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Introduction
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the things that are brittle right now where you're like oh my God like this is a little too expensive or it's a little
0:05
too fragile for me to use like all of that gets better like it'll get cheaper and like you know things will become
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less fragile and then like more complicated things will become possible like that that is the story of each
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generation in these models as we've scaled up [Music]
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on any given day Microsoft may be the most valuable company in the world and arguably no one has been more ambitious
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more strategic or more effective in its AI strategy than Microsoft the key architect behind that
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strategy Kevin Scott CTO of Microsoft we've had the pleasure of
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knowing Kevin for a couple of decades now dating back to his time at Google when he overlapped with our partner Bill
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cour Bill will join us today for a very special episode of training data we hope
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you enjoy Kevin thank you for being here on
1:15
training data yeah glad to be here so just to start I I know you've talked
Kevin's backstory
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about this before but for our listeners who might not be familiar with your story how does a kid from rural Virginia
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end up becoming the CTO of Microsoft who
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knows C certainly not a not a uh not a repeatable plan I don't think um I I
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don't know like it it is um the thing when I reflect back on my
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uh yeah my story is it's just a lot of being at the right place at the right
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time so I'm 52 years old so I was uh you know a 10 11 12 years old when the
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personal Computing Revolution started to uh hit Full Steam and so like right at
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that moment when you're a kid trying to figure out what you're about and like what you're going to latch on to like I
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had this like really convenient thing that captured my interest and was a good
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place for me to ground my curiosity on and you know and I I think that's you
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know one of the object lessons in general is if you happen to be
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interested in um like really motivated to learn
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more and do more with something that is at the same time growing really really quickly uh like you probably are going
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to end up in a reasonable place and so you know I I was interested in computers
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I um yeah I was the first kid in my um or the first person in my family neither
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my mom nor my my dad went to University so I was the first one to graduate with a bachelor's degree um like I majored in
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computer science and minored in English Literature Like I had this moment uh when I was uh um at this moment when I
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was trying to decide what I was going to go do after I got my undergraduate degree where my two advisors were
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arguing about uh whether it should be PhD in computer science or PhD in literature and I was very seriously
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considering both uh but I was so broke uh and just so tired of being uh busted
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all the time that I picked the pragmatic path uh you know not not that you know
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like I I still imagine what my life would have been like uh as you know person with a
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PhD in English literature and I think it would have been just fine but uh like I I chose you one of my two equal you know
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interest uh and then you know for a while I thought I was going to be a computer science Professor um and at the
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last minute this is where Bill and I uh intersected like I decided uh I was a compiler optimization and programming
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languages person uh through years and years in grad school and I got almost
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all the way to the end I was like I don't think I want to be a professor anymore like I I would would work on
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these things where it was you know 6 months of effort to write a paper and you make some synthetic Benchmark 3%
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better and I was like this doesn't feel to me like the way to have a lot of impact uh in the world and like I don't
4:43
want to do this over and over and over again for the next 25 years of my life
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and so I uh I sent my resume cold into
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Google in 2003 and um
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I got a email from this guy Craig Neville Manning who had just gone off to
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New York to open up Google's first engineer remote engineering office and
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uh like I had an amazing interview at Google uh I I don't know whether this
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was on purpose or not or like I just got luck of the draw but uh like uh it seemed like every compiler person who
5:24
was working at Google uh was on my interview slate and I was like this is amazing like all these people like uh
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know all of the stuff that I know and you know we can have easy conversations I I worked on nothing that was even
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remotely close to compilers uh at Google and I was confused why all of these people were there but uh it was a great
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interview and I was super stoked and I joined Google and Google is yet another one of those things just like the PC and
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just like the internet uh like it was a phenomenon that was growing crazy fast with a bunch of smart people working
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there and that uh that resulted in this opportunity I had to go uh join this
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startup AB mob when it was very early on like right at this pivotal moment when mobile mobile was taking off and you you
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needed things in Mobile like advertising infrastructure and I helped
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build you the the seminal company I think in in Mobile advertising uh and
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then was back at Google and then I helped uh link in go public uh running its engineering operations team and then
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I was at LinkedIn when we got acquired by Microsoft so like yeah none of that I
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think you can plan it's just like a lot of right place at right time and you
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trying at every point you can to do the most interesting thing you can do on the
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thing that's growing really fast you know when you talk about your personal history Kevin I guess
The role of PhDs in AI engineering
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you know the focus nowadays is on AI machine learning a lot of the
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practitioners are um people with phds how how do you think about sort of
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practical teams for AI uh since you're obviously doing a lot of that work at
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Microsoft and and involved with Partnerships with open Ai and others yeah I mean I I think if you are
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building the really complicated platform pieces
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of AI uh so like the big distributed systems for training and inference uh
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the big uh like networking and silicon and uh you know system software
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components or the algorithms that you're using to do training and inference I
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think a PhD is super helpful like there's just a huge amount of prior
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knowledge that you need to have in order to jump into the problem space and be
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able to like go uh quickly and like you know you need to be clever but like you
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don't PhD is I like yep I know you have
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a PhD uh Bill and are far clever than I am but like uh usually folks with phds
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are clever but like they're not the only people in the universe who are clever uh so like I I think it's mostly helpful in
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the that you've gone through like a pretty rigorous training regimen where you get a whole bunch of Prior Arts stuffed into
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your skull and like you you know demonstrably can do a very complicated project uh and you know the PHD
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projects uh you know look kind of like AI
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platform systems projects uh except the AI platform and systems projects are
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lots and lots of people working together whereas you when you're getting your PhD you often are like working in relative
9:03
isolation on like your particular thing so like that's the you know one of the things people have to learn is like how
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to get yourself docked into a group and to be able to collaborate effectively with a bunch of other people like
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yourself uh so useful but you know there's so much else in AI that needs to be done other than building the the
9:22
platform um you know and for those things you PhD is helpful but certainly
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not necessary um you know like figuring out how do I apply this to education how
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do I apply this to health care how do I uh like how do I build developer Tools around this how do I you do all of the
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million things that happen when new platforms emerge that you you know sort of complete the whole platform into like
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a portfolio of products and a portfolio of middleware and a portfolio of like all of the other stuff you
9:54
need well speaking of which Microsoft seems like it has about the most sort of
Microsoft's AI strategy
9:59
far-reaching or ambitious AI strategy of anybody out there can can you just kind
10:04
of say in a couple words what is the AI strategy for Microsoft and then just for fun if you're going to grade yourselves
10:12
what have you done particularly well what have you done maybe not as well as you could yeah so I mean we've been sort
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of talking about the strategy Microsoft as a platform company like we I think have participated or like helped drive a
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a hand full of the big platform waves in Computing uh like we were certainly one
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of the pillar companies in the personal Computing Revolution like we had a important part to play in the internet
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Revolution although I think that one was uh a far more diversely contributed to
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Revolution than uh yeah than personal Computing um yeah we kind of miss the
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mobile uh Computing Revolution um but
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like each one of those things like we have thought about uh how do you go build a technology platform for this
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particular era of technology that allows other people to go build on top of that
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platform to make useful things for other people and so that is our AI strategy it
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is uh like how do you from Frontier models to small language models to uh
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like highly optimized inference infrastructure uh you know like hypers scale on both
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training and inference uh like economies of scale like making the entire platform
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more accessible because it's cheaper and more powerful uh with every turn of the
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crank uh and like all of the developer tools and safety infrastructure and
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testing and everything that has to be there in order to have robustly built AI
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applications like go build that uh and like listen to developers and listen to
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people building uh building AIS uh as intently as you possibly can
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so that you are filling in all of the gaps that you can for them as they are encountering problems deploying this
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technology to to users um so that that is that is our strategy um and so yeah I
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think we're doing a reasonable job of it
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um um I don't know like I hate to grade myself it seems a little bit
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disingenuous right High to L lights well so so maybe before I do that like you
Highlights and lowlights
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know let me describe something about my own psychology so I I uh like I am an engineer and I think most Engineers are
12:52
uh like uh short-term pessimists long-term optimists uh and so the
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short-term p ism is like you come in every day and you're like oh my God this is like a bag of crap like I just don't like any of this and like everything's
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broken and like I gotta like I got I got so much stuff to fix and I'm so
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frustrated uh but you work on all of those things anyway because you're
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optimistic that all of the problems can be fixed and that uh they're going to be worth fixing at the end of the day um
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and so yeah I mean the there there's a bunch of stuff that I think we're doing really well like I
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think we have absolutely along with open AI made uh very powerful AI dramatically
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more accessible than it otherwise would have been to a larger group of people I think you because of that work that
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we've been doing alongside open AI we have uh we're just seeing lots and lots
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of customers who otherwise wouldn't be building powerful AI
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applications um and so like I I feel like we're doing a good job in uh the
13:58
way that we're partnering I think we're doing a good job in uh like having a
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really um particular point of view and it's not an immutable point of view but
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like it's a point of view about like what an AI platform ought to look like and we're trying to uh like make it as
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complete as we can um yeah low lights is I think we were a little bit late uh to
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like some of the basic AI stuff uh so it wasn't that we were
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not um investing in AI at all and like you can sort of look at some of the work that Microsoft research had done over
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the years and like MSR was an early AI leader um and I think you know
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the you B Bill Bill knows this just as well as I did just from his time at uh
14:52
time at Google and you where we overlap for a number of years but um
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many you know maybe most of the really important advancements in AI over the
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past 20 years have been uh a function of some kind of scale uh you know whe and
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it's usually you got data scale and compute scale in combination let you uh
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like do things that weren't possible at lower scale points and yeah at some
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point like that that scaling of data and compute is so exponential that you get
15:31
past the point where you can have fragmented bets uh where you can
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literally it's it just becomes economically impossible to bet on 10 different things uh that are all
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exponentially scaling or have the ambition or the need to exponentially scale simultaneously and so I I think
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one of the things that we were a little bit late to is like we we didn't put all of our eggs into the right basket uh
15:58
soon enough like we just yeah we were spending a lot on AI but it was fragmented across a whole bunch of different things and because we didn't
16:04
want to hurt any of the feelings of smart people or yeah whatever right like I I don't even know what the diagnosis
16:11
was because a lot of that was before I was at Microsoft um we like we we just
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weren't as quick as we should have been at uh like saying nope scale is what matters and like here's how we're going
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to focus uh focus our investments on scale in a principal way when did that
Accelerating investments
16:28
when did did you get religion that scale is what matters was there a particular event or a moment that really crystallized that for you yeah I mean I
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was so I've been at Microsoft for about seven and a half years now and uh
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like my um when I when I became CTO
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my my job was like take a take a scan left to right across uh both Microsoft
16:57
uh and the entire industry and try to see where yeah we we just had holes in
17:05
execution where like we we were not doing things uh at that point in time
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which was I guess 2017 early um
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where all right like what are we not doing today that we're going to deeply regret uh in 2019 or 2020 so like two
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three years out um and like the biggest thing on the list was like you know rate
17:29
of progress on AI was not fast enough so i' I'd say mid 2017 like I had religion
17:35
that that was going to be a big part of my job uh was like helping us figure out
17:40
what the strategy was going to be and then in 2018 uh like if anything the
17:48
um Bert the publication of the Bert uh Bert paper from uh from Google was like
17:55
a real crystallization of uh that that belief so like everything that I had uh
18:02
that was in my analysis I was like this this is as fine an example as anything of like why we have to really really
18:11
accelerate on getting more serious here and so like you know very shortly after
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that like I restructured a whole bunch of stuff inside of Microsoft to get us more focused on AI and then about a year
18:24
later we did that first deal with open AI um and yeah we we have been
18:30
accelerating our investments and like trying to get more focused more crisp uh more purposeful uh since them when you
The OpenAI partnership
18:38
were very early to appreciating the potential of open AI what did you see in them at that time when that first
18:44
partnership was true well we we had like or at least I had uh this like real belief that what
18:54
what was happening with these models as they scaled as they actually became a basis for building a platform that like
19:01
the big shift that was happening is it wasn't just you know I I ran one of these uh teams that uh at Google where
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you had a pool of data and a bunch of machines and an algorithm and you were
19:15
like training a model and like the model was for a specific thing like in you
19:20
know the case of the things I was doing at Google it was like click-through rate prediction for
19:26
advertising and you know like a hand of other things uh and like just outrageously effective right uh
19:35
but most of the work before this uh before
19:40
GPT um was about those sort of narrow
19:46
use cases like you were purpose building models for narrow things uh and it was just tough to scale like you couldn't
19:53
you'd invest a bunch of compute and like you couldn't amortize the cost of the compute across anything more than just
19:59
the narrow thing that you were building the model for and you had to have a lot of expertise uh that you know if you
20:06
wanted to replicate all of this it's like you had to have different data and like different you know Ai phds and you
20:14
know different processes every time you wanted to go build AI into an application
20:20
and what what was happening was like yeah you you had these big large language models uh that were useful for
20:28
lots of different things so you didn't have to you know have a separate model for machine translation and sentiment
20:33
analysis and you you like all of the different text
20:38
things that you were doing and I was like okay this is extraordinary and like they were also becoming more platform
20:44
like as a function of scale uh so transfer learning was working better as
20:49
things scaled up uh and yeah and this is still the general
20:55
pattern so uh like everything everything that we understand that large
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language models can do uh plus or minus like will get better when you get to the
21:06
next scale point and on top of that they will become slightly or maybe
21:12
dramatically more General in the sense that their capability set
21:18
broadens and open AI had that same belief and they also had like a very
21:24
principled analysis of like how the those platform characteristics emerged
21:31
over time as a function of scale uh and a bunch of experimental validation that said that
21:38
their forecasts were right and so like it just you know you you sort of like
21:43
look at what the forecast says and you're like this is how much money it costs to run the experiment to see if
21:50
you're going to be on forecast uh for the next uh turn of the crank and like you know it was felt like a big number
21:56
at the time like it was billion dollars uh but like relative to what was
22:02
happening it it just wasn't a large amount of money and
22:08
then you know gbd3 was on forecast and gbd4 was on for so like it it it just
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was like finding a partner that had the same platform belief that you did and like a track record of you being able to
22:21
execute through through these scale points like it was it it didn't like I I've done a
22:28
bunch of things before that I had way more reservation about uh in the past like just in terms of Investments like
22:34
this one didn't you like there was a bunch of people who didn't agree with me but like I I I had pretty high
22:40
conviction you touch on investment I guess you know there's a lot of um trade
Soon inference will dwarf training
22:47
Publications now speculating about the cost of doing training and so forth and
22:52
the you know rumors of billions and billions of dollars being spent and so forth uh and I guess based on my own
23:00
background I I think training's going to get dwarfed by inference here pretty soon uh how how do you see the should
23:08
hope so well yeah otherwise we're building models that nobody knows what to do with that might not be a great
23:14
investment but Y how how do you see kind of computing landscape evolving and
23:20
where's it going you know the I think people are joking that all the money is going to Nvidia at the
23:26
moment well look I think invid is doing a Nvidia is doing a good job uh so like
23:32
the you know the two interesting things that are happening with these models just in terms of um the efficiency of
23:39
the scale up is each Hardware generation's better price performance- wise
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uh usually by an extent greater than Mo's law used to work for general
23:50
purpose uh Computing so um you a100 was about you know three
23:58
three and a half times better price performance than v00 uh you know h100 like not quite that
24:06
much but you know close uh you know on paper the Next Generation looks uh very
24:12
good as well and so like you've got hardware for a variety of reasons uh
24:19
like you know part of its you process technology but part of its architecture and like a lot of it is like being able
24:25
to leverage uh um uh
24:31
narrower uh word size uh in the computation so like you know instead of needing 64-bit arithmetic like you're
24:38
you know you're doing arithmetic with much less Precision right now um and so
24:44
like that you know there's just an embarrassing amount of parallelism there then like we're getting better and better at extracting that
24:49
architecturally in the hardware and um you know there's a bunch of innovative
24:55
stuff happening with networking as well like we're past the point for the frontier models at least where you can
25:01
do anything interesting on a single GPU so for years and years now like both training and inference have been
25:08
multi-gpu multi compute node um problems uh and so like there's a bunch of
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innovation happening on the network side as well which allows you to strap all of the compute together uh like at the
25:21
chassis level the rack level the row level you data center level more
25:26
effectively um which is great because um you know for the Nerds
25:31
listening like we we haven't had effective um Power scaling uh uh or
25:38
dinard scaling since 2012 or so uh so
25:44
yeah we're not we we're we're getting more transistors but like they are not
25:50
uh they're not getting cooler uh um yeah know and like we we just we have a lot
25:56
of uh a lot of density issues just with power dissipation that we have to go uh
26:02
go deal with um do you do you see inferences driving different data center
26:10
uh architecture yeah I mean look we we already architect our training environments in our inference
26:15
environments differently they just need different things um and like I I I think
26:23
you know all the way down to you know silicon and uh like through the network hierarchy you need different things for
26:30
inference and like inference is inference is kind of easier than training like training the way that
26:35
we're doing it now is like we go build big environments that take you know a
26:40
few years to build um and yeah with inference like if somebody
26:47
came along with a like a better a better silicon architecture or a better Network architecture like a better cooling
26:54
technology like it's a much easier experiment to go run uh you just go swap
27:00
some racks out I mean like I'm like my data center people will yell at me like it's not quite that easy but it is uh it
27:07
is easier than like having to go like do a big Capital project like a like a
27:12
training environment looks like and so like I I you know intuitively you would think that that is going to result in
27:20
more diversity in the inferencing environment and more competition and
27:26
more like a faster rate of improvement um like
27:31
that's and like on the software side that's certainly what we see like the inference stack uh just because it's
27:38
such a large uh fraction of the overall compute footprint and it's constrained
27:43
uh uh because we have more demand than Supply at the moment uh like you just
27:49
have very very powerful incentives to go optimize the software stack to squeeze more performance out of it you think
27:55
we'll be in an environment anytime soon where that demand Supply balance changes
Will the demand/supply balance change?
28:01
not necessarily at Microsoft but it feels like we're seeing that at the market level as well yeah I I don't know um I mean if
28:10
we if we continue to see the platform uh continue to expand
28:19
capability wise uh and it just becomes more useful like I think demand increases if anything now the shape of
28:27
the demand is is probably going to move around like I I think you're already seeing a little bit of that
28:33
um yeah building uh building a Frontier Model is like a very
28:38
very resource intensive thing and as long as people are like building Frontier models and making them
28:44
accessible and like maybe they're not accept accessible quite the way that people want you know like they're only
28:50
API accessible and like there isn't an open source thing you can go instantiate and you know muck around with but like
28:57
it's way more accessible than it was uh you know six or seven years ago where
29:02
like the only way to access some of this stuff is you had to go work for like you know two two or three tech companies uh
29:11
and so anyway like I but I think you know you do have to ask yourself and
29:17
somebody else should do the asking right because I'm like all kinds of biased right uh but like I I don't know how
29:23
many Frontier models uh you actually need um
29:28
um if you know they're all roughly speaking in the same tier of
29:34
capability um yeah that's an awful lot of money to spend for you know things
29:39
that are you know roughly
29:45
equivalent you know it's sort of like you know if if you're starting a company right now and you believe that you have
29:51
to build your very own Frontier Model in order to go uh deliver an application to
29:57
someone that's almost the same thing is saying like I got I got to go build my
30:03
own smartphone hardware and operating system in order to deliver this mobile
30:08
app like maybe you need it but like probably you don't um and
30:15
so I mean that to Bill's point like I think the thing that makes sense you
30:21
know for the market is like you just you you're going to want to see lots of people doing lots of inference because
30:27
that means you got lots of products that have found product Market fit and those things are scaling um but like lot lots of
30:35
speculative dollars flowing into infrastructure uh
30:40
R&D like probably ends the same way that yeah the yeah many speculative
30:47
infrastructure booms have ended unless excuse me on the scaling
Business models for data
30:53
front um you know Microsoft published a paper uh sometime ago pointing out that
30:59
the quality of training data is uh maybe at least as important as as volume and I
31:07
think one of the speculations you see now in the industry is that we're running out of sources of high high
31:15
quality training data and and you're reading at least uh some articles
31:22
claiming that various Partnerships are being struck to to get access to training data and it might be behind pay
31:28
walls and so forth how do you how do you see that evolving because it it feels
31:33
like we have more and more computation but we may not have more and more training
31:40
data yeah I mean I think that was almost inevitable um it is you know
31:47
in my opinion a good thing that uh quality of data matters more than quantity of data because it gives you an
31:54
economic framework to go do the do the Partnerships uh that you need to go do
31:59
to yeah make sure that you're feeding your AI training algorithm of curriculum
32:04
that uh you know is going to result in smarter models um and like honestly like
32:10
not wasting a whole bunch of compute feeding at a bunch of things that are not um and I think one of the you know
32:17
from an infrastructure perspective one of the things people have been very confused about is like a large language
32:23
model is not a database it's not a repository of uh facts um you know it's
32:29
like important for it to you know quote unquote know some factual things uh but
32:35
it is uh like the world's crappiest database uh you know honestly like if
32:40
you need it to be your retrieval engine and so like you just shouldn't think about it as like hey I got this thing
32:47
and like it has to have everything baked into it uh like into the model weights themselves uh so that you can you know
32:54
recall a bunch of stuff uh you know like as you've seen like the
33:00
the recall is imprecise in the same way that human
33:06
recall is imprecise so they just much more efficient ways to do recall
33:12
um um yeah so look I I think you you are I mean the way that we say see
33:19
things developing is like you have data that is valuable for training models and then you have data that you need to have
33:26
access to for an application in order for the model to reason over and like those are two different things and I
33:32
think they're probably two different business models around those things so
33:38
and like at the end of the day this is all just this is about business models right like people who produce
33:46
data want to be compensated for uh you
33:52
know use of that data um and so yeah we we have all of this
34:00
data sitting inside of search engines right now like not not in you know randomized weights but like quite
34:06
explicitly it's like sitting in a you know sitting in indices and you know bang and Google and whatnot just waiting
34:13
to be retrieved and like you know plus or minus everybody's okay with that because there's a
34:20
business model there that makes sense uh so you like you enter a query and like
34:25
you're either sending traffic or you know like there's you SEO and like advertising like a whole bunch of like
34:32
business model that surrounds that I think we'll figure out a business model
34:38
for that um referral data so that like you know when an agent or an AI
34:44
application needs to retrieve some information from someone so that it can you reason over it and give the user an
34:51
answer like we will figure out the business model for that like it'll either be subscription rev share it'll
34:56
be licensing it'll be like some new flavor of advertising you I was just telling
35:01
someone the other day like if I was in my 20s right now like you know for for all of your entrepreneur like somebody
35:08
ought to be out right now figuring out what the new ad unit is for agents and
35:15
like just building the company uh because like it it it will have the same
35:21
characteristics and qualities is uh you previous ad units like you have people
35:27
with information in products and services who are going to want to get to the attention of someone who might want
35:34
those data and products and services and like quality is going to matter and you
35:40
know relevance is going to matter and a bunch of other things and I I would be shocked if there isn't a auction model
35:48
for uh you know that's going to be the right way to Value everything and you
35:54
know like maybe there's referrals and like referrals will have some economic value um I think for training is just a
36:01
little bit different because uh it's very very hard when you're building that
36:06
model uh to at the time that you're doing the building
36:12
to really be able to ascribe um a monetary value value to a
36:20
particular token of input yeah just because it's contribution to the model like the same way that a word from Moby
36:28
Dick has like a very defuse contribution to like your own human intelligence uh
36:34
you know even though you definitely read it at some point in your uh
36:39
career uh or your life um like how
36:45
valuable that is to like forming you know Bill Corin or Kevin Scott's uh you know useful intelligence like who
36:52
knows speaking of which this um one of the things that we hear a lot of times
The value function
36:58
is the the value function is in some ways the bottleneck to broader reasoning capabilities it's easy enough to
37:05
construct a value function when you're playing a game with a you know with a with a known winner and loser like go or
37:12
chess or poker or diplomacy but it becomes a lot harder to construct the
37:17
value function when you're going into broader domains and it's it's things like assigning the value of Moby Dick to Kevin Scott's life you know that sort of
37:24
thing um are there are there practical solutions to this are there practical
37:29
implications to this I guess the broader question would be where do you see the overall field of reasoning in llms going
37:36
well look I think people are trying to get at this uh so so you've got a bunch
37:41
of benchmarks like yeah GP QA and MML U and yeah like
37:49
we're just sort of rolling through a bunch of benchmarking paradigms to like try to come up with scores of
37:58
performance for these models like whether it's reasoning capability or yeah and yeah I think yeah one of the
38:04
interesting things we seen over the past handful of years is like we just are very quickly saturating these benchmarks
38:10
like where you uh like one emerges and then you within a model generation like you'll completely uh or like get very
38:17
close to saturating the particular Benchmark and then you got to go find something else to help be your Guiding
38:23
Light so so but like let let's just sort of assume that like you know you'll have some in interesting benchmarks that are
38:31
correlated with the reasoning capabilities you want models to have um
38:36
you know then the question it's just an expensive experiment to run like you can run an experiment where it's like okay
38:42
like I'm going to train a model like you know with this information in and or out and like does it get better or worse at
38:50
um you know performance on these reasoning benchmarks
38:55
um and you know like I think all of us have done different versions of those
39:01
experiments like that they they're just extraordinarily expensive to run at the most granular scale you can imagine
39:08
running them um part of that paper that bill referenced like textbooks are all
39:13
you need is like a um it it it's not the
39:18
full story but it's like part of a story that is like you know just sort of evaluating like token contribution
39:24
quality to model performance so yeah I like I I think
39:30
it's and and everybody's got every incentive in the world right now to try to figure out like what that is uh if
39:37
for no other reason than you uh like in a world where you're synthesizing data you're literally spending compute to
39:45
generate synthetic tokens for training you you really want to make sure that
39:50
the tokens that you're generating are uh actually useful or not
39:57
where do you think the models are at the moment I you know I think uh Microsoft
Copilots
40:02
has introduced a whole bunch of co-pilots to try to help end users with
40:07
with their with your products and so forth on the other hand I I see lots and
40:13
lots of companies trying to build agents that can be kind of autonomous actors now that that's a wide spectrum of kind
40:21
of expected performance of what the models can do where where do you think we are where do you think we'll be in a
40:27
couple of years well yeah so I think that's a super good
40:33
question and there's a you know there's even a philosophical thing there about you know what it is we should want
40:40
um well there is the the Spectre of everybody's job getting replaced right
40:46
so yeah you know somebody look the we we chose the the
40:52
name co-pilot for the things that we were doing relatively deliberately
40:57
because we we want to at the very least encourage everyone who's building these
41:03
things inside of Microsoft to think about how can I help augment someone
41:10
who's doing some form of cognitive work uh so like we want to build you know
41:15
assistive not substitutive Tech [Music]
41:20
um and and you know the good news is also easier to think about how to go
41:26
from you know sort of rough Frontier Model capability to useful tool when
41:32
you're narrowing it down to a domain um and so like I I think that's been a
41:39
reasonable deployment path and like we've got a handful of our co-pilots that have real Market traction right now
41:46
and are like you know in daily use by like a lot of people doing you real
41:53
non-trivial cognitive work uh you know and I think that will expand and over
41:58
time um what just on that what are some examples of copilot that have really
42:04
like hit the bullseye already versus maybe co-pilots where the technolog is not quite ready in terms of like jobs to
42:11
be done yeah like I I think you know GitHub GitHub co-pilot is like probably
42:16
the you know the thing we've talked most about and like there's you know the most
42:22
uh public conversation around uh yeah like it it it's it's been a been a hit
42:29
um you know it it is genuinely useful um yeah we we've
42:35
got some other co-pilots that are like that that are um you know that are that
42:41
are super useful um but you know I think the thing that bill was getting at like the more
42:46
General the more General the co-pilot is like the you know the the harder it is
42:53
to have it actually take uh very high Precision action on
43:00
your behalf autonomously um you know particularly if
43:05
you know it's doing something where it's representing you um where you there are stakes and
43:14
you know consequences and accountability back to you if this agent makes a mistake and we're trying to be very
43:21
deliberate there because I think one of the things that you don't want to do is uh introduce a thing that's going to
43:28
make a whole bunch of uh these sort of Errors where the user's first reaction
43:35
is like this doesn't work and like I'm not going to try it again for a good long while uh so we'd rather have it be
43:43
very good before we introduce it uh yeah which again you
43:49
means you're you're sort of optimizing for use cases not for like you know
43:54
super super broad things I mean there's a good like we um we did
44:00
a partnership recently with uh yeah with Devon uh um which I think is another one
44:07
of these like very interesting like you know use case specific things where it's like you know Frontier Model plus a
44:13
whole bunch of other stuff that is like optimized for like giving humans high
44:19
quality recommendations for actions uh that they can take and then when you click accept on the action uh like you
44:26
uh you have reason ly high confidence that you know it's going to work and you haven't made another set of problems for
44:33
yourself it's I like I'm I'm guessing too you all see this in your portfolio companies like there there seem to be a
44:40
bunch of companies out there right now that are like doing exactly this and and that it's you useful and working well
The 98/2 rule
44:47
it's interesting because we hear one of the things that we hear fairly consistently from the companies
44:53
that are further on in their AI Journey you know everybody kind of starts in the same way where they start playing with open Ai and then maybe they start using
44:59
some of the other proprietary Foundation models and then they incorporate some open source models and maybe they have some of their own stuff there's a vector
45:05
database in there somewhere yep from an architectural standpoint it feels like people tend to go on a not quite the
45:11
same Jour Journey but Journeys that sort of rhyme but then what we hear from them when they're 12 or 18 months down the
45:17
road is there's kind of this massive 8020 rule at play and maybe it's a 982
45:23
rule but you can automate most of a task pretty quickly and pretty effectively
45:29
but getting it to the point where it's actually end to end running
45:34
autonomously in a way that is compelling and consistent enough that you can actually trust it kind of that last mile
45:41
that last couple percent that makes you really trust it yeah it seems like that's been pretty elusive for a lot of
45:47
tasks and so I'm one of the things that we're really curious about is okay well when to the foundation models themselves
45:53
you know get good enough to knock out that last 2% or is that a domain specific thing and
45:59
that's really the job of the software vendor who who lives on top of the platform to figure out that last 2% I
46:06
look I think it's going to be both for a while like the the two things that I think you can
46:11
trust yeah I I know you you guys were probably going to ask this question at some point but um like we're we're
46:19
despite what other people think we're we're not at diminishing marginal Returns on scale up um and like I I try
46:27
to I try to you know help people understand like you know there there is an exponential here and like the
46:34
unfortunate thing is you only get to sample it every couple of years because it just takes a while to build supercomputers and then train models on
46:41
top of them uh and and so the the next sample is coming uh and like I I you
46:47
know I can't tell you when and I can't predict exactly how good it's going to be but it it will uh almost certainly be
46:58
better at like the the things that are brittle right now where you're like oh my God like this is a little too
47:04
expensive or it's a little too fragile for me to use like all of that gets better like it'll get cheaper and like
47:09
you know things will become less fragile and then like more complicated things will become possible like that that is
47:16
the story of each generation of these models as we've scaled up and so like
47:21
you know we even think about this inside of Microsoft and like one of the category errors that own developers who
47:27
are building these AI products can make is like get too convinced that uh the
47:32
only way to solve my problem is like I have to go uh take the current Frontier
47:38
and supplement it with a whole bunch of things but which you do have to do but like you want to be very careful
47:44
architecturally when you're doing that that it doesn't prevent you from taking the next sample when it arrives so you
47:50
just want to architect these these applications where when the new goodness comes you can go plug it in and like
47:56
you'll have have to go optimize that as well uh like I think that's just sort of the grind that we're on um but like you
48:03
just like the the the thing that was killing us internally is I I would have
48:10
teams inside of the company who would look at a Frontier Model and say oh my God there's no way that we can ever
48:15
deploy a product on top of this because like this is fragile and this is too expensive and so you know like please
48:22
give me like giant pools of gpus and like let me go spin up a big team doing uh you know like a very you know
48:29
tailored version of this and like we're going to build a specific model and like and and yeah they they would go off and
48:35
like spend a whole bunch of money and like the thing would be you know a little bit better uh cost wise at the
48:41
same level of performance as the current uh Frontier and then the frontier would snap to the new uh point and it would be
48:48
just doomed uh and and so like you you just architecturally don't want to get
48:54
trapped by that I don't think I mean like that'd be my advice I give to everyone is like just give yourself the
49:02
flexibility to snap to the new uh you know to the New Frontier when it
49:07
emerges and and like that lets you preserve all of your skepticism you can believe uh you know believe all you want
49:13
that the New Frontier not coming like go you know like you read your favorite Twitter troll that says it's all over
49:20
and a sham and like and like but but just give yourself the option that you know may maybe what's been been
49:26
happening for six years now is going to continue well well hearing hearing that
49:33
hearing that we are not at diminishing returns to scale I'm gonna count that as good news and so let's stay on the theme
Solving zero-sum games
49:39
of good news I know that you're um short-term pessimist long-term Optimist
49:45
can you give us some of the optimistic point of view for where where we're heading in this world of AI like what
49:50
are the some of the things that you're most excited to see in the world in five or 10 or 15 years or what whatever you C
49:56
as a longer term Horizon well look I think the thing that everybody ought to like spend some time thinking about is
50:03
where are the gnarliest zero some problems that we have in society like
50:10
where where are the things where like we just are fighting with one another or you know like we are IM miserating
50:18
people because uh whatever it is that people need there doesn't appear to be enough of it uh and I think for a good
50:26
number of those things like what you have to have to to to turn them into
50:31
non-zero sum games to you to create abundance uh and to
50:37
relax some of these constraints is you have to have technological breakthroughs
50:42
it's it's like the only thing that reliably that's ever turned zero some to nonzero sum in in human history is like
50:49
some tech has to come along that like you know lets us lets us have more um
50:56
you know like you know whenever Tech comes along and like creates more it doesn't mean that the more gets uh you
51:02
know equitably and uniformly distributed um and and like I think they're real
51:07
conversations to go have about that but like what you do want is the more uh and
51:14
you want it to you know be directed at things where like we we're just having a tough time right now um yeah like I I'll
51:22
tell this uh you know story that I've I've told a couple of other times recently but you know my mom
51:30
uh like I grew up in rural Central Virginia my mom's 74 years old and like she's suffered from this thyroid
51:36
condition called Graves disease for 26 years uh and so you know when you have Graves disease your thyroid is
51:43
hyperactive you're generating too much thyroid hormone and so they go in and
51:48
like irradiate your thyroid gland uh to reduce its activity level and then you take hormone uh replacement therapy to
51:55
like upregulate your hormones for the rest of your life and so she was having
52:01
some blood pressure issues and her doctor you know dorked around with her dosage of this hormone medicine and then
52:08
like she just had like some serious health issues as a consequence of that uh that landed her in the ER in you
52:16
rural Central Virginia like six times like in a pretty yeah short number of
52:22
weeks and yeah the the intro interesting thing there was the first time she went to the
52:28
ER like she was presenting all of these cardiac symptoms and it was pretty clear they hadn't even read her chart like it
52:35
hadn't registered on them that she had Graves disease and like the thing that they needed to do was like go order a
52:41
TSH panel to see what her uh like thyroid hormone levels were uh like if
52:47
they done that right away um like they would have said okay like we got to go you know adjust your
52:54
medicine um yeah and like I I'm not even ascribing ill intent like you know this is a a
53:02
Health Care system that is egregiously overburdened like this is not a place
53:09
where you know like you've got this influx of you know Talent like coming into you this part of the country and
53:17
and like it's a lovely part of the country like I I I I like love it um you
53:22
know so like I'm I'm not criticizing anything it's just you know they they have an aging population and they don't
53:28
have enough like young people coming in to be things like doctors to like help this Health Care System keep up with all
53:35
of the challenges that they have if some of those doctors had had
53:40
access to GPT 4 uh and like it was an approved you know product
53:46
use all they would have would have needed to do was like put the symptoms that she was presenting in and her
53:54
medical record and it would have said hey she needs a TSH test and if you
54:00
would put the TSH test result in the recommendation it would add was
54:05
like look at the dosage of her uh hormone replacement therapy that she's on and like if if like this isn't
54:13
theoretical like I I I did this um like it it could have helped
54:19
alleviate a massive amount of her suffering right now and like I think the only reason like that she got out of
54:26
this tough situation that she was in was I had to intervene like I I sent her to
54:34
a specialist that was you know 400 miles away that she couldn't have gotten into
54:39
without you know special you know and like it's it's it's ridiculous like you
54:45
there there are so many 74 year old old southern ladies or old Midwestern ladies
54:50
or like folks who are going through similar sorts of things who do not have someone who's going to go in and intervene
54:56
on their behalf who are suffering unnecessarily because we're not even deploying the technology that we've got
55:02
right now uh and like it's just going to get better and so like that's the thing
55:08
that I'm excited about it's like let's let's go let's go give kids a leg up in
55:14
education let's go fix some of these crazy problems we've got in a Health Care system that is just you know absent
55:22
technological intervention is just going to get more strained over time
55:27
um you know let's equip our scientists with better tools so that they can you
55:33
know find better Carbon capture catalysts so that uh you know like we can
55:39
design safer modes of transportation so we can more quickly get to a you know
55:44
like postc carbon economy like it's just so many things we can go do with this
55:49
stuff um like I'm super super optimistic about it and so you know like just kills
55:57
me like what we don't want to do is get
56:03
distracted on uh you know like things that
56:09
are um just you know effectively noise uh in the ecosystem right now um and you
56:19
know we we're we're getting so sideways sometimes with
56:24
um you know like that this model like said something that hurt my feelings or
56:31
you know and like and I don't I don't want to dismiss like you know people's feelings matter and you know I'm not
56:36
trying to be a jerk here but I I do want to make sure that as we're thinking
56:41
about how we develop and deploy the technology that we are always
56:47
remembering like what the cost of not deploying the good is um because that
56:54
that is a high high cost mhm very well put yeah yeah here
57:02
s probably a good note to end on I think well we have we have one other question
57:08
that we like to ask people on it's a quick one so I'm gonna ask I'm GNA ask you this one who do you admire most in
Lightning round
57:14
the world of AI you know I I I was I was thinking
57:19
about this so I I think it's uh Ray salamov um who was one of the
57:27
one of the folks who was at that Dartmouth Workshop in the in the 50s
57:32
where you know um Marvin Minsky
57:39
and Simon and uh you know like a whole bunch of folks convened that summer and
57:44
like they were all interested in machine intelligence and they uh they coined the the term artificial intelligence at that
57:52
workshop and like the reason that salamov is so interesting and like not not many people I think know who he is
58:00
outside of computer science is like he was the one from the very beginning who
58:05
was pushing on this whole notion that probabilistic methods were going to be
58:11
very important for the development of AI and you when when I was in grad school
58:17
in the 90s um the
58:24
prevailing uh academic theories about how we were going to get to AI were all about like okay well you know like there
58:31
there's some magic you know minimalist calculus about human intelligence and
58:36
like we've just got to figure it out and it's got to be you know rule-based systems and ontologies and you know
58:43
symbolic reasoning and like a bunch of like stuff where you know we like we do
58:50
in physics we were going to have to like you know Divine you know the the inherent Simplicity in the system like
58:56
figure out what the rules are and as soon as we understood the rules like we'd be able to make software emulate
59:01
human intelligence uh and salamov is like no no no like this this is just a intelligence is an extraordinarily
59:09
complicated phenomenon uh and like the only way that we're ever going to really
59:14
get there is modeling it with uh probabilistic methods and he was right
59:19
uh and like he was he was judged wrong for a very long
59:25
time uh um and so I I really admire uh
59:32
his contrariness um yeah all the way back in the 1950s
59:37
and like he's stuck with his uh he stuck with his beliefs his entire career and I don't know whether Ry actually uh like
59:44
lived to see how right he actually
59:49
was that's a great answer and a great story thank you Kevin yeah you're very
59:54
welcome thank you [Music]

---END TRANSCRIPT---

The above is a transcript of a 2024 interview with current Microsoft CTO Kevin Scott. Mention the most newsworthy things in the transcript that subreddit r/singularity would find interesting.