Hello, this is Eva and you're listening to the In Between Tech
and Trust podcast. This episode gets quite well,
personal and special because it's the first solo episode that
I'm going to record. And I thought it's a good moment
in time because I just come back from a week in Davos.
I've connected with a lot of experts on tech, politics, AI
and also the world in 2026. And so I did want to talk to you
about what's been in conversations and wanted to map
out some thoughts that I had afterwards and also some deep
dives that I thought could be interesting.
This year's World Economic Forum couldn't have come at a better
time because getting into 2026, we've already experienced how
many geopolitical tensions there are.
Also how collaboration is undermined by distrust and how
tech convergence impacts the business sector and how we can
also impact it over the next couple of years.
And I want to come to some topics that were discussed on
the ground because next to, you know, all the political speeches
that you might have heard of, or next to also the arrival of
Donald Trump, of Elon Musk and all other world leaders.
For me, there were four specific topics that I wanted to
highlight to you. And 1st, this was tech, second
were the politics of tech, Thirdly, it was serenity.
And then lastly, it was also the topic of collaboration and
trust. So let's start with the tech
aspects because the technological progress was, you
know, with focus on the impact on society, politics and also
the global collaboration. And it was basically on
everyone's talking point. And some of those you might have
heard. So basically in every
conversation that I attended was talk about agentic systems, AI
models and also how they are adopted for a time to business
impact. It's worthwhile to dig into
those because they will shape of how we also collaborate and
navigate the next months to come.
And I wanted to start with the agentic systems aspect because
they are considered and from the conversations that I had, it's
really about the transformation that they'll bring to the
business world. And there are three profound
reasons of why this transformation is in a way.
So first, they transform the world for knowledge workers and
those who use AI to deliver knowledge based output.
And it means that it can vary from very specific single state
type of output to whole products that can be provided, which is
also one of the second topics that I wanted to talk to you
about because a genetic systems will challenge how we look at
organizations and collaboration in itself.
And you might ask yourself, well, why is that?
And I do think that there are several reasons to it, but the
first one is because agentic systems only work if they're
connected via clear processes, that they need to be designed
towards specific outcomes and they need to be monitored for
performance and also for up to date models.
And while I want to talk to you about the model aspect in a
second, I want to linger a bit with the design part because
designing agentic systems required to be clear here on
what they are designed for. And it can be a specific
outcome. It can be a dedicated way of
working together, or it can also mean that you want to solve for
a specific process, a problem or an outcome that an organization
want to create. Now, if you look into the
heritage of potentially the organization that you might work
for, your processes and the way that you collaborate might have
been designed according to the skills hired according to the
output that you want to deliver. And you're used to collaborate
with the strategy department, with a product department, with
finance teams, HR and so on. And this is a really classical
way of how teams departments are structured and all of them are
feeding into like a specific process, but only like they
cover parts of it with their respective skills.
And now if we translate that into an agentic driven system,
agents will take either very specific parts of a task or, you
know, collaborate closely with yourself, for example.
But they will also be designed as systems overall and will be
built for several agents to form a system.
And you can think of them as a an agentic team that collaborate
and that produce uses and is designed for a dedicated
outcome. So it is required to know what
you're aiming for when designing those agents and also what is
needed to activate it, the conversation on the ground.
And that was really approached this view on how organizations
currently struggle with the design part, because it's super
complex and it also requires quite the clarity of what's
possible with AI models where human expertise need to be
catered to, and also how those systems interact and need to be
organized on an overarching and holistic organizational view.
So the complexity of what I've just described is what AI
leaders and levels have referred to when they talked about the
challenge to adopt AI. And there was this general
consensus that organizations need to be fast on the
implementation. Yet most of them feel
overwhelmed when it comes to the action plan of how that really
is to be done. And most of the conversation
that I've been part of during the day is like focused on the
urgency to prepare for those adoption challenges.
And it's been clear that an action plan is quite individual
to the size and the industry and the delivery model the
respective company is in. Though there has been a clear
call to action to train employees to also be clear on
what skills are required and to activate collaboration on an
organizational level. And interestingly, and this is
also what Satya Nadella, the CEO of Microsoft stated, he said
that AI is a platform shift and not an app nor a tool.
And he described it as the next computing layer.
He said that organizations need to go from auto complete to
agent mode to full project autonomy.
And the point that he made is the winners that adapt the
fastest aren't model builders, but they're the companies and
countries that reorganize work around reasoning, which then
again leads to the fact that an organizational chart becomes a
software problem. So leaders, organizations and
also adapting to AI and tech infrastructure need to look into
the way of how they they approach their collaboration
within the company and prepare their teams and transform them
into, you know, the way that software and platforms can then
be activated. The third part that I wanted to
talk to you about in terms of tech is the use of different
kinds of AI models. And it's been widely discussed
on the ground and also in the past couple of months that an AI
model provides the intelligence to drive overarching output
creation. So currently there are only a
few companies who have the means and the skills to provide that
to the rest of the world with those models.
And this particularly fuels the political and the sovereignty
discussion, which I'll get to you a bit later.
But for now, the design and the transparency of how those models
actually work cost some of the conversations because there are
those who call for world models and those who undermine the
tendency to specified and small models.
So I wanted to talk to you a bit about what are world models and
also what are specified or small model.
And starting with world models, these are like neural networks
that understand the dynamic of the real world and also include
physics and spatial properties. That means that they, you know,
have a lot of data in terms of also the environment that we're
in, and they can use input data to generate videos that simulate
realistic physical environments. The interesting part is that
within a world model, you can then build them for prediction
models. You can use them for style
transfer models or like further reasoning models to begin with.
But from what was discussed in Davos, we're also just at the
beginning of how these application contexts are
evolving. And the benefits are pretty
clear because they extend AI capabilities with a deep
understanding of how spatial relationships are being built
and also how physical behavior in three-dimensional
environments come into existence.
On the contrary, there is the rise of specified and small
models that are available for a particular application.
If I talk about particular applications, you can now think
it's for example, defence tech, it's state related, it's also
potentially government driven. And they are designed for narrow
and predefined tasks that are classified or that also generate
particular outcome. And they then On the contrary to
the world models, they use statistical correlations and
they also are specified models looking into the way of so how
they are focused on particular and high accuracy tasks because
that is where you do not want to be dependent on this one
specific, you know, large scale infrastructure that potentially
doesn't serve in the way that you want it to.
And everyone was very clear that going forward, the
implementation of I will consist of both worlds.
And we do need to decide the context that those models are
applied in and the impact that they should have.
So it's essential to integrate them in the different kinds of
facilities or workflows that we look at.
And that it's also orchestrated how their impact on business,
state, society are being monitored.
And it became clear that leaders and organizations are
responsible to put together the most efficient orchestration for
them and that on a state level, it's required to also dive into
the enablement of exactly this orchestration.
While on a societal level, there are still a lot of trust and
ethical unclarities that haven't been solved and that also now
get the more and more into contradiction of how we will
handle it. But if we linger a bit with the
business sides of it, it became quite clear that the future of
AI model application for businesses is about how they
select and orchestrate those models in their specific
context. And for organizations, it means
that they will have to choose from several different kinds of
specific and different model setups.
And also for organizations, it will impact their operating
model. So they need to stay flexible
and clear on the outcome. They will have to navigate
constant model updates and they will also need to still provide
a stable infrastructure so that it allows some sort of a degree
of flexibility, which then again also needs to be translated in a
whole probably other level of delivery.
But it's also required to think about what do we deliver
towards. And interestingly, and in this
context, it was Alex Karp, the CEO of Palantir, who reflected
upon that as a stress test for organizations and for reality,
because he made the point that it's really about institutions
and that real environments are adversarial, which means that
there is no connectivity, There is still messy data.
We need to talk about permissions.
Where is ethics anchored though AI doesn't fix that, but it
exposes it and essential for organization that we need to
provide an and reliable and auditable layer in terms of, you
know, infrastructure. And when we talk about
infrastructure, this was exactly a recurring topic that came up
as well because everyone was pointing towards the requirement
for it to be interoperable, particularly when it comes to an
organizational level. But if we look in to the
business aspects of interoperability, it means that
tools or AI models can interact with each other and ideally they
can do so seamlessly. Currently, it's one of the
biggest challenges for organization and particularly
also for tech companies. Why?
Because most of the tools work in their respective context or
environment and it's hard for them to be integrated with one
another because there are mechanisms and also how they
deliver an output might not work together, also might not be able
to be activated the way that a person, an organization works
with different kinds of infrastructure.
So currently there are several startups and also in Davos,
there were several ones pitching for an interoperable
infrastructure layer that want to provide exactly this kind of
integration of different kinds of tools, of different kinds of
ambitions, of different kinds of models to also serve as a
general connector and to also provide clarity for
organizations of how they can then orchestrate their AI tools
and models and integrate it into the way of how they produce
output. All of this leads into a larger
conversation that still lingers and that is the increasingly
need for an adoption rate that becomes faster in its time to
business impact. And a lot of organizations still
do not see critical business value generated.
There's still, you know, up to 50% of organizations on the
ground that said that for them, it's super hard to implement AI
and to navigate it in a valuable way.
And I think this is due to two topics.
First, it's like the design aspect that we've already dove
into earlier and it's also the lack of focus on the design
process and on organizational architecture required for AI to
really improve and provide impact.
And the second is like access to skill improvement for employees.
And the latter is one of the critical ones.
And there are two aspects of it. On the one hand side, it's the
provision of time and education material for employees
respectively, for people to adopt and learn to their
required background information. However, it also comes down, on
the other hand, to clear responsibilities.
And one of the main key takeaways that I had during
Davos is that it isn't really clear who provides also the
incentives for people to actually follow through.
And I'm not talking about an organizational level because
every, you know, organization does have and provide a lot of
effort for their employees. It is more about the holistic
view of it because tech companies somewhat point to
government to provide more frames, regulations or also
clear guardrails of how future models will be incorporated and
how then again, employees make use of it.
And because due to the lack of a unified approach, also how to,
you know, distribute the responsibilities when it comes
to implementing AI in organizations.
The transparency aspect is so important, but also the fact of
what actually gets implemented respectively, what output we can
expect. So there's a lot of, you know,
navigation to be done and also distrust to be addressed.
And the these are some of the most critical conversations to
be solved in the foreseeable future.
And who is it going to be solved by?
Well, This is why politics, and it's the second overarching
topic that I wanted to talk to you about, got tied into tech in
so many conversations. Either it was on the back of
geopolitical tensions and due to the large scale resource needs,
but also in the context of whether tech is up or downstream
of politics. And if I think that tech is up
or downstream of politics, you might ask, well, that's this
sounds complicated. What do you actually mean?
Well, at the core, the distinction is about direction.
And more explicitly upstream is when the things are defined,
designed and made possible. So you can think about this as
strategy part, the standardization, infrastructure,
resources, data generation and so on.
And downstream is where things are applied, distributed and
experienced. So you can think about it as
products, services, markets, users and social impact.
And so when it comes to tech, that means that chips, cloud
infrastructure, protocols, training data, open source
libraries and so on, these are upstream, while apps, platforms,
consumer devices, workplace tools are downstream functions.
Now you might have to come to the same conclusion and
technologies both upstream and downstream of politics.
And the direction flip depends on different kinds of layers and
also how we're looking into it. And this is at the core.
And I can tell you it was visible so many times of what
was experienced and why tech leaders were so present at
Devils, which particularly also so was shaped by politicians,
right? It became clear that the
interwovenness of how tech companies currently are both
upstream and define how tech impacts us as well as it is
downstream and shapes the application side of it, is why
we're in, you know, those discussions.
At the center of it all, and ideally on what everyone keeps
asking for is that politics, it's upstream of tech and that
it shapes what kind of technology gets built and by
whom, for example, through regulations, geopolitics, public
funding or standards, laws, etcetera.
So they define incentives, constraints, and the strategic
direction at the same time. And what we're witnessing and
what we've been also discussing in the past couple of months or
years is that tech sits upstream of politics and also at the
political inaugurations. If you just remember last year
when we saw inauguration of President Donald Trump, it
reshapes how politics itself functions through algorithms,
surveillance, decision systems and the finance of it all.
So this is how political behaviour gets shaped and also
how institutional capacities and power asymmetries come to view.
And so of course, there is this feedback loop that is defined by
a recursive relationship. Power roles and power structures
shape upstream tech decisions. And tech reshapes society and
politics itself. And politics again reacts
oftentimes too late to complicated or too far away from
operational reality regulations and redirective elements.
But that then again impacts how tech continues to apply and be
applied. And the discussion of upstream
and downstream tech mostly resonated in devils in the
conversations on energy supply data transparency.
To provision talent enablement and so on.
But interestingly and with the tech firms being so huge
economical conglomerates already, they now also shape the
labor markets, influence political processes and also
concentrate power in a few firms or states, which then again push
tech downstream into political outcomes.
And that again undermines why endeavors, the political
presence and the high attendance of tech CEO's was so omnipresent
this year specifically and interestingly.
And if you look a bit deeper into those who are now first
movers in tech and apply their advantage, they define the
standards and they also shape user behavior and they lock in
dependencies which are hard to reverse them again.
And that is what drives the narratives that are framed as
efficiency driven, providing safety or also how they designed
for user experience. And while these are all, and I
really don't want to downplay it, I do think it's important to
deliver exactly that and to also optimize for growth and
innovation. The conclusion is when tech
shapes political decisions as we've just arrived, there is a
certain amount of transparency needed on how those decisions
are determined. So tech needs to move fast and
at the same time, they also need to be hold accountable of how
they get there. And this then needs to be fed
back into the responsibility conversation we touched upon
earlier and the Nevos. This became essentially the core
but underlying non explicit conversation because tech
companies affect billions of users and like the companies and
revenues and so on. And at the same time their
systems are hard to inspect while only a few firms control
core infrastructure. And that then, shapes the
following. The dependency aspect on how
states rely on private tech for public functions and how they
rely on tech for public functions centers one aspect,
sovereignty. And well, sovereignty for all of
us is quite like the huge word. And in itself, it has been, you
know, a top topic and also in every other conversation for the
past two to three years or so. And on a political aspect, I
mean, this is some, this is where tensions on also state
aspects come into play. But from a tech perspective,
this gets really interesting because in the one hand side and
particularly from a European perspective, the need for
technological progress based out of Europe is like essential to
stay sovereign. On the other side, it was also
Satya Nadella again, who said the topic least talk about where
we most talk about this year. And it's the sovereignty of a
firm. But it inherently became a
statement about competitiveness, value and control.
And you might ask, well, why do we talk about it and why now?
Well, given the interwoveness of tech and politics as we've just
dove into, and also as the economical impact of AI on
society became quite clear and quite the central theme that we
need to also disentangle. And before we do that, we need
to talk about what sovereignty in itself and particularly what
it means in tech and AI. And sovereignty means being and
having the ultimate authority to the side and to make or to say a
final decision, set binding rules and also define the
impact. There are different
perspectives, different actors and also different aims to be
sovereign and it can be territorial, legal, political or
economical. But when it comes to tech and
AI, and in my opinion, there are several layers to it.
The compute energy sovereignty and so the ability to secure
economically viable reliable energy for AI compute over time.
So this is where it gets mostly interwoven with territorial
sovereignty and paired with the mining and raw material aspects
because only a few countries currently have the means to
power and the infrastructure needed for the future AI growth.
And this is also why the current conversation of energy supply
through alternative aspects get so important.
Second, infrastructure and platform sovereignty, which is
one of the most complex ones because it's about dependency
depth and some sub layers to look into it.
We talk about compute sovereignty.
How do you get access to GPUs and cloud contracts?
Then you talk about platform server entities.
Which cloud and ML OPS stack do organizations use?
And then there is also the network sovereignty, which
addresses the latency, routing and resilience of AI to work
with. And these layers particularly
are steered by only a few companies in the world, which
get even more specific when compared to the vast amount of
organizations they provide their infrastructure too.
So this is also the conversation you might have followed along
and most shaped by the term the Magnificent 7 with Alphabet,
Amazon, Apple, Meta, Microsoft, NVIDIA and Tesla and focus of
providing critical infrastructure to it.
And then looking into third, the data server and aspect, which is
the ability to control, protect and strategically also provide
continuous data flows generated by AI deployment and the
securance of ongoing data generations as well as the
control of feedback loops. And looking into the data aspect
a bit more. This is where is this underlying
need for transparency and regulation because it plays into
the way of how they do not have sufficiently the possible
ability to control, protect and provide continuous data flows
for them to be secured in the aspects and respective
environments without being dependent on the tech firm
overall. 4th, we talk about operational sovereignty, which
means that we can audit AI decisions, we can intervene in
real time and we can avoid single point of AI failure,
which is specifically critical and regulated or safety critical
industries. Here again, you could think
about the defense second environment and you can also
look, look into the geopolitical tensions.
But This is why it becomes one of the areas to watch and also
where we need to decide on how to move ahead, particularly from
a European perspective because this serenity currently is not
given. And this is like the main focus
areas of a lot of organizations of where they want to push into
to have this operational focus and ability to steer it in their
respective views and not being dependent on like a third party
vendor. There is the design and
behavioral sovereignty and this implies the the ability to
define what AI optimizes for and to also influence model
behaviour systematically and define the control over the
evaluation matrix. This is specifically the part of
dependency that all companies have on the models that they use
because this is also the value aspect the AI and tech companies
try to secure to ensure their level of intelligence and the
progress their respective large language models to keep and also
to make the race for general artificial intelligence.
The last serenity aspect then again is from a talent and
organizational perspective because organizations required
to the internal capabilities that provide an understanding
and operations and also an involvement with AI systems
without external gatekeepers. If you've now heard the seven
different kinds of types of sovereignty, you might get a
glimpse into why this is so important, but also why it's so
complex to well sort it because we have 7 different kinds of
ways of where we currently are dependent on non European
platforms. We're also looking into the way
of how to enable politicians or to enable also financial flow
across our, you know, or European environment.
When we pair this to the criticality of what we've
earlier looked into and that is the influence of tech companies
on politics, you realize that knowing Texas upstream politics,
knowing that tech also benefits from autocratic tendencies.
This is going to become one of the most crucial conversations
that we're going to have in the next couple of months and years.
It's been core of the discussion in Devils and it's also going to
be a core of the discussions that are going to be move ahead.
And interestingly, on the ground, there have been several
solutions tied to it. On the one hand side, it's been
said to focus on making sure that the auditing and
transformation of organization. And when it comes to the state
of infrastructure, it needs to be very clear of where big tech
companies currently have access to, of how they also are
dependent on the work that the different kinds of organizations
do, which tools and platforms could be good alternatives that
are being provided based out of Europe.
Second, it's all about the development of talent and making
sure that Europeans know and have like a respective skill set
to enable an infrastructure shift.
And this is a point which looks into how education systems are
set up. And then thirdly, it's also
about the unification of a financial landscape and how we
enable investment influx because currently there is a lot of
money that flows into the US and that is also enabling US based
company. Now you might have heard about
the EU Inc proposition that was a lot underline has proposed.
It's like one of the best steps into an implementation part and
also into the way of accumulate more financial means to enable
this infrastructure shift and to also attract talent in the mid
or long term, to sum it up. And I think sovereignty aspect
of like one of the most challenging ones to follow
through. And it also remains to be seen
of how we'll establish as European a mindset again that
provides us with a strong foundation.
And then lastly, 2 topics that I still wanted to talk to you
about this collaboration and trust.
And interestingly, both of them are quite tied to each other.
And that's also why I wanted to connect them with each other.
And when it came to collaboration on a global basis,
but then again, also on an organizational or team basis, I
sometimes got the feeling when I talked to, you know, experts
that there was this unspoken question tying into it.
So what does collaboration even still mean?
So can we trust each other? And we also look into a
commitment that we want to collaborate on a global basis.
And while I know and while I experienced a lot of commitment
to enable global scale collaboration, particularly in
tech, the requirements to cater to individual needs became clear
in many ways. Because if we continue to, you
know, provide this commitment from mutual collaboration and
trust, and also on a diplomatic and business level, we need to
enable it through action. And with that, I wanted to round
up a lot of topics that we touched.
We talked about the impact of tech, the interwovenness of tech
and politics, why sovereignty is so crucial but also so complex
to solve, and how we going to steer ahead with collaboration
and trust. And it's now end of January
2026. What I come home with is to be
very clear that this is a current conversation that needs
to to a context and commitment, being very specific of how we
want to shape the world ahead and making sure that the values
that we have, the ambitions that we want to drive forward are
enabled and not predefined by an infrastructure that we might
use. I am very curious to hear what
that came to your head, but until then, thanks so much and I
hear you next week.