Speaker 1: Pushkin from Pushkin Industries. This is Deep Background, the show
Speaker 1: where we explore the stories behind the stories in the news.
Speaker 1: I'm Noah Feldman. Today I'm speaking to one of the
Speaker 1: most influential and extraordinary scientists I know, Eric Lander. Eric
Speaker 1: is the president and founding director of the Broad Institute
Speaker 1: of MIT and Harvard. He's geneticist, a molecular biologist, a
Speaker 1: mathematician by original training, and he's also the host of
Speaker 1: a brand new Pushkin podcast called Brave New Planet. Eric
Speaker 1: has been at the epicenter of a great transformation in
Speaker 1: biology and indeed of science that's taken place over the
Speaker 1: last thirty years. A transformation focused most fundamentally on what
Speaker 1: can be done with the gathering of greater and greater
Speaker 1: and greater amounts of data about biological systems, including the
Speaker 1: human body. These developments are crucial to how science is
Speaker 1: being done every day, and they're absolutely crucial as well
Speaker 1: to how science has responded to COVID nineteen. It's a
Speaker 1: thrill to have Eric on the podcast. Eric, thank you
Speaker 1: so much for joining us. I want to start with
Speaker 1: the role you've played in really a transformative period in
Speaker 1: the history of modern science, a period which in certain ways,
Speaker 1: is being reflected in the cutting edge developments that are
Speaker 1: happening every day that we all care about most in science,
Speaker 1: including indirectly in the context of the vaccines for COVID nineteen.
Speaker 1: And that's a period in which big data has come
Speaker 1: to be a fundamental, some would say, the fundamental tool
Speaker 1: for solving our biggest scientific problems. You came across this
Speaker 1: already when you're working on the human genome project, but
Speaker 1: then it's been your central focus and your creation of
Speaker 1: the broad Now. I know this is a big picture question,
Speaker 1: but I wonder if you would just how a few bars,
Speaker 1: if you would for the audience about how this transformation
Speaker 1: in the way we think about what science is and
Speaker 1: how it works has come home. Now, Wow, what a
Speaker 1: small question to start with. No othing. I think there's
Speaker 1: a kind of high school cartoon version of what science
Speaker 1: is which runs something like this, Scientists make a hypothesis
Speaker 1: and then they set up a test, and then they
Speaker 1: test their hypothesis and see if it holds up or
Speaker 1: they should reject it. I think it misses a fundamental
Speaker 1: question where do hypotheses come from? Anyway, And many of
Speaker 1: the most important hypotheses come from just looking at the world.
Speaker 1: And what's happened in the last twenty thirty years in
Speaker 1: biology is we've been able to take almost entirely unbiased
Speaker 1: looks at parts of biology. The human genome was the
Speaker 1: first great example of that three billion letters of human instructions,
Speaker 1: and rather than diving in with a hypothesis I think
Speaker 1: this gene causes cystic fibrosis or some other aspect of
Speaker 1: a disease, you can ask questions like, amongst every gene
Speaker 1: in the genome, which ones show an inheritance pattern that
Speaker 1: matches up with cystic fibrosis, And instead of being limited
Speaker 1: to hypothesis driven science, you can do what some people
Speaker 1: you know call hypothesis free science. We're never free of hypothesis,
Speaker 1: but the limits are removed so much it's changed the
Speaker 1: way we approach biology because it's sort of big data.
Speaker 1: But what I really think about it is the big
Speaker 1: data is that life keep notes in its lab notebook,
Speaker 1: the genome, and in this generation, we've got access to
Speaker 1: that lab notebook and we can read and the questions
Speaker 1: we can ask are only limited by our creativity. Translating
Speaker 1: that into practical terms, how for example, today when scientists
Speaker 1: can affronted sars Covi two and said, okay, let's figure
Speaker 1: this out and then let's find something to do about it.
Speaker 1: How did their approach differ from the way it would
Speaker 1: have looked, say twenty five or thirty years ago, or
Speaker 1: four years ago actually in this case, so let me
Speaker 1: give you an example. There is something called the Human
Speaker 1: cell Atlas. It's probably this generation's successor to the Human
Speaker 1: Genome Project. The Human Genome Project was reading out all
Speaker 1: the genetic information in a human The Human Cell Atlass
Speaker 1: was reading out what are all the cells in the
Speaker 1: human body? By asking which of the twenty thousand genes
Speaker 1: are turned on in this cell or that cell. It
Speaker 1: became possible and eight years ago to start reading at
Speaker 1: a single cell basis the genes that are turned on
Speaker 1: and by how much in first dozens of cells, then
Speaker 1: thousands of cells, then millions of cells, and eventually it'll
Speaker 1: be billions of cells. And an international catalog. International program
Speaker 1: to create that catalog describing all possible cells and their
Speaker 1: expression patterns of genes and the states they find themselves
Speaker 1: in just caught fire around the world. So then stars
Speaker 1: comes along and people ask what cells might sars Covie
Speaker 1: two in fact, well, it was known that the previous
Speaker 1: coronavirus SARS, infected cells that had a particular gene that
Speaker 1: was active goes by the name ACE two. But that
Speaker 1: doesn't matter. People quickly figured out that the same gene
Speaker 1: was used as the receptor for the new coronavirus. And
Speaker 1: then you wanted to ask the question, what's every cell
Speaker 1: in the body that expresses the receptor for the new coronavirus. Well,
Speaker 1: in ancient times, meaning four years ago, one would have
Speaker 1: had to have done thousands of experiments to figure that out. Instead,
Speaker 1: the scientific community came together and then a matter of
Speaker 1: two weeks, sifted through all the data about which genes
Speaker 1: are on and off in which cell types to say, oh, yes,
Speaker 1: here are the different cell types that might be infectable.
Speaker 1: And you know, one of the things they found was
Speaker 1: the smell receptors in your nose. They made that discovery
Speaker 1: and then three days later people reported that people were
Speaker 1: losing their sense to smell when infected by the virus. Bingo.
Speaker 1: The idea that we have lookup tables, Yeah, pretty good now,
Speaker 1: but much much better in the coming years. For every
Speaker 1: cell in our body is going to mean that any process,
Speaker 1: whether it's a virus that's infecting us or some other
Speaker 1: physiological process, we're going to be able to look up
Speaker 1: signatures for it. It doesn't solve all problems, that doesn't
Speaker 1: make a disease go away, but maybe it cuts a
Speaker 1: year or more off the work. I guess an older
Speaker 1: example would be chemistry before there was a periodic table
Speaker 1: of the elements. In chemistry, after there was a periodic
Speaker 1: table of the elements, didn't solve all chemistry, but no
Speaker 1: chemists thought about chemistry the same way after the periodic
Speaker 1: table was in evidence. Do the mRNA vaccines that are
Speaker 1: in development and that have been so far attested in
Speaker 1: what looks like a very promising way to address SOARS
Speaker 1: covy two infection themselves owe something to these revolutions in
Speaker 1: biology My impressions that they do. Oh yeah, of course,
Speaker 1: How did anybody find this sarscovy two virus so quickly?
Speaker 1: You know, establishing what was the virus behind AIDS HIV
Speaker 1: took quite a long time. Even establishing some of these
Speaker 1: other more recent novel virus that have appeared have taken
Speaker 1: a long time. Now, with kind of a hypothesis free
Speaker 1: brute force sequencing approach, you could take cells from infected patients,
Speaker 1: sequence the genes that are getting expressed, and say, WHOA,
Speaker 1: are there any genes here that aren't supposed to be
Speaker 1: in humans? And when you sift the genes you see
Speaker 1: in the cells versus the genes you expect in the cells,
Speaker 1: the difference kind of turns out to be this virus
Speaker 1: that's infecting you. You didn't need to go in with
Speaker 1: the hypothesis that it wasn't necessarily a coronavirus, or that
Speaker 1: you were looking for a particular kind of gene. You
Speaker 1: could go in saying I think there's a virus to
Speaker 1: look for what novel stuff is getting expressed. So it's
Speaker 1: a great example of the ability to make discovery science
Speaker 1: by looking at the big picture in a comprehensive way.
Speaker 1: Kind of global views of biology have become possible, and
Speaker 1: they depend on people generating data, making those data freely available,
Speaker 1: a whole new generation of scientists growing up who live
Speaker 1: to analyze those kind of data, and incredible creativity because
Speaker 1: you'd think, oh, there's a big pile of data, you
Speaker 1: just crunched it through the computer. But given a big
Speaker 1: pile of information, you can ask a thousand different questions
Speaker 1: and it depends on the perspective you're bringing. So I'm
Speaker 1: just in awe of the creativity that people bring to
Speaker 1: these questions. I think at the beginning people thought, oh,
Speaker 1: all this data are boring, But of course they're an
Speaker 1: invitation for incredible diversity of intellectual questions. This is a
Speaker 1: philosophical question. But do you think that the changes that
Speaker 1: we've seen in these recent decades count as not just
Speaker 1: an evolution in the way the biology has done, but
Speaker 1: in fact as a revolution, especially with respect to being
Speaker 1: able to do something different than saying, here's our hypothesis. Now,
Speaker 1: let's search instead being able to look at a huge
Speaker 1: body of data and say, well, what are the associations?
Speaker 1: What are the associations across the genome. Let's drill down
Speaker 1: and see which ones turn out to be real and
Speaker 1: which ones turn out not to be real. At that
Speaker 1: point you can form my hypothesis. But as it were,
Speaker 1: the hypothesis comes from the associations. Yeah. I think people
Speaker 1: can fight over whether you should choose the word revolution,
Speaker 1: but it seems to me anything that radically changes your
Speaker 1: perspective deserves to be called a revolution. And this idea
Speaker 1: that we used to have that we'd be good at
Speaker 1: guessing what are the processes responsible for cystic fibrosis, or
Speaker 1: heart disease or schizophrenia, you know, just a mystery locked
Speaker 1: in people's brains. The idea that we're supposed to guess
Speaker 1: that based on some prior knowledge of biology was I think,
Speaker 1: doing biology with your hands tied behind your back. The
Speaker 1: idea that we could range across the genome and ask
Speaker 1: why there genetic variations anywhere more frequently found in people
Speaker 1: who have schizophrenia, Well it turned out that instead of
Speaker 1: finding one gene, it's pointed now to two hundred and
Speaker 1: sixty five genes across the genome that play roles. But
Speaker 1: no forward make a hypothesis based on prior knowledge could
Speaker 1: possibly have produced that picture. And biologists at some point
Speaker 1: argued over this new way of thinking, and now, as usual,
Speaker 1: they've just absorbed it into the way biologists think. But
Speaker 1: looking back, it was a shift that took place in
Speaker 1: the eighties and nineties and really took hold in the
Speaker 1: first decade of this century. You know, intellectual shifts like
Speaker 1: that are quite amazing. You know. I were still run
Speaker 1: a laboratory, and one of my students at some lab
Speaker 1: meeting a couple of years ago, was listening to a
Speaker 1: description and he just turned and he said, before you
Speaker 1: knew the sequence to the genome, how did you do anything?
Speaker 1: Of course lots got done, but it was the sign
Speaker 1: of what what intellectual progress is, which it so completely
Speaker 1: infects the way you think about things that you assume
Speaker 1: it's been in the woodwork forever. We'll be right back, Eric.
Speaker 1: I want to ask you about how the institutions that
Speaker 1: enable science to be done have been changing as a
Speaker 1: consequence of the apocal change in the cognitive part of
Speaker 1: science that you're describing. Your institute that you're the founding
Speaker 1: director of, the Broad Institute is a great example. It's
Speaker 1: tremendously influential throughout the areas of science which it touches upon,
Speaker 1: and it touches upon many areas of science. It's extremely
Speaker 1: well funded, it's not subordinated to the universities that it's
Speaker 1: affiliated with Harvard and m T the way a traditional
Speaker 1: institutent have been. And to be blunt, it's enormously powerful
Speaker 1: in the world of science. How coincidental is the evolution
Speaker 1: of the Broad to the developments you're talking about. I mean,
Speaker 1: had you started the Broad and it's not been the
Speaker 1: way science was done would it have been as significant?
Speaker 1: And similarly, had you not started the Broad But had
Speaker 1: these scientific developments occurred, could they have occurred with the
Speaker 1: same speed and efficiency Starting the Broad Institute it was
Speaker 1: a response to these scientific changes. The Broad was an
Speaker 1: answer to these new intellectual possibilities. But the word that
Speaker 1: you didn't mention, which I think, is that the heart
Speaker 1: of all of it is collaboration. We use the word
Speaker 1: collaboration lightly. It's doing anything as we collaborate with somebody. No, no,
Speaker 1: there's a sense of deep collaboration that underlies this new
Speaker 1: era of science. So you know, what the Broad really
Speaker 1: is is an intellectual meeting ground. What the Broad is
Speaker 1: about is a collaborative spirit that is I think necessary
Speaker 1: to take full advantage of where science is going right now.
Speaker 1: Everything else follows from that. I totally buy that collaboration
Speaker 1: is kind of the special sauce. It's also true, though,
Speaker 1: that once there is a site of collaboration, that site
Speaker 1: of collaboration can become tremendously empowered. I mean, think of
Speaker 1: a trading zone in ancient civilizations or even in modern civilizations.
Speaker 1: So if you find a spot where cultures converge in
Speaker 1: collaboration becomes possible, that place becomes enriched and becomes powerful.
Speaker 1: You know, Venice might be the classic example. It's a
Speaker 1: kind of crossroads of East and West a certain moment
Speaker 1: in history, and then tremendous wealth and power accrue in Venice.
Speaker 1: And I guess what I'm wondering about is as the
Speaker 1: resources needed to do biology at the highest level have
Speaker 1: become greater and greater, doesn't that empower the institutions that
Speaker 1: are at the center, that are the collaboration crossroads that
Speaker 1: can raise the funds and then everyone really wants to
Speaker 1: get a part of the action, which seems justifiable and
Speaker 1: good for science. I'm not objecting to this, I'm just
Speaker 1: trying to describe it. And no, I think biology is
Speaker 1: just much more diverse than that. There are so many
Speaker 1: different ways to do biology. As much as I champion
Speaker 1: the rise of the ability to generate large data and
Speaker 1: learn from it, it's one lens on biology. It's probably
Speaker 1: fair to say that ninety five percent of biological discovery
Speaker 1: today still is going on in traditional laboratory structures, and
Speaker 1: they are dramatically empowered by the fact that the five
Speaker 1: percent of places that are really into the let's generate
Speaker 1: large data and analyze it all that put the data
Speaker 1: freely out there because they use it. So the traditional
Speaker 1: biological laboratory, which remains an incredibly powerful model, it becomes
Speaker 1: a solid foundation that saves them tons of work. I
Speaker 1: think what this has done is this has allowed different
Speaker 1: approaches to grow up and interact, and it will continue
Speaker 1: to change. I don't see anything close to a monopoly
Speaker 1: of approach or a monopoly of power in modern biology,
Speaker 1: because a biology is way too rich for that. One
Speaker 1: of the consistent themes in your fascinating career, Eric is
Speaker 1: that you've been constantly in touch with as an advisor
Speaker 1: to or interacting with government. You've also been part of
Speaker 1: the broader field of academic science, always centrally, and you've
Speaker 1: also touched on the private sector corporate part, which is
Speaker 1: one of the three legs, as it were, of contemporary science.
Speaker 1: How do you see the relationship between those different moving
Speaker 1: parts changing. We all still very much have the COVID
Speaker 1: vaccine race in our minds, in which you've had government
Speaker 1: playing sum roble, you've had private sector, you've had academic centers.
Speaker 1: So that might be a concrete example to use, although
Speaker 1: you feel free to use others too. You're describing a
Speaker 1: model that was laid out in the closing months of
Speaker 1: World War two. Very famously. Franklin Roosevelt, a couple of
Speaker 1: weeks after his reelection in nineteen forty four, wrote to
Speaker 1: his science advisor Veniva Bush, saying, boy, this science and
Speaker 1: technology stuff's been pretty helpful in bringing the war at
Speaker 1: least a successful direction. Then and eventually conclusion, how can
Speaker 1: it make a big difference in peacetime? And Bush wrote
Speaker 1: this report that is sort of known as a foundational text, Science, Science,
Speaker 1: the Endless Frontier, and it laid out and eventually shaped
Speaker 1: a world in which we have three pieces. We have government,
Speaker 1: we have academia, we have industry. And there's this virtuous
Speaker 1: cycle where Bush said in this report scientific discovery, which
Speaker 1: during the war had been going on in government labs,
Speaker 1: it should go on in universities, and it should go
Speaker 1: on in the context of training the next generation. And
Speaker 1: governments should fund academia to make basic knowledge fundamental knowledge
Speaker 1: and make it broadly available. Industry is then able to
Speaker 1: pick up that knowledge and turn it into private goods,
Speaker 1: private products. And this virtuous cycle I've written about it,
Speaker 1: and I called it like this miracle machine is something
Speaker 1: that the United States perfected before and better than any
Speaker 1: other country of how these pieces work off each other.
Speaker 1: As an academic, I understand that our goal is create
Speaker 1: knowledge and make it broadly available. But I also know
Speaker 1: that it's never going to complete its mission if it
Speaker 1: doesn't get to patients, and so academia has to work
Speaker 1: with industry and government tests to think about what should
Speaker 1: its policies be on funding. But understanding that balance of
Speaker 1: those three partners in trying to create social products to
Speaker 1: make society healthier, wealthier, and more secure, I think it's
Speaker 1: really important to think about that. You depict a somewhat
Speaker 1: rosy picture there, and I think for those of us
Speaker 1: just coming out of we're not quite out of it yet,
Speaker 1: but a Trump administration viewing an administration that was deeply
Speaker 1: skeptical of science in a whole range of ways, from
Speaker 1: climate ultimately to the epidemiologists advice on how to handle COVID,
Speaker 1: and then which finally circled back to but we're doing
Speaker 1: such a great job because we're facilitating the emergence of vaccines.
Speaker 1: Left a lot of us skeptical about the productive role
Speaker 1: that government can play, and then apart from that, a
Speaker 1: lot of us have independent skepticism of the tremendous power
Speaker 1: of private companies. Is there anything you would say that's
Speaker 1: more critical about the relationship rather than that it's it
Speaker 1: is still the miracle machine that it was depicted as
Speaker 1: being in the rosy, good old Dask Miracle machines need
Speaker 1: a lot of tending and repair and care. They don't
Speaker 1: always get them right. I think, you know, we'll stand
Speaker 1: back with enough distance and see what things got done
Speaker 1: wrong and what lessons we can learn and what things
Speaker 1: got done right. I'm thinking at a broader level, do
Speaker 1: we have the model right? And I think the model
Speaker 1: surely needs repair at many levels, but I don't think
Speaker 1: it's fundamentally wrong. I think more fundamental science is getting
Speaker 1: done in industry that's really important. I think, yes, we've
Speaker 1: seen instances where the government has engaged in denying obvious
Speaker 1: scientific facts. Not a good thing. But I don't think
Speaker 1: there's any reason to think that that's a permanent condition.
Speaker 1: I would point to a constant revision of this model.
Speaker 1: Science does not sit on its laurels. It's always edgy,
Speaker 1: and as soon as Veneva Bush laid out his early model,
Speaker 1: there were efforts to rethink it and change it. If
Speaker 1: you ask me, do I think we're in for a
Speaker 1: period when there's both enormous need an enormous opportunity to
Speaker 1: rethink those things. Yes, absolutely. But the fundamental idea that
Speaker 1: this is one of the great engines of producing progress
Speaker 1: for society I very much agree with, and that makes
Speaker 1: it worth the trouble of figuring out how it needs
Speaker 1: to be fixed and changed and improved. Eric, You've done
Speaker 1: something pretty unusual for someone in your August position, which
Speaker 1: is that you started a podcast, Brave New Planet, I
Speaker 1: should say for disclosure purposes, produced by Pushkin, which also
Speaker 1: produces this show. Tell me why I have to say,
Speaker 1: I mean, it's someone who's relatively new to podcasting figures.
Speaker 1: I'm learning a little bit every day, make two mistakes
Speaker 1: for every step forward. I was kind of heartened to
Speaker 1: hear that you were doing it because I know that
Speaker 1: whatever you do your due to the highest standard. And
Speaker 1: sure enough, it's a great podcast. But why why did
Speaker 1: you decide to devote significant time to that pursuit. Well,
Speaker 1: it's actually related to what we're just talking about. I
Speaker 1: really do believe in this compact between science and society,
Speaker 1: and I do think it's frayed in certain ways. And
Speaker 1: so for me, Brave New Planet, which is seven episodes
Speaker 1: that try to take on really hard problems where I
Speaker 1: don't know what the answer is, deep fakes, for example,
Speaker 1: solar geoengineering. Should we engineer the Earth's atmosphere to mitigate
Speaker 1: climate change? Lethal autonomous weapons? Should we have killer robots? Biases?
Speaker 1: And predictive algorithms? And new technology is called gene drives
Speaker 1: to reshape species in nature. And what Brave New Planet
Speaker 1: is about is smart, thoughtful, passionate people trying to grapple
Speaker 1: with what should we do? People who agree on the facts,
Speaker 1: agree on the societal goals, and then don't agree on
Speaker 1: the solutions because they're hard. Brave New Planet as the
Speaker 1: tagline utopia or dystopia. It's up to us, and I
Speaker 1: think that's right. I mean, there are gonna be a
Speaker 1: lot of consequential decisions about science and technology that if
Speaker 1: we make wise choices, could leave us a lot better off,
Speaker 1: and if we don't make wise choices could leave us
Speaker 1: a lot worsolve. Brave New Planet was an invitation for
Speaker 1: that kind of a conversation and I hope we're gonna
Speaker 1: have a lot more of it. Eric let last question,
Speaker 1: and it derives from that tagline utopia or dystopia. It's
Speaker 1: up to us. There's a theme that I sense in
Speaker 1: you are thinking about your work over the last decades
Speaker 1: and in the direction where things are going now. I
Speaker 1: see you as on the whole an extremely optimistic, positive person.
Speaker 1: But I actually wonder if that might be one of
Speaker 1: the many secrets to your extraordinary success, that you're looking
Speaker 1: optimistically and what can come next, and how change can
Speaker 1: be productive, and how institutions can be evolved to make
Speaker 1: them better. And yet that word dystopia is still lingering
Speaker 1: there as a much more potentially You're not saying it
Speaker 1: will be dystopic, but a much more potentially worrisome picture
Speaker 1: of how our world is evolving, and particularly coming out
Speaker 1: of extraordinary technological innovations, of which big data biology is
Speaker 1: only one. So how do you think about the big
Speaker 1: picture risks that we face really as a society or
Speaker 1: as a civilization, which you know, at least when you
Speaker 1: go to Silicon Valley you hear a lot of smart
Speaker 1: people with a big steak in the future, very very
Speaker 1: worried about what their technologies are capable of producing. Look,
Speaker 1: I am an optimist, but I am a very realistic optimist.
Speaker 1: I'm not Poyanna that everything works out well automatically. You
Speaker 1: have to work really hard to make sure that you
Speaker 1: get the upsides. So, as a realistic optimist, every one
Speaker 1: of the episodes of Brave New Planet starts with all
Speaker 1: of the upsides that could come from something, and then
Speaker 1: pivots part way through the show to exactly the same
Speaker 1: question what could possibly go wrong? And it then unfolds
Speaker 1: layer after layer of how things can go off the rails,
Speaker 1: and in some cases are going off the rails. I
Speaker 1: try to be just completely clear eyed. There aren't quick fixes,
Speaker 1: but we do need fixes, and so that balance is
Speaker 1: something You're right. I could not do what I do
Speaker 1: without being deeply optimistic, and I couldn't do it responsibly
Speaker 1: without being deeply realistic. Eric. Thank you for the extraordinary
Speaker 1: work that you have been doing, that you're still doing,
Speaker 1: that you're going to continue to do. And thanks for
Speaker 1: coming in talking about it with me on deep background.
Speaker 1: Thank you a real pleasure. Noah, take care. Eric Lander
Speaker 1: is not only a tremendously influential figure in science, He's
Speaker 1: also a very, very talented science explainer, and I was
Speaker 1: thrilled that we had that on display here in the
Speaker 1: podcast as he talked about the transformation in biology, the
Speaker 1: emergence of what some have sometimes called hypothesis free science,
Speaker 1: which is really about forming hypotheses in new ways. For
Speaker 1: scientists and for me, listening to him describe these developments,
Speaker 1: it is really extraordinary to hear just how basic they've become,
Speaker 1: to how biologists can respond to real time crises like
Speaker 1: the crisis caused by Czars Cove two. At the same time,
Speaker 1: all transformations in scientific institutions drive changes in how power
Speaker 1: is deployed and how power functions, And although Eric preferred
Speaker 1: to emphasize the ways that power is spread out across
Speaker 1: the biological community, it's also true that big data science
Speaker 1: inevitably has some concentrating effects on those people, places, and
Speaker 1: institutions where the best and most cutting edge techniques can
Speaker 1: be found and consolidated, and Eric has been at the
Speaker 1: center of that development as well. Finally, I was very
Speaker 1: struck by the carefulness with which Eric addresses the problem
Speaker 1: of whether science is on the whole today bringing us
Speaker 1: in a better direction, or has the capacity to throw
Speaker 1: us into dystopia. I think it's highly significant that for
Speaker 1: his podcast Brave New Planet, Eric is exploring specifically technologies
Speaker 1: and scientific developments that have the capacity to go very,
Speaker 1: very wrong. He's a realist about how to try to
Speaker 1: fix those developments because they can't be put back in
Speaker 1: the bottle. I tend to agree with that instinct towards realism.
Speaker 1: Yet he knows that we cannot do the right thing
Speaker 1: unless we think hard about what the right thing to do,
Speaker 1: in fact is. In this moment, we're feeling or about
Speaker 1: to feel, tremendous gratitude to science for what it's accomplished
Speaker 1: with respect to a vaccine for Stars Covey two. Maybe
Speaker 1: we're not quite there yet, but it's entirely possible that
Speaker 1: we will be very grateful to science relatively soon. That
Speaker 1: gratitude will also call for common sense analysis of what
Speaker 1: the risks are and the downsides are of trusting the
Speaker 1: scientific community too much in terms of its response to
Speaker 1: a range of the most difficult social, political, and health
Speaker 1: problems facing us today. Until the next time I speak, you,
Speaker 1: be careful, be safe, and be well. Deep background is
Speaker 1: brought to you by Pushkin Industries, our producer is Lydia Gencott,
Speaker 1: our engineer is Martin Gonzalez, and our showrunner is Sophie
Speaker 1: Crane mckibbon. Theme music by Luis Gera at Pushkin. Thanks
Speaker 1: to Mia Lobell, Julia Barton, Heather Faine, Carlie mcgliori, Mackie Taylor,
Speaker 1: Eric Sandler, and Jacob Weisberg. You can find me on
Speaker 1: Twitter at Noah Arfeld. I also write a column for
Speaker 1: Bloomberg Opinion, which you can find at bloomberg dot com Slashfelder.
Speaker 1: To discover Bloomberg's original slate of podcasts, go to Bloomberg
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