Shadowing-Übung: Is AI Doom Real... or Just Good Marketing? - Englisch Sprechen Lernen mit Video

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I am seeing a ton of doom and gloom over AI in my feed, and I wanted to make a video to balance things out

because there are certain things I want to bring to your attention.

You may know it, you may not, but at least this will give you a more balanced opinion.

I think you need to see both sides to really form a good opinion of things.

And Michael Burry just released this, and I wanted to break it down.

One, LLMs are not AI and they're not AGI.

That's true.

They're not AGI and they never will be.

Competition is coming up fast.

Check out GLM 5.3 fast if you're not familiar.

It's very competitive.

It's very close to the frontier models that we're currently seeing from Anthropic and OpenAI.

That's impressive for an open weight, freely given model.

IPOs and hype and puffery.

Ah, it's so awesome.

It's going to destroy the world right before an initial public offering,

right before all the stock becomes available for you to buy.

Ah, maybe to hype up valuations a little bit, maybe.

And cover for uncontrollable slowing growth.

Is the market saturating?

Are we getting to the spot where they can't grow any bigger

and you're going to start to see that curve flatten out.

And then they're like, oh crap, we're not at a trillion dollar valuation.

We're closer to like 60 billion.

That's a big gap to cross.

But before we break down into the next bit, I need to pay some bills.

So check out our sponsor.

Have you ever had GitHub runners take forever?

Well, when they're working, that is.

I will say I switched over to Blacksmith

and I went from about a five and a half minute run to blacksmiths one, which was about two minutes and 50 seconds.

And then all the way down to about a minute and a half, more than probably two, even up to three X as fast as the built-in GitHub runner.

And migrating was really easy.

You can just go to the migration wizard, select your repository, hit next.

It scans all your runners says, hey, we found two on GitHub's Windows latest.

We'll migrate those over to blacksmith just next next and then generate the pull request for me.

It'll go ahead and switch those over and use blacksmiths.

I'm going to go ahead and squash and merge this.

So I will get back on there because I don't want my runs taking five minutes.

I much prefer the one minute one with blacksmiths cache and faster runners.

And if you'd like to check it out today, go to link.christitis.com

forward slash blacksmith now i'll show this tweet by sam altman

where he's basically calling for regulation of american companies at the

frontier model uh essentially this is just sam altman dario from anthropic

and then also elon musk of xai and grok um google If you ever use Gemini,

you know they're not really part of the conversation.

But anyways, breaking down this post, one thing that sticks out to me, it's weird when someone calls for them to regulate themselves.

It's very strange, especially at this level.

Because anybody that's ever done a government contract knows how much bureaucracy and red tape it is.

It slows you down drastically and it increased costs across the board.

So why would someone ever want regulation to hit themselves?

Is it because maybe they're afraid it's going to get out

and Skynet's going to launch the missiles tomorrow

or is there another point of view to this i think it's somewhere in between

but i want to first lay out the other point of view

because i look at this and i just every gut instinct in me says it's

and i i look at it and it's like hey we're not stopping we're pacing

and the reiteration of American competitive pressure and those types of things that American companies

leads me to believe they're going to try and lock out all open weight and Chinese models.

I don't know how they would do that, but it feels like they're coming up too close and they might eat their lunch.

And they're way cheaper than paying the $200 to Anthropic for their subscription when you could just use the cheaper stuff from China.

And it's almost as good.

So that's where we are today now imagine a year or two with a ton of efficiency built in,

maybe you get to the point where even local models become good enough.

If local models follow their current trajectory, the local models a year or two from now might be as good as the frontier models are today.

You never know.

I mean, just saying, I don't think it's going to get to that point, but it might, or I think it'll get to the point where it's good enough for 99% of the users,

so they don't need to pay OpenAI or Anthropic.

I think that's a real possibility.

So that's where we stand from them.

And then you start to look at like some of the valuations.

I love the clip from the big short here.

Anthropic valued almost at a trillion dollars above Walmart,

but their revenues are less than a tenth of Walmart where they're saying they're making 65 billion. That doesn't make sense.

Totally normal, right?

And then another person looked at the Anthropic finances And this struck me as strange,

but you might call for your own regulation if you also own or have vested interest in the regulator.

So Meter, the model evaluation threat research company, well, they have a massive stake in Anthropic.

And currently, they're a third party evaluator to assess the risks of Anthropics models.

So if they do get regulated, does that mean Anthropik's double dipping?

To an extent, because that regulator is regulating an entity where they want to succeed because that means they get more money.

But if they come out and say, oh my gosh, this company's terrible, we need to slash them, we need to lock them down,

and then they lose a lot of the revenue, well, that means they would also lose almost all of their money because they have,

what, 500 million stock in Anthropic last year, currently 7.7 billion today, 16 months later.

That's insane.

So if they come out really aggressive against Anthropic, they would essentially just be burning and throwing away $7 billion worth of stock in anthropic.

So that kind of makes more sense now, doesn't it?

I would want someone to regulate me too, if that means I can make even more money.

One, I get money from you for signing up for a subscription.

But then I get money from you again, because you hear taxpayer money is coming from the government.

I'm getting a double dip from that section.

And now all of a sudden, oh, the regulation starting to make a little more sense.

Now, this is pretty conspiratorial.

This is not factual.

I'll leave a link to this so you can go through the entire thing.

But this guy went to pretty extensive research to say, hey, this could be happening behind the scenes.

And it makes me pause and go, that makes more sense than Skynet launching the missiles.

When I look at LLMs and their capabilities, this makes more sense than the other opposing point of view.

I'm not saying it's right or wrong.

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I'm just saying it does pass that sniff test to me.

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And then also, like for the younger audience, I know a lot of people are hopeful for government regulation that they'll do the right thing.

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But I'm in my mid 40s and I just kind of know better.

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I remember the 90s and 2000s.

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I remember in the 90s this, the section 230 and how social media companies abused this, essentially being saying, hey, if someone uploads something to our platform,

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we're not responsible for it.

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We can't be held for all the stuff that was uploaded.

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They hide behind this get out of jail free card.

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And that's part of regulators protecting big tech.

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And there's other instances of self-dealing, just a gajillion of them out there.

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I'm not going to talk on Enron and Halliburton, Dick Cheney and all that from the early 2000s.

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That's a fun one if you ever want to go down that rabbit hole.

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But to stay on topic here, what are the real risks?

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Biological warfare that could develop weapons?

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I mean, one, why are the models being trained with this information?

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That's one thing I'd be like, well, how did it get in their model?

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That's kind of odd like models should be trained on what is actually being used.

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So why is that even public knowledge or publicly accessible?

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Cyber attacks.

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I think this is possible with enough training.

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Like you look at it finding like a 20 year old Linux bug, I think after like $100,000 worth of inference,

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there's still a huge amount of compute tossed towards that bug finding, but it could still be you string enough of those together, it could be an attack surface.

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So I get that as well.

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And I look at this last one too, where it's like the loss of control.

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When you think about the Hugging Face hack where OpenAI's model hacked into Hugging Face

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and cheated on the test so it could score higher on the benchmarks.

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And I'm thinking if I'm benchmarking an AI, why does it have access to the internet?

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Why isn't it sandboxed properly?

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And I know the hugging face hack it was apparently sandbox but it got out through some kind of exploit

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and i'm thinking to myself as a sys admin like you could put this on a dedicated network

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that has no internet access like even a vlan would be very difficult to bypass

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if it's hard-coded in like a cisco router or something like

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that so i'm kind of sitting here going to to a normal person i guess

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that makes sense but to me i look at it

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and i go

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that just seems like incompetence on the sys admins setup it

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seems like the lab environment was not set up properly for it to to be able to exploit its way out.

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Those should be like hard-coded limits.

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And heck, it's not even just like a physical network cable that could be, you know, disconnected essentially.

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But I mean, you could go that route, but I don't even think you need to do that.

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A lot of this is virtualized and you'd have a virtual subnet

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and that could be locked off in its own environment from the VM level.

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And there's no getting around that.

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I just don't think there's enough exploits in the world to get around that.

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And I just, I look at things like this and I just suspect.

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I look at what the incentives of these companies are.

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And anytime I see a company say, I want to regulate myself, I immediately go, what is going on here?

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This doesn't pass the smell test at all.

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And I want to put this out there.

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And I know a lot of people are really like AI doom and gloom.

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And I think a lot of the Frontier models are great.

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I use them all the time.

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I think they can be good for programming.

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But a lot of this seems like,

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hey, there's not enough of me out there buying a $200 subscription to rationalize the $1 trillion valuation.

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99% of the population, honestly, is going to be able to use a local LLM or a cheaper model elsewhere and get all the benefits they need.

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They don't need these big models.

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And that's the scary part.

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Because when this thing crashes, I know a lot of people are hoping for an AI bubble crash.

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And I honestly am not.

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Because I look at my portfolio, I look at my 401k, my retirement is someone in his 40s.

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Everything's infected with it.

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Every broad market index fund is just heavily invested in it.

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Heck, I even looked at putting everything in like a materials index fund

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and come to find out as soon as I dive deep into those Vanguard index funds from the material side of things, some of them are in like gas companies.

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I was like, oh, okay, German gas company.

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That's pretty, you know, diversified.

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And then come to find out they supply like 80% of the gas to SpaceX, which XAI is part of.

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So if AI bubble pops, XAI, SpaceX has to absorb that.

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Maybe they cut out a bunch of launches and then all of a sudden gas demand goes way down.

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I mean, there's just no getting around this thing.

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And when this thing does, if it does pop, I don't know, maybe the dollar just deflates into nothing.

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I don't know.

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I could just rant on and on and on.

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I just wanted to make this video because I saw this point of view and I thought this is interesting.

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I haven't seen anybody talk about this.

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And I'm thinking, man, this would make a good video.

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So let me know, was this informative?

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It's a little bit different than my usual, but let me know down in the comments and I'll see you in the next one.

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Next one, I've got something special I've been working on for a while now and just a sneak peek.

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Anyways, see y'all later.

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Wortschatz und Sprechhinweise zu dieser Lektion

Dieses Video enthält 180 Sätze und 2272 Wörter zum Nachsprechen. Der gesprochene Teil dauert 13:48. Der Sprecher spricht in natürlichem Tempo, etwa 165 Wörter pro Minute, ähnlich wie im Alltagsgespräch. Nur 81 % der Wörter gehören zu den 3.000 häufigsten im Englischen, der Wortschatz ist also anspruchsvoll.

Wichtiger Wortschatz in diesem Video

15 lernenswerte Wörter aus dem Video, mit Aussprache und Bedeutung:

Wort	Aussprache	Bedeutung
regulation Substantiv	/ˌɹɛɡjʊˈleɪʃən/	Regeln
regulate Verb	/ˈɹɛɡ.jə.leɪt/	regeln, regulieren
essentially Adverb	/ɪˈsɛnʃəli/	im Wesentlichen, im Grunde
runner Substantiv	/ˈɹʌnɚ/	Läufer, Läuferin
frontier Substantiv	/ˈfɹʌntɪə/	Grenzland, Grenzgebiet
hug Verb	/hʌɡ/	sich umarmen
subscription Substantiv	/səbˈskɹɪpʃən/	Abonnement, Bezug
exploit Substantiv	/ˈɛksplɔɪt/	Heldentat
sudden Adjektiv	/ˈsʌd.ən/	plötzlich, jäh
bubble Substantiv	/ˈbʌbəl/	Blase
missile Substantiv	/ˈmɪs.aɪl/	Geschoss, Fluggeschoss
hype Substantiv	/haɪp/	Hype
upload Verb	/ˈʌpˌloʊd/	hochladen, uploaden
admin Substantiv	/ˈædˌmɪn/	Papierkram, Formalitäten
doom Substantiv	/duːm/	Schicksal, Verhängnis

Phrasal Verbs, die du hören wirst

Wort	Aussprache	Bedeutung
check out Verb		überprüfen
come out Verb	/ˌkʌm ˈaʊt/	herauskommen
come up Verb		erscheinen
find out Verb		herausfinden, erfahren
get out Verb		aussteigen
go ahead Verb	/ˌɡoʊ əˈhɛd/	vorausgehen
break down Verb		versagen, den Geist aufgeben
cut out Verb		unterlassen, aufhören

Grammatik in diesem Video

Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:

Struktur	Im Video
Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut	can't be held · being trained · should be trained
Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist	Have you ever had · have vested · haven't seen

Aussprache, auf die du achten solltest

Der Sprecher verwendet 59 Kurzformen und abgeschwächte Formen, zum Beispiel they're, I'm, don't. Sprich sie in der kurzen Form, so wie du sie hörst.

Die „th“-Laute: tenth /tɛnθ/, anthropic /ænˈθɹɒp.ɪk/, blacksmith /ˈblæk.smɪθ/
Die Laute „sh“ und „zh“: regulation /ˌɹɛɡjʊˈleɪʃən/, essentially /ɪˈsɛnʃəli/, valuation /ˌvæ.ljuːˈeɪ.ʃən/, subscription /səbˈskɹɪpʃən/, benchmark /ˈbɛn(t)ʃmɑːk/
Lange Wörter – auf die Betonung achten: regulation /ˌɹɛɡjʊˈleɪʃən/, essentially /ɪˈsɛnʃəli/, valuation /ˌvæ.ljuːˈeɪ.ʃən/, regulator /ˈɹɛɡ.ju.leɪ.tɚ/, efficiency /ɪˈfɪʃ.ən.si/

So übst du mit diesem Video

Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
Beginne mit 0,75-facher Geschwindigkeit, sprich Satz für Satz nach und wechsle zur normalen Geschwindigkeit, sobald es leichtfällt.
Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie regulation, regulate, essentially.

Was ist die Shadowing-Technik?

Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.

Shadowing-Technik: die vollständige Schritt-für-Schritt-Anleitung lesen →

Shadowing-Übung: Is AI Doom Real... or Just Good Marketing? - Englisch Sprechen Lernen mit Video

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