Question 1

Doesn't NVIDIA manufacture their own chips?

Accepted Answer

No, and this surprises a lot of people. NVIDIA designs the chips but outsources all manufacturing to TSMC in Taiwan. NVIDIA is a fabless company, no factories, just engineers. The same is true for AMD and Apple. The actual physical manufacturing is extraordinarily capital intensive and TSMC has spent decades becoming essentially irreplaceable at it.

Question 2

What's the difference between a CPU and a GPU?

Accepted Answer

A CPU (Central Processing Unit) is a general purpose chip. It's fast at doing one thing at a time and handles everything your computer does day to day. A GPU (Graphics Processing Unit) was originally designed for gaming because rendering graphics requires doing thousands of simple calculations simultaneously. It turns out that's exactly what AI needs too, millions of parallel calculations at once. That's why NVIDIA, which dominated gaming GPUs, ended up accidentally dominating AI infrastructure.

Question 3

Why does AI use so much water and energy?

Accepted Answer

Training a large AI model requires running thousands of chips at full capacity for weeks or months. Chips generate enormous heat. Data centres cool that heat using water based cooling systems. A single large training run can use millions of litres of water and enough electricity to power a small town. This is why energy infrastructure, Bloom Energy, nuclear plays, alternative energy, is a genuine AI investment thesis, not a peripheral one.

Question 4

What does "open source" mean when Meta says Llama is open source?

Accepted Answer

It means Meta has released the underlying model weights and code publicly so anyone can download, run, and modify it. The alternative is closed source, like OpenAI's GPT 4, which you can only access via their API. Open source AI is controversial because it democratises access but also means anyone, including bad actors, can use it without guardrails.

Question 5

What is agentic AI?

Accepted Answer

This is the next big shift. Most AI today is reactive, you ask it something, it answers. Agentic AI takes actions autonomously on your behalf. You give it a goal and it figures out the steps, uses tools, browses the web, writes code, sends emails, and loops until the job is done, without you prompting each step. Think less chatbot and more digital employee. Palantir, ServiceNow, and Microsoft are all building agentic products. It's where most of the enterprise money is heading right now.

Question 6

What's the difference between training and inference?

Accepted Answer

Training is when an AI model learns, you feed it enormous amounts of data and it adjusts billions of internal parameters until it gets good at the task. It's extremely expensive and only happens once per model version. Inference is when the trained model actually answers your question, it's much cheaper and happens billions of times a day. Most of the chip and data centre demand you're hearing about is driven by inference at scale, not just training.

Question 7

Why are crypto miners relevant to AI?

Accepted Answer

Pure coincidence of infrastructure. Bitcoin and Ethereum mining requires cheap electricity, large buildings, and lots of cooling, which is exactly what AI data centres need. When crypto revenues collapsed, miners were left with sunk cost infrastructure sitting idle. Rather than write it off, the smart ones pivoted to renting that same infrastructure to AI companies. Core Scientific, IREN, Applied Digital, and Cipher Mining are all playing this transition.

Question 8

Isn't this just another tech bubble?

Accepted Answer

Possibly, at the margins. Valuations on some names are clearly pricing in perfection. But the infrastructure buildout, chips, power, data centres, is happening regardless of which AI application wins. The picks and shovels argument is that you don't need to know who strikes gold, you just need to own the people selling the shovels. That said, some of the application layer bets are speculative and the market is not uniformly rational right now. Caveat emptor.

Question 9

What is a foundation model?

Accepted Answer

A foundation model is a large, general purpose AI model trained on vast amounts of data that can then be adapted to specific tasks. GPT 4, Gemini, Llama, and Claude are all foundation models. They're called foundation models because everything else gets built on top of them, applications, tools, agents. The companies building foundation models are making a very expensive bet that being at the base of the stack is where the lasting value accrues.

Question 10

Why does everyone keep mentioning Taiwan?

Accepted Answer

Because TSMC, the company that manufactures most of the world's advanced chips, is based in Taiwan. Taiwan also sits in one of the world's most geopolitically contested regions. If anything were to disrupt TSMC's operations, a natural disaster, a conflict, a blockade, the global AI buildout would effectively stop. This is why the US and EU are spending billions subsidising domestic chip manufacturing. It's not just economics. It's national security.

Question 11

Does ASML have any competition?

Accepted Answer

Effectively no, and the reason is one of the more extraordinary stories in modern industrial history.

ASML makes the machines that use extreme ultraviolet (EUV) light to etch circuit patterns onto silicon wafers. The light source operates at a wavelength of 13.5 nanometres, just above the X ray range. To generate it, they fire a laser at a tiny tin droplet 50,000 times per second, which creates a plasma that emits the right wavelength of light. Then they focus and direct that light using mirrors so precise that if you scaled them up to the size of Germany, the biggest bump would be less than a tenth of a millimetre high.

It took ASML roughly 30 years and billions in R&D to get this working. The supply chain involves over 5,000 suppliers across dozens of countries. A single EUV machine costs around $200 million, weighs 180 tonnes, and ships in roughly 40 freight containers.

Canon and Nikon have tried to compete at various points. Neither has cracked EUV. ASML has a monopoly not because regulators gave it to them or because they crushed competitors, but because what they do is so technically complex that nobody else has managed to replicate it after decades of trying.

The geopolitical dimension is significant too. The Dutch government, under pressure from the US, now restricts ASML from selling its most advanced machines to China. That single export restriction is arguably one of the most powerful economic weapons in the US China technology war. One Dutch company, making one type of machine, in one city (Veldhoven), is a genuine chokepoint in the global AI race.

01Doesn't NVIDIA manufacture their own chips?

02What's the difference between a CPU and a GPU?

03Why does AI use so much water and energy?

04What does "open source" mean when Meta says Llama is open source?

05What is agentic AI?

06What's the difference between training and inference?

07Why are crypto miners relevant to AI?

08Isn't this just another tech bubble?

09What is a foundation model?

10Why does everyone keep mentioning Taiwan?

11Does ASML have any competition?