should we start at the beginning okay as a child were you good at mathematics like was was mathematics a natural thing for you which yeah it was very natural and i always liked it i like counting i like continually multiplying things by two although by the time i got to 1024 or whatever it is i was had enough of it uh but uh i like i like math i discovered as a very young kid maybe four something called zeno's paradox you ever hear of xenos paradox my father told me that the car could run out
of gas and i was disturbed by that notion that never occurred to me but then i thought well it shouldn't run out you could always use half of what it has and then you could use half of that and then half of that and it could go on forever and so it would never run out so now it didn't occur to me yes but it wouldn't get very far either but but the idea that in principle you didn't have to run out of gas was uh kind of a profound thought for a for a very
little boy were you a talented student were you always getting really good grades well i was a pretty talented student i knew i was very smart somehow on the other hand i was very careless so i sometimes screw up arithmetic tests just because i did it too fast or sloppily but i liked everything about it i liked everything about math i loved learning the formulas for the volume of a sphere four thirds pi r cubed i always thought that was a great uh a great formula when i got to high school we started with you
know plane geometry proofs and theorems that's where i really got gripped that i really loved that i loved working on some of the problems were harder than others and i liked i liked doing that i was never the fastest guy in the world but i would plod through it and uh with uh determination i just i just liked it did it feel like this was where your career was going to take you or were you like another boy who dreamed of being a a baseball or something no no no no the only thing i thought
about was i would be a mathematician whatever exactly that meant i didn't know quite what it meant except that mathematics was the only subject i really liked science was okay but it wasn't very well taught where i went to high school at least i don't think so and history english i love to read but i wasn't a good writer i read a lot when i went to mit i majored in mathematics of course and skipped the first year because i had some of those courses in high school and i even took a graduate course in
my freshman year of a second semester uh in math because it said no uh prerequisites required oh no prerequisites i'll take that course had a very hard time getting my arms around that course i finished it okay but i i was very puzzled by some of the concepts however that summer i got another book i've got a book on this this is algebra but not algebra like solving equations it was abstract algebra and then in a week everything came together for me i understood i was no longer puzzled i understood why they did everything other
parts of math got me stymied for a while but typically it would take some time for me to sort of grasp what it was what they were trying to do and then uh then it would go very smoothly so i graduated early from mit in three years did you imagine you would be a professor sitting in a university or like what you didn't had no grasp before i didn't yeah by by the time that my freshman year was over i i kind of realized that's that's what it would mean that someone would pay you for
thinking about mathematics and creating mathematics what i thought earlier was i would would do something that involved mathematics but it was mathematics that i wanted to major in and and i could see quickly that well that's the way if you're going to do that you're going to be a professor somewhere and uh profess you also talked about this week of algebra where everything sort of fell into place yeah is that a common thing is there is there like is this a a thing that always happens there's this moment where all the all the cogs move
into place and it's like oh i get it now uh i think yeah there are moments like that sometimes you're introduced to a new concept and it's you wonder what's why wh why are they talking about this why is this concept interesting and then you're thinking you see some examples and they say oh yeah okay this is this is really this is really good a lot in mathematics is making definitions you'll see things that really could come together under one aegis and and you can define this set of notions and sometimes just uh a structure
which which follows all these notions and that's a great way that mathematics has advanced because when you put a lot of things together that have similar characteristics then you can try to prove theorems about the the general set of things rather than some particular example and that turns out to be a very powerful approach so uh making a good definition is a is a good thing they wanted me to go to berkeley and get away from mit meet some new faculty because i was quite close to the mit faculty and they thought i think they
didn't want to get rid of me so much as they thought i was probably pretty good so i should get exposed to a certain guy named chern who was just coming to uh berkeley that year and i got this very nice fellowship and i went there i was very eager to work with churn me turned except he wasn't there he celebrated his first year at berkeley he had just come to berkeley and he celebrated that first year by taking a sabbatical so he wasn't there so i work with another guy which which was fine and
by the time churn came in the second year i was already pretty far along with the with the thesis project i was giving a seminar at the beginning of my second year at berkeley and in walk this tall chinese guy and i said to kai next me who's that i said that's churn that's churn i didn't know he was chinese i thought churn was probably short for chernowski or something or he was probably some polish guy who who had shortened his name to churn if it had been chen or chan i would have known it
was chinese but churn with the r i know but anyway uh so i met chern then and we became uh friends because i was much younger than he but we became friends and later collaborators can you give some idea about what your area of specialization is at this point what what are you zeroing in mathematically on well at that point and pretty much thereafter the field that i worked in was called differential geometry differential geometry is geometry but it's the geometry of curved spaces it's not flat things in the plane it's it's any any shape
or or form but typically with a with some distance function so that you knew how far apart things were and the basic object to study is what's called a manifold and a manifold is a lot of pieces of space glued together so for example the surface of a sphere is a manifold now you can't just take one little sheet of paper and make a sphere out of that but you could glue a bunch of pieces of paper together and make a sphere so a manifold is something that locally in your neighborhood looks like it's just
regular space maybe it's curved a little bit but globally it's uh it could be quite different so and the surface of a tire of a doughnut for example is fundamentally different from the surface of a spear you can't deform one into the other what really got me onto it is a certain theorem which is called stokes theorem now stokes theorem is the ultimate i think generalization of what was originally called the fundamental theorem of calculus now i don't know if you studied calculus but the basic theorem is if you integrate the derivative of a function
so you take a function you take its derivative first derivative and then you integrate that let's say from a to b well that gets you back the original function the answer when you integrate the derivative is of f it's well it's f of b minus f of a that's the answer if you integrate the derivative of a function you'll get back the value of the function well that was the fundamental theorem of calculus presumably newton knew that now that had been generalized into higher dimensions so there were other things you could integrate and differentiate and
go from boundaries to interiors and so on and the general statement of that theorem was to me almost breathtaking it was so beautiful that theorem and that's a a bit one of the basic theorems in differential geometry and it's about integrating something over the boundary uh and that's the same as integrating something else over the interior something related to it it's derivative in effect it's called it's differential so there's a whole calculus of things called differential forms and integration and all that and that takes place in manifolds in general and anything that looks like pieces
of space so i love that theorem and uh that's what got me into differential geometry so was it was it a beauty or an elegance you saw or was it a trophy and a prize you saw that could be chased no it was just a beauty it was just the beauty of it i just liked it i i learned a fair amount of algebra and that was fine but it didn't grab me but the geometry did so that's what i uh specialized in and and when i got to berkeley uh i was fooling around and
started i'd made some observations and i mentioned them to my thesis advisor the guy i was i was going to work with and he said oh that's interesting that makes me think about such and such a problem that was there was a problem floating around that uh people had been trying and failing to solve but i i thought oh that's very interesting so i started working on that and he advised me not to because so-and-so had tried and someone else had tried it and these those were big shots and i was just a little shot
but uh i didn't pay much attention to him and i i solved it and uh just worked through it and worked through it and you know had a little help along the way with for people but uh but basically i drove that i had done some other mathematics after my thesis work on something called minimal varieties and it was kind of a fundamental paper it took me six years to write that paper people were i think thought how could i be take six years but anyway it did and and that turned out to be a
very good paper that was where i was coming from at roughly that time frame or a year or two afterwards i went when i was 30 to stony brook to be chair of their math department stony brook university was a very new university they had a rather poor math department in fact it was very bad but an excellent physics department they offered me this job as being chairman and i thought that would be a lot of fun in that time frame i had decided to try to learn something about an area of topology which is
related to geometry it's the kind of handmaidens of each other called characteristic classes whatever that might be and i thought i'm going to just learn these classes because i didn't really know it and it was important and i wanted to learn about it so i kind of started from the beginning and worked my way up and i was trying to solve a problem as as i later discovered had intrigued many people and it's never been solved satisfactorily so since i say that you know i didn't solve it either but in the course of trying to
do that certain terms came up certain functions of a sort that began to look just very interesting to me and uh i saw well so they were a pesky term that i couldn't get rid of i needed to get rid of that term in order to make this formula that i wanted but the term seemed to have a life of its own and one thing led to another i defined a certain invariant of a three-dimensional manifold and use that to prove some what's called immersion theorems whatever those are this was a very handy thing and
i showed it to churn said look at these results in three dimensions and he looked at it and said oh well we could do this in all the dimensions because it was an area that he really knew about not the results that i'd gotten but the whole the general area so we worked together and we came up with uh with uh well uh this these results this whole structure in fact there's that's uh the slides of the presentation that churn made in uh at the international congress in the early 70s it was very nice geometry
i pushed on with it and we defined some things called differential characters which was another chapter uh with working with a guy named chiger but the chern simons invariants about 10 years later the physicists got a hold of it and it seemed to be very good for what ailed them or whatever what might have hailed them and so and it wasn't just string theory as i subsequently developed it was kind of all areas of physics including condensed matter theory even some astronomers seem to want to look at those terms that's really what's great about basic
science in this case mathematics i mean i didn't know any physics it didn't occur to me that this material that chern and i had developed would find use somewhere else altogether this happens in basic science all the time that one guy's discovery leads to someone else's invention and leads to another guy's machine or whatever it is uh you know so basic science is at the you know it's the seed corn of our of our knowledge of the world what did it feel like at the time you were coming up with it did it just feel
did it feel obscure and just like a little diversion and then ten years later it felt special but it never felt like a little diversion i really liked the uh i really liked the results i i loved the subject but i liked it for itself i didn't i wasn't thinking of applications you know i mentioned to uh cn yang who was at stony brook at the time who was a nobel prize winning physicist of great renown i said i've got these things maybe they'd be useful in physics because i knew the physicists were looking in
the same places using certain mathematical structures but he didn't bite and i didn't know how it would be useful so i just left it alone but i was very pleased with the math and then it led to another set of definitions and some math that i did with this guy jeff chiger which started as sort of a mini field although i didn't realize it at the time called it's now called differential cohomology and so that there's a fair amount of people working in that area what does that make you feel does it become like vindication
or what i did was more useful than i thought or like what do you think when it gets used 10 years later or by then are you divorced from it and you're like do what you want with it oh it's always made me feel very good of course naturally you like to think that something you did had far-reaching uh ramifications you know it was nice but it wasn't i didn't go to bed dreaming about ah now i've revolutionized physics with first of all i didn't revolutionize physics but but did some stuff there and but sure
it made me feel good actually in the middle of my mathematics career which ended when i was about 37 or 38 was that i spent four years at a place called the institute for defense analyses down in princeton which was a super-secret government based national security agency based a place for code cracking trying to break the enemies whoever it was russia i guess code machines and cypher machines i spent four years there in princeton during that time i was working on this remember i said i took me six years to work through all this stuff
in minimal varieties i did a lot of it while i was there but i also learned about computers and algorithms in the code cracking field i was no good as a programmer terrible but i was pretty good at coming up with algorithms and uh and trying to you know and i found that very exciting to think oh this might help crack this cold here's an algorithm which could work someone else would program it up then it would run on the computer uh and maybe it would work and maybe it wouldn't and i did one thing
there that was quite good because i can't tell you what it was it's all classified so i had a good career there both doing mathematics and learning about the fun of computer modeling let's say was that specialization that you'd had during your phd the manifolds and the topology was that related to the algorithms were the two or three oh completely so why were you drafted in for that in the first place then they paid money i was getting kind of bored simply being an academic and also this work this six-year project was maybe making me
feel gosh other guys are publishing papers and i'm not but i i wasn't thrilled with being an academic and this place hired mathematicians a handful you could do mathematics half your time and the other half of your time you were supposed to work on their stuff there was no teaching so it was like oh i could do as much mathematics as i was doing anyway and and they and they paid better and i thought this would be interesting and a nice change and it was and i i enjoyed it i did a lot of good
math or that i thought was good and it did good work for them but that ended after four years because i got fired and uh well you can't you can't just leave it at that you got fired okay why did you get fired well i got fired because this was in the middle of the vietnam war uh which i didn't like the war not that we were doing anything to support the war in this particular organization but nonetheless i just didn't like it and the head of the organization he was down in washington he was
a big shot named maxwell taylor general maxwell taylor he wrote an article in the new york times magazine section of a sunday magazine a cover story how we're really winning the war in vietnam we just have to stay the course and it's all going to be great and i thought that's a lot of baloney so i wrote a letter to the time saying not everyone who works for general taylor supports his views and uh in my opinion blah blah blah it was a good letter and they published it right away of course because it was
unusual that someone would write such a letter in his situation i didn't hear a peep no one said anything uh at the company they didn't say you shouldn't have done that but i was clearly on the watch list did you did you when you wrote it did you know you were doing something reckless no i didn't really think it was reckless because it was my opinion and it really didn't have anything to do with my work but then about three or four months later a guy from newsweek magazine came to see me and he said
he's doing an article about people who work for the defense department who are opposed to the war and he says i have a lot of trouble finding such people but could i interview you well i was 29 years old no one had ever asked to interview me before it sounded like hey maybe i'll i'll be interviewed okay you can interview me so we asked this and that but he really wanted to know so what is your policy so i sort of made up a policy although it was pretty much true i said well you know
here at ida you have to spend uh at least 50 percent of your time on their stuff in those days it was secret that it was even codes and ciphers so i just said their stuff uh but you could spend 50 of your time in mathematics and so my algorithm now is until the vietnam war is over i will spend all my time on mathematics and then after it's over i'll spend all my time on their stuff until the two things match up again and then uh and that was kind of largely true i was
doing mostly mathematics but i i was doing a little of their stuff so then it occurred to me to tell my local boss that i gave this interview and he said well what'd you tell him i said well i told him what i just said to you he said you did he says oh i better call taylor his boss and he called taylor and i came back into his office and he said you're fired he said i'm fired yes you're fired taylor fired you taylor told me to fire you so well i was fired i
said you know i don't know how you can fire me my title is permanent member which was i started as a temporary member and then i became a permanent member and the boss was very funny he said well here's the difference between a temporary member and a permanent member a temporary member has a has a contract a permanent member doesn't i had no contract so i was out of there and it was amusing i mean i i wasn't i had solved this big math problem that i told you about the six year thing i knew
i was going to get a job very easily so i wasn't really worried about that you've honed your skills with algorithms yeah and you've learned a lot about computers so i'm seeing the writing on the wall here for where things go next how how do we progress to yeah so uh at a certain point now i wasn't then i went to stony brook after this it was chair and uh did this work with churn and did some work with chiger and and we got stuck we were trying to prove something uh mathematical thing and it
was very very frustrating worked on it for about two years we got nowhere i mean it's okay to work for a long time if you feel you're getting somewhere but we have we got absolutely nowhere uh on this problem and uh my father had made a little bit of money and um i have had the opportunity to try investing it and that was interesting and i thought you know i'm going to try another career altogether and so i went into the money management business so to speak so you started with some of your dad's money
and that got you a taste of an interest in it some family money and then some other people put up some money and uh i did that uh no models no models for the first two years so what were you doing then you were just using cunning and uh you know just no like normal people do like normal people do and i brought in a couple of people to work with me and we were extremely successful i think it was just plain good luck but nonetheless we were very successful but i could see but this
was a very gut-wrenching business you know you come in one morning you think you're a genius the markets are for you we were trading currencies and commodities and financial instruments and so on not stocks but those kinds of things and the next morning you come and you feel like a jerk the market's against you it was very gut-wrenching and in looking at the patterns of prices i could see that there was something we could study here that there may be some ways to predict prices mathematically or statistically and i started working on that and then
brought in some other people and gradually built models and the models got better and better and finally the models replaced the fundamental stuff so it took a while i would have thought with your background and mathematician this would have almost occurred to you immediately like you would have straight away saying this what what was the two-year delay well two things i saw it pretty early but and i brought in a guy who was a wonderful guy also from the code cracking place and he was uh i thought together we'll we'll start building models that was
fairly early but it wasn't right away but he got more interested in the fundamental stuff and he says the models aren't going to be very strong and so on and so forth so we didn't get very far but i knew there were models to be made then i brought in another mathematician and a couple more and a better computer guy and then we started making models which really worked but you know the the general uh there's something called the efficient market theory which says that there's nothing in the data let's say price data which will
indicate anything about the future because the price is sort of always right the price is always right in some sense but that's just not true so there are anomalies in the data even in the price history data for one thing uh commodities especially used to trend uh not dramatically trend but trend so if you could get the trend right you'd bet on the trend and you'd make money more often than you wouldn't whether it was going down or going up that was an anomaly in the in the data but gradually we found more and more
and more and more anomalies none of them is so overwhelming that you're going to clean up on a particular anomaly because if they were other people would have seen them so they have to be subtle things and you put together a collection of these subtle anomalies and you begin to get something that will predict pretty well how elaborate are these things because in my head i'm imagining you know some equation like uh like pythagoras equation you put a few numbers in and something spits out but are these giant beasts of equations and algorithms or are
they are they simple things uh well the the system as it is today is is extraordinarily elaborate but it's not a whole lot of quite you know it's it's what's called machine learning so you find things that are predictive you might guess oh such and such should be predictive might be predictive and you test it out in the computer and maybe it isn't maybe it isn't you test it out on long term historical data and uh price data and other things and then you add to the system this if it works and if it doesn't
you you throw it out so there aren't elaborate equations at least not for the prediction part but the prediction part is the only it's not the only part you have to know what your costs are when you trade you're going to move the market when you trade now the average person will make a buy 200 shares of something and he's not going to move the market at all because it's too small but if you want to buy 200 000 shares you're going to push the price how much you're going to push the price how are
you going to you know are you going to push it so far that you you can't make any money because you've distorted things so much so you have to understand costs and that's something that that's important and then you have to understand how to minimize the volatility of the whole of the whole assembly of positions that you have and be uh so you have to do that that last part uh takes some fairly sophisticated applied mathematics not uh earth-shattering but but fairly sophisticated what discipline of mathematics or disciplines is it multi-disciplinary or are we talking
mostly statistics it's mostly statistics and uh some uh some probability theory and but i can't get into you know what things we do use and what things we don't use we reach for different things that might come that might be effective so we're very universal we don't have any any uh but it's a big computer model for one thing there's a there is a capacity to the major model it can manage a certain amount of money which is rather large but it can it can't manage an enormous amount of money because you're pushing you're going
to end up pushing the market around too much so it was kind of a sweet spot as to how much it's reasonable to manage therefore it would never grow into some behemoth which would uh you know take everybody out and you'd be the only player i mean well of course you were the only player that'd be known to play against there are there are limitations at least the way the way we the way we see it but we keep we keep improving it we have about a hundred phds working for the firm that's what i
mean i mean how did you get to that point did you start to think we need this we need that what did we just hired smart people my my algorithm has always been you get smart people together uh you give them a lot of freedom create an atmosphere where everyone talks to everyone else they're not hiding in a corner with their own little thing they talk to everybody else and you provide the best uh infrastructure the best computers and so on that people can work with and make everyone partners so that was the model that
we used in in in renaissance so we would bring in smart folks and uh they didn't know anything about finance uh but they learned what was your employment criteria then if they knew nothing about finance what were you looking for in that someone with a phd in physics and who'd had uh five years out and had written a few good papers and was obviously a smart guy or in astronomy or in mathematics or in statistics someone who had done science and done it well and was interested in you know applying his mind or her mind
although it was mostly his to uh you know modeling markets and making money but it's a very good spirit now i've been six years away from the company so i i'm not running it anymore but i'm the chair of the board and i go to a monthly board meeting i think the the morale is very good the spirit is good and it's really a very good way to work scientifically a it's a big collaborative effort and uh and everyone is happy to see someone else come up with something good because he the first person is
going to share in that because because everyone's shared in the profits so uh so okay you might wish it was you to show how smart you were but nonetheless good he did it i'm going to make money from it i would imagine lots of people want to be financially successful most most people want that of course i suppose and lots of people are good at mathematics and know a lot about computers like you know at your level i would imagine why did you do it why didn't someone else do it i don't know well first
of all some other people have done it uh i think that we're uh our firm is better but nonetheless uh i'm pretty sure of that but nonetheless other people have done some very good modeling and so we're not alone but it's not easy to do and there's a big barrier to entry for example huge data sets that we've collected over the years programs that we've written to make it really easy to test hypotheses and so on the infrastructure is exceptionally good so everything is tuned right it took years to learn how to do that people
don't leave our firm or if they do they believe to just do something else altogether everyone has signed a forever non-disclosure agreement and so on because we're you know we're very secretive about what we do because you can't patent that stuff or copyright it because then everyone would see what it is and someone will just work around it and say oh that patent's no good look i've made this twist and that tweak and it's different so you can't you what you have is your intellectual property and you have to keep that to yourself so uh
yes we were very successful continue to be as i say there are others but most very few uh investment operations are a hundred percent because this is a hundred percent model driven it's not ninety percent or eighty percent some people have models and for uh you know for advice so what does the model say oh yeah let's go oh i don't forget that i i don't want to pay attention to that so uh but you know uh renaissance is 100 model driven no trade is ever made because someone walks into the trading room and says
hey let's buy ibm it's a sure winner or anything like that uh you know we got too much uh google we got to show it nobody does that it may be that we had too much google but nonetheless uh he might have been right but uh it's just what the model says and that religious sticking to the model is the only way you can run such a business because you cannot simulate that guy who walked in and said hey let's google's too high let's sell it how can you simulate that you don't know what might
have happened but you can simulate a you can come up with a model or a new predictor and you can simulate it in the past and see how did it do so you have to stick to it i know you guys made the model so you do have the ownership of it and feel proud of it but is it hard to follow the model religiously like is it hard for your ego to think all the successes because of the computer like and i just i just sat there and watched no the computer's just a tool
that we used to i mean it's uh a good cabinet maker doesn't say it's all because of my wonderful chisel you know you may have great film equipment but that's not why you're a success at doing what you're doing you you're working with good equipment but uh another guy would make a mess of it with the same equipment so no we don't we don't feel oh the computer is doing everything the computer does what you tell it to do mathematics is very collaborative and things that are learned are built upon by sharing are you learning
things behind the locked doors at renaissance that could help mathematics but you're not sharing because you want to keep them a secret to help the business no there was there's nothing there's nothing that we're learning behind locked out doors and renaissance that would help the general field of mathematics or the general field of science as far as i can tell do you think there's something about your personality that made you able to do it or is it just this is luck well i think a lot of it's luck i probably have a good personality for
running a group of people but there are other people who are maybe even have a better personality for doing that we underestimate uh the role of luck it's typical that if someone fails at something he'll say i had bad luck and if he makes a success he'll say i was a smart guy and uh oh i was just lucky uh people don't usually say that uh when when uh if they make a big success and of course obviously not everyone can make a big success but i think the luck played a role i was in
the right place at the right time but i i really i think what i'm good at is getting good people to work together so it was more you would think it would more your managerial ability than your mathematical genius that resulted in it definitely wasn't my mathematical genius i think i'm a you know i'm a pretty smart guy i can understand this stuff so uh but i wasn't and i did come up with a few predictive algorithms so that was fine but uh many people did that's i knew i wasn't going to come up with
all them so that's why we have all these folks uh so it wasn't my mathematical genius but i think people respected me because i was a a good scientist so they said well he may act stupid but we know he's really smart so you know you had you had a few runs on the board i would say yeah are you given that given that you will put some of it down to luck what are you more proud of all of this in the business or the or the mathematics from that first half of your career
oh to the extent that i'm proud i think i'm proud of both i i think you know i've done some mathematics and some of it's had a positive effect and i'm i guess i'm proud of that and i've built a nice business and i'm proud of that i don't say i'm prouder of one than the other and now for the last number of years i've been working with my wife on this foundation which she started actually in 94 with my money but nonetheless she started the foundation and then i joined i got more and more
involved with the foundation as time went on and now that's my main thing and i'm pretty proud of the foundation let me focus your focus you more on which the mathematics versus the business then would you would you trade any of your any or all of your business success for for being the man who cracked the riemann hypothesis or something like that no that's a good question would i trade that for well i probably trade some of it i mean for the riemann hypothesis it would certainly uh i guess be a thrill to to solve
three men hypothesis i'm pleased mostly with what with the way my career has gone so would i trade part of it for something i don't know it's i've never looked back and said i wish at least in business i wish i hadn't done that or i wish i had done this and not that whatever it is i've never looked back that way i guess the thing i'm getting at is what do you define yourself as a businessman or a mathematician well i would not be very good at running an ordinary business if i had to
run a big manufacturing business or something like that i don't think i'd be very good at there's a level of detail that i would find tedious uh i'm not the best organized person in the world and frankly i would be bored so i'm not your typical businessman the kind of business that i did run was very natural to me so in that sense i was a businessman i i like business sort of i you know i i like i i always say what's the important mathematics skill in business is subtraction you have to understand when
your revenues are going to exceed your costs you know some some people they just look at revenues hey we're growing yeah but you're losing money every day well that'll take care of itself after a while so um but i i don't think i'd be uh i once had to for about six months run a small business in the computer area a business that we had started uh or invested in and then uh wasn't going well and we had to get rid of the head and find someone new and i found myself running it for about
three to six months commuting to philadelphia and i came to the conclusion i'm doing a better job than the guy was there before but i'm not doing a very good job and i found myself in business meetings like with the distributor from st louis at some and i remember saying i think what am i doing spending my time with some distributor from st louis i mean this is not the way i want to run my life finally we got someone in he really knew how to run a business and uh then that that was his
that kind of a business the foundation is focused on support of scientific research primarily basic science but not completely because we have a large autism project which involves a lot of basic science but the but treatments are a goal but the rest is support of mathematics physics computer science biology of all sorts neuroscience genetics we support basic science and that is what we like to do there's a certain amount of outreach we we have a math for america pro we spend maybe 10 or 15 percent on uh outreach outreach and education but uh 80 85
is uh support of basically basic science almost the nature of basic mathematical research is you can't really know where it's going to go that's right so what are you do you just are you are you throwing money at a wall and seeing what sticks or like how targeted are you about well to some extent yes uh we have investigator grants which give outstanding mathematicians and physicists and computer scientists uh 10-year run where they they have support for for 10 years and they can use that money to for a post doc or something like that to
help their work along we don't know what work it's going to be we we have no i well we know what the guy has done but it's it's it's not these are very very competitive and they're just the best people and it's helping their careers whatever they might do we also have collaborative projects that we support there there is a goal um but it can be a rather vague goal like one is origins of life well we'd like to know how we all got here what how did life originate outside inside what what was the
path to life uh so there's i don't know 40 or 50 people working on that and uh it's a very exciting project we're making some progress so there's a goal but do i think that in the next five years we're going to know everything about the org no i don't think so but we'll learn more about this path that started with you know primitive organic molecules and ended up with uh an interview like this one so there there is a goal but it's a but it's a relatively big goal this model of philanthropy of of
supporting the basic research and not knowing where it's going to end up almost seems the opposite of what you were talking about with your business totally you were talking about with business you know revenues and costs and making sure yeah well yes and no when we would bring smart guys into renaissance we didn't know what they were going to do we hoped that they'd come up with some good ideas to make the firm better but i didn't say oh this guy is going to discover i want this guy to discover this thing i don't know
what he's going to discover uh so but it's it's of course very focused on a narrow set of outcomes whereas uh the other basic science anything goes is uh is very different from that what's your attitude to risk because you you almost have created a business that's based on removing risk reducing it reducing risk uh and yet you took a risk by leaving tenure to go and start business i mean are you are you a risk taker or are you a risk minimizer or were you you don't you probably don't need to worry about well
i suppose as we get older we get a little less a little more risk averse i don't know uh i'd always been something of a risk taker i think uh but but i've had uh good taste and uh especially again as i say in people and uh have pretty much always partnered up with people so uh that that cuts the risk so uh but i did i i certainly when i started the company i had to finance it myself and uh i didn't know whether it would succeed or not and but i was pretty sure
it would but i didn't know you put a lot of money into mathematics so you're you've got some right to comment on it how are you feeling about it oh i think mathematics is is really uh going quite well uh worldwide uh the research then a bit uh a lot of new ideas are coming up new fields or sort of are are flourishing it feels like a pretty healthy enterprise to me what's not healthy is the state of mathematics education in our country that's very unhealthy for young people that's why we have we started this
thing called math for america and so on but we don't have enough teachers of mathematics who know it who know the subject and even of science and and that's for a simple reason uh 30 40 years ago if you knew some mathematics enough to teach let's say in high school there weren't a million other things you could do with that knowledge oh yeah maybe you could become a professor but let's suppose you're not quite at that level but you you're good at math and so on being a math teacher was a nice job but today
if you know that much mathematics you could get a job at google you could get a job at ibm you could get a job in goldman sachs i mean there's plenty of opportunities that are going to pay more than being a high school teacher right there weren't so many when i was going to high school such things so the quality of high school teachers in math has declined uh simply because if you know enough to teach in high school you know enough to work for google and well they're not going to pay that much in
high school so how do you redress that how do we redress that as a country well you have so we work a person works for a combination of financial reward and respect right so a guy becomes supreme court justice he's not doing it because he's going to make a fortune uh he'll be well paid i suppose but you know supreme court justice everyone says it's a big deal you have a lot of respect and you respect yourself presumably uh so there are many so you can't pay let's say high school teachers of math as much
as they would get at google but you can give them a bump pay them more we give people fifteen thousand dollars a year more than they would make their regular salary but we also create a community of math and science teachers uh which they love and it makes them feel important and they are important they interact with each other they don't they're not forlorn you know stuck in some high school no one to talk to so we've created a community with a great esprit de corps and these people don't walk out of the field but
turnover is very very low whereas ordinarily the turnover is quite high so in finland for example which has very good mathematics uh teaching those are real professionals and they they have a lot of status societal status a teacher in finland here in the united states teachers don't have such good societal status and and of course the the whole thing of uh getting rid of these bad teachers or get them out so on and so forth okay maybe we have some bad teachers but there's no nothing said about hey let's reward and and and recognize the
really good ones right so if you're running a business yeah you want to get rid of the dead wood if there are people who just aren't doing the job but most more important is to recognize the people who are doing a good job and reward them and extol them and make them feel good in one way or another we don't do that at all with teachers we're just bashing them and bashing them in passion why has that happened in the united states but but not in finland well and it has hasn't happened in most of
the european countries why well for one thing we have teachers unions but that shouldn't be the reason because finland probably has unions too i don't know uh there's been a shift and it's uh about maybe started about 10 years ago that we have to measure these teachers by outcomes all right fair enough yeah well what outcome oh well we're going to give their students standardized tests if they have value added and so on and well then then they're good and if they don't have value well it turns out judging people on standard student standardized tests
is a disaster it's a very very weak statistic it's not very highly correlated with how they're going to do the next year by the same measure and uh and the the but somehow the theory is we have to measure these people if they're not doing a good job we have to get rid of them somehow or other we have to get these people out of the schools well there's a old saying in the british navy you know floggings will continue until morale improves and that's kind of what this this is we're just beating these people
up so uh how did it start i don't know someone got the idea that we can measure output but we can't or at least we this is we've imposed a system which stinks for measuring output and uh if we the longer we stick with this system of rating teachers on performance of their students on standardized tests though the worse it's going to get and the solution is you put it sounds to me like just respect them a bit more and make them feel good about themselves yeah and and recognize and reward the very best ones
and that encourages people to say hey i could get maybe i could rise up and and and and get one of those become a master teacher or whatever and and uh or someone could even come into the field and say hey that my cousin is a master teacher there new york he's having a good life so yes uh but it's gonna take a we're doing it in new york pretty successfully but it needs to be all around the country and it needs to be bigger than it is in new york too you seem to be
a believer in markets markets in that though is this not something that would correct itself like over time like if the if the teaching drops and then the google can't get the mathematicians and they have to improve the team will this not take care of itself does it need no artists here's how we're taking care of it now we're importing people h1 visas and we're bringing in guys from other countries who do have this knowledge and all the technology companies only want more and more h-1 visas because they can't get enough home-grown talent the talent
that they need so now that's okay for a while but about 12 or 13 years ago for example of the graduates from the indian institute of technology i whatever they call it indian iit about 80 percent of them left india and would come to europe and united states to to get jobs now it's 20 because india is doing much better there's plenty of technical jobs in india so hey i can stay home i don't have to run to england or i don't have to run to united states uh to work so well there are people
from other countries who are coming and we're taking advantage of that but the day will come when we just don't won't have enough people here uh of our own folks who uh who know enough to to do this work basic research is as as i have said the sort of the wellspring of uh human knowledge knowledge about our world and but the federal funding for basic research has become restricted for one thing federal funding for science has come down anyway overall and second of all there's increasing tendency for these agencies to fund what's called translational
research rather than basic research translational means okay you're working on cancer great you know we're going to have a cure for this in three years you're okay fine we'll give you money you're working on how the basic cell is working uh well you know that applications are too far away so there's less the congress pushes the nih and the nsf too probably to hey you know we want to see some return on this money we want to see results so they're more conservative wild ideas less often get funded because you know so the government's not
doing such a good job at supporting basic science and so there's a role for philanthropy an increasingly important role of philanthropy do you give your money to basic research because you feel somehow indebted to it for your own success or do you do it just out of like a belief or do you do you feel like you're giving something back to us to what gave something to you that's an interesting question i do it because it feels good i like science i like to see it flourish i like to be around scientists i like to
learn some new things my wife feels the same way she loves science so we're very happy to to do this do i feel i'm giving back not especially you know i could give back in a lot of ways there's a lot of things i could do besides support science do you have a favorite number seven next question do you have a favorite mathematician well archimedes and euler are my current favorites but uh maybe you met somebody i'm very impressed with those two guys thank you so much for so much of your time all right well
this was kind of fun you
