Lesson: The Man in the Leather Jacket
A free editorial English lesson at three levels (B1, B2, C1) — for learners and teachers
He wears the same black leather jacket on stage every year, and his company’s chips now sit inside almost everything that runs on artificial intelligence. His name is Jensen Huang, and the company is Nvidia. Nearly twenty years ago he made a bet on a strange way of doing sums — thousands of small calculations all at once — that most people thought was a waste of time and money. Today that bet has made him one of the most powerful people in technology, and it has left a hard question hanging over the world: when almost all of AI depends on one company, is that a strength worth celebrating, or a danger we should worry about?
Contents
The same topic is scaled across three levels below. Pick the one that fits you — or, if you teach, read all three and choose per group.
Read the companion article → We Export Our Emissions (B1) · C1 version
About The BEBB Method — The Agency Loop
Every task in this lesson runs on the BEBB Method — The Agency Loop, a five-step framework for using AI in language learning without letting it replace your own thinking. It is my own configuration of established best practices in human-AI collaboration, built in direct response to Gerlich (2025) on cognitive offloading, and informed by the “AI Sandwich” tradition (Ippolito, 2023) and “AI as Critic” scaffolding (Mollick, 2024).
Agency = your own capacity to decide, think, and act. In this method, you keep your agency at every step — you, not the AI, remain the one deciding what happens to your thinking. The Agency Loop (dt. etwa: Handlungsfähigkeit im Umgang mit KI — du bleibst die Entscheider·in).
The five steps
Think first — form your own view before you touch any tool.
Use AI for facts — let the tool gather information, not opinions.
Draft yourself — write it in your own words, from your own head.
Challenge with AI — ask the tool to attack your position and find its weakest point.
Combine — fold the best of the challenge back into a stronger version that is still yours.
How the levels use the loop
B1 = Tasks 1 + 3 (two cycles): think first, then challenge and finalise with AI.
B2 = Tasks 1 + 3, with Task 2 optional if there is time.
C1 = Tasks 1 + 2 + 3 (the full loop, including the assigned-position debate).
For teachers
This lesson was tested with several groups in Germany over one week, at all three levels — the C1 profile “The Man in the Leather Jacket” and a rebuilt B2 version, “The Man Who Bet on the Future,” across mixed-industry corporate groups and 1:1 clients. It ran well as a 60-minute session at B1/B2 and a full 90-minute session at C1.
A few things to protect:
Stay neutral. The whole lesson turns on a genuine two-sided question. Do not signal your own verdict; let the room argue it out.
Protect the argument moment — the “what built Nvidia (vision, luck, or grit)” and “strength or danger” exchange is the heart of the lesson, not a warm-down. If you are short on time, cut elsewhere first.
Mixed-level guidance: in a mixed room, give stronger participants the C1 reading and the assigned-position flip; give others the B1/B2 reading and the simpler comprehension. Everyone can still join the same discussion.
Micro-research is the engine. The warm-up research task is what brings real facts into the room. Do not skip it to save time — it is what makes the debate substantive rather than opinion-only.
The discussion — and, at C1, the assigned-position debate — is where the language work actually happens. Everything before it is preparation for it.
60-minute block (B1 / B2)
Warm-up + micro-research · 11 min
Vocabulary · 6 min
Task 1 — Map Your Starting Position · 8 min
Reading · 9 min
Comprehension · 7 min
Task 3 — Challenge With AI (B2: Task 2 first if time) · 10 min
Discussion · 9 min
90-minute block (C1)
Warm-up + micro-research · 11 min
Vocabulary · 8 min
Task 1 — Narrate a Turning Point · 12 min
Reading · 11 min
Task 2 — Build & Defend Your Verdict · 16 min
Comprehension · 7 min
Task 3 — Challenge and Finalise · 11 min
Discussion · 14 min
45-minute fallback: keep the warm-up micro-research (shortened to items 1–3 only), the reading, and one focused round of Task 3 (challenge your position with AI, then revise). Drop Task 2 and cut the discussion to two questions. The lesson still completes one full Agency Loop this way — think first, gather facts, draft, challenge, combine — which is the minimum that makes it worth doing.
B1 version
Warm-up — micro-research (single prompt)
Before you search, take 30–60 seconds and write down what you already know or believe about Nvidia, Jensen Huang, or how AI chips are made. Just keywords. This is your starting point — your own thinking comes first. Agency means your own power to decide, think, and act; you keep it at every step, and the AI never decides for you.
Umbrella question: How does your item help explain Jensen Huang’s story — or the question of the world depending on Nvidia?
For your item, find:
What is it? (one or two simple sentences)
One key fact, number, or date.
Why does it matter?
Your item list (take one — items 1–3 first; 4–6 if there is time):
Nvidia — the company (started in 1993; what it makes today)
GPU vs CPU — the difference between a graphics chip and a normal chip
TSMC and Taiwan — where the world’s best chips are actually made
The dot-com crash of 2000 — what happened, and who lost money
The EU Chips Act — why Europe worries it does not make enough chips
CUDA — the software that makes it hard to stop using Nvidia
Paste this into your AI tool with your item filled in:
LEVEL: B1
I'm preparing for a Business English lesson about Jensen Huang — the founder
and CEO of Nvidia, the company whose chips power almost all of today's
artificial intelligence — and the question of whether the world depending on
one chip-maker is a strength or a danger.
My research item is: [MY ITEM]
In clear, simple B1-level English (maximum 100 words), give me:
1. What it is — one or two short sentences.
2. One key fact, number, or date.
3. Why it matters to Jensen Huang's story, or to the world depending on Nvidia.
Keep it short enough to say to my group in about two minutes. Define any
technical term in eight words or fewer.Vocabulary
Match each word or phrase (1–8) to its definition (A–H). Guess first, then check the key.
to pioneer
a calculated bet
resilience
to be vindicated
founder-led
indispensable
against the odds
to reinvent (a company)
A. to be the first to use a new idea or method
B. a risk you take on purpose, after thinking about the chances
C. the ability to recover quickly after a difficulty or failure
D. proved right after other people doubted you
E. run by the same person who started the company
F. so important that things cannot work without it
G. succeeding when success was very unlikely
H. to change a company so much that it becomes something new
Task 1 — Map Your Starting Position
Prepare alone (2 min). Think of a turning point in your own job or company — a risky decision, a near-failure, or a bet that paid off. Write 4–6 keywords only. No AI yet — this comes from your own memory.
Share (3 min). Tell a partner the story in about a minute. Use at least two vocabulary words from the list above.
Reading — The Chip Man
(1) Jensen Huang is the boss of a company called Nvidia. Nvidia makes computer chips. Today its chips run almost all of the AI in the world. But this was not always true, and Huang’s path was not easy.
(2) Huang was born in Taiwan in 1963. When he was nine, his parents sent him to live with a relative in the United States. His school in Kentucky was hard, and young Jensen cleaned the toilets. His first job was washing dishes in a Denny’s diner. He says it taught him to work hard.
(3) He studied engineering. In 1993, he started Nvidia with two friends. For years the company almost died. Its first big product failed, and more than once it nearly ran out of money. Huang still tells his staff: “Our company is always thirty days from going out of business.” This shows his resilience — he keeps going after failure.
(4) Nvidia survived by making graphics chips for video games. For ten years, people thought Nvidia was just a company for gamers. Nobody guessed it would become something much bigger.
(5) Then Huang made a big bet. A normal chip does one big sum very fast. A graphics chip does thousands of small sums all at once. Huang believed this second way would matter far beyond games. From around 2006, he spent a lot of money to pioneer software (called CUDA) that made his chips easy to use for new jobs. For years it looked like a mistake.
(6) Then the bet paid off. “Thousands of small sums at once” is exactly how modern AI learns. When the AI boom came, only Nvidia made the right chips. The gaming company became indispensable — the world could not do AI without it. Huang was vindicated.
(7) He runs the company in an unusual way. He wears the same black leather jacket on stage every year. He has led the same company for over thirty years, which is rare, and still speaks as if it could fail next month.
(8) There is a bigger question, and it is not about one man. Almost all of the world’s AI runs on chips from one company — designed in America but made on one island, Taiwan. Some people say this is fine: Nvidia is the best. Others say it is dangerous: if the whole world depends on one company and one place, a single problem could stop everything. Both sides have a point.
Comprehension
Answer from the reading. Note the paragraph.
Where was Jensen Huang born, and what was his first job in the United States? (¶2)
What did Huang tell his staff about the company, even today? (¶3)
What did Nvidia make first, before AI? (¶4)
What was the “big bet” Huang made from around 2006? (¶5)
Where are Nvidia’s chips designed, and where are they made? (¶8)
Task 3 — Challenge With AI
Write (alone, 4 min). Answer this question in 3–5 sentences, from your own head, no AI: One company makes almost all the world’s AI chips. Is that good or bad? Give one reason.
Challenge (3 min). Paste your answer into an AI tool with this prompt: “In simple English, what is the strongest argument against what I just wrote?”
Revise (3 min). Read the answer. Then rewrite your 3–5 sentences into a stronger version — but keep your own view unless the AI really changes your mind.
Discussion
Have you ever kept going after a failure at work, like Huang did? What happened?
Huang says his hard first job taught him good habits. Did a hard job ever teach you something useful?
Do you use anything with AI in it at work? What?
Is it a problem for your company to depend on one supplier for something important? Have you seen this happen?
Would you like a boss who says the company could fail next month? Why or why not?
B2 version
Warm-up — micro-research (single prompt)
Before you search, take 30–60 seconds and write down what you already know or believe about Nvidia, Jensen Huang, or how the chips behind AI are made. Just keywords. This is your starting point — your own thinking comes first. Agency means your own capacity to decide, think, and act; you keep it at every step, and the AI never decides for you.
Umbrella question: How does your item help explain Jensen Huang’s story — or the question of the world depending on Nvidia?
For your item, bring back:
What is it? (one or two precise sentences)
One key fact, number, or date that shows its scale.
Why does it matter to Huang’s story, or to the “strength or danger” question?
Your item list (take one — items 1–3 first; 4–6 only if there is time):
Nvidia — the company (founded 1993; what it does today)
GPU vs CPU — the difference between a graphics chip and a normal chip
TSMC and Taiwan — where the world’s most advanced chips are physically made
The dot-com crash of 2000 — what happened, and who lost money
“Digital sovereignty” and the EU Chips Act — Europe’s chip dependence
CUDA — the software that makes it hard to leave Nvidia (the “moat”)
Paste this into your AI tool with your item filled in:
LEVEL: B2
I'm preparing for a Business English lesson about Jensen Huang — the founder
and CEO of Nvidia, the company whose chips power almost all of today's
artificial intelligence — and the larger question of whether the world
depending on one chip-maker is a strength or a danger.
My research item is: [MY ITEM]
In clear B2-level English (maximum 150 words), give me:
1. What it is — one or two precise sentences.
2. One key fact, number, or date that shows its scale or significance.
3. Why it matters to Jensen Huang's story, or to the world depending on Nvidia.
Keep it tight enough to present to my group in about two minutes. Define any
technical term in eight words or fewer.Vocabulary
Match each word or phrase (1–10) to its definition (A–J). Guess first, then check the key.
to pioneer
a calculated bet
resilience
to be vindicated
founder-led
indispensable
a single point of failure
to reinvent (a company)
against the odds
a competitive moat
A. to be the first to develop or use a new method or idea
B. a risk taken on purpose, after weighing the chances carefully
C. the ability to recover quickly after difficulty or failure
D. proved right after others doubted you
E. run by the same person who originally started the company
F. so important that things cannot work without it
G. one part whose breakdown would stop the whole system
H. to change a company so completely that it becomes something new
I. succeeding although success was very unlikely
J. a lasting advantage that protects a company from its rivals
Task 1 — Map Your Starting Position
Prepare alone (3 min). Think of a turning point in your own company, team, or career — a risky decision, a near-failure, or a bet that paid off (or didn’t). Write 5–7 keywords only. Use three vocabulary items from the list. Work from your own memory first — no AI.
Exchange (5 min). Tell the story to a partner in about 90 seconds, in the past tense: the situation → the turning point → what happened next. Your partner listens for the three vocabulary items and the moment things turned.
Reading — The Man Who Bet on the Future
(1) If you have used anything powered by artificial intelligence lately, you have depended on a company run by a man in a black leather jacket. His name is Jensen Huang, and his company is Nvidia. The interesting question is how he rose — and whether the world should feel comfortable depending on him so completely.
(2) Huang was born in Taiwan in 1963. At nine, his parents sent him to live with a relative in the United States. His school in rural Kentucky was tough, and the young Jensen cleaned the toilets; his first real job was washing dishes at a Denny’s diner. It is tempting to read his resilience back into this hard childhood — the boy who succeeded against the odds — but Huang himself is careful not to over-claim.
(3) He trained as an engineer and, in 1993, founded Nvidia with two friends. For years the company nearly died: its first big product failed, and more than once it almost ran out of money. Out of that fear came a line Huang still repeats — “Our company is always thirty days from going out of business.”
(4) Nvidia survived by making graphics chips — the parts that draw fast, detailed pictures in video games. For most of the next decade, people thought Nvidia was simply a company for gamers. Few imagined it would one day reinvent itself into something far larger.
(5) Then Huang made the bet that changed everything. A normal chip does one big sum at a time; a graphics chip does thousands of small sums all at once. Huang became convinced this second way would one day matter far beyond games. From around 2006 he spent huge sums to pioneer software (called CUDA) for other jobs. For years it looked like an expensive distraction. Quietly, though, he was building a competitive moat: once researchers learned Nvidia’s software, switching to a rival became painfully hard.
(6) Then the pay-off came. “Thousands of small sums at once” is exactly how modern AI learns. When the AI boom arrived, the right chips already existed — and almost only Nvidia made them. The gaming-chip company had quietly become indispensable: the engine room of the entire AI age. Huang, long doubted, was suddenly vindicated.
(7) He runs the same founder-led company he started over thirty years ago — almost unheard of — and still speaks as if it could collapse next month. He has worn the same black leather jacket on stage for years.
(8) So how should we explain his rise? One story is vision: he saw a future nobody else saw. Another is luck: he was making exactly the right chips when a wave he did not create arrived. A third is grit: the company survived only because it refused to die. Probably none works on its own, which is why he is worth arguing about.
(9) Beneath the success sits a harder question. Almost all of the world’s AI now runs on chips from a single company — designed in America but made on one island, Taiwan. To his admirers, Huang built the essential backbone of the modern economy, and depending on the best is sensible. To others, that same dominance is the danger: when the whole world leans on one company and one fragile supply line, that line becomes a single point of failure. For an export economy like Germany’s — which makes almost none of the chips its future will run on — both things can feel true at once: the man built something remarkable, and the world now depending on it has reason to feel exposed.
Task 2 — Draft Your Argument
Optional — recommended if time permits.
Choose one of these three positions and write 5–7 sentences arguing for it, from your own head, no AI:
Nvidia’s rise was mostly vision.
Nvidia’s rise was mostly luck.
The world depending on one chip company is a danger, not a strength.
Push your case as hard as you honestly can. You will use this draft in Task 3.
Comprehension
Answer from the reading. Note the paragraph.
What was Huang’s first real job in the United States, and what does he say it taught him? (¶2)
What line does Huang still repeat to his staff, and what does it reveal about him? (¶3)
What did Nvidia make before AI, and what did people assume the company was? (¶4)
What is a “competitive moat,” and how did CUDA create one for Nvidia? (¶5)
Why do some people see Nvidia’s dominance as a danger rather than a strength? (¶9)
Task 3 — Challenge With AI
Draft (alone, 4 min). Take your Task 2 argument — or, if you skipped Task 2, write 5–7 sentences now on whether the world depending on one chip company is a strength or a danger. From your own head, no AI.
Challenge (3 min). Paste it into an AI tool: “In clear, simple English, what is the single strongest argument against my position?”
Revise (3 min). Read the answer, then rewrite your argument into a stronger 5–7 sentences that answers the objection — without giving up your view unless you are genuinely persuaded. Present your revised version to the group.
Discussion
Have you ever taken a “calculated bet” at work that others doubted? How did it turn out?
Huang says his hard early jobs shaped him. Do the most successful people really need to struggle first, or is that just a story winners tell later?
Would you want a boss who constantly says the company is thirty days from failing? Is that healthy drive or unhealthy fear?
One company makes almost all the world’s AI chips. For your business, is that a strength or a danger?
What could a European company do to reduce its dependence on suppliers it cannot control?
What might “digital sovereignty” mean in practice for Germany and Europe over the next ten years?
C1 version
Warm-up — micro-research (single prompt)
Before you search, take 30–60 seconds and write down 2–3 things you already know or believe about Nvidia, Jensen Huang, or how the chips behind AI are made. Just keywords. This is your starting point — your own thinking comes first. Agency = your own capacity to decide, think, and act; you keep it at every step, and the AI never decides for you.
Umbrella question: How does your item help explain Jensen Huang’s story — or the question of the world depending on Nvidia?
For your item, bring back:
What is it, in one or two precise sentences?
One key fact, number, or date that shows its scale or significance.
Why does it matter to Huang’s story, or to the “strength or danger” question?
Your item list (take one — items 1–3 first; 4–6 only if attendance is full or there is time):
Nvidia — the company (founded 1993; what it does today)
GPU vs CPU — the difference between a graphics chip and a normal chip
TSMC and Taiwan — where the world’s most advanced chips are physically made
The dot-com crash of 2000 — what happened, and who lost money
“Digital sovereignty” and the EU Chips Act — Europe’s chip dependence
CUDA — the software that makes it hard to leave Nvidia (the “moat”)
Paste this into your AI tool with your item filled in:
LEVEL: C1
I'm preparing for a Business English lesson about Jensen Huang — the founder
and CEO of Nvidia, the company whose chips power almost all of today's
artificial intelligence — and the larger question of whether the world
depending on one chip-maker is a strength or a danger.
My research item is: [MY ITEM]
In clear C1-level English (maximum 200 words), give me:
1. What it is — one or two precise sentences.
2. One key fact, number, or date that shows its scale or significance.
3. Why it matters to Jensen Huang's story, or to the question of the world
depending on Nvidia.
Keep it tight enough to present to my group in about two minutes. Define any
technical term in eight words or fewer.Vocabulary
Match each word or phrase (1–12) to its definition (A–L). Guess first from the word itself or from what you know about the topic, then check the key.
to pioneer
a calculated bet
resilience
relentlessness
to be vindicated
founder-led
indispensable
a single point of failure
an inflated valuation
to reinvent (a company)
against the odds
a competitive moat
A. proved right after others doubted you
B. a lasting advantage that protects a company from its rivals
C. so important that things cannot work without it
D. to be the first to develop or use a new method or idea
E. one part whose breakdown would stop the whole system
F. the quality of never stopping; continuing with full force despite obstacles
G. run by the same person who originally started the company
H. a risk taken on purpose, after weighing the chances carefully
I. the ability to recover quickly after difficulty or failure
J. succeeding although success was very unlikely
K. a price or level of excitement far higher than the real value justifies
L. to change a company so completely that it becomes something new
Task 1 — Narrate a Turning Point
Every career and every company has a moment when things could have gone either way. Tell the story of one from your own field.
Prepare alone (4 min). Think of a turning point in your own company, team, or career — a risky decision, a near-failure, a bet that paid off (or didn’t). Write 4–6 keywords only. Use at least three vocabulary items from the list. Work from your own memory first — no AI yet.
Tell it (6 min). Tell the story to a partner in about 90 seconds, in the past tense: the situation → the turning point → what happened next. Your partner listens for the three vocabulary items and the moment things turned.
Reflect (2 min). Was the outcome mostly vision, luck, or grit? One sentence. Hold that judgement — you will use the same lens on Huang.
Reading — The Man in the Leather Jacket
Read once for the story, once for the argument. Vocabulary items appear in bold on first use. Early finishers: in one sentence, what is the strongest counter-argument to this reading?
(1) If you have used anything powered by artificial intelligence in the last two years, you have depended on a company run by a man in a black leather jacket — and on a calculated bet he made nearly twenty years ago that almost everyone thought was a waste of time. The man is Jensen Huang. The company is Nvidia. And the question of how to explain him — and whether the world should be comfortable depending on him — is more interesting than either his admirers or his critics usually admit.
(2) Huang was born in Taiwan in 1963. When he was nine, his parents sent him and his older brother across the world to live with a relative in the United States. The school they were placed in, in rural Kentucky, turned out to be closer to a reform school than the academy the family had imagined; the young Jensen cleaned the toilets. His first real job was as a busboy and dishwasher at a Denny’s diner, and he has said, only half-joking, that it was the best job he ever had: it taught him to work hard, stay humble, and talk to anyone. It is tempting to read resilience back into this childhood — the boy who succeeded against the odds — but Huang himself is careful not to over-claim, and that caution turns out to be part of the puzzle of explaining him.
(3) He trained as an electrical engineer and, in 1993, sat in a booth at another Denny’s with two friends and founded Nvidia. For years it nearly died. Its first big product flopped. More than once the company was only weeks from running out of money. Out of that fear came a sentence Huang still repeats to his staff today: “Our company is always thirty days from going out of business.” The line is not motivational decoration. It is the worldview of a founder whose relentlessness was forged by almost failing, repeatedly, through a first decade that would have ended most businesses.
(4) Nvidia survived by making graphics chips — the parts that draw the fast, detailed pictures in video games. For most of the following decade, that is what the world thought Nvidia was: a company for gamers. Few outside the firm imagined it would one day reinvent itself into something far larger, or that the skill it had built for entertainment would turn out to be the exact skill the future needed.
(5) Then Huang made the bet that changed everything. Here is the one technical idea worth understanding, and it is simple: a normal computer chip does one big sum at a time, very fast — like a single brilliant mathematician working through a list. A graphics chip does thousands of small sums all at once — like a thousand schoolchildren each doing one easy sum in the same second. Huang became convinced that this second way of working — many small calculations in parallel — would one day be needed for something far bigger than games. From around 2006 he spent enormous sums to pioneer the software (a system called CUDA) that made Nvidia’s chips easy to use for these other jobs. For years it looked like an expensive distraction; investors complained, and the pay-off was nowhere in sight. What Huang was quietly building, though, went beyond the product itself: he was building a competitive moat — once researchers had learned to work on Nvidia’s software, switching to anyone else became painfully hard.
(6) The pay-off, when it came, was enormous. It turned out that “thousands of small sums at once” is exactly how modern artificial intelligence learns. When the AI boom arrived, the chips it needed already existed — and almost only Nvidia made them. The gaming-chip company had quietly become indispensable: the engine room of the entire AI age. The boy who had cleaned toilets in Kentucky became one of the most powerful people in technology, and Huang — long doubted — was suddenly and spectacularly vindicated.
(7) He runs the company in his own unusual way. He has worn the same style of black leather jacket on stage for years. Dozens of people report directly to him, instead of the usual handful, and he says he avoids private one-to-one meetings because he wants everyone to hear the same thing at the same time. He has led the same founder-led company for more than thirty years — almost unheard of — and still speaks as if it could collapse next month. Receiving an honour from a university, he told the students he did not wish them success but “pain and suffering,” because hardship, he said, is what built his own character.
(8) So how should we explain him? One story is vision: he saw a future nobody else saw and was brave enough to spend years preparing for it. Another is luck: he happened to be making exactly the right chips when a wave he did not create arrived, and he is wise enough to admit how much of it he cannot fully claim. A third is grit: the company survived only because it refused to die, again and again, through failures that would have finished most businesses. Probably none of these explanations works on its own — which is precisely why he is worth arguing about. The honest reader finishes the story unsure how much credit belongs to the man and how much to the moment that happened to arrive when it did, and Huang himself, tellingly, seems comfortable with exactly that uncertainty.
(9) Underneath the success story sits a harder question, and it has nothing to do with one man. Today an extraordinary share of the world’s AI runs on chips from a single company — chips designed in America but physically made on one island, Taiwan, in one of the most contested corners of the planet. To his admirers, Jensen Huang built the indispensable backbone of the modern economy, and depending on the best is simply rational. To others, that is exactly the danger: when the whole world depends on one company and one fragile supply line, you have not built a backbone — you have built a single point of failure.
(10) Not everyone, then, sees Nvidia’s rise as pure triumph. Some investors warn the enthusiasm has run far ahead of reality — that money is pouring into AI faster than anyone is earning it back, and that companies priced as if the boom will never end have been wrong before. Their favourite comparison is the dot-com crash of 2000, when a similar inflated valuation collapsed almost overnight. Others worry less about the money and more about the dependence: governments now treat advanced chips as strategic weapons, controlling who is allowed to buy them, precisely because one company and one supply line have become too important to leave alone. The sceptics’ point is not that Huang did anything wrong. It is that no economy — least of all an export economy like Germany’s, which designs and builds the machines of the physical world but controls almost none of the chips its future will run on — should let so much rest on so few chips, made in so few places. Both things can be true at once: the man built something remarkable, and the world that now leans on it has reason to feel exposed. That tension is the real subject of today’s lesson.
Task 2 — Build & Defend Your Verdict
Take a clear verdict, then argue the side you are given — including the side you would not have chosen.
Gut reaction (alone, 2 min). In one sentence: what built Nvidia — vision, luck, or grit? Write it down. Keep it private for now.
Assignment (1 min). Your trainer assigns you the opposite of your gut reaction (or, in a second round, one side of backbone vs single point of failure). You will now build the strongest possible case for the side you were given.
Argue it (9 min). Prepare for 3 minutes using evidence from the reading, then make your case to the group for about 2 minutes. Push the argument as hard as you honestly can, even if it is not what you believe.
Reflect (2 min). Did arguing the assigned side change anything about your real view? One sentence.
Optional second round: on the two-sided question — indispensable backbone vs single point of failure — assign one side to each half of the room and run 2-minute cases each. This is where the German and European exposure gets its sharpest airing.
Comprehension
Answer from the reading. Note the paragraph.
What does Huang’s early life (Kentucky, the Denny’s diner) seem to explain about him as a leader — and why does the writer add a note of caution? (¶2–3)
In plain terms, what was the bet Huang made from around 2006, and why did it look like a mistake at the time? (¶5)
Why does “thousands of small sums at once” turn out to matter so much for artificial intelligence? (¶5–6)
Explain the two readings of the same fact: how can Nvidia be both an “indispensable backbone” and a “single point of failure”? (¶9–10)
Does the writer pick a winner between the admirers and the sceptics? How can you tell? (¶8, 10)
Task 3 — Challenge and Finalise
Draft (alone, 3 min). Choose one verdict — vision / luck / grit, or strength / danger — and write your position from your own head, no AI.
Challenge (4 min). Paste it into an AI tool and ask: “What is the strongest argument against my position?” Read the response carefully.
Revise and present (4 min). Rewrite your position into a stronger 6–10 sentences that directly answers the counter-argument — without abandoning your view unless you are genuinely persuaded. Present it to the group, then reflect together: whose position moved, and which counter-arguments were hardest to answer?
Discussion
Vision, luck, or grit — what built Nvidia? Say which mattered most in one sentence; be ready to defend the opposite if assigned.
One company makes the chips almost all AI depends on. Is that a strength or a danger? (The indispensable backbone vs the single point of failure.)
Huang has run the same company for over 30 years and still tells staff it is “always 30 days from going out of business.” Is that healthy drive or unhealthy fear — and would you want a boss like that?
He says the hardship and “pain and suffering” in his life made him who he is. Do the most successful people need to suffer first — or is that just a comfortable story winners tell afterwards?
(German/European hook.) The chips are designed in the US and made in Taiwan. What does it mean for Germany and Europe to depend so completely on companies and places they do not control — and what might “digital sovereignty” mean in practice?
Governments now treat advanced chips as strategic weapons — controlling who may buy them, as they once did with oil or armaments. Is it right for a state to reach that deeply into a private supply chain, and where should the line sit between national security and open markets?
Looking ahead: is it ever acceptable for one company to dominate a technology the whole world depends on — and if not, what would actually have to change (regulation, rival chip-makers, spreading manufacturing across more countries) to make that dependence safe?
Answer keys
B1 vocabulary key
1-A, 2-B, 3-C, 4-D, 5-E, 6-F, 7-G, 8-H
B1 comprehension (model answers)
He was born in Taiwan, sent to the United States at nine, and his first job there was washing dishes at a Denny’s diner. (¶2)
That the company is “always thirty days from going out of business” — even today. (¶3)
Graphics chips for video games. (¶4)
He bet that a graphics chip’s way of doing thousands of small sums at once would matter far beyond games, and spent heavily on software (CUDA) to make it work. (¶5)
Designed in America, made on one island — Taiwan. (¶8)
B2 vocabulary key
1-A, 2-B, 3-C, 4-D, 5-E, 6-F, 7-G, 8-H, 9-I, 10-J
B2 comprehension (model answers)
Washing dishes at a Denny’s diner; he says it taught him to work hard and stay humble. (¶2)
“Our company is always thirty days from going out of business” — it shows a founder shaped by almost failing repeatedly, who still runs the company from a place of caution. (¶3)
Graphics chips for video games; people assumed Nvidia was simply a company for gamers. (¶4)
A lasting advantage that protects a company from rivals; once researchers had learned to work on Nvidia’s CUDA software, switching to a competitor became painfully hard, locking them in. (¶5)
Because almost all AI runs on chips from one company, designed in America but made on one island (Taiwan) — so one company and one fragile supply line become a single point of failure. (¶9)
C1 vocabulary key
1-D, 2-H, 3-I, 4-F, 5-A, 6-G, 7-C, 8-E, 9-K, 10-L, 11-J, 12-B
C1 comprehension (model answers)
It suggests hard work, humility and resilience built under pressure; the writer cautions because Huang himself avoids over-claiming, so reading the childhood as destiny may be too neat. (¶2–3)
He bet that graphics chips’ way of doing many small calculations at once would matter beyond games, and spent heavily on software (CUDA) to enable it; for years it earned nothing and looked like a costly distraction. (¶5)
Modern AI learns by doing huge numbers of small calculations simultaneously — exactly what graphics chips already did — so when AI arrived, Nvidia’s chips were already the right tool. (¶5–6)
The same dominance can be described as essential infrastructure (admirers) or as dangerous concentration on one company and one fragile supply line (sceptics); the fact does not change, only the framing. (¶9–10)
No — the writer presents both sides and states “both things can be true at once,” ending on the tension rather than a verdict. (¶8, 10)
No model answers are provided for the discussion questions or the TBLT tasks — the point is your own argument.
I write one editorial article and one paired lesson every week, teaching the same topic across corporate groups in Germany before publishing it here. This one came out of a week on Jensen Huang and Nvidia — read the companion article, We Export Our Emissions (B1 · C1), for the fuller argument.
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