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welcome to episode 43 Today
we're taking a deep dive
into the future of artificial intelligence
pulling insights from a recent AI
Ascent 2,025 keynote from Sequoia Capital
the venture capital powerhouse based in Silicon Valley
Sequoia discussed the current
state and future potential of AI
highlighted the rapid pace of AI development
and its potential to significantly disrupt and expand
markets
drawing parallels to the impact of the cloud transition
tune in as we unpack what's happening now
and what's coming next in the world of AI
today we're jumping straight into Sequoia Capital's AI
Ascent 2025 keynote
we know you want the core insights on AI's future fast
without getting lost in the weeds
yeah and that's exactly what we aim to deliver
this event it um
it brought together some really sharp people in AI
right lots of different perspectives definitely
but there was this common thread
you know this idea of a massive
trillion dollar opportunity in AI
but also how do you actually navigate it
it's moving so quickly exactly
so our goal today is simple
pull out those key insights for you
we'll look at where AI is now
where it seems to be heading mid to long term
and crucially what are some well
actionable takeaways let's
start right there with the so what
why should you listening now
really care deeply about this AI shift
they used a pretty interesting analogy
comparing it to the cloud transition right
but they immediately
pointed out a massive difference in scale
you might think OK
AI another service layer like the cloud was
but the numbers tell a different story
the cloud market big as it got
maybe $400 billion
was roughly the size of the software market
when it started exactly
now contrast that with AI
the projection is that the market for AI
and this includes services
and how it changes software itself
could be an order of magnitude bigger
right from the start an order of magnitude bigger
that's that's huge
it really is
and it's not just about adding a new service
the key inside I think
was that AI
could fundamentally change what software is
how so well
think about moving from selling tools
which fits neatly in a software budget to selling uh
actual outcomes
that might pull from completely different budgets
like labor budgets ah okay
so you're not buying the shovel anymore
you're buying the finished hole
so to speak something like that yeah
it potentially expands the whole pie
not just slices it differently
it really reframes where value gets created
now beyond just the size
they hammered home the speed
the acceleration they did
they use this layer cake analogy
which I thought was quite effective
explain that a bit
so the idea is that each big tech wave
like the internet mobile
cloud builds on the previous layers
AI is the latest layer OK
makes sense but two key things here
first the ingredients for this AI layer
the computing power the networks
the data the talent
they're already pretty mature
they're in place AI isn't some distant dream right
the foundations are solid
and second the pace of these layers is accelerating
each new wave arrives faster
and spreads quicker than the last
so AI isn't just bigger it's happening much
much faster that classic hockey stick curve
but maybe even steeper this time
precisely and they dug into why it's adopting so fast
what's really fueling it okay
what are the drivers three main things
people know about it people want it
and people can get it simple but powerful
awareness first yeah
you can thank things like chat
GPT for making AI incredibly visible
almost overnight to the general public
it's not some obscure tech anymore true
then the desire amplified by
you know massive online communities right
Reddit X places like that
they act as global accelerators
for discovering and wanting new tech
the buzz built incredibly fast
a built in demand engine and finally accessibility
nearly everyone's online the pipes
the infrastructure it's already there for AI to spread
unlike the early cloud days
where you had to build out the infrastructure
and convince people exactly
AI landed on a stage that was already
built globally it really shows how tech distribution
itself has changed okay
so massive opportunity accelerating fast
got it but where do you actually play to win that
the million dollar question
or I guess the trillion dollar question here right
and they acknowledged look
maybe the completely blank canvas
isn't quite as blank as a year ago
but that white space is still enormous
so plenty room still absolutely
if you look back at previous tech shifts
internet mobile
most of the value wasn't actually in the core
infrastructure it was in the applications built on top
e commerce social media
all those apps on our phones
exactly that's where the bulk of the value landed
mm hmm and the initial thought was
AI would follow that same pattern
build applications on top of the core models
but there's a wrinkle now
right with the foundation models themselves
getting so capable
that's the key evolution they pointed out
these big foundation models
yeah they're not just static platforms anymore
they're getting better and better
and starting to do things that look a lot like
applications themselves
making it more competitive at that app layer definitely
so for startups or anyone building here
the advice was pretty clear
which was focus customer back
really
really understand a specific customer's pain point
go deep into a vertical industry
or a specific business function
solve a real specific problem
yes tackle something complex
maybe niche where AI can offer clear value and
maybe for now
that solution even involves having a human in the loop
it doesn't have to be pure AI magic from day one
practical advice
they mentioned something called the Leoni
Merchandising cycle yeah
it was a quick mention but basically a framework
reminding everyone that even with AI
you still need to do the basics of building a business
you know develop the product
engineer it market it
sell it support it
the fundamentals don't disappear right
the tech is exciting
but the business building still matters
and this ties
into how you build a sustainable advantage
a moat there's this tension
right between focusing purely on the coolest tech
the tech out view
versus starting with the customer need
the customer back view
and they argued for the customer approach strongly
they argued that long term
defensibility in AI
is increasingly going to come from that
deep customer understanding
and the unique value you build around it
how do you build that moat practically
any examples yeah
they offered a few strategies
things like building an end to end solution
not just a point tool
becoming indispensable to the workflow exactly
or building data and the other big one was
getting really industry specific
like open evidence in healthcare
Harvey in legal
deep domain expertise creates a strong moat
it's all about that connection with the user
okay that makes sense
now they also talked about specific things
AI companies need to watch out for this idea
of revenue vibe versus real revenue caught my attention
yeah let's unpack that
revenue vibe is like the initial excitement
the buzz maybe some early sign UPS
it looks good on the surface
it might be superficial exactly
real revenue or maybe better put
real engagement is about sustainable user behavior
are people actually adopting it
using it regularly sticking with it
long term retention that's what matters
so look past the hype look at the core metrics
absolutely and tied very closely to that
is what they call good vibes with customers
yeah which is basically trust
trust seems huge right now
it's Paramount
especially now while the tech is still finding its feet
if your users trust you trust your vision
trust that you'll iterate and improve
that's maybe even more valuable than your initial
feature set interesting
build trust first what about margins
AI can be expensive to run
they address that yes
initial gross margins might be lower
because of compute costs
the a cost per token to run the models right
but the expectation
or maybe the hope is that margins improve over time
two reasons the underlying cost
per token is generally trending down
and companies should be able to charge more
as they deliver more value
moving from tools to those outcomes
we talked about
value based pricing as the AI gets better potentially
yes yeah
and finally they circled back to the data flywheel idea
with a key condition right
yes a
data flywheel isn't magic just because you have data
it only counts if that flywheel demonstrably improves
a key business metric better product
better user experience higher retention
something tangible otherwise
it's just data sitting there
got it data needs to do work
okay shifting gear slightly
they talked about the sheer momentum behind AI
this feeling of inevitability
yeah they use the phrase nature hates a vacuum
it said
there's this tremendous sucking sound in the market
pulling AI in everywhere that's a vivid image
what was the point the main point was
don't get overly distracted by macroeconomic headwinds
yes those exist
but the fundamental pull of this technological shift
this AI adoption wave is potentially so strong
it overrides typical economic cycles
so the tech trend is the bigger force right now
that was the argument
the implication being you need urgency
you need velocity if you're sitting on the sidelines
someone else is probably eating your lunch
or inventing a new kind of lunch entirely
a clear call for speed wait
okay then they did a kind of year in review
looking back at progress
from both the customer side and the tech side right
the customer back in technology out perspectives
and the customer data was pretty compelling
what stood out
engagement specifically for AI native apps
things built around AI from the start
they showed how the ratio of daily users
to monthly users for platforms like Chat
GPT has shot up it's approaching
the stickiness of established platforms like Reddit
so people aren't just trying it once
they're integrating it into their routines
it seems
so it suggests AI is delivering real ongoing value
not just novelty
and they mentioned real world impact expanding too
yeah beyond just chat
areas like advertising education
healthcare they name dropped open evidence again
suggesting AI is making real diagnostic differences
and then there was the her moment for voice AI
ah yes
like the movie meaning
meaning voice generation tech has gotten so good
it's basically crossed the uncanny valley
it sounds human which opens up well
a ton of possibility huge implications there
any other standout applications
coding that came up as a major breakout area
tools like anthropics Claude 3.5 sonnet
finding real product market fit
making developers significantly more productive
that's a big one changing how soccer gets built
absolutely now
on the technology outside the pure tech advances
they did note that maybe the pace
of just making the biggest models
even bigger
that pre training race might be slowing down a bit
some low hanging fruit might be picked
okay a potential plateau in the sheer scale potentially
but they quickly highlighted key areas
where breakthroughs are happening
such as better reasoning AI
getting better at actually thinking through problems
not just pattern matching open AI
I gotta mention there okay
also big strides in synthetic data
creating artificial data to train AI
which is crucial better tool use AI using
external software and APIs more effectively
and better
AI scaffolding techniques to build more complex
reliable AI systems
so refinement and capability building
not just raw size exactly
pushing towards more sophisticated tasks
and they also praised innovation
happening right at the edge of research and product
mentioning deep research in Notebook LM
which is now called Hugh I believe
taking cutting edge ideas and making them usable
right which brings us back to that key question
yeah in this whole AI stack from the chips to the apps
where does the most value end up
and their answer was still
still firmly at the application layer
despite the foundation models getting better
they believe
companies solving specific customer problems
r v open evidence for the examples again
are where the biggest value capture will be
even with the increased competition
from the models themselves seems
so the opportunity to build targeted
user loved applications is still seen as massive
though they did give a nod to Jensen Huang at Nvidia
of course can't ignore the hardware layer right now
he's kind of the king of the stack for the moment
you could say that yeah
but then focusing back on apps
they pointed to the first real cohort of AI killer apps
emerging who made the list
Chat GPT
actually Harvey Glean Sierra Cursor
a Bridge Listen Labs
Open evidence a diverse group
but all delivering significant value
and looking forward they see more agent first companies
yes companies
where the core product is an AI agent
moving beyond today's often brittle prototypes
towards really robust
reliable agents that can perform complex tasks
how do we get to those robust agents
two main paths are discussed
one involves clever orchestration
coordinating multiple AI models and tools
with really strong testing and evaluation
to ensure reliability okay
managing complexity
the other path is creating agents that are fine tuned
specifically for an end to end task
really specialized
Langchain and Openai were mentioned as enablers here
and this leads to the idea of vertical agents
exactly this seemed like a really big prediction
agents trained with deep expertise
in just one specific domain
like the examples they gave
yeah really striking ones
an AI agent
beating human penetration testers at cyber security
AI outperforming humans in complex Devops
or network troubleshooting
it suggests a future of highly specialized AI experts
wow AI specialist
which then connects to this idea of an abundance era
code was the first example
if AI makes certain types of labor like basic coding
super cheap and abundant then scarcity shifts elsewhere
right to things like taste judgment
quality control creativity
fundamentally changes the economic equation
that's a profound shift
so taking that agent idea further
what were the mid to long term predictions
it moved from individual agents to agent swarms
mm hmm the idea that agents won't just work alone
but will collaborate coordinate
form interconnected systems
okay teams of agents
and that evolves into the concept of an agent economy
a future
where these agents aren't just passing information
but are actually transferring resources
making transactions
operating with some understanding of trust
like autonomous economic actors
agents doing business with each other and with us
that's the vision but they cautioned
getting there
requires solving some major technical challenges
like what first
persistent identity
agents need a stable sense of self
memory
history to build reliable interactions over time
they can't just reset every conversation
they need to remember things
learn exactly second
seamless communication protocols
we need the equivalent of tcpip for agents
standard ways for them to exchange information
value and signals of trust
they mentioned MCP as a potential standard
emerging a common language for agents
makes sense and third
maybe most critical security
if agents are making autonomous transactions
trust and security become absolutely vital
you need to know
the agent you're dealing with is legitimate
and secure that's a huge challenge and opportunity
absolutely
trust in a world run partly by autonomous agents
okay beyond the tech
does this require us to change how we think
definitely they highlighted three key mindset shifts
the first is adopting a stochastic mindset
sarcastic meaning probabilistic
not deterministic moving
away from expecting computers
to give the exact same answer
every time like 2 + 2 is 4
AI responses might vary slightly
be probabilistic but still correct within context
we need to get comfortable with that uncertainty right
it's not traditional programming
second a management mindset
as we work more with agents
our role shifts from doing the task
to managing the agent understanding its capabilities
its limitations guiding it
overseeing its work less operator
more manager precisely and third
embracing increased leverage
but also accepting less certainty
agents can amplify our output massively
let us scale faster with
your people but that comes with
relying on these probabilistic systems
managing that inherent uncertainty and risk
more power more potential variants
a trade off it seems
so ultimately the picture they painted
was one of massive transformation
driven by these agents exponential leverage
faster scaling
reduced human workforce in some areas perhaps
and eventually maybe
these individual agents
merge into something even more complex
like neural networks of neural networks
hmm fundamentally changing work companies
the economy it's a pretty radical vision
it really is yeah
so to sum up this deep dive into the keynote
it's a huge AI opportunity moving incredibly fast
the tech is advancing rapidly
especially around reasoning
tool use and specialized agents
the value seems to be accruing at the application layer
focusing on customer problems
and the longer view
points towards this transformative agent economy
exactly which brings us back to you
our listener
you've now got a handle on these key insights
the scale the speed
the focus on applications and agents
you understand the shift from tools to outcomes
the importance of trust the rise of
AI in this potential abundance era
so maybe the final thought to leave you with is this
as these AI agents become more integrated
more autonomous operating within this agent economy
what happens next what are the challenges
the opportunities maybe the weird side effects
that weren't even discussed
yeah
what does it really mean for trust on a societal level
for human creativity what does work even look like
when your collaborators are increasingly AI agents
lots to think about
definitely a landscape that will keep evolving