On this episode of the CFA Society San Francisco Podcast, we had the pleasure of speaking with Alex Chitea, CFA, Founder and Managing Partner of Funston Capital, an entrepreneurial investment fund.
Prior to founding Funston, Alex led the programs and startup partnerships for cybersecurity and identity at Orange Silicon Valley - a subsidiary of Orange, a French multinational telecommunications company, that collaborates with startups, investors, researchers and other partners to bring innovation to the group’s operations around the world. He started his career as an early employee at Google and also held an intrapreneurial role at The Walt Disney Company. Alex holds both a Bachelor’s and a Master’s degree in Computer Science and an MBA from The University of Chicago Booth School of Business.
Listen to the full interview, where Alex discusses leveraging generative AI within private equity. Our conversation delves into the transformative impact of AI on small and medium-sized businesses within the private equity market, and how it's shaking up traditional approaches to due diligence, operational efficiency, and beyond. Alex shares his experiences and strategies for harnessing AI to drive business growth, enhance investor expertise, and unlock new avenues for value creation.
If you'd like to learn more about the show, have a topic or speaker to suggest, or would like to leave us a comment, email podcast@cfa-sf.org.
This podcast is produced by CFA Society San Francisco, a not-for-profit professional association, providing professional learning and career resources to over 13,000 investment industry professionals worldwide. To learn more about CFA Society San Francisco, visit our website or connect with us on LinkedIn.
The information contained in this podcast does not constitute financial or investment advice. Please consult your own financial advisor for information concerning your specific situation.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Lindsey Helman (00:06):
Hello and welcome to the CFA Society San
Francisco podcast, where weinterview and discuss trends
with leaders from across theinvestment and finance industry.
This month, our host TanyaSuba-Tang, membership Director
with CFA Society San Francisco,had the pleasure of speaking
with Alex Chitea, founder andmanaging partner of Funston
Capital.
(00:26):
Listen in as they discussleveraging generative AI within
private equity.
Tanya Suba-Tang (00:34):
Alex, good morning.
Great to see you again.
How are you?
Alex Chitea (00:37):
Good morning.
I'm great, Tanya, great to seeyou again.
Tanya Suba-Tang (00:41):
Thank you so much for joining for today's
today's podcast.
For listeners, I'm so excitedto have Alex because he is
managing partner for FunstonCapital, and Funston Capital is
a entrepreneurial investmentfund correct, where you source,
acquire, and operate a techcompany.
Alex Chitea (00:59):
Yes, that's right.
Pleasure to be here.
Tanya Suba-Tang (01:00):
Yeah, and you know - such an interesting
background you have, Alex,because you've had roles in your
career early on at Google andWalt Disney Company.
That's so impressive.
Alex Chitea (01:10):
Yes, thank you.
Thank you so much.
Tanya Suba-Tang (01:12):
So kind of jumping into our questions, you
know, having just mentionedthose two companies, you started
your career in technology andinvested in venture capital and
are now focusing on the SMBprivate equity market.
So what is it that interestsyou most about this market?
Alex Chitea (01:27):
Yeah, as you mentioned, you know, my career
spanned both operations andinvesting roles before I decided
to focus on the SMB privateequity market and right now I
got to a point where I'm lookingto bring the two operations and
investing capabilities byacquiring and growing a tech or
tech-enabled company.
So I'm looking at companiesthat have a history of stable
(01:48):
recurring customers andpredictable cash flows, and
those companies exist in thesort of SMB space in the private
equity market and that markethas historically had strong
returns and robust deal flowwithin the SMB market and the
companies are typically veryattractive to private equity for
(02:09):
those reasons.
They are, generally speaking,companies that are lesser known,
less established, so they havethe potential for market and
valuation inefficiencies thatcan eventually translate into
solid returns for private equityportfolios.
So, generally speaking, becausewe're looking at this, less
efficient markets and valuationsbelow those of larger buyout
(02:30):
deals and public comparablessponsors can typically acquire
SMB companies at relativelylower valuations.
So, because of that we are ableto sometimes employ a more
conservative capital structures,so that opens up multiple
routes to actual value creationat the end, including organic
revenue growth, operationalenhancements, add-on
(02:52):
acquisitions and managerialimprovements, just to name a few
.
And many of these lower middlemarket companies, they are
quality businesses butoftentimes they've been either
neglected or they haveunderdeveloped IT or quality.
You know, management,information systems and those
risk reward dynamics havehistorically contributed to
really good, strong returns inthe SMB market over the long
(03:14):
term for private equity.
Tanya Suba-Tang (03:16):
So, with the technological advancements of
the last decade, what hassurprised you the most about how
private equity is adapting?
Alex Chitea (03:23):
Yeah, that's a great question.
So we've all witnessedgenerative AI and I think that's
the talk of the town these daysand that has emerged as a
transformative force within theworldwide economy since ChatGPT
launched in late 2022.
We had that initial excitement,but afterwards we really have
been able to mine certainconsistent depth of content and
(03:44):
substance that has actuallyattracted the attention of
private equity investors.
So, if I can just take a quickstep back and frame generative
AI for our listeners,essentially for private equity
and for small businesses,generative AI serves as a
crucial reasoning engine that'scapable of participating in
open-ended dialogue withcustomers.
(04:05):
You can create a persuasivemarketing material, you can
scrutinize vast datarepositories that can provide
deep insights, and all of theselittle things are valuable
business tasks that can helpprivate equity investors be more
efficient in their jobs andhelp with their portfolio
companies at the same time.
So I guess what we've seen, youknow what's surprising is the
(04:26):
speed at which this privateequity forward thinking private
equity investors are actuallystarting to already harness
generative AI capabilities torevolutionize their portfolio
companies on the one hand,enhance their due diligence
processes and also augment theirexpertise as investment
professionals and also augmenttheir expertise as investment
professionals.
Tanya Suba-Tang (04:46):
Yeah, it's really interesting.
So how are investors leveraginggenerative AI within private
equity?
Alex Chitea (04:54):
Yeah, I'd say savvy investors are already using
these technologies to sort oftransform companies, make better
decisions and boost returns.
If I were to bucket the wayfirms are mobilizing this
technology, I would say withinthe portfolio, during the due
diligence process and then atthe firm level, and I can go
into each of these importantways.
So, within the portfolio, wesee some key questions that
(05:16):
private equity firms need toconsider when it comes to their
companies and specifically, theyneed to understand how
generative AI innovations candisrupt the portfolio company's
value chain or business model.
There are companies that aregoing to thrive in this new
environment and there arecompanies that are going to be
significantly affected oraltered by the generative AI
(05:38):
technologies.
So is there an opportunity tospearhead change through this
technology, right.
Will generative AI enable newcompetitors and how will the
competitive modes or barriers toentry be affected to protect
against this disruption?
So, in general, we see privateequity firms must work with
(05:58):
their portfolio companies totest and learn how to address
these questions and, mostimportantly, understand sort of
what works and what doesn't in avery iterative and very, very
fast way.
And then, once they do that,they should be able to
prioritize investmentsaccordingly.
So that's within the portfolio,right?
So at the portfolio companylevel, during the due diligence
(06:21):
process, the investmentprofessionals, at private equity
terms, they can do quickanalyses of any target company,
right?
So there's, you know, almostlike a scorecard-based protocol
with respect to generative AIand how generative AI will
impact the companies that theywould like to invest in.
So they need to do that toevaluate the threats and
opportunities presented by thetechnology.
(06:43):
So we believe that over time,you know, this assessment is
going to be normalized, similarto how we currently have legal
or commercial due diligencepractices, right so we also see
firms that are leveraging thesetools, ai tools, to expedite and
refine the underwriting process, right.
So generative AI offers adistinctive chance to sort of
(07:04):
develop these prototypes, duediligence and sort of validate
or refute certain hypotheses,prototypes during due diligence
and sort of validate or refutecertain hypotheses.
So we're seeing many firms sortof adopt that during the due
diligence process to kind ofswift, you know, sift through
their companies better and theirinvestment targets better.
And lastly, I would say, at thefirm level, you have generative
AI providing numerous avenuesfor streamlining or automating
(07:27):
back office operations, right.
So you know, within dealsourcing, there are a lot of
menial tasks that can now beeasily performed through AI
tools.
So we're seeing the truepotential in significantly
broadening the informationaccessible to these investment
decisions.
Right?
So you have these tools thatare ideal for scanning massive
data pools, for insights.
(07:48):
So the investment professionalsare not going to necessarily be
turning to robo-advisors orrobo-investors, but they will be
able to use their time moreeffectively after dealing with
the output of these tools andusing screening criteria and so
on and so forth.
So there will be a sort ofsupercharging of the investment
professional, if you will,across the full value creation
(08:08):
cycle, from sourcing, screeningdiligence all the way through
portfolio management and helpingthe companies all the way to
the exit.
So I'd say those are the threemost important ways.
We're seeing technology sort oftrickle its way down into
private equity firms andeventually in the larger economy
.
Tanya Suba-Tang (08:24):
So kind of taking the flip side, what are
some pitfalls of deployinggenerative AI?
Alex Chitea (08:29):
I would say you know the pitfalls are general
technology pitfalls.
Right, Focus is essential.
Right Like you want to directinvestments towards select
strategic or operational goals.
Right Like you can.
Ideally you'd like to have sometangible enhancements in
profitability.
So you have to do that reallygood match between the
(08:51):
investment and your goal.
Like any technology, generativeAI is a tool and it's best
deployed in service of strategyand not vice versa, and doesn't
create value by itself.
So you have to explicitly linkit to measurable business
objectives.
So these objectives, you know,we've heard them, they're sort
of everywhere in the pressthings like better serving our
(09:11):
customers.
You know understanding themetrics that we're trying to
move, understanding theprocesses that we're trying to
improve and then sort of thepeople that we're trying to make
more efficient.
You know customer service isthrown around a lot, so you need
to sort of understand, you knowthe business objectives and
then deploy the technologyaccordingly.
You know the businessobjectives and then deploy the
(09:32):
technology accordingly.
A lot of investors are askingthemselves already what the ROI
on generative AI is right sothey can understand intuitively
that there's the potential forproductivity gains.
Right, there's, you candetermine a clear return on
investment.
Determining a clear return oninvestment is actually pretty
difficult because there are manybenefits that have some
indirect or non-financial impact.
So, although you kind ofunderstand the productivity
(09:54):
gains, it's really hard tounderstand sort of the future
financial outcomes.
I'll give you just a quickexample like utilizing
generative AI to automate codegeneration, which is pretty.
One of the first use casescould enhance software
developers' productivity,allowing more time for
innovation and potentiallyspeeding up the time to market
for new features.
However, ultimately andultimately this would lead to
(10:17):
improved customer satisfaction.
But quantifying these benefitsin financial terms and actual
bottom line is a challenge andremains a challenge.
Tanya Suba-Tang (10:26):
So, Alex, I know you have a busy day, but
before you leave me I have toask given your background in
technology, what are trends ordisruptions can we expect over
the next decade?
Alex Chitea (10:37):
Yeah, it's really hard to venture an answer for
the entire decade, as technologyis developing at ever
increasing speeds.
I would also say that you know,when experts and people in
general try to forecast thefuture, we tend to extrapolate
the past, whereas we've seenwith entrepreneurs, they invent
the future that they personallyenvision.
So there's usually a disconnectbetween the two.
(10:58):
But with that caveat out of theway, I would say that AI in
general and you know, here wecan include the current
generation of large languagemodels, deep learning, et cetera
, but also other paradigms thatmight actually emerge in the
next decade.
So AI in general will continueto shape businesses and, more
broadly, society at large, andthat will have an impact on the
(11:20):
private equities bottom line.
We are at the beginning of theS-curve for this new technology
and we still have quite a longway to go until it's fully
deployed and implemented, untilit sort of works its way through
all the processes in society.
So what's already apparent isthat we have this sort of
marginal cost of knowledge thatcontinues to drop and perhaps in
(11:41):
the near future we'll reach apoint where expertise will be
nearly free.
So you know that the proverbialintern like we all hear this
phrase over and over again howgenerative AI is like an intern
that never tires, so that thatcost of knowledge, you know,
will continue to drop and I dobelieve at some point expertise
will be free.
So it's going to be interestingwhat you know knowledge worker
(12:02):
is going to do in that scenario.
We're also seeing a reallyinteresting intersection of AI
and generative AI, specificallywith robotics.
That speaks to the marginalcost of labor that might
eventually move towards zero asrobotics and AI come together.
We are seeing some, you know,demos online and in some Amazon
(12:27):
warehouses where you knowthey're sort of using certain
robots for certain tasks likepackaging, et cetera.
So, yeah, so I would say themarginal cost of knowledge and
marginal cost of labor movingtowards zero are going to have a
great impact on, you know,businesses and society and
eventually, you know, byextension to our discussion,
they will have an impact onprivate equity as an asset class
.
So, yeah, I would say we'restill in the early days.
(12:47):
There's definitely a lot oftime left to start adopting
these AI-enabled tools.
So, I hope that people are notgoing to be afraid and they're
going to sort of try to starttesting and learning and
harnessing data and informationin powerful new ways.
Tanya Suba-Tang (13:03):
Wow, I mean that's exciting, exciting things
happening in our future, right?
Alex Chitea (13:07):
Yeah, absolutely, I'm super excited about the
future.
Tanya Suba-Tang (13:10):
Yeah Well, Alex , thank you.
Thank you so much for your timetoday.
I know our listeners probablygained a wealth of knowledge on
generative AI and how it impactsprivate equity, so thank you so
much for your time, and I hopeyou'll join us again for maybe
an update on what's happening inthe industry.
Alex Chitea (13:27):
Absolutely, it would be my pleasure, thank you,
Tanya.
Lindsey Helman (13:32):
Thank you for listening to this month's
episode of the CFA Society SanFrancisco podcast.
We hope you enjoyed theengaging discussion.
Join us next month for anothernew episode.
This podcast is produced by CFASociety San Francisco, a
not-for-profit professionalassociation providing
(13:52):
professional learning and careerresources to over 13,000
investment industryprofessionals worldwide.
To learn more about CFA SocietySan Francisco, visit our
website at cfa-sf.
org or connect with us onLinkedIn.
Hello and welcome to the CFA Society San
Francisco podcast, where weinterview and discuss trends
with leaders from across theinvestment and finance industry.
This month, our host TanyaSuba-Tang, membership Director
with CFA Society San Francisco,had the pleasure of speaking
with Alex Chitea, founder andmanaging partner of Funston
Capital.
(00:26):
Listen in as they discussleveraging generative AI within
private equity.
Tanya Suba-Tang (00:34):
Alex, good morning.
Great to see you again.
How are you?
Alex Chitea (00:37):
Good morning.
I'm great, Tanya, great to seeyou again.
Tanya Suba-Tang (00:41):
Thank you so much for joining for today's
today's podcast.
For listeners, I'm so excitedto have Alex because he is
managing partner for FunstonCapital, and Funston Capital is
a entrepreneurial investmentfund correct, where you source,
acquire, and operate a techcompany.
Alex Chitea (00:59):
Yes, that's right.
Pleasure to be here.
Tanya Suba-Tang (01:00):
Yeah, and you know - such an interesting
background you have, Alex,because you've had roles in your
career early on at Google andWalt Disney Company.
That's so impressive.
Alex Chitea (01:10):
Yes, thank you.
Thank you so much.
Tanya Suba-Tang (01:12):
So kind of jumping into our questions, you
know, having just mentionedthose two companies, you started
your career in technology andinvested in venture capital and
are now focusing on the SMBprivate equity market.
So what is it that interestsyou most about this market?
Alex Chitea (01:27):
Yeah, as you mentioned, you know, my career
spanned both operations andinvesting roles before I decided
to focus on the SMB privateequity market and right now I
got to a point where I'm lookingto bring the two operations and
investing capabilities byacquiring and growing a tech or
tech-enabled company.
So I'm looking at companiesthat have a history of stable
(01:48):
recurring customers andpredictable cash flows, and
those companies exist in thesort of SMB space in the private
equity market and that markethas historically had strong
returns and robust deal flowwithin the SMB market and the
companies are typically veryattractive to private equity for
(02:09):
those reasons.
They are, generally speaking,companies that are lesser known,
less established, so they havethe potential for market and
valuation inefficiencies thatcan eventually translate into
solid returns for private equityportfolios.
So, generally speaking, becausewe're looking at this, less
efficient markets and valuationsbelow those of larger buyout
(02:30):
deals and public comparablessponsors can typically acquire
SMB companies at relativelylower valuations.
So, because of that we are ableto sometimes employ a more
conservative capital structures,so that opens up multiple
routes to actual value creationat the end, including organic
revenue growth, operationalenhancements, add-on
(02:52):
acquisitions and managerialimprovements, just to name a few
.
And many of these lower middlemarket companies, they are
quality businesses butoftentimes they've been either
neglected or they haveunderdeveloped IT or quality.
You know, management,information systems and those
risk reward dynamics havehistorically contributed to
really good, strong returns inthe SMB market over the long
(03:14):
term for private equity.
Tanya Suba-Tang (03:16):
So, with the technological advancements of
the last decade, what hassurprised you the most about how
private equity is adapting?
Alex Chitea (03:23):
Yeah, that's a great question.
So we've all witnessedgenerative AI and I think that's
the talk of the town these daysand that has emerged as a
transformative force within theworldwide economy since ChatGPT
launched in late 2022.
We had that initial excitement,but afterwards we really have
been able to mine certainconsistent depth of content and
(03:44):
substance that has actuallyattracted the attention of
private equity investors.
So, if I can just take a quickstep back and frame generative
AI for our listeners,essentially for private equity
and for small businesses,generative AI serves as a
crucial reasoning engine that'scapable of participating in
open-ended dialogue withcustomers.
(04:05):
You can create a persuasivemarketing material, you can
scrutinize vast datarepositories that can provide
deep insights, and all of theselittle things are valuable
business tasks that can helpprivate equity investors be more
efficient in their jobs andhelp with their portfolio
companies at the same time.
So I guess what we've seen, youknow what's surprising is the
(04:26):
speed at which this privateequity forward thinking private
equity investors are actuallystarting to already harness
generative AI capabilities torevolutionize their portfolio
companies on the one hand,enhance their due diligence
processes and also augment theirexpertise as investment
professionals and also augmenttheir expertise as investment
professionals.
Tanya Suba-Tang (04:46):
Yeah, it's really interesting.
So how are investors leveraginggenerative AI within private
equity?
Alex Chitea (04:54):
Yeah, I'd say savvy investors are already using
these technologies to sort oftransform companies, make better
decisions and boost returns.
If I were to bucket the wayfirms are mobilizing this
technology, I would say withinthe portfolio, during the due
diligence process and then atthe firm level, and I can go
into each of these importantways.
So, within the portfolio, wesee some key questions that
(05:16):
private equity firms need toconsider when it comes to their
companies and specifically, theyneed to understand how
generative AI innovations candisrupt the portfolio company's
value chain or business model.
There are companies that aregoing to thrive in this new
environment and there arecompanies that are going to be
significantly affected oraltered by the generative AI
(05:38):
technologies.
So is there an opportunity tospearhead change through this
technology, right.
Will generative AI enable newcompetitors and how will the
competitive modes or barriers toentry be affected to protect
against this disruption?
So, in general, we see privateequity firms must work with
(05:58):
their portfolio companies totest and learn how to address
these questions and, mostimportantly, understand sort of
what works and what doesn't in avery iterative and very, very
fast way.
And then, once they do that,they should be able to
prioritize investmentsaccordingly.
So that's within the portfolio,right?
So at the portfolio companylevel, during the due diligence
(06:21):
process, the investmentprofessionals, at private equity
terms, they can do quickanalyses of any target company,
right?
So there's, you know, almostlike a scorecard-based protocol
with respect to generative AIand how generative AI will
impact the companies that theywould like to invest in.
So they need to do that toevaluate the threats and
opportunities presented by thetechnology.
(06:43):
So we believe that over time,you know, this assessment is
going to be normalized, similarto how we currently have legal
or commercial due diligencepractices, right so we also see
firms that are leveraging thesetools, ai tools, to expedite and
refine the underwriting process, right.
So generative AI offers adistinctive chance to sort of
(07:04):
develop these prototypes, duediligence and sort of validate
or refute certain hypotheses,prototypes during due diligence
and sort of validate or refutecertain hypotheses.
So we're seeing many firms sortof adopt that during the due
diligence process to kind ofswift, you know, sift through
their companies better and theirinvestment targets better.
And lastly, I would say, at thefirm level, you have generative
AI providing numerous avenuesfor streamlining or automating
(07:27):
back office operations, right.
So you know, within dealsourcing, there are a lot of
menial tasks that can now beeasily performed through AI
tools.
So we're seeing the truepotential in significantly
broadening the informationaccessible to these investment
decisions.
Right?
So you have these tools thatare ideal for scanning massive
data pools, for insights.
(07:48):
So the investment professionalsare not going to necessarily be
turning to robo-advisors orrobo-investors, but they will be
able to use their time moreeffectively after dealing with
the output of these tools andusing screening criteria and so
on and so forth.
So there will be a sort ofsupercharging of the investment
professional, if you will,across the full value creation
(08:08):
cycle, from sourcing, screeningdiligence all the way through
portfolio management and helpingthe companies all the way to
the exit.
So I'd say those are the threemost important ways.
We're seeing technology sort oftrickle its way down into
private equity firms andeventually in the larger economy
.
Tanya Suba-Tang (08:24):
So kind of taking the flip side, what are
some pitfalls of deployinggenerative AI?
Alex Chitea (08:29):
I would say you know the pitfalls are general
technology pitfalls.
Right, Focus is essential.
Right Like you want to directinvestments towards select
strategic or operational goals.
Right Like you can.
Ideally you'd like to have sometangible enhancements in
profitability.
So you have to do that reallygood match between the
(08:51):
investment and your goal.
Like any technology, generativeAI is a tool and it's best
deployed in service of strategyand not vice versa, and doesn't
create value by itself.
So you have to explicitly linkit to measurable business
objectives.
So these objectives, you know,we've heard them, they're sort
of everywhere in the pressthings like better serving our
(09:11):
customers.
You know understanding themetrics that we're trying to
move, understanding theprocesses that we're trying to
improve and then sort of thepeople that we're trying to make
more efficient.
You know customer service isthrown around a lot, so you need
to sort of understand, you knowthe business objectives and
then deploy the technologyaccordingly.
You know the businessobjectives and then deploy the
(09:32):
technology accordingly.
A lot of investors are askingthemselves already what the ROI
on generative AI is right sothey can understand intuitively
that there's the potential forproductivity gains.
Right, there's, you candetermine a clear return on
investment.
Determining a clear return oninvestment is actually pretty
difficult because there are manybenefits that have some
indirect or non-financial impact.
So, although you kind ofunderstand the productivity
(09:54):
gains, it's really hard tounderstand sort of the future
financial outcomes.
I'll give you just a quickexample like utilizing
generative AI to automate codegeneration, which is pretty.
One of the first use casescould enhance software
developers' productivity,allowing more time for
innovation and potentiallyspeeding up the time to market
for new features.
However, ultimately andultimately this would lead to
(10:17):
improved customer satisfaction.
But quantifying these benefitsin financial terms and actual
bottom line is a challenge andremains a challenge.
Tanya Suba-Tang (10:26):
So, Alex, I know you have a busy day, but
before you leave me I have toask given your background in
technology, what are trends ordisruptions can we expect over
the next decade?
Alex Chitea (10:37):
Yeah, it's really hard to venture an answer for
the entire decade, as technologyis developing at ever
increasing speeds.
I would also say that you know,when experts and people in
general try to forecast thefuture, we tend to extrapolate
the past, whereas we've seenwith entrepreneurs, they invent
the future that they personallyenvision.
So there's usually a disconnectbetween the two.
(10:58):
But with that caveat out of theway, I would say that AI in
general and you know, here wecan include the current
generation of large languagemodels, deep learning, et cetera
, but also other paradigms thatmight actually emerge in the
next decade.
So AI in general will continueto shape businesses and, more
broadly, society at large, andthat will have an impact on the
(11:20):
private equities bottom line.
We are at the beginning of theS-curve for this new technology
and we still have quite a longway to go until it's fully
deployed and implemented, untilit sort of works its way through
all the processes in society.
So what's already apparent isthat we have this sort of
marginal cost of knowledge thatcontinues to drop and perhaps in
(11:41):
the near future we'll reach apoint where expertise will be
nearly free.
So you know that the proverbialintern like we all hear this
phrase over and over again howgenerative AI is like an intern
that never tires, so that thatcost of knowledge, you know,
will continue to drop and I dobelieve at some point expertise
will be free.
So it's going to be interestingwhat you know knowledge worker
(12:02):
is going to do in that scenario.
We're also seeing a reallyinteresting intersection of AI
and generative AI, specificallywith robotics.
That speaks to the marginalcost of labor that might
eventually move towards zero asrobotics and AI come together.
We are seeing some, you know,demos online and in some Amazon
(12:27):
warehouses where you knowthey're sort of using certain
robots for certain tasks likepackaging, et cetera.
So, yeah, so I would say themarginal cost of knowledge and
marginal cost of labor movingtowards zero are going to have a
great impact on, you know,businesses and society and
eventually, you know, byextension to our discussion,
they will have an impact onprivate equity as an asset class
.
So, yeah, I would say we'restill in the early days.
(12:47):
There's definitely a lot oftime left to start adopting
these AI-enabled tools.
So, I hope that people are notgoing to be afraid and they're
going to sort of try to starttesting and learning and
harnessing data and informationin powerful new ways.
Tanya Suba-Tang (13:03):
Wow, I mean that's exciting, exciting things
happening in our future, right?
Alex Chitea (13:07):
Yeah, absolutely, I'm super excited about the
future.
Tanya Suba-Tang (13:10):
Yeah Well, Alex , thank you.
Thank you so much for your timetoday.
I know our listeners probablygained a wealth of knowledge on
generative AI and how it impactsprivate equity, so thank you so
much for your time, and I hopeyou'll join us again for maybe
an update on what's happening inthe industry.
Alex Chitea (13:27):
Absolutely, it would be my pleasure, thank you,
Tanya.
Lindsey Helman (13:32):
Thank you for listening to this month's
episode of the CFA Society SanFrancisco podcast.
We hope you enjoyed theengaging discussion.
Join us next month for anothernew episode.
This podcast is produced by CFASociety San Francisco, a
not-for-profit professionalassociation providing
(13:52):
professional learning and careerresources to over 13,000
investment industryprofessionals worldwide.
To learn more about CFA SocietySan Francisco, visit our
website at cfa-sf.
org or connect with us onLinkedIn.
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