# Strategic AI Transformation: Sovereignty, Guardrails, and Business Viability

**Podcast:** HMZE
**Published:** 2026-11-05

## Transcript

Und jetzt die governance part, so die Sandboxen part, wird viel mehr mature, besonders durch die Push-Off NVIDIA, wie OpenShell Sandboxes und NemoClaw und all diese Open Source Projects, so dass Sie auch einfacher Design-Off-the-Shelf Sandboxes und nicht haben zu bauen etwas super custom, wie ein paar Monate vor.
Unless Sie nutzen die letzten OpenAI ornithropik Models, das Sie einfach aus der Every Sandbox.
No, just kidding.
Welcome to another episode of our new season of Beyond Vibe Coding, partnering with Impala Search.
The go-to tech and executive search agency in Germany.
In this podcast, we explore the transformational change in software engineering and knowledge work in general.
I'm Sebastian Heidemeyer, it's Erpnus, CTO at Ecosia.
And I'm Andrei, CTPO at Trusted Shops.
Great to have you back today.
Wir sind wieder mit dem Perspektive von einem Konsultant-Agenz.
Wir sprechen mit Thomas Wollmann von Merantik's Momentum.
Und wieder, ich muss sagen, einige sehr interessante Gedanken.
The most interesting aspects for me actually are that he combines a strong focus on engineering concepts with the business perspective.
We're talking about how Team Topologies shape his collaboration approach between humans and agents.
We talk about DDD and that many businesses currently are approaching Verantik's momentum with the question, will our business still be viable in 10 years?
That's true.
So interesting insights.
Enjoy the episode.
Welcome, Thomas, to our podcast episode.
We're very excited to speak to you.
And as usual, we'd like to introduce yourself to our listeners.
Hi, Sebastian.
I'm very pleased to be on the show today.
I'm Thomas.
I'm the Managing Director of Merantix Momentum, an AI consultancy based in Berlin.
We help organizations in the AI transformation.
And I'm building this organization since seven years.
We have done over 200 projects with organizations, especially in Europe, in pharma, finance, manufacturing and the public sector.
Everything where transformation is painful.
That's our cup of tea.
Before I did that, I built an e-learning startup.
Ich habe ein PhD in deep learning, als ich ein paar Zeit in healthcare und in automotive.
Vielen Dank.
Vielen Dank.
First short question, 7 years means that it was pre-Chechubi-T.
So I guess that the transformation of the kind of work that you're doing changed a lot in recent years compared to before, right?
Oh, yeah, definitely.
So 7 years ago?
Nobody knew about AI.
It was a niche topic where there was a lot of talk already in academia due to especially the achievements on ImageNet, but not yet in industry, especially in Europe.
And this is also why we started building momentum, because we saw the potential and we wanted to get this out as early as possible.
So the first things we did...
We're explaining executives the potential and then it also moved towards building POC, showing the value and it was also pre-in-context learning like what we do right now with ChatGPT and so on.
So building specialized models for specific tasks, building automations of verticalized problems and this shifted towards a more broad range application, but also more broad knowledge of our customer base, as well as their needs, obviously.
So now we have broad adoption, measuring the impact of AI investments, pushing foundation models in areas which is not text and so on.
There are lots of new needs that Das war nicht so, dass sie nicht seit Jahren existieren.
Und die Wirtschaft nicht bereit war.
Interessant.
Ja, und die Begründung der Impacten ist auch etwas, was es gut zu cover, weil das eine der Hauptthemenen, in unserer Ebene, wir sehen, ein erhöhter, ein Signifizantreise.
Not so much impact.
That's why this is a very important topic for us also to discuss later on.
But before we get there, let's go to our first segment, the reality check.
Sorry, not the reality check, but the status quo.
So how are you currently working?
So where do you utilize agents in your day-to-day work?
Quite a lot.
I think I have a very broad range as a managing director, but also as a basically the technical mind of the organization.
And I do slides.
I have to do strategy, look at data for controlling, have to manage my direct reports as well as building prototypes and advising on experience that we accumulated.
in our past projects and basically from being there from day one, sharing this knowledge with people who haven't been there.
And this all can be augmented with agents.
And you could now use like a zillion tools in your daily workflows.
But actually, I tried to bundle this.
So I'm a big fan of VS Code.
This means I try to integrate my workflows with that.
If it's like note-taking to building slides, to building prototypes or interacting with our internal data warehouse, which acts as our company brain and accumulates all the experience that we built over the past years.
And then this is basically my...
Let's say control tower for everything I do.
And in addition to that, I have some background agents running for recurring tasks.
But I'm actually more a fan of triggering actions myself than having a thing that is running all the time and is doing random stuff because Then you have to really take care again of all the stuff that gets produced by these agents and nobody has time for that.
This I can imagine is unlike what you do with organizations, right?
Because I assume there you have processes that you want to automate and then there are triggers.
And these are not always manual triggers, but probably also automatic triggers, right?
So something happens and then an agent picks up some work.
Yes, indeed.
The same principles apply.
So it's about the interfaces.
So if you also think about, or there's this marvelous book called Team Topologies, how to build interfaces between teams to optimize the communication and the cohesion in teams.
And the same also applies with agents.
If you need very high...
und haben eine Purly-Defined API zwischen Humans und LMs, dann ist das eigentlich mehr Arbeit und synchronization.
Dann ist es eigentlich mehr Arbeit und Synchronisation, wenn du eigentlich von einem zusätzlichen Person oder Agent in deinem Organisation hast.
So, was wir auch mit unseren Kunden machen, ist, dass wir die richtige Interfacke sind, so dass du are in control and have the best interface to offload tasks to an agent.
And this is, I think, what we tell our own employees as well as our clients.
Great.
And then maybe one or two last questions on your setup that you described.
VS Code is something we hear very rarely, if at all.
Usually people are using either as the app or CLI or something similar, Codex, whatever, right?
And VS Code is very rare.
And what kind of environment do you use behind it?
So what LLMs and, I mean, do you then use skills a lot?
And yeah, how do you work with VS Code as your interface?
For me, that's mostly the UI.
So, underneath I'm using Cloud, Codex, Copilot, the usual suspects.
And this also changes from month to month depending on what's the most capable model, the pricing and so on.
But I think humans are much faster if they are really...
in einem bestimmten Workflow für ihre User Experience, in diesem Fall, die Developer Experience.
So, wenn du die UI sehr gut kennst, wie du mit jeder Componenten kennst, dann ist es sehr produktiv.
Und du musst nicht durch diese Lernability-Curve von einem neuen Toolen gehen.
Und dann, einfach zu verwenden, zwischen einem AI-Assistenten mit einem anderen ist einfach.
Ich bin ein Fan von keeping das in einem Common Theme und du kriegst die Vorteile von all den anderen Tools.
Und ich benutze viele MCPs in meinen LMS, das sehr gut ist.
Ich habe schon gesagt, dass wir eine Unternehmen Data Warehouse haben.
where you can look at every aspect of the organization and leverage that when you are generating something.
As well as we have certain skills that we share in the organization to augment our workflow.
So in the past you would maybe write like a SLP, how people should do a process.
The skill is basically an SLP for an LM.
So we basically translate.
our SAPs into skills and use that so it acts like what you would expect from a human worker.
An automated SOP, if you wish.
If you say data warehouse, you mean a knowledge base or a real data warehouse where you have certain reportings?
Like as a database, which we use?
für Analytics und für LMS.
Und wenn wir denken, was wir machen, wir machen projects und helfen Organisations in Transformation.
Und alle können das.
Wir bringen ein paar smarte Leute in die room, die wissen, wie zu bauen, zu lösen Probleme und haben eine Service-Company.
But what is the thing that you have over time that others don't have?
This is the experience you've seen over the years.
And this is something that we conserved as data for the next generation of projects that we do.
So every project that we do compounds.
to a better product for our clients.
And this is something that we did in the past with humans, but we are also now doing it with our agents so that our agents get better over time.
And we will get into more details of that in the main section.
Last question for the first segment.
What is the environment your background agents are running in?
Ist das ein enthropisch, oder haben Sie Ihre eigene eigene eigene?
Wir haben unsere eigene eigene eigene eigene eigene eigene.
Das ist Teil der Unternehmen, wo wir reibringen Custom Agents.
All right.
Well, dann, vielen Dank.
Das war bereits interessant und etwas special.
Und jetzt geht's zur Hauptsichtung des Podcasts.
Ja, wir beginnen mit dem Beginn.
Was eigentlich Sie machen mit Meranteks Momentum?
Du hast es bereits auf einem sehr hohen уровnen.
Aber vielleicht könnten Sie ein bisschen mehr in die Details über wie Sie helfen, die Unternehmen adoptieren.
Ja, so vielleicht, wenn wir starten mit dem ich bereits gesagt habe.
Wir sehen uns als der AI-transformation Partner für Organisationen, besonders in Europa.
Das bedeutet, dass wir uns mit der Strategie aus dem Boardroom beginnen und sehen, wo eine Organisation braucht, wo es gibt, wo es in der Geschäfts-Business-Strategie gibt, von der Execution, aber auch von der Vision auf wie zu eigentlich with AI and the changes that is coming to every organization.
Is the business model still defensible?
Is the roadmap there to deal, for example, with the upcoming IPOs of AM providers and so on?
Do I have my supply chain secured so that I can still provide my product and Business Continuity in the Future.
So all the strategic topics where we help the organization shape their business strategy.
And then we don't stop there.
So we help also in the execution of the strategy.
This means rolling out software with clients helping, measuring how the software is actually bringing value, which is not a...
Easy thing, if you look decades back at how computers were introduced into organizations, I think none of the organizations back then saw immediate value of computers because you invested in the engine of how you run your company, but the bottom line didn't change immediately.
This is something where you have to look at.
how to actually see if your engine improves and then ensure that later you see this also on the balance sheet.
As well as we build custom systems for our clients.
Many of our clients have very high ROI, AI use cases, which there's just no product on the market.
So we build it for them.
This can be a new drug discovery tool.
This can be a demand forecasting algorithm, yad, yad, yad.
There are plenty of verticalized products that are very relevant for individual companies to build for themselves.
And sometimes this even goes to helping machine learning research teams progress on their research agenda.
So help them in experimentation in shaping their research agenda and sorting out what are promising research paths and what aren't.
And we do this because we can provide the full chain from strategy consultants to actually Full blown researchers, because we maintain a research team of around five postdocs, which I think is a very rare thing for a consultancy that you actually get real researchers who do this full time.
Interesting.
And you're, so if I understand correctly, then you're.
Pretty much in the execution phase, you're looking or screening the market for existing solutions that can be rolled out in order to support the actual AI-induced transition or transformation of the company.
Or you build custom solutions depending on if you can find something that suits the needs of the companies.
Yes, indeed.
So your angle is business, technology and research.
less the organizational transformation?
I think you can't decouple it.
I think there's also always a socio-technical system where you introduce a new technology and maybe if you think the technology is the solution, I think 80-90% of the solution is humans.
So I think the biggest mistake that we see over and over again is that Project champions who want to introduce the technology forget that they have a lot of legacy and processes structure, which has to be changed as well.
And this is often even more work for us than solving a technical problem.
I can imagine.
Yeah, definitely.
I mean, this is the cultural...
Das ist die schwierige Partei.
Ich bin noch ein bisschen mit oder mit den Dots von der Fokus auf den AI-Transformationen.
Und dann was du auf die Strategie-Levels hast du, wie du auf die AI-Auswahlstelle, wie du auf die AI-Auswahlstelle, wie die Unternehmen in der Unternehmen, die Unternehmen, die Unternehmen, die Unternehmen, die Unternehmen, die Unternehmen, die Unternehmen, die Unternehmen.
Pretty much the starting point and then derived from that you screen the market for existing solutions or build solutions.
I think it doesn't make sense to introduce AI just for the sake of it.
So it always starts with, okay, I have an organization.
I want to grow it in a certain direction or I still want to be there in 10 years.
So we start with that vision, which is usually like the first thing you do in a business strategy, and then evaluate how does AI interact with that.
And often nowadays, what we see is that company owners and executives are very afraid of if they still exist in 10 years, AI just ate up their whole business model.
So this is the thing that we do very often right now.
Consult on the threat of AI and also the opportunities.
And then you hopefully have a corridor where you can transform your organization into that you still stay relevant in the future and maybe capture a new market.
And then we help the organization to get there.
Super interesting.
This means you're starting actually pre-product strategy.
Actually, you're building the business strategy together with the companies.
Usually, I could also mention consultants come or join a company and then implementing the product strategy using AI technologies.
In your case, it's very different.
You actually start developing the business strategy together with the company.
Ich denke, das ist ein majorer Schritt in den letzten Jahren.
Vorher, ich denke, wir haben vieles zu helfen, mit dem konkreten Problem mit dem Produkt.
Und das ist auch noch ein Teil von was wir heute machen.
Aber was wirklich verändert hat ist, dass AI nicht nur Just not one tool in your organization or one little thing.
Organizations make this core of their business as well.
This is one of the biggest threats and also cost factors for an organization.
So you can't just see this very isolated in one part of the organization.
You have to include this into your core strategy.
And this is why we're also moving more and more on advising on business strategy because AI strategy and business strategy is not two things anymore.
Got it, yeah.
And now your initial statement actually connects the dots for me as well.
Yeah, thanks.
And this already hints basically probably at how the conversation with your clients has changed.
throughout the journey of the company, right?
So initially, it was probably more like, okay, there's this new thing, deep learning, AI, whatever you want to call it, and let's see how we can utilize it.
And now it's probably much more around, wow, this is going so fast and we cannot see where the endgame is.
Will our company still exist in five years, right?
Yes, indeed.
These are exactly the questions our clients ask themselves.
Und die Sache ist, aus dem Sphere, was du kannst du einfachen die Speed machen und sagen, okay, wir machen alle Agents, automatisch alles, transforme die Organisation zu max speed.
Aber wir müssen noch einmal consideren, dass, für zum Beispiel, LMS sind probabilistische Maschinen.
Wenn kann man eigentlich trust die Output, wenn man die Mistake macht und jetzt building everything on that.
At the same time, you take the humans out of the loop because you increase the velocity so much that humans can't even understand what's happening.
For example, if you have, let's say, software development, coding agents and all the code they produce, it's very hard to really grasp what is the impact of a thousand-line code change.
Und das bedeutet, dass man viel über die richtigen Regeln der Systeme der Räder sind, damit man nicht den Verlust durch die Velocität erhöht und man kann sich in der Fahrer-Seed sein.
Denn am Ende der Tag, ein Humanist Operator, der für these machines, if it's a coding agent, if it's a car or whatever device.
And if something blows up, still a human is liable for the consequences.
So you also as a human have to trust your AI to do the right thing with the right guard rates.
And I always compare this to a car.
Not everybody understands how a car works.
wie alles genau ist, aber ich habe es noch geholfen, dass die Brake ist, und es geht nicht, dass ich es nicht, wenn ich es.
Und wir müssen die gleichen Regeln für AI in unseren Prozessen.
Und ich denke, das ist etwas, was wir müssen, mit dem Anstrengen der Velocität zu still Wie ist die approach you take to guardrails?
Wenn du über eine neue Prozess hast, oder du bist in der Prozess automating?
Wie würde du das tun?
Und vielleicht kann du einen Beispiel?
Ich denke, das kommt in multiple layers.
Ich denke, first of all, da ist die große Worte, der Alignment.
actually, that a model or system does what it is intended to do.
And this gets more difficult if the tasks that a system can do is more open and there are just more chances that it does something which I didn't design it for.
So this means you can align the model through evaluation, through Density checks in the distributions through drifts, through fine-tuning, and so on.
So the model itself, so that the thing that it actually should do is more probable.
But this is just a soft guardrail.
What's really interesting, if you mix it with hard guardrails, which...
is additional models as a judge, which is handcrafted rules.
And this then provides hard guarantees in my process.
For example, if I know something is just not plausible, I can just forbid that the model is outputting that.
If I can build a proxy, for example, in code, which also LM can do itself, I can give feedback to the model output with an actual calculation rather than guessing what an LM is doing.
So deterministic stuff, actually.
Actually, yeah, deterministic stuff.
And I think a good combination of increasing the likelihood, so the probabilistic, Alignment, as well as deterministic rules, is what we need.
Yeah, I would say that in our world of agentic engineering, we are talking exactly about the same thing.
So also we talk about coding factories or dark factories as they are being called, and this is exactly the same, right?
So you have a mixture of...
Code being generated within deterministic checks that you anyways have, be it tests, be it lynching infrastructure, and then you add review agents, and then you have LLMs as judges on the final PR, whatever.
So very similar.
Yeah, I think if you go to coding, I think that's a very interesting thing because Wenn ich zurückkehren, haben wir eine Waffe von model-based Software-Developpensivität, das mich sehr daran erinnert, was passiert jetzt.
So, wir versuchen, sehr genau zu bezeichnen, die Software zu speichern.
Und dann haben wir eine Blackbox, eine Ruh-Based-Systeme, jetzt LMS, das dann implementiert etwas based auf die Speichern.
Aber ich denke, dass die Leute in Software Engineering für lange Zeit wissen, dass Menschen sehr schlecht an schreiben, die perfecten Spezifikationen sind.
Ich denke, was auch sehr relevant ist ein guter Loop, zu sehen, wo es sind, wo es sind, und die Wissen in der Spezifikationen sind, und zu entfernen oder zu klaren, von einem Loop.
Otherwise, we end up in the same state that the promise of generating code automatically is not fulfilled and then people start writing code by hand again.
The approach you just explained is that following a certain framework or a mental framework, so something...
So not a technical framework.
Is that something you came up on your own or is that following, I don't know, some external best practices?
I'm thinking more like from a machine learning perspective.
So here we would, for example, think about the counterfactuals in an explanation.
So if you think of, do I...
If I have examples that challenge my specification, then this is something where you can iterate on.
And if you think of how humans do that, just intuitively, they do the same when they are building specifications.
So I think what we have to do...
ist, dass wir nicht mit neuen Frameworks kommen, wie die Frameworks für Menschen für die Arbeit gemacht wurden, von einem Statistischen Punkt, als auch von einem Organisationen Punkt.
Ich nehme viele Ideen aus der klassischen Domain-Driven Design Frameworks, die ich denke, sind sehr relevant heute.
to decompose a problem into what are actually the functional and non-functional requirements that then can be implemented and where you can identify blind spots in the modeling of your business process.
No, not the first time that we hear this.
So compared to VS Code, which is a rare case, this case we have.
Ich denke, ich kann es noch einmal an zwei Mal wieder einmal mehrmals.
Das scheint ein guter Praktis zu hören.
Gut zu hören, dass gut old stuff ist, was nicht vergessen, was das Leben heute noch heute.
Also, ich habe auch deine Bemerkung über Team Topologies, als ich über die Interfacung zwischen Menschen und Agents bin, die sich mehr und mehr als wir uns wiederholfen haben.
Really interesting.
Bring these processes on the next level?
Or how do you do this normally?
So we pick up companies where they actually are right now and then try to help them with their most important decisions.
And this can be, for example, we have a lot of grassroots initiatives in the organization.
They are coding agents, they are co-pilot, whatever, and the organization maybe bought some licenses.
And now we want to adopt a common way of working across the organization.
So here we help with analyzing the organization, the processes, to understand what's actually going on there already.
And then see how we can support across the board with technology.
And in the end, maybe the decision is by this platform because it perfectly mirrors how you run your organization.
And maybe some functional, non-functional requirements.
For example, a big topic right now is AI sovereignty.
We are independent from the cost hikes that are happening right now with LM providers, as well as having access to models that no matter the geopolitics or having something that comes with European values.
And if you might know, we are building our own foundation model in the Zofi Consortium.
Wir rely on the big players, but we also bring our own foundation model and also the expertise to build these kinds of models.
And this is a big topic.
Where do you want to stay independent?
Where you want to maybe be quicker, but be dependent?
How important is cost?
What's the technology that mirrors my organization structure and processes?
And then we help with these decisions and then implement it with our clients.
For example, if you buy a platform, let's say Vertex or Azure Foundry.
Just to give two examples, then this just brings you a platform for a workload, but we haven't solved the business problem yet.
So we help building ML systems then on top of that and prioritize them and so on for our clients.
And picking up on the topic of sovereign AI, what is the current...
How do you implement Sovereign AI right now with companies?
I think Sovereign AI is having a choice.
And if you think about software, this is a stack.
So you have many layers of the cake from hardware to basically who runs the hardware, the model on top, the data.
Model is trained on, as well as the observability and so on.
So many layers where you could say, where do I want to have a choice?
And we have organizations in picking the right providers with the trade-offs that they are accepting to do.
I think being 100% sovereign is impossible.
Otherwise, you have to build chips on your own.
from your backyard and then build everything on top yourself.
You always depend on somebody or something.
But doing the trade-offs or the dependencies that you think are reasonable is something where we navigate with our clients through.
And if we think about what is the biggest worry of organizations right now?
I have data in my organization where I think this makes my organization defensible.
If I'm losing this data, then I'm basically killing my own organization because somebody else can replicate the knowledge that I built up over decades.
So organizations want to have inference at that they trust.
I think that's the biggest question that organizations ask themselves.
So we help them building inference systems locally or, for example, in Europe, helping with routing data depending on the content as well as then also based on how much you're willing to spend on the individual requests.
And I think a thing which is not yet there, but probably will come in the future is as well as basically having the systems in the cloud, having the systems more and more on the edge, actually in die Prozesse selbst, nicht wegen der Kosten oder wegen der Prozesse zu einem bestimmten Prozesse.
Aber in addition, Sie wollen sie bei dem, wo es passiert ist, weil Sie die Performance auf dem Shopfloor, in einem Robot, oder was auch.
Und Sie wollen sie nicht streamen die Daten in einem anderen Land.
As well as the old question still remains to a non-trusted organization.
And I think this is something which especially with the introduction of AI in the industrial sector, which is very prevalent in DAG, will arise more and more.
How to actually achieve that without in den Händen von non-trusten Organisationen und handen sie über deine eigene Factory.
Ja, das macht viel Sinn.
Ich habe deine Phrasing von Sovereign AI als eine Frage, die eine Frage zu haben und die verschiedenen levers.
Das ist eigentlich genau wie wir uns an AI approachen, Ecosia.
our product, but also in our agentic engineering efforts.
And now maybe last question on the actual execution before we then maybe come to the final point of impact and then close the main section.
Do you see a pattern of specific processes that you always or in most companies or in a lot of companies are?
trying to transform via AI?
Or is this totally different because your clients are totally different?
A big chunk is actually in a back office task where you have a lot of knowledge retriever.
And in the past, you had to search for knowledge, had to aggregate a lot of unstructured data, infrastructure data by hand.
And now with LAMs having at hand, this is something everybody wants to introduce.
And we see this across the board because no matter which product you have, which vertical you are in, everybody can pull this productivity gain.
I think another big one is Das ist eigentlich etwas, was nicht viele Leute sprechen, die prescriptive AI.
So dass du modelst, was dein Organisation ist, wie ein Digital Twin, und dann eine Planung zu machen, um besser zu machen.
Und das kann also mean, Test if there's a better pricing strategy in your organization.
Or you test, should I invest in this or that country?
How should I distribute my fleet in my organization?
So this combination of I'm building a replica of my organization and then testing different scenarios for better decision-making is something which I think is not that mainstream.
Aber es ist etwas, was wir in den Organisations sehen.
Und ich denke, auch, wo ein vieles ROI ist.
Und es bringt uns zurück zu der Diskussion, oder die Punkt wir hatten, wo es nicht über die Implementation ist, sondern über die Business-Decision.
Und ich wirklich mag das Fakt.
actually earlier in the process, like really from, actually from the beginning, beginning, beginning, right?
Not just putting this AI sticker on your products, but like think about that from the beginning on.
Maybe it sounds so obvious, but it's, I think it's actually, it creates quite some headache because you always need to keep in mind, okay, like now where we have the capabilities which haven't been there before, like now can I apply, now I can apply that, right?
And it actually, allows a very different kind of steering and organization, I couldn't imagine.
Yeah, 100%.
I really like the phrasing of one of my senior engineers who basically said every day I ask myself, why should not an AI do this?
Und er macht einfach etwas selbst, wenn er glaubt, dass eine AI nicht das tun sollte.
Und ich denke, wir können das gleiche, wie wir denken, wie wir in der Zukunft arbeiten, wie wir in der Zukunft denken.
Wir haben in der Vergangenheit gedacht, wie wir das können, wie wir das können, und wie wir das können mit Automation machen.
Jetzt, ich denke, das hat sich verändert.
And we have to think in how we are building businesses.
And let's imagine everything can be automated.
Where should a human should do something themselves?
And I need to stress that it's actually for me, the learning or the takeaway of that episode is really usually in the adoption, we think on the second half of that transformation, right?
We do not start from the very beginning on, I think.
Das ist eigentlich wo ich es soll, nicht nur benutzen AI, um die bereits bereits in der Lage zu implementieren, aber wirklich nachdenken, denken über, okay, was sind die Möglichkeiten?
Wie wird die Markt transformiert?
Was soll meine Positionierung sein?
Wo habe ich die Vorteile?
Und dann, wenn du es von dort ausprobieren, ausprobieren, ausprobieren, ausprobieren, ausprobieren.
using AI implementing that.
I think, yeah, I would not talk this down and say like this is simple, but probably not as powerful as the approach you just outlined.
Yeah, 100%.
As you mentioned, right, that's also where the strengths of LLMs are put to use because now you can actually do these different, run these different scenarios based on unstructured data before that.
Es war super expensive.
Now it's costing you a couple of minutes and obviously tokens.
But these are the two areas I also always look at.
So number one, you have obviously the efficiency gains.
If you can automate a process, then you have efficiency gains, right?
On the other hand, you can increase the top line, right?
If you're thinking about launching a new market or improving pricing, whatever, right?
So these are the two.
We already touched on one other topic several times, maybe before we close this episode, very briefly on impact measurement.
So how do you measure impact on your improvements that you implement with companies?
So I think best is if you can measure it directly in a monetary advantage.
But this is actually quite hard.
It just works in very specific use cases where you really know what you're doing.
For example, you have a process which creates some cost, for example, manufacturing a specific piece or running a delivery network.
And now you want to basically optimize it with AI.
In the end, you can look at the costs again, what you basically got invoiced, and then you can compare and you see the ROI.
The efficiency gains, that's something you can measure.
Yes, but that's very rare.
I think that's an example we can all work with.
But if you look in the wild, something like that is very rare.
So instead of that, As I said before in the episode, looking at the outcome is often delayed.
For example, if you, as in the example before, introduce a platform for better knowledge retrieval, you don't see this immediately on your top line because there's no cost immediately.
You will also not reduce a lot of headcount immediately because...
People will not just all quit or you fire them or even if you do, you have a lot of side effects which cost you money in the short term.
So you don't see this in a monetary value somewhere in your finance department.
However, what you can do is look at the engine.
Did it improve?
have a shorter process cycle, that you had less incidents, that you basically made decisions faster or didn't have to revert them, these kind of things tell you, okay, the engine is improving and then you should also be able to expect a return later on.
And then you can Hopefully, you'll see this more long-term also on the balance sheet.
That's again throughput, right?
So looking at throughput is giving you hints.
Yeah, indeed.
Also quality.
I think that's, I would like to stress that because increasing the velocity can also mean that you are producing more shitty stuff.
So we also need to have...
Both.
Exactly.
You need to have these balancing quality metrics, definitely.
Or even just focus on the quality metrics.
If the throughput is the same, but the quality is going up, then still you have a gain.
Absolutely.
Yes, indeed.
All right.
Cool.
Thanks.
With that, I would conclude the main segment of our little podcast episode and would now go to the final recurring segment.
So number one is the reality check.
So we usually ask if our guests have sometimes still WTF moments, or if there are topics or technologies or approaches that are really hot right now.
So do you have either or maybe both?
I have both.
I think there are so many WTF moments that I see.
I think especially on long-running agent tasks, the current LM still derail, sometimes randomly like crazy, and sometimes in very funny ways, where at some point it just decides to refactor your whole codebase and you just wanted to let it improve a certain section in a loop.
But since you're running in a loop at some point, by chance, it just wants to change everything.
There are so many of these things happening where you start an LAM and then in the end it just blows up.
And you see this either in the changes and then you can revert it, but also in your costs.
So you basically run through the roof with your tokens.
You would think harmless prompt, but in the end it just blows up everything.
Over and over again, I'm getting amazed how it can derail sometimes, even if I'm very deep into that field.
And what is hot right now?
I think Pi-based agents are very interesting.
because the ecosystem is evolving quite fast and it's super flexible and cool.
So this is a very small and flexible harness to build autonomous agents on top.
So not just like a co-pilot, but very autonomous.
And there's stuff like OpenClaw built on that.
I think that's the most prominent example.
And people build very amazing things with it.
Und ich denke, das ist sehr cool zu sehen.
Würde Sie das auch in einem Unternehmen-Kontext sehen?
Also wirklich large-scale, nicht nur für vielleicht engineering und localen environments, aber auch für die scale in Unternehmen?
Ja, wir tun das.
Es muss sich, wiederum, auch gut, gut, gut.
Das ist definitiv möglich.
Wir müssen einfach definieren die Interfaces, harden die Sandbox und dann I think it's ready to use in enterprises, just not blindly.
The reason why I was asking is there's still the question, some every now and then, are there companies around which are operating based on OpenClaw?
And that's why I was curious to better understand if you're running Pi somewhere at scale.
But yeah, thanks for sharing.
Ja, ich würde sagen, dass es eine Unterschiede ist.
Pi ist natürlich der Center für OpenClaw, aber OpenClaw kommt mit ein paar integriertionen bereits, die man kann, aber nicht haben, und man kann sich diejenigen, die man sich set up und wählen.
Aber wenn man eine spezielle Agente für bestimmte Tasks hat, dann kann man vielleicht besser mit Pi oder sogar Pi-Durable.
Das ist auch interessant.
Ja, 100 Prozent.
Es depends auf wie viel du willst.
Wenn du mit Pi startet, dann kannst du deinem Harness und kannst du es auf deine Neues.
Wenn du einfach nur ein Handelstuhl hast, kannst du mit OpenClaw.
die Verwendeninne Parts oder die Sandboxen Parts wird viel mehr mature, besonders durch die Push-Off NVIDIA, OpenShell Sandboxes und NemoClaw und all diese Open Source Projects, so dass Sie auch leichter designen off-the-shelf Sandboxes und nicht haben zu bauen, etwas super Custom wie ein paar Monate vor.
Unless you're utilizing the latest OpenAI or Enthropic models that basically jump out of every sandbox.
No, just kidding.
All right.
Last question, as usual.
Do you have a prediction for our listeners?
I think velocity will increase.
I think we just started with cranking up the speed in organizations in our society.
Und ich denke, dass wir nicht in einem Verlangenen in einem Verlangenen haben, wir müssen die richtigen System und Regenstern in die Lage sein und in die Lage leben in einem besseren Welt und nicht zu verhindern, was wir in den letzten Jahren haben.
Amen!
Regenstern, Regenstern, Regenstern!
Yes, velocity will increase.
And this actually makes head spinning, I feel like makes my head spin, because velocity already is crazy, right?
So how long can we still cope with that?
But we will have to.
There's no way around it.
We will have to.
We will have to.
Let's prepare for that.
Exactly.
All right.
Thanks, Thomas.
It was a great speaking to you.
Bye-bye.
The Beyond Vibe Coding Podcast is a project by Sebastian Heidemeyer zu Erpen and Andre Neubauer, partnering with Impala Search.
The content is created by us and our guests.
Join the discussion on LinkedIn or visit our website where we publish all episodes.
For questions and inquiries, feel free to reach out via LinkedIn.
Thank you for your time and see you in the next episode.