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Jukan
E-glass capacity cuts trigger production shifts for Taiwan CCL makers
The global AI data center boom is straining not only high-end computing chips but also adjacent industries such as memory and upstream glass fiber cloth, where significant capacity squeezes are rippling through to downstream customers' production layouts worldwide.
The pivot away from E-glass
Industry sources indicate that glass fiber cloth suppliers in Taiwan and Japan are fully focused on filling the supply gap for advanced capacities. To maximize limited factory space and production resources, they are dedicating efforts to ramping up specialty glass fiber cloth with low dielectric constant (Low Dk) and low coefficient of thermal expansion (Low CTE). As a result, some manufacturers plan to reduce or cease E-glass output starting in 2026.
Among the affected suppliers adjusting their production are Japanese giant Nitto Boseki (Nittobo) and its Taiwanese subsidiary Baotek, the US-Japan joint venture Asahi-Schwebel Taiwan, and Taiwanese integrated glass fiber yarn maker Fulltech Fiber Glass (FFG). Most shutdowns are scheduled for the second half of 2026 onward.
CCL makers follow suit
As glass fiber cloth producers shift their product focus, copper-clad laminate (CCL) manufacturers are following suit by revising their global production plans. They intend to gradually halt mid- to low-end product lines supplied from Taiwan to align with upstream material suppliers concentrating on the high-end market. Orders affected by E-glass discontinuation are expected to be fully transferred to Chinese factories.
Taiwan Union Technology Corporation (TUC) has notified customers that, due to upstream glass fiber cloth suppliers' adjustments to E-glass production, its Taiwan plants will stop producing the related specifications. The phase-out will proceed in two stages.
TUC's two-stage exit
TUC cited clear shifts in Taiwan's PCB market demand — particularly a sharp rise in Low Dk specialty materials usage — as the driver behind its main upstream glass fiber cloth suppliers' successively announcing a halt to E-glass production in order to redirect capacity toward weaving Low Dk glass fiber cloth. Consequently, TUC expects a substantial reduction in future E-glass procurement.
Given the overall change in the supply-demand structure, TUC faces limited E-glass availability and will coordinate with glass fiber cloth suppliers' subsequent supply plans, adjusting its Taiwan plants' product mix accordingly.
Per TUC's announcement, its Taiwan factory will begin reducing shipments of TU-662 (668/F), TU-768 (752), and TU-747 (742) series products starting February 10, 2026, with formal cessation of these affected materials planned by the end of 2026.
Broader market implications
Supply chain analysts note that E-glass materials are widely used across PCB products spanning smartphones, automotive, communications, servers, and memory sectors. Historically, Taiwanese and Japanese firms faced pricing pressure from China's large-scale capacity, prompting a gradual shift toward the high-end market — a trend that is now further driving production layout changes among CCL manufacturers.
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E-glass capacity cuts trigger production shifts for Taiwan CCL makers
The global AI data center boom is straining not only high-end computing chips but also adjacent industries such as memory and upstream glass fiber cloth, where significant capacity squeezes are rippling through to downstream customers' production layouts worldwide.
The pivot away from E-glass
Industry sources indicate that glass fiber cloth suppliers in Taiwan and Japan are fully focused on filling the supply gap for advanced capacities. To maximize limited factory space and production resources, they are dedicating efforts to ramping up specialty glass fiber cloth with low dielectric constant (Low Dk) and low coefficient of thermal expansion (Low CTE). As a result, some manufacturers plan to reduce or cease E-glass output starting in 2026.
Among the affected suppliers adjusting their production are Japanese giant Nitto Boseki (Nittobo) and its Taiwanese subsidiary Baotek, the US-Japan joint venture Asahi-Schwebel Taiwan, and Taiwanese integrated glass fiber yarn maker Fulltech Fiber Glass (FFG). Most shutdowns are scheduled for the second half of 2026 onward.
CCL makers follow suit
As glass fiber cloth producers shift their product focus, copper-clad laminate (CCL) manufacturers are following suit by revising their global production plans. They intend to gradually halt mid- to low-end product lines supplied from Taiwan to align with upstream material suppliers concentrating on the high-end market. Orders affected by E-glass discontinuation are expected to be fully transferred to Chinese factories.
Taiwan Union Technology Corporation (TUC) has notified customers that, due to upstream glass fiber cloth suppliers' adjustments to E-glass production, its Taiwan plants will stop producing the related specifications. The phase-out will proceed in two stages.
TUC's two-stage exit
TUC cited clear shifts in Taiwan's PCB market demand — particularly a sharp rise in Low Dk specialty materials usage — as the driver behind its main upstream glass fiber cloth suppliers' successively announcing a halt to E-glass production in order to redirect capacity toward weaving Low Dk glass fiber cloth. Consequently, TUC expects a substantial reduction in future E-glass procurement.
Given the overall change in the supply-demand structure, TUC faces limited E-glass availability and will coordinate with glass fiber cloth suppliers' subsequent supply plans, adjusting its Taiwan plants' product mix accordingly.
Per TUC's announcement, its Taiwan factory will begin reducing shipments of TU-662 (668/F), TU-768 (752), and TU-747 (742) series products starting February 10, 2026, with formal cessation of these affected materials planned by the end of 2026.
Broader market implications
Supply chain analysts note that E-glass materials are widely used across PCB products spanning smartphones, automotive, communications, servers, and memory sectors. Historically, Taiwanese and Japanese firms faced pricing pressure from China's large-scale capacity, prompting a gradual shift toward the high-end market — a trend that is now further driving production layout changes among CCL manufacturers.
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Javier Blas
RT @SecretaryWright: Visiting Venezuelan oil fields today to explore ways to boost oil production, update infrastructure, and unlock the country’s enormous economic potential.
Stronger commerce will benefit Americans AND Venezuelans, while delivering peace and prosperity across the Hemisphere! https://t.co/ZOlozhEf8H
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RT @SecretaryWright: Visiting Venezuelan oil fields today to explore ways to boost oil production, update infrastructure, and unlock the country’s enormous economic potential.
Stronger commerce will benefit Americans AND Venezuelans, while delivering peace and prosperity across the Hemisphere! https://t.co/ZOlozhEf8H
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God of Prompt
RT @godofprompt: I turned Matt Shumer's viral article into a prompt. The prompt inverts the article's structure. Shumer spent 4,000 words convincing people AI is real before giving advice. This prompt skips the convincing and goes straight to "what do I do Monday morning."
Prompt 👇 <contextAI capability is accelerating faster than public awareness. Models released in early 2026
can independently complete multi-hour expert tasks, write production-grade code, draft
legal briefs, build financial models, and iterate on their own output. Most professionals
are still evaluating AI based on experiences from 2023-2024, which is now irrelevant.
The gap between current AI capability and public perception is the largest it has ever been.
This gap is also the largest opportunity window for individuals willing to act now. <roleYou are a pragmatic AI adoption strategist who has helped hundreds of professionals
integrate AI into their daily workflows. You reject hype and theory. You only care about
what someone can do THIS WEEK to gain advantage. You understand that most people fail
at AI adoption not because AI is lacking, but because they treat it like a search engine
instead of a collaborator capable of doing hours of their actual work. <taskBuild a personalized 30-day AI integration plan that takes me from my current skill level
to actively using AI for real work output. Every recommendation must be specific to my
role, not generic "try asking AI questions" advice. The plan should make me the most
AI-capable person in my workplace within one month. <methodology1. AUDIT MY EXPOSURE: Based on my role, identify which parts of my job AI can already
do at or above human level RIGHT NOW (not theoretically, not "someday"). Be blunt
about what's already automated or automatable.
2. FIND MY HIGHEST-VALUE TASK: Identify the single task I spend the most time on that
AI could handle. This becomes my Week 1 focus. Provide the exact prompt template
I should use to delegate this task to AI.
3. BUILD MY DAILY PRACTICE: Create a structured 1-hour daily AI experiment schedule
for 30 days. Each day has a specific challenge tied to my actual work, not toy examples.
Difficulty escalates weekly.
4. SELECT MY TOOLS: Recommend the specific paid AI tool, the specific model to select
within that tool (not the default), and any domain-specific AI tools for my field.
Include exact settings to change and why the default configuration underperforms.
5. MAP MY RISK: Honestly assess how exposed my specific role is to AI displacement
on a 1-5 year timeline. Identify what parts of my job are hardest to automate and
tell me how to lean into those.
6. WRITE MY FIRST 5 POWER PROMPTS: Create 5 ready-to-use prompts customized to my
role that I can paste in and use immediately for real work output. These should
replace hours of manual work, not minutes. <guidelines- Zero fluff. Every sentence must be actionable or directly useful.
- Name specific tools, models, and settings. No "consider using an AI tool."
- When recommending prompts, write the full prompt I can copy-paste. Don't describe
what a prompt "might look like."
- Be honest about displacement risk. Don't soften it to be polite.
- If something in my field is already being done better by AI, say so directly.
- Assume I'm smart but have been treating AI like a search engine. Fix that.
- Prioritize tasks where AI saves HOURS, not minutes. Go for the biggest wins first.
- Include one "you probably don't think AI can do this, but try it" challenge per week. <avoid- Generic advice that applies to everyone ("stay curious!" "embrace change!")
- Recommending free-tier tools when paid versions are dramatically better
- Sugarcoating job displacement risk
- Suggesting I "ease into it" gradually. Speed matters. The window is closing.
- Listing capabilities without showing me exactly how to use them
- Any mention of "prompt e[...]
RT @godofprompt: I turned Matt Shumer's viral article into a prompt. The prompt inverts the article's structure. Shumer spent 4,000 words convincing people AI is real before giving advice. This prompt skips the convincing and goes straight to "what do I do Monday morning."
Prompt 👇 <contextAI capability is accelerating faster than public awareness. Models released in early 2026
can independently complete multi-hour expert tasks, write production-grade code, draft
legal briefs, build financial models, and iterate on their own output. Most professionals
are still evaluating AI based on experiences from 2023-2024, which is now irrelevant.
The gap between current AI capability and public perception is the largest it has ever been.
This gap is also the largest opportunity window for individuals willing to act now. <roleYou are a pragmatic AI adoption strategist who has helped hundreds of professionals
integrate AI into their daily workflows. You reject hype and theory. You only care about
what someone can do THIS WEEK to gain advantage. You understand that most people fail
at AI adoption not because AI is lacking, but because they treat it like a search engine
instead of a collaborator capable of doing hours of their actual work. <taskBuild a personalized 30-day AI integration plan that takes me from my current skill level
to actively using AI for real work output. Every recommendation must be specific to my
role, not generic "try asking AI questions" advice. The plan should make me the most
AI-capable person in my workplace within one month. <methodology1. AUDIT MY EXPOSURE: Based on my role, identify which parts of my job AI can already
do at or above human level RIGHT NOW (not theoretically, not "someday"). Be blunt
about what's already automated or automatable.
2. FIND MY HIGHEST-VALUE TASK: Identify the single task I spend the most time on that
AI could handle. This becomes my Week 1 focus. Provide the exact prompt template
I should use to delegate this task to AI.
3. BUILD MY DAILY PRACTICE: Create a structured 1-hour daily AI experiment schedule
for 30 days. Each day has a specific challenge tied to my actual work, not toy examples.
Difficulty escalates weekly.
4. SELECT MY TOOLS: Recommend the specific paid AI tool, the specific model to select
within that tool (not the default), and any domain-specific AI tools for my field.
Include exact settings to change and why the default configuration underperforms.
5. MAP MY RISK: Honestly assess how exposed my specific role is to AI displacement
on a 1-5 year timeline. Identify what parts of my job are hardest to automate and
tell me how to lean into those.
6. WRITE MY FIRST 5 POWER PROMPTS: Create 5 ready-to-use prompts customized to my
role that I can paste in and use immediately for real work output. These should
replace hours of manual work, not minutes. <guidelines- Zero fluff. Every sentence must be actionable or directly useful.
- Name specific tools, models, and settings. No "consider using an AI tool."
- When recommending prompts, write the full prompt I can copy-paste. Don't describe
what a prompt "might look like."
- Be honest about displacement risk. Don't soften it to be polite.
- If something in my field is already being done better by AI, say so directly.
- Assume I'm smart but have been treating AI like a search engine. Fix that.
- Prioritize tasks where AI saves HOURS, not minutes. Go for the biggest wins first.
- Include one "you probably don't think AI can do this, but try it" challenge per week. <avoid- Generic advice that applies to everyone ("stay curious!" "embrace change!")
- Recommending free-tier tools when paid versions are dramatically better
- Sugarcoating job displacement risk
- Suggesting I "ease into it" gradually. Speed matters. The window is closing.
- Listing capabilities without showing me exactly how to use them
- Any mention of "prompt e[...]
Offshore
God of Prompt RT @godofprompt: I turned Matt Shumer's viral article into a prompt. The prompt inverts the article's structure. Shumer spent 4,000 words convincing people AI is real before giving advice. This prompt skips the convincing and goes straight to…
ngineering" as a career path <information_about_me● My job title/role: [INSERT YOUR JOB TITLE]
● My industry: [INSERT YOUR INDUSTRY]
● My daily tasks (top 3-5 things I spend most time on): [LIST YOUR MAIN TASKS]
● My current AI usage: [NEVER / TRIED IT ONCE / USE FREE VERSION OCCASIONALLY / USE PAID VERSION]
● My biggest time sink at work: [WHAT TAKES YOU THE MOST HOURS PER WEEK]
● My comfort with technology: [LOW / MEDIUM / HIGH] <output_format**REALITY CHECK**
[2-3 sentences on where AI currently stands relative to my specific role. No hedging.]
**YOUR EXPOSURE MAP**
[Table: My top tasks | Can AI do this now? | How well? (1-10) | Timeline to full automation]
**WEEK 1-4 PLAN**
[For each week:]
- Focus area and WHY this week
- Daily 1-hour challenges (specific to my work, not generic)
- One "you won't believe this works" experiment
- Measurable outcome by end of week
**YOUR TOOL SETUP**
[Exact tool, exact model name, exact settings to change, monthly cost]
**5 POWER PROMPTS**
[Full copy-paste prompts customized to my role, each designed to replace 2+ hours of work]
**HARD TRUTH**
[Honest assessment: What's my 1-3 year outlook? What should I double down on?
What should I stop investing time in learning?]
tweet
● My industry: [INSERT YOUR INDUSTRY]
● My daily tasks (top 3-5 things I spend most time on): [LIST YOUR MAIN TASKS]
● My current AI usage: [NEVER / TRIED IT ONCE / USE FREE VERSION OCCASIONALLY / USE PAID VERSION]
● My biggest time sink at work: [WHAT TAKES YOU THE MOST HOURS PER WEEK]
● My comfort with technology: [LOW / MEDIUM / HIGH] <output_format**REALITY CHECK**
[2-3 sentences on where AI currently stands relative to my specific role. No hedging.]
**YOUR EXPOSURE MAP**
[Table: My top tasks | Can AI do this now? | How well? (1-10) | Timeline to full automation]
**WEEK 1-4 PLAN**
[For each week:]
- Focus area and WHY this week
- Daily 1-hour challenges (specific to my work, not generic)
- One "you won't believe this works" experiment
- Measurable outcome by end of week
**YOUR TOOL SETUP**
[Exact tool, exact model name, exact settings to change, monthly cost]
**5 POWER PROMPTS**
[Full copy-paste prompts customized to my role, each designed to replace 2+ hours of work]
**HARD TRUTH**
[Honest assessment: What's my 1-3 year outlook? What should I double down on?
What should I stop investing time in learning?]
https://t.co/ivXRKXJvQg tweet
X (formerly Twitter)
Matt Shumer (@mattshumer_) on X
Something Big Is Happening
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God of Prompt
RT @godofprompt: After interviewing 12 AI researchers from OpenAI, Anthropic, and Google, I noticed they all use the same 10 prompts.
Not the ones you see on X and LinkedIn.
These are the prompts that actually ship products, publish papers, and break benchmarks.
Here's what they told me ↓ https://t.co/CwG47vkWPV
tweet
RT @godofprompt: After interviewing 12 AI researchers from OpenAI, Anthropic, and Google, I noticed they all use the same 10 prompts.
Not the ones you see on X and LinkedIn.
These are the prompts that actually ship products, publish papers, and break benchmarks.
Here's what they told me ↓ https://t.co/CwG47vkWPV
tweet
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Pristine Capital
RT @realpristinecap: • US Price Cycle Update 📈
• IGV Software VPOC Incoming📉
• Memory Continued Relative Strength 💪
Check out tonight's research note. HAGE!
https://t.co/phVWmbQxj3
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RT @realpristinecap: • US Price Cycle Update 📈
• IGV Software VPOC Incoming📉
• Memory Continued Relative Strength 💪
Check out tonight's research note. HAGE!
https://t.co/phVWmbQxj3
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Michael Fritzell (Asian Century Stocks)
We'll all lose our jobs to AI eh?
"In their calmer moments, investors recognize their
inability to know what the future holds. In moments of
extreme panic or enthusiasm, however, they become
remarkably bold in their predictions; they act as though
uncertainty has vanished and the outcome is beyond
doubt. Reality is abruptly transformed into that
hypothetical future where the outcome is known. These
are rare occasions, but they are also unforgettable: major
tops and bottoms in markets are defined by this switch
from doubt to certainty." - Peter Bernstein
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We'll all lose our jobs to AI eh?
"In their calmer moments, investors recognize their
inability to know what the future holds. In moments of
extreme panic or enthusiasm, however, they become
remarkably bold in their predictions; they act as though
uncertainty has vanished and the outcome is beyond
doubt. Reality is abruptly transformed into that
hypothetical future where the outcome is known. These
are rare occasions, but they are also unforgettable: major
tops and bottoms in markets are defined by this switch
from doubt to certainty." - Peter Bernstein
tweet
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Dimitry Nakhla | Babylon Capital®
RT @DividendDynasty: Bullish $SPGI
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RT @DividendDynasty: Bullish $SPGI
$SPGI Director just purchased over $1 Million worth of shares
this is the largest insider purchase since 2015 https://t.co/5dxVtelq5O tweet
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Brady Long
RT @thisguyknowsai: BREAKING: Google Gemini just launched a new feature called Guided Learning.
You can now use it to learn literally anything, step by step, like a personal tutor.
Here’s how to access it 👇 https://t.co/cEDbogvX6v
tweet
RT @thisguyknowsai: BREAKING: Google Gemini just launched a new feature called Guided Learning.
You can now use it to learn literally anything, step by step, like a personal tutor.
Here’s how to access it 👇 https://t.co/cEDbogvX6v
tweet