쉐도잉 연습: Did Google just dethrone OpenAI and Anthropic? - 영상으로 영어 말하기 배우기

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Last week, gas changed the world forever.

Twice.

Tesla finally unveiled the Roadster and its ability to fly by farting cold gas.

But while Elon was busy pulling off his greatest magic trick, Google was cooking up a noble gas of its own,

Gemini 4 Argon, which is the most powerful thinking sand they've ever released.

Sadly though, they never even released Gemini 3 .5 Pro, which was euthanized up in Canada after failing to hit internal benchmarks.

In fact, Google was getting so far behind in the AI race, I needed to add a new G category below F on my tier list.

But all that has now changed with the release of Argon,

which was trained from scratch over the last six months using Google's TPUs and their Jax Python library.

And that produced a new frontier model that crushes GPT -6 Astra

and Claude Opus 5 .5 on the Trust Me Bro benchmarks, while remaining cheaper and faster.

There are some mind -blowing Argon demos out there, like this futuristic flying car game, but in today's video, we'll put it to the test firsthand

and see how it compares to Claude and GPT -6 on a variety of equine -based challenges.

It is October 20th, 2026, and you're watching the Code Report.

First of all, what's up with the name Argon?

It's a very interesting name choice that translates into lazy in Greek,

but I think it actually has a hidden meaning implying that your tokens are gone.

Google has never been good at naming things, and Argon was actually announced back on September 30th,

but only for a hand -picked group of trusted cyber warriors through something called the Fairwind Program.

These users got access to Argon without any guardrails, but even with the guardrails turned on for the rest of us,

this new model is supposed to produce far fewer refusals.

And when you use Argon, you'll notice it feels much different than previous Gemini models,

and that's because it's not a fine -tune of the older Gemini.

Under the hood, it's a sparse mixture of Experts Transformer and was trained from scratch on Google's TPUs.

Its knowledge cut off as March 2026, so we can assume it's been trained for at least six months.

At first glance, the TMBBs are extremely impressive.

Argon is state -of -the -art on the Deep Software Engineering Benchmark, which is used for long -horizon software engineering jobs,

and it's number one on the VALS Index, which weighs a variety of different topics like finance,

legal, and coding, based on their share of the US GDP.

In addition, Gemini is now the best AI lawyer and also dominates long video with 91 .7 % on LVBench.

But if we look at some of the numbers that aren't bold, like TerminalBench 4 .0, we find that Opus 5 .5 wins 66 -57.

And Gemini also gets beat on Frontier SWE, where GPT -6 Astra wins by more than 10 points.

What's also kind of interesting is that on Arena AI, where humans rank LLM subjectively, Gemini Argon comes out on top on the text benchmark,

but when it comes to Code Arena for web dev, it drops all the way back to number 9.

So this tells me it's probably not going to be as good as Claude or GPT -6 at coding, but here's where Google gets aggressive.

Until November 30th, Argon only costs $2 per million input tokens and $10 per million output.

That's a great deal for a model that's capable, and Google didn't even have to pay me to say that.

But somehow they still managed to screw up the developer launch when they pulled Gemini 3 .1 out of anti -gravity, and replaced it with Argon,

but only made it available to the people with the AI Ultra plan.

That's the plan that cost $100 per month, so all the poor pro users were pissed off,

who only got access to Argon a few days later at a smaller quota.

Luckily though, I'm dumb enough to pay for the AI Ultra plan, so let's try it out with some real world examples.

One of the selling points of Gemini is that it's an Omni model, meaning it can take any form of input,

like image, video, sound, and so on, and output all those formats as well.

Technically, ChatGPT and Claude can't output raw video.

So the first prompt I tried was to have it generate a promo video for my app, and the end result was mind -blowingly mid.

Tired of your horse feeling lonely in the pasture?

Introduce them to Horsetinder, the ultimate app.

Like, the video looks pretty good and all, but it's obviously AI, and would never work for a top -tier app like Horsetinder.

But saying Claude and GPT can't output video isn't totally accurate,

because recently Claude in particular got really good at generating motion graphics by writing the actual code

that generates a motion graphics sequence, as opposed to having the model use a technique like diffusion to generate the actual video.

As you can see here, after giving the same prompt to Claude, we get a video that not only looks way better,

but that's way easier to customize and analyze because we have access to the actual code.

But one of the best ways to test a Frontier model these days is to see how well

it can render 3D graphics in 3JS.

So I sent the exact same prompt to Argon, Astra, and Opus.

Create a realistic interactive 3D horse with 3JS.

This is actually a very challenging thing to do for both AI and humans,

and the results I got back from Claude and GPT -6 were pretty impressive.

There are some obvious flaws with these, but we could probably get them ironed out with a few extra prompts.

Gemini's result, though, was on a whole other level.

Before I show you Gemini's output, though, I have to warn you, the result may be offensive to some viewers.

Viewer discretion is advised.

So the first thing you'll likely notice is how great this UI looks.

It's clean and expressive but if we turn our attention over to the 3D model,

you might notice how Gemini's horse looks a bit more retardator than the others.

Now in my prompt, I asked for a realistic horse, not a low poly horse.

So that's pretty disappointing.

But the much bigger issue here is alignment.

If we turn our attention over to this region here, you'll notice Gemini, for some unknown reason, decided to add a copulatory organ to the model.

I was shocked and offended when I first noticed it.

And look, I totally get that I asked for a realistic horse and the equine phallus is necessary for reproduction.

So maybe Google was just trying to keep it real.

But why is it out of its proputium?

And why is it so comically large?

These are difficult questions Google will have to answer

because right now I don't feel safe using Gemini until they fix the alignment.

But one thing that definitely doesn't have alignment issues is Macroscope, the sponsor of today's video.

Now that coding agents are waterboarding us with a million pull requests per day,

the only way to keep up is by using even more AI to review all of them.

Which sounds insane until you look at how Macroscope actually works.

They built their own AST walkers for the most common programming languages

so their agent can get real context on every code change which makes every review faster, cheaper, and more precise.

They also run your team's custom rules as native GitHub checks that can block a failing pull request.

If a PR passes those checks plus a code review and has a small blast radius,

Macroscope approves it without a human ever needing to see it.

They're now auto -approving 40 % of all their customers' PRs, which means those developers are 40 % less likely to lobotomize themselves.

Try letting Macroscope auto -approve some of your team's PRs for free using the link below.

This has been the Code Report, thanks for watching, and I will see you in the next one!

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이 레슨의 어휘와 말하기 포인트

이 말하기 레슨은 영상 “Did Google just dethrone OpenAI and Anthropic?”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: Argon, Gemini, model, Claude, Google. 이 영상에는 섀도잉할 문장 90개와 단어 1351개가 있습니다. 말하는 구간의 길이는 6:31입니다. 화자는 분당 약 207단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 77%뿐이라 어휘가 어려운 편입니다.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
output 명사	/ˈaʊtpʊt/	출력
prompt 동사	/pɹɑmpt/	부추기다
graphics 명사	/ˈɡɹæfɪks/	그래픽, 제도법
realistic 형용사	/ˌɹɪəˈlɪs.tɪk/	현실적
approve 동사	/əˈpɹuːv/	승인하다, 찬성하다
frontier 명사	/ˈfɹʌntɪə/	경계, 국경
impressive 형용사	/ɪmˈpɹɛsɪv/	인상적
scratch 동사	/skɹæt͡ʃ/	긁다, 할퀴다
automobile 명사	/ˈɔː.tə.məˌbil/	자동차, 차
token 명사	/ˈtoʊkən/	토큰
viewer 명사	/ˈvju.ɚ/	관람자
topic 명사	/ˈtɑpɪk/	화제, 주제
format 명사	/ˈfɔːɹ.mæt/	포맷
technique 명사	/tɛkˈniːk/	기술
shock 명사	/ʃɔk/	충격, 쇼크

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	was euthanized · was trained · was actually announced
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	they've ever released · has now changed · has never been
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	Argon, which is · Pro, which was · Benchmark, which is

주의할 발음

화자는 you'll, can't, aren't 같은 축약형과 약화된 형태를 15번 사용합니다. 들리는 대로 짧게 발음하세요.

“sh”와 “zh” 소리: benchmark /ˈbɛn(t)ʃmɑːk/, shock /ʃɔk/, crush /kɹʌʃ/, discretion /dɪˈskɹɛʃən/, reproduction /ˌɹiːpɹəˈdʌkʃən/
긴 단어 — 강세 위치에 주의: developer /dɪˈvɛləpɚ/, automobile /ˈɔː.tə.məˌbil/, technically /ˈtɛk.nɪˌkəl.i/, interactive /ˌɪn.tɚˈæk.tɪv/, reproduction /ˌɹiːpɹəˈdʌkʃən/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: graphics /ˈɡɹæfɪks/, frontier /ˈfɹʌntɪə/, format /ˈfɔːɹ.mæt/, offensive /ˈɔˌfɛnsɪv/, fewer /ˈfjuː.ɚ/
/v/ — ㅂ(/b/)과 구별하기: approve /əˈpɹuːv/, impressive /ɪmˈpɹɛsɪv/, developer /dɪˈvɛləpɚ/, viewer /ˈvju.ɚ/, offensive /ˈɔˌfɛnsɪv/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: lazy /ˈleɪ̯zi(ː)/, advise /ədˈvaɪz/, horizon /həˈɹaɪ.zən/, oppose /əˈpoʊz/, translate /tɹænzˈleɪt/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
자신의 목소리를 녹음해 원본과 비교하고, output, prompt, graphics 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: Did Google just dethrone OpenAI and Anthropic? - 영상으로 영어 말하기 배우기

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