welcome to money is freedom
a podcast
exploring how finance and freedom connect in our lives
created from thoughtful research
and narrated by Notebook LM AI
this series brings you clear
meaningful insights into finance and beyond
welcome to Episode 11 where we're diving into
the transformative power of generative AI
in 2024
from redefining enterprise workflows
to sparking a wave of startup innovation
this technology is no longer a concept of the future
it's happening now
join us as we unpack the latest trends
challenges and predictions shaping the AI
revolution in the business world
let's get started
hey everyone
welcome back ready to dive into something really cool
today we're gonna take a deep dive into the world of AI
oh yeah
specifically how it's changing the game for businesses
absolutely
and we've got this fantastic report from menloventures
it's called 2024
The State of Generative AI in the enterprise
it's a great report and it's packed with insights
I think you're gonna find really fascinating
definitely and it's not just about the future anymore
right this is about what's happening now
right now this is all happening so fast
exactly the shift is happening incredibly quickly
okay so let's unpack this
the report highlights a massive increase in AI
spending just this year huge
we're talk $13.8 billion yeah
that's six times what it was in 2023
I mean that's a huge jump
what's driving that massive investment
well it's a clear sign that companies are moving beyond
just experimenting with AI
you know they're starting to see real potential for AI
to become a core part of their business strategy
so they're
not just dipping their toes in the water anymore
no they're diving in headfirst
they're going all in absolutely
and it's not just hype either
the report shows that
40% of that spending is coming from permanent budgets
not just innovation funds
so it's not just like a little side project
no it's becoming a core part of how they do business
okay so they're serious about this
hmm but where's all this money going
well that's where it gets really interesting
the report digs into the specifics of how
companies are actually using AI
and some of them might surprise you
okay late on it okay
one of the hottest areas is what they call code
co pilots oh yeah
I've heard about this it's like having an AI
partner that helps you write code right
exactly it's like having an AI
pair programmer right there with you
like Github co pilot yeah
Github co pilot's a great example
and it's already a massive success
it's got a 300 million dollar revenue run rate
wow so they're making serious bank
oh yeah
developers are really embracing these tools
it makes sense it's like
it frees you up to focus on the bigger picture
exactly and it's not just about general coding either
companies are building AI
tools for specific tasks within software development
okay so give me an example
how are these specialized AI
tools actually changing the game for developers
well think about things like automating code
testing or generating code for specific tasks
you know
those repetitive tasks that developers used to spend
hours on right
so harness for example
they created an AI Devops engineer and a Q a assistant
wow those AI
tools
are taking care of all those time consuming tasks
so the AI is doing the GRET work
and the developers can focus on
the more strategic stuff the creative stuff
absolutely I like it
what else are companies using AI for
well another major area is customer support okay
you know think about those 2047 chat bots
they can actually understand your questions
and troubleshoot your problems
okay but
are we talking about those basic jackpots
that just kind of frustrate you more than they help
no no no
we're talking about something much more advanced
like actual intelligence exactly
companies like Isara Decagon Sierra
they're creating AI chat bots that are so advanced
they can actually interact with customers in a
personalized way
okay so they understand context
they understand the context of your questions
they learn from pass interactions
and they can even adapt
their communication style to different customer
personalities that's wild
yeah
and it's not just about customer facing support either
observe AI is using AI
to support the human agents themselves
giving them real time guidance during calls
oh that's interesting
so they're like AI coaches
exactly wow
so we've got AI helping developers write code
we've got AI supporting customer service agents
what other tasks are companies handing over to these AI
assistants we'll think about
all
the information that's locked away in company's data
right you know emails
documents spreadsheets
it's a gold mine of knowledge
it is
but it but it's often really hard to find what you need
it's like trying to find a needle in a haystack
exactly and that's where AI
is proving to be incredibly powerful
companies like glean and Sanna
they're using AI to completely change how we search
and retrieve information so how does that work
what makes these AI powered search tools so different
well instead of just looking for keywords
these AI
tools can actually understand what you're looking for
the meaning and the context of your search
that's cool they connect to all sorts of data sources
and then they use natural language processing to find
the most relevant information
so it's like having an AI research assistant
that's a great way to put it
who just goes and digs through all that stuff
and pulls out exactly what you need
and they can even
extract data and transform it into a format
that's easy to analyze and understand
so it's about turning raw data into actional insights
that's really cool yeah
this is all so impressive
it seems like AI is becoming essential
for any business that wants to stay
ahead of the curve it absolutely is
yeah and we've only just scratched the surface
there's so much more to explore
okay well
I'm ready to keep going alright
how about AI powered meetings
okay you've got my attention
tell me about this AI meeting magic
imagine this
no more struggling to take notes during meetings
uh huh or missing key information
I like where this is going
there are tools like Fireflies Dot AI
Otter Dot AI okay
they can record and transcribe your meetings
then they use AI to generate concise summaries
highlight action items
and even flag important decisions
okay so no more going back and forth
trying to remember who said what exactly
and you can be sure that everyone's on the same page
about next steps this is great
I love it and it's not just for like business meetings
right right
it can be used in all sorts of contexts
there's even a company Ilayo's Health
that's using AI to automate documentation for doctors
wow that's amazing
doctors can finally spend less time on paperwork
and more time with their patients
it's a win win it really is
so we've got AI helping developers write code
we've got AI supporting customer service agents
we've got AI revolutionizing meetings
what's right what else
well we've talked a lot about AI augmenting human work
but what about AI taking over entirely
oh yeah that's a big question
is that something we're actually seeing happening
well it's still early days okay
but the report suggests that we're on the brink of a
transition to more independent AI system okay
so AI
actually making decisions
and taking actions on its own exactly
it's a big shift what would that even look like
well there are some fascinating examples popping up
in areas like financial back office operations
companies like Ford and simafor are developing AI
that can handle complex financial tasks
like reconciliation and reporting
with very little human intervention
exactly so it's like having an AI CFO running the show
it's getting close
but that brings up some interesting questions right
definitely
like how much control are we willing to give AI
yeah for sure
it's something we need to think carefully about
definitely
but before we start worrying about robots taking over
let's focus on what's happening right now okay
you mentioned this trend toward services as software
yes Menlo Ventures is calling it
the services as software era
can you tell me more about that
sure so basically
it means that AI solutions
can deliver
the same capabilities as traditional service providers
but with the speed and efficiency of software okay
it's a bold prediction it is
but considering how quickly AI is developing
it's not out of the realm of possibility
no it's not
uh huh
so with all these incredible AI solutions popping up
are companies building these in house
or are they buying them from vendors
what's the trend there that's a great question
and it turns out it's almost an even split
47% are building their own AI solutions
while 53% are going with vendors
that's really interesting
I would have guessed that
most companies would prefer to buy ready
made solutions well
that was the trend last year
80% were relying on vendors
oh wow
but this shift toward building inhouse
shows that companies
are
getting more comfortable with developing their own AI
tools so they realizing that they have the talent
expertise to do it themselves
exactly
it's a sign of growing confidence in the enterprise AI
space yeah
that's really cool
and regardless of whether their building or buying
companies are laser focused on ROI right
so they wanna make sure that their AI
investments are paying off exactly
they're not just jumping on the AI bandwagon
they're choosing solutions
that will actually make a difference
for their business and you know what's surprising
what's that
price isn't as big of a factor as you might think
hmm
only 1% of companies mentioned price as a major concern
that's right they're willing to invest in quality
solutions that deliver real value
even if it cost a bit more up front
exactly it's a long term investment
it's not a quick fix odd at all
but even with the best intentions
there are still some potential pitfalls
when implementing AI oh absolutely
it's not all smooth sailing
what kind of challenges are companies facing
well one of the biggest
traps is underestimating implementation costs okay
you know it's easy to get caught up in
the initial price tag of an AI solution
but then
you forget about the hitting costs of integration
training ongoing support
what else does extra expenses exactly
and in fact 26% of failed AI
pilot programs
cited implementation costs as a major factor
wow so it's a real issue
it is
and then there are data privacy issues to think about
right you know
AI systems often need access to sensitive data
so companies need to make sure they have strict
data governance policies in place
to comply with regulations
and protect customer privacy
especially with all the data breaches happening
these days it's a serious consideration
it is and then
there's always the chance that AI
projects just don't deliver the expected results right
right 18% of failed AI
pilots were attributed to disappointing ROI
it's a reminder that AI isn't a magic bullet
no it's not
it needs to be implemented
strategically to be successful
so what's the key takeaway here
what can companies do to avoid these pitfalls
well planning is crucial okay
businesses need to approach AI
implementations strategically
not impulsively exactly
so you gotta do your research
you gotta do your homework before you just dive in
right assess your needs
thoroughly evaluate different solutions
carefully consider
all the potential costs and challenges before
making any big decisions that's great advice
now earlier we talked about the rise of startups
in the AI space uh huh
is there any data
on how they're stacking up against the big
establish players yeah
that's a great question
and the report offer some fascinating insights
remember how last year
incumbent vendors were pretty much dominating
the market yeah
I felt like they were just kind of adding AI
features onto their existing products right
more incremental steps than
truly groundbreaking innovations
exactly but Menlo
Ventras predicted that startups
would start gaining ground
okay and the data now supports that prediction
interesting while 64% of businesses
still prefer to buy from established vendors
there
are signs that this dominance is starting to waver
really what are the signs
well 18% of decision makers
reported being disappointed with offerings from
incumbent vendors okay
and a whopping 40% question
whether their
current solutions were actually meeting their needs
wow so
that's a pretty big chunk of the market
that's open to new solutions
exactly
it's a prime opportunity for startups to come in
and disrupt the status quo
okay so
we've established that AI
is impacting businesses across the board
but is it really being adopted by all departments
or is it mainly concentrated in certain areas like it
that's a great question and
one of
the most remarkable findings in this report is that AI
adoption is happening across all departments really
it's not just a tech thing anymore
every part of the business is feeling the impact
so it's not just the it in
engineering departments leading the charge
well those departments
definitely get the lion's share of the AI budget okay
with it at 22%
product in engineering at 19% and data science at 8%
okay but
the remaining budget is spread across a wide range of
functions so give me some examples
where else are companies investing in AI
well
you've got customer facing departments like support
sales and marketing okay
they're all investing in AI
supports at 9% sales at 8%
marketing at 7% uh huh
then there are the back office teams
like HR and finance also at 7% each
and even smaller departments like design at 6%
and legal at 3% are getting in on the action
so AI really is becoming an organizationwide phenomenon
it's not just a niche technology anymore
it's something that every department is thinking about
exactly and it makes sense right
yeah
AI has the potential to transform how work gets done
regardless of the specific industry or department
I think that's a great point
and that's why we're seeing this shift towards more
specialized AI solutions
tailor to the needs of different industries okay
I'm intrigued
tell me more about these vertical AI applications
what
are some of the most exciting developments happening
in specific industries well
one of the most surprising findings in
the report is that health care is actually leading
the charge when it comes to AI adoption
you're pouring a massive $500 million into
enterprise AI spending this year
wow that's a huge investment
for an industry that's not always known
for being quick to embrace new technology
what's driving this spending spree in healthcare
well
one area that's really taking off is what they call
ambient scribes ambient scribes
these are AI powered tools
that can listen in on conversations between doctors
and patients and automatically generate clinical notes
so doctors can finally ditch the tedious paperwork
right and focus more on their patients
that's a win win for everybody
it really is companies like Abridge Ambience
Heidi Alayos Health they're all developing ambient
scribe solutions that
are gaining popularity in doctors offices
and that's just one example right
and then there's this whole area of clinical workflow
automation uh huh
where AI is being used to streamline everything
from patient intake in triage
to filling encoding
so it's really revolutionizing healthcare
absolutely from top to bottom
yeah it's not just about improving efficiency either
it's about enhancing the quality of care
and making healthcare more accessible and affordable
I think those are really important goals
absolutely and those are just a few examples of how AI
is transforming the healthcare landscape
another industry that's right for disruption
is the legal profession okay
you know they're traditionally resistant to change
but they're starting to embrace AI in a big way really
so how's AI actually being used in law
well
lawyers deal with massive amounts of unstructured data
you know contracts
legal briefs case files
and AI is proving
to be incredibly helpful in managing and analyzing
all that information
we're talking $350 million in enterprise spending
this year so it's like AI
is coming in to help them
sift through all those documents
exactly and find the key information they need
and it can also help them with those
really tedious tasks right
like contract review and legal research
so it's freeing up the lawyers too
to focus on the more strategic
and creative aspects of their work exactly
so it's the same story we're seeing across industries
AI taking care of the grunt work
so humans can focus on the high value task
a common theme
what other industries are jumping on the AI
well
financial services is another hot bed of AI innovation
they're investing $100 million in enterprise AI
this year okay
so what are the big AI plays in the world of finance
finance is all about data right
accounting research compliance
risk management AI
is automating a lot of those data intensive processes
giving financial professionals more time to focus on
strategic thinking and high level decision making
so less number crunching exact
more big picture thinking right
so for example
you have companies like Numerican Clarity
revolutionizing accounting with AI powered tools okay
Aki Fienrogo
are accelerating financial research through
advance data extraction and analysis
and companies like arch are using AI
to shake things up in back office operations
for investment firms so
AI
is having a pretty significant impact on how financial
institutions operate absolutely
and then there's this whole area of compliance
and risk management right
keeping up with
ever changing regulations is a huge challenge
for financial institutions
it is but AI is helping those
companies like Greenlight and Norm
AI are developing real time compliance
monitoring solutions that use AI
to flag potential violations
it was like AI
is becoming the compliance officer's new best friend
yeah you could say that
and then there's media and entertainment
which is also seeing a surge in AI adoption
we're talking $100 million in enterprise spending
this year okay
so AI is making its way to Hollywood
that's right tools like runway
have become essential for things like video
editing and special effects
uh huh and platforms like Black Forest Labs
Higgsfield ideogram
mid journey pika
wow that's a lot
they're all pushing the boundaries of AI
powered content creation
so we're talking about AI helping to generate scripts
compose music even create entire movies
it's not quite at that level yet
but it's getting there
and it's not just about big budget productions
AI is also empowering individual creators with tools
like captions and descript okay
which make it easier to create high quality
audio and video content
it's like AI is democratizing content creation
exactly
giving everyone access to tools that were once only
available to professionals
I love that it's really exciting to see how AI
is changing the game for creators of all levels
we've covered so much ground already
but I have one more question before we move on
we've talked about all these amazing applications
but I wanna step back
and look at the tech behind it all
what's the infrastructure that makes all this AI
magic happen that's a great point
all these incredible AI
applications wouldn't be possible without a solid
foundation so let's talk about the modern AI stack
what are the key components that power
all of this innovation well
after a year of rapid evolution
the modern AI stack has stabilized somewhat
businesses are
converging around a core set of building blocks that
form the foundation of most production AI systems
so there's like
a common blueprint emerging
for how to build and deploy AI solutions
exactly n at the heart of this blueprint
are what we call foundation models
okay these are large language models or L LMS
it has been trained on massive datasets
and can do a wide range of things
from generating text and translating languages
to writing code and analyzing images
so
foundation models are like the brains of the operation
wow that's a great way to put it
and these brains are getting bigger
and more powerful all the time
and businesses are pouring a massive
$6.5 billion into this LLM layer
but they're also learning that it takes
more than just a powerful LLM
to create successful AI solutions
what else do they need
they need what's called data scaffolding
okay it's about building the right
infrastructure around the LLM
to make sure it can access and process data effectively
and generate reliable outputs
so it's like
having the right support system to help that AI
brain function at its best
exactly that includes things like data pipelines
data cleansing tools integration with existing systems
it's a complex ecosystem working together
it sounds like there are a lot of moving parts
are there any clear trends emerging
in how companies are using these foundation models
to build solutions yeah
one interesting trend is the rise of multi
model strategies okay
businesses are realizing that it's often better to use
multiple foundation models
instead of relying on just one
so it's like having a team of experts
that's a great analogy each with their own specialty
exactly companies might use
one foundation model for text generation
another for image analysis
and yet another for co generation right
so it's about choosing the right tool for the job
exactly and in fact
the report shows that companies
are using an average of three or more
foundation models in their AI stacks
wow so is really mixing and matching
they are and this multimodel approach
probably plays into this whole debate about
open versus closed source AI right
and despite all the buzz around open stores
AI closed source solutions
are still dominating the market
with 81% market share
so companies are still playing it safe
opting for the control and security of closed systems
that seems to be the case
but open source is holding strong at 19% okay
LED by metas Lama two so it's not a complete shutout
so the competition is still on hmm
are there any other interesting shake UPS happening
in the closed source world
oh definitely
open AI the creator of Chat GPT was the early leader
right
but their market share has dropped from 50% to 34% wow
that's a pretty big drop what's behind that
philanthropic
a company that's focused on safe and ethical AI
seems to be the main beneficiary of this shift
their market share has doubled from 12% to 24%
thanks in part to their new model
Claude 3.5 sonnet okay
which is seen as a strong contender to open AI's GPT4
so businesses are really prioritizing safety in ethics
when it comes to choosing their AI tools
it's a trend that's likely to continue
as AI
becomes more powerful and integrated into our lives
yeah definitely
okay so we've got multi model strategies
and a growing emphasis on safe and ethical AI
any other major trends shaping the Lom landscape
the landscape is constantly evolving
but one of the most recent
and interesting developments is
Anthropic's release of Claude 3.5 sonnet
this new model has some groundbreaking capabilities
including the ability to use computers
wait AI using computers
that sounds a little too SCI fi for me
I know it's kind of mind blowing right
but anthropic is very focused on responsible AI
development and has
built in safeguards to ensure Claude 3.5
Sonnet uses computers safely and ethically okay
that makes me feel a little better
but what does it even mean for an AI to use a computer
can you give me an example
sure imagine an AI
that can not only access information on the internet
but also interact with software applications
run calculations and even generate its own code
so it's like an AI
that can basically do everything a human can do
on a computer exactly
and that opens up a whole new world of possibilities
for what AI can accomplish
this is getting pretty meta
it's definitely exciting
but also a little scary to think about
I understand the apprehension
it's a powerful technology
and it's crucial that we develop
and deploy it responsibly
but the potential benefits are truly groundbreaking
okay
so the LLM landscape is clearly a hot pit of innovation
and it's evolving at an incredible pace
but let's shift gears for a moment
and talk about design patterns okay
what are
the different ways that businesses
are actually using these foundation models
to build their AI solutions
well
the report highlights some interesting trends in AI
design patterns
and one of the most popular is called RGA
which stands for Retrieval Augmented Generation
R JAG
that doesn't sound very high tech
I know right
but
it's actually a really powerful technique that allows
AI
systems to access and use external knowledge sources
okay so how does RGA actually work
we'll think about
building a chat bot that needs to answer
customer questions about a specific product
okay with RG
you can connect that chat bot to a database of
product information
then when a customer asks a question
the chat bot
can instantly pull up the relevant information
and use it to generate a helpful response
oh okay
so it's like
giving the chat bot
access to a giant library of knowledge
exactly making it much smarter and more accurate
that's right and it's becoming increasingly popular
our egg adoption is up to 51% this year
compared to just 31% last year
that's a pretty big jump it is
it shows that businesses
are really starting to understand
the value of giving their AI
systems access to those external information sources
it's about giving them the tools to truly learn
and grow absolutely
yeah and then there's fine tuning
another design pattern that gets a lot of attention
yes
so fine tuning is like tweaking the settings on your AI
that's a good way to think about it
to make it work exactly the way you want
you're taking a pre trained foundation model
and training it further on a specific dataset
to make it even better at a particular task
so it's about customizing the AI to your specific needs
exactly but what's interesting is that fine
tuning is actually less common than you might think
really
only 9% of production models are being fine tuned
that's surprising I would
thought that most companies would wanna fine
tune their AI models to get that optimal performance
it's a common misconception
but it turns out that for many applications
fine tuning isn't necessary
oh okay
the pre trained foundation models are often good enough
right out of the box well
it's about choosing the right approach for the job
exactly
sometimes fine tuning is needed and sometimes it's not
and then
there's another design pattern that's gaining momentum
okay and it's probably the most disruptive of all
lay it on me egentic architectures
egentic architectures that sounds intense
what are we talking about here
imagine an AI system
that's not just capable of performing a single task
but can actually interact with the world
uh huh make decisions
take actions on its own
so it's like an AI that has its own agency
exactly it can actually act on its own
that's what agentic architectures are all about
that's a big deal
they're still in the early stages of development okay
but they're already being used in 12% of AI
implementation wow
that's a pretty big number
considering it such new concept
it is what
are some real world examples of
how agentic architectures are being used
well imagine a personal assistant AI
that can not only manage your calendar
and book appointments
but it can also proactively find opportunities for you
negotiate deals on your behalf
and even manage your finances
so it's not just an assistant
it's like a true agent exactly
representing you in the world
that's incredible
and that's just the tip of the iceberg
egenic architectures have
the potential to completely revolutionize everything
from customer service and logistics
to healthcare and education
this is exciting
but also a little intimidating to think about AI
having that level of autonomy
it's a lot to process I understand the apprehension
it's crucial that we approach this technology
cautiously
and ensure that we develop and deploy it responsibly
for sure but the potential benefits are immense
they are okay
so we've got orlegs for accessing external knowledge
fine tuning for customizing AI models
and agentic architectures for creating AI agents
they can act autonomously
yeah it's a lot to take in
but before we move on
I wanna circle back to RR for a moment
uh huh you mention that it lets AI
systems tap into external knowledge
but how's that knowledge stored in access
what's the infrastructure behind that
that's a great question
and it's an area that seeing a lot of innovation
right now to make our work
businesses need efficient ways to store and
access relevant knowledge
traditional databases like Postgras and Mungo d
B are still commonly used
yeah they account for 15% and 14% of deployments
respectively yeah
but AI specific solutions are gaining traction
so like
databases that are specifically designed for AI
applications exactly
and one of the leaders in this space is pinecone
an AI native vector database
that's already captured 18% of the market
okay so what makes a vector database so special
how is it different from a traditional database
well think of it this way
a traditional database organizes data into
rows and columns like a spreadsheet right
but a vector database stores data as vectors
which are mathematical representations of data points
okay this makes it much more efficient
for searching and retrieving information
based on similarity so
it's like a database that
understands the meaning of the data
it's storing not just the raw data itself
that's a great way to put it
and it's crucial for RJ because it allows AI
systems to
quickly and easily find the most relevant information
from huge amounts of data
that's really cool okay
so we got vector databases
for storing and retrieving knowledge
but what about getting that data into the database
in the first place what does that process look like
well that's where data ETL comes in
extra transform load
traditional ETL platforms are still widely used
accounting for 28% of deployments
but specialized tools
designed for the nuances of unstructured data
like pdfs in HTML are becoming more popular
so tools that can actually understand
the context of the data there processing
not just the raw text exactly
and one of the leading companies in this space is
unstructured which has a 16% market share
it seems like the entire
ecosystem is evolving to support
the unique needs of AI applications absolutely
and that's what's so exciting about this field
we're seeing innovation at every level
from the foundation models themselves
to the infrastructure that supports them
and it's all happening so fast
okay I'm definitely feeling the energy here
we've covered a lot of ground today
from the booming enterprise AI market
to the nuts and bolts of the modern AI stack
before we wrap things up I'm curious to hear what Menlo
Ventures is predicting for the future
well they've actually outlined three key predictions
okay and they're all pretty bold
I'm all ears with those predictions on me
okay prediction No. 1
is that agent driven transformation is going to be
the next big wave in AI
so those agentic architectures we talked about earlier
they're gonna be even bigger than we thought
that's what they're predicting
they believe that AI
agents will move beyond simple tasks
and start tackling more complex
multi step processes
imagine AI agents that can manage entire supply chains
optimize marketing campaigns
or even provide personalized financial advice
it sounds like something out of a SCI fi movie
it's pre mind blowing okay
so egetic architectures are prediction number one
what else do they see happening
well prediction No. 2
is that more incumbent companies are gonna fall to AI
native challengers so
we're talking about startups with AI powered solutions
coming in and disrupting established players
yeah like what happened with Cheg and Stack Overflow
exactly those are just the beginning
menloventures believes that other categories are right
for disruption as well
they specifically mention it
outsourcing firms like cognizant
and legacy automation players like UI Path
as being vulnerable to AI native challengers
so companies that haven't fully embraced AI
are at risk of being left behind
it's a case of adapt or die right
that's the reality of the situation
and they believe that over time
even software giants like Sales Force and Autodes
could face serious competition from AI native startups
so no one is safe no one is immune to disruption
okay so that's prediction number two
incumbent companies being disrupted by AI
native startups what's the third prediction
their third prediction is perhaps the most concerning
and it has to do with talent
they're predicting an intensifying AI talent drought
a talent trout what do they mean by that
they're basically saying that the demand for AI
talent is gonna far out strip the supply
as AI systems become more sophisticated and widespread
there's gonna be a huge need for people who can build
deploy and manage these systems
so it's not just about data scientists anymore right
it's gonna require a whole range of skills
from AI engineers and architects
to ethicists and product managers
who understand how to integrate AI
into products and services
companies are gonna be battling it out to
attract and retain top AI talent
absolutely and that's gonna push salaries higher
and create a very competitive job market
Minlove Ventures predicts that we'll see
two to three times salary premiums for AI
skilled enterprise architects
who are already well compensated
so the AI
talent crunch is a serious issue
that businesses need to be aware of
it's not just about the technology itself
it's about having the right people to make it all work
exactly
it's something they need to start planning for now
if they haven't already okay
so we've got three bold predictions
the rise of agent driven transformation
the disruption of incumbents by AI native startups
and an intensifying AI talent drought
it peeds a pretty dramatic picture of the future
it does but it's also an incredibly
exciting time to be involved in AI
we're on the verge of a major technological revolution
and the possibilities are truly limitless
I completely agree
it's mind boggling to think about where
all of this is headed speaking of exciting developments
I'm curious to hear about some of the specific
companies that Menlove
enters is backing in this space
they've highlighted several portfolio
companies that are using AI
to make a real difference in the world
I'd love to hear about some of them
it's always inspiring to see how AI
is being used to solve real world problems
one company that stands out is Vilia co
founded by Nobel Prize winning scientist David Baker
wow they're using AI to revolutionize drug discovery
using AI to develop new drugs
how does that even work well
they're using AI
to design potential new drugs on a computer
okay and to predict their structure and function
this is helping to speed up the development of new
life saving treatments for patients
that's incredible it sounds like AI
is playing a key role in advancing medical research
in bringing new treatments to market faster
are there any other companies using AI
in innovative ways like that
absolutely there's squint
a company that's transforming manufacturing
training with AI oh
they're working with big names like Continental
Michelin and Nestle
to capture the expertise of experienced workers
okay and create immersive
augmented reality
training experiences for new employees
so it's like having an AI
powered mentor right there with you
guiding you through the training process
exactly these AR experiences provide real time guidance
and verifications step by step
uh huh this helps to improve safety or reduce errors
and ensure consistent quality
that's amazing
what other exciting companies are they working with
another company worth mentioning is Code Signal
they're using AI
to help businesses find and hire the best
engineering talent
so AI powered recruiting for engineers
how does that work
they've developed a platform that uses AI
to automate coding assessments
personalized question difficulty based on skill level
and even detect plagiarism
that's cool
this helps businesses ensure they're hiring the most
skilled and qualified engineers
it makes sense it's like having an AI
powered recruiter who can quickly
inaccurately assess technical skills
exactly
and it helps to reduce bias in the hiring process
making sure that everyone has a fair
chance to show off their skills
that's a really important point
yeah it's great to see AI
being used to create a more equitable hiring process
what other companies are they excited about
while they're also highlighting abnormal security
a company that's using AI
to protect businesses from email attacks
like fishing and business email compromise
so AI is a cybersecurity watchdog
exactly
protecting businesses from those nasty email scams
Abnormal Security's AI powered platform
can spot suspicious patterns in email traffic
and stop attacks before they can do any damage
and that's so crucial these days
with cybertext becoming more common and sophisticated
absolutely and they claim
their platform
can prevent up to $4 million in potential losses
each year for their clients
that's a significant ROI
it sounds like a worthwhile investment
for any business concerned about cybersecurity
yeah what other companies are on their radar
one last company I wanna mention is typeface
which is using AI to help brands create personalized
on brand content at scale
so AI is a marketing assistant
you could say that Typeface's AI
powered platform
lets brands generate a wide range of content
from social media posts in email campaigns
to product descriptions and blog articles
that sounds like a dream for any marketing team
struggling to keep up with the demand for content
it frees up their time to focus on strategy
and creativity absolutely
and they're working with some big names including Nike
Starbucks and LG Electronics
they've got some impressive clients
wow sounds like
Menlo Ventures has a really diverse portfolio of AI
powered companies they do
and
they're clearly enthusiastic about the potential of AI
to transform a wide range of industries
okay so
we've reached the end of our deep dive
into a world of generative AI
in the enterprise been an incredible journey
and I feel like we've only scratched the surface
I agree it's a vast and rapidly evolving landscape
but I think we've captured some of the key trends
and insights that are shaping the future of AI
in business absolutely
and I'm sure our listeners are eager to hear
your final thoughts on this topic
what's the biggest takeaway for them
given the rapid pace of AI
advancement and the trends we've discussed
it's crucial for everyone to start thinking about
how generative AI will impact their industry
or field in the next few years
it's a great point
this isn't just something that's happening in Silicon
Valley AI is going to affect all of us
regardless of our profession or industry
exactly
the sooner we start thinking about the implications
the better prepare will be to navigate this exciting
new world and for our listener
I'm curious
what were your biggest takeaways from this report
what are you most excited about
or maybe a little apprehensive about
when it comes to AI's impact on your world
it's interesting you say that
because
what really stood out to me was this whole concept of
egentic architectures yeah
it's both fascinating and a bit unnerving
to think about AI systems
they can act autonomously
making decisions and taking actions on their own
yeah I get that
it's definitely a big leap forward from the AI
tools we're used to seeing today
for sure
and it raises some really interesting questions about
the future of work and how we interact with technology
exactly and it makes me wonder
how do we even begin to prepare for a world where AI
is not just assisting us but actually
acting as our agent in various aspects of our lives
it's a key question
and one that we'll need to grapple with as AI
continues to evolve
but even beyond agentic architectures right
there's so much to unpack about how AI
is changing the landscape of business
right from revolutionizing customer service
in streamlining software development
to transforming healthcare
and disrupting entire industries
it's clear that AI is already having a profound impact
and it's only gonna become more
pervasive in the years to come
absolutely the intelligent
enterprise is no longer a futuristic vision
it's becoming our reality
and the companies that embrace AI
strategically
are the ones that will thrive in this new world
so for anyone listening who's feeling overwhelmed
or maybe a little intimidated by all this talk about AI
what's your advice where should they start
I'd say the first step is to
simply start learning and exploring okay
there are so many resources available online
from introductory courses to in depth research reports
and don't be afraid to experiment with different AI
tools and applications yeah
dive in the more you understand about AI
the better equipped you'll be to harness its power
and navigate the changes it brings
that's great advice and remember
you don't have to become an AI expert overnight
just take it one step at a time
and focus on learning
the things that are most relevant
to your work and your interests
exactly
and don't hesitate to reach out to experts in the field
attend industry events or join online communities
there's a whole world of people
who are passionate about AI
and eager to share their knowledge and insights
yeah it's really a collaborative effort
to understand and shape the future of AI
we're all in this together
absolutely and for our listener
we'd love to hear your thoughts on all of this
hmm what are you most excited
or maybe a little apprehensive about
when it comes to AI hit us up on social media
we'd love to keep this conversation going
because this is just the beginning
it is the future of AI is full of possibilities
and it's up to all of us
to explore those possibilities responsibly
and create a future where AI benefits everyone
well said and as always
thanks for taking this deep dive with us
it's been a pleasure we'll catch you next
time for another fascinating exploration
of the trends shaping our world