South Korea has increased its semiconductor investment dramatically to ₩33 trillion (US$23.2 billion) as the nation moves to shield its important chip industry from mounting global pressures.

The expanded support package, announced Tuesday, represents a 27% increase from the ₩26 trillion that was pledged last year, underscoring Seoul’s determination to protect an industry that accounted for 21% of the country’s total exports in 2024. The announcement comes just days after president Trump stated he would reveal new semiconductor import tariff rates within the week, creating additional uncertainty for Korean chipmakers already navigating complex geopolitical tensions.

Strategic response to “increased uncertainties”

South Korean officials cited “increased uncertainties” with the United States as a driver behind the enhanced funding. The comprehensive package covers infrastructure development, low-interest loans for companies in the sector, and initiatives to recruit skilled workers in semiconductor firms.

Government officials confirmed that at least ₩4 trillion ($2.8 billion) will be deployed by 2026. Finance Minister Choi Sang-mok spoke about the government’s approach, following the announcement: “We will consult actively with the US over its Section 232 investigations into semiconductor imports to minimise any adverse impact on domestic companies.”

The expanded financial assistance programme directed specifically at the chip industry, will now reach ₩20 trillion ($14 billion), up from the previously allocated ₩17 trillion, according to the joint statement from multiple government ministries.

Protecting a vital economic engine

The significance of this South Korean semiconductor investment cannot be overstated. In 2024, the country’s semiconductor exports reached $141.9 billion, with shipments to China and the US standing at $46.6 billion and $10.7 billion respectively. South Korea hosts the world’s top memory chip manufacturers, Samsung Electronics and SK Hynix, the latter an important partner to chip giant Nvidia.

However, industry analysts have noted that Korean firms have fallen behind competitors in specialised areas like chip design and contract manufacturing, creating additional pressure to innovate.

Navigating Trump’s tariff threats

The timing of Seoul’s announcement appears calculated to have maximum impact, coming just days after Trump signalled imminent action on semiconductor tariffs. On Sunday, he indicated he would announce semiconductor import tariff rates in a week, though he suggested there might be “flexibility with some companies in the sector.”

The latest support package is the second announced by South Korea in recent days. Last week, the government unveiled emergency measures for its automotive sector, which has also been targeted by the Trump administration’s protectionist policies. Those measures included financial support, tax cuts, and subsidies to stimulate domestic demand. Industry observers view the parallel actions as part of a coordinated response to shield South Korea’s export-dependent economy from volatility.

Balancing act: US relations and Chinese competition

The expanded South Korea semiconductor investment package will address two issues: managing relationships with the US, and responding to intensifying competition from Chinese rivals.

South Korean chipmakers face a complex environment where they must maintain access to the US market and preserve their significant business with China, which remains still the country’s largest export destination. The balancing act has grown more difficult as US-China tech tensions have escalated. The government’s statement acknowledged the two pressures, noting that the measures come “in response to calls on the government to expand support at a time of growing policy uncertainty under the current US administration and rising competition from Chinese rivals.”

Industry context

The country’s semiconductor companies Samsung Electronics and SK Hynix lead the global memory chip market. However, pressure from US tariffs will impact exports, there’s the threat of aggressive Chinese investments in its domestic semiconductor infrastructure, and competition from Taiwan and the US comes in the form of chip manufacturing.

The additional South Korean semiconductor investment provides some financial reassurance to the industry, but the challenges extend beyond funding to include broader geopolitical and trade issues that financial measures alone cannot address fully. The Korea Semiconductor Industry Association has spoken in the past about the strategic importance of the semiconductor sector to the national economy and the challenges it faces currently.

Looking ahead

As Seoul ramps up its semiconductor support, diplomatic engagement will be equally important to financial backing. South Korean officials have indicated they will seek negotiations with the US to mitigate potential tariff impacts, similar to their approach with regards to the issues Korea faces in the automotive sector. The effectiveness of a dual strategy – increased domestic support coupled with international diplomacy – will help determine whether South Korea can preserve its position as a global semiconductor powerhouse. What remains clear is that South Korea views its semiconductor industry as non-negotiable for the nation’s economic future. It will to deploy significant resources to safeguard the sector regardless of international pressure.

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Pakistan SAF technology developments are positioning the agricultural-rich nation as a potential dark horse in Asia’s race toward aviation decarbonisation. As Asian airlines face mounting pressure to reduce emissions amid global climate imperatives, Pakistan’s unique approach of converting agricultural waste into sustainable aviation fuel (SAF) could offer a regional model worth examining.

Converting an environmental problem into economic opportunity

The smog that blankets major Pakistani and neighbouring Indian cities each harvest season has become an annual crisis, driven largely by the burning of agricultural residue. The practice, particularly prevalent in Punjab province, releases particulate matter that compromises air quality across borders.

However, this challenge presents a technological opportunity that Pakistan’s emerging SAF sector aims to capitalise on. “Crop residues burned during both winter and summer in Pakistan represent an underutilised resource with immense potential for SAF production,” noted experts in an April 2025 report by The Express Tribune, highlighting a practical technological solution to an entrenched environmental issue.

The technology equation

Pakistan SAF technology implementation focuses on two principal conversion methods, each suited to different agricultural inputs:

1. Hydroprocessing Esters and Fatty Acids (HEFA): For lipid-based feedstocks including used cooking oil and non-edible oils
2. Alcohol-to-Jet (ATJ): Optimised for converting sugar-based inputs like wheat straw and rice husks

The technologies produce aviation fuels chemically identical to conventional jet fuel, requiring no aircraft modifications, and deliver substantially improved carbon profiles. A third technology using carbon dioxide capture remains in development but holds promise for further emissions reductions. Dr Adeel Ghayur, described by The Express Tribune as an “eminent energy scientist and expert in circular economy,” indicated that commercial SAF technologies can scale from 100,000 to one million tonnes of annual production capacity, with corresponding economic impacts.

Asia’s first private SAF project

The December 2024 announcement of a $121 million SAF facility in Sheikhupura represented a milestone not just for Pakistan but for all of Asia. According to Pakistan Today, the Asian Development Bank (ADB) has committed $86.2 million to the project, with the International Finance Corporation (IFC) providing $35 million. What makes this development particularly noteworthy in the Asian context is its designation by the ADB as “the first private sector-led SAF project in Asia and the Pacific,” excluding China.

For a region where state involvement typically dominates energy infrastructure, this private-sector approach merits attention from investors and policymakers across Asia. The facility is operated by SAFCO Venture Holdings Limited and owned by Taimur Shaikh and Ali Shaikh, and presents compelling environmental and economic metrics: projected annual production of 200,000 tonnes of SAF, reduction of 500,000 tonnes of carbon dioxide yearly, creation of 300 direct jobs, and facilitation of approximately 20,000 indirect employment opportunities in the supply chain and tertiary industries.

The regional competitiveness question

While Pakistan’s SAF ambitions are technologically sound, important questions remain about its competitiveness in an Asian market where Singapore, Japan, and South Korea have already established advanced biofuel capabilities.

The price difference remains substantial, with SAF currently commanding approximately $2,500 per metric tonne versus $700 for conventional jet fuel. For price-sensitive Asian carriers navigating post-pandemic recovery, this cost gap presents significant challenges to adoption.

Dr. Ghayur said in The Express Tribune that “strengthening R&D is essential for Pakistan to remain competitive in the global SAF market, secure its position as a hub for innovation, and maintain leadership as SAF adoption rises across Asia.” The acknowledgement reflects awareness of the technological race underway in the region.

For Asian nations with similar agricultural profiles – Bangladesh, Vietnam, Thailand, and Indonesia – Pakistan’s SAF initiative offers a potential template for converting agricultural waste into aviation biofuels and addresses seasonal air pollution events. The multiplicative benefits – enhanced energy security, emissions reductions, rural economic opportunities, and foreign direct investment – align with development priorities across South and Southeast Asia.

Four-dimensional solution

Pakistan SAF technology implementation addresses four interconnected challenges that resonate in developing Asian economies:

1. Energy security: Reducing petroleum import dependence via domestic production
2. Economic development: Creating value-added manufacturing with substantial job creation potential
3. Foreign direct investment: Attracting capital for industrial-scale bioprocessing operations
4. Environmental mitigation: Addressing agricultural burning emissions and aviation carbon footprints

Critical path forward

For Pakistan’s SAF sector to achieve its potential, several hurdles will need to be surmounted. First, continued investment beyond the initial Sheikhupura facility will be necessary to achieve meaningful scale. Second, as SAF technologies evolve, Pakistan will need to maintain technological competitiveness through sustained R&D investment. Finally, efficiency improvements in agricultural waste collection and transportation will be essential to maintain favourable economics.

The byproduct potential enhances the business case further, with SAFCO’s facility projected to produce 18,000 tonnes of bionaphtha annually for sustainable plastics production, according to Pakistan Today.

As Dr Ghayur concluded in The Express Tribune, “The comprehensive policy roadmap will serve as both a blueprint and a catalyst to propel Pakistan to the forefront of the global SAF revolution.”

While significant challenges remain in scaling production, optimising costs, and matching competitive alternatives, Pakistan’s SAF technology trajectory represents a distinctive approach to circular economy implementation with potential regional application across all of Asia’s agricultural economies.

The post Could Pakistan SAF technology transform its agricultural waste crisis? appeared first on TechWire Asia.

After gaining attention with Studio Ghibli-style portraits, ChatGPT’s image generator is now powering a new wave of self-representation online – this time with users turning themselves into plastic action figures.

What began as a quirky trend on LinkedIn has now spread to platforms like Instagram, TikTok, Facebook, and X. The trend includes different takes, but the “AI Action Figure” version is among the most common. It typically shows a person recreated as a doll encased in a plastic blister pack, often accessorised with work-related items like laptops, books, or coffee mugs. That’s fitting, considering the trend’s initial traction among professionals and marketers on LinkedIn.

Other versions draw inspiration from more recognisable aesthetics, like the “Barbie Box Challenge,” where the AI-generated figure is styled to resemble a vintage Barbie.

The rise of the virtual dolls follows the earlier success of the Studio Ghibli-style portraits, which pushed ChatGPT’s image capabilities into the spotlight. That earlier trend sparked some backlash related to environmental, copyright, and creative concerns – but so far, the doll-themed offshoot hasn’t drawn the same level of criticism.

What’s notable about the trends is the consistent use of ChatGPT as the generator of choice. OpenAI’s recent launch of GPT-4o, which includes native image generation, attracted such a large volume of users that the firm had to temporarily limit image output and delay rollout for free-tier accounts.

While the popularity of action figures hasn’t yet matched that of Ghibli portraits, it does highlight ChatGPT’s role in introducing image tools to a broader user base. Many of these doll images are shared by users with low engagement, and mostly in professional circles. Some brands, including Mac Cosmetics and NYX, have posted their own versions, but celebrities and influencers have largely stayed away. One notable exception is US Representative Marjorie Taylor Greene, who shared a version of herself with accessories including a Bible and a gavel, calling it “The Congresswoman MTG Starter Kit.”

What the AI Barbie trend looks like

The process involves uploading a photo into ChatGPT and prompting it to create a doll or action figure based on the image. Many users opt for the Barbie aesthetic, asking for stylised packaging and accessories that reflect their personal or professional identity. The final output often mimics retro Barbie ads from the 1990s or early 2000s. Participants typically specify details like:

• The name to be displayed on the box
• Accessories, like pets, smartphones, or coffee mugs
• The desired pose, facial expression, or outfit
• Packaging design elements like colour or slogans

Users often iterate through several versions, adjusting prompts to better match their expectations. The theme can vary widely – from professional personas to hobbies or fictional characters – giving the trend a broad creative range.

How the trend gained momentum

The idea gained visibility in early 2025, beginning on LinkedIn where users embraced the “AI Action Figure” format. The Barbie-style makeover gained traction over time, tapping into a blend of nostalgia and visual novelty. Hashtags like #aibarbie and #BarbieBoxChallenge have helped to spread the concept. While the Barbie-inspired version has not gone as viral as the Ghibli-style portraits, it has maintained steady traction on social media, especially among users looking for lighthearted ways to express their personal branding.

https://youtube.com/watch?v=Z6S6zQQ8sCQ%3Fsi%3DPJOwLgHWngf21YhL

Using ChatGPT’s image tool

To participate, users must access ChatGPT’s image generation tool, available with GPT-4o. The process begins by uploading a high-resolution photo – preferably full-body – and supplying a prompt that describes the desired figurine.

To improve accuracy, prompts usually include:

• A theme (e.g., office, workout, fantasy)
• Instructions for how the figure should be posed
• Details about clothing, mood, or accessories
• A note to include these elements inside a moulded box layout

Reiterating the intended theme helps ensure consistent results. While many focus on work-related personas, the style is flexible – some choose gym-themed versions, others opt for more humorous or fictional spins.

Behind the spike in image activity

ChatGPT’s image generation tool launched widely in early 2025, and its use quickly surged. According to OpenAI CEO Sam Altman, the demand became so intense that GPU capacity was stretched thin, prompting a temporary cap on image generation for free users. Altman described the load as “biblical demand” in a social media post, noting that the feature had drawn more than 150 million active users in its first month. The tool’s ability to generate everything from cartoons to logos – and now custom action figures – has played a central role in how users explore visual identity through AI.

The post First it was Ghibli, now it’s the AI Barbie Box trend appeared first on TechWire Asia.

The Telecom AI Platform collaboration between Jio Platforms Limited (JPL), AMD, Cisco, and Nokia revealed at last month’s Mobile World Congress 2025 may represent a significant shift in how telecom networks evolve in coming years.

The partnership focuses on developing an open AI framework for network operations, and comes as service providers worldwide face mounting pressure to improve efficiency and create new revenue streams.

Announced on March 3, the alliance brings together expertise across RAN, routing, AI data centres, security, and telecom infrastructure to create what the companies describe as a “central intelligence layer” for telecom and digital services. The multi-domain intelligence framework aims to integrate AI and automation in all network operations, with Jio serving as the first implementation case.

“We are building a multimodal, multi-domain orchestrated workflow platform […] for the telecom industry,” said Mathew Oommen, Group CEO of Reliance Jio. He highlighted the platform’s potential to transform networks into “self-optimising, customer-aware ecosystems.”

Technical foundations and AI

What sets the Telecom AI Platform apart is its technological approach. The platform will be LLM-agnostic and use open APIs, using multiple forms of artificial intelligence including agentic AI, general and domain-specific LLMs, Small Language Models (SLMs), and non-GenAI machine learning techniques.

AMD’s chair and CEO Lisa Su heralded “more secure, efficient, and scalable networks,” made possible through the platform, which uses Cisco’s Agile Services Networking and Data Centre Networking, plus Nokia’s capabilities in RAN, Core, fixed broadband, and optical transport.

India first, then global expansion

The Open Telecom AI Platform’s first customer, Jio, describes a “replicable reference architecture and deployable solution for the broader global service provider industry.”

The company hopes to position India as a front-runner in AI-driven telecom innovation. The timing of the project is significant, as telecom operators worldwide face increasing pressure to enhance network performance and offer new services beyond those of traditional telecom infrastructure.

“The initiative goes beyond automation – it’s about enabling AI-driven, autonomous networks that adapt in real-time, enhance user experiences, and create new service and revenue opportunities across the digital ecosystem,” Oommen said.

Real-world applications and benefits

Industry analysts suggest the Telecom AI Platform could drive significant improvements in several key areas:

1. Network security: Enhanced threat detection and prevention through AI-driven analysis across network layers
2. Operational efficiency: Reduced total cost of ownership through automation and predictive maintenance
3. Self-healing networks: Autonomous identification and resolution of network issues before they impact service
4. Revenue generation: Creation of new AI-enabled services and applications for enterprise and consumer segments

Potential timeline and global impact

While specific deployment timelines weren’t disclosed in the announcement, the companies indicated that development is actively underway. The platform’s open architecture design suggests its impact could extend far beyond Jio’s network in India.

The Telecom AI Platform represents a significant step toward what industry experts call “cognitive networks” – telecommunications infrastructure with embedded intelligence that can learn, adapt, and evolve autonomously. For global telecom operators watching this development, the platform could provide a blueprint for integration that addresses their most pressing challenges: reducing operational costs, enhancing security posture, improving customer experience, and developing new revenue streams.

As telecom networks continue their evolution toward 6G and beyond, initiatives like this Telecom AI Platform may well determine which operators thrive in the future – and which countries will lead the next wave of telecommunications innovation.

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Google has introduced Ironwood, its seventh-generation Tensor Processing Unit (TPU) at Google Cloud Next 2025. The processor unit is specifically designed to support large-scale inference workloads.

The chip marks a shift in focus from training to inference, reflecting broader changes in how AI models are used in production environments. TPUs have been a core part of Google’s infrastructure for several years, powering internal services and customer applications. Ironwood continues with enhancements for the next wave of AI applications – including large language models (LLMs), Mixture of Experts (MoEs), and other compute-intensive tools that require real-time responsiveness and scalability.

Inference takes centre stage

Ironwood is designed to support what Google calls the “age of inference,” in which AI systems interpret and generate insights actively, rather than just responding to inputs. The shift is reshaping how AI models are deployed, particularly in business use, where continuous, low-latency performance is important.

Ironwood represents a number of architectural upgrades: Each chip provides 4,614 teraflops at peak performance, supported by 192GB of high bandwidth memory and up to 7.2 terabytes per second of memory bandwidth – significantly more than in previous TPUs.

The expanded memory and throughput are to support models requiring rapid access to large datasets, like those used in search, recommendation systems, and scientific computing.

Ironwood also features an improved version of SparseCore, a component aimed at accelerating ultra-large embedding models that are often used in ranking and personalisation tasks.

Scale and connectivity

Ironwood’s scalability means it can be deployed in configurations from 256 to 9,216 chips in a single pod. At full scale, a pod delivers 42.5 exaflops of compute, making it more than 24 times more powerful than the El Capitan supercomputer, which tops out at 1.7 exaflops.

To support this level of distributed computing, Ironwood includes a new version of Google’s Inter-Chip Interconnect, which can communicate bidirectionally at 1.2 terabits per second. This helps reduce bottlenecks so data can move more efficiently across thousands of chips during training or inference. Ironwood is integrated with Pathways, Google’s distributed machine learning runtime developed by DeepMind. Pathways allows workloads to run on multiple pods, letting developers orchestrate tens or hundreds of thousands of chips for a single model or application.

Efficiency and sustainability

Power efficiency metrics show that Ironwood has twice the performance per watt as its predecessor, Trillium, able to sustain high output under sustained workloads. The TPU has a liquid-based cooling system, and according to Google, is nearly 30 times more power-efficient than the first Cloud TPU introduced in 2018. The emphasis on energy efficiency reflects growing concerns about the environmental impact of large-scale AI infrastructure, particularly as demand continues to grow.

Supporting real-world applications

Ironwood’s architecture supports “thinking models,” which are used increasingly in real-time applications like chat interfaces and autonomous systems. The TPU’s capabilities also offer the potential for use in finance, logistics, and bio-informatics workloads, which require fast, large-scale computations. Google has integrated Ironwood into its Cloud AI Hypercomputer strategy, which combines custom hardware and tools like Vertex AI.

What comes next

Google plans to make Ironwood publicly-available later this year to support workloads like Gemini 2.5 and AlphaFold, and the unit is expected to be used in research and production environments that demand large-scale distributed inference.

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Chinese AI company DeepSeek has developed a new approach to AI feedback systems that could transform how artificial intelligence learns from human preferences.

Working with Tsinghua University researchers, DeepSeek’s innovation tackles one of the most persistent challenges in AI development: teaching machines to understand what humans genuinely want from them. The breakthrough is detailed in a research paper “Inference-Time Scaling for Generalist Reward Modeling,” and introduces a technique making AI responses more accurate and efficient – a win-win in the AI world where better performance typically demands more computing power.

Teaching AI to understand human preferences

At the heart of DeepSeek’s innovation is a new approach to what experts call “reward models” – essentially the feedback mechanisms that guide how AI systems learn. Think of reward models as digital teachers. When an AI responds, models provide feedback on how good that response was, helping the AI improve over time. The problem has always been how to create reward models that accurately reflect human preferences across many different types of questions. DeepSeek’s solution combines two techniques:

1. Generative Reward Modeling (GRM): Uses language to represent rewards, providing richer feedback than previous methods that relied on simple numerical scores.
2. Self-Principled Critique Tuning (SPCT): Allows the AI to adaptively generate its guiding principles and critiques through online reinforcement learning.

Zijun Liu, a researcher from Tsinghua University and DeepSeek-AI who co-authored the paper, explains that this combination allows “principles to be generated based on the input query and responses, adaptively aligning reward generation process.”

Doing more with less

What makes DeepSeek’s approach particularly valuable is “inference-time scaling.” Rather than requiring more computing power during the training phase, the method allows for performance improvements when the AI is used – the ‘point of inference’.

The researchers demonstrated that their method achieves better results with increased sampling during inference, potentially allowing smaller models to match the performance of much larger ones. The efficiency breakthrough comes at a important moment in AI development when the relentless push for larger models raises concerns about sustainability, supply chain viability, and accessibility.

What this means for the future of AI

DeepSeek’s innovation in AI feedback systems could have far-reaching implications:

• More accurate AI responses: Better reward models mean AI systems receive more precise feedback, improving outputs over time.
• Adaptable performance: The ability to scale performance during inference allows AI systems to adjust to different computational constraints.
• Broader capabilities: AI systems can perform better across many tasks by improving reward modelling for general domains.
• Democratising AI development: If smaller models can achieve similar results to larger models via better inference methods, AI research could become more accessible to those with limited resources.

DeepSeek’s rising influence

The latest advance adds to DeepSeek’s growing reputation in the AI field. Although founded only in 2023 by entrepreneur Liang Wenfeng, the Hangzhou-based company has made an impact with the V3 foundation model and R1 reasoning model. The company recently upgraded its V3 model (DeepSeek-V3-0324), which it said offered “enhanced reasoning capabilities, optimised front-end web development and upgraded Chinese writing proficiency.”

DeepSeek has also committed to open-source its AI technology, by opening five public code repositories in February which allow developers to review and contribute to software development.

According to the research paper, DeepSeek intends to make its GRM models open-source, although no specific timeline has been provided. Its decision could accelerate progress in the field by allowing broader experimentation with this type of advanced AI feedback system.

Beyond bigger is better

As AI continues to evolve rapidly, DeepSeek’s work demonstrates that innovations in how models learn can be just as important as increasing their size. By focusing on the quality and scalability of feedback, DeepSeek addresses one of the challenges to create AI that better understands and aligns with human preferences.

This possible breakthrough in AI feedback systems suggests the future of artificial intelligence may depend not just on raw computing power but on more intelligent and efficient methods that better capture the nuances of human preferences.

The post DeepSeek’s new technology makes AI actually understand what you’re asking for appeared first on TechWire Asia.

ChatGPT’s internet traffic has skyrocketed due to a spike in interest in AI-generated images styled after Studio Ghibli animations.

OpenAI noticed a large increase in engagement following the release of its image-generation tool, which enables users to create artwork reminiscent of classic titles like Spirited Away and My Neighbor Totoro. Data from Similarweb shows that weekly active users passed 150 million for the first time this year.

OpenAI CEO Sam Altman said on social media that the platform added one million users in a single hour – surpassing previous growth records. SensorTower reported that downloads and revenue through the ChatGPT app also increased. Weekly downloads rose by 11%, active users by 5%, and in-app purchase revenue by 6% compared to the previous month.

The rapid increase in use put pressure on the platform’s infrastructure. Users reported slowdowns and brief outages, forcing Altman to caution that future features may face delays while OpenAI manages capacity

Legal and copyright concerns with the ChatGPT x Ghibli

The viral trend has prompted discussion around copyright. Some legal experts have raised questions about whether closely-replicating distinctive animation styles could cross into infringement.

“The legal landscape of AI-generated images mimicking Studio Ghibli’s distinctive style is an uncertain terrain. Copyright law has generally protected only specific expressions rather than artistic styles themselves,” said Evan Brown, a partner at law firm Neal & McDevitt.

OpenAI did not respond to questions about how its models were trained or whether copyrighted materials influenced its image generator. Studio Ghibli has not issued a formal statement, but commentary from its co-founders has resurfaced.

Hayao Miyazaki’s 2016 reaction to an early AI-generated image drew attention last week. In a widely circulated video, he described the technology as “an insult to life itself.” The full clip shows him responding specifically to a zombie-like AI render, which he called “extremely unpleasant.”

In a recent interview, Studio Ghibli’s managing director Goro Miyazaki acknowledged the growing capabilities of AI. He claimed that AI-generated films could become a reality in the coming years, but questioned whether audiences would embrace them. He also acknowledged that while new technology could lead to new creative voices, it may be difficult to replicate the sensibilities of previous generations. “Nowadays, the world is full of opportunities to watch anything, anytime, anywhere,” he said, suggesting that younger artists may not share the same experiences that shaped Ghibli’s earlier works.

Studio concerns and industry shifts

Japan faces a shortage of trained animators, in part due to long hours and low wages in the industry. Goro noted that Gen Z may be less inclined to pursue the traditionally labour-intensive career path of hand-drawn animation.

AI tools are emerging as a faster, lower-cost alternative to visual storytelling. Studio Ghibli’s legacy includes a number of films that blend fantastical themes with personal and historical reflections. Miyazaki’s latest work, The Boy and the Heron, earned an Academy Award and may be his final project. Goro has contributed his own directorial efforts, including Tales from Earthsea and From Up on Poppy Hill, and helped develop the Ghibli Museum and Ghibli Park.

User privacy and data security

As more users upload personal images to generate stylised portraits, privacy advocates are raising concerns about how that data is collected and used. “When you upload a photo to an AI art generator, you’re giving away your biometric data (your face). Some AI tools store that data, use it to train future models, or even sell it to third parties – none of which you may be fully aware of unless you read the fine print,” said Christoph C. Cemper, founder of AIPRM.

OpenAI’s privacy policy confirms the platform collects user-provided and automatically generated data, including images. Unless users opt out or request data deletion, content may be retained and used to train future models.

Cemper said that uploaded images could be misused. Personal data may appear in public datasets, like LAION-5B, which has been linked to the training of tools like Stable Diffusion and Google Imagen. One reported case involved a user finding private medical images in a public dataset. Cemper said that AI-generated content has already been used to produce fabricated documents and images, adding that deepfake risks are increasing. “There are too many real-world verification flows that rely on ‘real images’ as proof. That era is over,” one user wrote on social media.

Navigating licensing and user rights between ChatGPT and Ghibli

Cemper urged users to be aware of broad licensing terms buried in AI platform policies. Terms like “non-exclusive,” “royalty-free,” and “irrevocable license” can give platforms broad rights over uploaded content. The rights may extend even after the user stops using the service.

Creating AI art in the style of well-known brands could also present legal challenges. Artistic styles like those of Studio Ghibli, Disney, and Pixar are closely associated with their original creators, and mimicking them may fall under derivative work protections.

In late 2022, several artists filed lawsuits against AI firms, alleging their work was used without permission to train image generators. The ongoing legal challenges highlight the tension between creative freedom and intellectual property rights.

Cemper added: “The rollout of ChatGPT’s 4o image generator shows just how powerful AI has become as it replicates iconic artistic styles with just a few clicks. But this unprecedented capability comes with a growing risk – the lines between creativity and copyright infringement are increasingly blurred, and the risk of unintentionally violating intellectual property laws continues to grow. While these trends may seem harmless,creators must be aware that what may appear as a fun experiment could easily cross into legal territory.

“The rapid pace of AI development also raises significant concerns about privacy and date security. With more users engaging with AI tools, there’s a pressing need for clearer, more transparent privacy policies. Users should be empowered to make informed decisions about uploading their photos or personal data – especially when they may not realise how their information is being stored, shared, or used.”

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“We’re all living inside the president’s head, and nobody knows anything,” wrote The Atlantic recently – an encapsulation of the market turmoil surrounding Trump’s tech tariffs.

The tariff policy has intensified rapidly, marking an escalation from the president’s previous trade approach. The latest threat to impose an additional 50% duty on Chinese imports unless Beijing withdraws its retaliatory measures would push the total tariff rate to 104%, more than quadrupling the cost of importing Chinese goods into the US.

Beijing’s 34% retaliatory tariffs, imposed in direct response to Trump’s initial tariff announcements, represent China’s own calculated approach – not seeking to match the complete US duties but sending a message that it won’t absorb economic punishment without a proportional response.

China’s Commerce Ministry stated, they “firmly oppose” the US threats and will “resolutely respond,” calling Trump’s approach “doubling down on its mistakes” and “exposing its nature of coercion.”

The severity of this action goes far beyond the 25% peak rates seen during Trump’s first administration, when economists warned of significant market disruption. Now, global technology supply chains that took decades to optimise face the prospect of a complete restructuring, as duties exceeding 100% would effectively close off the world’s largest consumer market to Chinese manufacturers.

This represents a continuation of Trump’s first-term policies and an amplification that creates immediate consequences for technology companies and consumers. “If China does not withdraw its 34% increase above their already long-term trading abuses by tomorrow, April 8th, 2025, the United States will impose ADDITIONAL Tariffs on China of 50%, effective April 9th,” Trump declared on his Truth Social platform this week.

Immediate market response to Trump’s tech tariffs

The escalating tariffs have created an unexpected short-term boom for companies like Apple, with consumers flooding stores to purchase electronics before potential price increases. “Almost every customer asked me if prices were going to go up soon,” reported one Apple store employee, who requested anonymity as they weren’t authorised to speak publicly.

Bloomberg reports that Apple’s US retail locations experienced higher sales this past weekend than in previous years. The sudden purchasing surges illustrate the real-world impact of tariff policies on consumer behaviour, with the prospect of iPhones potentially costing thousands of dollars creating what one employee described as an atmosphere resembling “the busy holiday season.”

The mathematical absurdity of “reciprocal” tariffs

What makes Trump’s approach particularly problematic is the disconnect between his “reciprocal tariff” rhetoric and the calculation method employed. Documents from the office of the US Trade Representative reveal that the tariff levels do not match other countries’ rates; instead, they are based on bilateral trade deficits. The formula mathematically ensures that any nation selling more to America than it buys faces punitive duties, regardless of their actual trade practices.

The reductive approach treats complex global trade relationships as a simplistic zero-sum game, ignoring the reality of how modern supply chains function.

Tech industry fallout

Few sectors stand to lose more from the escalation in trading relations than technology. The industry’s heavy reliance on transnational production networks means that components often cross borders multiple times before reaching consumers. Each crossing potentially incurs tariffs, creating a compound effect that industry analysts call a “tariff cascade.”

Apple exemplifies companies subject to this effect. While it has worked to diversify its manufacturing base since Trump’s first term, shifting some production to Vietnam, India, and other locations, China remains central to its supply chain. The company’s stock suffered its worst three-day rout since 2001 following Trump’s tariff announcements. Bloombergreported that Apple “lost more than half a trillion dollars in valuation” in just two trading days.

Beyond rhetoric: the real-world impact

Despite the president’s claims that tariffs will revitalise American manufacturing, economic forecasts paint a different picture. Fitch Ratings warned that the tariffs have “significantly raised the risk for a recession in the United States” through higher consumer prices, squeezed wages, and dampened business investment.

Larry Fink, CEO of BlackRock, said “most CEOs I’ve talked to would say we are probably in a recession right now.”

For tech consumers, the Yale Budget Lab estimates American households could face an additional $2,100 in annual costs due to the April 2nd tariff announcement alone. Lower-income households will bear a disproportionate burden of these increases, as they spend a higher percentage of their income on consumer electronics and other goods affected by the tariffs.

Strategic incoherence

Perhaps most concerning for the technology sector is the lack of coherent objective.

As Navin Girishankar, head of the economic security programme at the Centre for Strategic and International Studies, told the South China Morning Post, “The Trump administration has been transparent all along about its desire to use tariffs primarily as an instrument of choice for several different objectives,” but “less coherent, I would say incoherent, about the actual goals.”

The strategic ambiguity leaves tech companies in a precarious position, unable to make informed long-term investment decisions. Should they accelerate reshoring efforts, potentially at great expense, or hope for a negotiated resolution?

Cracks in support

Even some of Donald Trump’s most staunch supporters have begun to express concern. Elon Musk, who serves as a senior adviser to Trump, has expressed discomfort with the policy. Meanwhile, billionaire investor Bill Ackman stated bluntly, “I am just frustrated watching what I believe to be a major policy error occur.”

The coming days will determine whether Trump follows through on his threat to escalate duties to their new levels. What’s already clear is that his approach to trade represents a wrecking of the integrated global technology ecosystem that has fuelled innovation worldwide.

For tech companies and consumers across Asia, the message is unmistakable: the era of predictable trade in digital goods is over, at least for now. As markets reel and supply chains reconfigure, uncertainty is the only certainty.

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