Shadowing Practice: Gemini 4 Argon explained in 5min.. - Learn English Speaking with Video
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It's been about seven months since Google announced a new flagship model, Gemini 4 Argon.
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Even during Google I .O back in May, I expected Google to drop Gemini 3 .5 Pro.
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But Google has been releasing a lot of flash models instead,
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and a lot of them basically struggle to get market adoption given a variety of other flagship models
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and cheaper alternatives from Chinese open models.
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And Google finally made a flagship model with the codename Argon, and the preliminary benchmarks show a pretty strong number as you can see.
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The first thing you might have noticed here is coding benchmarks, more specifically, scoring 77 .9 % on DeepSuite,
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which would put Gemini 4 Argon above every other model we currently have.
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But it's always good to have some healthy skepticism here.
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Recently, EPOC found issues on DeepSuite Benchmark, finding 23 tasks that are flawed.
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And looking back to when the benchmark was actually released back in May, the entire 113 tasks and their solutions are made public,
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which means labs that have access to these could easily study them during post -training and benchmarks their models around public benchmarks.
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And when we look at other benchmarks that Google reported here, a large majority of them are public.
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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.
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And of course, as users, all of this makes it really difficult to track real progress in AI since
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so many public benchmarks like DeepSuite can be contaminated pretty fast.
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Not only that, benchmarks get saturated a lot faster now than before, which forces benchmarks to be refreshed more frequently.
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So consumers now carry a huge burden keeping track of real scientific progress in AI.
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But regardless of the case, achieving this kind of leap in benchmark is a huge improvement from Google.
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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,
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codecs, or open harnesses when it comes to coding.
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And despite being a strong model, Google still hasn't really solved making their harness anti -gravity a truly competitive option for developers.
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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.
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And on face value, Gemini 4 Argon currently undercuts both Anthropic
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and OpenAI when it comes to the cost of intelligence until the temporary discount ends and the price doubles again.
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But the cost benefit that we find here doesn't actually translate in Pareto Frontier, as you can see.
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Argon falls below the Pareto Frontier, showing you that Anthropic and OpenAI still leads ahead in both the harness and model adoption.
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Now, one angle we should think about, though, is the upcoming IPO for Anthropic and possibly OpenAI.
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While OpenAI and Anthropic have a huge incentive to subsidize the model through subscription pretty heavily to grow their users,
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Google already sits on top of one.
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And given that Google is already a publicly traded company,
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they want to be more careful about how to allocate their existing compute towards actually creating value within their ecosystem,
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which could help increase their perceived revenue.
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Even looking at how Argon is being rolled out in segments, the model is set to be made available to ultra -subscription users
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and paid API users where there's already a huge revenue being
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captured instead of fighting the subscription rates in channeling their compute towards that.
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On a recent Google's Q2 earning call, they said that their API is processing 22 billion tokens per minute,
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which is 6 billion more than the previous quarter.
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That's more than 11 quadrillion tokens per year in just API channel alone.
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And when you start factoring in other channels that Google owns in its distribution,
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we really get a closer picture into what Google really means when they said that they're supply constrained.
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And eventually, OpenAI and Anthropic will have to solve this scaling problem while maintaining healthy margins at the same time,
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not just subsidizing subscription to capture more users.
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So looking at Gemini for Argon, Google is demonstrating their capability in keeping pace at the frontier
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while they also try to figure out how to capture value at scale.
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One interesting note on Argon is how Google extended their output tokens from 64 ,000 to 1 million tokens.
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This might not sound like a big deal, but it's definitely a huge move from Google
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because maintaining coherence as autoregressive models generate longer and longer tokens get increasingly difficult the longer the output tokens get.
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Scientists like Yen Le Koon made criticism on autoregressive models for this very reason,
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where autoregressively generating one token after another can compound errors over long trajectory, let alone 1 million from Argon.
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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.
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Now, of course, that's just in theory, assuming that every token error is independent.
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But the fact
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that Google has enough confidence to extend their commercial product to
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1 million output tokens really opens up door for longer trajectory tasks like code migration
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and long -running research tasks that could benefit from a model that can generate more.
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Vocabulario y notas de pronunciación para esta lección
Esta lección de conversación se basa en el vídeo “Gemini 4 Argon explained in 5min.”. Las palabras que más se repiten: Google, model, benchmark, token, Argon. Este vídeo tiene 56 frases y 877 palabras para practicar shadowing. La parte hablada dura 4:56. El hablante habla a un ritmo natural de unas 178 palabras por minuto, cercano a una conversación cotidiana. Solo el 77 % de las palabras está entre las 3.000 más comunes del inglés, por lo que el vocabulario es exigente.
Vocabulario clave de este vídeo
15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| benchmark sustantivo | /ˈbɛn(t)ʃmɑːk/ | cota de referencia, estándar de comparación |
| token sustantivo | /ˈtoʊkən/ | seña |
| subscription sustantivo | /səbˈskɹɪpʃən/ | abono, suscripción |
| output sustantivo | /ˈaʊtpʊt/ | producción, output |
| capture verbo | /ˈkæp.(t)ʃɚ/ | capturar |
| generate verbo | /ˈd͡ʒɛn.ə.ɹeɪt/ | generar |
| frontier sustantivo | /ˈfɹʌntɪə/ | frontera |
| flagship sustantivo | /ˈflæɡʃɪp/ | buque insignia, capitana |
| harness verbo | /ˈhɑɹ.nəs/ | arrear, aparejar |
| solve verbo | /sɒlv/ | resolver, solucionar |
| extend verbo | /ɛkˈstɛnd/ | extenderse, ampliarse |
| gravity sustantivo | /ˈɡɹævɪti/ | gravedad |
| adoption sustantivo | /əˈdɑp.ʃən/ | adopción |
| trajectory sustantivo | /tɹəˈd͡ʒɛktəɹi/ | trayectoria |
| earn verbo | /ɝn/ | ganar, granjear |
Phrasal verbs que vas a escuchar
| Palabra | Significado |
|---|---|
| figure out verbo | averiguar, captar |
Gramática en este vídeo
Las estructuras que más usa el hablante, con las palabras exactas del vídeo:
| Estructura | En el vídeo |
|---|---|
| Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace | are flawed · was actually released · can be contaminated |
| Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora | It's been · hasn't really solved |
Pronunciación a tener en cuenta
El hablante usa 3 contracciones y formas reducidas, como doesn't, hasn't, they're. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “sh” y “zh”: benchmark /ˈbɛn(t)ʃmɑːk/, subscription /səbˈskɹɪpʃən/, capture /ˈkæp.(t)ʃɚ/, flagship /ˈflæɡʃɪp/, adoption /əˈdɑp.ʃən/
- Palabras largas — cuida el acento: trajectory /tɹəˈd͡ʒɛktəɹi/, primarily /ˈpɹaɪ.mə.ɹə.li/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, potentially /pəˈtɛnʃ(ə)li/, developer /dɪˈvɛləpɚ/
Sonidos difíciles para hispanohablantes:
- s + consonante al inicio — sin añadir una “e” delante: slight /slaɪt/, skepticism /ˈskɛp.tɪˌsɪ.zəm/
- /v/ — no es /b/: los dientes tocan el labio inferior: solve /sɒlv/, gravity /ˈɡɹævɪti/, developer /dɪˈvɛləpɚ/, incentive /ɪnˈsɛntɪv/, perceive /pɚˈsiv/
- /z/ — sonora, no /s/: translate /tɹænzˈleɪt/, subsidize /ˈsʌbsɪdaɪz/, skepticism /ˈskɛp.tɪˌsɪ.zəm/
Cómo practicar con este vídeo
- Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
- Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
- Grábate y compara con el original, prestando atención a palabras como benchmark, token, subscription.
¿Qué es la Técnica de Shadowing?
Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.