Shadowing Practice: Did Google just dethrone OpenAI and Anthropic? - Learn English Speaking with Video
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Last week, gas changed the world forever.
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Twice.
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Tesla finally unveiled the Roadster and its ability to fly by farting cold gas.
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But while Elon was busy pulling off his greatest magic trick, Google was cooking up a noble gas of its own,
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Gemini 4 Argon, which is the most powerful thinking sand they've ever released.
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Sadly though, they never even released Gemini 3 .5 Pro, which was euthanized up in Canada after failing to hit internal benchmarks.
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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.
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But all that has now changed with the release of Argon,
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which was trained from scratch over the last six months using Google's TPUs and their Jax Python library.
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And that produced a new frontier model that crushes GPT -6 Astra
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and Claude Opus 5 .5 on the Trust Me Bro benchmarks, while remaining cheaper and faster.
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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
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and see how it compares to Claude and GPT -6 on a variety of equine -based challenges.
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It is October 20th, 2026, and you're watching the Code Report.
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First of all, what's up with the name Argon?
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It's a very interesting name choice that translates into lazy in Greek,
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but I think it actually has a hidden meaning implying that your tokens are gone.
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Google has never been good at naming things, and Argon was actually announced back on September 30th,
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but only for a hand -picked group of trusted cyber warriors through something called the Fairwind Program.
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These users got access to Argon without any guardrails, but even with the guardrails turned on for the rest of us,
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this new model is supposed to produce far fewer refusals.
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And when you use Argon, you'll notice it feels much different than previous Gemini models,
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and that's because it's not a fine -tune of the older Gemini.
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Under the hood, it's a sparse mixture of Experts Transformer and was trained from scratch on Google's TPUs.
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Its knowledge cut off as March 2026, so we can assume it's been trained for at least six months.
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At first glance, the TMBBs are extremely impressive.
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Argon is state -of -the -art on the Deep Software Engineering Benchmark, which is used for long -horizon software engineering jobs,
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and it's number one on the VALS Index, which weighs a variety of different topics like finance,
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legal, and coding, based on their share of the US GDP.
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In addition, Gemini is now the best AI lawyer and also dominates long video with 91 .7 % on LVBench.
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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.
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And Gemini also gets beat on Frontier SWE, where GPT -6 Astra wins by more than 10 points.
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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,
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but when it comes to Code Arena for web dev, it drops all the way back to number 9.
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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.
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Until November 30th, Argon only costs $2 per million input tokens and $10 per million output.
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That's a great deal for a model that's capable, and Google didn't even have to pay me to say that.
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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,
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but only made it available to the people with the AI Ultra plan.
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That's the plan that cost $100 per month, so all the poor pro users were pissed off,
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who only got access to Argon a few days later at a smaller quota.
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Luckily though, I'm dumb enough to pay for the AI Ultra plan, so let's try it out with some real world examples.
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One of the selling points of Gemini is that it's an Omni model, meaning it can take any form of input,
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like image, video, sound, and so on, and output all those formats as well.
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Technically, ChatGPT and Claude can't output raw video.
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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.
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Tired of your horse feeling lonely in the pasture?
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Introduce them to Horsetinder, the ultimate app.
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Like, the video looks pretty good and all, but it's obviously AI, and would never work for a top -tier app like Horsetinder.
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But saying Claude and GPT can't output video isn't totally accurate,
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because recently Claude in particular got really good at generating motion graphics by writing the actual code
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that generates a motion graphics sequence, as opposed to having the model use a technique like diffusion to generate the actual video.
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As you can see here, after giving the same prompt to Claude, we get a video that not only looks way better,
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but that's way easier to customize and analyze because we have access to the actual code.
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But one of the best ways to test a Frontier model these days is to see how well
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it can render 3D graphics in 3JS.
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So I sent the exact same prompt to Argon, Astra, and Opus.
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Create a realistic interactive 3D horse with 3JS.
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This is actually a very challenging thing to do for both AI and humans,
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and the results I got back from Claude and GPT -6 were pretty impressive.
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There are some obvious flaws with these, but we could probably get them ironed out with a few extra prompts.
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Gemini's result, though, was on a whole other level.
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Before I show you Gemini's output, though, I have to warn you, the result may be offensive to some viewers.
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Viewer discretion is advised.
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So the first thing you'll likely notice is how great this UI looks.
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It's clean and expressive but if we turn our attention over to the 3D model,
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you might notice how Gemini's horse looks a bit more retardator than the others.
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Now in my prompt, I asked for a realistic horse, not a low poly horse.
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So that's pretty disappointing.
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But the much bigger issue here is alignment.
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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.
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I was shocked and offended when I first noticed it.
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And look, I totally get that I asked for a realistic horse and the equine phallus is necessary for reproduction.
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So maybe Google was just trying to keep it real.
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But why is it out of its proputium?
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And why is it so comically large?
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These are difficult questions Google will have to answer
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because right now I don't feel safe using Gemini until they fix the alignment.
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But one thing that definitely doesn't have alignment issues is Macroscope, the sponsor of today's video.
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Now that coding agents are waterboarding us with a million pull requests per day,
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the only way to keep up is by using even more AI to review all of them.
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Which sounds insane until you look at how Macroscope actually works.
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They built their own AST walkers for the most common programming languages
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so their agent can get real context on every code change which makes every review faster, cheaper, and more precise.
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They also run your team's custom rules as native GitHub checks that can block a failing pull request.
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If a PR passes those checks plus a code review and has a small blast radius,
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Macroscope approves it without a human ever needing to see it.
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They're now auto -approving 40 % of all their customers' PRs, which means those developers are 40 % less likely to lobotomize themselves.
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Try letting Macroscope auto -approve some of your team's PRs for free using the link below.
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This has been the Code Report, thanks for watching, and I will see you in the next one!
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Vocabulary and speaking notes for this lesson
This speaking lesson is built on the video “Did Google just dethrone OpenAI and Anthropic?”. The speaker keeps coming back to these words: Argon, Gemini, model, Claude, Google. This video has 90 sentences and 1351 words to shadow. The speech runs for 6:31. The speaker talks fast, about 207 words per minute, so expect linked and reduced sounds. Only 77% of the words are among the 3,000 most common in English, so the vocabulary is demanding.
Key vocabulary in this video
15 words from the video worth learning, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| output noun | /ˈaʊtpʊt/ | Production; quantity produced, created, or completed. |
| prompt verb | /pɹɑmpt/ | To lead (someone) toward what they should say or do. |
| generate verb | /ˈd͡ʒɛn.ə.ɹeɪt/ | To bring into being; give rise to. |
| benchmark noun | /ˈbɛn(t)ʃmɑːk/ | A standard by which something is evaluated or measured. |
| graphics noun | /ˈɡɹæfɪks/ | The making of architectural or design drawings. |
| realistic adjective | /ˌɹɪəˈlɪs.tɪk/ | Expressed or represented as being accurate, practicable, or not idealistic. |
| approve verb | /əˈpɹuːv/ | To officially sanction; to ratify; to confirm; to set as satisfactory. |
| frontier noun | /ˈfɹʌntɪə/ | The part of a country which borders or faces another country or unsettled region. |
| alignment noun | /əˈlaɪn.mənt/ | An arrangement of items in a line. |
| impressive adjective | /ɪmˈpɹɛsɪv/ | Making, or tending to make, a positive impression; having power to impress. |
| input noun | /ˈɪnpʊt/ | The act or process of putting in; infusion. |
| developer noun | /dɪˈvɛləpɚ/ | A person or entity engaged in the creation or improvement of certain classes of products. |
| scratch verb | /skɹæt͡ʃ/ | To rub a surface with a sharp object, especially by a living creature to remove itching with nails, claws, etc. |
| tier noun | /ˈtɪɚ/ | A row or range, especially one at a higher or lower level than another. |
| automobile noun | /ˈɔː.tə.məˌbil/ | A type of motor vehicle designed to move on the ground under its own stored power and intended to carry a driver, a small number of additional passengers, and… |
Phrasal verbs you will hear
| Word | Pronunciation | Meaning |
|---|---|---|
| cut off verb | /ˌkʌt ˈɔf/ [ˌkɐɾ ˈɔf] | To remove by cutting. |
| keep up verb | To maintain; to preserve; to prevent from deteriorating or falling. | |
| pull off verb | To remove by pulling. |
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Passive voice be + past participle — the focus is on what happens, not who does it | was euthanized · was trained · was actually announced |
| Present perfect have/has + past participle — a past action that still matters now | they've ever released · has now changed · has never been |
| Relative clauses who / which + clause — extra information about a person or thing | Argon, which is · Pro, which was · Benchmark, which is |
Pronunciation to watch
The speaker uses 15 contractions and reduced forms, such as you'll, can't, aren't. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: benchmark /ˈbɛn(t)ʃmɑːk/, shock /ʃɔk/, crush /kɹʌʃ/, discretion /dɪˈskɹɛʃən/, reproduction /ˌɹiːpɹəˈdʌkʃən/
- Long words — get the stress right: developer /dɪˈvɛləpɚ/, automobile /ˈɔː.tə.məˌbil/, technically /ˈtɛk.nɪˌkəl.i/, interactive /ˌɪn.tɚˈæk.tɪv/, reproduction /ˌɹiːpɹəˈdʌkʃən/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like output, prompt, generate.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.