May 22, 2025 • 30 mins
In this thought-provoking episode, fintech innovator and AI evangelist Peter Swain joins host Jim Jockle to unpack the rise of agentic AI and its sweeping impact on capital markets.
Swain shares a pragmatic roadmap for embracing this shift, starting not with grand-scale disruption but with small, high-impact wins—like automating supplier registration and managing calendar errors. These seemingly modest changes, he explains, can drive meaningful margin and set the stage for transformative growth.
Whether you're a financial professional navigating your future, a capital markets leader crafting an AI strategy, or simply curious about the future of work, this episode delivers candid truths and practical steps to stay ahead in the era of autonomous AI.
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Episode Transcript
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Jim Jockle (00:06):
Welcome to Trading Tomorrow Navigating Trends in
Capital Markets the podcastwhere we deep dive into
technologies reshaping the worldof capital markets.
I'm your host, jim Jockle, aveteran of the finance industry
with a passion for thecomplexities of financial
technologies and market trends.
In each episode, we'll explorethe cutting-edge trends, tools
and strategies driving today'sfinancial landscapes and paving
(00:29):
the way for the future.
With the finance industry at apivotal point, influenced by
groundbreaking innovations, it'smore crucial than ever to
understand how thesetechnological advancements
interact with market dynamics.
Today, we're turning our focusto artificial intelligence.
(00:56):
As we head deeper into 2025, aiis no longer a future disruptor
.
It's a present force.
From generative AI to agenticAI, from predictive models to
intelligent automation, we'reseeing unprecedented
transformation across capitalmarkets.
But which AI trends are trulypoised to take off this year,
and what will separate hype fromlasting impact?
(01:19):
To unpack this, I'm joined byPeter Swain.
Peter is not just a fintechleader.
He's a serial innovator,partner in next-gen lending
platforms, former bank founderand now full-on AI evangelist.
With a futurist mindset and ahands-on experience, he's at the
epicenter of where financialsystems meet machine
(01:39):
intelligence.
Peter welcome, thank you.
Thank you for having me, so youknow.
Let's start.
What was the pivot, what droveyou from moving, you know, from
banking to building AI, and whatwas the drivers behind that
switch?
Guest (01:53):
Yeah, I think all entrepreneurs really are in the
business of impact and legacy,and the money is hopefully the
virtuous side effect.
But when I had my children, itforced me to look at the world I
was creating and the part I wasplaying in it, and looking at
what can be the biggest shiftfor humanity as a whole.
(02:13):
And whilst access to equitablefinance is certainly on that
list, I think that AI is both amassive opportunity and an
existential threat for us ashumans, that we need to wrap our
heads around.
Jim Jockle (02:27):
So, you know, perhaps you know we can dive in
a little bit now in terms of.
You know, we started with kindof, you know, predictive, now to
generative in terms of AI, andnow we're in the let's call it
the phase of agentic.
You know, perhaps you can breakdown what agentic AI really is
and why is it more than just abuzzword?
Guest (02:47):
Sure.
So predictive and generative AI.
And there's the third type,which is transforming.
Transformer AI as well has beenaround for decades.
It's the raw compute power andthe attention we've put on it
recently that has changed it.
Where agentic is different fromthat is agentic is no longer
about building new software.
It's about buildingreplacements for the humans
(03:10):
using the software.
So if you talk about 2023 and2024, the focus was on building
the next piece of shiny kit,whether that was chat, gpt or
claude or mistral or moreindustry-specific applications.
The focus now has been well,hang on a second.
If we can get these things tounderstand, through vision and
(03:31):
speech, what's in front of them,why can't we get them to
navigate websites?
And if we can get them tonavigate websites, then do we
need the human to use the AI tonavigate the website?
So agentic AI is essentiallythe premise of replacing the
human operator with an AIoperator.
Jim Jockle (03:51):
And so where is that taking us, though?
In terms of you know, is thisthe new leap of replacement of
the human We've already got toquestion three and we're already
in dangerous murky waters.
Guest (04:05):
I mean, the simple answer is yes.
The more long-winded answer isAI has yet to touch one of those
jobs that I call the.
When I grow up, I want to bejobs, so that when I grow up, I
want to be an artist.
When I grow up, I want to be afireman.
When I grow up, I want to be aprincess.
When I grow up, I want to be afireman.
When I grow up, I want to be aprincess.
When I grow up, I want to be ahorse rider.
(04:28):
Those jobs, the things that thehuman spirit feels compelled
towards, where we find meaning,ai has very little part to play.
The jobs that AI are taking arethe jobs that, quite frankly,
we never wanted.
So the job of a customerservice agent, the job of a data
analyst, the job of a systemsadministrator, those kind of
(04:51):
risk and compliance, those kindof jobs are the jobs that AI is
taking, and what we do with thatas a society and as a species
is really up to us.
But it certainly challenges thefundamentals of capitalism, as
we know it.
Jim Jockle (05:07):
That's really an interesting way to think about
it, especially, you know, assomeone who has a young daughter
.
I think leaning into thatspirit of what I want to be is a
really great way to think aboutand potentially differentiate
yourself in this new age.
So thank you for that.
So you know, just bringing itback to capital markets a little
(05:32):
bit, you know where's thedisruption over the next few
years.
Is it trading?
Is it risk?
Is it compliance?
Is it the post-trade backoffice?
You know, where do you seedisruption?
Guest (05:40):
Well, if I could tell you a quick story, it might help on
this.
I was speaking to a gentlemanthat ran a debt fund out of New
York with 20 billion in debt outin the world, and we were
talking about exactly thissubject like what's your
concerns, what's your worries?
And his concern was that,essentially, if you're a debt
(06:03):
fund, there is nodifferentiation.
If you're going to lend $10million and I'm going to lend
$10 million, and if I lend it at12.6 and you offer it at 12.3,
then you're going to get thebusiness and I'm not.
There's very little value addthat you can bring to that
transaction to differentiateyourself from the competition.
So historically, what wouldhappen is you would say 12.3,
(06:27):
and I'd find a slightly moreefficient way to underwrite the
risk and I would offer it at 12.
I then become more successfulthan you and I then consume your
fund inside my fund and all ofthe analysts, all of the
compliance people, all the riskpeople, majoritively speaking,
stay employed.
Maybe you have a 10% cull asyou bring those two companies
(06:48):
together.
So that was his concern.
My concern was slightlydifferent and I think he
begrudgingly slash terrifyingly,that's not a word from a place
of terror agreed with.
The larger problem was asfollows If 20% of his fund goes
(07:10):
delinquent, his fund becomesupside down and because he has
no upside as a debt fund,there's no upside, there's only
potential downside.
So if he has $20 billion out inthe market and you're talking
about the wide-scale disruptionthat AI can bring, what if half
of his fund doesn't adopt ordoesn't survive that transition?
(07:33):
That would be 50% If you halfthat number.
So if my prediction of 50%won't make that transition is
correct, if you half that numberto 25%, he's still underwater.
And if you half that numberagain, 25% he's still underwater
.
And if you half that numberagain, he's still in a very
difficult position.
So, yes, it's going to disrupttrading desk.
(07:54):
Yes, it's going to disrupt risk.
Yes, it's going to disruptcompliance.
It's going to make those wildlyefficient.
And in the space of money andthe level that capital markets
move money around, a point of abasis point is enough to make a
huge difference to somebody'seffectiveness.
But the larger concern reallyis actually what happens in the
rest of the capital market.
(08:15):
So you've got, for example, theCEO of Anthropic, the producers
of Claude, predicting that 95%of software will be written by
AI by the end of 2025.
And Microsoft recentlyannounced that in Copilot and
GitHub, their two softwarerepositories, the number is
currently around 50% to 55%.
If you were to just displacethe software engineers in North
(08:40):
America, you would have a verybig change in how money gets
allocated in capital.
Jim Jockle (08:48):
You know it's fascinating to have this
conversation and not to go offtrack, but just thinking of
everything that is going on inthe world geopolitically, in
terms of changes and tariffs andwhatnot.
You know, having this kind ofthinking and overlay, you know,
can really take you down anentire rabbit hole that we're
not going to go down.
But and overlay, you know, canreally take you down an entire
rabbit hole that we're not goingto go down, but you know.
(09:08):
You know trust is one of thekey things that always comes up.
You know there's things thatthe AI can do better, but you
know it's.
We've seemed to move away fromhallucinations, you know, as is
being the, the being the basisof conversation 18 months ago,
but you know there are trustbarriers.
So where are those trustbarriers?
(09:31):
What are the biggest ones, orgovernance challenges that firms
are really going to need tosolve before they move to kind
of an agent-first mindset?
Guest (09:39):
Well, the first thing I'd say and it's a great question
because I think the trust andtransparency is a really big
side of this governance isincredibly hard in this area
because the the area moves soquickly that if you take a
fortune 500 company, by the timegovernance and regulatories are
drawn up and handed downthroughout the company.
Almost almost every time thething that they just governed is
(10:03):
different.
So it's more a case ofguidelines and ethos.
But in terms of trust andtransparency, the first thing I
say to people consistently istrust.
These conversations don'treally happen well in a vacuum.
So there's a lot of peopletalking about whether they
should or they shouldn't, theycan, they can't, they will, they
won't.
A better conversation is okay,we did.
(10:25):
What results did we get?
So the advice that we givepeople quite consistently is
find something very small andlook into the effects of agentic
AI as an example.
We just implemented inside myown company an AI agent whose
sole job it is to register a newsupplier, because we found out
(10:48):
that registering a supplier cantake us anywhere from 15 minutes
to four hours.
15 minutes is we send therequest, they send back the
information, we upload it toQuickBooks.
Thanks very much Four hours is.
We send the request.
They don't, uh, answer it.
We then chase it up.
They don't answer it.
They finally reply.
They don't mention whether it'sa wire or an ach.
They don't send in the w9.
(11:09):
So it's this back and forthperiod.
So we put an agent in placethat we literally cc into an
email and at that point it willreach out, it will ask the
questions, it will do thefollow-up, it will grade what
it's got as a return to makesure it's appropriate and then
send it back.
Now that's, that's not a bigwin by any stretch of the
imagination, but it's saving useight hours a month.
(11:32):
That's 96 hours a year.
Call it 100 hours.
Call it it a $50 charge out.
That's saving us $5,000 a yearand that's replacing one
function of one job.
So we got to see did this work?
How did it work?
What are the pros, what are thecons?
And, in terms of the cons,quite specifically, how do you
(11:55):
address this with the otherhumans in your business?
How do you talk to your teamand express is this a threat to
them?
Is it not a threat to them?
How should they respond to it?
That was probably the biggestarea of insight we gained.
But you don't gain anything bytalking about something in a
vacuum.
You only gain by actuallyapplying it, seeing it and doing
it.
(12:15):
So I think the answer to trustis to stop having conversations
in a vacuum and start playingplaying in very small ways so
that you can see what the actualresults are.
Jim Jockle (12:28):
And, just out of curiosity, what was?
What was the by-product of theconversation was?
Were the affected individualshappy?
Did they find more time?
Or, you know, was there aninherent distrust of oh you,
this has been automated for me.
And what's the next thingcoming?
You know how did that play?
Guest (12:46):
out.
Well, I was brutally honest,which most people that know me
know that I am.
So I told my entire team thatAI is coming for your job and I
told them I was actively lookingto replace their jobs, which
led to lots of open-mouthedlooks back on the Zoom.
But my second sentence was butit's not coming for your career.
(13:07):
So that's the question andchoice you all have to make.
You know what we do, you knowwho we're in service of.
You know our mission, you knowour vision.
So come back to me and tell mewhere you can help, tell me
where you can add value to meand to my customers and to my
community and to my experience,because the human is now a
(13:28):
hundred times more expensive,probably, than the AI equivalent
, which means you have to beadding a hundred times the value
.
I'm not saying that'simpossible by any stretch of the
imagination.
I'm not saying that'simpossible by any stretch of the
imagination.
I'm just saying that AI iscoming for everybody's job.
Everybody's job is at risk, butI don't believe anybody's
(13:48):
career is at risk.
I think that people have to makea conscious decision to
understand where they can addtrue value to an organization,
to an enterprise, and thendouble, triple and quadruple
down value to an organization,to an enterprise, and then
double, triple and quadrupledown.
So I don't know if my teamfound that loving or not, but I
find in almost every singleinstance the truth is a better
(14:13):
solution Because, as you said,they know that I'm trying to do
it.
Why wouldn't I try and do it?
So they know it's happening.
So if I pretend it's not, Idon't think I get their trust.
I don't think I get theirrespect.
I think by saying, listen, wecan't ignore the benefits that
this thing can bring us.
But you, as the humans in mycompany, have a great deal of
understanding in how we work,who we work for, why we work,
(14:33):
why do I wake up in the morning?
And if you can leverage thatversus leveraging the hours that
you're doing with me, you areworth an infinite amount to both
myself and my community.
If you think you're just hereto push a button very soon,
you're worthless.
Jim Jockle (14:50):
Well, you know, peter, just as an aside and a
reaction to that, that's a realdemonstration of being a leader
and I just wanted to call thatout.
Thank you for sharing that.
So, coming back in terms of youknow fintechs and the rest of
the world.
You know, so I know you'redeeply embedded in fintech
lending.
(15:10):
You know how are agentstransforming the full lending
lifecycle.
You know from originationunderwriting to servicing
collections.
You know how is that evolving.
Guest (15:20):
Well, I can tell you.
So we run a lending company inLatin America, amongst other
things I'm on the board of thatand one of the large AI
providers who I won't name.
But they came to us and saidhey, we want your data and we'd
like to do a sample project withyou just to show you what we
can do.
What can we do to show themwhat we can do?
(15:42):
And we went completions.
Now, for us, a completion iswhen we have somebody that has
made a loan application and theelectricity bill is in the wrong
address or their pay slip showsa different amount from their
loan application, and they'relike okay, how many have you got
?
And we said, uh, 32 000.
(16:03):
Okay, and what does the callsend to him?
Like, well, there's 30 peopleand they average 20 a day, so we
do about 600 a day.
All right, okay, we'll do that.
So they uh deployed an agenticvoice-based ai.
So this is actually callingpeople in Mexican, argentinian
and Peruvian Spanish, so verydifferent dialects.
(16:25):
And after six hours, thecompany in question finished the
project because they had nocompletions left to do.
So the human equivalent does600 a day with 20, 30 people in
the call center.
It's like 26 or something.
(16:45):
That means that they do 18,000a month and the company in
question did 32,000 in the firstday and the statistics were
shocking.
At the very first moment it wasterrible, it was absolutely
horrendous.
It was really really bad.
(17:05):
But it learned on every singlecall it did and it got better on
every single call it did.
And this is really where youstart seeing the power of AI,
because humans have ego.
We have massive amounts of ego.
So whenever we listen to adviceor feedback, we always filter
it through our own humanexperience, which is naturally
(17:27):
defensive.
The AI doesn't have thatproblem.
It literally is looking for thepatterns in its own data and
its own performance to improveitself.
So every time it's doing anaction, it's getting better at
the action it's doing.
So that was one example gettingbetter at the action it's doing
so as one example.
The other example was uh, backin full 23.
We fed in every lendingdecision we'd made into just a
(17:50):
high street llm um, largelanguage model um.
And I asked it just, this wasjust an experiment in the very
early days.
I said tell me something thatyou can see, that there's no way
I can see.
And it said you've got aproblem on Tuesday.
I'm like what it's like?
You have a problem on Tuesday.
Your acceptance rates are 2%higher and your average, your
(18:12):
API, is 2% lower.
I'm like how much does thatcost us?
It's like $220,000.
Wow, okay.
So I called together the C-suiteand said tell me about tuesday.
They're like what do you mean?
Tell me about tuesday.
I'm like, just tell me ontuesday.
And they're like it's the dayafter monday.
I'm like you know, I know it'sthe day after monday, but tell
me what is different abouttuesday?
And they're like what do youmean?
(18:33):
I'm like I can't tell you whyI'm asking, because if I tell
you why I'm asking, we won't getthe answer.
Tell me what's different aboutTuesday.
We were in the room for about35 minutes before somebody said
well, the head of risk has theirday off on Tuesday.
Could that be what you'retalking about?
I'm like yeah, that's exactlywhat I'm talking about.
So the ability for AI to takethese huge data sets and find
(18:56):
patterns is an unparalleledcapability that no human being
could ever possibly get near.
So I think you'll see winsacross every single piece of the
lending lifecycle, and thething about being in lending is
it's a great business if you'regood at assessing the person
(19:18):
that sits in front of you, it'sa terrible business if you're
not very good at assessing theperson that sits in front of you
.
It's a terrible business ifyou're not very good at
assessing the person that sitsin front of you.
So that's really.
The only thing that a lendingcompany does well or doesn't do
well is underwriting.
The deployment is normalizedacross the board.
(19:40):
It's commoditized.
Sorry, collections iscommoditized.
The only secret source that alending company really has is
their underlying data and theiralgorithm of who they're going
to lend and how they're going tolend and how they make that
decision, and AI will find thebias that humans have put into
that system pretty much straightaway.
Jim Jockle (19:58):
Here we are 2025, in terms of a revolution of AI.
Would you categorize this yearas being a potential tipping
point, especially aroundregulation?
Will tighter AI governance stopinnovation or is it going to be
(20:20):
a catalyst to building trust atscale?
Guest (20:23):
This is such a fantastic question.
If you'd asked me in December,I'd have said yes, 2025 will be
a year where AI regulation getstighter and it helps us level
the playing field.
But obviously, as we know, thatone of President Trump's first
executive orders was to repealthe safeguards around AI that
Biden has started putting inplace, which led to the UK then
(20:47):
following suit and opting out ofthe EU AI Safety Act as well.
So and I'm not making thispolitical, it's just a statement
of fact the guardrails haveactually been reduced, not
increased.
Now, if we were to getpolitical, we could say that's,
you know, spurring a huge waveof investment inside the us at
(21:08):
the moment.
We saw the the project withsoftbank stargate, I believe it
was for 500 billion dollars comethrough almost as soon as that
the executive order was repealed.
We've seen softbank actuallynow step up a week ago and say
they're actually looking at anumber closer to $2 trillion of
investment inside the US and theAI ecosystem there.
(21:30):
So I think we're actually goingto see less regulation, not
more, which, from a capitalperspective, is fantastic.
Jim Jockle (21:39):
From a humanity perspective, it's somewhat
debatable you've coined thephrase ai agents as, as
colleagues.
What does that look like inpractice?
And I even think back to youknow recently, uh, that you know
the ceo of salesforce at aconference.
He was out out talking abouthow he has multiplied his, his
workforce, and, and it's humanand uh, and and digital at this
(22:03):
point.
So, the agents as colleagues,what does that look like in
practice?
And are capital markets firmsready to work with AI as a?
Guest (22:12):
peer.
So what does it look like inpractice?
I think the example I gave youof registering a supplier is a
perfect example of as long asthe work gets done, then does it
really matter how it gets done?
You know, we have embedded adagencies nowadays that aren't
your employees.
We have, you know, w2s, and wehave contractors and permanent
(22:34):
members of staff.
The workforce, the makeup of aworkforce, is now so fluid.
Anyway, I'm not sure it'sreally a differentiator if it's
a human or an AI.
Now, are the capital marketfirms ready to work with AI is a
fantastic question, I think.
Well, they're both greatquestions, but that's the one
(22:54):
that really stands out to me,and the example that I'm taken
to is when I've you know, I'vebeen in digital for pretty much
all my life.
I had the 30-second listing onYahoo and I was one of the lead
developers for the UK version ofYelp back in the 90s.
So I remember when Facebookstarted doing these things
(23:14):
called lookalike audiences,which is where you can advertise
to people that look like otherpeople.
So you've got 1,000 customersand you can advertise to people
that look like other people.
So you've got a thousandcustomers and you can advertise
people that look like thosecustomers and at the time that
was invented, a lot of people inthe industry, myself included,
thought it was unethical.
We thought it was ethical tosay, hey, if Jim or Emily or
(23:37):
Cheryl or Bob or James hits ourwebsite, tagging them with
Facebook and then advertising isokay.
But if Jim lands on the websiteadvertising to Stuart because
he has the same behavioraltraits as Jim, that's not okay.
Now no one needs to understandthat example, because what then
(23:58):
happened was everybody starteddoing lookalike audiences
because it was too effective tonot do it.
It is now a perfectlynormalized behavior to use
lookalike audiences.
So my answer to are they ready?
Is it doesn't really matter,because if your competition
starts doing it and they startposting 12% IRR versus your 10
(24:24):
or 15, versus your 12 or 20,versus your 9 or 30 versus your
8, you're going to get on boardwith it.
The AI first future is reallynow inevitable.
There is too much money fromsovereign wealth firms.
There is too much money fromsovereign wealth firms.
There is too much investmentfor this not to succeed.
(24:47):
This is now a.
This is a given.
It's kind of like saying thatgreen energy is going to replace
the oil industry in any timesoon.
There's too much money in theoil industry for that to happen.
You know, no matter whatbenefits we can get from
renewables, no matter whatinvestments being put behind it,
the oil industry is amulti-trillion dollar industry.
(25:08):
The growth of AI is now amulti-trillion dollar industry,
so this is going to happen.
The phrase that capital marketsand the firms around that
ecosystem need to decide is areal, simple question of are we
going to be first or are wegoing to be second?
Do we want the competitiveadvantage and rewards that come
(25:28):
with being first that iscombined with the risk of going
first, or do we want the safetyof coming second, negating some
of our risk but losing some ofour reward at the same time?
That's the question that has tobe asked.
It's not shall we do this orshan't we do this?
It's we're going to do this.
When is the right time to dothis?
Jim Jockle (25:48):
And, for lack of a better term, I think a lot of
institutions are having thatcome to Jesus moment.
If you will, you know whatwould you say is from your
perspective, what is theplaybook for for scaling ai in
in 2025?
I mean what, what?
Where should someone start andwhat's something people should
(26:08):
stop doing at this point?
Guest (26:10):
so I think there's we have two phases for this.
The first phase is your, your,your own, uh, personal use of ai
and I, up until about a yearago, I had a sticky on my
desktop that said if it tooklonger than five minutes, I
should have probably used AI.
So I think we have to get ridof this sorry, this residual
(26:31):
automation that we have of notusing AI and going to like
Google.
You know I'm sure some of yourlisteners still have this, but I
often get like a phone callfrom my dad or something and say
how long does the drive fromLondon to your house?
Or something like that.
I'm like Google Maps man.
Why am I still answering thesequestions?
Google, google it.
(26:52):
I'm just going to Google it foryou.
Why do I need to do this?
So I really don't want peopleto be that in the AI space of
just not having the reflex, likethe muscle memory, to say AI
first, that's where we go first.
So I think that's the personalside of things.
Then, on the business side ofthings, as we said earlier, I
(27:13):
think the real thing to do isjust look for that low hanging
fruit.
Don't look for the biggest winto begin with.
Don't look for the biggestdisruption, look for the
smallest win that's actuallygoing to make a difference.
The first one we did wasregistering a supplier.
The second one I did was aroundanyone that used my calendar
link.
We noticed that often peopleget confused with time zones.
(27:35):
So they'll book it at fouro'clock in the morning, pacific,
and then obviously they don'tshow up because they didn't mean
to book at four o'clock pacific, um and?
But for my time, four o'clockpacific is noon, so it's
actually quite a busy time forme.
So we put an agent in placethat looks at the calendar
appointment that just landed andwe'll literally just email or
(27:55):
text the person and say hey, jim, you just booked a call with
peter.
I know he's looking forward tospeaking to you.
This is sally of his AIassistants.
Just wanted to check because itlooks like it's four o'clock in
the morning, your time.
If that was deliberate, no, youdon't need to do anything.
If that wasn't deliberate andthere was maybe a time zone
confusion.
This is the link to reschedule.
We've managed to eliminatealmost all of those no-shows
(28:20):
from that, which were probablytwo a week, so 104 a year, where
I'm sitting for 30 minuteswaiting, or 15 minutes, if I'm
being honest, waiting to see ifthe person's going to show up.
So that saved me 25 hours ayear and all of these small wins
this 25 hours and the $5,000for registering a supplier soon
(28:42):
start adding up because I'mdriving margin.
We have a phrase at this end,which is revenue is vanity and
profit is sanity.
We can all drive big revenuenumbers and they're fantastic.
I love those.
But the numbers I'm reallyinterested in, the numbers that
profit, they're the numbers thatend up and take home for
(29:02):
founders and take home forpartners and C-suites.
So always driving margin meansfinding those tiny little
efficiencies that startcompounding real quick.
Jim Jockle (29:15):
So sadly, we've made it to the last question of the
podcast.
We call it the trend drop.
It's like a desert islandquestion, and if you could only
watch or track one trend in AI,what would it be?
It would be the percentage ofagents deployed versus the
(29:36):
percentage of humans displaced.
Wow Well, peter, fascinatingconversation and, I would say,
probably the most quotes andthought-provoking statements of
any of our podcasts to date.
I want to thank you so much foryour time.
This was really fantastic.
Guest (29:52):
Thank you, I really appreciate it, and thanks for
having me Please.
Jim Jockle (29:55):
Anytime.
Thanks so much for listening totoday's episode and if you're
enjoying Trading Tomorrow,navigating trends and capital
markets, be sure to like,subscribe and share, and we'll
see you next time.
Transcription by CastingWords.
Welcome to Trading Tomorrow Navigating Trends in
Capital Markets the podcastwhere we deep dive into
technologies reshaping the worldof capital markets.
I'm your host, jim Jockle, aveteran of the finance industry
with a passion for thecomplexities of financial
technologies and market trends.
In each episode, we'll explorethe cutting-edge trends, tools
and strategies driving today'sfinancial landscapes and paving
(00:29):
the way for the future.
With the finance industry at apivotal point, influenced by
groundbreaking innovations, it'smore crucial than ever to
understand how thesetechnological advancements
interact with market dynamics.
Today, we're turning our focusto artificial intelligence.
(00:56):
As we head deeper into 2025, aiis no longer a future disruptor
.
It's a present force.
From generative AI to agenticAI, from predictive models to
intelligent automation, we'reseeing unprecedented
transformation across capitalmarkets.
But which AI trends are trulypoised to take off this year,
and what will separate hype fromlasting impact?
(01:19):
To unpack this, I'm joined byPeter Swain.
Peter is not just a fintechleader.
He's a serial innovator,partner in next-gen lending
platforms, former bank founderand now full-on AI evangelist.
With a futurist mindset and ahands-on experience, he's at the
epicenter of where financialsystems meet machine
(01:39):
intelligence.
Peter welcome, thank you.
Thank you for having me, so youknow.
Let's start.
What was the pivot, what droveyou from moving, you know, from
banking to building AI, and whatwas the drivers behind that
switch?
Guest (01:53):
Yeah, I think all entrepreneurs really are in the
business of impact and legacy,and the money is hopefully the
virtuous side effect.
But when I had my children, itforced me to look at the world I
was creating and the part I wasplaying in it, and looking at
what can be the biggest shiftfor humanity as a whole.
(02:13):
And whilst access to equitablefinance is certainly on that
list, I think that AI is both amassive opportunity and an
existential threat for us ashumans, that we need to wrap our
heads around.
Jim Jockle (02:27):
So, you know, perhaps you know we can dive in
a little bit now in terms of.
You know, we started with kindof, you know, predictive, now to
generative in terms of AI, andnow we're in the let's call it
the phase of agentic.
You know, perhaps you can breakdown what agentic AI really is
and why is it more than just abuzzword?
Guest (02:47):
Sure.
So predictive and generative AI.
And there's the third type,which is transforming.
Transformer AI as well has beenaround for decades.
It's the raw compute power andthe attention we've put on it
recently that has changed it.
Where agentic is different fromthat is agentic is no longer
about building new software.
It's about buildingreplacements for the humans
(03:10):
using the software.
So if you talk about 2023 and2024, the focus was on building
the next piece of shiny kit,whether that was chat, gpt or
claude or mistral or moreindustry-specific applications.
The focus now has been well,hang on a second.
If we can get these things tounderstand, through vision and
(03:31):
speech, what's in front of them,why can't we get them to
navigate websites?
And if we can get them tonavigate websites, then do we
need the human to use the AI tonavigate the website?
So agentic AI is essentiallythe premise of replacing the
human operator with an AIoperator.
Jim Jockle (03:51):
And so where is that taking us, though?
In terms of you know, is thisthe new leap of replacement of
the human We've already got toquestion three and we're already
in dangerous murky waters.
Guest (04:05):
I mean, the simple answer is yes.
The more long-winded answer isAI has yet to touch one of those
jobs that I call the.
When I grow up, I want to bejobs, so that when I grow up, I
want to be an artist.
When I grow up, I want to be afireman.
When I grow up, I want to be aprincess.
When I grow up, I want to be afireman.
When I grow up, I want to be aprincess.
When I grow up, I want to be ahorse rider.
(04:28):
Those jobs, the things that thehuman spirit feels compelled
towards, where we find meaning,ai has very little part to play.
The jobs that AI are taking arethe jobs that, quite frankly,
we never wanted.
So the job of a customerservice agent, the job of a data
analyst, the job of a systemsadministrator, those kind of
(04:51):
risk and compliance, those kindof jobs are the jobs that AI is
taking, and what we do with thatas a society and as a species
is really up to us.
But it certainly challenges thefundamentals of capitalism, as
we know it.
Jim Jockle (05:07):
That's really an interesting way to think about
it, especially, you know, assomeone who has a young daughter
.
I think leaning into thatspirit of what I want to be is a
really great way to think aboutand potentially differentiate
yourself in this new age.
So thank you for that.
So you know, just bringing itback to capital markets a little
(05:32):
bit, you know where's thedisruption over the next few
years.
Is it trading?
Is it risk?
Is it compliance?
Is it the post-trade backoffice?
You know, where do you seedisruption?
Guest (05:40):
Well, if I could tell you a quick story, it might help on
this.
I was speaking to a gentlemanthat ran a debt fund out of New
York with 20 billion in debt outin the world, and we were
talking about exactly thissubject like what's your
concerns, what's your worries?
And his concern was that,essentially, if you're a debt
(06:03):
fund, there is nodifferentiation.
If you're going to lend $10million and I'm going to lend
$10 million, and if I lend it at12.6 and you offer it at 12.3,
then you're going to get thebusiness and I'm not.
There's very little value addthat you can bring to that
transaction to differentiateyourself from the competition.
So historically, what wouldhappen is you would say 12.3,
(06:27):
and I'd find a slightly moreefficient way to underwrite the
risk and I would offer it at 12.
I then become more successfulthan you and I then consume your
fund inside my fund and all ofthe analysts, all of the
compliance people, all the riskpeople, majoritively speaking,
stay employed.
Maybe you have a 10% cull asyou bring those two companies
(06:48):
together.
So that was his concern.
My concern was slightlydifferent and I think he
begrudgingly slash terrifyingly,that's not a word from a place
of terror agreed with.
The larger problem was asfollows If 20% of his fund goes
(07:10):
delinquent, his fund becomesupside down and because he has
no upside as a debt fund,there's no upside, there's only
potential downside.
So if he has $20 billion out inthe market and you're talking
about the wide-scale disruptionthat AI can bring, what if half
of his fund doesn't adopt ordoesn't survive that transition?
(07:33):
That would be 50% If you halfthat number.
So if my prediction of 50%won't make that transition is
correct, if you half that numberto 25%, he's still underwater.
And if you half that numberagain, 25% he's still underwater
.
And if you half that numberagain, he's still in a very
difficult position.
So, yes, it's going to disrupttrading desk.
(07:54):
Yes, it's going to disrupt risk.
Yes, it's going to disruptcompliance.
It's going to make those wildlyefficient.
And in the space of money andthe level that capital markets
move money around, a point of abasis point is enough to make a
huge difference to somebody'seffectiveness.
But the larger concern reallyis actually what happens in the
rest of the capital market.
(08:15):
So you've got, for example, theCEO of Anthropic, the producers
of Claude, predicting that 95%of software will be written by
AI by the end of 2025.
And Microsoft recentlyannounced that in Copilot and
GitHub, their two softwarerepositories, the number is
currently around 50% to 55%.
If you were to just displacethe software engineers in North
(08:40):
America, you would have a verybig change in how money gets
allocated in capital.
Jim Jockle (08:48):
You know it's fascinating to have this
conversation and not to go offtrack, but just thinking of
everything that is going on inthe world geopolitically, in
terms of changes and tariffs andwhatnot.
You know, having this kind ofthinking and overlay, you know,
can really take you down anentire rabbit hole that we're
not going to go down.
But and overlay, you know, canreally take you down an entire
rabbit hole that we're not goingto go down, but you know.
(09:08):
You know trust is one of thekey things that always comes up.
You know there's things thatthe AI can do better, but you
know it's.
We've seemed to move away fromhallucinations, you know, as is
being the, the being the basisof conversation 18 months ago,
but you know there are trustbarriers.
So where are those trustbarriers?
(09:31):
What are the biggest ones, orgovernance challenges that firms
are really going to need tosolve before they move to kind
of an agent-first mindset?
Guest (09:39):
Well, the first thing I'd say and it's a great question
because I think the trust andtransparency is a really big
side of this governance isincredibly hard in this area
because the the area moves soquickly that if you take a
fortune 500 company, by the timegovernance and regulatories are
drawn up and handed downthroughout the company.
Almost almost every time thething that they just governed is
(10:03):
different.
So it's more a case ofguidelines and ethos.
But in terms of trust andtransparency, the first thing I
say to people consistently istrust.
These conversations don'treally happen well in a vacuum.
So there's a lot of peopletalking about whether they
should or they shouldn't, theycan, they can't, they will, they
won't.
A better conversation is okay,we did.
(10:25):
What results did we get?
So the advice that we givepeople quite consistently is
find something very small andlook into the effects of agentic
AI as an example.
We just implemented inside myown company an AI agent whose
sole job it is to register a newsupplier, because we found out
(10:48):
that registering a supplier cantake us anywhere from 15 minutes
to four hours.
15 minutes is we send therequest, they send back the
information, we upload it toQuickBooks.
Thanks very much Four hours is.
We send the request.
They don't, uh, answer it.
We then chase it up.
They don't answer it.
They finally reply.
They don't mention whether it'sa wire or an ach.
They don't send in the w9.
(11:09):
So it's this back and forthperiod.
So we put an agent in placethat we literally cc into an
email and at that point it willreach out, it will ask the
questions, it will do thefollow-up, it will grade what
it's got as a return to makesure it's appropriate and then
send it back.
Now that's, that's not a bigwin by any stretch of the
imagination, but it's saving useight hours a month.
(11:32):
That's 96 hours a year.
Call it 100 hours.
Call it it a $50 charge out.
That's saving us $5,000 a yearand that's replacing one
function of one job.
So we got to see did this work?
How did it work?
What are the pros, what are thecons?
And, in terms of the cons,quite specifically, how do you
(11:55):
address this with the otherhumans in your business?
How do you talk to your teamand express is this a threat to
them?
Is it not a threat to them?
How should they respond to it?
That was probably the biggestarea of insight we gained.
But you don't gain anything bytalking about something in a
vacuum.
You only gain by actuallyapplying it, seeing it and doing
it.
(12:15):
So I think the answer to trustis to stop having conversations
in a vacuum and start playingplaying in very small ways so
that you can see what the actualresults are.
Jim Jockle (12:28):
And, just out of curiosity, what was?
What was the by-product of theconversation was?
Were the affected individualshappy?
Did they find more time?
Or, you know, was there aninherent distrust of oh you,
this has been automated for me.
And what's the next thingcoming?
You know how did that play?
Guest (12:46):
out.
Well, I was brutally honest,which most people that know me
know that I am.
So I told my entire team thatAI is coming for your job and I
told them I was actively lookingto replace their jobs, which
led to lots of open-mouthedlooks back on the Zoom.
But my second sentence was butit's not coming for your career.
(13:07):
So that's the question andchoice you all have to make.
You know what we do, you knowwho we're in service of.
You know our mission, you knowour vision.
So come back to me and tell mewhere you can help, tell me
where you can add value to meand to my customers and to my
community and to my experience,because the human is now a
(13:28):
hundred times more expensive,probably, than the AI equivalent
, which means you have to beadding a hundred times the value
.
I'm not saying that'simpossible by any stretch of the
imagination.
I'm not saying that'simpossible by any stretch of the
imagination.
I'm just saying that AI iscoming for everybody's job.
Everybody's job is at risk, butI don't believe anybody's
(13:48):
career is at risk.
I think that people have to makea conscious decision to
understand where they can addtrue value to an organization,
to an enterprise, and thendouble, triple and quadruple
down value to an organization,to an enterprise, and then
double, triple and quadrupledown.
So I don't know if my teamfound that loving or not, but I
find in almost every singleinstance the truth is a better
(14:13):
solution Because, as you said,they know that I'm trying to do
it.
Why wouldn't I try and do it?
So they know it's happening.
So if I pretend it's not, Idon't think I get their trust.
I don't think I get theirrespect.
I think by saying, listen, wecan't ignore the benefits that
this thing can bring us.
But you, as the humans in mycompany, have a great deal of
understanding in how we work,who we work for, why we work,
(14:33):
why do I wake up in the morning?
And if you can leverage thatversus leveraging the hours that
you're doing with me, you areworth an infinite amount to both
myself and my community.
If you think you're just hereto push a button very soon,
you're worthless.
Jim Jockle (14:50):
Well, you know, peter, just as an aside and a
reaction to that, that's a realdemonstration of being a leader
and I just wanted to call thatout.
Thank you for sharing that.
So, coming back in terms of youknow fintechs and the rest of
the world.
You know, so I know you'redeeply embedded in fintech
lending.
(15:10):
You know how are agentstransforming the full lending
lifecycle.
You know from originationunderwriting to servicing
collections.
You know how is that evolving.
Guest (15:20):
Well, I can tell you.
So we run a lending company inLatin America, amongst other
things I'm on the board of thatand one of the large AI
providers who I won't name.
But they came to us and saidhey, we want your data and we'd
like to do a sample project withyou just to show you what we
can do.
What can we do to show themwhat we can do?
(15:42):
And we went completions.
Now, for us, a completion iswhen we have somebody that has
made a loan application and theelectricity bill is in the wrong
address or their pay slip showsa different amount from their
loan application, and they'relike okay, how many have you got
?
And we said, uh, 32 000.
(16:03):
Okay, and what does the callsend to him?
Like, well, there's 30 peopleand they average 20 a day, so we
do about 600 a day.
All right, okay, we'll do that.
So they uh deployed an agenticvoice-based ai.
So this is actually callingpeople in Mexican, argentinian
and Peruvian Spanish, so verydifferent dialects.
(16:25):
And after six hours, thecompany in question finished the
project because they had nocompletions left to do.
So the human equivalent does600 a day with 20, 30 people in
the call center.
It's like 26 or something.
(16:45):
That means that they do 18,000a month and the company in
question did 32,000 in the firstday and the statistics were
shocking.
At the very first moment it wasterrible, it was absolutely
horrendous.
It was really really bad.
(17:05):
But it learned on every singlecall it did and it got better on
every single call it did.
And this is really where youstart seeing the power of AI,
because humans have ego.
We have massive amounts of ego.
So whenever we listen to adviceor feedback, we always filter
it through our own humanexperience, which is naturally
(17:27):
defensive.
The AI doesn't have thatproblem.
It literally is looking for thepatterns in its own data and
its own performance to improveitself.
So every time it's doing anaction, it's getting better at
the action it's doing.
So that was one example gettingbetter at the action it's doing
so as one example.
The other example was uh, backin full 23.
We fed in every lendingdecision we'd made into just a
(17:50):
high street llm um, largelanguage model um.
And I asked it just, this wasjust an experiment in the very
early days.
I said tell me something thatyou can see, that there's no way
I can see.
And it said you've got aproblem on Tuesday.
I'm like what it's like?
You have a problem on Tuesday.
Your acceptance rates are 2%higher and your average, your
(18:12):
API, is 2% lower.
I'm like how much does thatcost us?
It's like $220,000.
Wow, okay.
So I called together the C-suiteand said tell me about tuesday.
They're like what do you mean?
Tell me about tuesday.
I'm like, just tell me ontuesday.
And they're like it's the dayafter monday.
I'm like you know, I know it'sthe day after monday, but tell
me what is different abouttuesday?
And they're like what do youmean?
(18:33):
I'm like I can't tell you whyI'm asking, because if I tell
you why I'm asking, we won't getthe answer.
Tell me what's different aboutTuesday.
We were in the room for about35 minutes before somebody said
well, the head of risk has theirday off on Tuesday.
Could that be what you'retalking about?
I'm like yeah, that's exactlywhat I'm talking about.
So the ability for AI to takethese huge data sets and find
(18:56):
patterns is an unparalleledcapability that no human being
could ever possibly get near.
So I think you'll see winsacross every single piece of the
lending lifecycle, and thething about being in lending is
it's a great business if you'regood at assessing the person
(19:18):
that sits in front of you, it'sa terrible business if you're
not very good at assessing theperson that sits in front of you
.
It's a terrible business ifyou're not very good at
assessing the person that sitsin front of you.
So that's really.
The only thing that a lendingcompany does well or doesn't do
well is underwriting.
The deployment is normalizedacross the board.
(19:40):
It's commoditized.
Sorry, collections iscommoditized.
The only secret source that alending company really has is
their underlying data and theiralgorithm of who they're going
to lend and how they're going tolend and how they make that
decision, and AI will find thebias that humans have put into
that system pretty much straightaway.
Jim Jockle (19:58):
Here we are 2025, in terms of a revolution of AI.
Would you categorize this yearas being a potential tipping
point, especially aroundregulation?
Will tighter AI governance stopinnovation or is it going to be
(20:20):
a catalyst to building trust atscale?
Guest (20:23):
This is such a fantastic question.
If you'd asked me in December,I'd have said yes, 2025 will be
a year where AI regulation getstighter and it helps us level
the playing field.
But obviously, as we know, thatone of President Trump's first
executive orders was to repealthe safeguards around AI that
Biden has started putting inplace, which led to the UK then
(20:47):
following suit and opting out ofthe EU AI Safety Act as well.
So and I'm not making thispolitical, it's just a statement
of fact the guardrails haveactually been reduced, not
increased.
Now, if we were to getpolitical, we could say that's,
you know, spurring a huge waveof investment inside the us at
(21:08):
the moment.
We saw the the project withsoftbank stargate, I believe it
was for 500 billion dollars comethrough almost as soon as that
the executive order was repealed.
We've seen softbank actuallynow step up a week ago and say
they're actually looking at anumber closer to $2 trillion of
investment inside the US and theAI ecosystem there.
(21:30):
So I think we're actually goingto see less regulation, not
more, which, from a capitalperspective, is fantastic.
Jim Jockle (21:39):
From a humanity perspective, it's somewhat
debatable you've coined thephrase ai agents as, as
colleagues.
What does that look like inpractice?
And I even think back to youknow recently, uh, that you know
the ceo of salesforce at aconference.
He was out out talking abouthow he has multiplied his, his
workforce, and, and it's humanand uh, and and digital at this
(22:03):
point.
So, the agents as colleagues,what does that look like in
practice?
And are capital markets firmsready to work with AI as a?
Guest (22:12):
peer.
So what does it look like inpractice?
I think the example I gave youof registering a supplier is a
perfect example of as long asthe work gets done, then does it
really matter how it gets done?
You know, we have embedded adagencies nowadays that aren't
your employees.
We have, you know, w2s, and wehave contractors and permanent
(22:34):
members of staff.
The workforce, the makeup of aworkforce, is now so fluid.
Anyway, I'm not sure it'sreally a differentiator if it's
a human or an AI.
Now, are the capital marketfirms ready to work with AI is a
fantastic question, I think.
Well, they're both greatquestions, but that's the one
(22:54):
that really stands out to me,and the example that I'm taken
to is when I've you know, I'vebeen in digital for pretty much
all my life.
I had the 30-second listing onYahoo and I was one of the lead
developers for the UK version ofYelp back in the 90s.
So I remember when Facebookstarted doing these things
(23:14):
called lookalike audiences,which is where you can advertise
to people that look like otherpeople.
So you've got 1,000 customersand you can advertise to people
that look like other people.
So you've got a thousandcustomers and you can advertise
people that look like thosecustomers and at the time that
was invented, a lot of people inthe industry, myself included,
thought it was unethical.
We thought it was ethical tosay, hey, if Jim or Emily or
(23:37):
Cheryl or Bob or James hits ourwebsite, tagging them with
Facebook and then advertising isokay.
But if Jim lands on the websiteadvertising to Stuart because
he has the same behavioraltraits as Jim, that's not okay.
Now no one needs to understandthat example, because what then
(23:58):
happened was everybody starteddoing lookalike audiences
because it was too effective tonot do it.
It is now a perfectlynormalized behavior to use
lookalike audiences.
So my answer to are they ready?
Is it doesn't really matter,because if your competition
starts doing it and they startposting 12% IRR versus your 10
(24:24):
or 15, versus your 12 or 20,versus your 9 or 30 versus your
8, you're going to get on boardwith it.
The AI first future is reallynow inevitable.
There is too much money fromsovereign wealth firms.
There is too much money fromsovereign wealth firms.
There is too much investmentfor this not to succeed.
(24:47):
This is now a.
This is a given.
It's kind of like saying thatgreen energy is going to replace
the oil industry in any timesoon.
There's too much money in theoil industry for that to happen.
You know, no matter whatbenefits we can get from
renewables, no matter whatinvestments being put behind it,
the oil industry is amulti-trillion dollar industry.
(25:08):
The growth of AI is now amulti-trillion dollar industry,
so this is going to happen.
The phrase that capital marketsand the firms around that
ecosystem need to decide is areal, simple question of are we
going to be first or are wegoing to be second?
Do we want the competitiveadvantage and rewards that come
(25:28):
with being first that iscombined with the risk of going
first, or do we want the safetyof coming second, negating some
of our risk but losing some ofour reward at the same time?
That's the question that has tobe asked.
It's not shall we do this orshan't we do this?
It's we're going to do this.
When is the right time to dothis?
Jim Jockle (25:48):
And, for lack of a better term, I think a lot of
institutions are having thatcome to Jesus moment.
If you will, you know whatwould you say is from your
perspective, what is theplaybook for for scaling ai in
in 2025?
I mean what, what?
Where should someone start andwhat's something people should
(26:08):
stop doing at this point?
Guest (26:10):
so I think there's we have two phases for this.
The first phase is your, your,your own, uh, personal use of ai
and I, up until about a yearago, I had a sticky on my
desktop that said if it tooklonger than five minutes, I
should have probably used AI.
So I think we have to get ridof this sorry, this residual
(26:31):
automation that we have of notusing AI and going to like
Google.
You know I'm sure some of yourlisteners still have this, but I
often get like a phone callfrom my dad or something and say
how long does the drive fromLondon to your house?
Or something like that.
I'm like Google Maps man.
Why am I still answering thesequestions?
Google, google it.
(26:52):
I'm just going to Google it foryou.
Why do I need to do this?
So I really don't want peopleto be that in the AI space of
just not having the reflex, likethe muscle memory, to say AI
first, that's where we go first.
So I think that's the personalside of things.
Then, on the business side ofthings, as we said earlier, I
(27:13):
think the real thing to do isjust look for that low hanging
fruit.
Don't look for the biggest winto begin with.
Don't look for the biggestdisruption, look for the
smallest win that's actuallygoing to make a difference.
The first one we did wasregistering a supplier.
The second one I did was aroundanyone that used my calendar
link.
We noticed that often peopleget confused with time zones.
(27:35):
So they'll book it at fouro'clock in the morning, pacific,
and then obviously they don'tshow up because they didn't mean
to book at four o'clock pacific, um and?
But for my time, four o'clockpacific is noon, so it's
actually quite a busy time forme.
So we put an agent in placethat looks at the calendar
appointment that just landed andwe'll literally just email or
(27:55):
text the person and say hey, jim, you just booked a call with
peter.
I know he's looking forward tospeaking to you.
This is sally of his AIassistants.
Just wanted to check because itlooks like it's four o'clock in
the morning, your time.
If that was deliberate, no, youdon't need to do anything.
If that wasn't deliberate andthere was maybe a time zone
confusion.
This is the link to reschedule.
We've managed to eliminatealmost all of those no-shows
(28:20):
from that, which were probablytwo a week, so 104 a year, where
I'm sitting for 30 minuteswaiting, or 15 minutes, if I'm
being honest, waiting to see ifthe person's going to show up.
So that saved me 25 hours ayear and all of these small wins
this 25 hours and the $5,000for registering a supplier soon
(28:42):
start adding up because I'mdriving margin.
We have a phrase at this end,which is revenue is vanity and
profit is sanity.
We can all drive big revenuenumbers and they're fantastic.
I love those.
But the numbers I'm reallyinterested in, the numbers that
profit, they're the numbers thatend up and take home for
(29:02):
founders and take home forpartners and C-suites.
So always driving margin meansfinding those tiny little
efficiencies that startcompounding real quick.
Jim Jockle (29:15):
So sadly, we've made it to the last question of the
podcast.
We call it the trend drop.
It's like a desert islandquestion, and if you could only
watch or track one trend in AI,what would it be?
It would be the percentage ofagents deployed versus the
(29:36):
percentage of humans displaced.
Wow Well, peter, fascinatingconversation and, I would say,
probably the most quotes andthought-provoking statements of
any of our podcasts to date.
I want to thank you so much foryour time.
This was really fantastic.
Guest (29:52):
Thank you, I really appreciate it, and thanks for
having me Please.
Jim Jockle (29:55):
Anytime.
Thanks so much for listening totoday's episode and if you're
enjoying Trading Tomorrow,navigating trends and capital
markets, be sure to like,subscribe and share, and we'll
see you next time.
Transcription by CastingWords.
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