シャドーイング練習: Gemini 4 Argon explained in 5min.. - 動画で英語スピーキングを学ぶ

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It's been about seven months since Google announced a new flagship model, Gemini 4 Argon.

Even during Google I .O back in May, I expected Google to drop Gemini 3 .5 Pro.

But Google has been releasing a lot of flash models instead,

and a lot of them basically struggle to get market adoption given a variety of other flagship models

and cheaper alternatives from Chinese open models.

And Google finally made a flagship model with the codename Argon, and the preliminary benchmarks show a pretty strong number as you can see.

The first thing you might have noticed here is coding benchmarks, more specifically, scoring 77 .9 % on DeepSuite,

which would put Gemini 4 Argon above every other model we currently have.

But it's always good to have some healthy skepticism here.

Recently, EPOC found issues on DeepSuite Benchmark, finding 23 tasks that are flawed.

And looking back to when the benchmark was actually released back in May, the entire 113 tasks and their solutions are made public,

which means labs that have access to these could easily study them during post -training and benchmarks their models around public benchmarks.

And when we look at other benchmarks that Google reported here, a large majority of them are public.

So the further we go out in time, labs that release their models later have a slight advantage to potentially include them during post -training.

And of course, as users, all of this makes it really difficult to track real progress in AI since

so many public benchmarks like DeepSuite can be contaminated pretty fast.

Not only that, benchmarks get saturated a lot faster now than before, which forces benchmarks to be refreshed more frequently.

So consumers now carry a huge burden keeping track of real scientific progress in AI.

But regardless of the case, achieving this kind of leap in benchmark is a huge improvement from Google.

Now, when we take a step back and look at how Google is competing with other labs, a lot of people still primarily use clock code,

codecs, or open harnesses when it comes to coding.

And despite being a strong model, Google still hasn't really solved making their harness anti -gravity a truly competitive option for developers.

For a recent report, Codex has more than doubled the amount of weekly active users at 5 million compared to 2 .4 million for anti -gravity.

And on face value, Gemini 4 Argon currently undercuts both Anthropic

and OpenAI when it comes to the cost of intelligence until the temporary discount ends and the price doubles again.

But the cost benefit that we find here doesn't actually translate in Pareto Frontier, as you can see.

Argon falls below the Pareto Frontier, showing you that Anthropic and OpenAI still leads ahead in both the harness and model adoption.

Now, one angle we should think about, though, is the upcoming IPO for Anthropic and possibly OpenAI.

While OpenAI and Anthropic have a huge incentive to subsidize the model through subscription pretty heavily to grow their users,

Google already sits on top of one.

And given that Google is already a publicly traded company,

they want to be more careful about how to allocate their existing compute towards actually creating value within their ecosystem,

which could help increase their perceived revenue.

Even looking at how Argon is being rolled out in segments, the model is set to be made available to ultra -subscription users

and paid API users where there's already a huge revenue being

captured instead of fighting the subscription rates in channeling their compute towards that.

On a recent Google's Q2 earning call, they said that their API is processing 22 billion tokens per minute,

which is 6 billion more than the previous quarter.

That's more than 11 quadrillion tokens per year in just API channel alone.

And when you start factoring in other channels that Google owns in its distribution,

we really get a closer picture into what Google really means when they said that they're supply constrained.

And eventually, OpenAI and Anthropic will have to solve this scaling problem while maintaining healthy margins at the same time,

not just subsidizing subscription to capture more users.

So looking at Gemini for Argon, Google is demonstrating their capability in keeping pace at the frontier

while they also try to figure out how to capture value at scale.

One interesting note on Argon is how Google extended their output tokens from 64 ,000 to 1 million tokens.

This might not sound like a big deal, but it's definitely a huge move from Google

because maintaining coherence as autoregressive models generate longer and longer tokens get increasingly difficult the longer the output tokens get.

Scientists like Yen Le Koon made criticism on autoregressive models for this very reason,

where autoregressively generating one token after another can compound errors over long trajectory, let alone 1 million from Argon.

If you can imagine each token that's 99 % reliable, chaining 100 independent steps together would leave about 37 % chance of every step being correct.

Now, of course, that's just in theory, assuming that every token error is independent.

But the fact

that Google has enough confidence to extend their commercial product to

1 million output tokens really opens up door for longer trajectory tasks like code migration

and long -running research tasks that could benefit from a model that can generate more.

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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「Gemini 4 Argon explained in 5min.」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：Google, model, benchmark, token, Argon。この動画には、シャドーイング用の文が56文、単語が877語あります。音声の長さは4:56です。話す速さは1分あたり約178語で、日常会話に近い自然なペースです。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
benchmark 名詞	/ˈbɛn(t)ʃmɑːk/	水準点
token 名詞	/ˈtoʊkən/	トークン
subscription 名詞	/səbˈskɹɪpʃən/	定期購読, 購読
output 名詞	/ˈaʊtpʊt/	出力
capture 動詞	/ˈkæp.(t)ʃɚ/	捕獲する
generate 動詞	/ˈd͡ʒɛn.ə.ɹeɪt/	生む, 生み出す
frontier 名詞	/ˈfɹʌntɪə/	境界, 国境
flagship 名詞	/ˈflæɡʃɪp/	旗艦
solve 動詞	/sɒlv/	解決する
adoption 名詞	/əˈdɑp.ʃən/	養子縁組
trajectory 名詞	/tɹəˈd͡ʒɛktəɹi/	軌道, 弾道
earn 動詞	/ɝn/	稼ぐ, 得る
temporary 形容詞	/ˈtɛmp(əˌɹ)ɛɹi/	暫くの, 暫時の
compare 動詞	/kəmˈpɛɚ/	比べる, 比較する
clock 名詞	/klɑk/	走行距離計

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	are flawed · was actually released · can be contaminated
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	It's been · hasn't really solved

注意したい発音

話し手はdoesn't, hasn't, they'reなど、短縮形や弱形を3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/, subscription /səbˈskɹɪpʃən/, capture /ˈkæp.(t)ʃɚ/, flagship /ˈflæɡʃɪp/, adoption /əˈdɑp.ʃən/
長い単語（アクセントの位置に注意）: trajectory /tɹəˈd͡ʒɛktəɹi/, primarily /ˈpɹaɪ.mə.ɹə.li/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, potentially /pəˈtɛnʃ(ə)li/, developer /dɪˈvɛləpɚ/

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: primarily /ˈpɹaɪ.mə.ɹə.li/, regardless /ɹɪˈɡɑɹd.lɪs/, reliable /ɹɪˈlaɪəbl̩/, ultra /ˈʌltɹə/, preliminary /pɹɪˈlɪmɪnɛɹi/
/v/ — /b/ にならないように、上の歯を下唇に当てる: solve /sɒlv/, gravity /ˈɡɹævɪti/, developer /dɪˈvɛləpɚ/, incentive /ɪnˈsɛntɪv/, perceive /pɚˈsiv/
/f/ — 「フ」ではなく、上の歯と下唇で出す: frontier /ˈfɹʌntɪə/, flagship /ˈflæɡʃɪp/, flash /flæʃ/, flawed /flɔːd/, refresh /ɹɪˈfɹɛʃ/

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。benchmark, token, subscription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: Gemini 4 Argon explained in 5min.. - 動画で英語スピーキングを学ぶ

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