I'm fresh off the Mongo DB dot local event.
They had a ton of developers, partners, startup founders
hanging around and showing how Mongo is trying to respond to
everything that's happening in artificial intelligence.
They are the sponsor of this podcast and they had me on site
at Mongo DB dot local to do a bunch of these interviews, which
I really enjoyed, so we decided to put them in our feed.
I had a great conversation with CJ Desai, the CEO of Mongo, who
dropped by my little studio at the conference.
We have another interview with the head of AI from Ripling
coming after that. This is the Newcomer podcast.
Hi, I'm Eric Newcomer, author of Newcomer.
We're here at Mongo DB's local event, the amazing drop in when
you get the CEO, what, 65 days into the job to show up on
stage. What I mean, this is, this is
your big event. What was What's the message that
you really wanted to carry to the attendees here?
You know, I would say, Eric, first, thank you for having me
and thank you to you for letting me crash the party of.
Course I love it. So we.
Just we just literally we just talked to a three person
company. So now we're going much larger.
Yeah. Sounds good, Eric.
So 65 days in and one of the things that our previous CEO,
Dev and the entire team, we realized, so first of all, from
just being in Silicon Valley for the long time, San Francisco
feels like it's back. San Francisco is back.
During the pandemic, people went a little dark on San Francisco.
But with this AI platform shift, San Francisco is back and as you
know, we had a New York headquarter, New York founded
company. Listen, I'm I'm a New York based
person who also believes in San Francisco.
So we have a sort of shared spirit on that realizing that AI
boom is here, we are also in New York.
So yeah, I feel it. So lot of you know, so San
Francisco is back. Mongo DB about 10 years ago did
a really nice job at San Francisco.
They were in front of software developers, builders saying
build on Mongo DB. Here is why.
Speed, agility, scale out many, many advantages.
And then the team realized, Dave and the team that we kind of as
the company succeeded, we took our eye off the ball for a lack
of better term. And so after four years, we
decided to reintroduce Mongo DB today in San Francisco.
So today's an auspicious day. Thank you for coming and we
wanted to launch and tell people that we are here, we are Mongo
DB. It's a great data platform you
can build on no matter what kind of applications you're building
on, digital native, AI native or you have AI plus plus, whatever
the case might be, it is a great database.
And you know, one of my biggest profound realization was that
when I, when I was doing my own diligence to join Mongo DB, even
though the founders didn't create with AI in mind,
unstructured data, flexible schema, you know, Symantec
Retrieval and all these things that are part of now the
platform, it is like the platform for AI applications.
And so we wanted to tell that to everyone.
And we got a great customer today to validate a great
founder who said he has built three companies on Mocodb.
You're talking about Mike Krieger.
Yeah, you had the nice self video Instagram founder.
I created artifacts, which you talked about briefly, which is
an interesting for me in the news world.
And then obviously he's at Anthropic.
Yeah, that was an exciting endorsement.
What what is your read on? You know, I don't know, AI hype
in 2026. On some level, you must think
this is obviously a phenomenon to say you're doing this big San
Francisco push. Dave, you know, I think I talked
to him last year. You know, he has a dose of
realism to it all. What's your what's your personal
perspective on where we are in this hype cycle?
He's smart and realist and I'm an optimist, so that's how I.
Brought in the guy like, Oh no, this thing is going strong and
we're we're going to get in front of it.
Yeah. I mean, I, I would say since
2022, Christmas ish or October ish, when you look at the
evolution of AI and you look at some of the killer AI companies
where they have scale business in a significant way has been of
course open AI. Everybody understands that that
is a killer app from my perspective.
But you also look at Grog and how fast they have grown X AI
and then Entropic, the entire team at Entropic Lab, that is a
truly killer AI app for coders. I mean, people load that
platform and they sometimes use it with cursor this that.
So you're seeing that these companies, if you look at like
Internet age in 90s, you look at mobile age, they did not scale
this fast. How how fast this companies have
scaled. That's like pretty amazing from
my standpoint. And in 2025, I think the killer
apps were the coding tools. There was a killer app besides
chat GPP of course. And now as we go into 2026,
which ones are going to take off?
I'm optimistic that some very specialized vertical apps
related to maybe a healthcare or insurance insurance assist or
something may take off, but you will see some take.
Off close to, you know, a bridge and open evidence.
Those are super interesting applications.
And health, Yeah. You interviewed Constantine and
Sequoia. Obviously, they're big.
They're bullish on Harvey, you know.
Yeah. So a lot of these vertical
applications are exciting. And Harvey, you know, I met them
a couple of years ago and they were still, you know, truly with
the legal firms they were using them.
And then now you have the in house counsels use them as well.
And so they have gotten a perfect product market fit and
they're expanding now their use cases.
So that's just it. But that's very specialized,
right? It's not just generic.
I come in and help me review this contract from a legality
perspective and so on. This is a drop by, so I don't
want to take too much of your time.
This will be the last question. How do you think about, you
know, there's so many models available.
How do you think about like, oh, where to provide your own versus
to just sort of say you're obviously going to bring models
from other places? We provide sort of the database
layer. For databases, we want to be
model agnostic, Yeah, we want to provide best embeddings.
So your retrieval quality is high, accuracy is high, but we
want to be model agnostic. And when I speak to customers,
including AI companies, like everybody who originally was
telling me open AI, then they shifted to cloud, I think the
innovation cycles are very fast. And then you look at some of the
firms in China and you look at DeepSeek and others that
innovation. Cycles come from anywhere.
Yeah, that could come from and the product cycles are shorter,
right. Product cycles are shorter.
So multi model, not multi modal, but multi model is will be a way
to go and it gives freedom to people to use whichever model is
best. Gemini, maybe today, better
tomorrow, maybe an topic again. And I think that's how it's
going to work. So we will be agnostic
regardless of who you use. Our goal would be always to give
best semantic retrieval capabilities and completely
provide a scalable data plan. Well, I love, you know,
newcomer, we use green so it was easy to share the stage, but
honoured that we get to share branding with Mongo.
Thanks so much for having me here and thanks for dropping by.
Thank you, Our stage. We appreciate it and we'll see
you in Brooklyn soon. Sounds good.
Alright, thank you. Thanks again to Mongo DB for
sponsoring this episode. I feel like I've talked to both
CEOs, the old and the new in the last six months.
So that's been a lot of fun. And now excited to get into it
with Anchor Bot, the head of AI at Ripling.
I know he's big boss Parker Conrad pretty well.
He's been on this podcast before.
Parker has been somewhat slow to embrace AI.
So it's fun to talk to his head of AI about, you know, the
company finding AI Jesus, believing in AIA little bit and
how they're making that cultural and technological
transformation. Give it a listen.
Thrilled to have anchor bot, the head of AI at Rippling here.
I'm just saying, before we got on, I go way back with Parker
Conrad, your Ceoi wrote about Zenefits back in the day.
I I feel like I was, you know, as far as reporters can be
bullish. I was pretty bullish on his
comeback in Rippling and have been following your work.
What's it like to be the head of AI for?
I feel like for a Parker is a technologist for the guy, but he
has not been wrapping himself in AI.
Like how's that How how is it to be the head of AI and CEO sort
of coming to the the AI hype? I think it's been exciting
specifically from the lens of there is so much happening in AI
everyday. So automatically everybody's
curiosity on what's the new model release, what's the new
capability release? And like like these days,
everybody's hyped up on cloud code as an example, what
Entropic is doing. So that automatically starts to
create this pull internally around.
What does this mean for Rippling from a product point of view?
What does it mean for Rippling in terms of day-to-day business
perspective? And what does it mean in terms
of customers expectation on Rippling it, right?
You get to you get to be the guy who knows what's happening
today. I say, oh, how do we respond to
this, this, that and the other we're obviously, you know, at
Mongo DB dot local, you know, obviously sprawling ambition
with Rippling, it's like, oh, we want to stack startup on
startup. So I can imagine strong data
organization capability is part of the business success.
But explain Ripling's relationship with Mongo.
Yeah. So we are being a long time
customer of Mongo from the beginning.
I think as you rightly pointed out, Parker's thesis was
compound startup like product over product.
And the core, heart of it is the employee graph.
And being even though yes people know us that we can run payroll
for you. Core of it is the employee graph
which captures not just your pay related information or benefits
related information. We also capture your ID and
identity related information, your device related information,
because at the end we also have a product portfolio of ID.
Similarly, we also have a product portfolio around
finance, your corporate card, your spend, your travel expense.
We just launched a travel expense product.
So that's certainly the amount of data you have about employees
gets connected in so many different ways.
And having a partnership with Mongo allows us to continue
keeping that graph sanity intact in terms of the relationships
you have in this business data. Rigorous are you guys about
making sure that everything built at Ripling uses the same
tech stack? I mean, if you're acquiring
startups and you're letting people sort of do their thing,
how do you balance the trade off of sort of a consistent stack
versus letting different people build what they want?
Oh, that's a great question. And speed versus consistency is
a constant dialogue we have internally.
I think there is a value in terms of letting people innovate
on the side, but it's a little bit of a trade off because as
soon as they are reusing the common capabilities we already
have in our platform, there is a dimensional speed which comes
with it. And certainly for example, the
employee graph comes with the workflow, it comes with the
notification, it comes with an ability to you know, track the
changes that's happening now. If I am building now our travel
expense product that are workflows for me.
Of course there is a dimension where I need to go into
integrating into flight booking, hotel booking, which is very
unique to my products niche. And there I'm free to choose
what I choose to do that and run faster.
But I think we've been able to guide the different product
teams even though running independently on the core value
of building on that common employee graph and common
capabilities. What has been the value of Mongo
like? Would would Rippling be a
different company who is built on Postgres?
I think, I think it would, no, Rippling would not be a
different company because of Parker's overall hypothesis of a
compound startup and really building it as a connected
ecosystem of products running on a common foundation.
I think it would have been just a little bit harder, I think and
having that partnership with Mongo from day one allowed us to
build this employee graph, build a capability set so that the
next product or the next product we were creating was a lot more
easy. And this is not just about
transactional data which we are maintaining and managing.
It's also about analytics and reporting.
Being able to take that information and have the ability
to report across a cross section of your product portfolio or
your business data, not just from HR or IT or finance allowed
has opened up those possibilities which having a
common data store, having this doc Mongo capabilities opened up
those possibilities for us, which we would have solved
eventually. It would just have taken a lot
more effort. So you're the head of AI.
We touched on some of that role means translating what's
happening in the broader San Francisco ecosystem to to
Rippling. But in terms of, you know,
products within Rippling, what what are the AI products?
Do you have an agent yet? What like what's that look like
inside of Rippling? Great question.
I think originally Rippling also started.
We have almost 70 plus products in the portfolio and originally
each product started innovating around how they can embed AI as
they're building that product, right.
It could be within our recruiting product portfolio.
When you're doing interviews, you are meeting candidates
summarizing those information. It could be within our IIT
product portfolio where you are issuing devices, you are
tracking compliance and security around those things and having a
summary of your policies. So people started embedding AI
as product capabilities very early.
Inadvertently, I think the change happened.
The point in time we actively started investing on for
building AI products, you have to use AI everyday.
Without that, I genuinely believe it's very hard for
people to reimagine what it means for their product.
So 10 months ago, we Albert, our CTO really put a charter out and
that's where I came in is driving the AI transformation of
Rippling internally that we use AI everyday, we embrace latest
and greatest of AI tools every day in our workflow whether it
is product design, engineering, legal, finance, sales, marketing
and that. What is that cursor?
Harvey Sierra? What do?
You use the tech stack is full of all the tools you can think
of, right? So of course Gemini chat GBD
cursor cloud codecs, so does. That engineer sort of pick their
preferred. I think so because Rippling
deals with a lot of enterprise customer data.
We do have AI pilot process and I have built up a checklist with
our security and legal so that we can quickly assess a new AI
solution or somebody will request.
So I get request every week. OK, I want to try granola.
I want to try Bisper flow. Typically what we do is we run
through it through an AI pilot process.
Do you know do you use factory? I'm going to be talking to the
factory. It's on my list of an ask from
somebody. Maybe it'll get accelerated
after yes. That could be.
I'm very keen to learn more, which keeps my job a little
curious because a lot of times I can't stay on top of things.
So then the rest of the company is constantly keeping top of
things and asking me can we try this or can we try that and.
Do you, do you have an overall agent at ripling or?
That's one of the new investments we are making.
I think Mongo's partnership has helped us because if you think
about Rippling, we are not just a payroll system, We are not
just a finance or a travel expense system.
We are a full suite of products. So for us, we can build an agent
which is not a payroll agent or a finance agent or an IT agent.
We can actually build a rippling intelligence agent which can
actually answer questions across your entire day-to-day business.
And that's essentially what we have embarked upon, right?
Parker is extremely excited about business.
Intelligence you think is a key output.
Or I think it is really productivity.
So if you think about AI productivity is the, is the
impact which we we see in engineering or product and
design or marketing, right. The same productivity impact is
what we are hoping to provide to our customers because as our
footprint has increased, our customers growth has also
happened. So they are growing from like
look at Andrew, one of our flagship customers.
As Andrew is growing, they are using more products from we're
playing, they have larger set of ibase.
So essentially their day-to-day operations has gotten complex.
Yeah. So now if you are able to offer
them an agent to operate Android payroll to their IT to their
travel and the agent takes care of, you know, proactively
working with their administrators and making sure
Android stays on top of things which they need to say on is the
value we are aiming for. I hate to bring up a competitor,
but ramp and rippling start in different places.
They overlap on some. You now have, I think credit
cards, yes. And you both want to be sort of
the what, what's the term of art, like the record of all
things business for somebody. They've clearly leaned into the
AI brand more than you have. I don't know, what do you, what
do you you're the AI guy? Like do you want, do you want
that AI brand? Or it's like we have different
sensibilities. Or how do you think about it?
I think there is a, there is obviously an equity in terms of
having a clear AI story around your company and your product
because customers are keen to understand that because
specifically because if I'm a ripping customer today or I'm a
prospect evaluating Rippling today, I come with that
expectation that there is a certain amount of AI automation
productivity I will get. So it's, I, I commend Ramp for
what they have been able to embark on, not just on the
product side, but as well as in terms of positioning themselves
as an AI forward company. I think Rippling is not very far
in terms of our AI transformation internally.
We are very further along than what people may be aware of in
terms of our design partnerships.
We have design partnerships with Cursor, with Open AI, with
Entropic, with AWS where we get early access to the latest and
greatest land chain data breaks early and even Mongo, so early
access to their product capabilities.
We are piloting that at tripling using that to create
productivity and give feedback back.
And when I see those interactions and when I see our.
The ability to influence product road map of so many ecosystem of
AI companies it reflects back on Rippling's AI impact is much
larger than what people may be aware of.
I should know that where is is enterprise search part of
Rippling's vision because that's been an obviously clean is very
promising AI company. I would say part of Rippling's
AI assistant will take care of answering questions you'll need
answers for right across your enterprise, right?
Being able to obviously be being the system of record of a lot of
information, right? You don't need glean in those
cases because we can just answer those questions for you
directly, right? What do you think?
There's been a lot of AI is going to replace software
companies. I mean, so you get it from both
ends, embrace AI, but also like, oh, you know, you know,
individual companies are just going to spin up their own
payroll because, you know, they can build it with cursor on
their own. Like, I don't know, payroll
obviously to me seems sort of absurd because there's all the
regulatory and it just like sensitive, but clearly there are
there must be pieces of your business where people are sort
of hacking together their own apps.
Like, what do you make of this narrative that the rise of
artificial intelligence is going to have all these sort of
homespun software applications? We actually embrace that
wholeheartedly because what we find at at Rippling is what we
find at Rippling is as customers are using our platform, there
are always niche unique use cases for which they like parts
of what we offer to them, but they want to extend, enhance and
add new capabilities on top of. Which is why beginning of last
year, middle of last year, we launched our custom app
capabilities where you can essentially vibe code an
extension app to Rippling ad Rippling's platform itself.
Interesting. And again, going back to since
we are at Mongo's conference, right, being on Mongo in terms
of ability to store generic artifacts and documents actually
opened up that possibility for us essentially then to create
custom objects which then can be used as a container for any
customer to bring in whatever data they want to bring.
In any interesting examples? That you get, oh, tons of very
unique, very interesting examples in in companies,
because we deal with a lot of workers who are dealing with
shifts, for example, and compliance comes up quite often
in terms of having them having gone through certain amount of
rigors in certain industries. Very unique cases in terms of
tracking, you know, ticketing, tracking, sliding sign outs,
tracking. So there are like niche sort of
use cases people are building, which has actually motivated
Rippling to even launch RFD team, which is actually now
going in to our customers and building these niche customer
apps for them. And then seeing how we bring
that into our platform as capabilities in terms of
enhancing a platform to make that easier going forward.
Do you think your code base gets a little worse the more people
use AI tools or what's the risk in terms of over reliance on
cursor? That's a great.
That's a great question. I think we've started to observe
that, yes, there is a dimension of an AI slop seeping in to the
engineering discipline of coding every day, right?
But I think we've been very strict about our AI stance on
couple of things. First, testing is non
negotiable. So any code which is being
checked into the main line and pushed to production has to be
fully tested and vetted. Second, accountability doesn't
just the fact that I used cursor in AI doesn't take away my
accountability of what that code does.
And I think we've been very clear to engineers from day one
that accountability is still there.
So that essentially creates sort of a responsibility
accountability in engineers to be cautious of it.
But that's just on the human side.
From a system side, we're also bringing in additional AI tools,
so AI coding, code review tools, AI tools around looking through
in terms of production outages and bringing that information
back to to engineers to be able to troubleshoot and then
improve. Because at the end of the day,
the AI enablement or AI use is not just about development.
AI can also be used in analysis of what's happening in
production. AI can also be used in reviewing
and verifying. And essentially by bringing
those systems in those places, overall keeps the health of the
code base at a peak and avoids the slops sort of just bleeding
into production. In terms of the rippling
product, maybe have you started to build it at all with the idea
that it's like someday soon people are going to have AI
agents that they treat as a worker?
It's like, yeah, I want to in some ways compare apples to
apple, this human and this AI agent.
And I'm going to like think about it in terms of identity in
the same way I would use rippling is that started to
creep into. Your great.
I don't know, it's like you're just just sort of picking up
things we are discussing internally because one of the
things we've been thinking about is agent identity, right?
Because now if we think about agents we are building for our
customers, at the end of the day, they'll start accessing
payroll information. They'll start accessing, you
know, information which has its own access management of who can
see what and how and what type of actions they can take.
Yeah, So it does need three things.
One, obviously an identity of an agent, which we know who's doing
what. But then second, it should still
inherit from a human, because in some ways that's one of the
conclusions. At least we landed on that.
At the end of the day, humans. Culpable for this agent?
Exactly, exactly, exactly who is who is on the hook for the agent
at the end of the day and whose, whose team This essentially
which translates into whose permissions, whose access
management this agent has to have.
And we found that it's a lot more easier to say that, OK,
there is always a human. There is always a human in the
system. Of course, what we are adding is
a productivity element. So instead of in future thinking
of adding more humans, you're essentially adding more agents.
But they still function as part of a payroll team and then they
get access according to being a payroll admin, right?
They are part of an IT team and they get access as part of an.
I feel like a lot of people are excited about what agents can
do, but not excited about being the one responsible for stopping
the agent from doing what it's not supposed to do.
Is Rippling willing to take that on at all?
Or like, do you, you see a world where you say, yes, we're the
barrier for your agent not getting access to some system
it's not supposed to? Have that already.
That already is something part of Rippling's ethos.
So we because we're not just a payroll company, we also have a
full blown identity system, we have a full blown access
management system. We already control what data who
has access to. We have a very well defined
access management platform essentially.
And if you which is what is allowing us to now bring in
agents and say, oh, this agent inherits from Encore and
whatever Encore can do in rippling this agent can do
right. And that philosophy essentially
allows us to guardrail not just when we are building agents
ourselves, but customers deploying agents in their
landscape, being able to then start performing tasks and
actions and leading to unintended consequences because
we will still keep that principle even for them
accessing ripping data. We guardrail that heavily.
The API access is only enabled through certain controls and
access management principles, so randomly customers can't deploy
an agent and start accessing ripping information.
Well, you have what every AI company wants, which is great
data. And now it sounds like you've
got the AI conviction. So exciting times ahead at
Rippling and thanks for joining us.
No, thanks for the great conversation and really enjoyed
talking to you. And yeah, let's continue this
dialogue. Sounds good.
Thanks so much. Thank you.
Thank you for tuning in to this week's episode of the podcast.
If you're new here, please like and subscribe.
It really helps out the channel. Listen in for new episodes every
week wherever you get your podcast.
Yes.