October 10, 2024 • 47 mins
Chet Kapoor, Chairman and CEO of Datastax, is all in on GenAI, aka Generative Artificial Intelligence. When ChatGPT burst into public consciousness in November 2022, Chet pivoted his team and the company to seize a leadership position in the fifth major technology wave of his 30+ year technology career.
Data at scale, vectorizing databases, and an open-source ethos are just a few ingredients necessary to enable developers to build and deliver the next revolution of GenAI native, and GenAI infused applications. This opportunity goes beyond merely saving money and increasing efficiency by a few points; to fundamentally reinventing processes, business models, and daily life assisted by human-like agents specifically designed around the context of our individual preferences, productivity, and priorities.
Please join me in welcoming Chet Kapoor to the Work 20XX podcast.
From RAG to LLMs, SLMs to hallucinations, and the exponential curves that will provide each of us with our own personal agents running locally, tuned to various processes, GenAI apps promise to change everything. From managing personal email to the marketing department, to our personal social calendars, these technologies will be based on the context and relevance of what matters most to each of us. The implications are broad and Chet and I discussed a number of important topics, from regulation and governance, developer tools, to the ramifications of 24/7 speech access to a supercomputer running apps tailored to our preferences. Exciting, scary, brave new world? Yes, yes, and yes.
What can we learn from the four most recent technology epochs (client-server, web, mobile, cloud), and what makes this GenAI chapter so unique?
Join me in this conversation with someone who has lived through prior transformations and has strong opinions on how this next chapter will unfold. Chet’s eyes still sparkle, the data-centric, open-source excitement burns as brightly as when he first picked up *The Little Kingdom: The Private Story of Apple Computer* by Michael Moritz in 1984, knowing then that he had to be part of this technology-powered transformation.
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(00:00):
All right.
So if you're ready to go, Chet.
Absolutely am.
All right,so I will,
I'll count us down,
and we will go
in three,
two,
one
Hey, welcome back everybody.
Jeff Frick here.
Coming to you for another episode of ‘Work 20XX’
And I'm excitedto have this next guest.
He's an industry veteran.
I think he's been around
this business and this valleyas long as I have.
(00:22):
And, we're kind of a rare breedto find these days,
so I'm excited to welcome in.
He's Chet Kapoor, the Chairman and CEO of DataStax.
Chet, great to see you today.
Thank you Jeff, it's great to be here.
Absolutely.
So before we jump inChairman and CEO of DataStax
give everyone kind of the 101on what DataStax is all about.
(00:43):
DataStax started about 14 years ago
with data at scale.
Right, if you think aboutall the different apps you use
Spotify, Apple, Netflix
ordering Starbucks,
ordering a phone from Verizon,
getting a FedEx package.
They all use technology
that DataStaxhelp create.
So anything that was data at scale,
(01:05):
that's what they did.
Now think about two years ago
in comes this thing called ChatGPT.
And guess what happens?
There is no AI without data.
There's no AI without data at scale.
We do data at scale.
So DataStax is the company to do GenAI apps
(01:25):
because we can
We make it easier for people to develop GenAI apps.
We make them more relevant
and we obviously scale them like crazy.
So that's what DataStax does.
All right. Great.
And we're going to get deepinto the whole AI conversation.
But before we go there,
I want to get a little bit technical.
Tell me a little bit aboutwhat is a vector database.
How is that different?
You know, DataStax was famousfor Cassandra back in the day.
(01:48):
You know, Oracle wasthe first one
with relational databases back in the day.
What is differentabout a vector database,
and how is that really an enabling technology
that we couldn'tdo before.
So the thing that GenAI needs.
So if you think about LLMs, right, so large language models [LLMs]
they need to knowthe proximity of information.
(02:08):
So Jeff's been in the Bay area for 30 years.
Chet's been in the Bay area for 30 years.
They need the proximity of both of us being in the Bay area,
not just Jeff's record and Chet's record.
Right. And so they don't
And it may not be zip codes,but so what you do is
you encode my informationinto an x, y axis,
and we take your informationinto a number, x, y axis
(02:32):
and actually put them together.
So when we search for the area
we get proximity of Jeff and Chetbeing in the Bay area together.
So what vectors are
is a great way for thingsto relate to each other.
Right. And there's a numerical
there are numerical representations
of what is availableto large language models
(02:54):
so that they can actually goand use that information
to make it happen.
So language modelsactually need vectors
to go off and make it happen.
They hadthey use it themselves.
But that's also how you interact with them
to create more relevant applications.
So if you think about
all of the data you have, let's just take, FedEx.
(03:14):
All my packages being tracked forever.
Now it is it is there.
But I need to ‘vectorize’ them
so that I can actually work with the LLMs
to create more relevant applications going forward.
And is it just a faster way to draw the relationships
[Jeff] or a faster way to connect the relationship?[Chet] No, it’s a different way.
[Jeff] Is that the secret? [Chet] So it is a different way
(03:35):
that makes it faster
for large language modelsto use the information.
Okay.
So then let's talk about your pivot to GenAI
and supporting GenAI.
What did you see specifically and
what was really involvedin this pivot
and kind of reprioritizing around this hot new thing
that hit the scenea couple Novembers ago.
(03:57):
So, we started tracking ChatGPT very closely and, you know,
a bunch of us from Google.
So we knew aboutfoundational models happening.
So we've been tracking it for a while,
and then we really saw ChatGPT come inin November of ‘22,
and we started talking about it at the all hands, we started
(04:17):
a bunch of us startedtalking about it at the dinner table.
It became a part of our
of how it was going to change the world.
And so we're geeks,we are technologists.
So we wanted to start talkingabout it and was very clear
by about January or February [2023]that we had to do something
about taking all the datathat we store.
Right, all these, all these terabytes,petabytes of data that we have,
(04:42):
how do we vectorize that
so that you can make more relevantGenAI apps happen?
So the first thing we did waswe said
We went and created some open source technology called ‘JVector’
which basically takes informationas it is coming in
vectorizes it,indexes it, and stores it
so that you can use itright away.
(05:03):
So that's the first thing that we did.
So we basically created the first
what people would call a hybrid vector database.
That means it actually servesas a regular database,
but it's also a vector database all in one.
So and it is the most scalableone in the industry.
And we did that in,I would say, June of ‘23.
(05:23):
Now the moment we did that,we realized that
that was not going to be good enough.
If you think about every stack, right.
This is my fifth waveclient server, web,
mobile, cloud.
And now GenAI.
Every time there's a new wave,there's a new stack
that developers use to build applications.
Databases arepart of them.
(05:44):
So we startedwith a vector database
and we deliveredthat to the market.
But it was very clearthat it was the wild, wild West.
And people did not figured outhow to build apps
to go and make this happen.
And so we said,we're going to go up the stack
and not just have a greatlyoptimized vector database,
but actually go up the stack
and give developersan opinionated view
(06:05):
from open source technologies
on how they should build apps.
And we've been on that journey.
We started about in August of ‘23,
and now one yearyou fast forward.
It is amazing.
We are delivering an AI PaaS,which is a platform as a service.
We have a visual editor.
We have a bunch of ways to ingest data.
We're using a bunch of technologies from Nvidia
(06:25):
for embedding services.
So we're making it likeat least 10x faster and more agile
for developers to deliver,relevant applications at scale.
So you've been on
on a little bit of a roadshowon your RAG++ Roadshow.
Yes
Which I was happy to attend,the one up in the San Francisco,
I think you were most recentlyin New York.
So, you know, when ChatGPT launched
(06:47):
and then I think the firstOpenAI developer conference
they talked about, you know,
have your own ChatGPT,you know, put in your own data,
customize it your own way.
Well, you know,that's not really training it.
RAG [Retrieval-Augmented Generation]is a real way to train it.
And, in, you know,kind of
put in your own data, make it relevant.
So tell us a little bit about the RAG adventure and
it’s still, it’s one step closer tothis vision of, citizen developers.
(07:12):
You know, it'snot quite there yet. I
I watched I'm like, I'm not
don't think I'm quite readyto write that demo,
Netflix appthat you had,
but it's getting closer every day.
This is,it's a great question.
So the first thing is,
LLMS do really well, and they'llalways have a great spot for us.
And you can talk about how OpenAI was
going after the search market
and goingafter advertisers.
(07:32):
All that stuffis for the consumer market.
Now, if you think about you and I
and we think about this, right. [Chet holds up his mobile phone]
You use an AI agent today.
What's the AI agent called?
It's called Google Maps, right?
It's not like we’re new to this.
We've been using AI agentsfor quite some time.
How does an AI agent perform?
(07:53):
How do you agent-ify something?
You take all this informationyou have about maps.
I'm going to give you an example.
All of this informationabout maps and things like that.
And then you need to takeall the information about me,
which is what is Chet like?
What kind of food does he want to do when he travels?
Does he actually have a
In the Bay Area, does he have a sticker?
Can he use thecarpool lane?
(08:14):
Things like that.
Right.
All that is informationthat's stored with me.
You are not going to give that to the LLM
because that's personal information
it’s PII information. [personally identifiable information)
Right?
So now when I’m going to create an applike Google Maps,
I’m going to take all of this information
about maps and freeways and roadsand all that stuff.
And then I'm going to take
all this information I haveavailable for Chet.
(08:35):
And I need to combine these two
to makeit relevant
so I can serve a relevant experience to Chet.
Right, and that
the mechanism to do that
is ‘retrieval augmented generation’ [RAG]
That's what RAG is.
RAG is taking everythingthat the LLM gives you,
everything that Chet has about him,
combining it so that we can deliverthe most relevant experience
(08:58):
for Chet on a personal basis
to make that happen.
Whether you're doing itthrough a chatbot,
your doing it through personalizationor whatever else it might be.
RAG is the best technique so far.
There'll be other techniquesthat come up as well.
But that's the best technique.
And we've jumped on itand so has the industry.
Right
And the open sourceecosystem is phenomenal.
(09:20):
It's growing really, really well,which is great.
Right?
We have a saying at DataStax,if it's not being done in open source
it's probably not worth doing.
Right? Right.
That's how fast things are going.
So we have this thing about RAG
and it is going exceedingly well now.
And taking off.
And so we had this conferencethat you were in San Francisco.
That was great.
You should have been in New York.
(09:40):
That was just two weeks ago.
We had standing room only over 500 people there,
and it was really interesting.
You generallya company like us,
you know, goes and says you want, you know,
you want a bunch of folksto come over
90% of the people in the room were not our customers.
That's great.
We had Nvidia on the stage, we're justit was a great time.
It was standing roomonly in San Francisco.
(10:01):
I was in the back
helping the catering people
because they weretripping over me
every time they came inand out from the back.
So yeah, you had a full house.
It was great.
[Jeff] No doubt about it[Chet] And we’re doing one in London
And we're doing onein London next week.
Yeah.
So, you know,you talk a lot about,
reminding everybody that we'restill in the early days
and I think one of your fun examples,
you talk about early mobile.
Everyone wanted toprogram Angry Birds.
(10:22):
I think I pulled up an original
or early, early Yahoo web page the other day
to highlight to people.
We're just gettingbarely started in this thing.
And if you look at whereweb apps are now,
you know, compared to Angry Birdsand everything is on our phone
this thing is hardly getting started.
So let's talk about adoptionand people being afraid of it
(10:42):
and how people shouldlearn to use it,
especially in the contextof an assistant.
Because that's the one we talk about, every day.
So we thinkthat 2024
is the year of production AI.
A lot of people found when we
when we said this in February,
people were like, ‘No!’
it was like very controversial.
(11:03):
Now you show up in the middleof September and you realize that
what you're doing here is thatevery enterprise that we know of,
right, 800 plus customersdoing lots of different things,
everyone will put one app in production this year.
It may be a small one,it may be just internal,
but they'll put something.
So I think 2024 is going to bethe year of production.
(11:25):
2025 is whenit gets interesting.
That's when we have what we call ‘transformative’ AI use cases.
Where people start saying,
Ah, I think I really likethis travel agent or
the assistant I have and a bot and things like that.
But now I'm startingto change my business model
and try to experimentwith different things.
(11:47):
So I believe
2024 is the year where people put static websites
on mobile apps, right?
They basically had stuffalready on the web
and all they did was made it
make it availablein mobile.
And that's where I call the‘Angry Bird’ stage, right?
You're just doing some things that are simple,
but they they're unique
because the form factor changesand things like that.
(12:08):
But next year and the year after,
it gets really, really,really awesomely interesting
because we have not seenthe Amazons of GenAI yet.
We've not seen the Netflixes of,
of GenAI yet.
They’re still yet to come.
We've not seen companieslike Walmart
(12:28):
who have reinvented themselvesaround GenAI yet.
I think all of that happensin ‘25, ’26, ‘27.
But the one thingthat's different.
This time around, it'll go faster.
It'll go much faster.
And the reasonis because,
this technology is more human like
than anything that we've ever seenin the last 500 years.
(12:51):
Right, or forever in the history of humankind.
We have never seen technology that is more.
I mean, you think about the steam engine, right?
It's not human like,but it changed our lives forever.
Right?
But this thing is, like, morehuman like than anything else.
I wish we had six hours,but we don't.
But we'll keep going so
on the app side,
(13:11):
what percentage of the appsdo you think are going to be
standalone apps that somebody engages with versus
AI influences within other apps,because it seems like
that's really wherethe giant impact is,
because, as you said, it's goingto be a part of the way
everything works in software.
I thinkyes and yes.
(13:31):
I think the answer
so let's take thethe first thing.
Every app will have GenAI features,
no doubt about it,
but I think people will rewrite those apps
to beGenerative AI apps.
And by the way, this is howtechnology works, right?
If you think about cloud,
let's take a different one than mobile.
(13:53):
What did customers doin the beginning of cloud?
Lift and shift.
It's the same app.
Shut down my data center,let me go and just run it on AWS.
That's whatthe use case was.
And thenthey realized,
Ahhhh, we got to like,rebuild the application
to take advantage of everythingthat the cloud does.
And so that'll bethe natural progression.
People will just say,
(14:13):
yeah, I have a website.
Priceline is a good example.
I have a website
I'm going to go and put Penny [Penny is Priceline’s AI chatbot]
Instead of just having a regular chatbot,
I'll have a Generative AI chatbot,so Chet can get relevant information
about his travel plans.
Right.
That's all good.
But how do we
change the experience of how Chet does
he’s still coming to a webpage.
And so people like Pricelineare going to start figuring out
(14:36):
what is the nextwhat is
what does GenAIgive me where
I can change the experience for Chet?
Right, and go proactivein making it happen.
Right
On the human centric side,which is such a good point.
And I actually think
the most underreportedaspect of this whole thing
is the fact that,as you said
I now have accessto a supercomputer,
(14:56):
first of all,
and I cantalk to it.
And the fact that it's trainedon text and literature and,
all these people generated,content databases
actually makes ita really good,
conversational assistantand really understands
the way people talk and the waypeople can communicate.
(15:18):
And I think that
the fact that you've got that at at your fingertips
all the time,you can ask it any question
and it comes right back and helpsyou sort of things out.
I think that is wayunderappreciated
because we finally haveconversational interface
with a computer.
I would agree 100%
I've been wanting.
(15:39):
So if you really think about it, UI [User Interface]
has not changed since what Parc did[Xerox PARC]
and what Steve Jobstook from PARC
and said, we're going to goand do it on the Mac, right?
The GUI interface.[Graphical User Interface]
And now instead of a mouse,
you use your fingers onon a phone
and things like that,which is great.
Lots of good progress.
You know, you can see everythinggoing in the right direction.
(16:01):
But it's not significantly changed.
It's like, you knowsaying
the steering wheel has being on the right or the left
and generally in the same placesince cars came to be,
since cars were invented,
they’re not significantchange there.
I think conversational interfaces will change the game significantly
in a massive way.
(16:21):
I think that is awesome.
LLMs can do that.
You can do that Chat
By the way, ChatGPTwouldn't have gotten to a,
to a billion people withouta conversational piece into it.
Right?
It’s, it went faster than any other technology in our past.
Right.
What I'm really looking forward to though
is I want the technologyto think for me.
(16:42):
Right?
I want it not just to think for everybody.
I don't want it to homogenize it.
I want it to actuallybe specific to me.
So I love the interface piecethat you mentioned,
but I want it to be next level
because I process my emaildifferently than you do.
Right?
And by the way,
my leadership team processesemails differently than I do.
And I want the agent,the email agent,
(17:05):
to be my email agent.
I want it to
to do itthe way I think,
and I want itto be specific to me.
That's what I'm looking forward to.
I want to train agents for me.
Right.
All right, so let's shift gears a little bit
and talk about work,because this is Work 20XX
and in the context of work and people
have a hard time adopting it.
(17:25):
It's still surprising how manypeople have not tried it out.
And I try to tell people,you know,
think of it as a calculator.
It's a tool that you need to use.
You need to getcomfortable using it.
The difficulty is at this stage in time with hallucinations
is I don't double check the work of the calculator
when I do asquare root.
And what I find really interesting in hallucinations is if
(17:47):
if it's a topic that you're aware of
and you're knowledgeable about,
you can pick outthe hallucinations.
And more importantly,
you can identifywhether it's important or not.
But the problem is,if you do like ten,
ten queries in a rowabout something you know about,
and then you do somethingthat you don't know about,
you know, it's a lot harderto pick up the hallucinations.
How do you think that people should think about
(18:09):
incorporating this tool into their life.
Because today, whatI would love for it to do
that it doesn’t do is tell me whatI should be working on right now.
You know, what's the highest?
What's the highest leverage activity
that I should be doing todaybased on my calendar,
based on my email,based on all these other things?
And it's still notdoing that.
It’s still coming back from queries,but it's not really,
(18:31):
taking the lead and helping mebe more efficient with my time.
This is a year and a half old
[Jeff] Right, right[Chet] Give it a little time
Angry Birds
And for a lot of people,don't forget
that in the early days of Google,
or Yahoo or everything you had,
search queries were not accurate.
(18:51):
In fact, they werenot very good.
You could actually go and say,
I did thisand you got a different result.
This is just technology.
It’ll mature, right in all different ways.
It'll mature with relevancy.
It'll mature with governance
and it’ll mature with security.
It'll happen across
the amount of the number of people
and the amount of capitalbeing invested is obscene.
(19:13):
Is obscene, right?
I mean, it is
absolutelywill get better
and will get better fasterthan most people think.
Right? Right.
So you've talked about thisthing, the product,
the productivity paradox.
And as we're recording this,you know,
Cisco just announcedanother round of layoffs.
They had a couple this year.
You know, Amazon just
(19:34):
announced their five days a week
back in the office,return to office,
which I thoughtwas kind of interesting.
Some people think that might bejust kind of a soft layoff
that nobody'stalking about.
But there is this ideawhere, you know,
can you substitutethe increases in productivity
based on this toolwith less people,
or should you really be thinking about it as a way
(19:55):
to get more out of the resourcesand the people that you have?
Because this isan increasingly
accelerating and competitive world,
it seems likeit really should be the latter.
So, great question.
Something that wethink a lot about. Right. So,
We think every enterprise will fallin one of three categories
(20:16):
delegate, accelerate and reinvent
or invent.
The people in the delegate category
are looking for efficiency.
And what they're going to say isI want to be 30% more efficient.
And I'm picking 30% could be 20%, could be 40%,
but a 30% more efficient.
And they will do lay offs
(20:37):
just the wayit goes.
Accelerate will be people who say,
I want to be,
30% more effective.
Yes, I'll takethe efficiency gains,
but I want to be 30%more effective.
And those peopleare signed
are to going to go and change the game,
and they're going to go off and say,
I'm going to accelerate.
I'm not just going to delegate down and say,
(20:58):
give me bottom line results,
but I want to actually start thinking about
being more effective.
And so I can actually increasemy top line and go from there.
The really fun stuff happenswhen people say
they want to reinvent themselves,and they want to be 300% better
not 30%, 300% better.
When they keep
they thinkthey take the model.
(21:20):
They don't say,I am better with GenAI
They flip the model and say,
I have GenAI, let me see how I would structure a company.
And that's what Amazon did, right?
Amazon reinvented what Walmart was doing
because they said it's the web.
Right, so we should thinkabout everything differently
because we have no legacy.We have a
We have what I calla blank sheet of paper.
(21:41):
And you can dowhatever you want with it.
[Jeff] Right[Chet] Right, so
Those are thethree categories
I think you will find companies
to actually go through those phases.
Right, there'll be companies
that go through delegateand go to accelerate.
They're not going to stay stagnant.
There'll be some companies
that just stay in delegatebecause they don't have
because they are regulated industry
and they do not havethe imagination or
(22:02):
they don't have the income statement to make that happen.
They don't have a balance sheetto make that happen.
So you’ll find different people.
But what's really encouraging
is people like JP Morgan, largest bank in the world,
have come back and said,we want to talk about
not just efficiencybut effectiveness.
Right.
Right, and that is extremely encouraging right.
Now does that meanthey won't do layoffs? No.
(22:24):
It means that they willprobably do layoffs as well.
But the good news is
they are looking at thisas top line growth,
not just bottom line.
So I'm
This point that a lot ofpeople don't realize is that.
You may notwhat, it’s just, let’s
Let's try to simplify this
(22:44):
I have 100 peoplein the company.
I get to be 30% more effective.
I have a choice.
I can take the 100 peopleand make it 70.
Or I can leave the 100 people
and just become 25% more effective on the top line.
And that is the decision thatCEOs and boards have to make.
(23:05):
I think most of them will realize
that they're going to come into the accelerate category,
then stay in that delegate category.
Right, but
So they will notI don’t
I think over time people realizethey're not going to do layoffs.
Now the models might change.
They might do some.
But I think they'll realize that this is a mode for them to efficient
become not just efficient,but also become more effective.
(23:26):
Yeah, it's really interesting, right
because that's so parallels the cloud,
the cloud story exactly
where people first thought about itas efficiency gained
and then the smart people looked at it
as an effectiveness game to change the model.
I just had that conversationwith Julie Whalen from CBRE,
even trying to get real estate people to start
think about from efficiency,how many butts per floor,
how many cubes per floor,etc., etc.
(23:48):
into effectiveness.
You know, how are we helping people
to deliver better resultsfor the company?
And it's such a different wayto really think about the world.
I’ll make it really simple.
We run the companyon a weekly basis.
We have a 40 page thingthat we do
that gives us stats and data and knobs or metrics about
across the company.
(24:09):
What we do is
we take everythingthat comes up for a week.
We actually go and, you know, send it to an LLM
that we’ve trained
and it gives us stats all over the place,
and it gives us of course, trending patternsand something, that now
that work was being done by somebodyfor about 10 or 12 hours a week.
Now, we actually just waitfor five minutes
and get a result back.
(24:30):
What do we do with that person's 12 hours?
We make them do other things.
Right, right
And that's how
if you simplify it to a task,it makes a difference.
Right
All right, well I'm going to take you back
in the hot tub time machine here.
We're going to go backto your early days.
And you actually read books back when people still read books.
[Jeff] You and you’ve talked the two big books[Chet] Still do, still do
(24:50):
that really had a big influence on you.
Mike Moritz’s‘The Little Kingdom’
talking about Apple and the excitementaround the development of Apple
and the other one thatthat I was amazed you brought up.
I actually read it,
‘The Cathedral and the Bazaar’[by Eric Raymond]
which is really the storyof open source.
And I think the open source
and it's kind of
it's kind of like,the Moore’s Law
(25:11):
There's the actual thingand then there's the concept
and really the spirit behind itthat I think is so powerful,
like in Moore's Law.
Open source, I think is one ofthe most amazing contributions,
to the world in terms of this ethos.
And, you know, I had Martina Lauchengco on,
she's at Costanoa
And she talked about when she was at Microsoft back in the day
(25:33):
working on Word.
She goes, you know,you were stuck with the product
you shipped in the shrink wrapfor three years.
So you better behappy with the features
because you can't touch it.
I mean, the open source changedeverything so much.
You just talked about the cadence at which you run your company,
and you also talked about,
you know, having lots of eyesand using the
accelerated development pace that that open source provides.
(25:57):
I wonder if you can sharea little bit more.
You've been in the businessfor a long time.
You've seen kind ofboth sides of the equation.
What is really the powerof open source
that has really transformed things?
I'll start by saying
I think I saidthis earlier.
In 2024, if it's not being done in open source
it's probably not worth doing.
That's how
(26:18):
And as a startup person, that's how I think about it right.
Now you may choose to havea different monetization model.
You may do 100 lines of code,and you may only want to
open source 80 lines of code.
I, we are not believers in that.
But there are a lot of people do that,
and they have premium, freemium models and things like that, but
But if it's not being done in open source,
(26:38):
it's probably not worth doing.
Open source is single handedly the best way to get innovation.
In the early days of a project.
It's the best way to get diversity.
It's the best wayto get transparency.
There's no hiding behind anything.
(26:59):
Everybody sees everything.
It's the best way to have meritocracy
because you're not.
It doesn't matter what school you went to
or what company you work for.
Your code does the talking.
Imagine those four things in companies
and how our social structuresmake it so hard
to makethat happen?
(27:21):
We have this beautiful thing called OSS, or open source software
that lets you go off and make it happen.
I am a firm believer, right,in that
you have to figure out a wayto make it happen.
Now, the one thing I'll tell you,
and the reason I saidearly days of a project
open source is very differentwhen you are starting out
(27:41):
versus whenyou mature.
So Linux was very different
when we started out,or even Apache Cassandra,
the project that we’re involved in
very differentin the first seven years
with Linux, the first 15 years
and very different now,
because now
you make a small changethat is wrong
and you have a CrowdStrike thing happening
(28:02):
because everything runs on it.
So you've got to bereally careful.
In the early days,
you're putting features in on a regular basis,
so projects changeand they morph, so
what ends up happening?
The reason I bring this upis because
this is where the cathedral and the bazaar comes in, right, which is
the four thingsI talked about
are very hard to implement in the cathedral.
And so for the early days,you just go and do it in the bazaar.
(28:23):
It's the way to do itbecause you get diversity,
transparency, innovation,just great.
And meritocracy.
But as time goes on
and things get more mature,
people are not going to startworking on really cool features.
The vector, our JVector product
was not somethingwe did in open source.
(28:43):
We actually did itin the cathedral.
Just for the record,we did it ourselves,
but the moment we finished it,
we dropped it into the bazaar and said,
please, people who want to use it can go off and do it.
But people were not getting up in the morning
thinking that they wanted ‘vectorization’ of a Cassandra database.
Right.
But OSS just, it just rocksin so many different ways.
(29:05):
Right, and I think
anybody that doesn't implement it,
they should do it.
They should
they just have a blind spot.
Right.
So different topic.
You might not smile quite so big,but hopefully you'll smile a little bit.
And that's governance andlaw of unintended consequences.
And there's a couple of things
I want to throw out before you
and have you react.
(29:25):
One is,
Dr. Rumman Chowdhury, used to bethe head of ethics at Accenture.
Her great line is ‘Good brakes allow us to go faster.’
Which when I heard that I was like,wow, that is just genius.
And, then you look atwhat's happening, say, in privacy
as a proxy for our ability to regulate.
And because of our, our system
(29:47):
with states and the feds, you know,
it's really hardto get a consistent rule,
even for somethinglike breach notification.
And you compare that with, say,
GDPR in Europeas a way to pull this about.
And then the last thing is,you know,
the law of unintended consequences,which we've seen
a little bit on the social media sidewhere, you know,
unfortunately, to get more clicks,
it turns out more extremecontent gets more clicks.
(30:10):
And you've got kind of these
things that weren'tnecessarily set out that way.
But it happens.
As we look at the power of this technology
and where it's going to be.
And, you know,there's always the push back.
We don't want to govern too much.
We don't want to kill innovation.
But, you know,unfortunately,
things left unfettered don't necessarilyalways turn out the way we wish.
(30:30):
What's kind of your takeas we're getting
we're getting enough tractionwhere this is becoming more and more
of a topic of conversation.
In fact, I think,
[California Governor Gavin] Newsom just signed some paper
sitting next to [Salesforce CEO Marc] Benioff up at Salesforce the other day.
Yeah.
So I'll start with, first my biases.
I'm a geek.
(30:50):
I like innovating stuff.
I like building great productsthat change people's lives.
That's the essence ofwhat I like doing, right?
And obviously building businesses around it.
But it starts from building a product
that changessomeone's lives.
And I don't want anybodycoming between me
and the productand the person who uses it.
Nothing.
Because that's whatthat's how innovation happens.
(31:12):
It's really hard, right?
It's not meant to be.
Right, and changing user behavioris really, really hard.
So what I want to do isI want to build a product
and I want the productto interact with the user,
and that's the only thingI care about.
That's my bias.
Having said that,
for all technology waves,until this one,
(31:33):
I would have said,
Ahh, don't worry about governanceand regulators and all that.
But this one, becauseof what we talked about earlier,
this being more human like than anything else,
I think I'm really glad
that we actuallyare getting people involved.
Now, the problem is
when you get this, when, you know,
minds are mature,
I build a product
and marketsand governments are not
(31:55):
because they have many minds involved.
That's just the natureof how things happen.
And so what is going to happen here is
we're going to
we're going todo it wrong.
Right?
There's not a single technology wave
there's not a single technology wave
in our entire historythat has not had
that has not had, you know,
(32:16):
it has had bad consequences.
Every technology
unintended consequences happenedwith every technology.
There’s a flip side, it's two sides to a coin.
It will happen with GenAI.
We're not.
You can put all the regulation you want
and you'll still happenwith GenAI
Right, it doesn't matter.
So I think we should
the regulators being involved now is great.
We are notgoing to get it right.
(32:37):
But the factthat we're working on it,
my just request would bethe people who work on this
should be people who understandthe technology a little bit.
Right? Right.
They should not be.
That is my one thingthat I want.
I do not
I want any policymakersinvolved in this
to take the time to understand the technology
and the basics of it.
They don't have to becomeprogrammers or things like that,
(32:58):
but they should actually be,well versed
rather than just say it's wrongwithout understanding it.
Right.
Because they, the technologyis moving very fast.
And please don't forgetwe're in the Angry Birds part.
[Jeff] Right, right[Chet] That's right.
Right.
Yeah, hopefully it's not the senators
that still have their emailsprinted for them. Right.
And handed to themon a piece of paper.
That's my point.That is my point.
(33:18):
Yeah. Yeah.
I just was fortunate Ray Kurzweil just came out
with his new book,‘The Singularity Is Nearer,’
and he was speaking at one of the local bookstores.
Was very cool to see,
and there's a lotof really interesting concepts.
I also interviewed, Jack Nilles,the grandfather of telecommuting,
who did the first research in 1973,
when the fastestbandwidth you could get was,
(33:39):
was a T1, which I think was 1.4 megs on a good day,
according to my friends in the business.
So exponential curves,
I want to talk a littleabout exponential curves.
Just to take you back.I had to look it up.
The Cray supercomputerin 1976
cost equivalent dollarstoday of $36 million.
(34:01):
It had eight gigs of RAM
or excuse me,eight megs of RAM, not gigs
It could do 80 millionfloating point operations
compared to a modern smartphonecan do 500 billion
So you can't even try to figure out orders of magnitude.
I tried to figure outfor the bandwidth.
How do you compare5G to 1.4 megs
(34:23):
struggled, ChatGPT and it and said,
okay, how about compared to a garden hose?
If a garden hose is 1.4 megs,
how much waterdo you need to have 5G?
And it was like 50 fire hoses,
you know, bigger,faster, stronger.
So when you think ofwhat's possible
in the not too distant future,
you said this is going to gofaster than anything before,
you know, where do you see thingslike small language models?
(34:45):
Where do you see things likelocal language models running,
running on your laptopor even more on your phone?
As you know, these exponentialcurves on the technology side
continue to ramp.
And, you know,
how do you run a business in theworld of exponential curves?
As these things get faster and faster and faster?
(35:07):
It's a great question.
Something I think about quite a bit, right, so.
I'll give youtwo perspectives.
I think I'll give youa consumer perspective,
and I'll give youan enterprise perspective.
From a consumerperspective,
small language models,
the Distil model
it's doneit's going to happen.
Right, I want my language model.
(35:28):
I want my small language modelthat is disconnected
from anything that's happeningwith a large language model.
This is mine.
And by the way, then I would wantone for DataStax.
And I would want one for DataStax and marketing
and on and onand on
So that part is done.
I think it is going to happen because something
I hadjust to be clear,
I don't want likewe talked about,
I process email differently
(35:50):
than somebody elsedoes in our same company.
And I want to make sure that they have
their ownsmall language model,
because it might startwith email,
but it could go to calendaringas an example could be
how do you create content?
How you process content.
What do I look for in content
versus somebody else, so
that ship has sailed.
People will figure outa way to do it.
And if I want it,
I want it available on this.[Chet pointing to his wrist watch]
(36:11):
I want it available on this [Chet’s watch]by the way, not my phone.
That's actually an easy one.
This I want itavailable everywhere.
[Jeff] Right[Chet] Right
I want it able in my glasses I wear.
Right, so small language modelsare going to show up
everywhere no matter where you go.
Right.
It'll show up in everything we do.
And thatis awesome
because it isall about me.
(36:33):
Right, and I don't mean thatin an egocentric way,
but it's about how does this change my behavior,
then changes a functional behavior?
Then changes a company behavior,
then anindustry behavior,
then a country's behavior,and then civilization's behavior, right.
But I think it needs toit needs to zoom in and out
through all of that.
So we're going to find some crazyawesome stuff happen
(36:56):
that along this way you have ‘agentification’ making it happen.
So I think that's one part
on the enterprise piece a little harder.
90% of the IT budgetin enterprises
is about running systems,
10% is oninnovation.
This AI stuff aint cheap.
(37:19):
And so it's going to create a crunch
on their IT budgets.
And you're not going to go from
a $5 billion budgetto a $10 billion budget.
You may increase it to $5.5, $5.7
And that's not goingto be good enough
for you to reinvent yourself.
But if your in the delegate phase, fine,
but you won't be able to reinvent
So it's going to change
(37:40):
how enterprises spendtime on technology.
And I’ll
the best example is
the same thing happened with web
same thing happened with cloud, right?
But they were replacements.
There's no replacement here.
You're actually going to gut outyour system and make it happen.
And so the enterprise IT function is going to go through
a bunch of changesin the next 4 or 5 years,
(38:00):
because it is going to createthis structural problem
where they have to continue to run
what they already have.
But while they're running it,
they need to completelyreinvent themselves
in an app to go off and make it happen.
And that is going to be a problem.
It's going to create.
It's going to create tensionon how much,
how much moneyyou spend on services,
(38:21):
how much money you spend on software,
how much you spend on LLMs,
on small language models, on databases,
all thosekinds of things.
And we think
we think it's long overdue.
Right
That structurehas not changed,
at least for the 34 yearsI've been doing this.
Yeah, well, and it's not goingto be expensive for long, right.
(38:42):
It's not going to be expensive for long.
That same example,
the T1, I think cost $8,000a month in today's dollars.
Correct
For, for a T1 compared to what you pay for your phone.
Very much so
Kind of piggybackingon that.
You know, a couple of years agoI did some stuff with GE
and there was a lot of talkabout digital twins,
you know, digital twins for modeling behavior.
So you could do differenttypes of tests
(39:03):
and you could do lots of stuffwithout actually,
say, putting a jet enginethrough a sandstorm to find out
how it's going to act.
So a lot of talkabout digital twins
and a lot of good things about it.
What's really crazywhen you start talking about
what you're doing now and RAGand training, you just talked
about the way you doemail versus the way I do email.
When you think about digital twins
and digital twins not for jet engines,
(39:25):
but digital twin of Chet.
And like you said,
it might be a couple of digital twins
the one that goes to work
and then the one that stays at home,
and plays with the family
[Chet] And does laundry [Jeff] How do you think about that
That's a whole different one.
That's actuallythat could be your next
billion dollar idea right there.The digital twin does laundry.
But as you think about digital twins
and how that's going to change the way
(39:46):
you know,there’s talk of
you know, my twin’s going to goattend meetings for me.
You know, there's so manydifferent ways that you can
it’s kind of mind bendingto think of what you would do
with a digital twin,scenario planning for health.
I mean, there's so many vectorsin which this is applicable
pretty, pretty interesting possibilities.
I um
(40:07):
I'll be controversial.
I don't think
I think there'll be variations
of what people call the digital twin
My perspective on this.
And we talk about job lossand things like that.
This is not about me versus AI.
This is not humans versus AI.
This is about humans
(40:28):
versus humanswith the AI.
And what I mean by humanswith AI or people with the AI
is I'm going to havea bunch of things helping me,
but I am stillthe center of everything.
And it’ll
The agents or the digital twinswill never be able
to do what I do,
because judgment will still be minefor quite a long time to come.
(40:53):
Quite a long time to come, right, I mean
otherwise the agenthas to learn my childhood issues.
Right?
[Chet] And that is great going to be hard[Jeff] Right
And so my point is,
I behave a certain way becauseI am thinking about
what I want to be,what I used to be, and what I am.
Right, and that's kind of hardfor an agent to pick up,
no matter how much they goand search the web
and say, you know, Chet was bornand brought up in Calcutta,
(41:15):
and he came here because of the Steve Jobs book
or whatever else it might be.
You
The nuances are only known to me.
Right.
And so my take is,I think digital twins,
there'll be variations of it,just like every other technology.
Right?
We reach,we reach, we reach
and we land up,you know,
we reach for the moonand we land on the trees.
Right, I mean,
we've been talking about self-driving cars
(41:36):
for a long time,
they we're going to be ready four, five years ago
they’re now coming into being right.
[Jeff] Right[Chet] Right
They’re still not readyto roll them out.
So there's
I think there's a part of thisthat we just have to realize.
But I am really looking forwardto a bunch of different agents
that are very specialized for me.
Like you said, for laundry,
for hanging out with the familyhelps me out, like.
For example,I would love to know
(41:56):
what my daughter's schedule is today.
Right, and instead of going and looking at it,
I would say, you know what?
Can I spend time with herbetween 4 and 6
because I think I'm goingto go home early and find out.
And so clicking 16 places,
looking at her calendarlooking at my calendar.
It would be an easy thingfor somebody to get done.
Right? Right.
And then say in aggregate,did I spend 14 hours with her
or 10 hours with her,or 6 hours with her this week?
(42:19):
How do we make sure we spendten hours with her next week?
And I say we meaning meand my agent, we.
Right, right.
Well, and the other thing too just
what is time?
And you talked about
self-driving cars have been on the horizon for a while.
They're just getting here.
But was that a long timeor a short time?
You know, in many waysit feels very, very short,
which is what we're kind ofgetting to the end of our time.
(42:40):
So I want to give you,the last word.
You've been through a lot, as yousaid, five different waves.
You know, back in the daywhen people's main function
was really being an APIbetween applications,
we took data from one piece of paper and keyed it into another piece of paper.
What are some of the thingsyou're excited?
I mean, I know you'reexcited about this
next year is going to bethe year of production GenAI
(43:01):
what are some of the other things,
maybe more specificallyor some examples that you've seen
or that you guys are involved with, whether it's in health care
or transportation or whatever,
that you can say, wow, this is really
this is really amazing
this is some of the reason I came here.
This is some of the magic
I read in the bookabout early Apple days.
It's now coming to fruitionin some of these other fields
(43:22):
I love.
I am really looking forward to
GenAI making individualsmore productive,
because if youcan start there.
And by the way, the individualscould be consumers, right?
if I was in the retail world
or if I wanted to go off and be Verizon
or Best Buy or Pricelineor whatever else it might be.
(43:44):
I'm looking forward to GenAI with LLMs
and with SLMs right,small language models with RAG
making it so much more productive
for individualsto do things right.
It could be my email,it could be me going on travel.
It could be mebuying something on BestBuy.
It could be any of those
or me talking about doing a bill problem, right.
(44:05):
How frustrating is that?
How do I make it easy?
I think that part happensin the next 18 months,
and I still think
that's, by the way,Angry Birds plus plus, right.
The really fun part I'm really looking forward to is
I’m not so, another thing in thatwe said, health care.
How do I take ten hours?
(44:26):
Take ten hours out of doctor's times.
Right, they spend a lot of time readingcharts and saying, and it’s like
Give me the TL;DRon what Chet’s about, right?
I've seen himfor 18 years.
Tell me what I should go and focus on.
But if any anomalies that you seein the last ten years
because he's been 18 years, he's coming to me for the same tests,
like a physical.
So just tell me what it is and let me go and take a look at.
(44:47):
Right.
Given how old he is,things like that,
that's all great.
What I'm really looking forward to is
how does it fundamentally change my life?
Right.
There is a
there, how do I’m I’m asked
I'm talking about somethingmore effective.
Amazon and I go back to Amazon or Uber
changed my life
like significantly changed my life
(45:09):
like it was a step function change.
Now I don't go to a store.
You meanand so
how do you makewhat is
What is the GenAI version of that?
That part I don't know yet.
And by the way, nor do all my colleagues who are building companies,
they don't know that yet.
Right.So we just
but you have to believe in the concept
(45:29):
and go off and make it happen.
Right
I love that,
we’ll all get past Angry Birds.
We can enjoy the Angry Birdswhile we're here,
but we'll all get past Angry Birds.
[Jeff] Well Chet, I really [Chet] My one thing
The one thingI would tell you though, Jeff,
is the one though,you know, it's
The interesting thingabout GenAI, you can see how
interested I am, how enthusiastic I am.
(45:50):
It comes from a placeof being a technologist.
But guess what?
If you're not.
My only one request to you is
lean into it.
This is not going away.
You have to lean into it because it's going to change your life
whether youlike it or not.
Yeah, well,the last little concept,
I just want to close on.
We didn't cover it earlier, but
I used to ask people all the time.
If storage, compute, and networking were free,
(46:14):
what would you build?
Right. And we're asymptotically approaching that every single day.
But what this is enabling is this thing it’s called
the Lost Einsteins concept,which is a paper
that came out in 2018,which is, if
you know some person in Africa,
some young girl has the cure to cancer in her head
that if she only got accessto the education
(46:34):
and the resources thatshe would cure cancer,
this now really startsto get to put the power again
of that supercomputerfor very little cost
in the hands of anyone today with a mobile phone,
tomorrow with a watch.
I think that is so, so, powerful.
I would agree.It's a great way to put it.
(46:54):
The Lost Einsteins
All right.
Well, Chet, thanks again for sharing your,
[Jeff] your perspective, your enthusiasm.[Chet] Thank you
I really appreciatethe time.
It was great.
I'm very glad you had me.
[Chet] Thank you, [Jeff] Absolutely, thank you
All right.
Well, he's Chet, I'm Jeff,
You're watching Work 20XX.
Thanks for watching.
Thanks for listeningon the podcast.
We'll see you next time. Take care.
(47:15):
Great.
Was that good
Thank you.It was awesome.
Okay, great. That was fun.
All right.
So if you're ready to go, Chet.
Absolutely am.
All right,so I will,
I'll count us down,
and we will go
in three,
two,
one
Hey, welcome back everybody.
Jeff Frick here.
Coming to you for another episode of ‘Work 20XX’
And I'm excitedto have this next guest.
He's an industry veteran.
I think he's been around
this business and this valleyas long as I have.
(00:22):
And, we're kind of a rare breedto find these days,
so I'm excited to welcome in.
He's Chet Kapoor, the Chairman and CEO of DataStax.
Chet, great to see you today.
Thank you Jeff, it's great to be here.
Absolutely.
So before we jump inChairman and CEO of DataStax
give everyone kind of the 101on what DataStax is all about.
(00:43):
DataStax started about 14 years ago
with data at scale.
Right, if you think aboutall the different apps you use
Spotify, Apple, Netflix
ordering Starbucks,
ordering a phone from Verizon,
getting a FedEx package.
They all use technology
that DataStaxhelp create.
So anything that was data at scale,
(01:05):
that's what they did.
Now think about two years ago
in comes this thing called ChatGPT.
And guess what happens?
There is no AI without data.
There's no AI without data at scale.
We do data at scale.
So DataStax is the company to do GenAI apps
(01:25):
because we can
We make it easier for people to develop GenAI apps.
We make them more relevant
and we obviously scale them like crazy.
So that's what DataStax does.
All right. Great.
And we're going to get deepinto the whole AI conversation.
But before we go there,
I want to get a little bit technical.
Tell me a little bit aboutwhat is a vector database.
How is that different?
You know, DataStax was famousfor Cassandra back in the day.
(01:48):
You know, Oracle wasthe first one
with relational databases back in the day.
What is differentabout a vector database,
and how is that really an enabling technology
that we couldn'tdo before.
So the thing that GenAI needs.
So if you think about LLMs, right, so large language models [LLMs]
they need to knowthe proximity of information.
(02:08):
So Jeff's been in the Bay area for 30 years.
Chet's been in the Bay area for 30 years.
They need the proximity of both of us being in the Bay area,
not just Jeff's record and Chet's record.
Right. And so they don't
And it may not be zip codes,but so what you do is
you encode my informationinto an x, y axis,
and we take your informationinto a number, x, y axis
(02:32):
and actually put them together.
So when we search for the area
we get proximity of Jeff and Chetbeing in the Bay area together.
So what vectors are
is a great way for thingsto relate to each other.
Right. And there's a numerical
there are numerical representations
of what is availableto large language models
(02:54):
so that they can actually goand use that information
to make it happen.
So language modelsactually need vectors
to go off and make it happen.
They hadthey use it themselves.
But that's also how you interact with them
to create more relevant applications.
So if you think about
all of the data you have, let's just take, FedEx.
(03:14):
All my packages being tracked forever.
Now it is it is there.
But I need to ‘vectorize’ them
so that I can actually work with the LLMs
to create more relevant applications going forward.
And is it just a faster way to draw the relationships
[Jeff] or a faster way to connect the relationship?[Chet] No, it’s a different way.
[Jeff] Is that the secret? [Chet] So it is a different way
(03:35):
that makes it faster
for large language modelsto use the information.
Okay.
So then let's talk about your pivot to GenAI
and supporting GenAI.
What did you see specifically and
what was really involvedin this pivot
and kind of reprioritizing around this hot new thing
that hit the scenea couple Novembers ago.
(03:57):
So, we started tracking ChatGPT very closely and, you know,
a bunch of us from Google.
So we knew aboutfoundational models happening.
So we've been tracking it for a while,
and then we really saw ChatGPT come inin November of ‘22,
and we started talking about it at the all hands, we started
(04:17):
a bunch of us startedtalking about it at the dinner table.
It became a part of our
of how it was going to change the world.
And so we're geeks,we are technologists.
So we wanted to start talkingabout it and was very clear
by about January or February [2023]that we had to do something
about taking all the datathat we store.
Right, all these, all these terabytes,petabytes of data that we have,
(04:42):
how do we vectorize that
so that you can make more relevantGenAI apps happen?
So the first thing we did waswe said
We went and created some open source technology called ‘JVector’
which basically takes informationas it is coming in
vectorizes it,indexes it, and stores it
so that you can use itright away.
(05:03):
So that's the first thing that we did.
So we basically created the first
what people would call a hybrid vector database.
That means it actually servesas a regular database,
but it's also a vector database all in one.
So and it is the most scalableone in the industry.
And we did that in,I would say, June of ‘23.
(05:23):
Now the moment we did that,we realized that
that was not going to be good enough.
If you think about every stack, right.
This is my fifth waveclient server, web,
mobile, cloud.
And now GenAI.
Every time there's a new wave,there's a new stack
that developers use to build applications.
Databases arepart of them.
(05:44):
So we startedwith a vector database
and we deliveredthat to the market.
But it was very clearthat it was the wild, wild West.
And people did not figured outhow to build apps
to go and make this happen.
And so we said,we're going to go up the stack
and not just have a greatlyoptimized vector database,
but actually go up the stack
and give developersan opinionated view
(06:05):
from open source technologies
on how they should build apps.
And we've been on that journey.
We started about in August of ‘23,
and now one yearyou fast forward.
It is amazing.
We are delivering an AI PaaS,which is a platform as a service.
We have a visual editor.
We have a bunch of ways to ingest data.
We're using a bunch of technologies from Nvidia
(06:25):
for embedding services.
So we're making it likeat least 10x faster and more agile
for developers to deliver,relevant applications at scale.
So you've been on
on a little bit of a roadshowon your RAG++ Roadshow.
Yes
Which I was happy to attend,the one up in the San Francisco,
I think you were most recentlyin New York.
So, you know, when ChatGPT launched
(06:47):
and then I think the firstOpenAI developer conference
they talked about, you know,
have your own ChatGPT,you know, put in your own data,
customize it your own way.
Well, you know,that's not really training it.
RAG [Retrieval-Augmented Generation]is a real way to train it.
And, in, you know,kind of
put in your own data, make it relevant.
So tell us a little bit about the RAG adventure and
it’s still, it’s one step closer tothis vision of, citizen developers.
(07:12):
You know, it'snot quite there yet. I
I watched I'm like, I'm not
don't think I'm quite readyto write that demo,
Netflix appthat you had,
but it's getting closer every day.
This is,it's a great question.
So the first thing is,
LLMS do really well, and they'llalways have a great spot for us.
And you can talk about how OpenAI was
going after the search market
and goingafter advertisers.
(07:32):
All that stuffis for the consumer market.
Now, if you think about you and I
and we think about this, right. [Chet holds up his mobile phone]
You use an AI agent today.
What's the AI agent called?
It's called Google Maps, right?
It's not like we’re new to this.
We've been using AI agentsfor quite some time.
How does an AI agent perform?
(07:53):
How do you agent-ify something?
You take all this informationyou have about maps.
I'm going to give you an example.
All of this informationabout maps and things like that.
And then you need to takeall the information about me,
which is what is Chet like?
What kind of food does he want to do when he travels?
Does he actually have a
In the Bay Area, does he have a sticker?
Can he use thecarpool lane?
(08:14):
Things like that.
Right.
All that is informationthat's stored with me.
You are not going to give that to the LLM
because that's personal information
it’s PII information. [personally identifiable information)
Right?
So now when I’m going to create an applike Google Maps,
I’m going to take all of this information
about maps and freeways and roadsand all that stuff.
And then I'm going to take
all this information I haveavailable for Chet.
(08:35):
And I need to combine these two
to makeit relevant
so I can serve a relevant experience to Chet.
Right, and that
the mechanism to do that
is ‘retrieval augmented generation’ [RAG]
That's what RAG is.
RAG is taking everythingthat the LLM gives you,
everything that Chet has about him,
combining it so that we can deliverthe most relevant experience
(08:58):
for Chet on a personal basis
to make that happen.
Whether you're doing itthrough a chatbot,
your doing it through personalizationor whatever else it might be.
RAG is the best technique so far.
There'll be other techniquesthat come up as well.
But that's the best technique.
And we've jumped on itand so has the industry.
Right
And the open sourceecosystem is phenomenal.
(09:20):
It's growing really, really well,which is great.
Right?
We have a saying at DataStax,if it's not being done in open source
it's probably not worth doing.
Right? Right.
That's how fast things are going.
So we have this thing about RAG
and it is going exceedingly well now.
And taking off.
And so we had this conferencethat you were in San Francisco.
That was great.
You should have been in New York.
(09:40):
That was just two weeks ago.
We had standing room only over 500 people there,
and it was really interesting.
You generallya company like us,
you know, goes and says you want, you know,
you want a bunch of folksto come over
90% of the people in the room were not our customers.
That's great.
We had Nvidia on the stage, we're justit was a great time.
It was standing roomonly in San Francisco.
(10:01):
I was in the back
helping the catering people
because they weretripping over me
every time they came inand out from the back.
So yeah, you had a full house.
It was great.
[Jeff] No doubt about it[Chet] And we’re doing one in London
And we're doing onein London next week.
Yeah.
So, you know,you talk a lot about,
reminding everybody that we'restill in the early days
and I think one of your fun examples,
you talk about early mobile.
Everyone wanted toprogram Angry Birds.
(10:22):
I think I pulled up an original
or early, early Yahoo web page the other day
to highlight to people.
We're just gettingbarely started in this thing.
And if you look at whereweb apps are now,
you know, compared to Angry Birdsand everything is on our phone
this thing is hardly getting started.
So let's talk about adoptionand people being afraid of it
(10:42):
and how people shouldlearn to use it,
especially in the contextof an assistant.
Because that's the one we talk about, every day.
So we thinkthat 2024
is the year of production AI.
A lot of people found when we
when we said this in February,
people were like, ‘No!’
it was like very controversial.
(11:03):
Now you show up in the middleof September and you realize that
what you're doing here is thatevery enterprise that we know of,
right, 800 plus customersdoing lots of different things,
everyone will put one app in production this year.
It may be a small one,it may be just internal,
but they'll put something.
So I think 2024 is going to bethe year of production.
(11:25):
2025 is whenit gets interesting.
That's when we have what we call ‘transformative’ AI use cases.
Where people start saying,
Ah, I think I really likethis travel agent or
the assistant I have and a bot and things like that.
But now I'm startingto change my business model
and try to experimentwith different things.
(11:47):
So I believe
2024 is the year where people put static websites
on mobile apps, right?
They basically had stuffalready on the web
and all they did was made it
make it availablein mobile.
And that's where I call the‘Angry Bird’ stage, right?
You're just doing some things that are simple,
but they they're unique
because the form factor changesand things like that.
(12:08):
But next year and the year after,
it gets really, really,really awesomely interesting
because we have not seenthe Amazons of GenAI yet.
We've not seen the Netflixes of,
of GenAI yet.
They’re still yet to come.
We've not seen companieslike Walmart
(12:28):
who have reinvented themselvesaround GenAI yet.
I think all of that happensin ‘25, ’26, ‘27.
But the one thingthat's different.
This time around, it'll go faster.
It'll go much faster.
And the reasonis because,
this technology is more human like
than anything that we've ever seenin the last 500 years.
(12:51):
Right, or forever in the history of humankind.
We have never seen technology that is more.
I mean, you think about the steam engine, right?
It's not human like,but it changed our lives forever.
Right?
But this thing is, like, morehuman like than anything else.
I wish we had six hours,but we don't.
But we'll keep going so
on the app side,
(13:11):
what percentage of the appsdo you think are going to be
standalone apps that somebody engages with versus
AI influences within other apps,because it seems like
that's really wherethe giant impact is,
because, as you said, it's goingto be a part of the way
everything works in software.
I thinkyes and yes.
(13:31):
I think the answer
so let's take thethe first thing.
Every app will have GenAI features,
no doubt about it,
but I think people will rewrite those apps
to beGenerative AI apps.
And by the way, this is howtechnology works, right?
If you think about cloud,
let's take a different one than mobile.
(13:53):
What did customers doin the beginning of cloud?
Lift and shift.
It's the same app.
Shut down my data center,let me go and just run it on AWS.
That's whatthe use case was.
And thenthey realized,
Ahhhh, we got to like,rebuild the application
to take advantage of everythingthat the cloud does.
And so that'll bethe natural progression.
People will just say,
(14:13):
yeah, I have a website.
Priceline is a good example.
I have a website
I'm going to go and put Penny [Penny is Priceline’s AI chatbot]
Instead of just having a regular chatbot,
I'll have a Generative AI chatbot,so Chet can get relevant information
about his travel plans.
Right.
That's all good.
But how do we
change the experience of how Chet does
he’s still coming to a webpage.
And so people like Pricelineare going to start figuring out
(14:36):
what is the nextwhat is
what does GenAIgive me where
I can change the experience for Chet?
Right, and go proactivein making it happen.
Right
On the human centric side,which is such a good point.
And I actually think
the most underreportedaspect of this whole thing
is the fact that,as you said
I now have accessto a supercomputer,
(14:56):
first of all,
and I cantalk to it.
And the fact that it's trainedon text and literature and,
all these people generated,content databases
actually makes ita really good,
conversational assistantand really understands
the way people talk and the waypeople can communicate.
(15:18):
And I think that
the fact that you've got that at at your fingertips
all the time,you can ask it any question
and it comes right back and helpsyou sort of things out.
I think that is wayunderappreciated
because we finally haveconversational interface
with a computer.
I would agree 100%
I've been wanting.
(15:39):
So if you really think about it, UI [User Interface]
has not changed since what Parc did[Xerox PARC]
and what Steve Jobstook from PARC
and said, we're going to goand do it on the Mac, right?
The GUI interface.[Graphical User Interface]
And now instead of a mouse,
you use your fingers onon a phone
and things like that,which is great.
Lots of good progress.
You know, you can see everythinggoing in the right direction.
(16:01):
But it's not significantly changed.
It's like, you knowsaying
the steering wheel has being on the right or the left
and generally in the same placesince cars came to be,
since cars were invented,
they’re not significantchange there.
I think conversational interfaces will change the game significantly
in a massive way.
(16:21):
I think that is awesome.
LLMs can do that.
You can do that Chat
By the way, ChatGPTwouldn't have gotten to a,
to a billion people withouta conversational piece into it.
Right?
It’s, it went faster than any other technology in our past.
Right.
What I'm really looking forward to though
is I want the technologyto think for me.
(16:42):
Right?
I want it not just to think for everybody.
I don't want it to homogenize it.
I want it to actuallybe specific to me.
So I love the interface piecethat you mentioned,
but I want it to be next level
because I process my emaildifferently than you do.
Right?
And by the way,
my leadership team processesemails differently than I do.
And I want the agent,the email agent,
(17:05):
to be my email agent.
I want it to
to do itthe way I think,
and I want itto be specific to me.
That's what I'm looking forward to.
I want to train agents for me.
Right.
All right, so let's shift gears a little bit
and talk about work,because this is Work 20XX
and in the context of work and people
have a hard time adopting it.
(17:25):
It's still surprising how manypeople have not tried it out.
And I try to tell people,you know,
think of it as a calculator.
It's a tool that you need to use.
You need to getcomfortable using it.
The difficulty is at this stage in time with hallucinations
is I don't double check the work of the calculator
when I do asquare root.
And what I find really interesting in hallucinations is if
(17:47):
if it's a topic that you're aware of
and you're knowledgeable about,
you can pick outthe hallucinations.
And more importantly,
you can identifywhether it's important or not.
But the problem is,if you do like ten,
ten queries in a rowabout something you know about,
and then you do somethingthat you don't know about,
you know, it's a lot harderto pick up the hallucinations.
How do you think that people should think about
(18:09):
incorporating this tool into their life.
Because today, whatI would love for it to do
that it doesn’t do is tell me whatI should be working on right now.
You know, what's the highest?
What's the highest leverage activity
that I should be doing todaybased on my calendar,
based on my email,based on all these other things?
And it's still notdoing that.
It’s still coming back from queries,but it's not really,
(18:31):
taking the lead and helping mebe more efficient with my time.
This is a year and a half old
[Jeff] Right, right[Chet] Give it a little time
Angry Birds
And for a lot of people,don't forget
that in the early days of Google,
or Yahoo or everything you had,
search queries were not accurate.
(18:51):
In fact, they werenot very good.
You could actually go and say,
I did thisand you got a different result.
This is just technology.
It’ll mature, right in all different ways.
It'll mature with relevancy.
It'll mature with governance
and it’ll mature with security.
It'll happen across
the amount of the number of people
and the amount of capitalbeing invested is obscene.
(19:13):
Is obscene, right?
I mean, it is
absolutelywill get better
and will get better fasterthan most people think.
Right? Right.
So you've talked about thisthing, the product,
the productivity paradox.
And as we're recording this,you know,
Cisco just announcedanother round of layoffs.
They had a couple this year.
You know, Amazon just
(19:34):
announced their five days a week
back in the office,return to office,
which I thoughtwas kind of interesting.
Some people think that might bejust kind of a soft layoff
that nobody'stalking about.
But there is this ideawhere, you know,
can you substitutethe increases in productivity
based on this toolwith less people,
or should you really be thinking about it as a way
(19:55):
to get more out of the resourcesand the people that you have?
Because this isan increasingly
accelerating and competitive world,
it seems likeit really should be the latter.
So, great question.
Something that wethink a lot about. Right. So,
We think every enterprise will fallin one of three categories
(20:16):
delegate, accelerate and reinvent
or invent.
The people in the delegate category
are looking for efficiency.
And what they're going to say isI want to be 30% more efficient.
And I'm picking 30% could be 20%, could be 40%,
but a 30% more efficient.
And they will do lay offs
(20:37):
just the wayit goes.
Accelerate will be people who say,
I want to be,
30% more effective.
Yes, I'll takethe efficiency gains,
but I want to be 30%more effective.
And those peopleare signed
are to going to go and change the game,
and they're going to go off and say,
I'm going to accelerate.
I'm not just going to delegate down and say,
(20:58):
give me bottom line results,
but I want to actually start thinking about
being more effective.
And so I can actually increasemy top line and go from there.
The really fun stuff happenswhen people say
they want to reinvent themselves,and they want to be 300% better
not 30%, 300% better.
When they keep
they thinkthey take the model.
(21:20):
They don't say,I am better with GenAI
They flip the model and say,
I have GenAI, let me see how I would structure a company.
And that's what Amazon did, right?
Amazon reinvented what Walmart was doing
because they said it's the web.
Right, so we should thinkabout everything differently
because we have no legacy.We have a
We have what I calla blank sheet of paper.
(21:41):
And you can dowhatever you want with it.
[Jeff] Right[Chet] Right, so
Those are thethree categories
I think you will find companies
to actually go through those phases.
Right, there'll be companies
that go through delegateand go to accelerate.
They're not going to stay stagnant.
There'll be some companies
that just stay in delegatebecause they don't have
because they are regulated industry
and they do not havethe imagination or
(22:02):
they don't have the income statement to make that happen.
They don't have a balance sheetto make that happen.
So you’ll find different people.
But what's really encouraging
is people like JP Morgan, largest bank in the world,
have come back and said,we want to talk about
not just efficiencybut effectiveness.
Right.
Right, and that is extremely encouraging right.
Now does that meanthey won't do layoffs? No.
(22:24):
It means that they willprobably do layoffs as well.
But the good news is
they are looking at thisas top line growth,
not just bottom line.
So I'm
This point that a lot ofpeople don't realize is that.
You may notwhat, it’s just, let’s
Let's try to simplify this
(22:44):
I have 100 peoplein the company.
I get to be 30% more effective.
I have a choice.
I can take the 100 peopleand make it 70.
Or I can leave the 100 people
and just become 25% more effective on the top line.
And that is the decision thatCEOs and boards have to make.
(23:05):
I think most of them will realize
that they're going to come into the accelerate category,
then stay in that delegate category.
Right, but
So they will notI don’t
I think over time people realizethey're not going to do layoffs.
Now the models might change.
They might do some.
But I think they'll realize that this is a mode for them to efficient
become not just efficient,but also become more effective.
(23:26):
Yeah, it's really interesting, right
because that's so parallels the cloud,
the cloud story exactly
where people first thought about itas efficiency gained
and then the smart people looked at it
as an effectiveness game to change the model.
I just had that conversationwith Julie Whalen from CBRE,
even trying to get real estate people to start
think about from efficiency,how many butts per floor,
how many cubes per floor,etc., etc.
(23:48):
into effectiveness.
You know, how are we helping people
to deliver better resultsfor the company?
And it's such a different wayto really think about the world.
I’ll make it really simple.
We run the companyon a weekly basis.
We have a 40 page thingthat we do
that gives us stats and data and knobs or metrics about
across the company.
(24:09):
What we do is
we take everythingthat comes up for a week.
We actually go and, you know, send it to an LLM
that we’ve trained
and it gives us stats all over the place,
and it gives us of course, trending patternsand something, that now
that work was being done by somebodyfor about 10 or 12 hours a week.
Now, we actually just waitfor five minutes
and get a result back.
(24:30):
What do we do with that person's 12 hours?
We make them do other things.
Right, right
And that's how
if you simplify it to a task,it makes a difference.
Right
All right, well I'm going to take you back
in the hot tub time machine here.
We're going to go backto your early days.
And you actually read books back when people still read books.
[Jeff] You and you’ve talked the two big books[Chet] Still do, still do
(24:50):
that really had a big influence on you.
Mike Moritz’s‘The Little Kingdom’
talking about Apple and the excitementaround the development of Apple
and the other one thatthat I was amazed you brought up.
I actually read it,
‘The Cathedral and the Bazaar’[by Eric Raymond]
which is really the storyof open source.
And I think the open source
and it's kind of
it's kind of like,the Moore’s Law
(25:11):
There's the actual thingand then there's the concept
and really the spirit behind itthat I think is so powerful,
like in Moore's Law.
Open source, I think is one ofthe most amazing contributions,
to the world in terms of this ethos.
And, you know, I had Martina Lauchengco on,
she's at Costanoa
And she talked about when she was at Microsoft back in the day
(25:33):
working on Word.
She goes, you know,you were stuck with the product
you shipped in the shrink wrapfor three years.
So you better behappy with the features
because you can't touch it.
I mean, the open source changedeverything so much.
You just talked about the cadence at which you run your company,
and you also talked about,
you know, having lots of eyesand using the
accelerated development pace that that open source provides.
(25:57):
I wonder if you can sharea little bit more.
You've been in the businessfor a long time.
You've seen kind ofboth sides of the equation.
What is really the powerof open source
that has really transformed things?
I'll start by saying
I think I saidthis earlier.
In 2024, if it's not being done in open source
it's probably not worth doing.
That's how
(26:18):
And as a startup person, that's how I think about it right.
Now you may choose to havea different monetization model.
You may do 100 lines of code,and you may only want to
open source 80 lines of code.
I, we are not believers in that.
But there are a lot of people do that,
and they have premium, freemium models and things like that, but
But if it's not being done in open source,
(26:38):
it's probably not worth doing.
Open source is single handedly the best way to get innovation.
In the early days of a project.
It's the best way to get diversity.
It's the best wayto get transparency.
There's no hiding behind anything.
(26:59):
Everybody sees everything.
It's the best way to have meritocracy
because you're not.
It doesn't matter what school you went to
or what company you work for.
Your code does the talking.
Imagine those four things in companies
and how our social structuresmake it so hard
to makethat happen?
(27:21):
We have this beautiful thing called OSS, or open source software
that lets you go off and make it happen.
I am a firm believer, right,in that
you have to figure out a wayto make it happen.
Now, the one thing I'll tell you,
and the reason I saidearly days of a project
open source is very differentwhen you are starting out
(27:41):
versus whenyou mature.
So Linux was very different
when we started out,or even Apache Cassandra,
the project that we’re involved in
very differentin the first seven years
with Linux, the first 15 years
and very different now,
because now
you make a small changethat is wrong
and you have a CrowdStrike thing happening
(28:02):
because everything runs on it.
So you've got to bereally careful.
In the early days,
you're putting features in on a regular basis,
so projects changeand they morph, so
what ends up happening?
The reason I bring this upis because
this is where the cathedral and the bazaar comes in, right, which is
the four thingsI talked about
are very hard to implement in the cathedral.
And so for the early days,you just go and do it in the bazaar.
(28:23):
It's the way to do itbecause you get diversity,
transparency, innovation,just great.
And meritocracy.
But as time goes on
and things get more mature,
people are not going to startworking on really cool features.
The vector, our JVector product
was not somethingwe did in open source.
(28:43):
We actually did itin the cathedral.
Just for the record,we did it ourselves,
but the moment we finished it,
we dropped it into the bazaar and said,
please, people who want to use it can go off and do it.
But people were not getting up in the morning
thinking that they wanted ‘vectorization’ of a Cassandra database.
Right.
But OSS just, it just rocksin so many different ways.
(29:05):
Right, and I think
anybody that doesn't implement it,
they should do it.
They should
they just have a blind spot.
Right.
So different topic.
You might not smile quite so big,but hopefully you'll smile a little bit.
And that's governance andlaw of unintended consequences.
And there's a couple of things
I want to throw out before you
and have you react.
(29:25):
One is,
Dr. Rumman Chowdhury, used to bethe head of ethics at Accenture.
Her great line is ‘Good brakes allow us to go faster.’
Which when I heard that I was like,wow, that is just genius.
And, then you look atwhat's happening, say, in privacy
as a proxy for our ability to regulate.
And because of our, our system
(29:47):
with states and the feds, you know,
it's really hardto get a consistent rule,
even for somethinglike breach notification.
And you compare that with, say,
GDPR in Europeas a way to pull this about.
And then the last thing is,you know,
the law of unintended consequences,which we've seen
a little bit on the social media sidewhere, you know,
unfortunately, to get more clicks,
it turns out more extremecontent gets more clicks.
(30:10):
And you've got kind of these
things that weren'tnecessarily set out that way.
But it happens.
As we look at the power of this technology
and where it's going to be.
And, you know,there's always the push back.
We don't want to govern too much.
We don't want to kill innovation.
But, you know,unfortunately,
things left unfettered don't necessarilyalways turn out the way we wish.
(30:30):
What's kind of your takeas we're getting
we're getting enough tractionwhere this is becoming more and more
of a topic of conversation.
In fact, I think,
[California Governor Gavin] Newsom just signed some paper
sitting next to [Salesforce CEO Marc] Benioff up at Salesforce the other day.
Yeah.
So I'll start with, first my biases.
I'm a geek.
(30:50):
I like innovating stuff.
I like building great productsthat change people's lives.
That's the essence ofwhat I like doing, right?
And obviously building businesses around it.
But it starts from building a product
that changessomeone's lives.
And I don't want anybodycoming between me
and the productand the person who uses it.
Nothing.
Because that's whatthat's how innovation happens.
(31:12):
It's really hard, right?
It's not meant to be.
Right, and changing user behavioris really, really hard.
So what I want to do isI want to build a product
and I want the productto interact with the user,
and that's the only thingI care about.
That's my bias.
Having said that,
for all technology waves,until this one,
(31:33):
I would have said,
Ahh, don't worry about governanceand regulators and all that.
But this one, becauseof what we talked about earlier,
this being more human like than anything else,
I think I'm really glad
that we actuallyare getting people involved.
Now, the problem is
when you get this, when, you know,
minds are mature,
I build a product
and marketsand governments are not
(31:55):
because they have many minds involved.
That's just the natureof how things happen.
And so what is going to happen here is
we're going to
we're going todo it wrong.
Right?
There's not a single technology wave
there's not a single technology wave
in our entire historythat has not had
that has not had, you know,
(32:16):
it has had bad consequences.
Every technology
unintended consequences happenedwith every technology.
There’s a flip side, it's two sides to a coin.
It will happen with GenAI.
We're not.
You can put all the regulation you want
and you'll still happenwith GenAI
Right, it doesn't matter.
So I think we should
the regulators being involved now is great.
We are notgoing to get it right.
(32:37):
But the factthat we're working on it,
my just request would bethe people who work on this
should be people who understandthe technology a little bit.
Right? Right.
They should not be.
That is my one thingthat I want.
I do not
I want any policymakersinvolved in this
to take the time to understand the technology
and the basics of it.
They don't have to becomeprogrammers or things like that,
(32:58):
but they should actually be,well versed
rather than just say it's wrongwithout understanding it.
Right.
Because they, the technologyis moving very fast.
And please don't forgetwe're in the Angry Birds part.
[Jeff] Right, right[Chet] That's right.
Right.
Yeah, hopefully it's not the senators
that still have their emailsprinted for them. Right.
And handed to themon a piece of paper.
That's my point.That is my point.
(33:18):
Yeah. Yeah.
I just was fortunate Ray Kurzweil just came out
with his new book,‘The Singularity Is Nearer,’
and he was speaking at one of the local bookstores.
Was very cool to see,
and there's a lotof really interesting concepts.
I also interviewed, Jack Nilles,the grandfather of telecommuting,
who did the first research in 1973,
when the fastestbandwidth you could get was,
(33:39):
was a T1, which I think was 1.4 megs on a good day,
according to my friends in the business.
So exponential curves,
I want to talk a littleabout exponential curves.
Just to take you back.I had to look it up.
The Cray supercomputerin 1976
cost equivalent dollarstoday of $36 million.
(34:01):
It had eight gigs of RAM
or excuse me,eight megs of RAM, not gigs
It could do 80 millionfloating point operations
compared to a modern smartphonecan do 500 billion
So you can't even try to figure out orders of magnitude.
I tried to figure outfor the bandwidth.
How do you compare5G to 1.4 megs
(34:23):
struggled, ChatGPT and it and said,
okay, how about compared to a garden hose?
If a garden hose is 1.4 megs,
how much waterdo you need to have 5G?
And it was like 50 fire hoses,
you know, bigger,faster, stronger.
So when you think ofwhat's possible
in the not too distant future,
you said this is going to gofaster than anything before,
you know, where do you see thingslike small language models?
(34:45):
Where do you see things likelocal language models running,
running on your laptopor even more on your phone?
As you know, these exponentialcurves on the technology side
continue to ramp.
And, you know,
how do you run a business in theworld of exponential curves?
As these things get faster and faster and faster?
(35:07):
It's a great question.
Something I think about quite a bit, right, so.
I'll give youtwo perspectives.
I think I'll give youa consumer perspective,
and I'll give youan enterprise perspective.
From a consumerperspective,
small language models,
the Distil model
it's doneit's going to happen.
Right, I want my language model.
(35:28):
I want my small language modelthat is disconnected
from anything that's happeningwith a large language model.
This is mine.
And by the way, then I would wantone for DataStax.
And I would want one for DataStax and marketing
and on and onand on
So that part is done.
I think it is going to happen because something
I hadjust to be clear,
I don't want likewe talked about,
I process email differently
(35:50):
than somebody elsedoes in our same company.
And I want to make sure that they have
their ownsmall language model,
because it might startwith email,
but it could go to calendaringas an example could be
how do you create content?
How you process content.
What do I look for in content
versus somebody else, so
that ship has sailed.
People will figure outa way to do it.
And if I want it,
I want it available on this.[Chet pointing to his wrist watch]
(36:11):
I want it available on this [Chet’s watch]by the way, not my phone.
That's actually an easy one.
This I want itavailable everywhere.
[Jeff] Right[Chet] Right
I want it able in my glasses I wear.
Right, so small language modelsare going to show up
everywhere no matter where you go.
Right.
It'll show up in everything we do.
And thatis awesome
because it isall about me.
(36:33):
Right, and I don't mean thatin an egocentric way,
but it's about how does this change my behavior,
then changes a functional behavior?
Then changes a company behavior,
then anindustry behavior,
then a country's behavior,and then civilization's behavior, right.
But I think it needs toit needs to zoom in and out
through all of that.
So we're going to find some crazyawesome stuff happen
(36:56):
that along this way you have ‘agentification’ making it happen.
So I think that's one part
on the enterprise piece a little harder.
90% of the IT budgetin enterprises
is about running systems,
10% is oninnovation.
This AI stuff aint cheap.
(37:19):
And so it's going to create a crunch
on their IT budgets.
And you're not going to go from
a $5 billion budgetto a $10 billion budget.
You may increase it to $5.5, $5.7
And that's not goingto be good enough
for you to reinvent yourself.
But if your in the delegate phase, fine,
but you won't be able to reinvent
So it's going to change
(37:40):
how enterprises spendtime on technology.
And I’ll
the best example is
the same thing happened with web
same thing happened with cloud, right?
But they were replacements.
There's no replacement here.
You're actually going to gut outyour system and make it happen.
And so the enterprise IT function is going to go through
a bunch of changesin the next 4 or 5 years,
(38:00):
because it is going to createthis structural problem
where they have to continue to run
what they already have.
But while they're running it,
they need to completelyreinvent themselves
in an app to go off and make it happen.
And that is going to be a problem.
It's going to create.
It's going to create tensionon how much,
how much moneyyou spend on services,
(38:21):
how much money you spend on software,
how much you spend on LLMs,
on small language models, on databases,
all thosekinds of things.
And we think
we think it's long overdue.
Right
That structurehas not changed,
at least for the 34 yearsI've been doing this.
Yeah, well, and it's not goingto be expensive for long, right.
(38:42):
It's not going to be expensive for long.
That same example,
the T1, I think cost $8,000a month in today's dollars.
Correct
For, for a T1 compared to what you pay for your phone.
Very much so
Kind of piggybackingon that.
You know, a couple of years agoI did some stuff with GE
and there was a lot of talkabout digital twins,
you know, digital twins for modeling behavior.
So you could do differenttypes of tests
(39:03):
and you could do lots of stuffwithout actually,
say, putting a jet enginethrough a sandstorm to find out
how it's going to act.
So a lot of talkabout digital twins
and a lot of good things about it.
What's really crazywhen you start talking about
what you're doing now and RAGand training, you just talked
about the way you doemail versus the way I do email.
When you think about digital twins
and digital twins not for jet engines,
(39:25):
but digital twin of Chet.
And like you said,
it might be a couple of digital twins
the one that goes to work
and then the one that stays at home,
and plays with the family
[Chet] And does laundry [Jeff] How do you think about that
That's a whole different one.
That's actuallythat could be your next
billion dollar idea right there.The digital twin does laundry.
But as you think about digital twins
and how that's going to change the way
(39:46):
you know,there’s talk of
you know, my twin’s going to goattend meetings for me.
You know, there's so manydifferent ways that you can
it’s kind of mind bendingto think of what you would do
with a digital twin,scenario planning for health.
I mean, there's so many vectorsin which this is applicable
pretty, pretty interesting possibilities.
I um
(40:07):
I'll be controversial.
I don't think
I think there'll be variations
of what people call the digital twin
My perspective on this.
And we talk about job lossand things like that.
This is not about me versus AI.
This is not humans versus AI.
This is about humans
(40:28):
versus humanswith the AI.
And what I mean by humanswith AI or people with the AI
is I'm going to havea bunch of things helping me,
but I am stillthe center of everything.
And it’ll
The agents or the digital twinswill never be able
to do what I do,
because judgment will still be minefor quite a long time to come.
(40:53):
Quite a long time to come, right, I mean
otherwise the agenthas to learn my childhood issues.
Right?
[Chet] And that is great going to be hard[Jeff] Right
And so my point is,
I behave a certain way becauseI am thinking about
what I want to be,what I used to be, and what I am.
Right, and that's kind of hardfor an agent to pick up,
no matter how much they goand search the web
and say, you know, Chet was bornand brought up in Calcutta,
(41:15):
and he came here because of the Steve Jobs book
or whatever else it might be.
You
The nuances are only known to me.
Right.
And so my take is,I think digital twins,
there'll be variations of it,just like every other technology.
Right?
We reach,we reach, we reach
and we land up,you know,
we reach for the moonand we land on the trees.
Right, I mean,
we've been talking about self-driving cars
(41:36):
for a long time,
they we're going to be ready four, five years ago
they’re now coming into being right.
[Jeff] Right[Chet] Right
They’re still not readyto roll them out.
So there's
I think there's a part of thisthat we just have to realize.
But I am really looking forwardto a bunch of different agents
that are very specialized for me.
Like you said, for laundry,
for hanging out with the familyhelps me out, like.
For example,I would love to know
(41:56):
what my daughter's schedule is today.
Right, and instead of going and looking at it,
I would say, you know what?
Can I spend time with herbetween 4 and 6
because I think I'm goingto go home early and find out.
And so clicking 16 places,
looking at her calendarlooking at my calendar.
It would be an easy thingfor somebody to get done.
Right? Right.
And then say in aggregate,did I spend 14 hours with her
or 10 hours with her,or 6 hours with her this week?
(42:19):
How do we make sure we spendten hours with her next week?
And I say we meaning meand my agent, we.
Right, right.
Well, and the other thing too just
what is time?
And you talked about
self-driving cars have been on the horizon for a while.
They're just getting here.
But was that a long timeor a short time?
You know, in many waysit feels very, very short,
which is what we're kind ofgetting to the end of our time.
(42:40):
So I want to give you,the last word.
You've been through a lot, as yousaid, five different waves.
You know, back in the daywhen people's main function
was really being an APIbetween applications,
we took data from one piece of paper and keyed it into another piece of paper.
What are some of the thingsyou're excited?
I mean, I know you'reexcited about this
next year is going to bethe year of production GenAI
(43:01):
what are some of the other things,
maybe more specificallyor some examples that you've seen
or that you guys are involved with, whether it's in health care
or transportation or whatever,
that you can say, wow, this is really
this is really amazing
this is some of the reason I came here.
This is some of the magic
I read in the bookabout early Apple days.
It's now coming to fruitionin some of these other fields
(43:22):
I love.
I am really looking forward to
GenAI making individualsmore productive,
because if youcan start there.
And by the way, the individualscould be consumers, right?
if I was in the retail world
or if I wanted to go off and be Verizon
or Best Buy or Pricelineor whatever else it might be.
(43:44):
I'm looking forward to GenAI with LLMs
and with SLMs right,small language models with RAG
making it so much more productive
for individualsto do things right.
It could be my email,it could be me going on travel.
It could be mebuying something on BestBuy.
It could be any of those
or me talking about doing a bill problem, right.
(44:05):
How frustrating is that?
How do I make it easy?
I think that part happensin the next 18 months,
and I still think
that's, by the way,Angry Birds plus plus, right.
The really fun part I'm really looking forward to is
I’m not so, another thing in thatwe said, health care.
How do I take ten hours?
(44:26):
Take ten hours out of doctor's times.
Right, they spend a lot of time readingcharts and saying, and it’s like
Give me the TL;DRon what Chet’s about, right?
I've seen himfor 18 years.
Tell me what I should go and focus on.
But if any anomalies that you seein the last ten years
because he's been 18 years, he's coming to me for the same tests,
like a physical.
So just tell me what it is and let me go and take a look at.
(44:47):
Right.
Given how old he is,things like that,
that's all great.
What I'm really looking forward to is
how does it fundamentally change my life?
Right.
There is a
there, how do I’m I’m asked
I'm talking about somethingmore effective.
Amazon and I go back to Amazon or Uber
changed my life
like significantly changed my life
(45:09):
like it was a step function change.
Now I don't go to a store.
You meanand so
how do you makewhat is
What is the GenAI version of that?
That part I don't know yet.
And by the way, nor do all my colleagues who are building companies,
they don't know that yet.
Right.So we just
but you have to believe in the concept
(45:29):
and go off and make it happen.
Right
I love that,
we’ll all get past Angry Birds.
We can enjoy the Angry Birdswhile we're here,
but we'll all get past Angry Birds.
[Jeff] Well Chet, I really [Chet] My one thing
The one thingI would tell you though, Jeff,
is the one though,you know, it's
The interesting thingabout GenAI, you can see how
interested I am, how enthusiastic I am.
(45:50):
It comes from a placeof being a technologist.
But guess what?
If you're not.
My only one request to you is
lean into it.
This is not going away.
You have to lean into it because it's going to change your life
whether youlike it or not.
Yeah, well,the last little concept,
I just want to close on.
We didn't cover it earlier, but
I used to ask people all the time.
If storage, compute, and networking were free,
(46:14):
what would you build?
Right. And we're asymptotically approaching that every single day.
But what this is enabling is this thing it’s called
the Lost Einsteins concept,which is a paper
that came out in 2018,which is, if
you know some person in Africa,
some young girl has the cure to cancer in her head
that if she only got accessto the education
(46:34):
and the resources thatshe would cure cancer,
this now really startsto get to put the power again
of that supercomputerfor very little cost
in the hands of anyone today with a mobile phone,
tomorrow with a watch.
I think that is so, so, powerful.
I would agree.It's a great way to put it.
(46:54):
The Lost Einsteins
All right.
Well, Chet, thanks again for sharing your,
[Jeff] your perspective, your enthusiasm.[Chet] Thank you
I really appreciatethe time.
It was great.
I'm very glad you had me.
[Chet] Thank you, [Jeff] Absolutely, thank you
All right.
Well, he's Chet, I'm Jeff,
You're watching Work 20XX.
Thanks for watching.
Thanks for listeningon the podcast.
We'll see you next time. Take care.
(47:15):
Great.
Was that good
Thank you.It was awesome.
Okay, great. That was fun.
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