シャドーイング練習: Did Google just dethrone OpenAI and Anthropic? - 動画で英語スピーキングを学ぶ

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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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このレッスンの語彙とスピーキングのポイント

このスピーキングレッスンは、動画「Did Google just dethrone OpenAI and Anthropic?」を教材にしています。繰り返し出てくる語は次のとおりです：Argon, Gemini, model, Claude, Google。この動画には、シャドーイング用の文が90文、単語が1351語あります。音声の長さは6:31です。話す速さは速く、1分あたり約207語です。音のつながりや弱く発音される音が多くなります。英語の頻出3,000語に含まれる単語は77%だけなので、語彙は難しめです。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
output 名詞	/ˈaʊtpʊt/	出力
generate 動詞	/ˈd͡ʒɛn.ə.ɹeɪt/	生む, 生み出す
benchmark 名詞	/ˈbɛn(t)ʃmɑːk/	水準点
graphics 名詞	/ˈɡɹæfɪks/	グラフィック
realistic 形容詞	/ˌɹɪəˈlɪs.tɪk/	現実的
approve 動詞	/əˈpɹuːv/	承認する, 賛成する
frontier 名詞	/ˈfɹʌntɪə/	境界, 国境
impressive 形容詞	/ɪmˈpɹɛsɪv/	印象的な
scratch 動詞	/skɹæt͡ʃ/	引っ掻く
tier 名詞	/ˈtɪɚ/	層, 階層
automobile 名詞	/ˈɔː.tə.məˌbil/	自動車, 乗用車
token 名詞	/ˈtoʊkən/	トークン
viewer 名詞	/ˈvju.ɚ/	視聴者
topic 名詞	/ˈtɑpɪk/	話題, テーマ
trick 名詞	/ˈtɹɪk/	欺き, 企み

動画に出てくる句動詞

単語	発音	意味
cut off 動詞	/ˌkʌt ˈɔf/ [ˌkɐɾ ˈɔf]	断つ

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	was euthanized · was trained · was actually announced
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	they've ever released · has now changed · has never been
関係詞節 who / which + 節 — 人や物について情報を加える	Argon, which is · Pro, which was · Benchmark, which is

注意したい発音

話し手はyou'll, can't, aren'tなど、短縮形や弱形を15回使っています。聞こえたとおりの短い形で発音しましょう。

「sh」と「zh」の音: benchmark /ˈbɛn(t)ʃmɑːk/, shock /ʃɔk/, crush /kɹʌʃ/, discretion /dɪˈskɹɛʃən/, reproduction /ˌɹiːpɹəˈdʌkʃən/
長い単語（アクセントの位置に注意）: developer /dɪˈvɛləpɚ/, automobile /ˈɔː.tə.məˌbil/, technically /ˈtɛk.nɪˌkəl.i/, interactive /ˌɪn.tɚˈæk.tɪv/, reproduction /ˌɹiːpɹəˈdʌkʃən/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: realistic /ˌɹɪəˈlɪs.tɪk/, translate /tɹænzˈleɪt/, refusal /ɹɪˈfjuːzl̩/, guardrail /ˈɡɑɹdˌɹeɪl/
/v/ — /b/ にならないように、上の歯を下唇に当てる: approve /əˈpɹuːv/, impressive /ɪmˈpɹɛsɪv/, developer /dɪˈvɛləpɚ/, viewer /ˈvju.ɚ/, offensive /ˈɔˌfɛnsɪv/
/f/ — 「フ」ではなく、上の歯と下唇で出す: graphics /ˈɡɹæfɪks/, frontier /ˈfɹʌntɪə/, format /ˈfɔːɹ.mæt/, offensive /ˈɔˌfɛnsɪv/, fewer /ˈfjuː.ɚ/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。output, generate, benchmarkなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: Did Google just dethrone OpenAI and Anthropic? - 動画で英語スピーキングを学ぶ

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