October 15, 2025 • 11 mins
Most enterprise knowledge lives in chaos—PDFs, email threads, scans, recordings—and for years that chaos resisted automation. We sit down with Bernard Aceituno, Co-Founder of Stack AI, to unpack how generative models and agentic systems finally tackle the messy 90% of data and convert it into measurable actions.
Bernard draws a sharp line between keyword search and real understanding, showing how reasoning traces and tool use enable AI to read, decide, and act—sending the email, filing the claim, extracting the clause, and updating the record.
We go deep on what “agentic” really means in plain language: planning steps, choosing tools, and executing with auditable logs. From banks and hospitals to defense and civil engineering, Bernard maps where AI agents are already reducing handle time and error rates across collections, payments, compliance, and business development. He also outlines near-term constraints—accuracy under pressure and context window limits—and how retrieval, external memory, and better infrastructure are closing the gap.
The conversation demystifies AGI as a reliability benchmark for specific tasks and frames superintelligence as both a risk and an opportunity frontier, especially in drug discovery and complex research.
If you’re wrestling with legacy systems, data silos, or unclear starting points, this episode offers a pragmatic roadmap: target high-friction workflows, layer agents with guardrails, measure outcomes, and expand. Ready to turn unstructured data into operational advantage and move beyond demos to dependable ROI? Follow the show, share with a teammate who owns the process you want to fix, and leave a review with the one task you’d hand to an agent first.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_01 (00:10):
Ladies and gentlemen, we have Bernard
Acetuno.
Hope I'm pronouncing thatcorrectly.
Co-founder of Stack AI.
No pressure.
Last one on the document.
Thank you so much.
No pressure.
SPEAKER_00 (00:24):
There's never any pressure, Bernard.
Thank you, George.
I appreciate it.
Nice, nice to see you in person,finally.
Finally, right?
By all means.
Beautiful.
SPEAKER_01 (00:32):
So here we are, Bernard on the sidelines of Unga
and um at the NASDAQ today.
Yeah.
And uh we've talked a lot aboutthe issue that you are
apparently working on, which isAI.
Very much, very much.
And I'd like to ask you, how isStack AI transforming?
Well, let me go back.
How is AI transformingenterprise operations?
(00:56):
And tell us, because a lot ofpeople are curious, how can AI
tools be leveraged to solvecomplex global problems while at
the same time, here's nopressure here, ensuring ethical
and responsible implementation?
SPEAKER_00 (01:10):
Absolutely, absolutely.
So just in your risk, we havethe same thing.
It's a whoop.
The whoop.
It's a device that we're doing.
SPEAKER_01 (01:16):
Rory McElroy.
SPEAKER_00 (01:17):
Yeah, I know we are.
Absolutely, yeah.
So we live a healthy life.
So the Whoop is consistentlycollecting data from our hands
and from our vitals, giving usnumbers and statistics on, you
know, our different signals, youknow, heart rate, sleeping,
sleeping quality, whatnot.
And the kind of data that wework with, this kind of device
in general, is what people callstructured data, historical,
(01:39):
time series data, informationthat we always have collected
over time.
Historically, the computers andoverall compute have managed to
successfully interact and makeanalysis and insights from data
that is well structured, wellcleaned, well organized.
However, that's only about 10%of the data that exists in the
(02:00):
digital world.
90% of the data that exists inthe world is what we call
unstructured data.
Scans of documents, Excelsheets, Word documents, you
know, notes of documents, youknow, audio recordings, videos,
all sorts of, as we call it inour company, garbage that
companies have.
But unfortunately, most of theknowledge in the world exists
(02:20):
there.
Since the past decade, since2010, we had a major revolution
where AI researchers were ableto create systems that could
analyze and operate on this kindof data, on structured data.
Eventually came to the point atthe beginning of this decade
where we could have systems likeGPT analyzed that could generate
text, could generate video,could generate images, which
would be called generative AI.
(02:41):
Generative AI overall can nowunlock a series of new
operations where now automationcan reach that 90% of the data
in the world.
Now we can work with documentsand we can find information in
them.
We can search for the thingsthat we care about.
We could collect payments, wecan have conversations, we can
generate poems, etc.
We can take AI outside of thenew with the world of numbers
(03:04):
and tables and put it into thereal world.
Thanks to that.
SPEAKER_01 (03:07):
Tell me just a practical question.
How is that different from whatwe used to do with Google?
Like we used to search, youknow, I'm searching for a
restaurant in New York.
SPEAKER_00 (03:14):
Why is that different than great question?
Because you know what happenedwith Google is that what Google
would do was that they will takeall the information in the
internet, they will map it intoa way that is structured, into
what we call a graph, and thenrun a search over it.
It wouldn't be AI in the sensethat it's not like understanding
what's under the information.
It doesn't really extract thingsfrom what's on the website.
(03:34):
It's just structuring thewebsite into a way that is like
searchable, and then it's tryingto like traverse the web.
And that's why many times youget things that, okay, they have
the word you're searching for,but they don't really answer the
question.
Right.
Because it doesn't reallyunderstand what you're looking
for.
Generative AI, on the contrary,does understand what you're
looking for.
SPEAKER_01 (03:52):
It does.
SPEAKER_00 (03:52):
It does.
SPEAKER_01 (03:54):
And what about agentic?
We've heard about agentic.
Yeah.
So describe that in plainEnglish.
SPEAKER_00 (03:58):
Very good, very good.
So, you know, it's a greatquestion.
Imagine you have someone that isa copywriter.
Basically you tell you whatsomething to write and it writes
it.
It can just write stuff.
You tell it, okay, now write mea recipe and you know make me a
via sandwich.
It will go and write the recipefor a sandwich, but it will make
you the sandwich, you can't doanything.
If you give it the tool now tolike go to a kitchen and cook,
(04:21):
you know, take a pan, turn onthe stove, you know, take bread,
toast it, put cheese, you know,whatever, then the system is not
just a copywriter, it canactually do stuff.
SPEAKER_01 (04:32):
Create new new ideas.
SPEAKER_00 (04:34):
Yeah, it can take actions over the world.
More simply, if I go to thethinking.
It is the definition of thinkingis complicated.
When you say thinking, whatyou're asking is, is there a
logical reasoning behind everystep of what it's doing?
Over the past year, it has beena big breakthrough in the eyes.
They were able to embed alogical reasoning change for
every decision of a languagemodel.
That's where Jopen AI GPT 5 nowcomes to the market, Cloud Opus
(04:56):
4.
They're able to like backtracethe logic they're following
before they give an answer.
That's the closest we can embedto thinking to a system like
this.
We humans do things that aremuch more complicated in our
brain.
But the point where we canunderstand why they make a
decision and how they get to aconclusion.
As they decide to, like, youknow, when you tell them, send
me an email, they decide to openGmail, write something, put a
(05:17):
subject, and then send thatemail, you can see the trace of
actions they took.
And that's why now agents orsystems that can not only
reason, but interact with itsexternal systems now exist.
What we do in Stack AI is thatwe have a software that helps
enterprises build custom AIagents to automate internal back
office operations.
We're with banks, hospitals,large defense contractors, large
(05:38):
government contractors and civilengineering as well, in order to
automate key manual workflowsthat we have in collections,
payments, business development,compliance, and the like.
SPEAKER_01 (05:48):
So where do you see this going?
You're sitting in the in a seatthat's really unique because you
know, as I like to tell people,and I and I so many of you in
the room have educated me on AI,that it's not coming, that it's
already here.
How far are we from thatsentient thinking, from the
(06:08):
ability of AI to go beyondagentic?
What's the next level afteragentic?
SPEAKER_00 (06:13):
So what agent is more of a framework of AI,
right?
And that what people want to gobeyond agent is not really
towards actually adding morecapabilities.
AI today is as complete as itgets to achieve arbitrary tasks.
The real next level of success,uh the next big leap, the way we
get to what people callartificial general intelligence.
Meaning AGI, J artificialintelligence, AGI, which means a
(06:36):
system that is able to, given aset of tools, perform a task at
the same reliability that ahuman would.
Given, let's say I take I takean AI like Chai UBT, and I give
it the ability to write emails,I give it the ability to collect
payments, I give it the idea towrite content, I give it the
ability to like, you know,respond to emails, I could tell
her, become my content marketer,research the web every week,
(06:56):
give me a nice post that iscreative, it's different we see,
and then send an email with itevery Monday.
If it could do that at thereliability that we have for a
human, then we will have what wecall AGI.
And this is the example I woulddo for marketing, but this will
apply for anything.
I could have an AI that is afinancial analyst, I tell it,
write me a model that forecaststhe performance of the Google
stock, giving it to assumptions,and it could research the
information, find what it needsto do.
(07:16):
So, the same level of ananalyst, then we will have what
we call AGI.
That's one level at the level ofperformance of a human.
The next unlock, and this iswhat people are actually
concerned about, AGI, we're,people, some people say we're
quite close.
I think that we are missing manysteps together, but we're, it's
tangible how to get there.
The part that is reallyconcerning for many, it's SSI,
(07:37):
ASI, artificialsuperintelligence, meaning
intelligence that transcends theability of a human per of a
human.
Meaning if I ask a system, goand you know, figure out
everything you can in order tobreak it to this computer, it
could, at a scale beyond what100 humans could do, you know,
iterate with many algorithms,tries, you know, different
different systems, create emailscans on, et cetera, in order to
(07:58):
do so, to a level that is beyondthe ability of an individual
human.
And that's where most concernsare.
Nevertheless, it was also wheremost opportunities.
You could tell AI, okay, now runa bunch of experiments over
these molecules in order to likecure this disease.
And it could do it at the scalebeyond what you know a major
university and state life woulddo so, all just by itself.
And that's quite promising formany fields, in medicine and in
(08:19):
sciences especially.
SPEAKER_01 (08:20):
Isn't that already somewhat happening?
SPEAKER_00 (08:22):
It's happening to a small degree, but the problem is
that AI language models, whichare probably what most people
are use as AI today, they haveessentially two limitations.
First of all, the degree ofaccuracy at which they can
reason, meaning like at what ifwe all play with ChatGPT, we all
have seen that at some point.
After trying enough questions,we can always get him.
We can always like get it tolike you know make a mistake or
(08:42):
like skip something, you know,and correct it.
So a degree of accuracy it has.
The second thing that they canwork with is what we call
context window, meaning how muchtext they can and how much they
can memorize of a conversation.
Language models, by beingsystems that live inside of a
GPU, they can only handle somuch information.
And once they cross thatinformation, it just stops
working.
Maybe you have seen that youknow if you give ChatGPT a
(09:03):
document that is way too large,like multiple thousands of
pages, it just doesn't work.
Tells you this is too much text,I can work with this.
So in order for a language modelto get to the proper
superintelligence level, it mustbe, first of all, very accurate,
and second of all, be able towork with like a very vast
amount of data, way beyond whatwe can do with existing GPUs and
existing compute.
That's why projects like StarGais so important, because it
(09:24):
allows to take this technologyto the next level beyond the
physical barriers we have today.
SPEAKER_01 (09:28):
And so, what what do you see uh the world looking
like?
Um, I won't go to 2030 becausethat's too far, just in a year
or two.
How different are things gonnabe?
SPEAKER_00 (09:38):
So, of for sure the underlying technology will
continue improving at the sameexponential rate.
I have no doubt about it.
You know, it's uh the peopleworking on it haven't even
advertised on it most of thethings they have already
developed.
So the technology will keepimproving and we'll become more
accessible and of course moresecure and more affordable.
But more than the technologybecoming more available, the
biggest the biggest leap I willtherefore see is organizations
(10:03):
and individuals understandingwhere to apply the technology.
I think that over the past threeyears there's been this kind of
like a promise of AI that hasbeen very exciting, and those
that have managed to leveragecorrectly have seen tremendous
gains.
Very few organizations havemanaged to do that.
But if you manage to achievetremendous gains with very
little payroll, or the contrary,like you know, take an
(10:24):
exponential organization towhere they are today.
I think as the years comethrough and organizations find
the specific places within anindustry where with an
organization where AI can bevery impactful, this will be
very transformational.
The problem with Gen AI is thatit's a very transformational
software.
In order to like properly useAI, a company must be properly
using cloud, first of all, whichis already a hard conversation
(10:45):
for many banks.
You know, oh, you're usingyou're not using cloud, okay.
You must be using softwares thathave open APIs, you must be
using softwares that are thathave you know systems that you
can interpret code from, MCCservice, why not?
And that's challenging for manyorganizations.
So there will be blockers to getin there that transcend the
abilities of technology.
They just rely on theinfrastructure we have today.
But towards getting to a pointwhere those first few wins that
(11:08):
really help the first batch ofAI users and creators, they'll
you know, we will see a time, atime in the world where most
organizations and people will beable to benefit from those.
SPEAKER_01 (11:18):
Incredible.
What's your final call to actionbased on what you're seeing in
AI?
SPEAKER_00 (11:22):
Yeah, if you're working for an organization that
is always asking, oh, what do Ido with AI, why is AI good for?
Go to Stack.ai and click on bookat demo and you'll you we'll
we'll help you figure it out.
We'll help companies.
Yeah, for sure.
We'll help companies likeNubank, BAE systems, LifeMD, YC
retirement all over the globe touse this.
Well, are you are you bullish onthe future?
You think it's gonna bepositive?
(11:43):
Absolutely.
I think I think we are underhyping the future.
Wow.
The best is yet to come.
Ladies and gentlemen, we have Bernard
Acetuno.
Hope I'm pronouncing thatcorrectly.
Co-founder of Stack AI.
No pressure.
Last one on the document.
Thank you so much.
No pressure.
SPEAKER_00 (00:24):
There's never any pressure, Bernard.
Thank you, George.
I appreciate it.
Nice, nice to see you in person,finally.
Finally, right?
By all means.
Beautiful.
SPEAKER_01 (00:32):
So here we are, Bernard on the sidelines of Unga
and um at the NASDAQ today.
Yeah.
And uh we've talked a lot aboutthe issue that you are
apparently working on, which isAI.
Very much, very much.
And I'd like to ask you, how isStack AI transforming?
Well, let me go back.
How is AI transformingenterprise operations?
(00:56):
And tell us, because a lot ofpeople are curious, how can AI
tools be leveraged to solvecomplex global problems while at
the same time, here's nopressure here, ensuring ethical
and responsible implementation?
SPEAKER_00 (01:10):
Absolutely, absolutely.
So just in your risk, we havethe same thing.
It's a whoop.
The whoop.
It's a device that we're doing.
SPEAKER_01 (01:16):
Rory McElroy.
SPEAKER_00 (01:17):
Yeah, I know we are.
Absolutely, yeah.
So we live a healthy life.
So the Whoop is consistentlycollecting data from our hands
and from our vitals, giving usnumbers and statistics on, you
know, our different signals, youknow, heart rate, sleeping,
sleeping quality, whatnot.
And the kind of data that wework with, this kind of device
in general, is what people callstructured data, historical,
(01:39):
time series data, informationthat we always have collected
over time.
Historically, the computers andoverall compute have managed to
successfully interact and makeanalysis and insights from data
that is well structured, wellcleaned, well organized.
However, that's only about 10%of the data that exists in the
(02:00):
digital world.
90% of the data that exists inthe world is what we call
unstructured data.
Scans of documents, Excelsheets, Word documents, you
know, notes of documents, youknow, audio recordings, videos,
all sorts of, as we call it inour company, garbage that
companies have.
But unfortunately, most of theknowledge in the world exists
(02:20):
there.
Since the past decade, since2010, we had a major revolution
where AI researchers were ableto create systems that could
analyze and operate on this kindof data, on structured data.
Eventually came to the point atthe beginning of this decade
where we could have systems likeGPT analyzed that could generate
text, could generate video,could generate images, which
would be called generative AI.
(02:41):
Generative AI overall can nowunlock a series of new
operations where now automationcan reach that 90% of the data
in the world.
Now we can work with documentsand we can find information in
them.
We can search for the thingsthat we care about.
We could collect payments, wecan have conversations, we can
generate poems, etc.
We can take AI outside of thenew with the world of numbers
(03:04):
and tables and put it into thereal world.
Thanks to that.
SPEAKER_01 (03:07):
Tell me just a practical question.
How is that different from whatwe used to do with Google?
Like we used to search, youknow, I'm searching for a
restaurant in New York.
SPEAKER_00 (03:14):
Why is that different than great question?
Because you know what happenedwith Google is that what Google
would do was that they will takeall the information in the
internet, they will map it intoa way that is structured, into
what we call a graph, and thenrun a search over it.
It wouldn't be AI in the sensethat it's not like understanding
what's under the information.
It doesn't really extract thingsfrom what's on the website.
(03:34):
It's just structuring thewebsite into a way that is like
searchable, and then it's tryingto like traverse the web.
And that's why many times youget things that, okay, they have
the word you're searching for,but they don't really answer the
question.
Right.
Because it doesn't reallyunderstand what you're looking
for.
Generative AI, on the contrary,does understand what you're
looking for.
SPEAKER_01 (03:52):
It does.
SPEAKER_00 (03:52):
It does.
SPEAKER_01 (03:54):
And what about agentic?
We've heard about agentic.
Yeah.
So describe that in plainEnglish.
SPEAKER_00 (03:58):
Very good, very good.
So, you know, it's a greatquestion.
Imagine you have someone that isa copywriter.
Basically you tell you whatsomething to write and it writes
it.
It can just write stuff.
You tell it, okay, now write mea recipe and you know make me a
via sandwich.
It will go and write the recipefor a sandwich, but it will make
you the sandwich, you can't doanything.
If you give it the tool now tolike go to a kitchen and cook,
(04:21):
you know, take a pan, turn onthe stove, you know, take bread,
toast it, put cheese, you know,whatever, then the system is not
just a copywriter, it canactually do stuff.
SPEAKER_01 (04:32):
Create new new ideas.
SPEAKER_00 (04:34):
Yeah, it can take actions over the world.
More simply, if I go to thethinking.
It is the definition of thinkingis complicated.
When you say thinking, whatyou're asking is, is there a
logical reasoning behind everystep of what it's doing?
Over the past year, it has beena big breakthrough in the eyes.
They were able to embed alogical reasoning change for
every decision of a languagemodel.
That's where Jopen AI GPT 5 nowcomes to the market, Cloud Opus
(04:56):
4.
They're able to like backtracethe logic they're following
before they give an answer.
That's the closest we can embedto thinking to a system like
this.
We humans do things that aremuch more complicated in our
brain.
But the point where we canunderstand why they make a
decision and how they get to aconclusion.
As they decide to, like, youknow, when you tell them, send
me an email, they decide to openGmail, write something, put a
(05:17):
subject, and then send thatemail, you can see the trace of
actions they took.
And that's why now agents orsystems that can not only
reason, but interact with itsexternal systems now exist.
What we do in Stack AI is thatwe have a software that helps
enterprises build custom AIagents to automate internal back
office operations.
We're with banks, hospitals,large defense contractors, large
(05:38):
government contractors and civilengineering as well, in order to
automate key manual workflowsthat we have in collections,
payments, business development,compliance, and the like.
SPEAKER_01 (05:48):
So where do you see this going?
You're sitting in the in a seatthat's really unique because you
know, as I like to tell people,and I and I so many of you in
the room have educated me on AI,that it's not coming, that it's
already here.
How far are we from thatsentient thinking, from the
(06:08):
ability of AI to go beyondagentic?
What's the next level afteragentic?
SPEAKER_00 (06:13):
So what agent is more of a framework of AI,
right?
And that what people want to gobeyond agent is not really
towards actually adding morecapabilities.
AI today is as complete as itgets to achieve arbitrary tasks.
The real next level of success,uh the next big leap, the way we
get to what people callartificial general intelligence.
Meaning AGI, J artificialintelligence, AGI, which means a
(06:36):
system that is able to, given aset of tools, perform a task at
the same reliability that ahuman would.
Given, let's say I take I takean AI like Chai UBT, and I give
it the ability to write emails,I give it the ability to collect
payments, I give it the idea towrite content, I give it the
ability to like, you know,respond to emails, I could tell
her, become my content marketer,research the web every week,
(06:56):
give me a nice post that iscreative, it's different we see,
and then send an email with itevery Monday.
If it could do that at thereliability that we have for a
human, then we will have what wecall AGI.
And this is the example I woulddo for marketing, but this will
apply for anything.
I could have an AI that is afinancial analyst, I tell it,
write me a model that forecaststhe performance of the Google
stock, giving it to assumptions,and it could research the
information, find what it needsto do.
(07:16):
So, the same level of ananalyst, then we will have what
we call AGI.
That's one level at the level ofperformance of a human.
The next unlock, and this iswhat people are actually
concerned about, AGI, we're,people, some people say we're
quite close.
I think that we are missing manysteps together, but we're, it's
tangible how to get there.
The part that is reallyconcerning for many, it's SSI,
(07:37):
ASI, artificialsuperintelligence, meaning
intelligence that transcends theability of a human per of a
human.
Meaning if I ask a system, goand you know, figure out
everything you can in order tobreak it to this computer, it
could, at a scale beyond what100 humans could do, you know,
iterate with many algorithms,tries, you know, different
different systems, create emailscans on, et cetera, in order to
(07:58):
do so, to a level that is beyondthe ability of an individual
human.
And that's where most concernsare.
Nevertheless, it was also wheremost opportunities.
You could tell AI, okay, now runa bunch of experiments over
these molecules in order to likecure this disease.
And it could do it at the scalebeyond what you know a major
university and state life woulddo so, all just by itself.
And that's quite promising formany fields, in medicine and in
(08:19):
sciences especially.
SPEAKER_01 (08:20):
Isn't that already somewhat happening?
SPEAKER_00 (08:22):
It's happening to a small degree, but the problem is
that AI language models, whichare probably what most people
are use as AI today, they haveessentially two limitations.
First of all, the degree ofaccuracy at which they can
reason, meaning like at what ifwe all play with ChatGPT, we all
have seen that at some point.
After trying enough questions,we can always get him.
We can always like get it tolike you know make a mistake or
(08:42):
like skip something, you know,and correct it.
So a degree of accuracy it has.
The second thing that they canwork with is what we call
context window, meaning how muchtext they can and how much they
can memorize of a conversation.
Language models, by beingsystems that live inside of a
GPU, they can only handle somuch information.
And once they cross thatinformation, it just stops
working.
Maybe you have seen that youknow if you give ChatGPT a
(09:03):
document that is way too large,like multiple thousands of
pages, it just doesn't work.
Tells you this is too much text,I can work with this.
So in order for a language modelto get to the proper
superintelligence level, it mustbe, first of all, very accurate,
and second of all, be able towork with like a very vast
amount of data, way beyond whatwe can do with existing GPUs and
existing compute.
That's why projects like StarGais so important, because it
(09:24):
allows to take this technologyto the next level beyond the
physical barriers we have today.
SPEAKER_01 (09:28):
And so, what what do you see uh the world looking
like?
Um, I won't go to 2030 becausethat's too far, just in a year
or two.
How different are things gonnabe?
SPEAKER_00 (09:38):
So, of for sure the underlying technology will
continue improving at the sameexponential rate.
I have no doubt about it.
You know, it's uh the peopleworking on it haven't even
advertised on it most of thethings they have already
developed.
So the technology will keepimproving and we'll become more
accessible and of course moresecure and more affordable.
But more than the technologybecoming more available, the
biggest the biggest leap I willtherefore see is organizations
(10:03):
and individuals understandingwhere to apply the technology.
I think that over the past threeyears there's been this kind of
like a promise of AI that hasbeen very exciting, and those
that have managed to leveragecorrectly have seen tremendous
gains.
Very few organizations havemanaged to do that.
But if you manage to achievetremendous gains with very
little payroll, or the contrary,like you know, take an
(10:24):
exponential organization towhere they are today.
I think as the years comethrough and organizations find
the specific places within anindustry where with an
organization where AI can bevery impactful, this will be
very transformational.
The problem with Gen AI is thatit's a very transformational
software.
In order to like properly useAI, a company must be properly
using cloud, first of all, whichis already a hard conversation
(10:45):
for many banks.
You know, oh, you're usingyou're not using cloud, okay.
You must be using softwares thathave open APIs, you must be
using softwares that are thathave you know systems that you
can interpret code from, MCCservice, why not?
And that's challenging for manyorganizations.
So there will be blockers to getin there that transcend the
abilities of technology.
They just rely on theinfrastructure we have today.
But towards getting to a pointwhere those first few wins that
(11:08):
really help the first batch ofAI users and creators, they'll
you know, we will see a time, atime in the world where most
organizations and people will beable to benefit from those.
SPEAKER_01 (11:18):
Incredible.
What's your final call to actionbased on what you're seeing in
AI?
SPEAKER_00 (11:22):
Yeah, if you're working for an organization that
is always asking, oh, what do Ido with AI, why is AI good for?
Go to Stack.ai and click on bookat demo and you'll you we'll
we'll help you figure it out.
We'll help companies.
Yeah, for sure.
We'll help companies likeNubank, BAE systems, LifeMD, YC
retirement all over the globe touse this.
Well, are you are you bullish onthe future?
You think it's gonna bepositive?
(11:43):
Absolutely.
I think I think we are underhyping the future.
Wow.
The best is yet to come.
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