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Taiwan Rejects Washington’s 50-50 Semiconductor Production Proposal Amid Intensified Trade Talks

Overview Of Negotiated Terms

Taiwan’s senior trade negotiator and vice premier, Cheng Li-chiun, made clear that the island will not entertain the U.S. proposal requiring Taiwan to manufacture only half of the chips it currently supplies into America. This firm rejection comes after intensive discussions in Washington, where the focus was on reducing tariff rates, eliminating tariff stacking, and easing levies on Taiwanese exports, which presently face an additional 20% reciprocal tariff.

U.S. Ambitions For Onshore Chip Production

The United States has long pursued a more balanced approach to semiconductor production to diminish its dependence on Taiwanese chips—a dependence that currently satisfies 95% of domestic demand. U.S. Commerce Secretary Howard Lutnick outlined a vision wherein production would be split evenly between Washington and Taipei, emphasizing the need to significantly bolster domestic chip manufacturing capabilities. However, Taiwan’s top trade officials have firmly dismissed the idea, choosing instead to prioritize more traditional trade issues over proposals to share chip production responsibilities.

Political And Economic Implications

The proposal has sparked intense backlash within Taiwan’s political landscape. Eric Chu, chairman of the opposition Kuomintang, condemned the idea as an exploitative move that undermines Taiwan’s technological sovereignty and its defense mechanism, often encapsulated in the ‘Silicon Shield’ theory. This theory has historically served as a bulwark against external pressures, notably from China, which views the island through a lens of territorial reclamation.

Strategic Considerations And Future Prospects

While Lutnick argues that a balanced semiconductor production plan could enhance Taiwan’s security, critics insist that such a move would erode the technological foundation that fortifies the island’s geopolitical standing. With Beijing’s unwavering claims over Taiwan and its pledge to use force if necessary, Taiwan continues to delicately balance economic interests with national security imperatives. As trade talks press on, the island remains resolute in safeguarding its semiconductor industry—a critical asset in global technological and defense circuits.

Conclusion

This latest development underscores the broader geopolitical and economic challenges at play in the semiconductor sector. For Taiwan, the priority remains to secure favorable trade terms while preserving the integrity of its dominant role in the global chip manufacturing landscape—a balance that will undoubtedly be tested as U.S. ambitions for a more autonomous semiconductor supply chain intensify.

Meta Unveils Muse Wonder Models In A Bid For Frontier AI Leadership

Meta has introduced two new AI models, Muse Wonder and Muse Wonder Full, signalling a more ambitious move into frontier AI. The releases build on Muse Spark but target more demanding tasks, including coding, while placing greater emphasis on long-term memory and personalised assistance.

Larger Models Target More Demanding Tasks

Unlike Muse Spark, which was designed for low latency and lightweight use, Muse Wonder and Wonder Full are significantly larger and, according to Meta, more capable. The company reports stronger benchmark performance and improvements on demanding workloads, including coding.

According to Meta’s model card, both models achieved benchmark results comparable to Fable 5.1 and GPT-6 Astra in the company’s internal testing. Meta described the release as “the next step on our scaling ladder and a major leap toward personal superintelligence” in its announcement.

Pricing Reflects Meta’s Frontier Ambitions

The models’ pricing places them in the premium AI segment. Through the Meta Model API, Muse Wonder will cost $4 per million input tokens and $20 per million output tokens, putting it in a similar pricing tier to GPT-6 Sol and Opus 5.5.

Muse Wonder Full, by contrast, will not be offered through the API. Meta says it will be exclusive to Muse Code subscribers, tying access to its most advanced model to a premium product. Both models will also power the Muse agent.

Long-Term Memory Takes Centre Stage

Meta is increasingly positioning its AI strategy around assistants that can support users over time rather than respond only to individual prompts. A central part of that approach is what the company calls “active memory”: the ability to retain, organise and synthesise information provided by users across extended periods.

Meta points to the MemoryBench benchmark, which evaluates systems designed for continual learning and memory construction. The company says Muse Wonder now outperforms Fable, Opus and Astra on this measure.

“We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond,” research scientist Kavya Kopparapu said in a launch video.

New Models Follow Meta’s AI Reorganisation

The launch is also significant within Meta’s broader AI strategy. It is the company’s first frontier-model release since Scale AI founder Alexandr Wang reorganised its AI operations in August 2025. Meta has since introduced Muse Spark and the Muse agent, while making fewer high-profile model announcements than competitors such as OpenAI and Anthropic.

The new models were trained at Meta’s Prometheus data centre in central Ohio, which came online earlier in 2026, according to the company. The facility highlights the growing importance of infrastructure in the race to develop advanced AI systems, where performance depends not only on algorithms and data but also on computing capacity, data-centre investment and access to capital.

As competition intensifies, Muse Wonder and Wonder Full represent a broader bet on larger models, persistent memory and premium AI products. Their longer-term significance will depend on how their reported capabilities translate into real-world performance and whether users find enough value to pay for access.

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