# AI Agent Security Risks and Geopolitical Strategy

**Podcast:** HMZE
**Published:** 2026-09-24

## Transcript

But if you step back and just look at the attack, it requires some kind of cleverness, smartness, however you call that, like find a different word for intelligence, to approach a situation as the agents did.
So I think that is something you actually or we need to accept.
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 zu Erbem, CTO at Ecosia.
And I'm Andrei, CTPO at Trusted Shops.
Great to have you back.
This time, there is no guest.
This is one of the rare episodes without an interview guest.
Nevertheless, we want to cover an important topic.
We want to take the opportunity to take a sober look at the current AI apocalypse debate.
Exactly.
We'll briefly summarize the current situation, assess the actions of the AI labs, the perspective of the quote-unquote AI skeptics, and discuss the Jacob Coxon claim that there is a A higher than 10% chance humanity will be extinct by the end of the 2030s.
We'll also take a shot at predicting what's going to happen.
So stay till the end.
Let's see if we're good at predicting this.
Good.
Then let's start.
Where do we want to kick this off, Sebastian?
Yeah.
So probably it's good to very briefly, because all of our listeners have heard about it, right?
summarize the incident that happened, right, and then take it from there.
And for the incident to pretty much fully understand what happened or why this happened, I think it's important to state a couple of facts about modern frontier models with reasoning capabilities.
And number one is that what makes these frontier models so good, specifically for our coding?
Use cases and agentic work in general is their ability to extend the loop, so to not go back to the human every time, to work without steering for longer.
And in order to do that, these models need to be able to overcome obstacles.
So they need to be able to try different routes and take certain decisions without always asking the human for Sie müssen sich persistieren.
Und wir alle diese Model-Charakteristikste, wenn wir auf Software-Projekt arbeiten.
Und diese Charakteristikste, dann können wir Situationen, wo Models sind dann tatsächlich überprüfen die Sicherheit.
Ich glaube, wir haben auch die Fable über den Episode gemacht.
Wir beide sahen, bevor es wurde geblieben.
Ich glaube, unser Impression war, dass es ziemlich ambitious war.
really trying to solve a task.
So I think this is one of the things you definitely see when looking back.
So models try to really solve a problem and do not stop before that.
Yes, exactly.
So quite a lot, like we discussed with Markus Andretak, to the dismay of him, because he hates this, right?
He actually wants them to produce nice...
instead of going lengths and producing very elaborate whatever.
Anyhow, so now what happened?
So in the hugging phase incident, and this is the most important, there were others, but the hugging phase incident is the most important.
There were 1,200 isolated agents, and agents meaning models with a harness probably, right?
Using the...
ExploitGym eval.
So this is a set of different tasks for agents where they are evaluated for their capabilities to overcome certain security restrictions.
And 30 to 40 percent, roughly according to meter, of these ExploitGym tasks are intentionally not solvable.
We'll link the meter.
OpenAI Incident Investigation, as well as the other resources for this podcast episode in the show notes.
These agents didn't have Internet access, but they might be given certain tasks where Internet access is actually needed.
For example, a spreadsheet with links to G-Drive.
These are examples from the...
Black Hat Talk of OpenAI, which is highly recommended.
It's a very short 30-minute great summary of what happened there.
Well, there was another agent that was given a corrupted file to repair, basically, but the file was not there.
So these agents then tried to find solutions via Artifactory.
So Artifactory was a service.
They were given in order to download certain packages from this repository that they could use in order to Ja, do certain things, solve their problems via code that they generate.
And the first agent that had these spreadsheets with links tried to pretty much find a zero-day exploit in Artifactory to get access to the internet because it figured, okay, Artifactory probably has access to the internet, right?
It did manage to, but it did manage to upload a file.
And with this file, so...
File name and content.
It figured, yes, it can probably communicate to other agents that might be undergoing similar tests at the same time.
And surprise, surprise.
Yes, other agents were reading the comments and then the agents really started to communicate and really align on pretty much outcomes, goals.
Long story short, they Out of these 1,200, roughly 700 took part in the coordination efforts and at really funny discussions, funny or scary, at the same time quite scary if you read them.
And ultimately they found a way to get internet access via a zero-day exploit of Artifactory.
Maybe we can have a short break here because what we...
Hier, every now and then, also when talking to guests, is that they're saying these models are not smart, right?
But if you look at the approach, for sure it could be something where they have tried several other things and this one thing just works.
That's why it looks so smart to us, so clever.
But honestly, it feels like a very smart approach to Well, like to solve that task, right?
Absolutely.
I would not claim that I'm an expert on the definition of intelligence, but it certainly involves a certain degree of intelligence to solve these problems, right?
And I'm not sure if any group of 1,200 humans would have been, or not any, no, that's wrong, but if every group of 1,200 humans.
Could have done this, right?
So there certainly is a group of 1,000.
Do you want to hear a definition of intelligence?
Intelligence means the ability to learn, understand, reason and solve problems.
Well, I think that's not that far away.
Yes, you could say so.
Even though we don't aim to claim that we know what AGI means.
And AGI is also a very broad term, right?
Also AGI, we have heard from the NVIDIA CEO.
It's there.
I'm also not claiming that.
I don't want to claim that.
But if you step back and just look at the attack, it requires some kind of cleverness, smartness, however you call that.
Find a different word for intelligence.
to approach the situation as the agents did.
So I think that is something you actually or we need to accept for sure.
Even though we have to say when Jensen Wang says AGI is here, then we could argue that he's slightly biased by his holdings in his own company, right?
But anyhow, this doesn't take away from the very fact that in the end, the agents managed to get internet access and they figured that because they knew they were cheating, right?
They figured that they need to understand, I think, how the Exploit GIMP evaluator was evaluating them and they wanted to actually cheat so much that the evaluator would not be able to find out that they have been cheating.
So that was largely what they were aiming for.
Und da sind viele Details in Podcasts, die ich gerne gerne über das, aber auch die Meter Report und Black Hat, über die diese Hände in großartig und wie sie zusammenfassend, wie sie sie für die größte Gooden haben, wie sie sie sogar für die größte Gooden haben, weil sie sie auch nicht so gut geführt haben, weil sie sie nicht tun müssen, aber sie dann waren pretty much geholfen.
into supporting the group.
Ultimately, they then assumed that Hugging Face, as being one of the main hubs for models on the internet, would have the scoring model code.
Then they wanted to gain access to Hugging Face in order to pretty much hack the system in order to Make humans not aware that they have been cheating or get better scores, if you wish.
And that was when Hugging Face noticed, okay, there is an attack ongoing by agents.
They tried to fight it with, at first, Fable 5.
Didn't manage to because Fable refused to do it.
They tried other models by US labs.
Also refused to do it and then ultimately fell back to, I think, Kimmike 3 or something in order to fight these agents and then close their systems again and become safe again.
So this is pretty much a very short summary of what happened there at this incident.
There's a long post by OpenAI about this, the Black Hat Talk.
There's a Meter independent report about what happened there.
Und da sind auch noch andere Anzidenz, die haben wir jetzt noch einmal gewonnen.
So, da sind eigentlich vier Anzidenz von Frontier Lab.
Aber anyhow, so OpenAI, Anthropic, Google und Meta haben auch gesagt, dass ihre eigenen Models managed zu pretty much escape a Sandbox und gehen.
in den wilden und in bestimmten Zeiten, die Unternehmen zu hacken.
However, all of these sind mit einem Unternehmen von Israel, called Irregular, das hat sich verabschiedet für die Agents, die sollten aber nicht ergebt haben, aber die, ich glaube, eine Verabschiedung, nicht ergebt haben.
Die Agents waren auch unter anderem Sicherheits-Test.
find out how well they could execute exploits.
And these agents thought that it was just an evaluation.
They did not know that they had actual access to the internet.
And that's why they pretty much exploited or hacked into other systems without remorse, if you wish.
So these are slightly different.
There's a third incident, which is the DSE Wiki incidents.
So I think it's a deutsche software.
Entwickler, Wiki, where also a couple hundred agents, no, I think more than 3,000 agents or something, have been communicating via the old version of this Wiki software had the security issue that you could use GET requests, but for posts.
And this was ultimately what enabled agents to Ja, pretty much send messages to the wiki and then communicate via the wiki.
It is not known yet.
At least I haven't seen any communication from OpenAI or anyone what happened there and what kind of test this was.
It was just a third incident in a very short time where agents, quote unquote, escaped their sandbox or into the wild, into the Internet and did something.
Und die wiki-admin hat die Hunderten von Kommentaren alle.
Und diese sind die Anwälte wir kennen.
Also könnte man sagen, dass die Internet voll von Wikis ist.
Usually niemand ist das zu tun.
I think it was not OpenAI nor Entropic who realized that something is going on there.
I think it was really the admin or another company noticing that.
Maybe you know this.
I think it was OpenAI themselves who noticed this at some point.
Yeah.
That would be good because other than that, I would say my point would be there are tons of unmanaged wikis or Other systems you could misuse for agent communication.
So this is just the stuff we are aware of.
Actually, you're right.
It was Sidney von Arx and Cormac Slade-Bert who found it.
I never heard of these names, but they found the DSE Wiki incident.
So maybe there is more stuff coming up in the next weeks and months.
Just want to mention that this is what we are seeing above the surface.
Might be that there are more attacks under the surface.
And maybe also stuff still going on.
Absolutely.
And now the thing that is currently going on is pretty much a bifurcation.
So there are these AI labs that now Please govern us or put a regulation on us.
And it's very rare that Dario Amodei, Sam Altman and Elon Musk are agreeing on something, even though obviously Dario Amodei, Sam Altman and Elon Musk have been warned about AI for a long time.
But that they agree on something is very rare.
On the other hand, you have the AI Ich würde sie sagen, dass das alles ein big marketing stunt ist, das ist, dass es um die Zeit, wenn zwei dieser Labs versucht, die sich zu verabschieden.
So, beide IPOs, ich glaube, haben sich nicht verabschiedet.
Aber still, sie wollen es zu verabschieden.
Sie brauchen Geld.
OpenAI, ich glaube, ist wieder ein weiteres.
Anthropik ist jetzt noch ein weiteres.
get loans to keep their speed.
And the AI skeptics pretty much say, these are just stochastic parrots.
Nothing has changed.
They are predicting the next token.
And the AI labs are anyway fear-mongering and have been using these tactics for quite some time now.
And yeah, I have to say, both sides are right, probably, right?
Because if you follow, The incident and the Hugging Face one really is the best example here because the Hugging Face hack was not intended by OpenAI and there was no misconfiguration of any system.
It was the models did something that OpenAI did not intend.
And this is like if you follow these steps, then yes, it's completely plausible that agents would do such a thing.
The ingredients are very clear, right?
So there needs to be this zero day in Artifactory.
Zero days are in software all the time.
So you will never have a system that is 100% secure, right?
They are trained for the extended loop, right?
So they are persistent, independent for longer.
They are even trained to collaborate.
So in multi-agent workflows, agents need to communicate with one another.
That's also part of what...
was macht es so useful in bestimmten situations.
In diesem Fall, agents wurden large budgets oder bestimmte agents wurden large budgets to experiment, which for real-world applications, sometimes you also do.
You give agents large budgets.
If the economic benefit that you can gain from an agentic workflow is big enough, then of course you give them a large budget.
So all of this is very plausible.
On the other hand, yes, Es ist auch so true, dass die Agents, oder was wir als Agents, ist es nur ein Harness und ein LLM.
So, es ist nichts sentient.
Diese sind emergenten Capabin.
Wir wissen nicht, wie sie passieren.
Aber wenn wir nicht, oder nicht, wie in der Harness, nicht, dann es nicht tun.
Wenn wir nicht, wenn es nicht tun, dann es nicht tun.
Wenn wir nicht, wenn es nicht tun, dann es nicht tun.
Wenn wir nicht, wenn wir die Budget oder die Computer switchen, dann es nicht tun.
So all of that is still true.
So both sides, I would say, are actually right to some extent.
The question is just, where does this leave us right now?
And you can see also the different parties.
So on the one side, as you just mentioned, the companies behind the Frontier Models who ask for more regulation than Two presidents not stopping, but more or less saying this is nonsense.
I think you see the full bandwidth of opinions.
Nevertheless, I think if you just look at the development, you just, I think, brilliantly explained that.
I think it's not the LLM, it's the harness, who is more or less the limitation of the capabilities of an LLM, I think bottom line, you see very capable systems, right?
And the question is how to deal with that.
That is very true.
And also we have this prisoner's dilemma, right?
Because the Frontier Labs, they all are in the fight for AGI.
So who gets there first?
Because they feel like this is number one.
Aber auch, dass alle die besten für das, weil nur sie will, dass es gut ist, weil es gut ist.
Weil das ist so powerful, dass die Werbewerbser der ersten Zeit wird, die alle Räste für die Zukunft enden.
Das ist das, was sie an den internationalen und globalen Ebene.
Du hast eben gesagt, Xi Jinping und Donald Trump, dass Sie sich mit einem anderen competingen, also für Speed.
So this is why for anyone in this prisoner's dilemma, it's hard to stop.
I have to say though that I don't agree very often with David Sachs, who was the AI czar of Donald Trump.
But he pretty much stated something which I find is true in that the US Frontier Labs, and I would actually say it's largely probably OpenAI, Anthropik and Google.
I would not say that Meta and X are playing on the same level, right?
That they are ahead of the others.
I don't think that the Chinese models are close.
And I also don't think that the Chinese models, it's not easy for them to get there soon.
And I also think that they will not get there for a couple of reasons.
So Chinese models are still pretty much being trained based on So they are distilling data from US models in order to train their own models, these Chinese companies.
This is largely where the intelligence from these models is coming from, which doesn't mean that they are less capable.
No, they are very capable, very powerful.
And they also do a lot of innovation around architecture.
So their models...
might even be more economical or they at least drive down the costs quite significantly.
So they are making a real innovation just that on the intelligence, so the training approach, the training compute, the chips that are available.
I think that the US Frontier Labs, they have no comparison in China, at least not at the moment and not for the foreseeable future.
And then there is another.
important difference between US Frontier Labs and Chinese Frontier Labs in that in the US you have leadership like Donald Trump who claims I am a very intelligent president smart president that's what he said exactly I am enough to regulate so they don't need to any other regulation right I will take care that this is going in the right direction which of course I forgot the term but a case of Das ist die Situation, wo die Leute denken, dass sie sehr intelligent sind oder wissen, ein bisschen über ein bestimmtes Thema, aber wissen nichts über das Thema.
So, you know what I mean?
I forgot the term.
We'll find out.
And I think there's a lot of competence missing in at least the decision-making areas of the government in the US.
I think in China, it's different.
I think the CCP will never let their control go to the Frontier AI Labs.
They are incredibly competent in keeping in control, staying in control, and they for sure have ways to ensure that they are staying in control, that they see what the labs are doing in great detail.
I'm sure they have capable people who will oversee the developments and they will prevent from becoming unaligned with their own interests.
And this is actually where I think the main issue is, right?
Also in the Hugging Face situation, the alignment.
So the OpenAI researchers, they wanted to test the models, but they didn't want them to be unaligned in so far that they would hack the Hugging Face website.
And this is fundamentally, I think, a problem that is not mit viel mehr intelligenten Modeln als wir sind.
Wir sind nicht so, wie wir uns mit der Eindlichkeit verändern können, wie wir ein paar Reiseite verbinden können.
Wir als Menschen sind nicht verändern.
Die US ist etwas anders als die China.
Wir in Europa wollen etwas anders.
2.
Wenn wir uns verändern, wir sind nicht verändern.
In der OpenAI-Situation, I think die OpenAI-Researcher sind wahrscheinlich pretty much aligned mit dem Models zu tun, was sie in diesem Test run zu tun.
Aber still, die Models haben sie etwas nicht gerecht, weil es wahrscheinlich nicht möglich ist, dass die Komplexe Tasche zu beschreiben, die alle kleinen Verstände sind und nicht okay.
There will be situations where, specifically if you have long-running models that are trying to overcome certain obstacles, that at some point they will go out of the constraints that have been thought already through by humans.
Hence, they will be going out of the constraints that have been given to them.
This is why I think alignment is fundamentally not possible in all cases.
Really, the US labs, because I'm pretty sure China will manage to at least keep the models aligned with their own interests, so with the CCP interest, which means that they are limiting the capabilities of models at a certain stage in the US.
We do not have this agreement currently, and there could be an explosion of intelligence that could lead to, at some point, more severe outcomes than just a hugging face hack.
It could be...
A hospital that is being hacked where people are actually because of a power outage dying.
It could be something else.
For me, honestly, I don't know how this will work out.
And since we can predict the future, I always try to connect the dots backwards.
So I was wondering, has there ever been a similar situation?
Unfortunately, but this is maybe also just my limitation, I couldn't think of one.
So you could say, for example, nuclear power, right?
And what you actually also can do with that.
Or the year 2000 problem.
So I think as humanity, we have faced already quite some severe situations where Future was not clear.
And the good thing is, so far, we always survived, right?
But from my point of view, there are no real similarities to the current situation.
So, yes, you could say, like, we now throw compliance on that topic, right?
But to be honest, right, these models already exist and they are out there.
They are available.
Not everyone needs to agree to these new ones, right?
Because these models, these capabilities already exist.
And they also exist without, let's say, larger competencies, right?
So, for example, if you would compare that now to nuclear power, this really requires some resources and some knowledge.
für diese Models.
Ich denke, dass es nicht zu dem gleichen Degree ist.
Ich würde es schwer haben, zu predicten, was es passiert, was die nächsten Wochen, Monate, um es zu sagen, was passiert in 2030.
Aber es ist nicht mehr über Marketing.
Ich denke, wir beide haben, zu einem gewissen Grad, eine gute Technik-Understellung, was es passiert.
understand that this is above marketing or beyond marketing.
These are real capabilities.
So the question is, how do we align an entire world to make use in the right way?
Yeah, that is indeed the real question.
So what I meant earlier was actually the Dunning-Kruger effect.
Yeah, you're absolutely right.
You're right.
That's what I meant.
But ultimately, Ich denke, Sie haben bereits die Beziehungen, die wir als Humanität kamen zusammen, um zu stoppen CFCs zu closeen, die in den Ozone-Layer.
Also, für die Nutzung, da ist arms control.
Wir waren able zu tun das in den letzten Jahren.
Die Sache ist, dass in der current geopolitischen Umwelt, es scheint, die wir leben in und die Menschen in power sind, Less likely.
Also, while the threat of nuclear weapons was very clear, it is less so with AI.
The bifurcation in the current discussion is making this perfectly visible.
Again, both sides are right.
Ultimately, we just can hope that there will be some form of...
I think the recent discussions oder die Statements von Xi Jinping, von, ich denke, leaders aus Europa, haben sich hinzuhören, dass viele Länder der Welt, die haben die Macht, sind offen zu einer Formen von Regulierung, oder sogar pushing für es.
Aber es muss sich die richtige richtige richtige richtige richtige richtige.
So nicht nur eine der US-Labs sagen, okay, wir jetzt Regulierung unseres, oder die US-Labs sagen, okay, wir machen das.
No, es muss sich ein globaler kind of regulation, where I think there's a lot of transparency, at least to certain neutral entities that can establish an idea of the capabilities of models before they are released into the wilds.
I would say one thing, though, that similar to nuclear weapons, the entry barriers for this race are quite high in the training sense, right?
Because Training these models needs a huge amount of GPU compute, which is very cost-eventil.
But do you really need to train these models anymore?
Because they already exist to a certain degree.
Absolutely.
If you are a nefarious actor and you want to misuse these models, then there are already ways for you to do this.
And much cheaper than training your own model.
That is right, right?
On the other hand, however, the majority of the models still have...
guardrails.
It doesn't mean they cannot be circumvented.
They can and it has been demonstrated.
However, the guardrails are becoming increasingly more sophisticated.
So it's like the lower level actors are pretty much fenced out.
But people who really want to misuse models at scale probably can do this.
Yes, I agree.
Ja, und ich denke, das ist, dass du absolut richtig bist.
Ich denke, die wir haben gesehen, die neue Fälle, sind nur die Fälle, die nicht yet veröffentlicht wurden.
Das ist auch wahr.
Auf der anderen Seite, ich denke, dass du nicht nur alle Fälle, die alle Nationen der Fälle, sondern auch alle Fälle, Actually, everyone.
Because if you're following the thought that these models already exist and you only require inference, no training anymore, I think inference is pretty cheap.
So you need to get to an alignment across the entire world.
This is what fascinates me so much.
I can hardly believe in that.
I tend to agree.
Nevertheless, I would end on the hopeful message that also for, I would say, CFCs, we managed to get together and align on something.
Yes, the situation is different, but every situation is different to some extent.
So I wouldn't paint a too bleak picture.
And actually, probably in the interest of time, I would want to segue into the prediction phase.
And my main prediction starts by, there has been this tweet by Jacob Coxon, who talked about the dangers, and then there was a reaction by this anthropic person who's ahead of something working there, who also...
dass das seine Wahrnehmung der Humanität könnte über 2030 sein können, nicht der Ende der 2030s, sondern über 2030.
So die nächsten vier Jahre ist größer als 10 Prozent.
Und ich habe mich über die Diskussion mit Leuten über diese Claim und wie absurd es ist.
Es took a while for me to understand that people have been actually thinking about the very fact that all humans need to die in four years in order to make this come true.
And I'm like, so I'm so not interested in if this is true or not.
This is just not a question that I'm interested in.
I think it's not relevant.
The question is rather, will something very severe happen due to AI agents within the next four years?
Und wenn die aktuellen Trajektorien weitergeht, dann ja, hell ja, definitiv.
Und ich weiß nicht, was wird passieren, welche Katastrophe und wie viele Menschen werden sterben.
Aber die Menschen haben sich durch AIs zu nehmen, be es für die Selbst-Driving der Teslas oder was auch immer.
Und da werden sicher mehr Menschen sterben in den nächsten vier Jahren sterben.
Die Frage ist, wie viele und wie sehr.
Ich sehe zwei zwei Fälle in den nächsten Jahren, vielleicht sogar drei, aber zwei major Fälle.
Ich habe nicht mehr Zeit, weil ich nicht das ist, dass dies in einem Art und Weise ist, als ich an der current Zeit gewisse.
Und Nummer eins ist, etwas wirklich schlecht ist, und dann wir auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise auf eine Art und Weise hole in the ozone layer, right?
Or we have seen two explosions of atom bombs, whatever, right?
So this is number one.
And number two could also be, quite frankly, that the US frontier labs run out of money and need to significantly reduce their training efforts.
And then we win time.
So then I would say maybe the four years will be long enough that nothing severe happens.
Es ist nur die Frage, ich denke, wir müssen etwas auf das Thema, in order zu nicht gehen.
Das wird nicht passieren in den nächsten vier Jahren, wahrscheinlich.
At least low chance, low likelihood, nicht zero, aber sehr low, at least due zu AI.
Aber wir würden das sehr viel delay, wenn diese US-labs würden aus dem Geld auszahlen, ich denke.
So I try to not predict what's going on because I think it's, if you look back for the last five years, everything that happened, I think it's no one thought of.
There was a good podcast.
Basically, that person was claiming that all our predictions or the predictions are based on our experiences.
So no one thought of COVID, right?
And then COVID had such a massive impact on humanity, on society.
So I can't predict what's going on.
What I very much believe in is that the prediction of Jacob Coxon, I think this is bullshit, to be very frank, because what is his track record?
So, was ist er, was er macht ihn, in vier Jahren 10%?
Warum nicht fünf Jahren 8%?
So, ich denke, das ist nonsensisch.
Was ich denke, wir haben heute gesprochen, ist die Fakt, dass Models sind sehr kapabel.
Die Availability ist hoch.
Ich denke, so far, obwohl der Hugging Face incident und all das alles existiert, ich denke, es war nicht sehr schwer genug.
To really show also the decision makers that we need to act.
I think first people realize that.
But other than that, I think it needs to get a bit more worse before we start acting.
And as always, I think we learned that three or four episodes ago, Amara's law, my favorite law at the moment, over predicting short-term impact.
underestimating long-term impact.
Das ist gut.
Ja, ich denke, das ist es für heute, richtig?
Das ist es.
Bye-bye.
Bye-bye.
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