welcome to money is freedom
a podcast
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created from thoughtful research
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this series brings you clear
meaningful insights into finance and beyond
in the state of AI 2025
Bessemer Venture Partners highlights
2025 as first light following the 2023 AI Big Bang
mass release of Chat GPT revealing emerging AI
galaxies of foundational companies and best practices
this episode explores new benchmarks for AI startups
categorizing them into two archetypes
supernovas
which are explosively scaling with unprecedented growth
often trading distribution for profit
with approximately 25% gross margins
and shooting stars which are fast growing
capital efficient companies
achieving strong product
market fit and approximately 60% gross margins
welcome back to the deep dive
uh your shortcut to being well informed
today we're navigating the well
the rapidly expanding
universe of artificial intelligence
that's right specifically
we're looking through the lens of Bessemer
Venture Partners The State of AI 2025 report
really important one you know
if 2023 was the AI Big Bang
as they put it then 2025 feels more like first light
hmm first light
I like that yeah
the initial chaos seems to be subsiding a bit
and we're starting to glimpse
you know foundational companies
emerging best practices clearer paths forward
yeah things solidifying
and the report's message is well
pretty unequivocal AI
is driving the biggest wave of technology change
we have ever witnessed
and that's precisely why this deep dive into Best
Summer's report is so crucial for any founder
investor or really
anyone
just trying to separate the genuine innovation from
let's face it the sheer volume of hype
there is a lot of hype oh yeah
there's still plenty of wild
unpredictability in dark matter out there
as they call it but Besemer
having deployed over $1 billion into AI
native startups since 2023
well
they're kind of uniquely positioned to map this out
$1 billion definitely gives them perspective exactly
so their mission with this report
share concrete benchmarks
tour roadmaps across five key AI areas
surface those unanswered questions that dark matter
and importantly offer five bold predictions
it's like they're handing us a constellation chart
for the future then
that's a great way to put it
a constellation chart okay
let's unpack this
the report kicks off with a really fascinating
look at what great actually means
for AI startups
now in 2025
it's moving beyond just traditional sauce metrics right
they highlight this tale of two AI startups
introducing what they call supernovas
and shooting stars it's a brilliant distinction
really captures the two main types of uh
explosive growth recently
yeah first up
the AI supernovas these are companies showing truly um
unprecedented velocity
sprinting to around 40 million dollar a R R
in their first year of commercialization
it's one 40 million year one
and then hitting a staggering 125 million dollar a R R
by year two wow
now
what's crucial to understand is that these supernovas
often operate with low gross margins
sometimes even negative really negative
yeah they're actively trading profit
for that rapid distribution
for market share but get this
their revenue per full time employee is incredible
about $1.13 million a R
R per F t e whoa
which is like
four to five times higher than a typical song
you gotta be super efficient in that sense extremely
but the report does caution
you know
this hyper growth can sometimes signal vulnerability
maybe low switching costs
or what they call thin wrapper concerns
dazzling but potentially fragile
that's a sharp warning so
are these supernovas just like a necessary early stage
or is there a real danger
many won't last past that fragile phase
with those low margins well
it's probably a bit of both
isn't it the supernovas definitely push the boundaries
they prove out entirely new categories
but yes the risk is real
many might
struggle to convert that rapid adoption
into sustainable profit
if they don't build strong defensibility
they set the pace
but maybe the long term winners evolve differently
which brings us to the second type
I guess exactly
then you have the AI shooting stars
these are more like stellar sauce companies
just accelerated OK
they achieve strong product market fit
great retention impressive expansion
they grow faster than traditional saws
but at rates that are you know
more capital efficient more sustainable
makes sense what do those benchmarks look like
so approximately $3 million AR in year one
growing to maybe $12 million in year two
then $40 million in year three
and hitting around $103 million in year four
still very fast growth oh absolutely
but crucially they maintain healthy gross margins
typically around 60%
and they achieve about $164,000 a R
R per FTE in their first year
okay so a different profile entirely right
and this growth trajectory is so significant
best summer actually coined a new term for it
Q2 T3
Q2 T three yeah
stands for quadruple quadruple triple
triple triple growth over five years
quadruple quadruple triple
triple triple
that's serious acceleration
does this mean the rules
for scaling have fundamentally changed
for all startups now not just AI ones
that's a great point
because the report really emphasizes that
while supernovas grab headlines
shooting stars are the most important benchmark to aim
for scalable
capital efficient growth
that's now uniquely achievable
absolutely I think speed just matters more than ever
AI has undeniably unlocked
faster product development cycles
quicker go to market more rapid distribution
that q2t3 isn't just some pie in the sky dream anymore
it's becoming an achievable benchmark for many
so the bar's been raised for everyone pretty much
but the tools to reach it are also more powerful now
okay so with those benchmarks set
the report then shifts gears
it takes us on this tour
through what they call the roadmaps of the AI cosmos
yeah I like that framing
it's where we see these early galaxies of AI
forming but also bump into that dark matter
those big unresolved questions still shaping everything
precisely and they start with the foundation
yeah AI infrastructure right
and here you can clearly see galaxies forming
in the model layer you've got the big players
open AI anthropic Gemini
Llama Xai
dominating headlines usual suspects
but it's not just them right
the open source models like Kimi Deepseek
Quinn Mixtrell are also showing real strength
sometimes in specialized areas
what does that dual track mean for the ecosystem
well
it creates this really vibrant competitive landscape
which is generally good
the report calls this phase AI infrastructure
second act okay
second act yeah
the first act was you know
algorithmic breakthroughs
proving AI could solve problems
now the shift is from just solving problems
to building systems that can define
measure and solve problems with experience
clarity and purpose more operational
more integrated exactly
so we're seeing new infrastructure tools emerge
not just for scale
but for grounding AI in real world context
for continuous learning think reinforcement learning
environments like fleet and matrices
or new evaluation frameworks from companies like Big
Spin Dot AI
kill an AI and also the rise of compound AI systems
integrating knowledge retrieval
memory planning that brings up the dark matter
they mention here the bitter lesson of AI
which is this idea well
you explain it right
the bitter lesson essentially poses the question
will simple scalable architectures
fed with vast amounts of data always
went out in the long run
or will more complex technique
specific approaches from betting
context and domain expertise ultimately prove superior
it's still an open debate
so for a founder building here
the challenge is
how to best embed that domain knowledge
is it about the data or the techniques
it's really both the
how of getting specific intelligence into your model
effectively is still a massive area of innovation
you probably can't just throw more generic
data at every single problem
and expect the best outcome
technique matters okay
from infrastructure it's a natural jump to the builders
the developers and
the report sees a huge shift in
how they'll even interact with these systems
doesn't it oh absolutely
in developer platforms and tooling
the big shift they highlight is natural language
becoming the new programming interface
prompts as programs exactly
prompts are programs
and LLMs are acting like a new kind of computer
it's a paradigm shift
AI isn't just an incremental change for developer tools
it's a completely
new way to think about software development
and within that they talk about galaxies forming
around something called the Model
Context Protocol MCP
described as the USB C of AI
what's that about huh
the USB C of AI it's becoming a universal way for AI
agents to access external APIs tools
real time data think of it as a standard plug
OK it allows for persistent memory
cross sessions complex multi tool workflows
fine grain permissions this
means agents can actually chain tasks together
reason over live systems and truly act for users
not just assist passively
so they can actually do things right
and key enablers here things like Prefex
Fast MCP and tools like arcade and keycard
it's all about empowering agents to handle complex
tasks securely and effectively
letting them plug and play with different tools
this seems to connect to the next bit of dark matter
memory context and beyond
you mentioned memory earlier
can you break down the difference between context
and memory as the report sees it
yeah it's a critical distinction
context is the data the model sees during inference
basically it's immediate working memory
for that specific interaction
okay like what's in the prompt window
sort of yeah
memory though
is the information retained across interactions
this is what allows for multistep reasoning
personalization continuity over time for an agent
ah okay
so memory builds up exactly
and the leading stacks are combining short term memory
via large context Windows
long term memory using vector databases
maybe memory OBIS
and semantic memory through techniques like hybrid REEs
and the report says this
context and memory might be the new moats
for AI application founders
that's the key insight
when your product accumulates intelligence about a user
or their specific world
it creates what they call emotional switching costs
emotional switching costs yeah
those accumulated insights
become incredibly sticky assets
it's not just what the AI knows generally
but what
it remembers and understands about you specifically
that's hard to replicate that makes a lot of sense okay
moving on to horizontal and enterprise AI
this is where it gets really interesting
I think the report suggests
AI is starting to disrupt massive
horizontal systems of record
Saures like Salesforce SAP
stuff that seemed untouchable
it's a potential seismic shift for sure
we're seeing galaxies forming
as these traditional systems of record
might get replaced by what the report calls
systems of action systems of action
how does AI enable that well
AI's ability to structure unstructured data
like emails or call transcripts
and its ability to generate code on demand
that dramatically speeds up migration
away from legacy systems
those painful multi year implementations
maybe they become obsolete
replaced by by agentic workflows
doing the rote data entry
think CRM tools like day Dot AI or Adio
automatically
logging customer interactions from everywhere
or AI native ERPs like Everest
DOS roulette automating finance tasks
so what are the key unlocks
for founders trying to build these
the report highlights a few
using AI
as a Trojan horse feature to get access to data flows
achieving like 90% faster implementation
thanks to code generation
doing data migrations in maybe a day using AI
and delivering a clear 10 x ROI
compared to the old systems
it's a completely different value proposition
a total reinvention
we're seeing similar wedges in HR recruiting
enterprise search F P N a too right yes
similar patterns are emerging
across those horizontal categories
but OK the dark matter here is enterprise
ERP and the long tail of soar
so while disrupting Salesforce might be happening
replacing a giant complex manufacturing ERP system
that's further off much further off according to report
while there's exciting stuff in AI
native accounting and ERP for SMBs
and maybe mid market
large scale enterprise ERP replacement
especially for complex industries
with intricate supply chains
that's still years away why is that so much harder
just the sheer inertia the existing deep integrations
regulatory hurdles the complexity
it's immense
and then there's the long tail of other source
identity platforms computer aided dispatch systems
content management systems
disrupting those is likely a decade long journey
patience and capital needed there
a lot of both got it
now let's shift to vertical AI
the report seems really bullish here
noting how AI is tackling those technophobic verticals
that traditional saws kind of struggled with
what's AI's edge in these specific industries
AI's edge is its ability to handle high value tasks
that are often multimodal
dealing with images voice
sensor data or heavily language based
these were often manual service
intensive processes that old school
sauce couldn't automate effectively
okay so it cracks problem
sauce couldn't exactly
and we're seeing galaxies forming
with really concrete examples
of vertical specific workflow automation
like what okay
in healthcare a bridge automates clinical notes
smartdicks health hospitals find missed revenue
legal even up generates
demand packages for personal injury cases
Ivo reviews contracts education
brisk teaching in magic school
are building AI tools for teachers
real estate Elise AI automates property management
communications home services
Hatch uses AI for customer service teams
Rilla analyzes sales calls
lots of specific examples
are there patterns to these successful vertical AI
companies yeah
the report identifies three key patterns
1 they start with a compelling wedge
often a language heavy or multimodal task
maybe involving voice or audio
deeply embedded in a workflow
hmm 2
context is key they need deep domain expertise
and build data models specific to that vertical
3 they're built for value
delivering clear
often 10 x productivity gains or massive ROI right away
makes sense but then there's the dark matter
integrating with or competing against legacy systems
in these verticals dealing with incumbents waking up
and whether data moats are truly defensible
and fragmented
often privacy sensitive industries like healthcare
how do founders navigate that
it's definitely tricky that's the challenge
deeply embedding into workflows
helps create switching costs
building truly proprietary data insights
especially from unique data types like voice
in that vertical builds defensibility
but yeah it's a constant battle
against the inertia of the old systems
and incumbents who are starting to react
you have to deliver undeniable value
undeniable value okay
we've covered enterprise verticals infrastructure
what about us the everyday users
the report also looks at consumer AI
noting a shift beyond just basic productivity
absolutely the consumer space is evolving
we're seeing usage move into deeper areas like therapy
companionship self growth
but the core galaxies forming are still around AI
assistance for everyday tasks
and interestingly creation
assistance and creation yeah
general purpose LLMs like Chat
GPT and Gemini
are becoming daily habits
for literally hundreds of millions
voice interaction is also getting much bigger
and then you have perplexity
emerging as a breakout darling for AI
native search I've heard a lot about perplexity right
and their agentic browser concept
comet could potentially redefine how we think about
ambient agents
AI just working for you in the background
plus
AI is dramatically lowering the barrier to creation
think create dot X y
z for building apps Suno and audio for Music
Moon Valley Runway for multimedia
exactly making creation accessible to way more people
and we are seeing those purpose built AI assistants too
especially in mental health
Rosebud Finch although areas like email
calendar seem tougher due to trust issues
OK so what's the dark matter in consumer AI
clear unsolved consumer pain points
like what well
it seems many obvious
high value consumer use cases are still underserved
because the underlying agentic infrastructure
the tech that lets AI
take complex actions safely and reliably
is still maturing
so the tech isn't quite there for some big ideas
not quite robust enough yet
for things hiding in plain sight
like a truly personalized
end to end travel concierge that books everything or
smart shopping agents that don't just find deals
but actually browse compare complex options
and securely checkout for you across different sites
that would be amazing right
but it raises the big question
who will ultimately own these powerful
agentic use cases will it be the browser
a general LLM assistant
or a new wave of specialized purpose built agent apps
a battleground for the next generation of consumer tech
exactly the answer will likely determine
the next big consumer platform winners
okay that's a fascinating space to watch
and speaking of the future
let's pivot to Bessemer's five big predictions for AI
in the coming years
these are based on their partner's consensus
yeah these are their boldest calls
prediction number one the browser
will emerge as a dominant interface for agentic AI
we touch on this with comet
why the browser
they argue these new agentic browsers like comet or DIA
aren't just add ons
they're embedding AI right at the operating layer
this allows for multi step automation
intelligent actions across different websites and tabs
seamlessly so it's deeply integrated
exactly it's ubiquity in that deep integration make it
in their view
the most capable and inevitable interface layer
for agents
they even say the new browser wars have begun
the browser as the iOS interesting
okay prediction 2
2026 will be the year of generative video
we had images hitting maturity in 2024
voice really taking off in 2025
video is next it feels like video
generation is moving incredibly fast
it is models like Google's VO3
cling from China open AI's Sora
Moon valley's Marie
they're rapidly improving in quality
controllability realism
they see it nearing a tipping point
for real commercial viability
at scale soon
beyond just cool demos but what are the hurdles
besides just making it look good
well there are big questions about market structure
will the big labs dominate
can open source compete
what about real time video generation
and maybe most importantly
the IP and copyright
issues are getting exponentially more complex
with video
that's a major hurdle for widespread commercial use
illegal and ethical minefield
pretty much okay
prediction three evils
and data lineage
will become a critical catalyst for AI
product development this one sounds a bit technical
but it's crucial evaluations okay
why so important because enterprises
aren't just looking for raw performance anymore
they need confidence they need to trust the AI
public benchmarks aren't enough for specific
real world business applications
so they need custom testing
exactly the prediction is a big shift in 2025
2026 towards private
grounded and trusted internal evaluation suites
Taylor to a company's own data
their specific risks their industry regulations
this sounds like the boring
but essential plumbing that makes AI trustworthy
especially in regulated fields
like finance or healthcare
that's a perfect way to put it
for founders
the real differentiator won't just be accuracy scores
it'll be proving exactly how
when and why your model works
reliably in their specific environment
tooling for this is emerging
multi metric evils synthetic data environments
from companies like Brain Trust
Lane Chain Arclex
you need built in evil infrastructure
got it prediction four
this one feels a bit SCI fi
ah yeah
a new AI native social media giant could emerge
whoa like what
well they draw parallels to past tech shifts
PHP enabled Facebook mobile birth
Instagram mobile video fueled TikTok
what fundamental AI
capability enables the next social giant
what are the possibilities
they speculate it can take different forms
maybe a network where AI
agents manage a lot of our social interactions for us
or maybe platforms populated by emotionally intelligent
AI influencers
or even clones of real people
building on things like character dot AI
or replica AI agents managing our social lives
or platforms full of Ais that's a wild thought
could that make things like echo chambers worse
or maybe offer different kinds of connection
that's the huge unanswered question
isn't it it's a real tension
AI could personalize connections deeply
or it could create incredibly sophisticated
manipulation or echo chambers
yeah or just feel uncanny
the breakthroughs in voice memory
generative media are the fuel
but the societal implications are massive and unknown
definitely one to watch okay
the final prediction No. 5
the incumbents strike back as AI MNA heats up
basically
after a couple years of watching startups disrupt
the big enterprise
giants are now aggressively buying AI capabilities
so the big guys are waking up and opening their wallets
yep especially in vertical software
healthcare logistics
legal tech they're requiring AI native companies
not just to add features
but often as a way to fundamentally
reinvent their value proposition
so for founders
does this change the build versus sell calculation
is the window closing or are new exit doors opening
it definitely shifts the landscape
it means founders need really strong technical modes
strong data modes
they need to be thinking about MNA strategy earlier
perhaps
and understanding where the incumbents are weak
so they can position themselves
as the essential missing piece
it's both a threat
and potentially a huge opportunity for strategic exits
wow okay
those are some really thought provoking predictions
yeah so let's try to bring this all together
what does this mean for you
the listener maybe the builder
the innovator out there
what are the absolute key takeaways from this
deep dive into the state of AI 2025
yeah Best summer
helpfully boils it down to 10 crucial points
for founders first
really understand those two AI startup archetypes
are you aiming for supernova velocity
or sustainable shooting star growth
know your path okay second
remember that memory and context are the new modes
build products that learn
adapt personalize
that creates real stickiness
got it memory in context third
systems of action are replacing systems of record
don't just sprinkle AI on old workflows
use it to completely reimagine them
reimagine not just enhance exactly fourth
start with an AI wedge solve a narrow
painful high friction problem with obvious 10 x value
then expand from there find that wedge
what's fifth fifth
the browser is your canvas for agenic AI
think about how your application
can leverage the browser
as this emerging AI operating layer
okay browser as the platform 6th
private continuous evaluation is mission critical
especially for enterprise
build in ways to prove your AI is trusted
explainable and performs reliably in their world
build in trust make sense
seventh
speed of implementation is a strategic advantage
use AI to crush on boarding times
and make switching to your solution painless
that undermines vendor lock in
faster time to value precisely eighth
vertical AI is the new sauce
those supposedly technophobic industries
they're adopting AI fast
when you solve their core high value problems
with clear ROI go deep in verticals ninth
comments are awake and acquisitive
build strong notes know your value and be MNA ready
if that's your goal be prepared for MNA
and the last one and finally
10th and maybe the most important
taste and judgment are your differentiators
in a world overflowing with AI capabilities
human insight into what should be built
what users truly need
what constitutes a great product experience
it's becoming the ultimate differentiator
taste and judgment I like that
it's not just about the tech itself
not at all it's about applying it wisely
this deep dive really hammers home
just how dynamic this AI universe is
the rules are changing so fast
but the opportunities for real innovation
for those with the right insight
the right judgment they seem immense
thank you for joining us
on this exploration of the state of AI 2025
absolutely and maybe a final thought
building on the report's conclusion
they say you don't just need a better model
you need a better model of the world hmm
the companies that win next won't do more AI
they'll do the right AI at the right altitude
with the right outcome
so as you navigate this AI cosmos
maybe reflect on that
what is the right AI as the right level
for the specific problem you're trying to solve
and the value you truly wanna create