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Adrian’s Arena: Navigating the Complexities of AI Copyright Across Asia

Discover how Asia is tackling AI and copyright challenges with innovative laws, landmark cases, and a focus on balancing creativity and innovation.

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AI Copyright Asia

TL;DR:

  • Asia is at the forefront of AI copyright regulation, with diverse legal frameworks tailored to foster innovation while safeguarding intellectual property.
  • Singapore’s 2021 Copyright Act and China’s 2023 landmark rulings highlight progressive approaches to AI-generated content.
  • Key challenges include defining authorship, using copyrighted data for AI training, and balancing creator rights with AI development.
  • Asia’s leadership is shaping global standards, offering valuable insights for navigating copyright in the AI era.

Artificial intelligence (AI) is revolutionising the way content is created, consumed, and protected. From generating music to writing articles and producing digital art, AI has become a key player in industries reliant on creativity and intellectual property. As this technology advances, it brings with it significant questions about copyright—particularly in Asia, where diverse legal frameworks, cultural practices, and technological innovation intersect.

In this article, we’ll explore how Asia is addressing copyright in the AI era, examining the legal landscapes of key nations, highlighting challenges, and forecasting the region’s influence on global standards.

The Intersection of AI and Copyright

AI’s ability to produce content has sparked a debate: Can works created by machines truly be copyrighted? If so, who owns the rights? Traditional notions of authorship hinge on human creativity, but AI blurs those lines by operating as both a tool and an independent creator.

This has led to critical questions about copyrightability, the use of copyrighted works for training AI, and the responsibilities of human oversight. The answers are far from uniform, especially in Asia, where the legal and cultural contexts vary widely.

Current Legal Landscapes in Asia

Singapore: Leading the Way

Singapore has emerged as a leader in adapting its copyright laws for AI. The 2021 Singapore Copyright Act introduced a defence for copyright infringement related to machine learning, making it the first Southeast Asian country to do so. This amendment allows businesses to conduct computational analysis using copyrighted material, fostering an AI-friendly environment while maintaining safeguards against misuse. By providing a safe harbour for companies engaging in AI development, Singapore aims to attract global investments in the sector. However, purely AI-generated works remain unprotected, as human authorship is still a requirement for copyright protection.

China: Landmark Rulings

China has taken bold steps to address AI and copyright. In November 2023, the Beijing Internet Court ruled in favour of granting copyright protection to an AI-generated image, provided there was substantial human involvement in its creation. The court emphasised the importance of “intellectual inputs” and “personal expressions,” recognising that the prompts and aesthetic judgments of a human user are key to establishing originality. While this case-by-case approach reflects caution, it also sets a precedent for recognising AI-generated works under certain conditions.

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Japan: A Balancing Act

Japan has adopted a permissive stance regarding the use of copyrighted materials for AI. The revised Copyright Act of 2019 allows for the ingestion of copyrighted works in AI training without requiring permission, provided it serves technological development. This flexibility has spurred AI innovation, but it has also raised concerns among content creators. Recent discussions suggest Japan may impose stricter protections for copyright holders while maintaining its innovation-friendly policies.

South Korea: Prioritising Human Creativity

South Korea has taken a cautious approach, requiring evidence of human thought and emotion to grant copyright for AI-generated works. This policy underscores the importance of preserving human creativity while navigating the ethical and legal implications of AI.

India: Co-Authorship Approach

India is unique in its recognition of co-authorship for AI-generated works. Rather than introducing new laws, the country relies on its existing intellectual property framework, which it considers sufficient to address these challenges. This pragmatic approach allows for flexibility while protecting human contributions.

Other Asian Nations

  • Taiwan: Requires consent or licensing for using copyrighted materials in AI training, considering such activities as “reproduction” under copyright law.
  • Hong Kong: Exploring exceptions to copyright infringement for AI training, similar to Singapore and Japan.
  • Philippines: The Intellectual Property Office of the Philippines (IPOPHL) is working on drafting guidelines for AI-generated artwork, currently, copyrightable works in the Philippines require a “natural person” as the creator.
  • Indonesia: Indonesian Copyright Law is currently silent on the protection of AI-generated works, making the country’s position uncertain. Yet the Directorate General of Intellectual Property (DGIP) in Indonesia has clarified that copyrightable works require a “human touch,” which purely AI-generated works cannot meet.
  • Vietnam: The current Intellectual Property Law in Vietnam does not specifically address AI-generated content that infringes on IP rights. Only human individuals or organisations can hold copyright under Vietnamese law; entities like computers, robots, and AI are not considered copyright holders
  • ASEAN Initiatives: In March 2024, ASEAN released a non-binding Guide to AI Governance and Ethics, encouraging member states to harmonise approaches to AI regulation and intellectual property.

Key Challenges

Copyrightability of AI-Generated Works

The question of whether AI-generated works qualify for copyright protection is at the heart of the debate. Countries like China and Singapore require significant human involvement, while Japan allows for more permissive use in technological development. This divergence highlights the challenge of creating unified standards in a fragmented regulatory environment.

Training Data and Infringement Risks

The use of copyrighted materials for training AI models has raised legal concerns across Asia. While some nations, like Japan, allow this under specific conditions, others are still grappling with how to balance innovation with the rights of content creators.

Balancing Innovation and Protection

Governments face the challenge of fostering AI innovation while safeguarding intellectual property. Striking this balance is critical for ensuring both technological progress and the protection of creators.

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Asia’s Role in Shaping Global Standards

Influencing International Frameworks

Singapore and Japan’s AI-friendly copyright laws provide valuable case studies for other regions. By addressing copyright concerns proactively, these nations are influencing global debates on AI governance. China’s landmark rulings on AI-generated works further contribute to shaping international norms.

Driving AI Innovation

The permissive copyright environments in countries like Singapore and Japan are attracting AI investments and fostering regional innovation. Initiatives like ASEAN’s guide encourage harmonisation, which could create a more cohesive regulatory landscape.

Challenging Traditional Concepts

As countries like China redefine the relationship between human creativity and machine output, traditional notions of authorship and originality are being reexamined. These developments could have far-reaching implications for global intellectual property laws.

Future Outlook

Asia’s diverse approaches to AI and copyright will likely continue to evolve as technology advances. Emerging challenges, such as voice cloning and AI-generated art, will test the limits of current laws and inspire new solutions. By taking the lead in addressing these issues, Asian countries are not only shaping their own futures but also influencing global standards.

For businesses and creators, staying informed about these developments is essential. As the legal landscape becomes increasingly complex, adaptability and awareness will be critical to thriving in this dynamic environment.

Conclusion

Asia is at the forefront of the global conversation on AI and copyright, demonstrating leadership through diverse legal frameworks and innovative policies. By balancing the rights of creators with the need for technological advancement, the region is setting a precedent for how the world can navigate the complexities of AI-driven creativity.

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As this journey unfolds, Asia’s experience will provide valuable insights for shaping a fair and innovative global framework for copyright in the AI era.

Join the Conversation:

What do you think? Should AI-generated works be granted the same copyright protections as human-created content, or does this risk undermining the value of human creativity? What’s your take on how Asia is handling this balance? Leave your thoughts in the comments section below.

Share your thoughts and experiences with AI technologies, and don’t forget to subscribe for updates on AI and AGI developments here. Let’s build a community of tech enthusiasts and stay ahead of the curve together!

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  • Adrian Watkins (Guest Contributor)

    Adrian is an AI, marketing, and technology strategist based in Asia, with over 25 years of experience in the region. Originally from the UK, he has worked with some of the world’s largest tech companies and successfully built and sold several tech businesses. Currently, Adrian leads commercial strategy and negotiations at one of ASEAN’s largest AI companies. Driven by a passion to empower startups and small businesses, he dedicates his spare time to helping them boost performance and efficiency by embracing AI tools. His expertise spans growth and strategy, sales and marketing, go-to-market strategy, AI integration, startup mentoring, and investments. View all posts

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Build Your Own Agentic AI — No Coding Required

Want to build a smart AI agent without coding? Here’s how to use ChatGPT and no-code tools to create your own agentic AI — step by step.

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agentic AI

TL;DR — What You Need to Know About Agentic AI

  • Anyone can now build a powerful AI agent using ChatGPT — no technical skills needed.
  • Tools like Custom GPTs and Make.com make it easy to create agents that do more than chat — they take action.
  • The key is to start with a clear purpose, test it in real-world conditions, and expand as your needs grow.

Anyone Can Build One — And That Includes You

Not too long ago, building a truly capable AI agent felt like something only Silicon Valley engineers could pull off. But the landscape has changed. You don’t need a background in programming or data science anymore — you just need a clear idea of what you want your AI to do, and access to a few easy-to-use tools.

Whether you’re a startup founder looking to automate support, a marketer wanting to build a digital assistant, or simply someone curious about AI, creating your own agent is now well within reach.


What Does ‘Agentic’ Mean, Exactly?

Think of an agentic AI as something far more capable than a standard chatbot. It’s an AI that doesn’t just reply to questions — it can actually do things. That might mean sending emails, pulling information from the web, updating spreadsheets, or interacting with third-party tools and systems.

The difference lies in autonomy. A typical chatbot might respond with a script or FAQ-style answer. An agentic AI, on the other hand, understands the user’s intent, takes appropriate action, and adapts based on ongoing feedback and instructions. It behaves more like a digital team member than a digital toy.


Step 1: Define What You Want It to Do

Before you dive into building anything, it’s important to get crystal clear on what role your agent will play.

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Ask yourself:

  • Who is going to use this agent?
  • What specific tasks should it be responsible for?
  • Are there repetitive processes it can take off your plate?

For instance, if you run an online business, you might want an agent that handles frequently asked questions, helps users track their orders, and flags complex queries for human follow-up. If you’re in consulting, your agent could be designed to book meetings, answer basic service questions, or even pre-qualify leads.

Be practical. Focus on solving one or two real problems. You can always expand its capabilities later.


Step 2: Pick a No-Code Platform to Build On

Now comes the fun part: choosing the right platform. If you’re new to this, I recommend starting with OpenAI’s Custom GPTs — it’s the most accessible option and designed for non-coders.

Custom GPTs allow you to build your own version of ChatGPT by simply describing what you want it to do. No technical setup required. You’ll need a ChatGPT Plus or Team subscription to access this feature, but once inside, the process is remarkably straightforward.

If you’re aiming for more complex automation — such as integrating your agent with email systems, customer databases, or CRMs — you may want to explore other no-code platforms like Make.com (formerly Integromat), Dialogflow, or Bubble.io. These offer visual builders where you can map out flows, connect apps, and define logic — all without needing to write a single line of code.

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Step 3: Use ChatGPT’s Custom GPT Builder

Let’s say you’ve opted for the Custom GPT route — here’s how to get started.

First, log in to your ChatGPT account and select “Explore GPTs” from the sidebar. Click on “Create,” and you’ll be prompted to describe your agent in natural language. That’s it — just describe what the agent should do, how it should behave, and what tone it should take. For example:

“You are a friendly and professional assistant for my online skincare shop. You help customers with questions about product ingredients, delivery options, and how to track their order status.”

Once you’ve set the description, you can go further by uploading reference materials such as product catalogues, FAQs, or policies. These will give your agent deeper knowledge to draw from. You can also choose to enable additional tools like web browsing or code interpretation, depending on your needs.

Then, test it. Interact with your agent just like a customer would. If it stumbles, refine your instructions. Think of it like coaching — the more clearly you guide it, the better the output becomes.


Step 4: Go Further with Visual Builders

If you’re looking to connect your agent to the outside world — such as pulling data from a spreadsheet, triggering a workflow in your CRM, or sending a Slack message — that’s where tools like Make.com come in.

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These platforms allow you to visually design workflows by dragging and dropping different actions and services into a flowchart-style builder. You can set up scenarios like:

  • A user asks the agent, “Where’s my order?”
  • The agent extracts key info (e.g. email or order number)
  • It looks up the order via an API or database
  • It responds with the latest shipping status, all in real time

The experience feels a bit like setting up rules in Zapier, but with more control over logic and branching paths. These platforms open up serious possibilities without requiring a developer on your team.


Step 5: Train It, Test It, Then Launch

Once your agent is built, don’t stop there. Test it with real people — ideally your target users. Watch how they interact with it. Are there questions it can’t answer? Instructions it misinterprets? Fix those, and iterate as you go.

Training doesn’t mean coding — it just means improving the agent’s understanding and behaviour by updating your descriptions, feeding it more examples, or adjusting its structure in the visual builder.

Over time, your agent will become more capable, confident, and useful. Think of it as a digital intern that never sleeps — but needs a bit of initial training to perform well.


Why Build One?

The most obvious reason is time. An AI agent can handle repetitive questions, assist users around the clock, and reduce the strain on your support or operations team.

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But there’s also the strategic edge. As more companies move towards automation and AI-led support, offering a smart, responsive agent isn’t just a nice-to-have — it’s quickly becoming an expectation.

And here’s the kicker: you don’t need a big team or budget to get started. You just need clarity, curiosity, and a bit of time to explore.


Where to Begin

If you’ve got a ChatGPT Plus account, start by building a Custom GPT. You’ll get an immediate sense of what’s possible. Then, if you need more, look at integrating Make.com or another builder that fits your workflow.

The world of agentic AI is no longer reserved for the technically gifted. It’s now open to creators, business owners, educators, and anyone else with a problem to solve and a bit of imagination.


What kind of AI agent would you build — and what would you have it do for you first? Let us know in the comments below!

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Is AI Really Paying Off? CFOs Say ‘Not Yet’

CFOs are struggling with AI monetisation, with many failing to capture its financial value, essential for AI’s success in the boardroom.

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AI monetisation

TL;DR — What You Need to Know:

  • AI monetisation is a priority: Despite AI’s transformative potential, 71% of CFOs say they’re still struggling to make money from it.
  • Traditional pricing is outdated: 68% of tech firms find their legacy pricing models don’t work for AI-driven economies.
  • Boardrooms are getting serious: AI monetisation is now a formal boardroom priority, but the tools to track usage and profitability remain limited.

Global Bean Counters are Struggling to Unlock AI Monetisation, and That’s a Huge Issue

AI is being hailed as the next big thing in business transformation, yet many companies are still struggling to capture its financial value.

A new global study of 614 CFOs conducted by DigitalRoute reveals that nearly three-quarters (71%) of these executives say they are struggling to monetise AI effectively, despite nearly 90% naming it a mission-critical priority for the next five years.

But here’s the kicker: only 29% of companies have a working AI monetisation model. The rest? They’re either experimenting or flying blind.

So, what’s the hold-up? Well, it’s clear: traditional pricing strategies just don’t fit the bill in an AI-driven economy. Over two-thirds (68%) of tech firms say their legacy pricing models are no longer applicable when it comes to AI. And even though AI has moved to the boardroom’s priority list — 64% of CFOs say it’s now a formal focus — many are still unable to track individual AI consumption, making accurate billing, forecasting, and margin analysis a serious challenge.

The concept of an AI “second digital gold rush” has been floating around, with experts like Ari Vanttinen, CMO at DigitalRoute, pointing out that companies are gambling with pricing and profitability without real-time metering and revenue management systems.

This is where the real opportunities lie. Vanttinen’s insight?

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“Every prompt is now a revenue event.”
Ari Vanttinen, CMO at DigitalRoute
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So, businesses that can meter AI consumption at the feature level and align their finance and product teams around shared data will unlock the margins the market expects.

Regional differences are also apparent in the study. Nordic countries are leading in AI implementation but are struggling with profitability. Meanwhile, France and the UK are showing stronger early commercial returns. The US, while leading in AI development, is more cautious when it comes to monetisation at the organisational level.

Here’s the key takeaway for CFOs: AI is a long-term play, but to scale successfully, businesses need to align their product, finance, and revenue teams around usage-based pricing, invest in new revenue management infrastructure, and begin tracking consumption at the feature level from day one.

The clock is ticking — CFOs need to stop treating AI as a cost line and start seeing it as a genuine profit engine.

So, what’s holding your company back from capturing AI’s full value?

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Anthropic’s CEO Just Said the Quiet Part Out Loud — We Don’t Understand How AI Works

Anthropic’s CEO admits we don’t fully understand how AI works — and he wants to build an “MRI for AI” to change that. Here’s what it means for the future of artificial intelligence.

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how AI works

TL;DR — What You Need to Know

  • Anthropic CEO Dario Amodei says AI’s decision-making is still largely a mystery — even to the people building it.
  • His new goal? Create an “MRI for AI” to decode what’s going on inside these models.
  • The admission marks a rare moment of transparency from a major AI lab about the risks of unchecked progress.

Does Anyone Really Know How AI Works?

It’s not often that the head of one of the most important AI companies on the planet openly admits… they don’t know how their technology works. But that’s exactly what Dario Amodei — CEO of Anthropic and former VP of research at OpenAI — just did in a candid and quietly explosive essay.

In it, Amodei lays out the truth: when an AI model makes decisions — say, summarising a financial report or answering a question — we genuinely don’t know why it picks one word over another, or how it decides which facts to include. It’s not that no one’s asking. It’s that no one has cracked it yet.

“This lack of understanding”, he writes, “is essentially unprecedented in the history of technology.”
Dario Amodei, CEO of Anthropic
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Unprecedented and kind of terrifying.

To address it, Amodei has a plan: build a metaphorical “MRI machine” for AI. A way to see what’s happening inside the model as it makes decisions — and ideally, stop anything dangerous before it spirals out of control. Think of it as an AI brain scanner, minus the wires and with a lot more math.

Anthropic’s interest in this isn’t new. The company was born in rebellion — founded in 2021 after Amodei and his sister Daniela left OpenAI over concerns that safety was taking a backseat to profit. Since then, they’ve been championing a more responsible path forward, one that includes not just steering the development of AI but decoding its mysterious inner workings.

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In fact, Anthropic recently ran an internal “red team” challenge — planting a fault in a model and asking others to uncover it. Some teams succeeded, and crucially, some did so using early interpretability tools. That might sound dry, but it’s the AI equivalent of a spy thriller: sabotage, detection, and decoding a black box.

Amodei is clearly betting that the race to smarter AI needs to be matched with a race to understand it — before it gets too far ahead of us. And with artificial general intelligence (AGI) looming on the horizon, this isn’t just a research challenge. It’s a moral one.

Because if powerful AI is going to help shape society, steer economies, and redefine the workplace, shouldn’t we at least understand the thing before we let it drive?

What happens when we unleash tools we barely understand into a world that’s not ready for them?

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