Every modern economy runs on chips. Understanding why requires going inside the machines that make them, understanding who controls those machines, and realizing what that control is worth.
Phones need them, cars need them, factories need them, hospitals need them, banks need them, satellites need them, gaming consoles need them, cloud platforms need them, and every serious AI model depends on them.
That is why semiconductors have moved from being a technical supply-chain topic to becoming one of the most important industries in the world. Chips now sit inside the biggest conversations in markets: artificial intelligence, national security, inflation, energy demand, industrial policy, data-centre capex, and the future of computing.
The industry has always been cyclical. Demand rises, companies build capacity, inventories reset, prices fall, and the market starts again. That cycle still matters. What has changed is the size of the demand driver now sitting on top of the old cycle.
AI has turned compute into a scarce resource. It has also turned memory bandwidth, advanced packaging, power efficiency, and leading-edge manufacturing into strategic assets.
This creates the main debate for investors.
Semiconductors have some of the strongest profit pools in public markets, but the value is not shared equally across the chain. The companies that control scarce technology, design software, AI architecture, leading-edge manufacturing, advanced memory, and equipment chokepoints capture the best economics. The weaker players still move with the cycle and can look cheap right before earnings fall.
Our view is clear: semiconductors remain one of the most important sectors for the next decade, but investors need to map the value chain before buying the story. ASML is not Micron. Micron is not NVIDIA. NVIDIA is not TSMC. TSMC is not Amkor. They belong to the same industry, but they do not have the same economics.
The short version
Semiconductors solve one large problem: the world keeps demanding more computation, more storage, more connectivity, and more control over electronic systems. That demand now comes from AI training, AI inference, cloud computing, smartphones, PCs, cars, factories, industrial machines, defence systems, medical devices, and connected products.
The business model changes depending on where the company sits in the chain. Some companies license IP. Some sell design software. Some design chips. Some manufacture chips. Some sell the machines used to manufacture chips. Some package and test the final product. Some distribute chips into fragmented customer markets.
The strongest profit pools are usually found in 5 areas: EDA and IP, fabless AI design, semiconductor equipment, leading-edge foundry, and high-bandwidth memory. These are the places where technical difficulty, customer dependency, scale, and switching costs create strong pricing power.
The industry also has real risks. Fabs are expensive. A leading-edge fab can cost tens of billions of dollars. EUV tools can cost hundreds of millions. Manufacturing cycles are long. Inventory can build quickly. Memory prices can rise fast and fall fast. Export controls can change demand overnight. Taiwan remains the biggest geographic risk in the global technology stack.
The next 5 years will probably be decided by 6 questions:
Can hyperscalers keep spending on AI infrastructure?
Can memory suppliers turn HBM demand into more stable long-term contracts?
Can TSMC keep leading at advanced nodes while managing Taiwan risk?
Can ASML continue to control the EUV and High-NA transition?
Can China’s mature-node expansion pressure pricing in older chips?
Can advanced packaging become a bigger profit pool as chiplets grow?
That is the whole industry debate.
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What semiconductors actually do
A semiconductor controls electrical signals. That sounds basic until you realise that modern life is built on the ability to control signals at enormous scale and speed.
Some chips process information. CPUs, GPUs, NPUs, AI accelerators, and custom ASICs fall into this group. They decide how fast a system can calculate, train, infer, render, simulate, or respond.
Some chips store information. DRAM, NAND, and high-bandwidth memory sit here. The AI world is learning this lesson very quickly: compute without memory bandwidth is limited.
Some chips move information. Networking chips, modems, RF chips, optical components, and connectivity chips allow devices and data centres to communicate.
Some chips manage power. Power semiconductors help convert, distribute, and control electricity. They matter in electric vehicles, factories, power grids, renewable energy, and data centres.
Some chips sense the physical world. Image sensors, accelerometers, temperature sensors, radar chips, and industrial sensors turn physical signals into digital information.
That is why the semiconductor market is much broader than AI GPUs. AI is the current centre of attention, but the industry also includes automotive chips, industrial chips, analog chips, memory, sensors, communications chips, microcontrollers, and power devices.
The useful investor question is simple: which chips are scarce, differentiated, and hard to replace?
That is usually where the money goes.
How the industry is structured
The semiconductor industry has 8 main layers.
Before walking through each one, here is the full value chain in one view:
1. Materials and chemicals
This is the base of the industry. Chip manufacturing needs silicon wafers, specialty gases, photoresists, wet chemicals, deposition materials, rare gases, ultra-pure water, and cleanroom infrastructure.
Key players include Shin-Etsu, SUMCO, Siltronic, GlobalWafers, Linde, Air Liquide, Air Products, JSR, Tokyo Ohka Kogyo, and other specialist suppliers.
This layer rarely gets the same attention as NVIDIA or TSMC, but it matters. A fab cannot run without the right materials at the right purity. Even small contamination can ruin wafers that have already absorbed weeks of process time and huge production cost.
Materials also create geopolitical exposure. Neon, helium, bromine, tungsten, rare earths, and specialty chemicals can become supply-chain issues very quickly when trade, war, export rules, or logistics routes change.
2. EDA software and IP
Before a chip can be manufactured, it must be designed.
That is where Electronic Design Automation, or EDA, comes in.
The main players here are Synopsys, Cadence, and Siemens EDA. Arm also sits in this upstream layer through processor architecture and IP licensing.
This is one of the best business models in the industry. EDA tools are deeply embedded inside engineering workflows. If a chip company is spending hundreds of millions on a new design, it will not risk failure by using weak tools. The cost of the software is small compared with the cost of a failed chip.
That gives EDA companies strong pricing power, high margins, recurring revenue, and exposure to the whole industry. They do not need to guess which AI chip wins. If the world keeps designing more complex chips, demand for design software and IP keeps rising.
3. Fabless design
Fabless companies design chips but outsource manufacturing.
This is where NVIDIA, AMD, Qualcomm, Broadcom, Marvell, MediaTek, Apple’s internal silicon teams, and many AI chip startups sit.
The model is attractive because fabless companies avoid the huge capex burden of owning fabs. They spend on architecture, software, engineers, product design, and customer relationships. If the product wins, returns on capital can be very high.
NVIDIA is the clearest example today. The company’s advantage is not only the GPU — it is the full AI computing stack: GPUs, networking, systems, CUDA, software libraries, developer adoption, and deep customer relationships with hyperscalers.
The weakness of the fabless model is dependence. A great chip design still needs TSMC, Samsung, advanced packaging, HBM supply, substrates, and testing capacity. The chip can be designed perfectly and still be constrained by manufacturing or packaging supply.
4. Semiconductor equipment
This is the machinery layer.
Fabs need tools for lithography, etching, deposition, inspection, metrology, cleaning, ion implantation, process control, and testing.
Key players include ASML, Applied Materials, Lam Research, Tokyo Electron, KLA, Advantest, and Teradyne.
This is one of the strongest parts of the value chain because the equipment is hard to build and hard to replace. A fab can change some suppliers over time, but it cannot avoid using highly specialised tools.
ASML has the most obvious chokepoint. It is the only supplier of EUV lithography systems, which are needed for leading-edge chips. No EUV means no leading-edge production at scale.
KLA also matters because fabs need inspection and metrology to control yield. In a high fixed-cost industry, yield is money. A small yield improvement can have a large impact on gross margin because the wafer cost is already incurred.
5. Foundries and IDMs
Foundries manufacture chips for other companies.
TSMC is the leader. Samsung Foundry is the other major advanced-node competitor. GlobalFoundries, UMC, and SMIC focus more on mature and specialty nodes.
IDMs design and manufacture their own chips. Intel, Samsung Electronics, Texas Instruments, Analog Devices, STMicroelectronics, Infineon, NXP, and Micron all have elements of this model, depending on the product category.
Manufacturing is where the industry becomes physically difficult. Fabs require extreme precision, cleanrooms, power, water, process knowledge, expensive tools, long production cycles, and large engineering teams.
