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in Episode 45
we're exploring what working means for AI apps
based on insights from top VC
firm and Dreisen Horowitz from Silicon Valley
the generative AI era has ushered in a period where
startups are growing faster than ever
often with fewer resources
we're seeing a significant shift in benchmarks
for the average AI company
what was once considered best in class growth
like $1 million in ARR in the first year
for enterprise startups
is now considered on the lower end
if you're interested
in the cutting edge of tech and startup growth
tune in to understand why
there's never been a better time to build
an application layer
software company let's dive in
alright let's dive in
there's this uh
this question that feels really central right now
doesn't it
what does working actually mean for a startup today
especially you know
with AI everywhere how much has the game really changed
oh definitely
it's more than just a feeling
the um
the traditional signals what we used to look for
they seem to be shifting and pretty dramatically
the old rules might just not cut it anymore
yeah and we've actually got some
really interesting data to look at here
we're digging into this piece from Andreesen Horowitz
you know a
16 is it's based on what they've seen across like
hundreds of companies over the last year and a half
that's right
it's called what working means in the era of AI apps
uh huh so it's grounded in
you know real world observation
not just theory exactly
so our mission for this deep dive anyway
is to unpack what they found
what are the new benchmarks
especially for those app layer companies
the ones building the stuff you actually use
and what does it mean for you right
whether you're a founder
or just trying to keep up with all this
this pace and it's pretty surprising stuff
it really is prepare yourself
because the numbers aren't just like a little faster
they suggest growth is accelerating significantly
and maybe not where you'd expect okay
so maybe let's set the scene first
like before this whole AI wave really hit
there was a pretty common benchmark
wasn't there for what you know
amazing looked like for an enterprise startup
in its first year first 12 months after launch
or starting to charge money
yeah absolutely
that magic number was often around
$1 million in annual recurring revenue
$1 Mar if you hit that in year one
you were basically considered top tier best in class
it showed you really had something in the b to B world
right a strong signal
yeah but for consumer companies
totally different story
revenue was like way down the road
maybe years later the whole
focus was just getting users
millions of them tens of millions sometimes
then you figure out how to make money
usually ads or something so that was the setup
$1 AR for top enterprise users
first for consumer okay
so now we've got this a 16 is data last 18 months
hundreds of companies hmm
what did they actually find
what's changed well
the picture is uh yeah
very different looking at the companies they know well
the numbers have just jumped right
and this is where it gets really compelling
the median enterprise company in their data
it's not hitting 1 million dollar ARR in year one
it's reaching over 2 million dollar ARR
and get this they're typically raising their Series a
just nine months after starting to monetize
just pause on that the median
so like the typical company in their sample
is doing more than double
what we used to call best in class
just a few years back that's well
that's a huge acceleration right
and then the consumer side is even Wilder
the median consumer AI companies in their data
they're actually doing better on early revenue
they're hitting something like $4.2 million in AR
in the first year and raising Series a even faster
maybe eight months in so that journey
that zero to $1 million AR
which used to be the gold standard
it's now kind of well
it's at the lower end for these AI
companies that are getting traction in year one okay
okay the speed is just undeniable
faster money faster funding rounds
but why what is it about AI specifically
that lets companies accelerate like this
yeah that's the key question
is it probably a few things working together
um first
AI
can let you iterate on the product incredibly quickly
you know improving the core function
might just mean training a new model
or plugging in a better one
that can be way faster than rewriting
big chunks of traditional code
right faster building
and second these AI
models can often deliver value almost immediately
like right out of the box
that initial hook for users or businesses
it can happen faster less setup
maybe less configuration than older software needed
okay faster value delivery too
and then there's just you know
massive market pull right now
people and companies are actively looking for AI tools
the demand is just huge so faster building
faster value hot market makes sense
and the article hints that this speed
this velocity it's becoming a competitive edge itself
like a mote how does speed work as a mote here
well it's more than just being first right
speed lets you iterate on your models
your features faster than others
and if your AI learns from user data
well
moving fast means you potentially get more data sooner
and that creates this cycle right
better data better models
better product more users
more data ah
the flywheel effect
so the gap widens because you're learning
faster exactly
it can create a lead that slower
companies just find really hard to close okay
that makes sense speed to launch
but also speed to improve
so faster revenue faster rounds speed as a mode
that sounds like the early game plan
but um the article also throws in a pretty
important dose of caution
doesn't it it's not all about that topline growth
especially later on oh
absolutely crucial point that initial speed
that velocity that gets you noticed
gets you that fast series a
but when investors look at later rounds
series B series C
they're gonna dig into the fundamentals
the traditional software metrics okay
so things like usage
how much are people actually using it
engagement and uh critically retention
are they sticking around
you can't just buy that initial revenue spike
and expect it to last precisely yeah
if that early surge is just
you know burning cash unsustainably
or if users aren't really engaged
or sticking around long term
it won't pass the test later
the speed proves you can get started
but retention proves you built something
people actually value over time
something sticky hmm
and how does AI itself affect those metrics
does having an AI
feature automatically make retention better or worse
and how do you even measure engagement
with some of these generative AI tools
you might use them intensely for a short burst
then not for a while yeah
that's definitely something
the whole ecosystem is still
figuring out but what the data seems to hint at is
for the users who do convert to paying
for generative AI apps
their retention often looks pretty similar to older
pre AI consumer apps oh interesting
so once they pay they stick
it seems like it yeah
the challenge
might be getting them to pay in the first place
that free to paid conversion could be trickier
but if they do convert they seem quite sticky
which you know
pushes companies to really focus on proving that value
clearly and consistently to the paying users
okay let's circle back to that point
that really jumped out earlier
the consumer AI companies making serious money
really fast that just completely flips the old script
doesn't it build users for years
then worry about money totally clips it
yeah yeah
the data shows the median revenue for B to C AI
is actually higher than B to B
early on based on their sample
and the way they grow can look different too
how so different shape
well think about it
a lot of these consumer AI companies raise big money
specifically to train their own models right
their own large AI models
and what they often see is a huge jump in revenue
right after they release a new
better model ah
so it's not a smooth curve
more like steps big jump when the model gets better
kind of yeah
more like step function growth tied to those big R&D
moments you get a spike maybe things level off a bit
then another spike with the next big model improvement
and like we said maybe converting free users is tough
but those paying users seem to stick around
okay so the big picture take
away from this
a Sixteen's data seems pretty loud and clear
startups are working faster
much faster businesses consumers
they seem genuinely willing to pay for new AI
powered stuff right from the start
yeah and based on this
it feels like a really dynamic time
to be building an application layer
software company
the potential for just really rapid traction
significant early revenue seems
kind of unprecedented which is well
it's exciting if you're a founder with a great idea
but also probably pretty daunting
because the bar for showing that early traction
it just moved way way higher
yeah
and the time you have to prove it before that Series A
it seems shorter speed is definitely table stakes now
yeah but like we talked about
it's not the only thing not for the long haul
you absolutely need that underlying product strength
that stickiness to back up the early speed right
so just to quickly recap
what we pulled from this deep dive into the A16
z's numbers
that old $1 ARR year one benchmark kind of blown away
median AI companies are hitting over $2 for enterprise
and an amazing four dollars and two cents for consumer
fundraising like Series a is happening way faster 8
9 months maybe yeah
and consumer AI is a real revenue story now
right from the beginning changes that whole sector
but crucially
while that speed and early cash get you in the game
maybe get you funded initially
the traditional metrics usage
engagement and especially retention
they are absolutely vital for later rounds
for actually building something that lasts
speed gets you the meeting quality keeps you going
it really underlines it doesn't it
the AI era isn't just faster
it's actually redefining what early success
even looks like so thinking about that
this incredible speed these new high benchmarks
this potential gap between the fast and the slow
it makes you wonder
what does all this velocity really mean
for the whole startup world
beyond just getting funded faster
how does speed as a mode
really change the fundamental game
not just building but competing
surviving in this AI age something to think about