's really what's at play, right?
1946
01:22:48,360 --> 01:22:50,100
It and it's not just national security,
1947
01:22:50,100 --> 01:22:51,690
it's economic competitiveness.
1948
01:22:51,690 --> 01:22:53,940
It is so much better to
be the standard setter
1949
01:22:53,940 --> 01:22:56,160
and that's really in play right now.
1950
01:22:56,160 --> 01:22:59,850
- Okay, so this is
directly follows on our,
1951
01:22:59,850 --> 01:23:03,870
the discussion we just had,
which is what is the percentage
1952
01:23:03,870 --> 01:23:08,620
of basic research that
leads to valuable products
1953
01:23:08,620 --> 01:23:10,870
and what is the incremental investment?
1954
01:23:10,870 --> 01:23:12,610
I'd like to know the answer to that too.
1955
01:23:12,610 --> 01:23:15,460
I think the discovery
to product ratio is low.
1956
01:23:15,460 --> 01:23:16,930
I think this is to Mark's point
1957
01:23:18,160 --> 01:23:21,040
and would be a lot lower if
there were downstream strings
1958
01:23:21,040 --> 01:23:23,050
attached, right?
1959
01:23:23,050 --> 01:23:25,660
On the other hand, the cost from discovery
1960
01:23:25,660 --> 01:23:28,990
to product is relatively
high, even for software.
1961
01:23:28,990 --> 01:23:31,690
It seems like the reward
for basic research
1962
01:23:31,690 --> 01:23:34,960
with unfettered government
funding is about right
1963
01:23:34,960 --> 01:23:36,310
for its level of productivity
1964
01:23:36,310 --> 01:23:38,995
and the cost to market.
Let's take the first,
1965
01:23:38,995 --> 01:23:42,915
- The, the one thing I'll
say for sure is that the,
1966
01:23:42,915 --> 01:23:43,930
the discovery
1967
01:23:43,930 --> 01:23:48,850
to product ratio is
definitely extremely low.
1968
01:23:48,850 --> 01:23:52,810
The, this whole thing is
justified by the huge wins
1969
01:23:52,810 --> 01:23:56,020
that are very rare now is
economically justified.
1970
01:23:56,020 --> 01:23:57,670
There are other justification, but,
1971
01:23:57,670 --> 01:24:00,790
but having said that, those
things that could be construed
1972
01:24:00,790 --> 01:24:04,810
as not wins are bricks
in a hou, oh, excuse me,
1973
01:24:04,810 --> 01:24:07,600
are bricks in a house that we're building
1974
01:24:07,600 --> 01:24:08,950
that even those things
1975
01:24:08,950 --> 01:24:11,050
that don't look like they're
gonna lead to products,
1976
01:24:11,050 --> 01:24:13,570
they build a, a, a, a brick house
1977
01:24:13,570 --> 01:24:18,190
of knowledge upon which the
killer innovations happens.
1978
01:24:18,190 --> 01:24:22,510
So you, you can't really
dissect very well the
1979
01:24:22,510 --> 01:24:24,100
downstream butter.
1980
01:24:24,100 --> 01:24:25,420
It, it really is one
1981
01:24:25,420 --> 01:24:27,310
of these butterflies
flapping in the breeze.
1982
01:24:27,310 --> 01:24:29,740
I write this paper,
it's not that important,
1983
01:24:29,740 --> 01:24:31,900
it's a small incremental,
1984
01:24:31,900 --> 01:24:33,910
but then one of my competitors reads it
1985
01:24:33,910 --> 01:24:36,220
and says, boy, Altman got it wrong.
1986
01:24:36,220 --> 01:24:37,690
I know how to get it right.
1987
01:24:37,690 --> 01:24:41,860
And all of a sudden we
have a major new invention
1988
01:24:41,860 --> 01:24:46,360
that I only contributed to by
irritating my competitor. So,
1989
01:24:46,360 --> 01:24:48,880
- So I skip, do you wanna say something?
1990
01:24:48,880 --> 01:24:50,350
- Well, why don't you go
first? I'll come in after you.
1991
01:24:50,350 --> 01:24:51,520
Okay.
1992
01:24:51,520 --> 01:24:53,980
- I just wanna say that I
don't know any of the numbers.
1993
01:24:53,980 --> 01:24:55,360
I would be very interesting.
1994
01:24:55,360 --> 01:24:58,450
I do know that I think the
United States has benefited
1995
01:24:58,450 --> 01:25:00,430
tremendously from an
economic sense from the
1996
01:25:00,430 --> 01:25:01,930
research that was done.
1997
01:25:01,930 --> 01:25:04,900
And I don't know about the
ROI for the research dollars,
1998
01:25:04,900 --> 01:25:08,560
but there are numerous
industries that are there.
1999
01:25:08,560 --> 01:25:12,580
I i I just wanna point
out that if you view this
2000
01:25:12,580 --> 01:25:15,760
as the sort of idea generation, you know,
2001
01:25:15,760 --> 01:25:18,070
the research is funding idea generation.
2002
01:25:18,070 --> 01:25:22,390
I think you're missing a
large fraction of the value
2003
01:25:22,390 --> 01:25:25,180
of funding research, which is the product
2004
01:25:25,180 --> 01:25:27,520
of a university is first
2005
01:25:27,520 --> 01:25:30,700
and foremost the students that graduates.
2006
01:25:30,700 --> 01:25:33,310
So that is the product of a university.
2007
01:25:33,310 --> 01:25:38,230
Many of those graduates
in the STEM areas le less
2008
01:25:38,230 --> 01:25:39,250
so in the humanities,
2009
01:25:39,250 --> 01:25:41,260
but in the STEM areas are funded
2010
01:25:41,260 --> 01:25:43,420
by government funded research.
2011
01:25:43,420 --> 01:25:45,610
Okay? So they,
2012
01:25:45,610 --> 01:25:49,150
that funding provides two value streams.
2013
01:25:49,150 --> 01:25:53,950
The first is this highly
variable, you know,
2014
01:25:53,950 --> 01:25:56,980
uncertain output of killer ideas
2015
01:25:56,980 --> 01:26:00,130
that cause new industries to form.
2016
01:26:00,130 --> 01:26:03,740
But the much stabler output
2017
01:26:03,740 --> 01:26:06,920
is the trained students
2018
01:26:06,920 --> 01:26:11,570
who have now an ability to
do leading edge research
2019
01:26:11,570 --> 01:26:16,570
that go off and pri pri
primarily don't become faculty,
2020
01:26:16,790 --> 01:26:21,440
but work in basically
the, the tech industry,
2021
01:26:21,440 --> 01:26:23,240
the science industry that happens
2022
01:26:23,240 --> 01:26:25,670
that supports manufacturing
2023
01:26:25,670 --> 01:26:29,270
and generation of products
throughout the United States.
2024
01:26:30,860 --> 01:26:35,660
And I think if you look at
those two value streams, they
2025
01:26:36,500 --> 01:26:39,140
benefit the com country.
2026
01:26:39,140 --> 01:26:42,530
Their, their benefit to the
country is far in excess
2027
01:26:42,530 --> 01:26:44,345
of the money that the government spent.
2028
01:26:44,345 --> 01:26:46,010
- If, if I could add a bit to that,
2029
01:26:46,010 --> 01:26:48,320
I mean there's different types
of ROI, social, cultural,
2030
01:26:48,320 --> 01:26:51,110
economic, but if I can
just focus on economic,
2031
01:26:51,110 --> 01:26:52,520
it's, it's hard to measure.
2032
01:26:52,520 --> 01:26:54,290
But here's the thing
that actually happens.
2033
01:26:54,290 --> 01:26:55,640
Biden administration comes in,
2034
01:26:55,640 --> 01:26:57,470
and I don't know if you remember,
but within the first week
2035
01:26:57,470 --> 01:26:59,360
he wanted to talk about infrastructure.
2036
01:26:59,360 --> 01:27:02,780
So we get a call, I get a
call nine 30 in the morning,
2037
01:27:02,780 --> 01:27:04,760
it's from the White House and it's like,
2038
01:27:04,760 --> 01:27:07,940
does NSF know anything
about the ROI on r and d?
2039
01:27:07,940 --> 01:27:09,410
I'm like, yeah, we could
find some things out.
2040
01:27:09,410 --> 01:27:11,150
Like can you guys write us a report? Okay.
2041
01:27:11,150 --> 01:27:13,880
Like when do you want it close a business?
2042
01:27:13,880 --> 01:27:16,340
So we take everybody off a grants,
2043
01:27:16,340 --> 01:27:19,400
Danny Goroff is there from
Sloan, we get everybody on board
2044
01:27:19,400 --> 01:27:22,100
and we just have this intake
and everybody's working on it.
2045
01:27:22,100 --> 01:27:24,440
And by the end of the day we
produce this 20 page report
2046
01:27:24,440 --> 01:27:26,090
that still stands up.
2047
01:27:26,090 --> 01:27:27,800
And here's, here are the key things.
2048
01:27:27,800 --> 01:27:29,660
There are some like natural experiments
2049
01:27:29,660 --> 01:27:31,460
where there's a dis there's a disruption
2050
01:27:31,460 --> 01:27:34,190
or something weird happens and
all of a sudden you throw r
2051
01:27:34,190 --> 01:27:37,100
and d into a community or,
or a place where it wasn't
2052
01:27:37,100 --> 01:27:38,180
before, right?
2053
01:27:38,180 --> 01:27:40,070
And so that's, that's
kind of the best like
2054
01:27:40,070 --> 01:27:41,750
natural experiment you can get.
2055
01:27:41,750 --> 01:27:44,990
The ROIs are like 10, 20, 30 x right?
2056
01:27:44,990 --> 01:27:46,520
So when you look at it that way,
2057
01:27:46,520 --> 01:27:49,040
and it's not, again, these
were like natural experiments.
2058
01:27:49,040 --> 01:27:50,810
This was like a thing
happened that drew the money.
2059
01:27:50,810 --> 01:27:53,270
It was just kind of some
alteration in the universe.
2060
01:27:53,270 --> 01:27:56,480
But like that's the economic
ROI and I think to Russ and,
2061
01:27:56,480 --> 01:28:00,080
and Mark's point, it's of
that order of magnitude,
2062
01:28:00,080 --> 01:28:02,000
but sometimes it's harder to see.
2063
01:28:02,000 --> 01:28:03,950
- Well, we we have one minute left
2064
01:28:03,950 --> 01:28:07,370
and I'm determined to end
on an optimistic note.
2065
01:28:07,370 --> 01:28:11,060
So what, what gives you, what excites you?
2066
01:28:11,060 --> 01:28:13,880
What is promising for
you about universities
2067
01:28:13,880 --> 01:28:16,250
and the sort of future of science?
2068
01:28:17,360 --> 01:28:18,890
Mark, let's start with you.
2069
01:28:18,890 --> 01:28:21,560
Tell us one thing that gives
you optimism for the future
2070
01:28:21,560 --> 01:28:23,215
and then we'll go to Russ
and then end with Skip.
2071
01:28:23,215 --> 01:28:25,400
- I, I, I, I come to Stanford every day
2072
01:28:25,400 --> 01:28:28,250
because I love talking to the
students and my colleagues.
2073
01:28:28,250 --> 01:28:31,610
They are some of the most
creative people I've ever met.
2074
01:28:31,610 --> 01:28:33,140
And it's their creativity
2075
01:28:33,140 --> 01:28:35,360
that gives me optimism about the future.
2076
01:28:35,360 --> 01:28:38,360
There's just some amazing
stuff that we can still do.
2077
01:28:39,740 --> 01:28:41,510
- Russ, same answer.
2078
01:28:41,510 --> 01:28:45,140
Talking to young people who
want to do work in this field
2079
01:28:45,140 --> 01:28:46,790
and make contributions.
2080
01:28:46,790 --> 01:28:49,460
They're nothing but excitement and talent
2081
01:28:49,460 --> 01:28:51,410
and it just keeps you feeling young.
2082
01:28:53,300 --> 01:28:54,620
- Skip, you get the last word.
2083
01:28:54,620 --> 01:28:57,140
- Okay. Every day at places like Michigan
2084
01:28:57,140 --> 01:28:59,600
and Stanford, we do work that saves lives,
2085
01:28:59,600 --> 01:29:02,460
creates opportunity, improves
quality of life at a scale
2086
01:29:02,460 --> 01:29:05,700
that few institutions in
history have ever done.
2087
01:29:05,700 --> 01:29:08,310
And every day we have an
opportunity to do that better.
2088
01:29:08,310 --> 01:29:10,470
And people on this call,
the people, we, there's
2089
01:29:10,470 --> 01:29:12,000
so many people working on this.
2090
01:29:12,000 --> 01:29:13,560
It, it's an amazing thing.
2091
01:29:13,560 --> 01:29:15,510
We have agency like that's to,
2092
01:29:15,510 --> 01:29:17,370
to do these incredible things.
2093
01:29:17,370 --> 01:29:19,530
- Well, I couldn't ask
to end on a better note.
2094
01:29:19,530 --> 01:29:21,600
I'm gonna turn it back over
to Erin. Thank you all.
2095
01:29:22,470 --> 01:29:24,840
- Thank you Amy. Thank you
to our distinguished speakers
2096
01:29:24,840 --> 01:29:27,750
for sharing your insights
and expertise with us today.
2097
01:29:27,750 --> 01:29:29,850
And thank you to everyone
for joining our conversation
2098
01:29:29,850 --> 01:29:31,530
and your thoughtful engagement.
2099
01:29:31,530 --> 01:29:33,810
For the latest on the
Hoover technology policy,
2100
01:29:33,810 --> 01:29:36,780
accelerators work on
transformational technologies,
2101
01:29:36,780 --> 01:29:37,920
we invite you to check out
2102
01:29:37,920 --> 01:29:41,700
and download the 2026 Stanford
Emerging Technology Review.
2103
01:29:41,700 --> 01:29:43,710
We have a link in the chat.
2104
01:29:43,710 --> 01:29:46,230
Also, please stay tuned for a
special webinar on the civic
2105
01:29:46,230 --> 01:29:49,590
profile on April 28th,
moderated by Checker Finn
2106
01:29:49,590 --> 01:29:50,670
and on May 6th,
2107
01:29:50,670 --> 01:29:53,010
understanding the civilian
military relationship
2108
01:29:53,010 --> 01:29:56,070
and American democracy
with generals Chris Koli
2109
01:29:56,070 --> 01:29:57,450
and Joe Dunford, moderated
2110
01:29:57,450 --> 01:30:00,990
by Hoover's own Lieutenant
General HR McMaster.
2111
01:30:00,990 --> 01:30:04,590
Have a wonderful rest of
the day. Thanks everybody.