Hello, this is Eva and you're listening to the In Between Tech
and Trust podcast. It's the first.
Episode of 2026 and it's going to be a quite interesting one
because we're diving into relationships and the kind of
relationships that we can form with AI companions in
particular. And I had an expert on that
regard with me in conversation and that's Leo Oren.
And Leo is the Chief Technology Officer at Replica.
And some of you might have heard of Replica before because it's
one of the largest AI companion companies with a community of
over 35 million people that interact with their platform.
And what made our conversation? So special is that we.
Had an intersection and look into the way of how we form
trust in conversations from a human and then again from an AI
perspective, but also from a tech perspective in general,
which means that we talked a lot about also so the way that
infrastructure needs to be formed and built.
So that it also is. Flexible enough with user
requirements and guiding along the experience that we have and
also the design principles that one must follow when you really
want to put together a platform that intersects with all three
of them. So the conversation really
covered many. Aspects of.
The intersection of tech and trust and also made us reflect
of what actually is valuable and having human relationships
overall. Hello Lior.
And welcome to the in. Between Tech and Trust Podcast.
Hi Eva, thank you for having me and special thanks to Grisha for
Miro for introducing us. Shout out to you Grisha.
It's so great for the connection and I'm happy to now talking to
you Lior I I do want to start with you personally also with
your background and like engineering and technology part
and dive right into the topic of trust.
What does it mean for you personally?
Also when technology becomes a companion rather than a tool?
So trust is, is is. Everything we have in
relationships, right? I I have a trust in my
relationship with humans. I have trust in my relationship
with myself. I trust myself.
I trust with. The police officer around the
corner. And and with everything then
comes kind our our tools. I have a trust in my hammer.
To do its. Job and I have trust in in
technology now. Sometimes with technology I can
have more. Trust because I have much.
More control than humans even than myself and and when we come
to to companion to AI companion. It it blurs the borders a bit,
but there's still. Very strong ability to enforce.
The trust I have. I can have much more
transparency, I can have much more controls, and AI Companion
is actually designed to. Serve our well.
Being right, it's a tool that is designed to serve us and to make
us feel better. So in in this term, I think that
while AI companion is kind of blurring the the borders between
humans and and and machines, it's actually a more trustworthy
machine than what we used to. And if we now talk about
blurried lines and that replica particularly oversee like the
systems that interact with people's emotions and like their
identity and also the private thoughts, how do you see the
boundaries between human and humans, but then again, also
compared to human and machines? And where does trust lived in?
Like the in between of it all? It's again.
Blurry as as as a. Person who grew into the social
media revolution and also worked for a while at at my time in the
integrity teams. That's supposed to protect.
The community the line is already.
As a blurred so I have a. Relationship with the machine,
with the platform, but my relationship is actually with
humans on the other side of the platform.
So I have expectations. And this leads a bit to a
discussion about regulations and, and, and our whole
discussion about what we expect from the platforms.
We expect certain level of trust and actionables, but eventually
there is a human from the other side.
So I'm, I'm trusting a machine which is easier to trust
sometimes, which is more capable because it's, it's, it's
involved heuristics. But eventually there are humans
on the other side. So this is already very blurry.
Border now sometimes when it comes to to an AI, to a
companion, I can have better relationship with with my
companion than the human on the other side.
You know this bully on the other side?
Of of Twitter so these borders. Are moving and I'm not.
Sure that there is a. Concrete border, it's really
different who's on the other side?
Which service on the other side? And and of course me and how I
understand the technology, my borders, how to set borders in
front of other people or other machines and so.
Yeah, these borders are very. Vague to my opinion.
And when it now comes into the design of them and also
particularly with your background and like building up
large scale conversational AI platforms and intriguingly also
emphasizing the reliability, scalability, also the measurable
performance of it. If you now look into the tech
architecture and like from a point of view of that, what are
key technical design decisions that underpin trustworthy or
reliable conversational AI experiences?
So the there are few aspects for it, but don't forget that in the
in the in the place of of companion AI, one of the main
KPIs or metrics. Is user, is, is, is trust
because your. Dissatisfaction is how user
feels comfortable with the conversation because just won't
come back if they won't feel comfortable with the
conversation. When you have AI bolts or
assistants, how they? Call they just help you buy a
car or help you. Take a service ticket.
This is very clear success. But in our case success is a
well done conversation is is a conversation I want to continue.
It is sickness. I will come back and that by the
way, very hard to measure in a companion wall.
It's very hard to measure what is a good conversation, but.
Trust. Is, is one of the things that
keeps user in and coming back. So it's built in a KPI, built in
metric in what we're doing daily.
And I think that like any other software, and I've been for a
while in the industry, you have things you need always to have
on the road map like security and Dev X and reliability and,
and cost efficiency and with the AI with, with conversational AI.
Trustworthy guardrails. How they're called today to put.
Things so that the the conversation won't go off or you
can detect when it's going off is just another new thing we
need to to clear our road map for and to put inside exactly
like security and and and data protection so.
This is a it's it's, it's a part of our daily work and it's a
part. Of the road map and I think
every team, every engineer, every engineering manager, every
product manager need to know what are the tools in the
market. What the big?
Players are doing to protect, to make the conversation more
trustworthy. It's a very young place, the
whole AI. So.
We are a bit more reactive than proactive tools are there.
They're not yet perfect, but you need to always try the newest
tools and to know what what are the standards on top of that.
I think that's me. It's the last thing I'm going to
say about it, but that's maybe the most important thing is.
Observability if observability used to.
Be something help our trust in the machine help us as engineers
to trust our systems here observability in this unknown
world of of AI that things are still new in this statistical
machines are are still a black box and unknown.
The ability to know that things went off and to immediately.
Be able to see what? Is going on and most of the
times why and then to take action is super important.
So I wouldn't build any platform without full observability from
day one. Just because these these are.
Not heuristics you you cannot predict, but things it will
happen and you need to. Have the ability to.
See what happened and to try to. Figure out why.
And could you maybe make it a bit tangible in a way that you
shared one to two examples of how exactly a design principle
could look like for like a platform or a companion to
provide a good conversation? Well.
There are many, many, many aspects.
Right, because good. Conversation means it should be
fast. It should be intuitive.
So the whole. Normal system and UX decisions.
There is a. Lot.
A lot. That is being invested, of
course, in the prompt, right? You you you prompt the normal
assistant. To to just.
Solve the user's problems and you and a companion needs to
more take care of the user, be more empathetic as the user
about them. So it's it's.
The different approach on the prompt, but again the ability
to. Observe to see what is.
Going on to see when things went off, the ability to measure our
core principles. For.
A conversation of the, I think and that's my approach in last
three platforms that I've been involved in.
Because this is. Evolving all the time and keep
changing. And you know, I had talking cold
talks few weeks ago in Humboldt and I and I was talking about
how when I submitted. The talk that was 6.
Months ago, until when the conference started everything
changing our world so whatever I submitted was completely
irrelevant in six months. So everything we build now needs
to be. Very.
Flexible for changes and that's also very, very important.
So we cannot put guardrails and how good conversation looks like
now when tomorrow there will be another model.
Out there which will be. Better Now if I will add to
that, think about in conversation, in companion.
Tone of voice is something that one model has.
You cannot upgrade the model or replace the model.
The user will get a different tone of voice, a different
companion. This is something you wouldn't
expect from your friends or your relatives.
You cannot expect it from your companion, so the system must be
so flexible to changes that you need to figure out on the.
Way what you'll do? OK, what do I do with all these
users we used? To talk with this model, I now
found there is a new model, cheaper, better, more
conversational, but I cannot replace it for them because they
will get a new companion. What do I do?
So you need to build a platform that will be very flexible for
changes because they keep happening and very, very fast.
And. The fastness of it all and let's
call it velocity of also the innovation part is a particular
aspect of like your role at Replica as well.
And the company's story also shows both like the potential
and like the real ethical regulatory challenges that you
might have when interfering or like looking into emotional
attachment style with data privacy aspects or also
particularly in Europe, the regulatory aspects of it.
Can you share some examples or insights of how you and your
team balance innovation speed with like the Safeguard rails
also when designing for user emotional engagement with AI?
Yes. So as said earlier in our.
Case we're a bit lucky. Because safety and all the.
Stuff are part of our. KPI, our product won't be
successful if those things won't be embedded if, if and one of
the things that helps a lot, our engineers are engaged very much
with what's going on with the users.
I think I I've been advocating for years that engineering
needs. To see the closest to.
Customer support. Because usually they are so.
Far away from the users, they don't hear the stories, they
don't know what's going on. And in our case, our engineers
are so on Slack, they are pasting stories from Reddit.
We have Reddit community very, very active.
They're pasting story from Reddit.
The good. The bad ones, they're very much
connected to what's going on with the users and how they're
using the product. So that will be the.
First thing, it is embedded in our culture, the good and the
bad, how emotionally people attached to their product.
And when it's embedded in the culture, it's embedded in the
code eventually and it will be embedded in the road map and in
the demand. So that's first thing it really
needs to be a part of the culture.
And you know, when people are emotionally connected to a
platform, crazy things are happening.
For example, when we have a downtime and we had because.
It's software, people took it really bad.
And it's on one hand it's scary, on the other hand, it's, it's
amazing to see how the system influencing.
People how this product is. Really, really helping people
and how connected. They are to it.
So it's a part of the culture, it's a part of the.
KPI and we, we keep, we are listening.
Of course, I said earlier. You need to put place on your
Rd. not especially at scale. Maybe when you're startup you
doing MVP POC it's you don't need to care about this thing so
much your. User is only the VC.
That needs. To to to support.
But at scale, you must put a place on the road map.
For security for all these. Things and and on top
regulations I remember how old the.
The the years of the. Cookie banner and the GDPR
companies needed to invest so much engineering firepower and
road map. Space just for this cookie.
Banner and and maintaining it, and sometimes it looked
ridiculous and sometimes. It feels like regulators.
Don't really understand how engineering works when they
implement these things, but the intentions are good and
eventually we want to protect the users and we want a better
Internet. So you must clear and invest in
that. I'll give again an example from
Meta. Actually that Meta has huge
integrity teams and. It just to protect users.
Right. And and part of it is because
regulators and also the media are kind of demanding that, but
eventually a company is investing a lot in that.
Now this is not necessarily killing innovation.
It's just an extra investment that could have been invested in
innovation, you could say, but it's it's not when it's embedded
in the culture, when a company decide to invest, it's not
necessarily kill innovation. And by the way, I've seen
amazing innovation in this area. Exactly.
And for us investing in innovation, in safety, again, it
pays because eventually a good conversation, safe conversation
is something that will make user come back to us.
Earlier you just said one sentence that I loved and I want
to pick up on that. You said if it's embedded in the
culture, it ideally and eventually is embedded in the
code. Is that so and why?
On purpose, go to another example which is easier.
For example, when you have engineers who care so much about
security, when you have engineers who care so much about
testing, you know some companies.
Say test coverage. Must must be 80% and stuff like
that. It's a.
Part of the culture. Right, we are doing only test
driven development part of our culture.
It is eventually embedded in the code it is built in.
You can say oh develop this feature and.
After that, write the tests. Or you can have just a.
Culture that. Writing a test is a part of
implementing the feature and it's not even negotiable.
It's it's not a separate numbers on the ticket on JIRA.
And that's exactly it. It engineers while they're
building their thinking about it, while we are writing the
prompt, we are thinking about it.
It's not a separate boring task when it's a part of the culture,
when people care, when people see what they wrote make people
having very negative emotional effect when they know it, when
they see it, they will think about it next time.
So. It's it's really a part of the
culture and a. Part of the engineer's way of
thinking. And if we look into the way that
AI companions are engineered and knowing that they are or
eventually become part of like the everyday life that we also
explore. And we enter new spaces where
like the culture or the ownership of how the code shapes
the companionship of AI within our lives is like required to
have a trustworthy also systems and setup.
And but in our own agency, it's also hard to adapt it in the way
that we interact with like exactly those companions.
How do you see user agency in the context of Replica and also
at the intersection of influential AI responsibility
and the adoption of it all? So we if we'll start with a.
Simple. Software.
We have ways to. Control.
Most of the time the service provider will need to build.
This with space. For it.
But if I'm talking just about engineering, we can control
everything and that's the beauty of it, we can decide.
What to do with the? Data if to store.
It what to delete? Now if you implement this.
Feature for the user. Allowing the user to delete.
Their their data to delete. Their name, the right to be
forgotten and everything regulators are really trying to
to implement and. You give the users this.
Control. Now there are more features into
that. For example, we allowed to we
can show you what we remember about your relationship.
With with the replica. Or or in other platforms that I
build the same. We show you what we.
We remember about you and we allow you not only to delete it,
but sometimes to. Edit it which is.
Feeling much more control. So you have first transparency,
then you have control. And these are the engineering
parts of it. But if you think about it,
what's so special about conversational AI?
You're in control where the conversation will go.
Okay, you can tell the AI, hey, I don't like when you mention my
ex-girlfriend or I don't like when you speak with this slang
word. So we had the discussion at the
beginning. So you need adaptable
concessional AI. So if a business person use use
the companion, we cannot use some words like lol and stuff
like that. Just an example.
You're in control where this conversation will go, and the
challenge generally today in the AI world is the memory.
If we do good job. With remembering it always,
you're in control from now on that I will never mention your
ex-girlfriend. And that's amazing.
That's another mean of control that you have that we never had
before. And you don't even notice it.
It's not something you click and say, delete my data.
You're just saying it. And the conversation shifted.
And I find this quite amazing and from the engineering
perspective, very challenging to remember that you asked me these
things. And there is another layer of
trying under the hood to adapt to what you want.
And this is kind of you're in control, but you don't even know
that you're in control. This is even more complicated,
but we're also trying to implement that.
If I may challenge that on the other side, what you describe,
you could also say that you're playing with reality.
So you could say me, let's say asking the AI to forget about my
ex-boyfriend or to adapt the style that like things that I've
previously written shouldn't be remembered even though it was
part of like the earlier life that I lived through.
Isn't that also influencing the way that I perceive or the way
that I have experienced interactions beforehand?
And wouldn't that make it artificial?
And so let's compare it to you talking to a friend.
You would expect that once you asked your best friend to stop
talking about a certain topic. That that.
Person will consider your request and and be empathetic to
to your needs and to stop talking about it.
Would that be artificial? You will say no now if you're in
a crisis, and that's the time to remind you this, although you've
asked sometimes something we do as humans or you need to
escalate it and let's say some. Things which are very severe.
Someone went through a very severe event and that you need
to escalate even. This is a very good.
Question. Maybe that's a completely new
topic that we can discuss. We are not there yet, and again,
it's matter. That they have.
Escalations and they need to escalate to law enforcement when
when stuff are going off. But in this case it's a very
extreme case. But generally it's not
artificial. It's what I'm expecting, a
person or a companion, human or machine that care about me or
about what I. If we now take that on a like
even larger level and look into the regional dependencies that
we have with technology in itself and not only on a
personal level, when we as a society and trust technology
with personal and emotional domains and also the
interactions that we have with it.
What do you say? And from your experience that
you've had, what role does digital sovereignty play?
And are users losing control of potentially also gaining a new
form of agency through it? So first of all, we are.
Always in controlling what we share.
This is true to our. Friends, this is true to any,
any system. This is true to social media and
this is also. True to to social.
Too sorry to conversational AI and we can share emotional
private stuff we can be influenced by.
The feedback that we get. From the other.
Side about what we shared. And this is always true now.
First of all, we are. In control and it's very
important that. People, of course, will first.
Know that. It starts with you.
Now, on top of that, AI, you can always stop things.
I can close the conversation, I can shut down the computer, I
can ask for help, OK, I can change direction like I
mentioned earlier, Hey, stop talking about that something
that is not always possible with human to human.
So I actually gain more control to an extent.
I know that sometimes things can be.
Harder to ask to stop. And I know that not.
All people are always. On top of things and understand
that. Hey, I have this ability.
Do I know that when I have a conversation with another human,
it could be the same difficulty, right?
So I think we're gaining more control and we decide what the
AI know or the system knows or forgets and what are we can
define the boundaries now the. Beauty of it is is we have
control as a user but we also have control.
As an employee of the company that provide a service, and
that's very important to know. There is a human in the loop,
There are several humans in the loop here that can stop
something, that can give control because we can get alerts at
certain points or user can alert us as something is going off and
we can also stop things and in really bad case even escalate
them. So I think there is much more
control, but the awareness is maybe much more limited.
And when we. And look ahead.
On the way that conversations are shaped and also the way that
AI is scaled rapidly, what do you believe are the three non
negotiable capabilities that you yourself as ACTO must bring to
today to build up like new systems, but also to ensure the
trusted users have in future interactions with companions
tools or the systems that you build up?
Reflecting upon all that you've just said and also shared, when
it comes to the sovereignty of that potentially users want to
keep as well. It's user trusting this the
product. It's also when engineers or the
company employees are trusting the system, so I believe I
mentioned that. Before that, the first.
Thing, especially in this unknown field and again, it's
developing so fast and I'm lucky enough to.
To to already build several. Of these platforms but but
always helped and what was so important when things are
developing so fast. Is to have observability, so.
I want to know that when something has happened, I can
immediately see or even get alerts immediately see what
happened and be able to to to understand and to fix it if
needed. I don't think you can in this.
Field where there are black. Boxes and unknowns and new
models all the time, and new systems without observability.
You're just blind. And so that's the first thing.
Always needs to be in the system from day one.
The whole topic. Of human in the loop is
important we touch it in the last question so.
The user is always able. To control the conversation.
To stop the conversation to. Ask for help.
And then the other human, the employees, we can do something
about it and it's certain cases as certain products also law
enforcement and or teachers, parents, etcetera, etcetera.
So this human in the loop, there are several humans that could be
in the loop and that's very important to allow that and not
just that, you know, autopilot and, and hope that it will work
and everything will be OK. And third thing, and I mentioned
that before also and I tend to build platforms in this case.
Which are very very flexible. Which means I don't know what
will happen tomorrow, I don't know which regulation policy
will come tomorrow, and regulators don't always
understand the engineering as mentioned so.
So I need to. Be able to be.
Flexible, adjustable. So every new model, every new
system, every new memory. I will be.
Able to to experiment with and implement if needed or remove
when I say that something is not working.
So we touched observability, human in the loop and
flexibility and. May I just briefly look with you
into the flexibility part a bit further because from my
experience, that is the hardest part for organizations because
like in the past time we've set teams, we've set structures,
we've set control systems and so on.
And now if you say that we need to be flexible and adapt with
the evolving technology or also the user requirements that are
set, what does that actually imply for teams and particularly
in the technology area of how they will collaborate and of
also how they will be set up from a skill set perspective?
That's. Super complex.
So first of all, I think that any organization today, even big
ones that is not an adoptable organization, that doesn't.
Work like a start. Up in a.
Way will die. It's, it's really, really it's,
it's a very fast. Growing or fast changing
environment and and organizations cannot allow
themselves to to be slow and not adoptable.
And it's it's it's just impossible.
It doesn't mean that you need to adopt every new fancy thing and
the market, but this? Is where you put.
Professionals who knows what should be experimented with and
and what doesn't fit and and and is not needed.
So flexible. Flexibility.
An organization. It's a mindset.
It's a culture that always. That that is needed having roles
in engineering teams. Which are very very.
Complex. Today, what is the type?
Of engineer that I need in an AI team to it's very complex
context engineering and you know the traditional Python.
People. Coming from data engineering
teams, we need software engineers that did client
facing, API client facing products for many years with
Python. They're really rare to find and
and then not mention all the new vibing tools that are coming in
agents. So people who enjoy playing with
all these configurations and all these new tools that there is no
name for it yet. It's very complex to build a
team with not real defined roles.
So you need to find talents. You need to find talents which
are. Flexible that likes to explore.
Things and experiment with things and are not afraid from
changes. From that.
Of course you get the software that eventually is very flexible
and software which is very thing and and and you can throw parts
and implement new parts. Daily.
And if you now look. Ahead and I know in AI and in
technology, it's super hard to look ahead and like let's say at
least two years from now. But if we do, and if you look
into the field of AI companions and their evolution and how they
integrate deeper into our lives, what would you like to see
changed or also regarded when we talk about trust and when we
talk about AI? And what would you you want this
shift or change to feel like? I have.
Opinions from my my my learnings and experience and perspective.
I'm not sure that they are right.
It's a very complex topic I think.
I think the discussion about AI. Should should go back.
A bit to then to to what are the engineering?
Capabilities of these? Things It's still a statistical
machine. We sometimes expected to have
100% success in being trustworthy or not to do.
Stuff we don't expect it to do for.
Example if we want to prevent all the false positive so we
want some some AI system not to mention certain things or not
to. Suggest people to do.
Bad things, we need to eliminate all the false positives or it's
also when when we want to catch fraud or catch crime over social
media, we want to eliminate. All the false positive.
But then we will also hurt innocent people and block them
or even send the police to their door.
So it's a game. With these classifiers that
that. That you need to play with and
it can never be 100%. And sometimes people have
expectations of a 100% and they forget that these things are
statistics. So this discussion is A and and
B. We will have much.
Healthier products also if. Journalists, regulators will
will understand how these things are working and BI think we
should have a discussion. I don't really have a full
opinion about it. I had a very good discussion
about. It with with with a.
Friend the other day, he's a journalist and he said, I think.
We should just make laws that should.
Force all these companies. To have age.
Protection and that AI will never talk about sexual things
etcetera, etcetera and always like.
The law will. Prevent it and we had I think 10
minutes discussion. I tried to explain to him, but.
And that's a very good friend. Yeah.
So we have this opening discussion, but you don't
understand it's. Impossible.
This is not how. Engineering works, no, I expect
my the the law to to protect me and my child and this is quite
problematic thing to expect and I think we should have also a
discussion around. What we as society expect.
AI can can can do things that we want users not to do with
humans. Okay, for example, sex talks.
I will be. Happier if people.
Will go and talk about it with AI then people who are getting
exploited in only fence or things like that.
But do we want it? Do we want to encourage that?
Now we kind of open this thing and said okay.
That's okay because it's with AI.
Where is the border? If they print.
Pictures fake. Pictures not of real people.
Do we want to allow that or we don't want to allow the time?
I think we need to have this discussion, but I don't think it
is discussion that has a real solution, but it's a very
important one. That we as a society.
Should have shouldn't end immediately.
With with the law regulators. It's so interesting because we
look into, if we look into those discussions.
For me, it always comes down to questions that are as old as
humanity is like, what is, you know, right, what is wrong?
Where do you start? Where do you also frame it?
And what is real or what is also like empathetic towards each
other. And I'm just wondering how you
would like how you would open up the space to have the discussion
on a way that there can be a result.
Do you have an opinion on that? I think.
We are having this discussion as I said.
For a long time, but for me kind of from from what I've been
dealing with for for many years, again, social media is a great,
great example. We had these discussions all the
time, what to allow on social media, who is responsible to
what happens on social media there and.
There's a very big. Difference between how the
European Union see it and how the the the US regulator seeing
it, the platform is responsible or the humans at the edge.
It's it's it's a very problematic.
I I don't think there is an end solution.
I think you can say that the market.
Will will solve it. So good products that will
protect the users will will get. Gain the trust and they will win
the market. It's not always true.
You also need a good balance of regulations and I think what I
find the best is the discussions on, on, you know, the blog post
and newspapers and journalism and.
Podcasts, of course, think this is where it should happen.
Because in this place you have people from all sides.
People could listen, make up their opinion, learn.
And that's the most important thing.
We actually didn't talk enough. About it, but educate people.
Learn that I have control. Learn that.
This is right. This is wrong.
But my kids can still do those things and I need to to to watch
for it and. Decide what what is good?
For my kid, you know, it's like the decision kids at certain age
getting an iPhone or not. It's my decision as a parent
many times as much as other things won't influence it.
But but I I can still be. I can be.
Educated that this. IPhone has some protection
measures. For kids.
So I can use it. If we will have these
discussions openly. On the television, with
podcasts. Blog posts everywhere.
People will, will, will. Get educated will know what they
can, what they cannot, and. That's hopefully will push.
Also the industry and the. Products to a good.
Place you know we will. Always have products that will
do. Less good things and people.
Will use it. I don't see a way to avoid it.
You can have some regulations in the EU and then another country.
Will will serve. From from their.
Server as a product. That is doing something against
it and you cannot really prevent it.
It's the Internet. Yeah.
I was just in my head, I was running down the rabbit hole of
like global decision and regulations versus local ones.
But that will be a whole different conversation and we're
already. Yeah, let this topic I always.
Have in my head the example of the.
So I I always have this example in my head and and in the past
there was. This whole topic of Dolly the
Ship. That it was not allowed to to
clone animals in the US, in Europe, in the whole world and
then somewhere in Asia. I don't remember in which
country it happened, so I don't see a.
World where we. Can enforce regulation globally.
It's just impossible. I I just don't see this
happening. We need to be smarter a bit.
Yeah, I would. I'm super cute.
Curious if that's gonna turn out true.
Because also like on a European level, there is like a whole
conversation about how to regulate on European level when
there is like national interest and then globally as well.
So I think it's gonna be super interesting the next coming
years to define and also a trade on the way forward.
What happened now is the age gate in Australia that it was
implemented, but OK, You're not allowed to log in to register as
a team, but you can still see the feed when you're not logged
in. So what?
Did we do there but. So then I'm if I have a website
that sits in Europe, I'm not. Allowed to serve it to.
Australia or I need to serve it differently to Australia or it's
only Australian website? It's so complex on the Internet.
Who is not allowed the? User or the website or?
How do you? Enforce it Really.
Globally Super. Complex.
Absolutely. And I'm coming to the end of our
podcast episode. I always have those so-called in
between moments with my guests, which I love, which are also
sometimes a bit tricky because I ask you in particular to answer
like a prompt that I give quickly, intuitively and just
with a reflection of what comes to your mind instantly.
And I want to start with what trust feels like to you.
Perfect relationship. And what should an AI companion
not do? I think it shouldn't be what I
don't want it to be. I think it it should be what I
want myself to be, the best thing of me and plus.
The the superpowers of of. Machines like searching stuff
really fast and answering me. What is agency for you?
Having control over my boundaries.
What is the biggest risk in AI companionship?
Unrealistic expectations from all sides.
And when we look into 20-30 and that's five year heads now, what
will users still need? First, humans around them.
This will never change. We won't become so lonely.
We'll need technology to help. US.
Thank you for a perfect ending. I really appreciate the
conversation and the time that you took and all the insights
that you share was great having you Lear.
Thank you very much, Eva. Thank you.