The demand for AI chips has moved beyond GPU bottlenecks. The bottleneck now begins at TSMC’s advanced nodes, continues through CoWoS packaging, passes through HBM memories, substrates, assembly, testing, and even reaches mature process factories producing auxiliary chips for AI servers. The supply chain is no longer suffering from a single component. It’s struggling because almost all critical parts are being pushed simultaneously.
Digitimes summarized it clearly this week: Nvidia and other AI chip designers still face shortages because TSMC’s capacity for advanced nodes and CoWoS packaging remains tight, which is shifting demand toward foundries, advanced assembly, testing, and factories outside Taiwan.
TSMC itself has acknowledged the tension. C.C. Wei, the company’s president and CEO, stated in June that AI demand continues to grow so rapidly that not only TSMC but also its upstream suppliers and partners are struggling to keep up. Reuters reported that the company is working to avoid becoming the bottleneck in the semiconductor supply chain, although cost and capacity pressure are already evident.
The problem is no longer just chip manufacturing
For years, much of the semiconductor discussion focused on nodes: 7 nm, 5 nm, 3 nm, 2 nm. This language remains important, but AI has shifted the critical point toward advanced packaging. A modern accelerator isn’t just a logic die fabricated on the latest node. It must be integrated with high-bandwidth memory, dense interconnects, complex substrates, and an assembly process capable of combining multiple components into a single functional module.
That’s where CoWoS comes in—TSMC’s technology that has become one of the foundations of the most advanced AI accelerators. The company describes CoWoS as a wafer-level integration platform for high-performance applications, featuring large interposers, multiple HBM memory stacks, and packages capable of combining logic chips with high-bandwidth memory into dense modules.
Put simply: manufacturing the chip isn’t enough. The logic die and HBM must be combined in a package capable of moving data at enormous speeds. If CoWoS capacity is lacking, the chip can’t reach the server even if the wafer already exists. That’s why the bottleneck has shifted toward a part of the industry that was previously less visible to the public.
TrendForce estimates that TSMC’s monthly CoWoS capacity could reach between 120,000 and 140,000 wafers in 2026, with additional capacity from OSAT partners potentially adding another 50,000 to 60,000 wafers, bringing the total ecosystem capacity close to 200,000 wafers per month. The figure is huge, but the market continues to absorb it rapidly.
Demand spills over to OSATs, testing, and alternative foundries
When TSMC cannot absorb all the growth, the pressure gets distributed. OSAT companies, specialized in assembly and testing, gain importance because part of the packaging and backend capacity must come from outside TSMC. Companies like ASE, Amkor, SPIL, or King Yuan Electronics are increasingly on analysts’ radar because value isn’t just in advanced lithography anymore; it’s also in turning those chips into ready-to-integrate AI products.
The effect also extends to mature-node foundries. AI needs GPUs, ASICs, and accelerators, but also requires chips for networking, controllers, PMICs, retimers, power management components, sensors, microcontrollers, and support circuits. Many of these chips are fabricated on 8-inch or 12-inch processes rather than the most advanced nodes. Digitimes already highlighted that AI demand in 2026 is spreading into ASICs, networking, PMICs, and peripherals, straining both mature processes and advanced nodes.
The result is a more strained and less flexible supply chain. If a packaging provider lacks capacity, if there’s a shortage of ABF substrates, if testing takes longer than expected, or if a power chip falls short, the entire system is delayed. In AI, the final product isn’t just an isolated chip. It’s a board, a module, a server, a rack, and ultimately an entire cluster.
| Supply Chain Layer | Why AI is Causing Strain | Likely Effect |
|---|---|---|
| Advanced Nodes | AI GPUs and ASICs require cutting-edge processes | More competition for capacity at TSMC |
| CoWoS and Advanced Packaging | Integrates logic and HBM in one package | Delays even if the logic die is fabricated |
| HBM | Large models demand high memory bandwidth | Dependence on SK hynix, Samsung, and Micron |
| Advanced Substrates | Sizeable and complex packages require higher quality substrates | Potential bottlenecks with specialized suppliers |
| OSAT and Testing | More complex chips need more assembly and validation | Increased load on ASE, Amkor, SPIL, KYEC, and others |
| Mature Nodes | AI servers need PMICs, networking, and auxiliary components | Greater utilization in UMC, Vanguard, PSMC, or SMIC |
| Foreign Foundries | Clients seek to diversify geographic risk | More pressure on Arizona, Japan, Europe, and local partners |
Substrates may be the least visible bottleneck
One of the most noteworthy warnings comes from financial analysts. MarketWatch quoted a Nomura report highlighting that, although TSMC is expanding its CoWoS capacity, reliance on small substrate suppliers could hamper their production targets and impact the AI chip ecosystem.
This aspect often gets less attention than GPUs or HBM, but it’s critical. AI packages are physically large, consume a lot of power, and require high-density interconnections. The substrate isn’t just a support; it’s a vital part of the chip’s ability to communicate, dissipate heat, and maintain electrical stability.
If the industry scales wafer capacity and CoWoS but doesn’t increase substrates, chemicals, tools, testing, skilled labor, or logistics capacity at the same rate, the supply chain will remain limited. AI is pushing the growth of components that traditionally expanded with more predictable cycles.
Expanding CoWoS isn’t a silver bullet
TSMC is investing in capacity, but the physical timeline for semiconductors doesn’t accelerate by decree. Building lines, installing tools, qualifying processes, training staff, and achieving stable yields take time. The same applies to TSMC’s expansion in Arizona, where the company has committed $165 billion, but Reuters notes that progress is constrained by environmental permits and labor availability.
Moreover, alternatives aren’t yet at the same level. TSMC maintains that panel-level packaging won’t replace CoWoS in the short term for major AI processors. Tom’s Hardware reports that CoWoS still holds an advantage in integration density and tooling maturity, while panel-based technologies could complement rather than immediately replace current processes.
This leaves the industry in an awkward position. Everyone wants more capacity, but not all capacity is suitable for the most demanding chips. It’s not enough to have a factory; that factory must deliver performance, quality, and reliability in extremely complex packages.
Implications for Nvidia, AMD, and hyperscalers
For Nvidia, TSMC’s capacity and CoWoS capabilities are part of its advantage and limitation. Its AI GPUs need a steady flow of advanced nodes, HBM, and packaging. Securing more capacity than competitors maintains its business edge; if the chain tightens, even huge demand might limit deliveries.
AMD, Broadcom, Marvell, Google, Amazon, Microsoft, and other ASIC designers also compete for this infrastructure. AI no longer depends solely on general-purpose GPUs. Hyperscalers are designing their own chips, specialized networks, internal accelerators, and complete platforms for training and inference. This increases pressure on TSMC and the entire ecosystem.
According to MarketWatch, Nomura expects component mismatches to worsen as Nvidia’s and Amazon’s new AI platforms arrive, with servers expected to see substantial growth in 2026 and 2027.
For infrastructure buyers, the message is clear: AI capacity availability won’t depend only on GPU prices. It will depend on allocations, manufacturing schedules, HBM memory, packaging, racks, power, cooling, networking, and full server delivery capabilities.
A more profitable yet more fragile supply chain
The AI boom is distributing revenue throughout the entire semiconductor supply chain. TSMC leads but isn’t the only beneficiary. Alternative foundries, OSATs, substrate suppliers, memory manufacturers, testing companies, metrology firms, chemical producers, and data center infrastructure providers are seeing increased demand.
However, this expansion also reveals fragility. When all players depend on a few critical technologies, delays in packaging, substrate bottlenecks, or HBM issues can halt products worth billions of dollars.
That’s why the industry is starting to talk less about a “chip shortage” in singular and more about systemic capacity. The question isn’t just how many wafers TSMC can produce, but how many complete accelerators and systems the ecosystem can deliver.
AI has transformed the semiconductor industry into a race of physical integration. Models are trained in software, but they require an enormous, precise industrial supply chain beforehand. That chain is reaching its limits precisely when demand from customers is surging.
Frequently Asked Questions
What is TSMC’s bottleneck in AI?
The pressure is concentrated on advanced nodes and especially on CoWoS packaging, which is essential for integrating high-performance logic chips with HBM memory.
What is CoWoS?
It’s an advanced packaging technology from TSMC that allows the integration of logic chips and high-bandwidth memory into high-density packages for HPC and AI applications.
Why does it impact the entire semiconductor supply chain?
Because an AI accelerator requires advanced wafer processing, HBM, substrates, packaging, testing, auxiliary components, networking, power supplies, and server assembly. If any layer fails, the whole product faces delays.
Who benefits from the demand overflow?
Besides TSMC, OSAT companies, testing providers, substrate suppliers, mature-node foundries, HBM memory makers, and semiconductor equipment vendors are gaining opportunities.
Will expanding CoWoS solve the issue by 2026?
It will help, but doesn’t eliminate all bottlenecks. Demand continues to grow, potentially shifting pressure to substrates, HBM, testing, tools, and operational capacity.
