October 17, 2024 • 41 mins
In the latest episode of our podcast, we sit down with Gokul Rajaram - a Product Leader, Operator and Board member who has helped build seven of the largest tech companies globally - Google, Facebook, Square, DoorDash, Coinbase, Pinterest and Trade Desk.
He is popularly known as the 'Godfather of Google Adsense', here he grew it from zero to over $1 billion in revenue. Later, he founded an NLP company which was acquired by Facebook, where he then led the Ads Product team as Product Director, helping grow revenues from $0.75 billion to $6.5 billion, and helped Facebook transition its advertising business to become mobile-first.
He helped Square, DoorDash and Coinbase go public (IPO) as management team and board member, additionally he is a prolific Angel Investor for 300+ startups including Airtable, Airtable, CRED, Curefit, Figma, Learneo, Pigment, Postman, Whatfix and more.
In this episode, Gokul shares invaluable insights on how to grow from startup to scale-up quoting stories from his rich experience. He stresses the importance of product-market fit (PMF), exploring its critical link to monetization and sound unit economics.
He also addresses the formidable challenges startups face in the fiercely competitive AI sector and how can young entrepreneurs build in this exciting sector.
In this podcast, below are the topics covered:
0:00 - Journey from India to Silicon Valley
8:10 - Three Stages of a Company: Start-up, Early-Growth, Scale-up
13:41 - Discovering Product Market Fit and Monetization
23:23 - Challenges for Startups in AI
28:20 - Vertical SaaS and Indian Tech Innovation
Gokul offers a masterclass in entrepreneurial excellence - his experiences and strategies provide a roadmap for navigating the complex and ever-evolving tech landscape, making this episode a must-listen for aspiring entrepreneurs and seasoned professionals alike.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Sanjay Swamy (00:00):
The one and only Gokul Rajaram.
Gokul Rajaram (00:02):
The only work experience I've had in India is
actually working at IASC for asummer and so dropped out after
my master's, joined Google,which was at that point, the
only company that I was hiring.
In the aftermath of the dot-combust, there was almost no one
at Google who had worked inadvertising before.
It was a privilege to see thatjourney from zero to multiple
billions of dollars.
Went through some ups and downs, but from zero to multiple
(00:25):
billions of dollars I wentthrough some ups and downs but
ended up at the end was acquiredby Facebook.
Helped Facebook go public?
Was the management team ofSquare helped the company go
public there?
Joined DoorDash.
Was the management of DoorDashHelped DoorDash go public?
Or helped Coinbase go public asa board member?
Stock markets are vayingmachines in the long term and
voting machines in the shortterm.
(00:46):
Gpt-5 is probably going to costnorth of a billion dollars to
train A Google, a Facebook, anAmazon, etc.
You are not going to want tolose this race.
I think most of Sam's time isalmost suddenly spent in raising
capital.
At this point, there's $2.3trillion that are being spent on
various IT and businessservices.
Hard for startups to break outin functional AI companies.
Sanjay Swamy (01:13):
Hi everybody, Sanjay Swamy, here again from
Prime Venture Partners, and forthis next episode of our
entrepreneur-oriented podcast,we have someone that I have been
wanting to have on the show forthe better part of several
years at least, certainly twoyears but I'm thrilled to
welcome the one and only GokulRajaram.
(01:34):
Gokul has been both a friend andan advisor, a mentor, someone
we've all looked up to, and alsoa partner.
Amit has worked with Gokul inthe past at Google and someone
we've all tracked over the yearsand got to know him reasonably
well over the past few years anddone some co-investments
(01:57):
together.
And Gokul, of course, has beenvery well known in Silicon
Valley, notably for his roles atGoogle, in the AdSense product,
at Facebook, which is now Meta,and the AdPro, as chief product
officer at Square, which is nowBlock, I guess, as well as
several other things that couldgo on and on and also has been a
(02:22):
board member at Coinbase, aprolific angel investor, not
just in the US but also aroundthe world and notably in India.
So I would love to first of allwelcome Gokul to our show and I
look forward to exchangingviews on various topics that are
dear to our hearts and to ourlisteners, Thank you, Thank you,
(02:43):
Sanjay, our hearts and to ourlisteners.
Gokul Rajaram (02:44):
Thank you.
Thank you, Sanjay.
It's great to be here and I'vebeen a huge fan of Prime and you
and excited to finally get achance to do this.
Thank you for having me.
Sanjay Swamy (02:53):
Superb.
That's kind of you.
So, gokul, maybe we'll dive into a little bit about your
journey.
You know right from your daysgrowing up here and your journey
in the US.
Maybe you can give us a littlebit of a snapshot.
Gokul Rajaram (03:10):
Yeah, I grew up near Delhi and I went to
undergrad at IIT, kanpur.
The only work experience I'vehad in India is actually working
at IASC for a summer inBangalore.
I was doing some research forProfessor Raja Raman, who was
the chair of the CS departmentat IADK many years ago, and was
a great experience, and thatwhetted my appetite to do more
(03:32):
research.
So I went to the US for gradschool, joined the PhD program
in computer science at UT Austin, but then after a couple of
years I wanted to writeproduction code and so dropped
out after my master's to gobecome a software engineer at a
startup company called Juno,which was a very early ISP in
(03:53):
the US in the mid-90s.
It was one of the few companiesin the New York ecosystem which
was building ad softwarealongside DoubleClick back in
the day.
So I worked in order tomonetize the free email and
internet service they provided.
We needed to build an ad system, so that was the monetization
engine and I got exposed tobuilding ads products fairly
early and so, after a few yearsof doing that, got my MBA.
(04:17):
But I landed up in SiliconValley and joined Google, which
was at that point the onlycompany that was hiring.
In the aftermath of the dot-combust in the early 2000s and I
was lucky enough because I hadinitially thought of joining
telcos because I was reallyinterested in networking from an
infrastructure point of view.
But they all rescinded myoffers because they were all
(04:38):
going through hiring fees andlayoffs.
So I landed at Google, which ismy third choice, strangely
enough and just destiny, and Iwas put on advertising because I
had worked on ad productsearlier there was almost no one
at Google who had worked inadvertising before and ended up
working on a product which laterbecame called Google AdSense
(05:00):
and I accounted for over half ofGoogle's gross revenue at the
time of going public.
I was privileged to see thatjourney from zero to a fairly
large scale I think it wasmultiple billions of dollars and
after that started a companythat basically after two and a
half years again an interestingtime, it was during the global
financial crisis Went throughsome ups and downs, but ended up
(05:22):
at the end was acquired byFacebook.
Again, timing was right becauseFacebook was just starting its
journey.
It was in 2010, about two yearswith Facebook in public, but
got to experience the growth ofFacebook ads, led the Facebook
ads product team and helpedFacebook go public A lot of good
learnings there and then workedat Square Again really good
(05:43):
experience Was on the managementteam of Square, helped the
company go public there and thenworked at Square Again really
good experience was on themanagement team of Square,
helped the company go publicthere and then joined DoorDash.
As a manager of DoorDash,helped DoorDash go public Really
interesting IPO.
I call it the Zoom IPO becauseit was during COVID.
We went public in December of2020.
And so, instead of going to NewYork like we did with Square,
with DoorDash the wholemanagement team was on a Zoom
(06:06):
screen with the NASDAQ people inthe middle, and that's how we
saw ourselves go public and thatwas a screenshot that even
appeared in the news channel.
So it was really interesting.
We were sitting in the livingroom right over there with a
good background behind me, andthen I also participated or
helped Coinbase go public as aboard member, and that is an
(06:29):
interesting one is Coinbase waswhat is called a direct listing,
where it was a different kindof IPO than normal.
Google was a Dutch auction backin the day, so each IPO had
some unique characteristics.
Doordash is a Zoom listing.
Facebook was the first time thatNasdaq ever came to campus.
They actually came down toFacebook campus and we rang the
bell from the Facebook campus.
(06:49):
Mark rang the bell, so each ofthem was a unique experience and
a unique just learning of what,the different types of ways,
but all of them.
Ultimately, they say, stockmarkets are weighing machines in
the long term and votingmachines in the short term,
which means, ultimately,companies have to perform well
over the long term and so,thankfully, all these companies
have performed well and havedone well and the stock market
(07:11):
has responded appropriately.
Now I am a full-time investor,hoping to follow in your
footsteps and Prime Ventures'footsteps and invest, like we
have done alongside a fewcompanies.
It's been a great experienceworking with you and hopefully
get to do more of those thingsand support great entrepreneurs
building legendary companies.
Sanjay Swamy (07:31):
Wow, that's quite the background, gokul.
I mean any one of those topicsor those experiences is probably
a podcast in itself.
But what are maybe, you know,for entrepreneurs here sort of
getting into perhaps growthstage stage, where several of
the companies you work withthey're probably already at that
stage?
What are some common thingsacross this journey that have
(07:58):
served you very well and thecompanies very well, and what
are perhaps some unique thingsabout each of the companies?
Gokul Rajaram (08:03):
Maybe a couple of anecdotes would be great, yeah
I think, um, the growth stage isa really interesting point.
I always think the the earlygrowth versus scale up.
So there are three stages toany company.
There's a startup stage, whichis the only goal is to find
product market fit at that pointand you want to keep burn as
low as possible and be scrappy.
The second stage is I call itthe early growth stage, where
(08:28):
you're trying to buildrepeatability and you're trying
to figure out okay, was thisproduct market fit an illusion
or is it a real thing?
So you're trying to figure outthe channel, you're trying to
figure out a little bit ofthings on repeatability.
And then finally there's scaleup, where you figure out
repeatability.
You're just adding capital andjust trying to scale as fast as
possible.
That early growth stage is themost important thing because
(08:50):
that's when you start addingsome process.
So I think the biggest thingwhere people get wrong is they
either add too much process ortoo little process.
I think that secret sauce isthe right thing to get right
what is the right amount ofprocess you add so that you can
run the machine but not devolveinto chaos or not become too
(09:11):
bureaucratic.
So you've got to draw the fineline between over-bureaucracy
and chaos.
And right there is that sweetspot, the Goldilocks zone, and I
think process is in a fewplaces.
One around people.
I think hiring is probably themost important thing.
It was super important.
But the first 10 hires you havemostly from your network.
For the most part now it's thefirst time you're going out to
(09:32):
your network to hire in thisphase.
So how do you put a processinto place where you're just
like with customers, you're topof the funnel, you're getting
enough people top of the funneland you're getting people
through the funnel to get to anoffer stage in a systematic way.
So you've got to put someprocess into place.
Second, you've got to putprocess around goal setting.
So I think now there's enoughpeople that most of you can't
(09:54):
fit into one room.
People fit into multiple roomsor even multiple locations, and
so how do you set goals?
How do you align people aroundgoals so each person knows what
their goal is and how it fitsinto the broader goal of the
company?
So OKRs start coming into play.
Sanjay Swamy (10:09):
But yeah, I think that's the second piece.
Gokul Rajaram (10:14):
The third piece is around communication.
I think it's very important onan ongoing basis to figure out,
as a leader of a company and asleaders of different teams, how
you're communicating.
Things like all hands, thingslike potentially weekly emails,
things like even staff meetingsthose things didn't happen.
(10:37):
What is this?
Product reviews, weekly reviewswhat is the set of
communication processes you needin the company so that everyone
is executing and orchestratingthings together?
And, finally, people processbeyond hiring.
So I think you have a group ofpeople.
This is when people startthinking, okay, how is my career
going to evolve in this company?
And so you start having to putin some lightweight performance
(10:58):
plans, performance reviews,figuring out what ladders look
like, compensation structures.
So people have been againduring the PMF phase.
No one's focused on okay,what's my cash comp going to be?
It's like die or get to PMF.
Right, that's the only thing.
But once you get to PMF, peopleare like, okay, now you raised
maybe another round of funding.
Okay, you know, do I stay herefor two more years?
(11:19):
Am I going to become a seniorsoftware engineer from a
software engineer?
What's my path look like?
Et cetera, et cetera, and Ithink, again, it's possible to
overdo it.
It's possible to underdo it.
For each of these things, thetrick is figuring out how to not
have too much bureaucracy.
So you create 20 layers of acareer ladder or you don't get
any layers.
The answer probably is forengineering, you get three
(11:40):
layers.
You have a junior engineer stepin the ladder, you have a
mid-level engineer and you havea senior engineer based on
experience, based oncontributions, based on scope,
impact, etc.
And that's it.
And then, maybe the next stage,you have a little bit more
layers.
So, again, the goal is to tryto do the minimum possible thing
so that you can.
Still, the goal is to haverepeatability, so everyone is on
(12:02):
the same page, executing,marching to the same tune,
versus doing their own thing andthe company just dies yeah.
Sanjay Swamy (12:13):
So you know, I think one of the challenges uh,
I've noticed at least iscompanies where product market
fit itself is not well defined.
Right, and what is productmarket fit?
Is it people using the product?
Is it people paying for theproduct?
Is it people coming back andrepeating?
Um, uh, you know theirsubscriptions.
(12:34):
You know we've had interestingdebates with SaaS companies
where they say, well, thecustomer is prepaid for a year.
It's not always sure that that'sa good thing, because it may
mean that at the end of the yearthey may churn out and they
stop using it.
And obviously you need to seehow they're engaging, versus
perhaps even giving them theoption to just go and leave
monthly in the beginning so thatthey're only staying in the
system if they're getting value.
(12:55):
Go only monthly in thebeginning so that they're only
staying in the system if they'regetting value.
And what is many of thecompanies that you've worked
with also right?
I mean, product market fit wasprobably established in a few
dimensions, but perhaps notquite in the monetization side
of things, right, and, of course, as the world has evolved and
in the post-2021 era, it's allabout monetization these days
(13:17):
and everybody's looking foralmost profitability, but
certainly monetization andrepeatability there.
So for early stage founders,you know, again in the PMF zone
we'll start and then come to theother two.
What are your views on theimportance of monetization and
establishing unit economics and?
Gokul Rajaram (13:37):
things like that.
I think that's a great question, sanjay.
Product market fit has beendefined in numerous ways.
Like you said, there's no Iwould say there's no one clear
definition.
The way I think of it isproduct market fit has two parts
.
One I call problem solution fit, which is basically can you, on
(13:58):
one side, consistently when youget a new customer, can you
consistently get them to seevalue in what you're offering?
And second, for the first time,so can you activate them on a
consistent basis.
Second, can you retain themOnce they see value?
Do they stick around?
So those two together,activation and retention
(14:18):
together, I callproblem-solution fit.
So are you solving a clearproblem and are you solving a
clear problem on a consistentbasis?
So there the metric is aroundretention.
So you want to see activation,retention together In some ways.
There's also you start lookingat can you acquire customers
efficiently?
But efficiency is not asimportant as because that goes
(14:39):
into the cost piece of thingsCan you acquire a customer in
the first place, show them valueand get them to retain?
Let's just focus on that.
So, problem solution are yousolving a problem that they have
and can you get them tounderstand this?
So activate and then retain.
The second piece is, I think,can you, can you this?
This I call go-to-market fit,and I think this is important
(15:01):
because I think if we just stopthere, it basically doesn't
doesn't really show whether ornot you can get enough of these
customers, do they efficiently,et cetera.
So first part of go-to-marketfit is can you actually
consistently get in front ofyour ideal customers, so can you
reach them in an efficient way,and then can you consistently
(15:22):
close deals with your idealcustomer profile, so can you
then convert them?
So can you basically reach themand then convert them.
And I think that togetherstarts to think about, starts to
get the channel into place.
I do so again, problem-solutionfit, which I think is the most
important one you've got to findout.
And the next part is you'retrying to build repeatability on
(15:45):
the go-to-market front.
Now, where does monetization fitin?
I think it's a very goodquestion.
The challenge is, I think, forcompanies.
We know for certain kinds ofthings monetization can be
deferred.
For example, if you have amedia company or a company that
relies on consumer engagement.
We know time and again this hasimproved that if you can get
(16:06):
enough engagement, enoughengaged users, you can use
advertising to monetize, andevery time a new engaging media
came over, people said thatcan't monetize.
It did monetize, whether it wasSnapchat, pinterest, twitter,
tiktok all of them have figuredout how to monetize using ads.
So I think, for consumer mediaproperties, but you need massive
scale.
You need massive scale.
(16:27):
You need at least 100 millionMAUs probably more hundreds of
million MAUs to build areasonable business.
For almost everything else,where the value exchange is not
in media time or attention, butit is in paying for a service, I
think we've got to figure outare customers willing to pay?
So willingness to pay is, Ithink, a very important part of
(16:49):
the problem-solution fit, whichI think it has to be included,
because activation can't just befree activation.
It could be, but retentionprobably does involve because
it's a freemium product maybe,so you could activate with a
free product.
But retention you've got totest whether they're willing to
pay or not.
Why?
Because if they are free, youdon't really get a signal of
(17:10):
what the real customer behavioris going to look like and so I
don't trust.
So that's why you've got to.
If you have a business modelwhich relies on exchange of
value through customers payingyou for value, you've got to
test that out, because otherwiseI don't believe the customer
behavior will hold true when youintroduce payment.
So I do think it's important tofully simulate or fully test
(17:35):
PMF with the right businessmodel that you're going to have,
because if you say, oh, I havefree users, now I'm going to
introduce payments, I've seen Ihave a few companies in my
portfolio that try to basicallykeep services that you otherwise
would think of as payingservices free for a very long
period of time, thinking, oh,we'll monetize later and you
(17:56):
could with things like takerates et cetera.
But it becomes very, very, verychallenging, especially over
the last few years.
I think investors have refusedto believe one's hypothesis
around this and they basicallyare much more conservative than
entrepreneurs are in figuringout what the attach rate is of
payments.
So I think it's very importantfor entrepreneurs to figure this
(18:18):
out early, otherwise it'll beto their detriment if they don't
got it now.
Sanjay Swamy (18:23):
It's also interesting that um, one of the
things we experienced, um,trying to not name the company,
but I'll have to give theexample.
Yeah, where you know, theywanted to offer a service for 50
rupees per user per month andtypically the customer expected
another auxiliary service to beincluded which would have cost
(18:48):
15 rupees per transaction.
There was a bank card,basically, so an ATM transaction
, right.
And they said my bank cardgives me three free in a month.
So the next day we went backand said, okay, it's 100 rupees
a month.
Card gives me three free in themonth.
So the next day we went backand said okay, it's 100 rupees a
month and we include three freeATM withdrawals.
And people were fine with it,whether they used it or not.
And then the next day we wentand we sold to another customer
(19:09):
and we doubled the price to 200rupees per month and they were
fine with it.
Then the next day we tried 400rupees and we literally went
every customer, we just doublethe price and see where the
breaking point is right.
And then 400 people said that'stoo expensive.
And then we said okay, you know, the cac is so high.
How the heck are we going torecover?
And so we said what if we did a10 card minimum or a 10 user
(19:32):
minimum, like which flew?
And so that's how the price wasdiscovered, at you know, 2000
rupees with a 10-user minimum,and it made the you know sort of
the CAC, like you know,recovery period six to nine
months and made it a veryattractive proposition.
Gokul Rajaram (19:48):
It's a great point.
Price discovery can result inchanging how you go to market
and so on, because the price youdiscover might be different
than what you assume up front.
So I think that's anotherreason why it's important to you
know, understand early on,though, that said, your
portfolio company my gate, Ithink has an extremely well you
know, monetizing much later.
If I remember correctly, theyhad a free service for a very
(20:09):
long period of time.
So there are exceptions,amazing exceptions that prove
the rule that you can actuallynot.
Sanjay Swamy (20:15):
Yeah, I think in.
I think in their case becausethey're sort of pretty much the
only player in town and have avery sort of compelling, almost
mandatory daily use case, right,I mean because you can't.
You know your Amazon packagecan't come home unless you
approve there was enough sort ofbenefit.
(20:35):
Yes, yeah, yeah.
And of course they've also beenvery thoughtful about when they
start monetizing.
You know, get the habit goingand dependency.
But yeah, you're right, thoseopportunities are kind of rare.
I think for the most part thecustomer has a lot of choice,
because here the stickinesscomes, because 400 people in the
(20:58):
community have got to changefrom their product to somebody
else and there's always inertiathere.
Gokul Rajaram (21:05):
Because what happens is retention, which is
the primary measure of PMF,could instantly change if you
start pricing, unless you're aproduct like my gate.
So that's why it's so important, because you could say I have
80% retention, oh yeah.
So that's why it's so important, because you can say I have 80%
retention, oh yeah, that's fora free product.
Let's see what price does to it.
Right Price could reduce it to20% and then all your
assumptions fly out of thewindow.
Sanjay Swamy (21:24):
Right.
So, um yeah, look, I think thisis a topic in itself.
I know Shripati has writtenabout.
My partner has written a blogon the high price of mispricing
your product because, especiallyin a category creating company,
sometimes you price it so lowthat you just reduce the TAM
right and you reduce theattractiveness, although the
value to the customer might beextraordinary.
(21:46):
So we'll switch gears a littlebit.
Gokul is just cognizant of thetime here.
There's so much happening onthe AI front these days across
the board and I broadly havebeen thinking about it as three
things.
One is obviously the platformplays, which are very well
(22:09):
documented and talked about.
Would still love to get yourquick views on those.
But in terms of opportunitiesfor startups and VCs, one is
sort of what I call thescaffolding around the business
things like customer serviceautomation, things like sales
marketing and things like that.
And then the second is productswhich are inherently built on
AI, some which are just enabledbecause of all the advances in
(22:32):
AI and others where AI can makethem much better, business flows
and things like that.
I would love to get your takeon how you view this landscape
in the first place and theopportunity space, certainly for
startups and which are the onesthat you see as opportunities,
especially for India-basedcompanies that might be
(22:52):
targeting, you know, us-basedcustomers.
Gokul Rajaram (22:55):
Yeah, I think it's a.
You know, again, it's a topicwe could spend hours on, but,
like you said, I think at thehighest level there's
infrastructure and applicationswith a middleware layer in the
middle.
I think the infrastructurepiece is both chips NVIDIA, of
course and also foundationalmodels.
I think the challenge withfoundation models in general for
any startup, let alone India orthe US or anywhere in the world
(23:16):
, is that the capital intensity,the data intensity and the
computing needed for thesethings is exponentially scaling.
I think the estimate is thatnow you can with GPT 3.5-like
models, you can now $10 million.
You can train GPT-4 model willcost $100 million to train.
Gpt-5 is probably going to costnorth of a billion dollars to
(23:38):
train, and so basically, andthen the thing is, it won't
change because it's an arms race.
If you are a hyperscaler aGoogle, a Facebook, an Amazon,
et cetera you are not going towant to lose this race because
you don't want and even Apple,you don't want to be beholden to
someone else's platform, sothey are not going to stop
investing.
So if you're a startup, you'rebasically competing with the
(23:59):
largest companies in the worldwho have made this a strategic
priority and who have saidthey're willing to over-invest
versus under-invest at the risk,because they don't want to risk
missing these platforms.
So you're competing with thefour largest and most ferocious
competitors and most capitalintense and the most profitable
companies in the world.
So it's almost impossibleunless you're Sam Alpin and
OpenAI, maybe, maybe, maybe,anthropic.
(24:20):
So I think there's maybe fiveor six companies in the world
who are going to be able toinvest in this for the next
several years, because it's notstopping.
That's the thing.
If you stop, you're done.
You need continuous pool ofcapital.
That's why I think most ofsam's time is almost certainly
spent in raising capital at thispoint, more than anything else.
And you go to just like venturefunds became really large and
(24:42):
started going to the middle eastand so on.
You heard news of him going tosaudi arabia and so on, and you,
you probably have to do thesame thing.
You've got to now get sevenbillion dollars, next 50 billion
, $50 billion, who knows what.
So I think that's why, ingeneral, it's not a fruitful
endeavor for startups.
I think.
Chip layer there are somecompetitors.
We'll see how it goes.
As you know, nvidia has built avery compelling software
(25:03):
platform also which is part oftheir lock-in, not just the
hardware, but I think thatgenerally is a harder nut to
crack.
So I think the opportunitiesare in the middleware layer and
in the application layer, and inapplication layer there are two
kinds of applications, I think.
So in the middleware layerthere's a bunch of tooling.
I think the challenge I've seenwith these tooling companies so
(25:26):
far I'm an investor in a few isthat adoption, especially
amongst enterprises, is a littleslower than you're expecting.
Why?
Because enterprises don'treally know what they want to
build yet, so tooling is tooearly in many cases to test, and
so in many cases, even ifthey're adopting these tools,
they're not paying much and soand they're just using.
(25:48):
Maybe the one interesting kindof tool you have is an
orchestration layer that allowsenterprises to switch between
different models.
But the reality is, as modelsconsolidate, people are seeing,
well, I don't need 10 models,two or three models I need to
use.
So there's some stuff there,but many of the other tooling I
think is going to be built intothe foundation platforms
themselves or the cloudproviders and I think the
(26:09):
margins in tooling is going tobe squeezed.
There is some stuff I'm excitedabout around safety of models,
around observability of modelsand so on.
That will need to be done by athird party because you can't
trust the models to policethemselves.
So I think there's a system oftools around that and I think
there's good middleware aroundthat.
I mean, in Western, I gotPetronas.
(26:29):
That's doing some good stuffthere.
There's other companies there,but I think the app layer is the
most fertile of all.
Just like you mentioned, I thinkthere are various ways you can
categorize apps.
One of the best ways I think ofis functional horizontal apps
and vertical apps,industry-specific apps.
So there are apps that, like Ifyou look at accounting AI app,
accounting AI app applies acrossall industries in theory, but
(26:53):
it's more of.
It takes a function likeaccounting and basically
automates it as much as possible.
But then you take an app forthe construction industry or the
auto industry.
Those are vertical apps.
So I think those are two kindsof apps.
Even within that, I think themost interesting opportunities
(27:13):
are within the verticalecosystem.
Why?
Because in functional appsthere have been SaaS.
There are SaaS companies thathave automated over the last 10,
20 years many of thesefunctions, not with AI, but with
general, like you know, justheuristic based software.
There's Salesforce in sales andmarketing, there's HubSpot,
there's like 20 companies,there's Outreach, there's Gong,
et cetera, in sales andmarketing alone, and each
vertical QuickBooks inaccounting right, and these
(27:35):
companies have customers, theyhave data and, thanks to the
foundation world investments,they've been able to easily spin
up AI-based functionalitywithin their products or embed
AI functionality.
Now they're not as good asAI-native products, but they're
good enough.
They're good enough that theircustomers are placated for a
little bit while these guys areworking on AI-native products.
(27:57):
So in general, I think it isgoing to be fairly challenging
for startups to break out.
There's going to be exceptions,obviously, that prove the rule,
but it's going to be hard forstartups to break out in
functional AI company, infunctional AI categories such as
sales and marketing, such asaccounting, such as finance,
(28:18):
unless there is no incumbent inthem.
I think anytime you have anincumbent that is between five
and 10 years old, thesecompanies are fairly innovative
and fast moving.
For example, I think in customerservice AI, which was a very
fertile part, very fertiledomain for AI.
In fact, it's probably the onearea where customers are using
it fairly actively to displacehuman agents or augment human
(28:41):
agents.
There's at least 20 companies.
I think one of the YC batcheshad like eight companies and I
think there's easily and they'reall focused on different things
.
But I know of at least twocompanies that were started five
to seven years ago that withinthe last one and a half years,
they said okay, you know what?
Our old school customer serviceautomation business is going to
(29:03):
be disrupted.
Let's just migrate all ourcustomers.
Let's just build an AI customerservice agent and migrate all
our customers.
And they are at almost 10x thescale of any of these startups
within one and a half years,because they already were at
significant scale, close to 50to 100 million, and now they are
at the double digit millionsvery, very quickly as a
(29:23):
migrating customer.
And that's what you're going tosee.
I think there's a lot ofstartups that are raising rounds
from great VCs, but the realityis their scale is dwarfed by
the scale of the incumbents.
Even if they move just 10% inthe first year, that's more
revenue than most of thesestartups are doing, and these
are good products that they'relaunching, these incumbents,
because it's not that hard.
The foundation models are madeand not that hard, and now
(29:44):
coding agents all of thesetogether.
So now all of this serves tosay that I think the opportunity
is in verticals and I thinkwithin verticals and in
something that, as you know, iscalled service as a software is
the other opportunity which inmany cases deals with verticals,
where you take an existingservice, a business process or
service, and you completelyverticalize it and so you
(30:06):
basically make it an APIendpoint where you can basically
take almost an IT consultingservice.
Say, actually, a managementconsulting service is a great
example McKinsey offers.
You tell McKinsey I'm going topay a million dollars at
McKinsey BNDCG, literally, thereare companies that are doing
McKinsey as a service where youbasically ping that software and
(30:28):
it gives you an amazingMcKinsey-style report on
something with a bunch of inputsthat you give it, and so that I
think that has the opportunityto take.
I believe there's $2.3 trillionthat are being spent on various
IT and business services.
So in theory you can take allof those services and make them
a software endpoints very, verycustomized for that service.
(30:50):
And I think Indian companies inparticular are very, very are in
a pole position because I thinkeverything that Infosys offers
or TCS or Wipro, you could dowith AI without humans, so at
least augment them.
And similarly everything thatMcKinsey offers.
As you know, many of theselarge managing consulting firms
have their teams in India and Ithink there's a lot of knowledge
(31:10):
there.
So I'd be excited to seeex-McKinsey folks in India leave
and offer McKinsey as a serviceoffer, goldman Sachs analyst
reports as a service offer.
You know Wipro or Infosys orTCS as a service, all those
enterprise IT integration as aservice.
So I think software as aservice is a huge option.
The other one is what isinteresting to me is I've been
(31:31):
now meeting Service as asoftware.
You mean Service as a software.
I've been now meeting companiesin India that have gone after
US and European verticals andthere's a company I met called
Spine S-P-Y-N-E.
Really interesting company.
All their customers are autodealerships in the US and Europe
(31:51):
and they basically builtmerchandising software for these
folks and they have basically,using a sales team in India,
been able to sell this prettycool merchandising software and
they now have hundreds ofdealerships in basically nothing
in India all their businessesin the US and now they're
building a full suite ofproducts.
(32:12):
They race around, et cetera, etcetera.
I was like wow.
I met Sanjay, the CEO, and I'msuper impressed with what he's
built and what the team hasbuilt completely out of India.
So I suspect, because speed ofiteration is very fast Good name
too, right, it's?
Yeah, exactly, it's a greatname.
And so I think they're going tobe, and I've met more and more
founders who are taking, who arebasically taking, verticals in
(32:35):
the US or in the West and goingafter, just like Zoho and
FreshBooks did with horizontals,with functions, sitting in
India.
I think you're going to see thenext generation of vertical AI
companies, many of them.
I think Indian companies have asmuch a right to win as any
company anywhere because theycan build software fast.
You need to build a bunch of AIagents.
(32:57):
They have a lot of data and Ithink that.
And I think the other thingIndian companies in general have
a right to win, I think is infundamental needs, in the
Maslow's hierarchy, things likehealth, education, et cetera,
because you have a lot ofinteresting data and operations
that you can train your businesson in the Indian context and
(33:18):
you can really bulletproof yourproduct and your operations
operating in India and then youcan take those learnings and
bring them to the rest of theworld in a much more
cost-efficient and operationallyrobust way.
One of them is a portfoliocompany, dozy, which, as both of
us know, is building thisAI-enabled healthcare platform
that is super low price but isincredible.
(33:41):
It transforms any bed into anICU bed and basically lowers the
need for staffing, fordedicated staffing and nurses.
The nursing shortage is achronic shortage everywhere in
the world, including in Indiaalso, but I think definitely in
the West, in the US.
So they allow one-tenth orone-fifth of the number of
nurses to be able to monitor aset of beds using AI, using
(34:02):
signals, using just.
This is technology that they'veproven for a few years in India
and now it's used by all themajor Indian hospitals and now
they're seeing strong successhere and I think that model
taking the same human needsright.
I mean the human beings havethe same need for medical care,
whether it's in India or the US,and now you have technology
that is making it better.
(34:23):
I think, similarly, radiology,all of these things training
data is so much more availablein India compared to anywhere
else, because you have so manymore lives and so many more
people, so you can train yourmodels much better in the Indian
context and then you can easilyleverage those things in other
countries.
Sanjay Swamy (34:40):
Yeah, actually it's interesting that I was
chatting with Mudit and Gauravthe other day about the US
market opportunity for Dozee andthey almost have to dumb down
the product.
Right, because the product thatthey've got in India that is
working here in the hospitals.
People are running a verystrong operation on Dozee, but
(35:03):
in the US, now that they're FDA510K approved and they're
rolling it out there, I thinkthere are subcomponents of the
product that are extraordinarilymore valuable to the users and
they're almost building asimplified version or lighter
version of the product becausethe market is ready for it, has
(35:24):
been looking for and has alreadybeen using not-so-elegant
solutions.
This is a very simple to thecustomer, it's just okay.
It's a better version of what Ialready know I need, whereas
here is a lot more inevangelization and stuff that
needs to be done.
So, but you're right, I meanthe benefit of training.
This and especially, you know,in the case of clinical grade
(35:48):
products, right, it makes a bigdifference, and I think we're
seeing another young startup ofours called ZooAI that's doing
this IB curriculum like aself-study buddy for students.
There are enough kids in Indiathat are also studying the IB
curriculum and now that it'strained here they're able to go
to other markets.
(36:08):
It turns out I was surprisedthat Turkey is one of the large
markets of IB curriculum and ofcourse, the rest of the world,
the US included, is also huge.
So they actually got theirsecond batch of pilot users.
There's a lot of downloads fromTurkey and a lot of usage and
fine tuning of the product.
So, yeah, I think it's a superexciting time.
(36:33):
And on the vertical SaaS, we'vealso had companies that were
working on good, I would say,operating systems for mid-sized
businesses, verticalizedsolutions, like we have a
company called Bookie that'sdoing the how should I say the
studios, for, you know, smallgyms and those types of studios
(36:56):
are small to midsize and the AIinnovations that they've layered
into the product now havesuddenly, you know, had given
them explosive growth, right.
So I think the customers alsoare looking for that kind of an
edge.
In fact, one of the thoughtswe've also had is bringing in
the services and softwareelement, because with the human
in the loop in India, you canactually do a lot of like, for
(37:22):
example, the front officemanager is the hardest role to
hire and train and retain right.
Well, that can be done, youknow, with a combination of
software and a trained human inthe loop.
So I think some of theseparadigms are going to evolve
and I think it's going to createvery interesting new
opportunities.
Gokul Rajaram (37:40):
And what is crazy is that many of the current
companies today already areusing their gross margins
actually 40% or 50% because theyhave humans in the loop and so
almost everyone is in the loopto get training, data use,
reinforcement learning, feedbackand so on.
I think Indian companies can doit much more cost-effectively
and at scale compared to anyother company.
Sanjay Swamy (38:01):
Rajat Mittal.
Yeah, having said that, there'salso the risk for the larger
Indian companies If so much ofthe software side of it, of the
development side of it, uh, Imean, I guess there are two
schools of thought.
One is saying, hey, you know,there isn't a whole lot of very
senior talent in india and theco-pilot approach might actually
(38:22):
level the playing field and youmight be able to get a lot more
value out of the not soexperienced talent.
And then there's the other sidesaying, well, we need this
talent at all.
So it'd be interesting to getyour thoughts on is there always
going to be a continuous demandfor talent?
And, with all of the stuffhappening in AI, is there a
(38:45):
future for software developers?
Gokul Rajaram (38:47):
I believe.
So I believe what you said.
The first case, the optimisticcase, is what I believe in, in
that the number one thing thatAI does is narrow the skill
divide where it makes someonewho's a junior developer
equivalent to a mid-leveldeveloper and a mid-level
developer equivalent to seniordevelopers, so the skill premium
starts going away.
The notion of a 10x engineer or100x engineer who's paid 10x the
(39:08):
compensation of a 1, 1xengineer, I think you're going
to start seeing that, seeingthat shrink.
And I think that's where indiahas a lot of, you know, 2x, 5x,
maybe fewer 10x engineers.
But I think those 2x and 5xengineers can become 5x and 10x
engineers using ai tools.
So I think, because, because Ithink you still need engineers
(39:31):
to build software, but you maynot need, like, super expensive
engineers, you might need justreasonably talented analytical
engineers whose skills areaugmented by AI.
So I very much believe thatengineer, that AI will augment
engineers and improve theirskills and reduce the skill
premium, which very muchbenefits India skills and reduce
(39:52):
the skill premium, which verymuch benefits India, because
India, you know, and I thinkthat I think you're going to see
happen, versus just replacingengineers en masse.
Sanjay Swamy (39:59):
Wonderful.
So let's switch gears a bit.
Gokul, I know you've been avery prolific angel investor
over the years and obviouslylove having you on board in a
meaningful way at Dozy, and yourinsights and inputs have
already been very valuable tothe team.
But you're also transitioningnow or transition now into
(40:20):
becoming more of a venturecapitalist, from where you're
largely investing your own moneyand can make a decision and
look in the mirror and say whatwas I thinking to when it's went
to capital and there's anexpectation and responsibility
that goes with it you know itmight?
(40:43):
Or when it's like a hybrid ofyou know, both yours and a fund
that's raised with third-partycapital.
How are you seeing thesimilarities and differences and
any early insights into yournew journey?
Prime Venture Partners (41:01):
Dear listeners, thank you for
listening to this episode of thepodcast.
Subscribe now on your favoritepodcast app for free and you'll
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Just search for Prime VenturePartners Podcast in Apple
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You get your podcasts, Then hitsubscribe and if you have
(41:22):
enjoyed the show, we would bereally grateful if you leave us
a review on Apple Podcast.
To read the full transcript,find the link in the show notes.
The one and only Gokul Rajaram.
Gokul Rajaram (00:02):
The only work experience I've had in India is
actually working at IASC for asummer and so dropped out after
my master's, joined Google,which was at that point, the
only company that I was hiring.
In the aftermath of the dot-combust, there was almost no one
at Google who had worked inadvertising before.
It was a privilege to see thatjourney from zero to multiple
billions of dollars.
Went through some ups and downs, but from zero to multiple
(00:25):
billions of dollars I wentthrough some ups and downs but
ended up at the end was acquiredby Facebook.
Helped Facebook go public?
Was the management team ofSquare helped the company go
public there?
Joined DoorDash.
Was the management of DoorDashHelped DoorDash go public?
Or helped Coinbase go public asa board member?
Stock markets are vayingmachines in the long term and
voting machines in the shortterm.
(00:46):
Gpt-5 is probably going to costnorth of a billion dollars to
train A Google, a Facebook, anAmazon, etc.
You are not going to want tolose this race.
I think most of Sam's time isalmost suddenly spent in raising
capital.
At this point, there's $2.3trillion that are being spent on
various IT and businessservices.
Hard for startups to break outin functional AI companies.
Sanjay Swamy (01:13):
Hi everybody, Sanjay Swamy, here again from
Prime Venture Partners, and forthis next episode of our
entrepreneur-oriented podcast,we have someone that I have been
wanting to have on the show forthe better part of several
years at least, certainly twoyears but I'm thrilled to
welcome the one and only GokulRajaram.
(01:34):
Gokul has been both a friend andan advisor, a mentor, someone
we've all looked up to, and alsoa partner.
Amit has worked with Gokul inthe past at Google and someone
we've all tracked over the yearsand got to know him reasonably
well over the past few years anddone some co-investments
(01:57):
together.
And Gokul, of course, has beenvery well known in Silicon
Valley, notably for his roles atGoogle, in the AdSense product,
at Facebook, which is now Meta,and the AdPro, as chief product
officer at Square, which is nowBlock, I guess, as well as
several other things that couldgo on and on and also has been a
(02:22):
board member at Coinbase, aprolific angel investor, not
just in the US but also aroundthe world and notably in India.
So I would love to first of allwelcome Gokul to our show and I
look forward to exchangingviews on various topics that are
dear to our hearts and to ourlisteners, Thank you, Thank you,
(02:43):
Sanjay, our hearts and to ourlisteners.
Gokul Rajaram (02:44):
Thank you.
Thank you, Sanjay.
It's great to be here and I'vebeen a huge fan of Prime and you
and excited to finally get achance to do this.
Thank you for having me.
Sanjay Swamy (02:53):
Superb.
That's kind of you.
So, gokul, maybe we'll dive into a little bit about your
journey.
You know right from your daysgrowing up here and your journey
in the US.
Maybe you can give us a littlebit of a snapshot.
Gokul Rajaram (03:10):
Yeah, I grew up near Delhi and I went to
undergrad at IIT, kanpur.
The only work experience I'vehad in India is actually working
at IASC for a summer inBangalore.
I was doing some research forProfessor Raja Raman, who was
the chair of the CS departmentat IADK many years ago, and was
a great experience, and thatwhetted my appetite to do more
(03:32):
research.
So I went to the US for gradschool, joined the PhD program
in computer science at UT Austin, but then after a couple of
years I wanted to writeproduction code and so dropped
out after my master's to gobecome a software engineer at a
startup company called Juno,which was a very early ISP in
(03:53):
the US in the mid-90s.
It was one of the few companiesin the New York ecosystem which
was building ad softwarealongside DoubleClick back in
the day.
So I worked in order tomonetize the free email and
internet service they provided.
We needed to build an ad system, so that was the monetization
engine and I got exposed tobuilding ads products fairly
early and so, after a few yearsof doing that, got my MBA.
(04:17):
But I landed up in SiliconValley and joined Google, which
was at that point the onlycompany that was hiring.
In the aftermath of the dot-combust in the early 2000s and I
was lucky enough because I hadinitially thought of joining
telcos because I was reallyinterested in networking from an
infrastructure point of view.
But they all rescinded myoffers because they were all
(04:38):
going through hiring fees andlayoffs.
So I landed at Google, which ismy third choice, strangely
enough and just destiny, and Iwas put on advertising because I
had worked on ad productsearlier there was almost no one
at Google who had worked inadvertising before and ended up
working on a product which laterbecame called Google AdSense
(05:00):
and I accounted for over half ofGoogle's gross revenue at the
time of going public.
I was privileged to see thatjourney from zero to a fairly
large scale I think it wasmultiple billions of dollars and
after that started a companythat basically after two and a
half years again an interestingtime, it was during the global
financial crisis Went throughsome ups and downs, but ended up
(05:22):
at the end was acquired byFacebook.
Again, timing was right becauseFacebook was just starting its
journey.
It was in 2010, about two yearswith Facebook in public, but
got to experience the growth ofFacebook ads, led the Facebook
ads product team and helpedFacebook go public A lot of good
learnings there and then workedat Square Again really good
(05:43):
experience Was on the managementteam of Square, helped the
company go public there and thenworked at Square Again really
good experience was on themanagement team of Square,
helped the company go publicthere and then joined DoorDash.
As a manager of DoorDash,helped DoorDash go public Really
interesting IPO.
I call it the Zoom IPO becauseit was during COVID.
We went public in December of2020.
And so, instead of going to NewYork like we did with Square,
with DoorDash the wholemanagement team was on a Zoom
(06:06):
screen with the NASDAQ people inthe middle, and that's how we
saw ourselves go public and thatwas a screenshot that even
appeared in the news channel.
So it was really interesting.
We were sitting in the livingroom right over there with a
good background behind me, andthen I also participated or
helped Coinbase go public as aboard member, and that is an
(06:29):
interesting one is Coinbase waswhat is called a direct listing,
where it was a different kindof IPO than normal.
Google was a Dutch auction backin the day, so each IPO had
some unique characteristics.
Doordash is a Zoom listing.
Facebook was the first time thatNasdaq ever came to campus.
They actually came down toFacebook campus and we rang the
bell from the Facebook campus.
(06:49):
Mark rang the bell, so each ofthem was a unique experience and
a unique just learning of what,the different types of ways,
but all of them.
Ultimately, they say, stockmarkets are weighing machines in
the long term and votingmachines in the short term,
which means, ultimately,companies have to perform well
over the long term and so,thankfully, all these companies
have performed well and havedone well and the stock market
(07:11):
has responded appropriately.
Now I am a full-time investor,hoping to follow in your
footsteps and Prime Ventures'footsteps and invest, like we
have done alongside a fewcompanies.
It's been a great experienceworking with you and hopefully
get to do more of those thingsand support great entrepreneurs
building legendary companies.
Sanjay Swamy (07:31):
Wow, that's quite the background, gokul.
I mean any one of those topicsor those experiences is probably
a podcast in itself.
But what are maybe, you know,for entrepreneurs here sort of
getting into perhaps growthstage stage, where several of
the companies you work withthey're probably already at that
stage?
What are some common thingsacross this journey that have
(07:58):
served you very well and thecompanies very well, and what
are perhaps some unique thingsabout each of the companies?
Gokul Rajaram (08:03):
Maybe a couple of anecdotes would be great, yeah
I think, um, the growth stage isa really interesting point.
I always think the the earlygrowth versus scale up.
So there are three stages toany company.
There's a startup stage, whichis the only goal is to find
product market fit at that pointand you want to keep burn as
low as possible and be scrappy.
The second stage is I call itthe early growth stage, where
(08:28):
you're trying to buildrepeatability and you're trying
to figure out okay, was thisproduct market fit an illusion
or is it a real thing?
So you're trying to figure outthe channel, you're trying to
figure out a little bit ofthings on repeatability.
And then finally there's scaleup, where you figure out
repeatability.
You're just adding capital andjust trying to scale as fast as
possible.
That early growth stage is themost important thing because
(08:50):
that's when you start addingsome process.
So I think the biggest thingwhere people get wrong is they
either add too much process ortoo little process.
I think that secret sauce isthe right thing to get right
what is the right amount ofprocess you add so that you can
run the machine but not devolveinto chaos or not become too
(09:11):
bureaucratic.
So you've got to draw the fineline between over-bureaucracy
and chaos.
And right there is that sweetspot, the Goldilocks zone, and I
think process is in a fewplaces.
One around people.
I think hiring is probably themost important thing.
It was super important.
But the first 10 hires you havemostly from your network.
For the most part now it's thefirst time you're going out to
(09:32):
your network to hire in thisphase.
So how do you put a processinto place where you're just
like with customers, you're topof the funnel, you're getting
enough people top of the funneland you're getting people
through the funnel to get to anoffer stage in a systematic way.
So you've got to put someprocess into place.
Second, you've got to putprocess around goal setting.
So I think now there's enoughpeople that most of you can't
(09:54):
fit into one room.
People fit into multiple roomsor even multiple locations, and
so how do you set goals?
How do you align people aroundgoals so each person knows what
their goal is and how it fitsinto the broader goal of the
company?
So OKRs start coming into play.
Sanjay Swamy (10:09):
But yeah, I think that's the second piece.
Gokul Rajaram (10:14):
The third piece is around communication.
I think it's very important onan ongoing basis to figure out,
as a leader of a company and asleaders of different teams, how
you're communicating.
Things like all hands, thingslike potentially weekly emails,
things like even staff meetingsthose things didn't happen.
(10:37):
What is this?
Product reviews, weekly reviewswhat is the set of
communication processes you needin the company so that everyone
is executing and orchestratingthings together?
And, finally, people processbeyond hiring.
So I think you have a group ofpeople.
This is when people startthinking, okay, how is my career
going to evolve in this company?
And so you start having to putin some lightweight performance
(10:58):
plans, performance reviews,figuring out what ladders look
like, compensation structures.
So people have been againduring the PMF phase.
No one's focused on okay,what's my cash comp going to be?
It's like die or get to PMF.
Right, that's the only thing.
But once you get to PMF, peopleare like, okay, now you raised
maybe another round of funding.
Okay, you know, do I stay herefor two more years?
(11:19):
Am I going to become a seniorsoftware engineer from a
software engineer?
What's my path look like?
Et cetera, et cetera, and Ithink, again, it's possible to
overdo it.
It's possible to underdo it.
For each of these things, thetrick is figuring out how to not
have too much bureaucracy.
So you create 20 layers of acareer ladder or you don't get
any layers.
The answer probably is forengineering, you get three
(11:40):
layers.
You have a junior engineer stepin the ladder, you have a
mid-level engineer and you havea senior engineer based on
experience, based oncontributions, based on scope,
impact, etc.
And that's it.
And then, maybe the next stage,you have a little bit more
layers.
So, again, the goal is to tryto do the minimum possible thing
so that you can.
Still, the goal is to haverepeatability, so everyone is on
(12:02):
the same page, executing,marching to the same tune,
versus doing their own thing andthe company just dies yeah.
Sanjay Swamy (12:13):
So you know, I think one of the challenges uh,
I've noticed at least iscompanies where product market
fit itself is not well defined.
Right, and what is productmarket fit?
Is it people using the product?
Is it people paying for theproduct?
Is it people coming back andrepeating?
Um, uh, you know theirsubscriptions.
(12:34):
You know we've had interestingdebates with SaaS companies
where they say, well, thecustomer is prepaid for a year.
It's not always sure that that'sa good thing, because it may
mean that at the end of the yearthey may churn out and they
stop using it.
And obviously you need to seehow they're engaging, versus
perhaps even giving them theoption to just go and leave
monthly in the beginning so thatthey're only staying in the
system if they're getting value.
(12:55):
Go only monthly in thebeginning so that they're only
staying in the system if they'regetting value.
And what is many of thecompanies that you've worked
with also right?
I mean, product market fit wasprobably established in a few
dimensions, but perhaps notquite in the monetization side
of things, right, and, of course, as the world has evolved and
in the post-2021 era, it's allabout monetization these days
(13:17):
and everybody's looking foralmost profitability, but
certainly monetization andrepeatability there.
So for early stage founders,you know, again in the PMF zone
we'll start and then come to theother two.
What are your views on theimportance of monetization and
establishing unit economics and?
Gokul Rajaram (13:37):
things like that.
I think that's a great question, sanjay.
Product market fit has beendefined in numerous ways.
Like you said, there's no Iwould say there's no one clear
definition.
The way I think of it isproduct market fit has two parts
.
One I call problem solution fit, which is basically can you, on
(13:58):
one side, consistently when youget a new customer, can you
consistently get them to seevalue in what you're offering?
And second, for the first time,so can you activate them on a
consistent basis.
Second, can you retain themOnce they see value?
Do they stick around?
So those two together,activation and retention
(14:18):
together, I callproblem-solution fit.
So are you solving a clearproblem and are you solving a
clear problem on a consistentbasis?
So there the metric is aroundretention.
So you want to see activation,retention together In some ways.
There's also you start lookingat can you acquire customers
efficiently?
But efficiency is not asimportant as because that goes
(14:39):
into the cost piece of thingsCan you acquire a customer in
the first place, show them valueand get them to retain?
Let's just focus on that.
So, problem solution are yousolving a problem that they have
and can you get them tounderstand this?
So activate and then retain.
The second piece is, I think,can you, can you this?
This I call go-to-market fit,and I think this is important
(15:01):
because I think if we just stopthere, it basically doesn't
doesn't really show whether ornot you can get enough of these
customers, do they efficiently,et cetera.
So first part of go-to-marketfit is can you actually
consistently get in front ofyour ideal customers, so can you
reach them in an efficient way,and then can you consistently
(15:22):
close deals with your idealcustomer profile, so can you
then convert them?
So can you basically reach themand then convert them.
And I think that togetherstarts to think about, starts to
get the channel into place.
I do so again, problem-solutionfit, which I think is the most
important one you've got to findout.
And the next part is you'retrying to build repeatability on
(15:45):
the go-to-market front.
Now, where does monetization fitin?
I think it's a very goodquestion.
The challenge is, I think, forcompanies.
We know for certain kinds ofthings monetization can be
deferred.
For example, if you have amedia company or a company that
relies on consumer engagement.
We know time and again this hasimproved that if you can get
(16:06):
enough engagement, enoughengaged users, you can use
advertising to monetize, andevery time a new engaging media
came over, people said thatcan't monetize.
It did monetize, whether it wasSnapchat, pinterest, twitter,
tiktok all of them have figuredout how to monetize using ads.
So I think, for consumer mediaproperties, but you need massive
scale.
You need massive scale.
(16:27):
You need at least 100 millionMAUs probably more hundreds of
million MAUs to build areasonable business.
For almost everything else,where the value exchange is not
in media time or attention, butit is in paying for a service, I
think we've got to figure outare customers willing to pay?
So willingness to pay is, Ithink, a very important part of
(16:49):
the problem-solution fit, whichI think it has to be included,
because activation can't just befree activation.
It could be, but retentionprobably does involve because
it's a freemium product maybe,so you could activate with a
free product.
But retention you've got totest whether they're willing to
pay or not.
Why?
Because if they are free, youdon't really get a signal of
(17:10):
what the real customer behavioris going to look like and so I
don't trust.
So that's why you've got to.
If you have a business modelwhich relies on exchange of
value through customers payingyou for value, you've got to
test that out, because otherwiseI don't believe the customer
behavior will hold true when youintroduce payment.
So I do think it's important tofully simulate or fully test
(17:35):
PMF with the right businessmodel that you're going to have,
because if you say, oh, I havefree users, now I'm going to
introduce payments, I've seen Ihave a few companies in my
portfolio that try to basicallykeep services that you otherwise
would think of as payingservices free for a very long
period of time, thinking, oh,we'll monetize later and you
(17:56):
could with things like takerates et cetera.
But it becomes very, very, verychallenging, especially over
the last few years.
I think investors have refusedto believe one's hypothesis
around this and they basicallyare much more conservative than
entrepreneurs are in figuringout what the attach rate is of
payments.
So I think it's very importantfor entrepreneurs to figure this
(18:18):
out early, otherwise it'll beto their detriment if they don't
got it now.
Sanjay Swamy (18:23):
It's also interesting that um, one of the
things we experienced, um,trying to not name the company,
but I'll have to give theexample.
Yeah, where you know, theywanted to offer a service for 50
rupees per user per month andtypically the customer expected
another auxiliary service to beincluded which would have cost
(18:48):
15 rupees per transaction.
There was a bank card,basically, so an ATM transaction
, right.
And they said my bank cardgives me three free in a month.
So the next day we went backand said, okay, it's 100 rupees
a month.
Card gives me three free in themonth.
So the next day we went backand said okay, it's 100 rupees a
month and we include three freeATM withdrawals.
And people were fine with it,whether they used it or not.
And then the next day we wentand we sold to another customer
(19:09):
and we doubled the price to 200rupees per month and they were
fine with it.
Then the next day we tried 400rupees and we literally went
every customer, we just doublethe price and see where the
breaking point is right.
And then 400 people said that'stoo expensive.
And then we said okay, you know, the cac is so high.
How the heck are we going torecover?
And so we said what if we did a10 card minimum or a 10 user
(19:32):
minimum, like which flew?
And so that's how the price wasdiscovered, at you know, 2000
rupees with a 10-user minimum,and it made the you know sort of
the CAC, like you know,recovery period six to nine
months and made it a veryattractive proposition.
Gokul Rajaram (19:48):
It's a great point.
Price discovery can result inchanging how you go to market
and so on, because the price youdiscover might be different
than what you assume up front.
So I think that's anotherreason why it's important to you
know, understand early on,though, that said, your
portfolio company my gate, Ithink has an extremely well you
know, monetizing much later.
If I remember correctly, theyhad a free service for a very
(20:09):
long period of time.
So there are exceptions,amazing exceptions that prove
the rule that you can actuallynot.
Sanjay Swamy (20:15):
Yeah, I think in.
I think in their case becausethey're sort of pretty much the
only player in town and have avery sort of compelling, almost
mandatory daily use case, right,I mean because you can't.
You know your Amazon packagecan't come home unless you
approve there was enough sort ofbenefit.
(20:35):
Yes, yeah, yeah.
And of course they've also beenvery thoughtful about when they
start monetizing.
You know, get the habit goingand dependency.
But yeah, you're right, thoseopportunities are kind of rare.
I think for the most part thecustomer has a lot of choice,
because here the stickinesscomes, because 400 people in the
(20:58):
community have got to changefrom their product to somebody
else and there's always inertiathere.
Gokul Rajaram (21:05):
Because what happens is retention, which is
the primary measure of PMF,could instantly change if you
start pricing, unless you're aproduct like my gate.
So that's why it's so important, because you could say I have
80% retention, oh yeah.
So that's why it's so important, because you can say I have 80%
retention, oh yeah, that's fora free product.
Let's see what price does to it.
Right Price could reduce it to20% and then all your
assumptions fly out of thewindow.
Sanjay Swamy (21:24):
Right.
So, um yeah, look, I think thisis a topic in itself.
I know Shripati has writtenabout.
My partner has written a blogon the high price of mispricing
your product because, especiallyin a category creating company,
sometimes you price it so lowthat you just reduce the TAM
right and you reduce theattractiveness, although the
value to the customer might beextraordinary.
(21:46):
So we'll switch gears a littlebit.
Gokul is just cognizant of thetime here.
There's so much happening onthe AI front these days across
the board and I broadly havebeen thinking about it as three
things.
One is obviously the platformplays, which are very well
(22:09):
documented and talked about.
Would still love to get yourquick views on those.
But in terms of opportunitiesfor startups and VCs, one is
sort of what I call thescaffolding around the business
things like customer serviceautomation, things like sales
marketing and things like that.
And then the second is productswhich are inherently built on
AI, some which are just enabledbecause of all the advances in
(22:32):
AI and others where AI can makethem much better, business flows
and things like that.
I would love to get your takeon how you view this landscape
in the first place and theopportunity space, certainly for
startups and which are the onesthat you see as opportunities,
especially for India-basedcompanies that might be
(22:52):
targeting, you know, us-basedcustomers.
Gokul Rajaram (22:55):
Yeah, I think it's a.
You know, again, it's a topicwe could spend hours on, but,
like you said, I think at thehighest level there's
infrastructure and applicationswith a middleware layer in the
middle.
I think the infrastructurepiece is both chips NVIDIA, of
course and also foundationalmodels.
I think the challenge withfoundation models in general for
any startup, let alone India orthe US or anywhere in the world
(23:16):
, is that the capital intensity,the data intensity and the
computing needed for thesethings is exponentially scaling.
I think the estimate is thatnow you can with GPT 3.5-like
models, you can now $10 million.
You can train GPT-4 model willcost $100 million to train.
Gpt-5 is probably going to costnorth of a billion dollars to
(23:38):
train, and so basically, andthen the thing is, it won't
change because it's an arms race.
If you are a hyperscaler aGoogle, a Facebook, an Amazon,
et cetera you are not going towant to lose this race because
you don't want and even Apple,you don't want to be beholden to
someone else's platform, sothey are not going to stop
investing.
So if you're a startup, you'rebasically competing with the
(23:59):
largest companies in the worldwho have made this a strategic
priority and who have saidthey're willing to over-invest
versus under-invest at the risk,because they don't want to risk
missing these platforms.
So you're competing with thefour largest and most ferocious
competitors and most capitalintense and the most profitable
companies in the world.
So it's almost impossibleunless you're Sam Alpin and
OpenAI, maybe, maybe, maybe,anthropic.
(24:20):
So I think there's maybe fiveor six companies in the world
who are going to be able toinvest in this for the next
several years, because it's notstopping.
That's the thing.
If you stop, you're done.
You need continuous pool ofcapital.
That's why I think most ofsam's time is almost certainly
spent in raising capital at thispoint, more than anything else.
And you go to just like venturefunds became really large and
(24:42):
started going to the middle eastand so on.
You heard news of him going tosaudi arabia and so on, and you,
you probably have to do thesame thing.
You've got to now get sevenbillion dollars, next 50 billion
, $50 billion, who knows what.
So I think that's why, ingeneral, it's not a fruitful
endeavor for startups.
I think.
Chip layer there are somecompetitors.
We'll see how it goes.
As you know, nvidia has built avery compelling software
(25:03):
platform also which is part oftheir lock-in, not just the
hardware, but I think thatgenerally is a harder nut to
crack.
So I think the opportunitiesare in the middleware layer and
in the application layer, and inapplication layer there are two
kinds of applications, I think.
So in the middleware layerthere's a bunch of tooling.
I think the challenge I've seenwith these tooling companies so
(25:26):
far I'm an investor in a few isthat adoption, especially
amongst enterprises, is a littleslower than you're expecting.
Why?
Because enterprises don'treally know what they want to
build yet, so tooling is tooearly in many cases to test, and
so in many cases, even ifthey're adopting these tools,
they're not paying much and soand they're just using.
(25:48):
Maybe the one interesting kindof tool you have is an
orchestration layer that allowsenterprises to switch between
different models.
But the reality is, as modelsconsolidate, people are seeing,
well, I don't need 10 models,two or three models I need to
use.
So there's some stuff there,but many of the other tooling I
think is going to be built intothe foundation platforms
themselves or the cloudproviders and I think the
(26:09):
margins in tooling is going tobe squeezed.
There is some stuff I'm excitedabout around safety of models,
around observability of modelsand so on.
That will need to be done by athird party because you can't
trust the models to policethemselves.
So I think there's a system oftools around that and I think
there's good middleware aroundthat.
I mean, in Western, I gotPetronas.
(26:29):
That's doing some good stuffthere.
There's other companies there,but I think the app layer is the
most fertile of all.
Just like you mentioned, I thinkthere are various ways you can
categorize apps.
One of the best ways I think ofis functional horizontal apps
and vertical apps,industry-specific apps.
So there are apps that, like Ifyou look at accounting AI app,
accounting AI app applies acrossall industries in theory, but
(26:53):
it's more of.
It takes a function likeaccounting and basically
automates it as much as possible.
But then you take an app forthe construction industry or the
auto industry.
Those are vertical apps.
So I think those are two kindsof apps.
Even within that, I think themost interesting opportunities
(27:13):
are within the verticalecosystem.
Why?
Because in functional appsthere have been SaaS.
There are SaaS companies thathave automated over the last 10,
20 years many of thesefunctions, not with AI, but with
general, like you know, justheuristic based software.
There's Salesforce in sales andmarketing, there's HubSpot,
there's like 20 companies,there's Outreach, there's Gong,
et cetera, in sales andmarketing alone, and each
vertical QuickBooks inaccounting right, and these
(27:35):
companies have customers, theyhave data and, thanks to the
foundation world investments,they've been able to easily spin
up AI-based functionalitywithin their products or embed
AI functionality.
Now they're not as good asAI-native products, but they're
good enough.
They're good enough that theircustomers are placated for a
little bit while these guys areworking on AI-native products.
(27:57):
So in general, I think it isgoing to be fairly challenging
for startups to break out.
There's going to be exceptions,obviously, that prove the rule,
but it's going to be hard forstartups to break out in
functional AI company, infunctional AI categories such as
sales and marketing, such asaccounting, such as finance,
(28:18):
unless there is no incumbent inthem.
I think anytime you have anincumbent that is between five
and 10 years old, thesecompanies are fairly innovative
and fast moving.
For example, I think in customerservice AI, which was a very
fertile part, very fertiledomain for AI.
In fact, it's probably the onearea where customers are using
it fairly actively to displacehuman agents or augment human
(28:41):
agents.
There's at least 20 companies.
I think one of the YC batcheshad like eight companies and I
think there's easily and they'reall focused on different things
.
But I know of at least twocompanies that were started five
to seven years ago that withinthe last one and a half years,
they said okay, you know what?
Our old school customer serviceautomation business is going to
(29:03):
be disrupted.
Let's just migrate all ourcustomers.
Let's just build an AI customerservice agent and migrate all
our customers.
And they are at almost 10x thescale of any of these startups
within one and a half years,because they already were at
significant scale, close to 50to 100 million, and now they are
at the double digit millionsvery, very quickly as a
(29:23):
migrating customer.
And that's what you're going tosee.
I think there's a lot ofstartups that are raising rounds
from great VCs, but the realityis their scale is dwarfed by
the scale of the incumbents.
Even if they move just 10% inthe first year, that's more
revenue than most of thesestartups are doing, and these
are good products that they'relaunching, these incumbents,
because it's not that hard.
The foundation models are madeand not that hard, and now
(29:44):
coding agents all of thesetogether.
So now all of this serves tosay that I think the opportunity
is in verticals and I thinkwithin verticals and in
something that, as you know, iscalled service as a software is
the other opportunity which inmany cases deals with verticals,
where you take an existingservice, a business process or
service, and you completelyverticalize it and so you
(30:06):
basically make it an APIendpoint where you can basically
take almost an IT consultingservice.
Say, actually, a managementconsulting service is a great
example McKinsey offers.
You tell McKinsey I'm going topay a million dollars at
McKinsey BNDCG, literally, thereare companies that are doing
McKinsey as a service where youbasically ping that software and
(30:28):
it gives you an amazingMcKinsey-style report on
something with a bunch of inputsthat you give it, and so that I
think that has the opportunityto take.
I believe there's $2.3 trillionthat are being spent on various
IT and business services.
So in theory you can take allof those services and make them
a software endpoints very, verycustomized for that service.
(30:50):
And I think Indian companies inparticular are very, very are in
a pole position because I thinkeverything that Infosys offers
or TCS or Wipro, you could dowith AI without humans, so at
least augment them.
And similarly everything thatMcKinsey offers.
As you know, many of theselarge managing consulting firms
have their teams in India and Ithink there's a lot of knowledge
(31:10):
there.
So I'd be excited to seeex-McKinsey folks in India leave
and offer McKinsey as a serviceoffer, goldman Sachs analyst
reports as a service offer.
You know Wipro or Infosys orTCS as a service, all those
enterprise IT integration as aservice.
So I think software as aservice is a huge option.
The other one is what isinteresting to me is I've been
(31:31):
now meeting Service as asoftware.
You mean Service as a software.
I've been now meeting companiesin India that have gone after
US and European verticals andthere's a company I met called
Spine S-P-Y-N-E.
Really interesting company.
All their customers are autodealerships in the US and Europe
(31:51):
and they basically builtmerchandising software for these
folks and they have basically,using a sales team in India,
been able to sell this prettycool merchandising software and
they now have hundreds ofdealerships in basically nothing
in India all their businessesin the US and now they're
building a full suite ofproducts.
(32:12):
They race around, et cetera, etcetera.
I was like wow.
I met Sanjay, the CEO, and I'msuper impressed with what he's
built and what the team hasbuilt completely out of India.
So I suspect, because speed ofiteration is very fast Good name
too, right, it's?
Yeah, exactly, it's a greatname.
And so I think they're going tobe, and I've met more and more
founders who are taking, who arebasically taking, verticals in
(32:35):
the US or in the West and goingafter, just like Zoho and
FreshBooks did with horizontals,with functions, sitting in
India.
I think you're going to see thenext generation of vertical AI
companies, many of them.
I think Indian companies have asmuch a right to win as any
company anywhere because theycan build software fast.
You need to build a bunch of AIagents.
(32:57):
They have a lot of data and Ithink that.
And I think the other thingIndian companies in general have
a right to win, I think is infundamental needs, in the
Maslow's hierarchy, things likehealth, education, et cetera,
because you have a lot ofinteresting data and operations
that you can train your businesson in the Indian context and
(33:18):
you can really bulletproof yourproduct and your operations
operating in India and then youcan take those learnings and
bring them to the rest of theworld in a much more
cost-efficient and operationallyrobust way.
One of them is a portfoliocompany, dozy, which, as both of
us know, is building thisAI-enabled healthcare platform
that is super low price but isincredible.
(33:41):
It transforms any bed into anICU bed and basically lowers the
need for staffing, fordedicated staffing and nurses.
The nursing shortage is achronic shortage everywhere in
the world, including in Indiaalso, but I think definitely in
the West, in the US.
So they allow one-tenth orone-fifth of the number of
nurses to be able to monitor aset of beds using AI, using
(34:02):
signals, using just.
This is technology that they'veproven for a few years in India
and now it's used by all themajor Indian hospitals and now
they're seeing strong successhere and I think that model
taking the same human needsright.
I mean the human beings havethe same need for medical care,
whether it's in India or the US,and now you have technology
that is making it better.
(34:23):
I think, similarly, radiology,all of these things training
data is so much more availablein India compared to anywhere
else, because you have so manymore lives and so many more
people, so you can train yourmodels much better in the Indian
context and then you can easilyleverage those things in other
countries.
Sanjay Swamy (34:40):
Yeah, actually it's interesting that I was
chatting with Mudit and Gauravthe other day about the US
market opportunity for Dozee andthey almost have to dumb down
the product.
Right, because the product thatthey've got in India that is
working here in the hospitals.
People are running a verystrong operation on Dozee, but
(35:03):
in the US, now that they're FDA510K approved and they're
rolling it out there, I thinkthere are subcomponents of the
product that are extraordinarilymore valuable to the users and
they're almost building asimplified version or lighter
version of the product becausethe market is ready for it, has
(35:24):
been looking for and has alreadybeen using not-so-elegant
solutions.
This is a very simple to thecustomer, it's just okay.
It's a better version of what Ialready know I need, whereas
here is a lot more inevangelization and stuff that
needs to be done.
So, but you're right, I meanthe benefit of training.
This and especially, you know,in the case of clinical grade
(35:48):
products, right, it makes a bigdifference, and I think we're
seeing another young startup ofours called ZooAI that's doing
this IB curriculum like aself-study buddy for students.
There are enough kids in Indiathat are also studying the IB
curriculum and now that it'strained here they're able to go
to other markets.
(36:08):
It turns out I was surprisedthat Turkey is one of the large
markets of IB curriculum and ofcourse, the rest of the world,
the US included, is also huge.
So they actually got theirsecond batch of pilot users.
There's a lot of downloads fromTurkey and a lot of usage and
fine tuning of the product.
So, yeah, I think it's a superexciting time.
(36:33):
And on the vertical SaaS, we'vealso had companies that were
working on good, I would say,operating systems for mid-sized
businesses, verticalizedsolutions, like we have a
company called Bookie that'sdoing the how should I say the
studios, for, you know, smallgyms and those types of studios
(36:56):
are small to midsize and the AIinnovations that they've layered
into the product now havesuddenly, you know, had given
them explosive growth, right.
So I think the customers alsoare looking for that kind of an
edge.
In fact, one of the thoughtswe've also had is bringing in
the services and softwareelement, because with the human
in the loop in India, you canactually do a lot of like, for
(37:22):
example, the front officemanager is the hardest role to
hire and train and retain right.
Well, that can be done, youknow, with a combination of
software and a trained human inthe loop.
So I think some of theseparadigms are going to evolve
and I think it's going to createvery interesting new
opportunities.
Gokul Rajaram (37:40):
And what is crazy is that many of the current
companies today already areusing their gross margins
actually 40% or 50% because theyhave humans in the loop and so
almost everyone is in the loopto get training, data use,
reinforcement learning, feedbackand so on.
I think Indian companies can doit much more cost-effectively
and at scale compared to anyother company.
Sanjay Swamy (38:01):
Rajat Mittal.
Yeah, having said that, there'salso the risk for the larger
Indian companies If so much ofthe software side of it, of the
development side of it, uh, Imean, I guess there are two
schools of thought.
One is saying, hey, you know,there isn't a whole lot of very
senior talent in india and theco-pilot approach might actually
(38:22):
level the playing field and youmight be able to get a lot more
value out of the not soexperienced talent.
And then there's the other sidesaying, well, we need this
talent at all.
So it'd be interesting to getyour thoughts on is there always
going to be a continuous demandfor talent?
And, with all of the stuffhappening in AI, is there a
(38:45):
future for software developers?
Gokul Rajaram (38:47):
I believe.
So I believe what you said.
The first case, the optimisticcase, is what I believe in, in
that the number one thing thatAI does is narrow the skill
divide where it makes someonewho's a junior developer
equivalent to a mid-leveldeveloper and a mid-level
developer equivalent to seniordevelopers, so the skill premium
starts going away.
The notion of a 10x engineer or100x engineer who's paid 10x the
(39:08):
compensation of a 1, 1xengineer, I think you're going
to start seeing that, seeingthat shrink.
And I think that's where indiahas a lot of, you know, 2x, 5x,
maybe fewer 10x engineers.
But I think those 2x and 5xengineers can become 5x and 10x
engineers using ai tools.
So I think, because, because Ithink you still need engineers
(39:31):
to build software, but you maynot need, like, super expensive
engineers, you might need justreasonably talented analytical
engineers whose skills areaugmented by AI.
So I very much believe thatengineer, that AI will augment
engineers and improve theirskills and reduce the skill
premium, which very muchbenefits India skills and reduce
(39:52):
the skill premium, which verymuch benefits India, because
India, you know, and I thinkthat I think you're going to see
happen, versus just replacingengineers en masse.
Sanjay Swamy (39:59):
Wonderful.
So let's switch gears a bit.
Gokul, I know you've been avery prolific angel investor
over the years and obviouslylove having you on board in a
meaningful way at Dozy, and yourinsights and inputs have
already been very valuable tothe team.
But you're also transitioningnow or transition now into
(40:20):
becoming more of a venturecapitalist, from where you're
largely investing your own moneyand can make a decision and
look in the mirror and say whatwas I thinking to when it's went
to capital and there's anexpectation and responsibility
that goes with it you know itmight?
(40:43):
Or when it's like a hybrid ofyou know, both yours and a fund
that's raised with third-partycapital.
How are you seeing thesimilarities and differences and
any early insights into yournew journey?
Prime Venture Partners (41:01):
Dear listeners, thank you for
listening to this episode of thepodcast.
Subscribe now on your favoritepodcast app for free and you'll
be the first one to know whennew episodes are available.
Just search for Prime VenturePartners Podcast in Apple
Podcast, Spotify, Castbox orhowever.
You get your podcasts, Then hitsubscribe and if you have
(41:22):
enjoyed the show, we would bereally grateful if you leave us
a review on Apple Podcast.
To read the full transcript,find the link in the show notes.
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