The short version
Part 2 mapped the nine layers and named the leaders. This part asks the obvious follow-up. These are the most profitable positions in the global economy, and every large technology company has both the money and the motive to attack them. So why does almost nobody succeed?
The answer is different at each layer, and the differences matter.
ASML: the monopoly that is not really about patents
Start with the most extreme case. ASML, a Dutch company, makes the extreme ultraviolet lithography machines used to print the finest features on advanced chips. It has no competitor. Not "dominant": there is no second supplier, anywhere.
To see why, consider what the machine does. It needs light at a wavelength of 13.5 nanometres, far into the ultraviolet. No convenient source of that light exists, so the machine makes its own: a jet of molten tin droplets is fired across a chamber, and each droplet is struck twice by a high-power laser, roughly fifty thousand times per second, vaporising it into a plasma that emits the required light.
That light is then absorbed by essentially everything, including air and glass. So the whole optical path runs in a vacuum and uses mirrors rather than lenses. Those mirrors, made by Zeiss in Germany, are among the smoothest objects ever manufactured. ASML's own way of putting it is that if the mirrors were the size of Germany, the tallest mountain on them would be about a millimetre high.
Now note who built all this. Not ASML alone. Zeiss made the optics. Trumpf made the lasers. Cymer, later acquired, worked on the light source. Development ran for roughly two decades with funding and cooperation from the chipmakers who would eventually buy the machines. It was less a product launch than a multi-decade industrial programme.
What kind of moat is this? Accumulated engineering, distributed across an entire supply chain, plus the time cost of qualification. Even a well-funded new entrant with a working machine would then face years of customer testing before anyone would risk production on it, while ASML continued to advance. That is why the price tags hold: hundreds of millions of dollars for a standard Low-NA EUV system, and substantially more for the High-NA generation.
What could break it? Realistically, only a change in the underlying physics of patterning, meaning some approach that does not need EUV at all. Several are researched. None is close.
TSMC: the flywheel
TSMC took 72 percent of the top ten foundries' revenue in the first quarter of 2026, according to TrendForce, roughly eleven times second-placed Samsung's share. At the leading edge, where AI chips are made, its position is stronger still.
The technical explanation is incomplete on its own, because Samsung and Intel have both been technically close at various points. The fuller explanation is a loop that feeds itself.
TSMC has the most customers, so it runs the most wafers. Running the most wafers means encountering the most defects, and every defect encountered is a defect understood. That knowledge improves yield, the share of chips per wafer that work. Better yield means lower effective cost per working chip, which attracts more customers, which produces more volume. Round and round.
Layered on top is something harder to replicate: TSMC designs no chips of its own. When Nvidia hands over its most valuable design, it is handing it to a company that will never compete with it. Samsung cannot make that promise, because Samsung designs phones, processors and much else besides. Intel cannot either. In an industry where a leading design represents years of work and billions in investment, that assurance is worth a great deal.
TSMC is now compounding the advantage into packaging. Its CoWoS technology, which joins processor dies to HBM stacks, has been a well-documented bottleneck in AI chip supply. Capacity has expanded substantially since late 2024, and even after that expansion supply is expected to fall short of demand. Controlling both the manufacturing and the assembly of AI chips is a considerably stronger position than controlling either one.
What kind of moat is this? Scale, learning and structural neutrality, reinforcing each other.
What could break it? A sustained, loss-tolerant challenge from a company that can fund years of unprofitable capacity while building trust. Samsung's Taylor fab, anchored by a large, multi-year Tesla contract, and Intel's 18A programme are both exactly this attempt. Neither can be judged for several years.
Memory: consolidation plus difficulty
Three companies dominate DRAM: Samsung, SK Hynix and Micron, with China's CXMT now a fourth entrant holding a modest but growing share. In HBM it is the same three, in a different order.
The three-company structure is the residue of a brutal history. Memory was a commodity for decades, subject to violent cycles: shortage, high prices, everyone builds capacity, glut, prices collapse below cost, the weakest go bankrupt or merge. Dozens of manufacturers were reduced to three by simple attrition. What survives is the set of companies with balance sheets large enough to keep investing through the crashes.
HBM then raised the barrier a second time. Stacking a dozen or more memory dies with thousands of vertical connections drilled through the silicon is difficult in a specific and unforgiving way: a defect anywhere in the stack ruins all of it, so the yield penalty compounds with height. Being excellent at conventional DRAM does not automatically transfer. That is precisely why the market rankings differ between the two products, with Samsung leading DRAM at 39 percent in the second quarter of 2026 while SK hynix led HBM at 58 percent in the first quarter.
What kind of moat is this? Capital scale plus a genuinely hard manufacturing process, in a market where the customer's cost of a bad part is enormous.
What could break it? This is the least secure of the major choke points, and it is being tested from two directions. Micron has been gaining fast, reaching 25 percent of DRAM in the second quarter of 2026 and pulling within about a percentage point of SK Hynix. And China's CXMT is ramping aggressively, covered in part 5. Memory is the layer where the incumbents have the most to lose.
Nvidia: four moats stacked
Nvidia is the most discussed and the most misunderstood. Its position does not rest on having the fastest chip. It rests on four things that reinforce each other, and a competitor has to beat most of them at once.
The chip. Genuinely excellent, and iterated on a roughly annual cadence that competitors struggle to match.
The network. Nvidia sells NVLink to connect GPUs inside a rack at 1.8 terabytes per second per GPU, plus InfiniBand or Spectrum-X Ethernet between racks. Because training requires constant communication, a competitor with a comparable chip and a worse network delivers a worse cluster.
The system. Nvidia increasingly sells complete racks rather than components, having done the integration, power delivery and liquid cooling work itself. A customer buying a rack is buying a working system rather than a project.
The software. CUDA is roughly two decades of libraries, tools, tutorials, published papers and trained engineers, all assuming Nvidia hardware. This is the piece most often described as unbreakable, and it is the piece currently eroding fastest. AMD's ROCm has improved substantially, and the largest AI labs employ engineers capable of working close to the metal, which is why AMD's arrangement with OpenAI for up to 6 gigawatts of Instinct capacity, the first gigawatt of it MI450, is credible rather than aspirational.
What kind of moat is this? Vertical integration plus ecosystem habit.
What could break it? It is already being tested, from below rather than head-on. The cloud companies are not trying to build a better GPU; they are designing narrower chips for their own specific workloads, where they can trade flexibility for efficiency. TrendForce projects custom AI chip shipments growing significantly faster than GPU shipments in 2026. That does not displace Nvidia, but it caps how much of the market it can hold.
Broadcom: the quiet one
Worth naming because it is routinely overlooked. Broadcom occupies two strong positions at once: a leading share of the high-end Ethernet switching silicon the industry buys, and, with Marvell, the large majority of the market for co-designing custom AI chips.
That second business is unusual. When Google, Meta or OpenAI decides to build its own accelerator to reduce dependence on Nvidia, it generally hires Broadcom to do the engineering. Broadcom therefore profits from the effort to escape Nvidia, whichever customer wins. Counterpoint expects it to hold around 60 percent of AI server ASIC design work in 2027.
Its networking position is strengthening for a structural reason: cloud operators actively prefer open Ethernet to a single vendor's proprietary interconnect, so the industry's dislike of dependence pushes business toward the merchant supplier. Broadcom's Tomahawk 6 shipped at 102.4 terabits per second roughly a year before Nvidia's equivalent Ethernet part was scheduled to arrive.
How these positions actually end
Read across the four cases and a pattern emerges about what does and does not work.
What does not work: a startup with a better product, on its own. Capital requirements, qualification timelines and switching costs are high enough at every layer that a well-funded startup with a better product is not, by itself, a plausible route in.
What does work, historically, is one of three things.
A technology transition that makes the incumbent's accumulated advantage irrelevant. The move to EUV did exactly that in lithography equipment: Nikon and Canon had led optical lithography for decades, did not follow ASML into EUV, and neither builds a leading-edge scanner today.
A customer large enough to fund an alternative deliberately, accepting worse economics for strategic reasons. This is exactly what the cloud companies are doing with custom silicon, and what several governments are attempting with subsidies.
A political intervention: export controls, subsidies, antitrust. This has become the most active force of the three, and it is the subject of part 5.
Part 4 turns to a constraint that no amount of capital or policy resolves quickly, because it is made of concrete, copper and electricity.