# AI Cognitive Debt and Engineering Ownership

**Podcast:** HMZE
**Published:** 2026-11-12

## Transcript

So we're going to have the spec at the beginning, we're going to have the input, we're going to have the output, but that magic in the middle, okay, that makes us think, assume, err, right?
So we make mistakes and redo is disappearing.
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.
Ich bin Sebastian Heidemeyer, der Erbroom CTO at Ecosia.
Und ich bin Andrei, CTPO at Trusted Shops.
Great to have you back.
In diesem episode, which is again somewhat different, we take a look at how AI will influence our way of thinking and how we remain responsible for the work we do with AI.
Yes, we're speaking with Ilya Sakharov, Executive Director of Engineering of Red Care Pharmacy.
And we are exchanging...
Vielen Dank, Ilya.
Ich freue mich auf die Gespräche.
So I cannot be held responsible for all the different slips that can happen.
It's our fault.
Yeah, it's your fault.
So my name is Ilya.
I'm based in Berlin.
I've been in software engineering and software engineering industry long enough to remember that everybody was afraid of a cloud and Kubernetes was somehow a good idea.
My career started around quality and testing, which probably explains a lot about mich sehr kritisch über die Sache.
Aber ich habe ein paar Jahre mit Plattform Engineering, mit Site Reliability, Developer Productivity, Architecture und Engineering Leadership, um mich über meine Karriere und immer noch immer zu fragen.
The same question with increasingly more expensive answer is how to make engineers move faster without setting the production on fire.
So this is also my passion.
So this also took me through work with Microsoft, with RSA, with HelloFresh, ETH, Codility.
And today I'm Executive Director of Engineering for RedCare.
RedCare Pharmacy is the biggest e-pharmacy in Europe, which you...
Probably know by the name Shopapoteke.
True that.
But that's only just one brand out of many.
Platform engineering was always a part.
It's always a weak spot.
I love it.
I like to build roads and not just drive cars, but now we are adding the autonomous cars to those roads, and those are the agents, I believe.
And those are occasionally...
und fahren in die Fläche hier und da.
Aber die Plattform ist, in meiner Meinung nach, der der underlying layer für alles ist.
Ich bin froh, dass ich hier sein und sehen, wo die Diskussion leads.
Vielen Dank.
Wir sind auch interessiert, wo die Diskussion leads.
Aber bevor wir zum Hauptpartnerin gehen, die wir...
Always ask our guests to talk a little bit about how they utilize AI in their day-to-day work and would like to do the same with you.
So what kind of tools, what kind of tech are you using?
Yeah.
So as the name of the podcast, Beyond the Hype, right?
Beyond the Vibe Coding.
I think we are beyond the models.
We are beyond the, is Opus 5.x better than whatever model.
I think we are into durable working tools.
And I've been, as an engineering leader and also with managerial responsibilities, I'm finding myself in this weird connection between utilizing AI for my kind of corporate managerial work, but also making sure that the organization follows and adopts and really takes the benefit of those tools.
And we moved, I mean, the AI explosion.
von individualen engineers.
So, der eine engineer, die die GPT-3.2, was es?
In March 23.
Und versucht, es zu tun, und dann hat sie die Personal Subscriptions für Corporate Code gebraucht.
Und dann hat sie gesagt, okay, wir kaufen alle ein subscriptionen.
So, es hat sich gemacht, wie es aus dem Engineering hat.
Und ich denke, wir sind...
Es ist ein sehr hochwertiges Leveres, und es ist Zeit, die wir benutzen, die wir benutzen, in etwas, das kann man kontrollieren, usabliert, finanziell, und dann sprechen wir über die Skills, über die Dokumentation, die Geschichten, die Geschichten, die Geschichten, die Geschichten, die Geschichten.
In RedCare, for example, we do have access to both Frontier providers of AI tooling, which is OpenAI Anthropic, and we use them for different tasks.
We do have internal LLM-MCP-LLM gateway that allows us to separate different models for different tasks, but this is mostly purely for financial.
weil wir sind sehr großartig und die Spend ist, natürlich, aufgrund der wir öfters benutzen.
Und ich denke, es geht nicht wirklich darum, was wir heute benutzen.
Wir haben beide.
Wir haben beide.
Wir benutzen Codex, wir benutzen Codex, und Cloud ist.
in parallel plus in other functions, because sometimes Cloud is actually better for functions outside of engineering.
Our marketing department, the pricing, all of those, they are all using AI tools.
And for them, we have an option to offer Cloud for engineering codecs pretty much fills the gap that we need.
Sorry, just an end for...
Für mich, ich benutze, für meine Managerial-Tasks und für meine Corporate-Tasks, ich benutze both Tools und finde, was besser works.
Okay, das eigentlich answered my question.
Ich war wondering, oder ich wollte, ob du auch noch contribuint auf die Software-Projekte, oder wie du benutze AI-Tools?
Vielleicht ist der einzige Frage, ob es da A harness you are using for your managerial work?
Is there, I don't know, a special setup?
No, not specifically.
I have my own GPTs that were created.
They helped me to perform certain tasks that are relatively repeatable.
The issue is that I'm also not really a big fan of calling this automation.
And I hear a lot of...
der Ausgabe der Wort Automation in der Zusammenarbeit mit AI.
Es ist ein Task, man muss automatisch, man muss AI zu tun.
Und in meinem Geist, das kommt aus Qualität und aus Automated Tests 26 Jahre alt, Automation ist ein deterministisches Prozess.
Und was jeder AI ist, ist es.
So we invest quite a lot of effort of creating those harnesses and guardrails and agents checking agents and one LLM validates the output of the other LLM in all this just to make sure that we are not deviating too much from our target.
But eventually those machines, those LLMs in general, they are not made to be deterministic.
So I would not call this automation.
Ich würde sagen, dass Menschen nicht deterministisch sind, und dass Standard Operating Procedures sind sozusagen Specs.
So, Sie definieren ein Prozess, in der past, das Menschen haben, und sie haben das nicht in einer sehr deterministisch Art.
Das ist deshalb, dass es diese Prozess Descriptions gibt, um die ...
pretty much the amplitude of deviation of results.
And I would say that that's fairly similar to what is being done with AI, at least under the label of automation, which is not, I think, everything that is being done with AI, right?
Yes, but I'm not saying that humans are better in determinism than LLMs.
What I'm saying is that we invented automation to achieve deterministic results.
Weil Menschen nicht sind.
So, instead of repeating the same error-prone, human error-prone process, by hand we introduced automation.
And then we know we can rely on this process to come to a certain conclusion in a very deterministic way.
If you automate with LLMs, you don't.
I see where you're coming from.
And I think that's also an interesting philosophical discussion.
Maybe we can even continue it in the main section of our discussion.
But first, I would say, let's move over to the main section of our podcast, to the meat, as we call it.
And there you created or came up with a quite interesting topic, which is about...
Cognitive, we already had in the pre-discussion a bit of a discussion on the terms cognitive load leading to, cognitive debt.
And yeah, please introduce our listeners to your ideas around this.
Yes, so I'm trying to look at what AI does, not only on the assisted coding or...
und die nächsten Software und so weiter.
Aber in general, was es tun uns als Menschen?
Und es kann, was ich sagen, kann es beklassungsvolle zu engineering, aber es kann auch nicht mehr als andere Kognitive Worker.
So, was passiert ist, dass wir zu sehen, viele Produkte und Medien und Texts generiert.
Und wir callen, among uns, die Slope.
LinkedIn even introduced that beautiful tag that you can mark a post you don't like.
It looks like AI Slope.
And it actually gets a hit in exposure.
But it also means that we are outsourcing the understanding.
Und das ist etwas, was ich denke, um viel zu viel zu denken.
Und ich denke, um dieses processus in dem, was thinking ist, was ein specialist macht, was ein Person, was ein Person, was ein System, was ein Person, was ein Teil der System, das Person, das Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die Person, die So we're going to have the spec at the beginning, we're going to have the input, we're going to have the output, but that magic in the middle, okay, that makes us think, assume, err, right?
So we make mistakes and redo is disappearing.
We are starting to have input and output and we are sourcing in the middle, which means that as eventually we are responsible for the things that we build, okay, we actually are creating a system that we don't know how it works.
And we don't know how it evolved up from the moment of conception or idea towards the product it is today.
We don't know this chain of architectural decisions and this chain of changes that were made.
And practically what we have is a black box.
And this black box is a very difficult thing to own, right?
Because I think ownership is about...
your ability to influence, and your ability to control.
Because if you're owning something you cannot control, you cannot really own it.
It's going to be a strange type of ownership.
It's going to be mostly scapegoating.
And in this case, we're creating those systems, and those systems are very far from understanding of everyone who is involved in building it, and we have no idea what that is.
Und wir sind sogar in diesem, ich kenne es, die Fantasy-Koncept der Magik.
Weil, wenn du Fantasy-Buch bist, sie sprechen über eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art, eine Art.
Und das ist genau wo wir, und mein größter Begründnis, with what AI is doing to us is that thing.
Okay?
Is that we are accumulating just like technical depth, something that we made decisions that were not completely right, but we made it under the pressure of time and the circumstances.
We are accumulating a cognitive depth that we will not be able even to process, even if we wanted to comprehend the system that we created.
And then the question is, who owns the system?
Who is responsible for system behavior?
And what do we need to do if we need to change the system behavior?
And in some cases we do want to change not only evolutionary as in adding additional feature, but maybe we need to fix a problem.
So we're going to outsource that too.
So there is a problem, there is a misbehavior, here is the stack trace.
I mean, we won't have stack traces anymore.
And here is the problem, here is the behavior, fix it.
Und wenn es nicht fixiert wird, dann wird es auf der nächsten Generation fixiert werden, oder auf der nächsten Generation, auf der nächsten Generation.
Wir haben noch nicht die Frage, was in unseren System passiert.
Ja, ich denke, wir sind bereits in die Prediktsland hier.
Es ist wirklich schwer, wie das alles funktioniert.
Ich höre, was du sagen.
Ich denke, dass ich auf der anderen Seite auch ein paar similarities bin.
For example, you mentioned in the beginning certain technologies, right?
I think not everyone is really aware how these technologies are really functioning under the hood.
I think also today there is not 100% control.
And then I think the second argument, but this is at least from my point of view already a weak one, it's always going up the ladder.
So, the languages, for example, we're talking about the programming languages today, right?
They haven't been there back in the days.
Back in the days, it was very low-level programming.
And then people came up with abstraction, abstraction, abstractions.
You could also say this is another abstraction.
However, it's not deterministic.
I think that is my main concern with that.
Not only that, let me address the two points, right?
I think we are on this very thin edge in which AI is treated as a tool, but also as already not only.
So we started from a point in which AI-assisted coding and all those code generators that we had, they were the tool, right?
Tool to generate a function or a class or a file or even a small system from the spec.
And then...
A human would actually look at it, understand, fix, maybe optimize and release.
So we use the tool.
Usually people who are really like AI pioneers and enthusiasts, they say, okay, but you didn't complain about the calculator.
The calculator was introduced and they usually post this picture of people.
Demonstrating with banded calculators and stuff like that from the last century.
It's not the same thing.
A calculator is a tool that performs a complicated action.
But it is, one, it is deterministic.
And second, it is taking away the technicality.
It doesn't take away the thinking.
I think AI is threatening us to take away the thinking.
Und wir sind überreinigend auf diese Systeme, um die Gedanken zu nehmen und die beste Weise zu tun.
Ich denke, das sollte mit Menschen bleiben.
So das ist eine Argumentation, die erste.
Und die zweite, die Abstruction ist, praktisch aus dem gleichen Ort.
Abstruction ist simplification.
Simplification aufgrund der Komplexität.
So you increase complexity, but you achieve simpler way to do something.
And here is where my problem is.
What is this something that we want to achieve to do?
If it is code generation, I'm perfectly fine with it.
If it is the idea behind how we build a certain engine, a 3D graphics engine, to finally beat the Unreal one, if it's even possible.
I think it's a problem, because then nobody understands how it works.
And the cognitive load, and as you mentioned, Sebastian, of inability to process the amount of things that AI is producing, leads to cognitive depth in which even if we wanted to understand that we would open this in the files and start reading the code and profiling it, we will not be able to.
Because of the size, because of the complexity, because of the verbosity of LLMs.
And then, what's left for us?
That's the interesting question here, right?
So I also think, and there's, I think, a vivid discussion currently going on, that there is a risk of us humans losing the ability to Ja, think through things, right?
So that we're outsourcing our thinking and are just, I don't know, consuming, like thinking very cheaply, very, very simple in simple terms and having the AI do the complex stuff, right?
And that's something that definitely right now would not work because the AI doesn't understand the concepts and doesn't have the context that is normally needed in order to do that.
Maybe it could work in the future.
But also they're not sure if this is something we would actually want as humans, because I think this is really pretty much a big part of what sets us apart from other living beings, right?
Living organisms.
And I think in code, exactly as you mentioned, right?
Or in software engineering, we still have code as the deterministic like at least intermediate result from what we're doing with LLMs right now with agents, right?
And I think that it's important to understand the systems and to do not treat them as a black box.
However, I also do see areas where we already pretty much have these very complicated black boxes.
And I do think that this will like become a very normal element in our daily lives going forward.
I think there are autopilots for planes and autonomous AVs are pretty much advancing.
We already utilize machine learning of different variations in different kinds of fraud detection or I don't know be it KYC processes.
Numerous situations where we already are utilizing these systems in a black box kind of way where we pretty much we train these things and then verify that the algorithms with a specific data set work according to what we expect, right?
And then we let them out in the wild and then we see if they behave similarly good in the wild, right?
Ja, aber ich habe eine Frage, um das erste Mal zu sagen, um uns zu verlieren, um die Möglichkeit zu denken zu können.
Ich denke, das größte Problem ist, dass wir die Möglichkeit haben, die Möglichkeit zu lernen.
Und das hat bereits angefangen, bevor die Bildung des Screen Generation kam.
Und so die Bildung des Bildungssystems war bereits ringen die Bellen, wie die Kinder haben, die Probleme zu lernen.
Und ich denke, das ist genau das was, was passiert.
going to be amplified because that thinking thing in the middle is what makes us remember stuff.
Right now we operate on input-output, so even if the output is correct and it is deterministic, we will not remember the same output when we will face this problem reasonably in a longer period of time from now.
If I face the problem again, I will automatically go and ask the LLM again.
Okay, I will not remember because I didn't go through the hurdle, okay, of try and understand and comprehend and dissect and combine in my head, which actually creates the connection between the neurons.
So I think the problem, the physiological problem is very real.
Okay, we are really losing that ability if we're going to rely on this.
Regarding your second comment, I agree with you.
And I think it's also inevitable.
I think we are going there regardless of what I think about, is it good or bad?
Wir sind da, da zu gehen.
Und wenn du starten, ich dachte, du bist über die komplikative Librerien.
Wenn du jemanden geschrieben hast, etwas fantastisch, und wie die 3D-Rendering-Engine, ich nicht will, ich habe es, ich integriere meine Librerien, und hier wir sind.
Ich muss nicht verstehen, wie es funktioniert.
Aber du bist noch mehr mit den Dingen aus dem Kode.
Und ich glaube, es geht und geht und geht.
Ich glaube, wir sind in einem Ort, wo die Begründung sind Lego-Blocks und nicht eine komplette Lösung.
So jetzt sage ich, okay, ich brauche den KYC-Modell, okay, ich brauche es hier.
Ich brauche den Engine, ich brauche es hier.
Und zusammen, sie erreichen, was ich brauche es.
Ich denke, wir sind zu dem Punkt, in dem all diese Gedanken über was ich brauche, und was blocks es ist es gemacht?
ist es komplett irrelevant.
Und ehrlich, vielleicht bin ich alt, aber es kind of schützt mich.
Ja, ich kann gar nicht mit dem Arschluss.
Es ist, zu einem gewissen, sehr scary.
Ich fahre, dass ich das.
Ich würde sagen, dass, also, dein Beispiel, dass du die KYC-Bootel da und dann die andere System da da bist, und du hast die vollständige Verständnis von der System.
Für mich, ich würde ein bisschen mehr connectionen zu, was du gesagt hast, dass es etwas wie magic, was wir erleben mit diesem Technologien.
Und jeder neue Technologie, die sich erst als magic fühlen, und später dann wird es normal.
Und ich würde sagen, es ist das gleiche mit dem wir erleben heute.
Let's take the phone.
Someone had the idea that we could send signals through Vias.
On the other hand, these signals could be received and then we could have these Morse codes and could pretty much decrypt the messages, which was a very strange thing and only a few people could do it.
And then these could translate these messages into normal.
Und aus dem, wir haben landlines, wir haben die Internet, die Smartphones und was nicht.
Und heute ist es einfach, wie natürlich, jeder hat eine iPhone.
Und das ist sehr normal.
Und so, ich sehe ein world, wo das ist passiert mit pretty much any Artificial Intelligence, das wir in der day-to-day-zeitigen Welt.
Ja, ich glaube, es war Graham Bell, richtig?
Ja.
Und du kennst, wenn er die Telefon, die erste Telefon er hat, hat er zwei missed calls von Chuck Norris.
Wir haben jetzt unsere Outtake.
Ja, ich denke, du bist correct.
Wir gehen das so.
Although, if I look back and I look at those magic moments that happened, I think people still asked how they work.
And there were still people who were actually digging into the nitty-gritty details of how things work, how the electronics work.
We have electronics enthusiasts.
We have those people who are creating systems at home.
I don't think this conversation actually happens regarding AI.
It is very, very niche.
There are specific groups that are really building those things, but the consumers, I don't think they are bothering themselves to really ask the question, how does it work?
You can say the regular iPhone user also doesn't ask how the iPhone works.
I was about to say that.
But I still have hopes.
Aber auch für Ihre Arbeit, ich kann mir vorstellen, dass wir ein Zukunftsleben sehen, wo die Organisationen ist, die Menschen, die diese kinder Systemen sind, wie sie mit Automatischen und AI sind, und Menschen, die sie einfach nutzen, ohne eine größere Entstehung zu verstehen, wie diese Technologie eigentlich funktioniert.
To some extent, you could also say we will see some kind of Taylorism, where you have the people working on the system, so building the system, having a good understanding for those.
I think critical thinking is super much needed because it will not...
taken from their shoulders, right?
They need to build a system.
And then there are people who are utilizing that.
I think that was also one of the latest episodes when we talked with Bastian, a friend of us.
We actually worked together at ImmobilienScout.
I think he made a point or he made the observation during their AI transformation at the Getaway Group.
Maybe it's not the right approach that everyone needs to deal with AI technologies, because not everyone is also capable of understanding that.
Actually, for some people, digitization is already too much.
In our company, we are facing this quite a lot.
We are trying to digitize healthcare.
Und wir sehen sehr interessant, die Leute, die auch abnative, die wirklich sehen, wie eine normalen Evolution von wie du receive, wie du medizinisch Medikation hast, wie du regieren, aber auch die Gruppen gehen, die Leute, die die Pharmacy, weil das ist, was muss passieren.
Und Regulations und Governmenten nicht wirklich helfen.
Ich glaube, dass ich das, was ich, das ist, dass wir die Systeme, die Menschen, die es in einem mehr sicherer und confidenten und mehr, ist, dass das Transition von der aktuellen völlig wilden AI-Markt nach dem Plattformen ist, dass es sich um das Problem ist.
Und wieder, es starten mit Engineering.
Engineering ist es konsumierende AI und Agents in eine sehr natural und organische Weise, aber dann spüren über andere Funktionen.
And andere Funktionen sind nicht technisch savvy, um ihre eigenen Agents zu entwickeln.
So das ist warum die Unternehmen wie NA10 oder so etwas kamen mit Low-Code, No-Code Situation.
that allows someone to create whatever they need.
I don't like those approaches because eventually the engineering is going to own that thing that they created.
But I think it's also up to engineering to allow marketing to use AI in the proper way, which was the second point that we decided not to discuss.
And that was...
Platform engineering now serves not only engineering.
It's a revolutionary moment.
It is sales, it is marketing, it is finance, it is legal.
All of those functions are now going to rely on AI-tailored products delivered to them by the technical department and within the guardrails of security, privacy, and NPI protection.
And there the question...
ist, Nummer 1, vielleicht eine Leading Frage, ist das inevitable oder avoidable?
Ich denke, dass wir in dieser wilden arms race zwischen den zwei AI-Superpowers in der Welt, die nicht wirklich stoppen sind, das ist nicht stoppest.
Ich denke, das ist inevitable.
Ich glaube.
Und wir müssen sicher, dass wir uns protect.
was wir uns nicht zu beurteilen, weil es nicht ein Problem kommt von einem Frontier-Modell-Produk.
Ja, das ist auch genau das, was ich dachte, in der Sinne, dass wir die Situation brauchen, die wir jetzt vorhanden sind, die Änderungen sind, die jetzt vorhanden sind, und wir müssen uns auf die Vor-Fronten, so dass wir sie in die richtige Richtung steigen können.
in our own spheres of influences.
Look what's happening with security recently.
Look at the PHP thing that happened and all of this.
The amount of new vulnerabilities being found today is 100 times faster or bigger than it was before the latest frontier models appeared.
So, everyone is scanning now things, everyone is finding things, and the amount is unprecedented.
Now, one would say that, you know, it's actually great, because we will then fix all of them, and then eventually we will get to the point where there is none, right?
Because we will find all the problems and we will fix them, and it's great that it's being exposed.
But I think the amount of software and buggy and leaky and vulnerable software produced right now out there by the same LLMs is exponentially higher.
So that's the spiral of death.
Yep.
I was just about to ask how you see that point.
Spiral of death.
There will be a very positive ending for the main section.
Just kidding.
But I mean, in the end, I think that's it, right?
That's the reality we have to face.
And I mean, we're all in different...
Und ja, natürlich, die zwei letzten Wochen haben sich mit scramblingen, zu mitigieren, oder zu erstellen, zu assessen, ob es da sind, wenn es da sind, dann zu schramblingen, zu mitigieren.
Ich würde sagen, die E-Mudra, was wahrscheinlich, depending auf deinem TechStack, aber diese waren die zwei größten, und wir haben auch Diskussionen.
Wie können wir unsere Posture verbessern, so dass wir schneller reagieren können?
Wie können wir die Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort-Effort- Es ist auch ein Wack-up-Call für die Sicherheit in den Organisationen.
Usually, die Sicherheit in den Organisationen sind, insbesondere die large Organisationen, die nicht mit Sicherheit auf den Tag zu verändern, das ist nicht der Bremsen oder Business, sind sie reaktiv.
Es ist die Faster Patch, es ist die Vulnerabilität, es ist die Bug Bounties, es ist die External-Researcher und so weiter.
Das ist wie es funktioniert.
Ich denke, es ist Zeit für die Internal Offensive.
Das ist eigentlich finden die Dinge in der Software, die die Unternehmen produzieren, bevor es den Kunden gibt.
Ja, ich würde sagen, dass das eine der Hauptverantwortlichen Hauptverantwortlichen Anwendungen ist.
Es sollte eigentlich nicht sein, aber die Anwendung der Investitionen Das ist wahrscheinlich auch ein bisschen mehr, relativ zu den overallen...
Ja, wir alle wissen, die meme, right, der Security Budget vor der Breach und nach der Breach, right?
Ja.
So, ja, aber ich denke, dass das Offensive...
Es ist nicht nur App-Scan oder App-Penetration-Test, das Leute usually machen, nur um die Certification zu bekommen.
Es ist eigentlich, dass das Teil der EURSDLC mit proper...
System tuned to attack your software as it would be attacked in the wild.
Yes, definitely.
And so you're talking about proactive security, right?
Yeah, fully agree there.
I think, Andrei, you also wanted to say something?
Well, like earlier to the points, I'm just saying maybe let me try to sum it up.
Ich denke, alles, was ich gehört hat, dass wir als tech-people sollten alles tun, um die Organisationen mit der Arbeit zu machen, um die Möglichkeiten zu machen, aber nur zu einem bestimmten extent.
Give some kind of direction how to handle that situation.
I think that is for me the takeaway.
Because I think what we discussed in the last two minutes was actually the opposite from what we discussed before.
At the moment we are in this war where on the one side people are really using the capabilities without having a clue.
What are the consequences?
Because they don't know.
They do this in the best interest, for sure.
But at the end, it doesn't matter whether engineering is responsible or not, but at a certain point, they will ask you.
So something went down the road.
What can we do?
How can we fix that?
On the other side, you see that even the most technical person, those who should understand how this is working, are struggling because they don't know.
We try to find patterns back from what we have learned, but there is actually no pattern because this technology, this change is so massive that there is no cognitive model yet.
And I think until then, we need to somehow guard this.
And I'm also not a fan of this low-code thing.
I'm more fan of that.
Forward deployed engineers.
I think that is one.
This is a model to somehow overcome that situation.
But I'm really curious if we maybe meet in a year or two how this evolves.
And if there are, I'm more or less always looking for this best practice, how to build the organization to ensure you make use of the capabilities, but in a way that it puts your company not fully at risk.
Sorry, that was a long summary.
No, no, I agree with you and I think the risk is real and because of the, and you said that the change is massive and I think the change hasn't happened yet.
Right?
Don't scare me.
I think this change is just evolving and unwrapping, okay, in a form that we cannot really understand what's the end state.
Right?
Because again, if we take the internet or the telephone or the iPhone moments, they are kind of momentarily in time, right?
Nobody thought it's possible, now it is.
Boom, okay?
And then you evolve, you make it better, bigger, brighter, battery longer, blah, right?
Here, next Monday, we will wake up to the new world.
And that's actually a key point here, also too.
Ich will ein bisschen meiner Summary oder zu Ihrer Summaries.
Ich denke, dass, ein, da ist kein End-State.
Also, da war kein End-State mit den letzten Chancen.
So, die Internet hat sich schon seit Jahren verändern.
Die Speed von Change ist massiv, also es ist viel schneller und schneller.
Das ist ein, das ist ein.
Und ein, das ist ein, das also bedeutet, dass da eigentlich keine Continuous best practice, right?
If you also compare to previous times, you should do extreme programming and then you should do Scrum and you have Scrum Masters and product owners and then suddenly Scrum Masters are actually not needed anymore because the companies are agile and then you go into Kanban and then POs are not really POs but PMs and you follow Maybe, I don't know, the Silicon Valley product, whatever, right?
So it's constantly evolving and the speed of change is increasing and that's just the reality that we have to face and that we, based on what we know in this moment, we need to make the best decisions, right?
And yes, next Monday, there is a different situation and we need to make different decisions.
By all means, yes.
I think we still need to do extreme programming.
All the rest is not worth it.
I cannot disagree with that statement necessarily.
All right.
But I think that's a great ending for the main segment of our podcast.
So let's go to the two small recurring segments towards the end.
Yeah.
So we always ask our guests what the fuck moments they have still with...
und auch was die hottest Sachen sie sehen.
Ja, du hast noch etwas für unsere listeners.
Ich denke, für den WTF-moment ist es das, die misinterpretation der User-Intent.
Und ich denke, es passiert uns oft zu uns, wenn wir etwas sagen, und wir wollen den AI-agent oder ein Chat oder etwas machen, und es macht absolut And it sees into our soul our true intent, right?
So we didn't express through words.
But recent What the Fuck moment was again, we use also Copilot with modern models and engineer just added Copilot for advice, tagged agent, Copilot agent into their code.
und die Staten der Testen für den Tests geplanten, und das Agent interpretiert es als eine Einwittung zu completely rewriteieren.
Und es hat die Funktion zu passen, die Existenz zu passen und wirklich spirale aus der Weg.
Ich denke, das ist, dass man sich beherrschte, was, was du willst, und wer du willst, und wer du willst.
Und wenn du es, dann mit intentionen.
Das wäre wahrscheinlich das FAC-Moment.
Es ist nicht so catastrophic, es ist einfach so weird zu sehen, und es einfach nur die nondeterministische Natur und auch vielleicht die Inablierung der Menschen zu expressen, ihre Intent in eine clear Art.
Und ich denke, die HOTA-Stuf sind, dass wir die Models passen, wir passen diese Tape.
Not the hype, but maybe the overly enthusiasm about things.
So now kind of AI becomes part of our world, part of our toolbox and everything.
So I think it's super important to understand the financial implication.
And it becomes more and more important, not only in engineering.
Again, as we said, engineering may be the owner of those tools, but it's the whole organization that uses it.
And I think measuring the impact, not by Adoption, percentage of adoption, or consumption of tokens, right?
That people are kind of token maxing and everything.
I think it's going away because of the price now.
But really measuring how to build the economics and FinOps and measuring the value, okay, of your AI usage in the end value to the customer, right?
And I haven't found yet a comprehensive way to do it.
There is a simplistic way, there are very complicated mathematical ways, but none of them feels right.
And I think we are still circling around it and I would really love to see more and more thoughts about it.
So for me, it's kind of hot stuff because I own the P&L.
Makes sense.
Yeah.
Right.
Thank you.
And then last but not least, do you also have a prediction for our listeners?
Ja.
Das ist etwas, was ich bereits mit LinkedIn und anderen, und es ist ein derivative von unserer Gespräche.
Wir sind in der Erre of die Disposable Code, in der die nur die Konstanz ist, wahrscheinlich wird die Architektur.
Schaffolding, oder whatever.
Und every part of the software that we want will be completely regenerated.
Every time.
So there will no longer be builds.
There will no longer be CI-CDs.
There will be regeneration of the model, maybe directly into production.
So it's going to be like Ultimate CD.
And the issue is that if we take it further and say, okay, and people don't need to understand what's in it.
then maybe extension of the existing function with the new capability that we want is going to be just another regeneration.
Fix is going to be regeneration.
Now, with this regeneration, because of non-deterministic behavior, the core of their function will change.
And I foresee that as, if you remember, some experiments were done by Microsoft and others of having two bots talking to each other, and they invented their own language.
This might be the death of the programming language.
There will be a language created between machines, understood by machines, and it's probably not going to be binary.
But we are going to be completely squeezed out of that process.
Thanks for the prediction, right?
Closing word from my side.
When we started this podcast season, so we had a podcast three years ago, and then we stopped it and we restarted it.
Earlier this year, because of AI, and we said, okay, we need to have an outlet, we need to discuss this.
Our first guest was Stefan Schmidt, and he is a CTO coach in Europe, quite well known.
And his claim in our first episode, I think, was that code will go away.
So that was exactly this prediction that you were just making.
Cool.
All right.
Thanks a lot, Elia.
Es war ein Prazer.
Danke für mich zu haben und zu patienten und zu führen diese Diskussion mit mir.
Ich komme ein bisschen smarter, als ich in.
Danke für das.
Wir kommen immer ein bisschen smarter.
Danke, Silja.
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