First Time Founders: Is Cohere the Next AI Powerhouse?

So for for today's show comes from Dark Trace. Dark Trace is a cyber security

defenders deserve and the one they need to defend beyond. Dark Trace is AI

cyber security they can stop novel threats before they become breaches across

email clouds networks and more. With the power to see across your entire attack

surface cyber defenders including IT decision makers CISOs and cyber security

professionals now have the ability to stop zero days before day zero. The world

needs defenders defenders need Dark Trace visit darktrace.com slash defenders

for more information oh hey sorry love to chat but I'm busy shopping all the

rollback some more at Walmart grab a what cancel that I got to grab these big

savings on the Walmart app online and in store like right now see who oh no

on a mail the only thing I want to see are the prices just lowered on tech

home and all men must have wait you want to shop Walmart with me all right I

think I can fit you in support for this show comes from Toyota before the

trophies before the glory every world class athlete began as a kid with

someone who believed in them from carpools to championships Toyota helps you

go further with the reliability and confidence to keep showing up from early

morning practices to the road games and rides home every destination has a

beginning and from day one Toyota is right there with you celebrating the

pursuit of greatness because it's not just about getting there it's about being

there every step of the way visit Toyota dot com slash athletes

welcome to first time founders I'm Ed Elson artificial intelligence has become

one of the most heavily funded sectors in the world more than 30 startups have

raised over one hundred million dollars this year alone as AI becomes more

embedded in how the world operates a handful of firms have emerged as the key

players behind that transformation among them is a company building the kind of

AI most people don't see that is the AI that is powering the systems that run

businesses and governments founded in 2019 by three former Google engineers this

company has focused squarely on the enterprise market developing large

language models for clients like Dell SAP and Salesforce it even recently signed a

deal with Canada's government to bring its technology into public operations

now valued at nearly seven billion dollars it is under place alongside giants

like open AI and then drop it helping define what the next era of AI will

actually look like this is my conversation with Nick Frost co-founder of co here

all right Nick Frost good to have you on the program thanks for having me so for

those who don't know what co here is I think we should probably just start

there what is co here what does co here do when you guys building in AI so we're

a foundational model company and we are uniquely and singularly focused on

the enterprise so there's there's about ten companies in the world that can

make foundational models so foundational models the large language models that

are largely these days synonymous with AI if somebody's talking about AI

they're probably talking about large language models there's about ten

companies in the world that can make them we are unique amongst them in our

singular focus on the enterprise so we make large language models that are

good at the stuff that enterprises need them to be good at we make them easy to

deploy and efficient to deploy for enterprises we deploy them securely and

privately so that we can't see the data that our customers are passing into the

model that allows them to access the truly useful data out there and we make

them easy to work with via an agentic platform so we do kind of the whole thing

in order to get AI to work at work so these foundational models I think most

people who are interested in tech kind of know what they are but just at a very

basic level the foundational models are the models that all of these AI

startups are building offers or all of these companies if you're building AI or

you're building AI products you need the foundational model which companies

like open AI and anthropic and co here your company are building what am I

missing if you talk about AI companies there's a lot of companies building

stuff with AI mostly when people say they're building stuff with AI they mean

they're building stuff with foundational model these days there's still a

huge amount of work being done on more traditional machine learning smaller

systems and a lot of those people working on that will still very rightfully

cold so that they're an AI company but if you are talking to someone and they

say hey I've got a startup and it's an AI startup or something chances are they

mean they're building off of a foundational model they're making getting a

large language model to do something useful for their customers there's a

relatively smaller number of companies that actually make the foundational

models so that actually make the large language models that take in a bunch of

words and then predict the next words that should come next yeah there's about

10 of those so there are 10-ish companies building foundational models which

is basically the backbone of AI or at least it's one of I don't know the

vertebra of AI maybe we could call the chips the backbone but what is so

striking is there are thousands of AI companies and we're seeing many of

them and so many of these companies building AI and yet there are only 10

companies that are building these foundational models why is that so in short

it's really hard and it's enormously resource intensive building large

language models is a lot more like building a rocket than it is like building

other computer science projects it requires a huge number of really smart

people who have experience doing it working in tight unison there's a whole

bunch of different things that need to go well in order for it to be successful

there's a whole bunch of experimentation that needs to get done and there's

still you know and there's huge amounts of resources that need to get put

into it in order to make the thing work right so you have to get a huge amount

of compute so rent rent all those chips you know that that you were talking

about you need to get a huge amount of data you need to have a huge amount of

people helping you create that data getting data annotators you need to have a

whole bunch of really smart engineers working together in order to make it go

well and even then it's still challenging so yes there's really only about

10 companies in the world that are doing it because of that reason in the

same way that there's not that many companies building rockets either right so

how did you end up being one of the people who built one of these rockets take

us back to the beginning how are you how did you get into this before co

founding co here with Aiden and Aiden Gomez and Ivan Zhang I was a researcher at

Google Brain so I worked with Jeff Hinton for a few years there working on

explainability and adversarial examples and capsule networks and stuff which

was really fun and it was there that I met Aiden and Aiden was just finishing up

a stint in Google Brain in California where he worked on the paper

attention is all you need which introduced the architecture that we still use

today so that he helped write that paper in 2017 and you know almost 10

years later we're still using the same the same architecture so after he worked

on that and when I met him in Google Brain Toronto he was obviously very excited

about the architecture and about what it could do and he showed it to me and I

thought it was also really really exciting so you know we noticed something

about the nature of this new model that created an opportunity and indeed a

need for companies to make foundational models what we noticed was that for

the first time in machine learning's history if you wanted to solve a task

like a language task the best model to solve that task was not a model trained

on that task alone it was a model trained on a whole bunch of tasks so that was

really exciting and that made us realize hey like there's going to be a need if

companies are going to actually make this stuff useful and get this to work for

them there's going to be a need for companies to create really big and really

good foundational models that other companies can use so we had that

realization in 2020 and we've been delivering on that since then we can try to

make language models useful for the enterprise by making them really

affected at the things that they care about. You mentioned Jeffrey Hinton there who

you who you studied under who for those who don't know is considered to be the

Godfather of AI why is he the Godfather of AI what did you learn from him and I

mean I think people generally recognize him and his name maybe if you're

into tech but perhaps they don't if you're not super plugged into what's

happening in AI so what was his role in the story of AI and what did you learn

from him so I studied with him as an undergrad so I only have an under I don't

have a master's or a PhD or anything so I had an undergrad from U of T and I

did take his course while I was there and I sat in the front and asked lots of

annoying questions and and I really only worked with him closely when I was

at Google so I was a research researcher at Google Brain and I worked with him

for I was working at a waterloo for a little bit and then I found out that he

was working in Toronto and then we started to work together and then I helped

start up the Toronto brain group with him and worked there for a few years and

it's during those three years four years that I learned most of what I know

about research and machine learning and neural nets and I learned it from him

so I learned a huge amount from him but I'm not I don't have a PhD or a master's

right now that's for his contribution I really can't be understated neural

nets neural nets have been an idea for a while people have been thinking about a

neural net architecture at a particular jet's been thinking about neural

net architectures since the like mid-Ais there was a long time where people

thought they were not gonna work and there was this whole wave of you know first

perceptrons and that which is just a single layer neural net and people thought

that was kind of interesting for a little bit and then some work came out to

show that they had some fundamental flaws and that really cooled people down on

them people weren't excited about neural nets and then people started working on

multi-layer neural nets or multi-layer perceptrons and that solved some of the

critiques but still people were not excited about it and they generally thought it

was a bad idea and if they wanted to build AI they were much better doing things

like search or symbolic reasoning or things like that and so very few people

worked on it and largely they thought it was done except for a few people Jeff

being one of them so Jeff tirelessly worked on neural nets in the face of

general ridicule for decades for decades until around 2011-2012 they were

finally able to show that neural nets were suddenly the best at image

recognition that was the first thing that they really they really knocked out of

the park on and I was done at U of T with a bunch of other brilliant U of T

students the reason we are where we are with neural nets in general which of

course is the precursor to transformers right so there's kind of if you think

of it broadly there's like AI as a concept there's machine learning as one

strategy for doing that neural nets as one strategy for machine learning

transformers as one type of neural net it's kind of where we are so the neural

net part in particular Jeff can claim a huge amount of responsibility for and it's

really his tenacity that that's in his dedication to continuing to work on it

even when everybody else around him was saying now this is a bad idea it's not

gonna work that we have to thank for where we are today so when we look through

the hit when the the history books are written about AI I mean AI is having

its moment right now what changed I mean AI had been worked on and neural

nets had people have been working on the stuff the decades Jeff Hinton had been

working on it for decades he makes this breakthrough with image recognition in

the early 2010s now it's ubiquitous was chat GPT that the breakthrough

moment like what will the textbooks tell us about what changed when AI

became mainstream there have been other AI moments have been other times when

people are when the whole world's really thinking about AI this is the first

time that it I would say it's been this dominant narrative of the economy for

the past few years and that's a first like and technology that's been the

dominant narrative of technology for the past few years and it's been the

dominant area of the economy even more for the past few years so that's that's

kind of a first but there have been moments where people have been as really

excited about AI and thinking that they're in some kind of AI moment before you

got to separate AI as a property versus any implementation trying to get at

that property so people have been thinking about artificial intelligence like

what happens if a machine has intelligence the way a person has intelligence for

a really long time there's a myth that I cite pretty often that was written

in that like a around you know 1500s 1400s I believe a Yiddish myth about the

the column which talks about you know some rabbi imbuing intelligence into a

clay man and then he asks the he asks the the column to go get fish from the river

and then he leaves his house for a little bit and when he comes back the

house is filled with fish and the river is empty and like like it's a joke

right like it's it's it's effectively you know a comedic story that's told at

that moment and the joke is oh like intelligence is complicated and there's

nuance and language and if we gave an artificial thing language maybe you

wouldn't understand that nuance that's about 500 year old joke yeah right so

people have been thinking about this for a really long time more recently you

know after the computer was invented there was a whole wave of people thinking

about that now Alan Turing was thinking about the Turing test thinking about

intelligence after that there was search there was the deep blue moment when

the search algorithms beat Casper off at chess and that had a similar moment so

people have been thinking about this all the time this is different this is a

different moment and it's different in its scale and when people write the

history of AI this is certainly going to be a pivotal moment and I'm convinced

that neural nets are certainly going to be a central component of of machine

learning and AI going forward like they're so good they're so fantastic at

they do all kinds of things that there's no other way we could get them to do

yet and transformers in particular large language models are very easy to use

for the average person and that is I think really why this feels different so if

you look at the other moments when people were talking about AI like deep blue

let's look at that one as an example right like there you can read tons of

articles about people talking about what's happening with the machines or our

computers getting as smart as people they beat the best chess player in the

world like what's going on but if you're an average person you couldn't really

interact with that like maybe if you're good at chess you could try the chess

bots and that people did and actually you know chess in some ways chess is more

popular than it has ever been before and in part that's because you can be at

your home playing against something better than a grandmaster but you could

interact with it that way you couldn't really interact with a search algorithm

like an a-star search algorithm in anything else so your experience of it's

pretty limited same with machine learning like when we made image recognition

the best image recognition model suddenly yeah your phone you could go on

Google photos and you could search up pictures of you know dogs and see all the

pictures of dogs you've seen over the years like that's new that's cool but

you couldn't that's still directive that's still like somebody made the model

that does the thing it's telling you how to use it transformers are the first

time that any person without any experience in computer science or AI can go

up to the model you know open up a chat window ask it to do something and it'll

do it or will not do it and that'll be interesting itself but you could

interact with it without it being prescriptive of how you interact and that's

I think the reason why this is suddenly so much bigger it's suddenly so much

more interesting so much more widespread and why it's become the dominant

narrative of tech over the past few years so when people write the history of

AI and I want to be clear that I think the history of AI is not done I think

yeah I don't think transformers are gonna get us to artificial general

intelligence so I think there's gonna be more waves of new independent

spontaneous inventions I'm sure that's gonna happen but I'm convinced that the

transformer is gonna be a central component of that and when the history of AI

has written a hundred years from now a thousand years from now this moment will

be talked about as relevant and interesting and a moment when a lot of

stuff happened really quickly as a result of the tenacity of a handful of

people yeah it's interesting that in a way it was the consumerization that

really took things in a completely different direction which is almost a

testament not necessarily to the underlying technology but almost to like the

productization and being able to put this kinds of technology into the hands of

millions and then eventually hundreds of millions of people is that when you

see all of these big tech companies that are spending hundreds of billions of

dollars building out their AI capabilities building out data centers rent and

compute buying chips are men spending money on on on models like like the ones

you've built to build their own products do you think it was sort of a moment

where they kind of woke up to what the capabilities and what the prospects of

AI could be because they just saw it a lot or was it something else was it that

the technology changed a fundamental way I mean to what extent was this sort of

the narrative that suddenly captured people's imaginations versus something in

the technology actually changed which made Mark Zuckerberg think now we need to

get on this I think everything we're experiencing today is largely predictable

from around 2020 2019 now that's not a coincidence that's when I left Google to

start cohere with and I don't so that's the reason why I think it was largely

predictable around that time is because that's what I predicted it so I'm sure

other people predicted it before I got on board at that time at the time I think

when I remember telling people I'm going to leave to go create this foundational

model we I don't think we use the word foundational model we just a large

language model company we're going to be a large language model company we're

going to make large language models I remember everybody saying yeah that's

probably a good idea I don't think anybody was thinking like oh that that makes

no sense the question was not low limit the question was like oh you know is

Google is Google just going to do it or the other other big companies just

going to do it but I think at the time it made sense now it really still wasn't

popular and when we had conversations for the first few years of coheres

history the conversation was this is a large language model and here's why we

think it can help you the conversation now is okay cool like why you know why

your large language model or like how this actually helped me get into

production how can I have it access my my private data without giving that away

like how can I how can I deploy it in a secure and safe way so that I can

handle regulated industries how can I connect it to my specific data in an

enterprise like those are all the questions we answer now so it's changed a

lot and what changed in particular and a thing I did not predict at the time was

the success of chat fine tuning so you train this big generic language model when

language models were first created what they did was they just completed the

ends of sentences because they were trained on the web so you really can think

of it as like a web we're calling it a large language model at the time it

wasn't a large language model it was a web text model yeah it's like a

Reddit language model yeah so you wrote the first part of a website it would

write the second part of a website not even the HTML just the text on the

website and you could do a lot of stuff with that but it was confusing and

weird and then opening eye and a few other companies at the same time fine

tuned that large language model on chat dialogue and that suddenly suddenly

people understood it and at the time actually I remember thinking I was

surprised at how efficient that was because when you think about it you're

training a model on the entirety of the web so a huge amount of language and

then you fine tune it on a relatively small amount of chat and yet actually

it learns how to chat pretty well so that is I think responsible for for the

difference between 2020 and 2022 it was the data efficiency of chat fine

tuning that allowed people to untill like for the model to meet them where

they're at they kind of expected users kind of expected chat to work when you

told them there's a large language model and it didn't it was just like weird

texting so then making it work in the way they expected it to work seems to

have really gotten like woken people up to the effect and the utility of these

models yeah I'm sure it was also the volume to the idea that if you keep on

chatting with this AI you're contributing more and more data for it for it to

train itself on I want to get to the specifics of co here in a moment but you

know it's interesting you're describing that the model gets better when it's

subjected to or when it's fed large amounts of data and also like diverse

forms of data and originally we were kind of just limited to the web but the

web isn't all of life there's more beyond the web that these models could be

trained on and so too you could say the same thing about these these chats I'm

wondering if there's other forms of data that you think will be prevalent for

model training in the future you know things in in the physical world I mean I

mean typing words onto it onto a keyboard and seeing words on a screen isn't

everything but to AI right now it seems to be close to everything so is there

a way are there other forms of data that you think in the future AI will be fed

and therefore that would sort of take us I guess on the path to AGI let me

first talk a little bit about the way the way we train these models okay so the

first step is to train them on everything on the web everything on the open

web so you have you create a data set of all the text that's available for

training from the web and that turns out to be a huge amount of text orders of

magnitude more text than you will ever read I like like a thousand people a

thousand years reading 24 hours a day volumes of text like that's how you know

that's how much text so first step is training on that then you make a

data set with people so you have people create like talk to the model and if

the model gives a good response they say that's great if it gives a bad response

to say that's bad and they write what the model should have said if you do that

process you'll create both ratings I guess it a good response or a bad response

and you'll also create the S what's called supervised fine tuning data SFT data

so that's like here's the input to the model and here's a gold standard of

what a person wanted like they wrote out the sentence like that's what the

model should have said that's called SFT data so then you trade the model on

that SFT data after that you can do reinforcement learning which was a type of

machine learning invented before transformers where the you're training a

model without access to the to the right answer the model kind of tried stuff and

then you say hey this is better this was worse than you and you update the

weights of the model based on that that signal so then you can do reinforcement

learning now we do a whole bunch of reinforcement learning with synthetic data

so now we use the model itself to generate data and then do reinforcement

learning on that synthetic data so that's a big component of training now so

there's like the data you get from the web the data you make with people and

then the data you make with the model itself and those all of those are super

relevant for making the models that people use today your question about

models being restricted to the to the web and missing the stuff in the real

world is that a blocker to AGI like yeah definitely that's a blocker to AGI if

when you say AGI you mean human-like intelligence yes that's a blocker to

AGI we are embodied creatures we have we learn our intelligence through

interactions with the real world and intervention into the real world it's

lots of interesting psychological work that suggests learning and interaction

are super related so interactions super important is that a blocker to AGI

like yeah definitely but I don't there's a whole bunch of blockers to AGI and

that's just that's just one of them and the technology as it exists today is

massively impactful massively useful absolutely transformative on the nature

of computers and subsequently the nature of work massively transformative on

the economy in general then I don't think it's AGI and nor do I think the

transformer alone will get a stage AGI nor do I care I don't really I don't

really look out in the world and say oh geez I wish my computer was a person I

look out in the world I say oh man there's so much stuff that a computer should

be doing and not me my time should be free to to think strategically to think

creatively there's so much work that a large language model when connected

into the things that I'm using can do for me and and and subsequently allow me

to do the interesting in the human and like that's what that's what I want to

make I want to make a technology that does that as good as possible do you think

that AI the people building AI that the leaders of the AI industry Sam Altman

probably being the the high priest right now at least do you think that there's

not enough appreciation of that do you think that people are too obsessed with

we need AGI we need human like intelligence I just look at the contract

between Microsoft and open AI which basically one of the stipulations in the

contract is you know the terms will change once we achieve AGI I mean there are

many questions like what does that even mean but the fact that AGI is sort of

the benchmark for everyone and I'm even asking you like how do we get to it do

you think there's too much obsession with this concept of AGI in the AI

industry right now yeah yeah I mean you said yeah look high priest is a good

term a lot of the thought around AGI and discussion around that AGI feels

religious to me it's calm down a little bit right like if we back in 2023 20

like 2024 my views on this were a little heretical people would disagree if I

said hey AGI is probably not around the corner people would disagree and say

why do you think that like I would get a lot of pushback I don't get much

pushback these days I'm like yeah guys guys guys we know transformers incredible

super awesome super good can definitely be way better than they are need to be

deployed correctly need to have lots of stuff you know to get them into

production that's what we focus on but AGI like no and everybody's like yeah

yeah yeah totally I get it and and if you use a large language model you which

has everybody does these days you'll feel that pretty soon you'll be like yeah

they're amazing at these things and then I assume some other things they don't

understand at all completely different and that they have a completely

different it's very different talking to a language model as it is chatting to

a person and people you know kind of know that when they're grounded in an

environment the focus on it is I think you know a narrative device more so

than it is a scientific belief we'll be right back

support for the show comes from LinkedIn it's a shame when the best B2B

marketing gets wasted on the wrong audience like imagine running an ad for

cataract surgery on Saturday morning cartoons or running a promo for this

show on a video about roadblocks or something no offense to our general

listeners but that would be a waste of anyone's ad budget so when you want to

reach the right professionals you can use LinkedIn ads LinkedIn is

ground to a network of over one billion professionals and 130 million

decision-makers according to their data that's where it stands apart from

other ad buys you can target buyers by job title industry company role

seniority skills company revenue all suit and stop wasting budget on the wrong

audience that's why LinkedIn ads both one of the highest B2B return on ad

spend of all online ad networks seriously all of them spend $250 on your

first campaign on LinkedIn ads and get a free $250 credit for the next one

just go to LinkedIn.com slash Scott that's LinkedIn.com slash Scott terms and

conditions apply

support for this podcast and the following message is brought to you by E-Trade

from Morgan Stanley simplify your finances and discover the convenience of

investing in banking all in one place with E-Trade from Morgan Stanley choose

from a wide range of investment choices and award-winning banking solutions

together in one platform plus get up to $1,500 when you open a brokerage

account with a qualifying deposit today learn more at E-Trade.com slash offer

banking products and services are provided by Morgan Stanley Private Bank

National Association member FDIC terms and other fees apply investing

involves risks Morgan Stanley Smith Barney LLC member SIPC

new markdowns up to 70% off are at Nordstrom Rack stores now and that means so

many new reasons to rack because I always find something amazing just so many

good brands because there's always something new join the Nordic Club to unlock

exclusive discounts shop new arrivals first and more plus buy online and pick up

at your favorite rack store for free great brands great prices that's why you rack

we're back with first-time founders I'm going to ask you the question that you

said everyone asks about co here which is what is the difference between co here versus

the other foundation model companies what is it is drink co here and open AI

between co here and and drop it those are the two big ones in my head what is the difference

the big difference is we are not a consumer company and we're only an enterprise company

so we don't have we can't pay $20 a month to get access to our tech we're not trying to build

a product that people use in their personal lives we are instead selling only to large and

medium enterprise companies and we create language models and search models and an

agentic framework for using them that is tailored to the needs of those companies that strategic

difference comes from a philosophical difference which is in a different view on the technology

right like I don't think the technology is going to get us to AI and I don't think the biggest

utility of the models is in people's personal lives I think the biggest utility of these models

is in work I think that like their ability to augment and automate work at a desk behind a

computer is I think what they are the best at and so we have that different view of the technology

that leads us to think differently about where we can add the most value to the world and that leads

us to being an enterprise a singularly enterprise focused company and that is as mentioned unique

amongst the foundational model players just so we can picture like what kinds of work is being

done what is an example of a use case then an enterprise is is adopting because of using co here

as a foundation model lots of people will go into work open up north which is the name of our

agentic platform so it's like a chat app with automations and you can make custom agents and you

can share those across people like it's but it's a chat app that on its surface you would be

familiar with so going to work they'll open that up and they might open up our model and say you

know hey you know somebody emailed me yesterday about a brief for a meeting read that email then

cross reference that with our Salesforce data and then make a table sharing like telling me the

state of that customer that's something the model can do for you or they might say hey you know I

just got this data room from this company I'm trying to evaluate read through the data room do

some analysis come up with a cited and detailed document on how you think that company looks and

then send a slack message to my co-workers with that PDF just looking at where you are in the AI

world you are automating tasks that are done at businesses and enterprises that as we are all

talking about would otherwise be done by humans which introduces the question of is AI going to

replace people and this has been a large debate we're obviously seeing a lot of layoffs in tech

right now a very charged debate how do you think about all of this how do I think about it

frequently yeah so there's a lot so I think this technology is you know there is a huge amount

of stuff that people do that large language models should be doing for the large language models

will do a better job of them the work itself is not very enjoyable humans are really good at a lot

of stuff that large language models are very that at and largely they enjoy the stuff that they're

good at and don't enjoy the stuff that large language models are good at so I think you know in

the same way that we've had previous industrial revolutions that's augmented and automated a huge

amount of stuff that people generally didn't really like doing and we look back on those periods

of time as kind of chaotic but largely a good idea no one's running around saying hey the steam

engine was stepping the wrong direction or hey the industrial revolution was bad we you know we

should all still be farmers I think there's something similar going on with this now I do think

this technology is fundamentally augmentative right I think this technology anybody work

behind a computer I think this technology can automate I don't know 20 or 20 30% of their work

I don't think it can automate 100% of pretty much anybody's

huge amounts of the work that we do is not just text on a computer or images on a computer

it's personal it's understanding the cultural context it's talking to people and coordinating

and aligning it's thinking strategically it's doing all the stuff and that's true at every level

of an organization so I think it's there's a lot of people say oh this is just going to take out

the bottom bit of an organization like no what this is going to do is make it augment and improve

and increase efficiency and productivity across the entire organization is that going to have

consequences on the labor market yes absolutely it is just as the industrial revolution had

huge consequences on the labor market just as you know the widespread adoption of computers had

huge influences on the labor market like in our lifetime you're in my lifetime we have seen

wild changes in the way that work is done as a result of technology it was not so long ago that

every organization had a huge number of people working as typists to type stuff up

because because people didn't have computers and that was that needed to get done that doesn't

exist anymore but the labor market evolved labor market figured out all those people are you

know still are doing good work just doing different work so I think that this will have similar

effects to the computer to the internet the industrial revolution on the labor market and I think

governments and organizations and unions and businesses should be thinking about how to make

sure that that goes well how to make sure that that is largely that that uplifts people and that

builds a resilient economy and that allows people to do things they like to do and I really

like that's the conversation that I'm encouraging everybody to have like what are the what are the

policy decisions that can be made in order to make sure that that is good for all people

but I think recently like all the talk of you know there's been a lot of tech layoffs and I know

that's kind of tried to be tied towards AI I don't I think that's a lot more related to the

overhiring that happened during the pandemic then I think it's related to having those people

suddenly like an AI is doing that job for them yeah I think that's kind of borne out if you look

at the look at the data yeah so I do think it's going to have consequences on the labor market I

think in in when history looks back at this we'll largely say that it was a good idea the same way

people say the computer was a good idea the same way people say the industrial revolution was a good

idea but it is going to be a chaotic moment and it does require ten. Do you have concerns about

what this will do in terms of inequality I mean I think about the downsides I think long time

it's you know value a creative which means that's a good thing for society in the same way

that the steam engine was the internet was but I think the the the big concern that seems super

likely to me is that the value is accrued to the people who own the AI and that yes maybe some of

us might might be getting some value out of using AI but we won't be the ones who own it

and it will only make wealth inequality even worse which could have all of its own impacts do you

worry about that I do worry about that yeah income wealth inequality is the thing I is one of the

things I think is the most pressing issue I think yeah I think it's one of the most pressing

issues for the world right now and I do worry that this technology similar to other technologies

stands to exacerbate the wealth inequality that was already rising over the past you know a few

decades I think the correct solution to that is policy oftentimes when people thinking about

the economy that they kind of forget that this is a system we create and it's a system that can

be subtly pushed in one direction or another direction and you can add policies you can change

things in order to make sure that this works for everybody for all people in your country or in

yeah whatever organization you're within um I I think that's the conversation I want the

world to be having and one of the reasons why I'm very vocal about saying hey I don't think we're

getting the H.E.I. is that the H.E.I. conversation often distracts from that conversation

because if you're talking about oh no what if we make a digital god and it kills all people

it's very difficult to have the conversation hey like you know do we have the right policies in place

to encourage better income distribution such that we don't we don't end up in a bifurcation

society which I don't think anybody wants what kinds of policies or what what what do you think

that would look like is my first question and then my second question is as someone who cares

about that are you a pariah in the tech industry because from what from my understanding there is

a feeling of if you're talking about policy and regulation then you're a luddite and you're just

trying to hold AI back and you're just scared so I guess how do you think about those two questions

am I a pariah for talking about that stuff um no no but I also don't live in Silicon Valley

right like I don't I live in Toronto I'm certainly in the tech scene you know I talked to

and I talked to VCs all the time I talked to other tech people I have I talked to you know

lots of people thinking about this but would I you know would I be a pariah

in I certainly have I certainly have lots of different views than people would have hanging out

in the the remnants of the effective altruist parties in Silicon Valley right I certainly have very

different views than than the culture that developed there I mean I am I I'm certainly not a

lot I right like I'm certainly not against the creation of technology but you know having been to

what was that town I went as a town in England that's that was kind of the epicenter of that and

I went to a museum there on Luddites that was very interesting I wish I had but I know I've

studied I know what you're talking about I wish I had been there yeah you know and a lot of the

people at the time were were you know what they were frustrated at the loom for making their

economic situation worse right now I again we all look back at the automated loom and

we think that was a good idea and the economic situation that people living now is better than

the economic situation that they were living in during that time but I'm empathetic I'm empathetic

to saying hey like my economic situation is shitty and it's shitty at a systematic level at a

population system level let's figure out how to make that better right so I am empathetic with that

so what I be a pariah I don't think so I think actually a lot of people know this I think if you

go to Silicon Valley and you tell somebody hey you know income inequality is bad it's hard to

live in that city and not think that just looking at the future of care here reportedly care here

is looking to go public I don't know if you can talk about that but that's what we've been

reading are can you tell us about those plans in and Ivan and my goal in creating this company

was to create something that lasted us was to create a generational company like that's motivating

that's exciting that's a fun thing to be part of that's what we're excited about I think the right

way to do that is to become a public company I think that's how you can build that I think that's

like I like those mechanisms I think that's how you can build a company that is bigger and longer

than you and that's exciting I think the tech we're building is speaks for itself and is getting

there I think the the customers that we've closed and the relationships we've built and the

value we've been able to add to our customers is there's something I'm enormously proud of

and something I want to keep doing and something I think would be best done through a public company

we'll be right back

this episode is brought to you by Nespresso introducing virtual up the latest and a long line of

innovation from Nespresso it's innovation you can touch sense and taste in every single cup

with a three-second start easy open lever and dedicated brew over ice button it's even easier

to enjoy your coffee your way zip for yourself shop virtual up exclusively at nespresso.com

America leads the world in medicine development it matters we get new medicines first nearly

three years faster five million Americans go to work because we make medicines here at home

and not relying on other countries keeps us safe but China is racing to overtake us will we let them

or will we choose to stay ahead when America leads America cures let's tell Washington to keep

us in the lead learn how at america cures.com pay for by pharma a kfc tail in the pursuit of flavor

the greatest insult the kernel ever suffered was being served a wrap that was just a snack by a friend

so he took two crispy tenders lettuce tomatoes and pepper mayo and wrapped them in a soft tortilla

it wasn't a snack it was a meal he called it a twister and never called that friend again

the kernel lived so we could chicken the twister now back at kfc classic or with bacon also try

it spicy it's finger lick and good prices and participation may vary

we're back with first-time founders we've discussed the sort of the decline of the IPO on on our

markets podcast a little bit just the fact that there are there are fewer public companies in

america than ever and also the fact that so many of these massively transformative companies are

taking so long to go public the idea that overnight is only now i'm in there the nonprofit issues

but the reality is this company is supposedly going to go public at a trillion dollar valuation

that's crazy uh so how did you balance it it seems as though the startup world is more

interested in staying private as long as possible uh for or at least that's what the data would tell us

um bus is going public so how did you how do you think about going public what are the the

pros what are the cons and and why now ish well i've made no promises to timing yeah

no promises to time um but i do think look i i don't yeah again i don't know what openly i

is doing um i i think they're a very i i look forward to the interesting books that will be

written about that company over the next 10 years i i don't know what's going on and i don't know when

they're going to go public but i know they've announced stuff i i don't know it's it's a very it's

its own beast it's its own unique and interesting um company then what i'm sure people lots will be

written to describe that story for us our business looks a lot different because we don't have an

enterprise we don't have a consumer offering we don't have the same losses that they have

we're not using money on every customer our margins are actually you know they look a lot

more like sass margins when we work with a customer we deploy our models into their environment

right and that that allows that model to access their private data securely so that we can't see

it it also makes the nature of our margins completely different um you know i think that puts us

in a very different position than the consumer companies that are out there and i think that puts

us in a position that makes it a lot more resonant with a public market a lot more understandable a lot

more uh yeah it looks a lot better um so i i do think the right way for us to make sure that this

company outlasts us and continues to deliver is to eventually go public um when yeah i don't know

and there's an interesting thing you're pointing out about there being a smaller number of public

companies there's an interesting thing you're pointing out uh about companies staying private for

longer i don't think those are unrelated to the income inequality wealth inequality stuff that

we were talking about earlier right this an interesting dynamic in the economy going on right now

and all like all of those things are kind of related um but for us like yeah no that's what we're

going for um and i think that's the path that we're on you are a Canadian company you're based in

Canada uh how do you think about AI as a sort of international geopolitical race um we've got

some big AI companies in america some big AI companies in china i guess mistral is another one

that's in france and there's us in canada and that's right yeah there are four countries in the

world that can make this technology tell us more about what that means for for society yeah it's

that's a strange one um i think that's when you think about how difficult it is to make this

technology and how resource intensive it is it's not super surprising that there aren't that there

like just in it's not surprising that there aren't that many companies doing it it's not surprising

that there aren't that many countries so but it is it is a strange reality that yeah there are

four countries in the world that can build this tech when i think about the what that means for

geopolitics i think this technology is best in the similar you know i use this analogy of like

rockets it's like building rockets um another analogy is to say it's like building power plants

it's like building infrastructure the technology is is a lot more like infrastructure

than previous computer science efforts um and so i think it's a good idea for countries to

have the ability to build infrastructure themselves but it gets a good idea for countries to be

able to build their own nuclear power plants like that's useful that sets up the country for success

um it's a good idea for them to be able to build their own roads like infrastructure for people

is good and it's good for countries to be able to do that from a strategic perspective from a

security perspective like from an economic perspective it's generally a good idea so i think this

technology is important for countries to to be able to build i think there's ways that that

countries can work with the providers in order to give that ability to their country um and i think

that's something that the lot of the world is seeing right now you know we had two decades of

the history of tech really being american really being centered on america and america is a dynamic

fast-moving ingenious place that will can is going to continue to be defining on the technology

and technology in general but i do think it's good for the world to have tech that comes from

other places you know to have a more distributed um view on on how technology is developed and what

it can do for people so that's one of the reasons why i'm happy to be building out of Canada

is that the the thing that changes the trajectory of geopolitics like for example you made the

comparisons to to to other technologies i think some people would also make the comparison to the

nuclear arms race not to say that a i as like a nuke but to say that it was the belief of nations

that this is what will tilt the balance of power across the world we have to build these things

because if we don't and if rush or anyone else gets their hands on this transformative technology

it will completely upend uh the geopolitical structure of earth do you view a i in the same way

not in the sense that it would be i'm not making a nukes and it's going to be destructive point

i'm making a point of the power of it is it is it is it is it a question of whoever builds the

the the a i first and whoever builds the best a i they will be the most powerful force in the world

do you see it that way no that's a little extreme okay yeah i see it as like a strategic and a

imperative for countries to have this technology to facilitate economic growth to do stuff in the same

way i see it's imperative for them to build you know roads really great health care build you know

nuclear power plants build wind real weather other pieces of of infrastructure i don't think i

would go so far as to say it will be the defining thing um and certainly yeah the the nuclear

bomb analogy i disagree with um and i know that's often used when people are talking about a i

as an existential threat but it didn't because i don't think transformers are going to get us to

a g i i i don't think they pose an existential threat and so i don't i don't think that analogy

serves us um in conversation uh i do think it's important to think about the technology as

infrastructure and infrastructure that's good to build but one piece of infrastructure amongst

many pieces of infrastructure that are good to build and important building this moment and i do

were certainly in a dynamic and changing geopolitical time right like this um you know these

are unprecedented times as has been said for the past decade uh but uh yeah and i and i think

the technology will have an impact on that but i don't think it will be the defining thing

you are one of the leaders in a i which is the most important and transformative technology

it's certainly of my time i i'm jenzy and i i was not there to see the internet be created and built

so i think you know this is an extremely important moment not just for america or canada but for

the world does that weigh on you what is it like to be a founder who is at the forefront of this

world changing technology yeah it weighs on me yeah it's totally a strange place to end up in

and not a place i thought i would it excites me i love working with co here working at co here i

love working with all the people echo here and i get really excited and i'm enormously proud

and occasionally deeply moved by the work that we're doing and the group of people that i get to

spend time with working on it it is a complicated emotional experience to think hey this technology is

you know the defining narrative and we are one of ten companies in the world

yeah more four countries in the world that are building it you know i still love i

mentioned earlier i still i love the tech and i moved by it and that's that influences how i

think about this like i think we're building something beautiful and cool and can be useful

and it's it's very meaningful to the world and to the you know to the people around me and

that's interesting there is a subsequently a pressure and an intensity that i did not anticipate

when we started this company i don't i don't think anybody did um i think i solved that by staying

grounded in things that have nothing to do with tech sometimes right i think that's an important

part of the way that i'm that i live my life is by doing stuff occasionally that is completely

unrelated to to AI to transformers to to tech itself and i think that might be why i have pretty

different views than the rest of the people in similar positions to me a lot of young people

watch this show what would be your advice to young people not necessarily just founders but

i think young people in general perhaps even young people who are concerned about their job prospects

that career prospects people who believe that AI could be taking their jobs i mean from the guy

who's building the AI what would your advice be to young people in terms of jobs my advice

is to it has been the same for young people for a while which is that i know i meet a lot of people

young people who are like anxious about making the right decision like i got to work on this because

that's going to be the right thing or that's going to be the right thing and my my advice has often

been look the world's two chaotic for you to predict what's right you can't like every every year

you could read an article of somebody saying the next big job is this and you got to go into this

and they're almost always wrong and so it's just two chaotic you can't predict it what you should

instead do is focused on what you're interested in and what you can optimize for is your own

excitement your own curiosity your own interest and when you're thinking about what career you

want to pick or something you should first and foremost be like well what am i excited about

what am i interested in and conditioned on that your ability to be successful is much higher

than conditioned on you choosing you know what you think is the optimal decision at that moment so

i would really encourage young people like follow their curiosity follow their passion more than they

think follow what's optimal just because you can't predict it it's really hard um my other advice

is to when the central when the narrative around the world these days is one of like it's an

unprecedented chaotic absolutely crazy time i definitely encourage people to learn about history

just read just whatever history from whatever time like well whatever you find ancient history

prehistoric pre history um you know enlighten history modern history like wherever literally wherever

yes we live in unprecedented times yes stuff this chaotic and weird right now and i think

when the history of this moment i think people are going to read the history of these

few decades with curiosity in the future um but there's been a whole lot of crazy times

there's been a whole lot of absolutely nuts stuff that has happened in the history of humanity um

and and it is calming sometimes to read about those and understands the good things that happened

the bad things that happened the way stuff continued in the face of it i find that grounding and

that grounding is helpful for keeping you focused on like what you're interested in what you're

passionate about what you're curious about make frost is the co-founder of co here nick

this was great we really appreciate your time you as well thanks for the conversation

this episode was produced by Allison Weiss and engineered by Benjamin Spencer

our research associates are Dan Shalon and Christian O'Donohue and our senior producer

is Claire Miller thank you for listening to first time founders from prof. G media

we'll see you next month with another founder story

rinse knows that greatness takes time but soda's laundry so rinse will take your laundry and

hand-deliver it to your door expertly cleaned and you can take the time pursuing your passions

time one spent sorting and waiting folding and queuing now spent challenging and innovating

and pushing your way to greatness so pick up the Irish flute or those calligraphy pens or that

daunting beef Wellington recipe card and leave the laundry to us rinse it's time to be great