# AI Strategy: Overestimating Short-Term Gains

**Podcast:** HMZE
**Published:** 2026-08-27

## Transcript

I think we humans are really, really bad at predictions.
And I want to maybe have like a dispel a bit of predictions to play a game of how we are really bad at overestimating short-term gains and how bad we are at underestimating long-term gains.
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 diesem Podcast, wir beschäftigen die transformational change in Software Engineering und Knowledge Work in general.
Ich bin Sebastian Heidemeyer, zu Erpen, CTO at Ecosia.
Und ich bin Andrei, CTPO at Trusted Shops.
Great to have you back.
This time, it's more a philosophical talk.
Und, well, let me start with a question.
Who knows Amaro's Law?
Yes.
Amara's Law basically describes that we usually overestimate the impact of change in the short term, but underestimate the long term impact.
And the question is, is this also true for AI?
That's what we discussed today with Ras Shruti.
So hope you enjoy.
Welcome to the show, Ras.
We're excited to have you on the show.
And as usual, I'd like to ask you to please introduce yourself to our listeners.
Yeah.
Hi, thanks for having me.
I'm Raz.
I am currently the Group CTO of Fox Money.
And I've been very much active in the Berlin tech community for a very long time.
I was part of working in many different companies.
I think we probably even intersected in some where we had friends working with each other.
I know I had lots of friends at Scout24, for example.
Worked in Berlin for 12 years now in management roles.
Started at a company called Oxenata, which was a startup that Selly went under back in the 2014 days.
Worked Wikimedia, moved from there to DeliverHero, a very well-known company where I spent five years of my time.
And now I'm already, what is it, two and a bit years at Ox Money?
Und für diejenigen, die nicht aus dem Berlin-Ecosystem sind, ist ein Dissadolf-Based-Besystem.
Aber die Pandemie wurde, und das hat mich auch aus dem Berlin-Ecosystem gearbeitet.
Und das hat mich auch aus Berlin gearbeitet.
Und das hat mich auch aus Berlin gearbeitet.
Und Ox Money ist die Europäische größte Lending-Platform, für diejenigen, die nicht wissen.
Und ich habe eine Artikel, die uns in der ersten Zeit gewonnen hat.
fourth place of fintechs in Germany, which is a nice, let's call this a nice break from my side, with companies like N26, Scalable Capital and so on.
So Ox Money is very much less known, I think, because it's not in Berlin.
So it's less in the cool sphere, but very much one of the most successful fintechs we actually do have right now.
Interesting.
Yes.
All right.
And as usual, let's start with the status quo.
So how are you currently?
Was kind of tools are you using?
Was kind of workflow skills?
Be it in your coding or in your managerial capacity?
Yeah.
Fantastic question.
So let's start with my private setup.
I like my private setup.
And I can talk about work a lot, but I actually don't get to write code almost at all, unless it's for my private projects.
To be honest, I've been through so many different setups right now for my private projects.
My current one that works really well is, I wonder, have you read this book called GUST?
It's Growing Object-Oriented Software Driven by Tests.
No, I haven't.
I love that book.
It talks about basically architecture and mostly outside in TDD.
I'm going to drop some stuff that maybe will get me killed by the audience.
I was never a fan of TDD until AI.
Ich denke, AI hat mich tatsächlich lieb TDD, weil mein Gehirn never war, wenn ich über die kleinen Unen und dann von einem Unen zu schreiben, und dann von einem Unen zu großartig.
Ich war also immer besser an den Outside In, so ich habe einen wirklich großen Test, und dann habe ich meine Features, und dann habe ich die Unen Testen.
Aber jetzt mit AI, es ist eigentlich eine fantastische Situation.
So mein Setup ist eigentlich sehr viel ein Grill Me.
where I have a lot of gray on me until we're done.
So I want to basically find every stone and turn all of them as much as I can.
Then I get to have my unit test written and I can basically go and review those structures as I go from my project.
And then from there I go to integration tests.
I see implementation.
And then I go to some tests on the outside.
So basically I have those kind of like keg cleared.
Ich habe das erste Buch geschrieben, die ich in diesem Buch habe, die Spezifizierung-Driven-Development, die in diesem AI-Framework ist, und es hilft mir ein sehr, was ich mit Goost und das Setup-Mine-Setup.
Wie funktioniert mein Team?
Das ist nicht wirklich das, das ist eine persönliche Wahl von meinem Seite.
In Oxmine, wir versuchen, die Menschen zu geben.
Ich liebe die Namen Copilot, aber nicht für die Microsoft-Tool.
Ich würde einfach nur gerne an AI als mein Co-Pilot oder mich als Co-Pilot.
Das heißt, es ist mir als Co-Pilot.
Es ist mir als Co-Pilot.
Aber wir haben Co-Pilot, aber später mit dem Zeit, wir haben Cursor, wir haben Klaus.
Wir haben Klaus, so wir haben unsere Entwickler zu finden, was die Setup funktioniert.
Und als es folgt unsere Prinzipien und die Veränderung von wie wir wollen, und wir haben Es kam also, weil viele meine Devs ...
For example, our PHP devs, they really like to have their environment, so many of them struggle to move to Cursor from PHPStorm.
So giving them Claude and using Bash is a much easier setup.
Some people actually do enjoy Cursor and how it works.
It gives them more ability to, in a sense, interact with their IDE.
So we've kind of given them many different opportunities to basically be as productive as they can.
So that's basically, I guess, I hope I answered it and didn't babble too much.
Thanks a lot.
The fact that you're a fintech also, and you hinted at that a little bit, makes pretty much compliance and security much more important, right?
And you said, you hinted a little bit that you have a lot of protective systems around pretty much what the engineers are using in order to develop code.
Can you, like...
Detail this out a little bit more without going into any trade secrets, obviously.
Of course, exactly.
That's what I'll do.
So I think the one most important thing I'll start before, you know, the basics.
Everyone will say, first of all, education.
You cannot put a system that in the end, any system can be abused.
So what you want to do is to educate.
So we do give a lot of education around AI and how to use AI.
Whether it is external courses or internal information that we share.
So oversharing information is always good.
Let's quickly ignore that because we're talking about tools.
For us as a fintech, a lot of the important parts is honestly, let's even start with which AI can we use.
So even when we're looking at a, I don't know, like a chat that you want to roll out to the entire organization to make people, you know, what they do right now.
Proudly self-evaluate themselves with their chatbot or send emails and so on.
You're exposing potentially company information into a whatever, a model.
And it's not necessarily something that we can allow, right?
Like those models are trained onto that.
So if you have information going to the US, that's also not okay.
So we started by actually in a harsh way blocking every external chat GPT and so on and so on that we can.
No access to that, but we've given them LangDoc.
LangDoc is basically, I don't know if you know, it's a Berlin-based company and they aggregate different models.
And the agreement with LangDoc is that they don't train on that dataset.
The dataset is only staying here in Germany and it's basically enterprise-ready.
You can have role-based access, you can do many things.
And we also told our people, you're welcome to use it as your own from your private laptop if you want to ask about.
Das ist der Grund, das ist der Grund, das ist der Grund für uns.
After that, we have GitLab Ultimate.
So basically we see that on the pipeline itself, we want to automate as much as we can to avoid the hugging face type of scenario or any type of NPMs that you don't want from supply chain.
So we want to have our systems working to block from any type of mistakes that can happen.
So anything that goes into our production goes through a rigorous CI, CD pipeline that has a lot of automatic tests.
At the end of the day, it's again about the process.
And process for me would be a 4i principle.
And AI is not a 4i principle.
It's great that I can have my AI co-pilot, not the tool, but as a concept.
And I work with my AI co-pilot.
But the code review should be done by another set of eyes, also with the support of AI.
That's fantastic.
But at the end, I am the human who ships the code.
And there is a human that actually signs off on the code.
So we test ourselves and we also have a testing period, right?
So similar to any other company with a secure software development lifecycle, we also make sure that there is a QA phase.
We do not have QAs, by the way, but we have at least a testing period.
Either it is automation end-to-end or if there is a bit more complexity, then the developer that takes the code also does a manual check themselves.
So this is basically how we play with it.
So of course, a lot of automation tests, unit testing, integration testing.
A lot of security tests with GitLab Ultimate.
And at the end, of course, policies that set us to what we can and can't do according to regulations, which is for FinTech super important because protecting the data of our customers is our number one priority.
And is this also the target state?
So what you are aiming for?
Is there stuff where you say you want to achieve that, but like we don't know how to do this at the moment?
To be honest, I think all of us are chasing, like we're dogs chasing an imaginary car and the car is faster than us.
And also when we catch the car, what do we do with it?
You know what I mean?
AI is moving so fast.
So for example, hugging face, everything that happened there was a big shocker for us, right?
Because we didn't know it's happening and now it's there.
So a lot of the thoughts we have right now is how do you minimize and how do you...
Make sure that supply chain attacks don't happen.
And that's a very difficult task to have.
So we're working on that, of course, to kind of at least bring it into a better environment from our side.
And again, a lot of it will also be adding more and more systems that we can to make sure that we can deliver code faster in a more secure way.
So adding more checks into the pipeline while we try to also optimize to...
Es ist ein bisschen wie das mit den Schadern, wie man die beiden zusammenfassen.
Wir versuchen beide zu tun.
Wir versuchen, die die Velocität der Verlösen, während wir die Ressourcen reduzieren.
Wir sind weit von der Target State, aber ich kann nicht sagen, was die Target State ist.
Denn every drei Tage die Target State sieht anders.
Und es ist für uns immer immer zu halten, was es geht.
Und es ist für uns, dass wir uns auf die Augen auf was.
Und basically, an einem Punkt, ich denke, die Target State wird sich selbst verändern.
Basically, a softer design, like a garden, right?
You basically plant small things and you kind of find yourself with the garden that is revealed to you rather than planning a garden in advance and then realizing that the soil doesn't fit or the sun is not coming from the right direction and so on and so on.
I think the picture with the garden is a nice one, but to some degree incorrect because for the garden you...
Probably can plan for that because there is a lot of knowledge around and a lot of experience and people who did this already.
But with AI and there, I absolutely agree.
It's a bit of unfair to say this is the target set.
At least you need to adjust that.
And which is actually also a great segue to our meat section, right?
It's super hard to.
predict what will be the future about.
But may you want to introduce Demit on your own?
Yeah, absolutely.
I think the discussion was our short-term overestimation versus our long-term underestimation.
So we are really, really bad at estimation.
We as humans, all of us, I think.
Like this funny thing with, I think, Bill Gates.
Wir haben über die Kilo von 512 Kilobytes von RAM gesprochen.
Das ist in X amount of Jahren und wir haben das über ein paar Gigte.
Wir sind wirklich schlecht an überestimating short-term und wirklich schlecht an überestimating long-term.
Ja, ich fully agree.
Und ich denke, das ist auch so für AI.
Even though, jetzt, es fühlt sich ein bisschen anders.
In der ich nicht, und viele Leute würden nicht, uns zwei Jahre alt haben, oder was es noch mehr, drei Jahre alt?
Wenn Chagabert kam, dann haben wir jetzt automatisch kodingen agents.
Aber zumindest die Impacts auf den laboren Markt.
are less clear and predictable, right?
In the sense that the short-term impacts are not measurable as of yet.
If you discount pretty much the fact that some companies are using this as a nice financial market explanation for laying off people, right?
So it's not necessarily that the gains in productivity already would allow them to let people go.
Probably are productivity gains, but they usually would be overcompensated by the new ideas that companies would have to keep people busy, essentially, right?
Yeah, on that side, actually, I'm quite an optimist.
I think, I mean, if you look at the idea of how the washing machine was invented to reduce labor from the home front and what happened in the end is we just do laundry more.
I think most companies are realizing that, hey, if we have a superpower that can improve productivity, Letting people go means I invest less in myself, but actually having the same team or growing that can do 200% means I can actually improve my business much faster.
So at some point, I believe that it will also equalize a bit.
And even on that, I think if I look at AI, AI is just another, at the end of the day, another obstruction from machine language.
And if you look at how machine language started, There was one company, a couple of universities in Nassau that you wrote code in.
So if you were a software engineer, you worked in five places.
C came in, basically brought us Microsoft and other companies, so we basically spread the workforce over.
I generally believe that AI will just very soon, I'm just an optimist, what can I say, will also create many different opportunities that other people will create startups and then they will need their Vibe coders or agentic coders to come in and then they'll be...
Maybe smaller teams, but more work.
So I generally think that the software engineering position is not dead, it's just transforming.
We just need to be kind of part of this transformation to figure out what are the right toolings, processes, and how to do it well.
But I'm on the optimistic side, by the way.
So this is my maybe great future estimation.
And is this now an overestimation of the short term or an underestimation of the long term?
Ja, das ist, ich denke, vielleicht ein überstimmungsvoller Anerkennung von meinem Seite.
In der Shorten, wir alle sehen, dass die Markt ist ein bisschen stalling.
Ich denke, es ist ein bisschen verändert.
Ich weiß nicht, ob die beiden Sie, aber ich habe mehr Positionen open und weniger Candidates.
So vielleicht ist das mein eigenes Ziel.
Aber ich denke, mehr Leute sind wiederholt.
Aber ich bin nicht sicher.
Es ist schwer, zu schauen, es auszulich.
We all went through the COVID hypergrowth.
All of us, instead of optimizing for profit, we've optimized to who can hire the most people.
And I think Spotify even admitted that.
They said, yeah, we chased numbers of overhiring.
Ich denke, andere Unternehmen haben nicht erwähnt, aber sie sind auch die gleichen Sache.
Ich generell nicht anwenden AI zu diesem, weil ich kann es aus dem Ausmutter von der Aux Money side sagen.
Wir genannt haben die extra Abilität zu tun.
Es gibt einen Weg zu bewegen.
Ich habe andere C-Levels mit mir als CTO gearbeitet.
The two of you probably can relate.
You always get people coming with ideas to you.
And usually as a CTO, you say, I am the protector of the roadmap.
The team can only do X.
So if you want to add one more, I need to make sure that something gets out of the roadmap.
Now we are able to say, yeah, let's give us more.
And it changes a bit the mindset.
You know what I mean?
Back in the day, we wanted a lot of data points.
Should we or shouldn't we invest in this?
Because this will be seven weeks to prototype to even figure out if it makes sense to build.
Do we really want to invest a team?
Today, we can prototype that quite quickly.
So it gives me the ability to say, hey, why don't we try it out and throw it to the trash if it doesn't work?
It's an easier negotiation way for me right now than before.
So again, I think if we change a bit the ways we're thinking, it's quite an optimistic future here.
Again, this is me overestimating, I think, for...
But the optimistic enemy sees, at least on my day-to-day, how working with my chief growth officer and my CFO and my COO, when they come and they want something, I'm able to give them some quicker results and then we can test the water if it's worth it or not, rather than back in the day.
So why would I want to cut my workforce if my workforce can do that and drive the business forward?
So there are two thoughts in my head right now.
Double click on that one second.
So number one, maybe how big is the engineering workforce at Aux Money currently?
Yeah, fantastic question.
We are about including our data team, our AI team, the technology and the infrastructure, approximately, what is it, close to 100 people, I would say, with the AI.
Yeah, all right.
And how do you pretty much...
...
in front of customers.
There needs to be some guardrails around, okay, what kind of innovation needs security up front?
What kind of innovation?
Maybe not because it's just a marketing tool or whatever.
It's not that relevant, right?
So yeah, how do we do that?
It's exactly strategically where you can and you cannot, right?
I'll distinguish POC is not MVP.
So we can prototype things.
It doesn't mean that we take this prototype and put it in production.
Es ist mehr für uns zu testen, und dann, wenn es gut ist, und es gut ist, und es macht Sinn, dann können wir ein Produkt, das vielleicht eine andere, eine andere, eine andere Prozesse zu bauen.
Und dann kommt es auch die Strategie.
Eine Sache, die wir haben, und ich glaube, es ist nicht neu, alle machen es jetzt, ist ein Chatbot.
Aber ein Chatbot ist ein sehres Ding, um zu bauen, schnell zu haben, haben die Möglichkeit, zu können, um die Möglichkeit, zu können, um die Möglichkeit, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, um zu können, Und es ist etwas, dass es relativ gut ist.
Wir haben experimentiert mit es.
Wir haben eine kurze Prototypen.
Wir sahen, dass es funktioniert.
Und dann haben wir gesagt, okay, wir bauen es in der App.
Und das ist wo wir, okay, das ist wie es funktioniert.
Das ist also wo wir uns mit der Security Parten gemacht haben.
Und nachdem wir das gemacht haben, wir sahen, es funktioniert.
So, again, wir bauen die Frontend in der Web Parten.
So, das ist das, was wir able to do relativ schnell.
Ich denke, es hat uns für Monate zu basically...
Do a lot of tests to see that you cannot inject AI into it and so on.
Make sure the answers look good.
And then build it properly from basically prototyping it with a chat, with a LangDog chat, to then put it properly in Bedrock under proper security that is available for the customers.
And then build the UI for Mobile and then later for the web.
That was quite simple for us because in the end of the day, Die goal war zuerst, dass es Leute mit den Leuten spielen.
Wir sahen, dass es sich nicht so gut macht.
Und dann haben wir es, dass es sich nicht so gut macht.
Und dann haben wir es, dass wir es in einem Langdok-Check haben.
Es wird ein Bedrock, es wird ein Teil unserer AWS Infrastruktur, es ist unter our API Gateway und so weiter.
So, of course, security comes first.
Aber die Logik selbst, das Chat-Agent und wie es...
We already knew how it's going to work from day one because we basically use our FAQs.
We trained it with our information.
We trained it what it can and cannot say.
We put the right boundaries.
That gave us this, in two days, the ability to let everyone in the company kind of play with it and see if it works well before we even put it in development.
And then development was relatively quick.
But again, this is where if you have your core, right, and you have a tree coming from the core, it's easier to do those things at the outskirts.
Aber es ist nicht etwas, was ich in die Mitte der System plugge.
Das ist wo die Realität ist wirklich tricky.
Das ist wo ich nicht prototypfe Dinge und es ist haste.
Aber was man das Plugge auf die Satelliten oder wenn man die Moon auf die Planet hat, sind die Dinge sehr leichter zu tun.
Ja, und dann, wie du es machst, ist, dass du ein POC hast.
You test it internally, which means that you have the audience under control.
You know it's people who work for you and who wouldn't abuse the system.
And then once you validate that it makes sense internally, then you have strong signal that it also makes sense.
The further investment in order to pretty much secure it and bring it on the production infrastructure.
Und die andere Partie kann man das automation machen.
Ich denke, Software Engineering ist eine der besten Dinge, dass wir nicht nur die Dinge tun haben, sondern auch die Automatischen Prozesse haben.
Du kannst die Lohnprozess machen.
Was Ox Money ging, du gehst zu einem Bank, du sprichst zu einem Person, der paperwork ist dann zu einem Automatischen Prozess.
Wenn du es auch als internaler Konzept, AI ist wirklich helfen uns auch Automatischen Prozesse für die Firma.
So basically, wir können internen und dann haben die AI-Team See how team X, Y, or Z work at the company and figure out how can we support them to be more productive, to be able to basically address more cases with the customers or how do we improve flow of similar to what we do in software engineering, the velocity of the team through the flow and use AI in those steps.
And I think that's also been a fantastic way for us to let our employees experiment because now Even people who were not using AI back in the day are now trying because they see, oh, I can now play with some things, talk to the AI, come with a concept and then go to talk to the team and say, hey, I've done this thing and with my Lambda Doc chat, I was able to potentially optimize here 20% of the work.
Can we automate that?
Can you take this and make it into Salesforce so I can basically have it automated for the team?
And that happens and that's a fantastic thing for us.
where people who are less tech-savvy are able to come in with tech-savvy ideas because they basically have a solution architect that can prototype stuff for them and they just talk to the thing.
And then it gives them really good solutions and they know the context.
And that's one of the most fantastic things that is happening in MapAni.
Yeah, I think that's a huge leverage point for every company actually that has software engineering.
und die Möglichkeiten, die das internally für die Proactivity gains sind.
So, haben Sie Teams mit denen die Fokus auf die internalen Stakeholders und Kunden?
Technically, our structure is built in a way where we have teams that are more on the back office side and teams that are more on the front facing side.
So we have our customer acquisition that is more trying to bring people in.
And for example, our customer management, which is the app and how do we train our customers.
But of course, we have internal tools and those internal tools are built with our software engineers and our own.
So those teams are focusing inside.
And we also have an AI and automation team.
And the AI automation team are solely focusing on looking at what we can improve with AI.
They have software engineers and AI engineers.
So basically, in a sense, it gives us a cross-functional structure, even in the AI team.
And then they can go and support a team that works with Salesforce, for example, how to automate their processes or the risk analysis team, and also focus on the chatbot and help the software engineering team, for example, with properly setting up the guardrails of the AI chatbot.
Let's bring this back a bit more to the meat.
What are some of the thoughts that you have when you think of short-term overestimation and long-term underestimation?
My favorite one is, I think last year, people came to me and said, in about a year or two from now, we will not need software engineers anymore.
And I am a year in since that statement was given, and many people said software engineering is dead.
Ich denke, das ist eine Laufbeilfe.
Nicht nur weil du siehst, dass du noch nicht mehr als Software Engenhez.
Es ist generell also eine Laufbeilfe, wo, in der Ende des Tages, wenn du es oder nicht, jemand muss verstehen, die Spezifikation, die Interplay der Systeme.
Und ich denke, wir werden nicht mehr Software Engineersen haben, wir müssen einfach nur noch mehr Software Engineersen suchen, vielleicht.
Aber ich denke, wir werden uns in die nächsten, ich werde mir machen, hier.
By 2030, Software Engineersen werden noch existieren und wahrscheinlich werden wir mehr Software Engineersen suchen, mehr Software Engineersen suchen, dann less.
Das ist mein general stupides Prediktion, dass ich ein egg on my face kann, vielleicht, wenn ich falsch.
Für mich ist das Ziel, was die Verantwortung ist?
Was ist die Verantwortung für die Software Engineering?
Ich denke, das wird dramatisch verändern.
Ich denke, wir auch hier haben.
Ich habe eine schwierige Zeit, so ich wirklich geteilt die Punkt über überestimating short-term gains und überestimating long-term gains.
Ich denke, es gibt auch eine Art von dem Law.
Ja, ich denke, dass es Amara's Law ist.
Roy Amara.
Ich habe ein bisschen nachgedacht.
Ich finde, dass es eigentlich Nath's.
So, even though ich würde agree zu haben, ich sehe eine Herausforderung.
Vielleicht kannst du mich hier helfen.
Ich denke, wenn du überstimmst eine short-term impact, kann das lead...
too slowing down.
And I think that can be dangerous, right?
Because at the end, also, even though the short-term impact is underestimated, according to Amaro's law, this compounds, right?
So there is a high chance that at a certain point you are behind, you get overtaken because you underestimated too much.
Ich denke, wir alle wollen richtig sein, aber vielleicht ist das hier nicht der Punkt.
Vielleicht ist es eher wichtig, dass wir mit der Kurve stehen, oder zumindest mit der Kurve, als dass wir zu schnell sein und vielleicht richtig sein.
Aber in einem Moment, wo du nicht richtig bist, ich denke, du bist eher aus der Gange.
Ich glaube, ich glaube nicht, dass ich mit dir 100% mit dir agree.
Ich denke, es ist toll, dass es Basically, you shoot for the moon.
And in two years, I think the general idea of stating we will not need software engineers in two years is a wishful thinking.
But someone needs to say it to keep 99% of the people basically on their edge and saying, oh, okay, it's a big event and I need to change.
I agree with you 100%.
I just also think that saying, I don't know, when you liquid those posts in two years.
No one will need software engineers.
It's also, for me, it was laughable.
Sadly, the problem with that is there is a full new generation of students that will probably be afraid to actually take that role.
And we are good at that as an industry, in the tech industry.
We might treat ourselves as a feat to not get the new generation of developers because we are basically kind of promoting this.
We will not need you.
Don't come in.
And we've seen through 50 plus years of software engineering that every time something became easier, it actually opened the market for more and more people to join.
And that's where my biggest fear is.
We're kind of scaring away the students, the juniors from coming in.
But don't get me wrong.
I think setting those goals and shooting for the moon is the only way to go because you're either moving forward and you chase everyone that are having high ambition goals or you kind of stay behind and you die.
And that's...
Das ist die Wahrheit der Stagnation in Technologie.
Es geht so schnell, dass wenn du nicht die Hörstest, um alle Software Engineers in zwei Jahren zu entfernen, bist du eigentlich aus der Raze.
Even wenn es eine Unabhängige ist.
So, basically, dann die ramification der Statement, oder der Amara Law, Royal Amara Law, ist, dass du have a pragmatic view basically on things.
You should still follow the curve and not say we're anyway overestimating the short term so we can lay back.
That's nothing you should do.
But I think it's also important for us and the engineers.
All of us are impacted by this heavily right now and we I think have a responsibility to bring a little bit of this pragmatic view.
auf der table und calm down, Leute.
Ich fully agree, dass wir nicht die Hälfte der Team in 2026 oder 2027 haben.
Wenn wir das tun, dann wird es vielleicht etwas falsch machen.
Oder es gibt andere Reasons, die Einkommenswürden, aber es ist nicht der Ziel.
Was wir eher tackling, und das ist auch ein streamer über die verschiedenen Episodes, dass wir eher Moving bottlenecks.
And pretty much if we remove one bottleneck, the writing code bottleneck, then we pretty much make other bottlenecks visible following the theory of constraints.
And then we need to address these bottlenecks in the organization.
And it's usually not that we lack ideas.
So that's rarely the bottleneck.
Absolutely.
100% aligned here.
Und ich möchte auch sagen, weil ich uns die Juniors ein bisschen schaffe, die ich ein bisschen glaube ich, und vielleicht ein bisschen ein bisschen an audience hier, das sind schon believers, weil ich glaube, wer hört sich das Podcast schon mal in die Podcast ist, aber es ist wichtig, weil ich schon viele developers in diesem Thema habe, die ich nicht benutze, AI, AI-slop und all diese Dinge.
Die Realität ist, dass diese Industrie ist changing.
Und ich gebe dir meine besten advice hier von einem Person mit 20 Jahren in meinem Beruf.
Engineering ist über die Erwachsenen und die Person, die in der Zeit war, die in der Zeit, die Karte zu printen und assembly, zu sagen, ich bin gegen C, weil es nicht Computer-Language ist, würde ich sagen, dass du dich nicht left bist.
Und ich denke...
Basically saying, I want to be like those monkeys with my hands on the ears and my eyes and ignore it.
AI is not going to go away.
And you basically need to also step up and learn how will your role shape up and what your role is now.
And this is something that I've been also trying to convey to my team.
Since I've joined, it was okay.
If you like it, you don't like it.
You have to understand that this is...
where we're going and this is the reality and you need to educate yourself because it's going to be a situation where you are going to compete at some point with people who are super eager and they know what they're doing and they know how to use this and that will be their advantage and that's something that you cannot ignore.
So that's also where those goals are fair.
It puts a bit of fire under our asses to also say, okay, I need to learn something new because there is a risk here.
I understand why we're doing it, but again, it's a bit of a marketing-ish as well into it.
Ja, und es ist, sorry, einfach nur die Ebenen.
Die Ebenen, die Telefonnummern, die Plug-In-Cables in order to connect people, oder die Lift-Boys, die machen Lifts-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen-Gruppen- Das ist die Frage, wie du mit dem Veränderungen veränderst.
Es ist deine Wahl, wie du mit dem Veränderungen reagieren, wie du das Veränderungen veränderst.
Wie du das Veränderungen veränderst, wie du es veränderst, und wie du es wirklich veränderst, oder eher beherrschst, in der Ende des Veränderungen nicht AI, gerade nicht, das ist nicht AI, das ist die Person, die die AI veränderst, um mehr produktiv zu nutzen.
Ich denke, es ist immer gut zu haben die opposite Meinung in der Podcast.
Ich nehme das Wort.
Ich sage nicht, dass wir halb der Leute wegnehmen in 2027 sind.
So, if someone would tell me, well, the shift is very comparable to cloud or microservices or internet, I don't know, I would disagree.
I think maybe industrialization is getting closest to that.
Even there, I'm not sure, but I wasn't there when the industrialization happened.
So, I can't relate.
For that reason, Honestly, I don't know.
Usually, you also can't predict the future.
You can't just look backwards and connect the dots there.
So I'm really, really unsure.
For me, at the end, if you look what happened with industrialization, I think it went way slower than we all believe.
But no one is telling us how fast it will go.
Und das ist was ich nicht so scared, aber ich finde es sehr schwierig, zu sagen.
Wenn du auf LinkedIn openst, ich weiß nicht, wie dein Feed ist, aber es ist 99% AI-Sachsinn.
Das Framework hier, das Improvement hier, ein neues Modell, ein neues Harness, ein neues...
I would really like to travel back in time to get a better understanding how industrialization happened.
Well, there was no LinkedIn, so probably people didn't realize, right?
But it's to some degree overwhelming.
So I totally get this feeling also maybe juniors or other people have looking at our industry at the moment.
Und für gute Wissen.
Ich würde nicht negate nichts, André.
Ich so fühle mich.
Vielleicht ist es zu friendly.
Und ich würde auch nicht sagen, mit Ihrer Meinung.
All ich möchte sagen ist, ich denke, dass wir ein höchstenswert sind.
Weil wir vielleicht unterestimieren.
Das ist wirklich die Sache, right?
Are we underestimating the short term now or not?
Because AI, to some extent, and that's what I tried to say in the beginning, it surprised me how quick it became so good.
Initially, we all were like, co-pilot, wow, now you can do auto-complete for lines of code and then you could write some comments and it would...
great small functions, right?
And like you blinked with an eye and suddenly there was cloud code and it would actually produce working software and could do TDD and now skills and different models and you named it, right?
I fully agree.
The thing, and that's something that Pip Gluckner is always saying, the speed or the theoretical maximum of speed that such a change can have is vastly different from the Das ist, ich denke, für sehr kleine Startups, die jetzt starten, die diese Problemen grundlegend werden.
Sie werden weniger Leute, für sicher.
Sie können mehr Automate, sie können in einem anderen System entwickeln.
that have more people have such a degree of complexity that it will be hard, just hard, to reduce the amount of people significantly.
I mean, Elon pulled it off with Twitter, then named it X, and I don't know, it's still working, right?
Even though he did many things in order to get the load down, right?
Because there's less people on it now, I think, and less people who advertise.
But anyhow, I mean, there's this...
This efficiency is in all companies, right?
And it always depends on where you're at, at the growth curve, right?
So do you have growth ahead of you?
Then probably you have many ideas and have appetite to invest more and you rather want to go for productivity gains in order to free up more of this capacity.
If you, however, are in an economically challenging situation, then yes, it...
Probably makes sense to look at it from a different angle.
And I think this is really different from company to company.
However, the huge cuts that due to AI productivity gains, we might see this in 2027, right?
So we don't know.
Predicting the future.
No, predictions are hard, specifically if they are about the future.
Ja, genau.
Sebastian, ich denke, du hast den Nail auf den Kopf, weil in der Ende, was du gerade erwähnt, ist wie ein Unternehmen mit oder ohne AI läuft.
So, in der Ende des Tages, wenn du eine Growth Trajektor hast, dann in general, du wirst die Organisation als ist, oder du wirst die Organisation als ist, oder du wirst die Organisation als ist, um sozusagen mehr Möglichkeiten zu zeigen.
Wenn du in der Entwicklung einverlässig, dann du wirst die Organisation, weil du wirst die Organisation für optimise für profit und zu halten.
Ich denke, das hat nicht verändert.
Die Regeln der Businessen, ich bin sehr nerdy hier, ich liebe Star Trek und DS9 und Star Trek, Deep Space Nunders Quark, die immer noch zu sagen, dass sie 224 Regeln der Reise gewinnen und sie quatschen.
So, am Ende der Tag, die Regeln der Reise der Reise ist, dass du nicht anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt anstatt an Das ist die general Idee.
Und das ist jetzt wo wir sind.
Und du hast einen guten Punkt, in meinem Meinung nach, wo neue Firmen werden, und sie werden sehr effizienter, wahrscheinlich mit lesseren Teams, aber es wird auch demokratis die Idee von Bildung.
So in meinem Hoffnung, viele mehr Firmen werden mit mehr Ideen und dann vielleicht nicht mehr haben, aber vielleicht werden wir nicht mehr in einem Unternehmen, aber vielleicht werden wir Wir finden uns jetzt mit vielen, vielen Unternehmen, vielleicht mit smaller Teams, aber ich denke, You will still need a pilot or a co-pilot to your AI.
And that's where I'm very hopeful.
But again, I have no clue really what will happen tomorrow.
I've made every mistake possible.
Try to predict the future.
I'm really good at analyzing the past.
I'm good at the retrospective side.
All right.
I think we need to come to the end of this episode.
So what are what the fuck moments you still experience?
Maybe what are wow moments that you still experience with AI?
Good question.
Und die WTF-Momenten, ich finde mich, ich sehe, dass AI nicht gut ist, mit einem sehr großen Code-Base und Kontext.
So, du manchmal produzierst etwas mit AI und ich kann den Problem sehen, und die Sache keeps mir, Gaslighting mich, dass das nicht der Problem ist, und ignores, basically, etwas wie ein Junior-Developer will finden.
Und es macht mich immer, um mich zu verlieren, um mich über das Problem, Can I tell the AI, no, this is the issue.
This is what you've done wrong.
And I get it.
Yes, yes, yes, you're absolutely right, but it's not here.
It's somewhere here.
I'm like, no, just listen to me for a second.
But honestly, what wows me, I sat with my son a couple of weeks ago and he described to Claude in my home a game he wants to create.
Basically, it's a nine-year-old, right?
I sat down, wrote everything with a grill me.
Und er hat eine Web-Game gemacht.
Und das ist ein wower Moment für mich, weil ich als 9-jähriger, wenn ich wie Pascal lerne, ich lerne, ich weiß, wenn 10 ist größer als 9, dann wird es nicht mehr als True oder False.
Das ist basically wo ich war.
Mein Vater hat eine Web-Game gemacht.
So das ist wo ich wowed.
Wie viel hat es öffnet everyone zu versuchen, und demokratisieren die Idee von Building, was fantastisch.
As a parent, at least that was a wow moment for me, is to see how my son can do amazing things with AI.
Thanks for sharing.
Yeah.
Thank you, Raz.
It was great chatting with you.
Yeah.
Thanks for coming on the show.
And bye-bye.
Bye-bye.
Thanks for having me.
The Beyond Vibe Coding Podcast is a project by Sebastian Heidemar, Zerpen, and Andre Neubauer, partnering with Impala Search.
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