Across the quiet machinery of the world's factories and vehicles, a shortage that began with the hunger of AI data centers for memory is now reaching the smaller, older chips that govern power and everyday control. Speaking in Seoul, Texas Instruments Korea's Park Joong-seo described a world where capital chases the newest, most advanced silicon while the humble mature nodes that run industry are left underbuilt, because outside foundries cannot find enough profit in them. It is a reminder that the most sophisticated technologies depend on modest components no one thinks about until they are m
AI Data Center Demand Spills Into Industrial Chip Shortages, Texas Instruments Says Mature Nodes Are the Choke Point
Building more advanced fabs does not increase the supply of industrial chips.
Why does a shortage of cheap chips matter when the headlines are about memory and advanced nodes?
Because the AI servers need both. Park says they carry lots of voltage regulation chips and motor control chips for cooling, and those are made on older processes between 28nm and 130nm.
And why isn't anyone building more of that capacity?
Money. He cited $1,000 to $2,000 per legacy wafer versus $40,000 to $50,000 for advanced ones, so outside foundries see poor returns and avoid expansion.
Worth noting that's Park's account, and he works for a company that sells the alternative. The wafer prices are his figures, not independently verified here.
Fair. What is TI's advantage, then?
Its own 300mm production. Park says that cuts chip cost about 30% and more than doubles shipments, and that 95% of its embedded equipment is internalized.
Again, those are company claims. The revenue numbers are more concrete: $6.055 billion in the third quarter, up 28%, with data center up about 95%. But only growth rates were given for that segment, so we can't see its size.
The 800V design also came up.
Efficiency goes from 84.6% to 85.2%, and an expected 86.7% after 2027 with further changes. Small gains, but at data center scale they matter.
And that 86.7% is a projection, not a result. Same for the humanoid robot market forecast around 2035.
O Pulso
- AI data center demand is pulling power and embedded chips into a shortage that spreads well beyond memory.
- Outside foundries are reluctant to add mature-node capacity, leaving industrial and auto makers exposed as wafer economics favor 2nm, 3nm and HBM.
- TI is answering with its own 300mm factories and nearly fully internalized embedded production, promising steadier lead times and lower costs.
- Customers now weigh the expense of redesigning products against the risk of depending on a single supply chain.
- Data center revenue surging roughly 95% signals that the squeeze is still tightening rather than easing.
Across the quiet machinery of the world's factories and vehicles, a shortage that began with the hunger of AI data centers for memory is now reaching the smaller, older chips that govern power and everyday control. Speaking in Seoul, Texas Instruments Korea's Park Joong-seo described a world where capital chases the newest, most advanced silicon while the humble mature nodes that run industry are left underbuilt, because outside foundries cannot find enough profit in them. It is a reminder that the most sophisticated technologies depend on modest components no one thinks about until they are missing, and that the companies able to make their own supply may quietly gain the advantage.
On November 11, at the Embedded Labs 2026 conference in Seoul's Gangnam district, Park Joong-seo, CEO of Texas Instruments Korea, told reporters that the chip squeeze driven by AI data centers has moved past memory. Power and embedded semiconductors are tightening as well, and he argued that building more advanced fabs does little to relieve shortages of industrial chips.
His explanation rests on a mismatch between where chips are made and where money is going. Microcontrollers, communications chips and radar components are mostly produced on mature processes between 28nm and 130nm, while global investment flows toward 2nm, 3nm and high-bandwidth memory. A leading-edge wafer costs $40,000 to $50,000, a legacy one roughly $1,000 to $2,000, and outside foundries find the older business too thin to justify new capacity.
TI's response is to lean on its own factories. The company runs 15 manufacturing, assembly and test sites across eight countries and regions, and it began moving to 300mm wafers in 2010, the first in analog to do so. Park said the larger wafers cut production costs by about 30% and more than double shipments at the same process and lead time, and that TI has brought 95% of its embedded production equipment in-house, which he believes keeps lead times stable even when outside capacity runs short.
Demand is visible in TI's third-quarter results, with revenue of $6.055 billion, up 28%. Data center revenue rose about 95%, industrial more than 40%, and automotive in the mid-teens. AI servers account for part of this, carrying voltage regulation and motor control chips, and TI has worked with Nvidia on an 800-volt DC design that improves delivery efficiency.
Park also pushed back on the industry's fixation on raw TOPS figures, arguing that affordable, practical functionality matters more, and that Level 3 autonomy can run at 400 to 500 TOPS. On humanoid robots, he said price will decide the mainstream moment, with a large market expected around 2035. For customers weighing a move away from mature-node suppliers, TI is offering development tools and migration support to lower the cost of switching.
On Nov. 11, at a conference called Embedded Labs 2026 at COEX in Seoul's Gangnam district, Park Joong-seo, CEO of Texas Instruments Korea, told reporters that the squeeze created by AI data centers has moved beyond memory. Power and embedded semiconductors are getting tight too, he said, and building more advanced fabs does nothing to increase the supply of industrial chips.
His explanation is a mismatch in process nodes. Microcontrollers for general-purpose and real-time control, along with communications and radar chips, are mostly made on mature processes between 28nm and 130nm. Global investment, meanwhile, is concentrated on 2nm and 3nm advanced nodes and high-bandwidth memory. Park put numbers on the economics: advanced wafers cost $40,000 to $50,000 each, while legacy wafers run roughly $1,000 to $2,000. From the standpoint of outside foundries, he said, the legacy business is not profitable enough to justify new capacity, so they avoid adding it.
TI's answer is to rely on its own factories. The company, an American analog chipmaker that introduced the first silicon transistor in 1954, runs 15 sites for manufacturing, assembly and testing across eight countries and regions, including the United States, Germany and Japan. In 2010 it began moving to 300mm (12-inch) wafers, the first in the analog industry to do so. A 300mm wafer yields 2.2 to 2.3 times as many usable dies as a 200mm one. Park said that cuts chip production costs by about 30% and more than doubles shipment volumes at the same process and lead time. He added that TI has internalized 95% of its embedded production equipment, covering its mainstay 45nm and 65nm processes and extending to 28nm in the future, which he said guarantees stable lead times even when outside foundry capacity is short.
The demand side is visible in TI's third-quarter results. Total revenue was $6.055 billion, up 28% from a year earlier, with analog accounting for 81.1%. Embedded revenue reached $858 million, up 21%. Data center revenue rose about 95%, industrial revenue grew more than 40%, and automotive posted a mid-teens increase. Only growth rates, not dollar figures, were disclosed for those three segments.
AI servers explain part of that growth. Beyond GPUs and CPUs, they carry large numbers of voltage regulation chips that deliver power and remove heat, plus sensor and motor control chips for cooling equipment. TI has worked with Nvidia on an 800-volt direct current design meant to reduce power loss. Delivery efficiency is 84.6% on the existing 48V-centered structure and 85.2% with 800V DC, and is expected to reach 86.7% after 2027 if transformer and distribution structures also change. Park said every conversion stage between high-voltage power and the sub-1V levels AI processors use adds loss and heat, and TI is working with major global platform companies to cut the steps from as many as five to three.
Park also questioned the industry's focus on TOPS figures. Setting price and power aside, he said, one could build a 10,000-TOPS chipset, but an autonomous driving platform costing 20 million won (about $15,000) inside a 20 million won car would not be accepted by the market. He argued that competitiveness lies in delivering practical functions at affordable prices through software optimization, and that Level 3 functions can be implemented at 400 to 500 TOPS.
On humanoid robots, he said price, not technological maturity, will decide when they go mainstream. A 500 million won (about $370,000) robot would sell only for research, he said, while one in the 10 million won (about $7,500) range doing human-level labor would open the market explosively. The industry expects a large market to form around 2035, starting from 2030. He named GaN-based power chips, motor drive integration for longer battery life, and the integration of functional safety and cybersecurity standards as challenges.
TI is also trying to ease switching costs. Its Code Composer Studio development environment has shortened development from years to months or weeks, and migration tools let customers short of other companies' parts carry over existing firmware expertise. For SimpleLink wireless chips, TI said wireless battery management network availability exceeds 99.999%. The assessment accompanying Park's remarks is that suppliers with their own 300mm capacity hold an edge in lead times and costs, while industrial and automotive customers dependent on mature nodes must weigh supply diversification against the cost of redesigning their products.
Citações Notáveis
Demand set off by AI data centers is making supply and demand tight not just for memory but for power and embedded semiconductors as well.— Park Joong-seo, CEO of Texas Instruments Korea