Culture in the Digital Age
Te Ahurea i te Ao Matihiko 

How will technology change the way New Zealanders create, share and protect their stories in 2040 and beyond? 
Ka pēhea te huri a te hangarau matihiko i te āhua o te waihanga, te tiri me te tiaki a ngā tāngata o Aotearoa i ā rātau kōrero ā te tau 2040, ki tua hoki?

Manatū Taonga Ministry for Culture & Heritage

June 2025

Acknowledgments
Ngā kupu whakamihi

Manatū Taonga Ministry for Culture and Heritage would like to acknowledge all those who have been so generous with their time, experiences and insights in helping us to develop this draft Long-term Insights Briefing 2025: Culture in the Digital Age.

In particular, we acknowledge those who have engaged in the topic consultations and content development process thus far. Your contributions have been invaluable in shaping this document and ensuring it reflects the diverse perspectives and expertise within Aotearoa New Zealand’s cultural and creative sectors and across government.

Foreword
Takamua

As Secretary for Culture and Heritage, it is my privilege to present the draft Long-term Insights Briefing (LTIB) 2025: Culture in the Digital Age. This document represents a significant milestone in our ongoing efforts to navigate the profound changes that digital technologies bring to Aotearoa New Zealand’s cultural landscape.

By 2040, our cultural system will be deeply intertwined with artificial intelligence (AI) and other emerging technologies. These advancements offer unprecedented opportunities for creating, sharing and protecting our stories. However, they also introduce significant ethical, legal, cultural and governance challenges that we must address thoughtfully and collaboratively.

This draft briefing builds on the insights from our 2022 LTIB, which highlighted the transformative impact of digital technology on how people engage with, create, and experience arts, culture and heritage. The 2025 LTIB delves deeper into these themes, focusing on the future implications of digital technology through the lens of storytelling. It explores key trends, risks and opportunities, and presents policy options to ensure that our cultural system remains vibrant, inclusive and resilient in the digital age.

I extend my thanks to everyone who contributed to this draft. Your insights and feedback have been invaluable in shaping our understanding of the challenges and opportunities ahead. Special thanks go to the individuals and organisations who participated in our consultations and workshops, shared their expertise and provided thoughtful feedback. Your contributions have enriched this document and will help guide our future actions.

As stewards of New Zealand’s cultural system, Manatū Taonga is committed to ensuring that culture can thrive. The LTIB is a crucial tool in this endeavour, providing a long-term overview that helps us plan and adapt our activities to meet the evolving needs of our society. It aligns with our strategic intentions to foster an inclusive and reflective cultural system, increase access and participation, and build a resilient and sustainable cultural sector.

The insights and policy options presented in this briefing can guide efforts to grow and harness the potential of digital technologies while safeguarding the values and traditions that define us. We will continue to work closely with our partners across government, the cultural and creative industries, and the wider community to ensure that our cultural system remains a source of pride and inspiration for all New Zealanders.

I invite you to engage with the draft LTIB 2025 and join us in this important journey. Together, we can navigate the complexities of the digital age and ensure that our stories continue to be created, shared and protected for generations to come.

Ngā mihi nui,

Leauanae Laulu Mac Leauanae

Tumu Whakarae
Secretary for Culture and Heritage

Executive summary
Whakarāpopoto matua

This draft Long-term Insights Briefing (LTIB) explores how digital technologies may transform the ways New Zealanders create, share and protect stories in 2040 and beyond. It provides strategic foresight to inform future creative and cultural policy, grounded in Te Tiriti o Waitangi the Treaty of Waitangi and focused on stewardship of a cultural system that is inclusive, sustainable and resilient in the digital age.

Contemplating the future

To consider what the future may hold, the briefing reviews a set of transformative emerging digital technologies including generative and agentic artificial intelligence (AI), virtual and extended reality, and decentralised platforms; and external drivers likely to reshape New Zealand’s cultural system by 2040 such as demographic shifts, declining institutional trust, widening digital divides, and accelerating cyber and data risks. This culminates in four exploratory scenarios, which imagine how culture in New Zealand could evolve under different technological, social and geopolitical conditions:

Aotearoa Unfiltered – rapid innovation, little regulation

Hyperlocal Renaissance – decentralised communities and platforms

Digital Guardianship – strong oversight and governance, less creative freedom

A New Digital World – radical technological advancements erase boundaries

Te ao Māori and cultural intellectual property

The draft LTIB recognises the importance of mātauranga Māori, te reo Māori and indigenous governance in shaping how cultural stories are created, managed and protected using emerging technologies. Māori-led innovation, kaitiakitanga and indigenous data sovereignty are critical principles throughout the analysis.

Key insights for the future

Overall, this draft highlights that by 2040, New Zealand’s cultural system will be deeply intertwined with AI and other emerging technologies. While these technologies bring unprecedented opportunities for creating, sharing and protecting stories, they also introduce significant ethical, legal, cultural and governance challenges. Discussion of these issues is presented through a series of insights structured around three central creative and cultural functions:

Create – examines how new digital tools will expand opportunities for cultural and sporting endeavours, reshape the creative workforce, and redefine what creativity means in 2040.

Share – analyses the changing dynamics of content distribution, algorithmic curation and digital inclusion, while highlighting risks related to misinformation and loss of trust.

Protect – considers how we can preserve and safeguard New Zealand’s stories, taonga and cultural heritage, including the issues of digital storage, data sovereignty and authenticity.

Policy levers and options

The briefing identifies a wide range of potential government levers across the ‘Create’, ‘Share’ and ‘Protect’ domains, which could be considered in the future. These include public-private partnerships, workforce transformation and digital literacy initiatives, international cooperation and cultural diplomacy, digital infrastructure and data innovation enablers, legislative and regulatory actions, among others.

Within these levers, the briefing presents ideas for future policy options or tools, such as innovation sandboxes, inclusive digital storytelling platforms, a sovereign New Zealand AI system, future technology apprenticeship programmes, human-made content labels and rights-based frameworks for personalisation and provenance. Each option is assessed for its effectiveness, equity, feasibility, cost-benefit and risk profile.

Conclusion

Based on the insights analysis presented, the LTIB concludes by suggesting that, regardless of which future scenario emerges, any policy approach to address the risks and opportunities of digital technology in New Zealand’s arts, heritage, media and sports sectors will need to consider some key factors, including:

• Balancing regulation with innovation – Supporting effective AI and digital technology oversight without stifling culture and creativity.
• Increasing equity and inclusion – Investing in reaching underrepresented or vulnerable communities (e.g., youth, older people, people with disabilities, Māori, Pacific peoples, migrants, rural and LGBTQ+communities) to ensure all parts of society benefit from technology-driven opportunities.
• Leveraging private investment – Using government investment to attract industry co-investment and enable limited public funds to be maximised and meet New Zealand’s emerging technology aspirations.
• Strengthening cyber and privacy protections – Safeguarding New Zealand’s digital cultural knowledge systems, data, infrastructure and taonga.
• Future-proofing the workforce – Helping AI and digital technologies to enhance (not replace) cultural, creative and sports professionals and practitioners.

What’s next?

The LTIB is intended to stimulate public discussion, inform long-term policy development and support future decision-making about how New Zealand’s arts, heritage, media, and sports and recreation sectors can thrive in a fast-evolving digital world.

This draft briefing will be refined based on public feedback and finalised by the Chief Executive and Secretary for Culture and Heritage at Manatū Taonga, before being presented to the House of Representatives later in 2025.

Introduction
Whakataki

About Manatū Taonga and the Long-term Insights Briefing

Manatū Taonga Ministry for Culture and Heritage (Manatū Taonga) is the steward of Aotearoa New Zealand’s cultural system, supporting the arts, heritage, media, and sport and recreation sectors. Manatū Taonga provides leadership, policy advice and funding to support these sectors to thrive and contribute to the nation’s economy, identity and wellbeing.

About us

Under the Public Service Act 2020, Manatū Taonga is required to produce a Long-term Insights Briefing (LTIB) every three years. The LTIB is a think piece that helps us to plan for the future by considering important issues and trends that may affect New Zealand society, and the related risks and opportunities. It contains information, impartial analysis and policy options for responding to these matters.

LTIBs (Department of the Prime Minister and Cabinet)

Manatū Taonga completed its first LTIB in 2022, considering potential pathways that will influence the vibrancy and resilience of the cultural ecosystem into the future. Five themes emerged as key focus areas for Manatū Taonga to consider over the longer-term: te ao Māori; funding, investment and value; population change; digital technology; and climate change.

Manatū Taonga LTIB 2022

LTIB 2025: Culture in the digital age

The topic: How will technology change the way New Zealanders create, share and protect their stories in 2040 and beyond?

Building on the insights from LTIB 2022, this LTIB focuses on the future implications of digital technology and explores connections with the creative process, New Zealand’s knowledge and information systems, and te ao Māori. This draft includes evidence from research and feedback received during the topic consultation and insights from engagement with subject matter experts. See Appendix 1 for details on the methodology taken to date and limitations.

Linking technology and New Zealanders’ stories

Stories are an essential part of people’s everyday lives, and the cultural and creative sectors play a vital role in creating, sharing and protecting New Zealanders’ stories, both locally and on the global stage.

In this LTIB, the term ‘stories’ takes on diverse meanings. Stories provide an entry point to consider the many ways that people create, share and consume knowledge, information and content. Stories are a way to express creativity, cultures and identities—in words, images, sounds and symbols—across time, place and technology. Stories inform, educate and entertain, and play an important role, for example, in:

• preserving and sharing knowledge and information
• teaching and learning about other cultures and ideas
• fostering and navigating social, political and economic relationships
• developing trust and connections between people, institutions and communities
• establishing shared values and societal norms
• building and expressing individual and collective identities, culture and creativity
• showing the importance of things we value
• protecting heritage and taonga for future generations.

Throughout history, changes in technology have had a profound impact on how people create, share and protect their stories. Consider, for example, the radical transformations brought about by technology—such as the printing press, telephone, airplane, radio, television, cassette, mp3, mobile phone and the internet—on how people create, express and consume information and culture. Consider also who controls, contributes and benefits along the way.

The changing world of digital engagement

How New Zealanders engage with and through digital technologies has changed. Globally, internet access doubled between 2013 and 2023 (Hedges et al., 2023, p. 1), and over 90 percent of New Zealanders have been online for more than 10 years, with internet access in New Zealand growing exponentially from only five percent in 1995 to 96 percent in 2022 (World Bank, 2024). Likewise, the 45 percent of adult New Zealanders connecting to the internet at home via fibre in 2018 grew to 69 percent by 2023 (InternetNZ, 2023).

Increased connectivity and access to digital technology has led to shifts in people’s attitudes, preferences and behaviours. People currently spend more time using digital media like streaming platforms and social media than traditional media such as linear television (NZ On Air, 2023). The majority of New Zealanders spend two to four hours online every day outside of work—nearly half spend most of their time on social media alongside emailing and streaming, while far fewer use the internet for news or education (InternetNZ, 2023). People are also using digital technology to engage in cultural activities in new ways, with digitisation, online streaming and virtual service delivery offering further opportunities for digital participation.

Recent developments—and the pace at which these developments are now occurring—have raised new possibilities for technology to further transform New Zealanders’ lives. Digital technology continues to rapidly evolve, along with how people create, share and protect their stories. These developments hold promise to increase productivity, learning, communication and participation, while also presenting challenges and risks relating to privacy, ethics and security. As such, questions persist about how to harness the opportunities provided by these technologies while safeguarding knowledge, culture and creativity for future generations. This LTIB considers how these issues might impact New Zealand’s future, in 2040 and beyond.

“The current period of technological acceleration we are living in is transformative, impacting every aspect of human life in ways never seen before. Pivotal technologies including the Internet, software and AI, cloud computing, spatial computing, biotechnology, nanotechnology, 3D printing, robotics, renewable energy and aerospace, are all rapidly redefining what it will be like to be human in the future. Ready or not.” – Ben Reid (2024, p. 11)

How to read this briefing

Section 1 highlights emerging trends in digital technology and broader drivers of change that are likely to impact how New Zealanders’ stories are created, shared and protected in 2040 and beyond, and introduces four scenarios for the future.

Section 2 discusses te ao Māori as a cross-cutting lens, highlighting New Zealand’s unique cultural context under Te Tiriti o Waitangi the Treaty of Waitangi, and exploring issues relating to cultural intellectual property and Māori data governance and sovereignty.

Sections 3 to 5 explore emerging issues, risks and opportunities through a series of key insights that have emerged from research and stakeholder engagement. This analysis is organised into three areas:

• Create – evolving creative tools and cultural expression
• Share – transforming content distribution and engagement
• Protect – safeguarding culture and heritage.

Each of these sections also identifies potential future policy levers and a range of ideas for options or tools to guide the responsible adoption of new emerging technologies while safeguarding New Zealanders’ culture and creativity, aligned with the strategic policy framework described below. These options are informed by New Zealand’s constitutional and legislative context including:

• Te Tiriti o Waitangi the Treaty of Waitangi 1840 (upholding responsibilities between the Crown and Māori)
• New Zealand Bill of Rights Act 1990 (freedom of expression, privacy, fair trial)
• Human Rights Act 1993 (protection against discrimination)
• Copyright Act 1994 (intellectual property)
• Public Records Act 2005 (digital preservation)
• Privacy Act 2020 (data sovereignty, digital rights).

It is important to note that in keeping with the statutory requirements and guidelines for the LTIB, this briefing is future focused, looking beyond New Zealand’s current context and reality, and does not prioritise or recommend which actions the current or future government should take, or allocate responsibilities based on existing government portfolios or structures.

The appendices provide further information to support this briefing, including the methodology taken to date and limitations (Appendix 1), policy framework rationale and evaluation criteria (Appendix 2), key drivers of change (Appendix 3), and four future scenarios (Appendix 4).

Strategic framework for policy analysis

To translate the insights identified in this briefing into potential policy options, this analysis draws on the Organisation for Economic Co-operation and Development (OECD) AI Principles (given their digital focus and comprehensive, multi-dimensional lens) and Manatū Taonga strategic outcomes and priorities for the cultural system (given their outcomes-focus and grounding in New Zealand’s unique social and cultural context).

OECD AI Principles

The OECD AI Principles, as presented in the New Zealand Public Service AI Framework (2025), provide a robust basis for assessing whether potential policies are ethical, inclusive, internationally aligned and adaptable to future technological advancements.

New Zealand Public Service AI Framework (Digital.govt)

Inclusive, sustainable development

• Innovation
• Efficiency
• Resilience
• Address inequalities and transition equitably.

Human-centred values

• Privacy and data ethics
• Upholding democracy and the rule of law
• Human and labour rights
• Human oversight.

Transparency and explainability

• Promote awareness and understanding of AI systems and interactions
• Explainable outcomes.

Safety and security

• Safety by design
• Data protection
• Traceable data
• Robust risk management, including national security risks.

Accountability

• Governance
• Regulatory frameworks
• Auditing with human oversight
• Capability growth.

Manatū Taonga strategic outcomes and priorities for the cultural system

Manatū Taonga strategic outcomes and priorities for the cultural system (Ministry for Culture & Heritage, 2024, p. 17) provide a high-level, values-driven framework which can be applied to assess policy options and inform analysis. Applying these strategic outcomes and priorities could help ensure AI and emerging technology policies enhance and protect New Zealand’s cultural identity, creative economy and social cohesion, while balancing global best practice with New Zealand's unique cultural context. See Appendix 2 for further explanation of the strategic framework and policy evaluation criteria.

Section 1: Contemplating the future  
Wāhanga 1: Te whai whakaaro ki te anamata

Futures thinking, often called strategic foresight, is a “structured and systematic approach of exploring plausible futures to anticipate and better prepare for change” (OECD, 2024). Futures thinking can be beneficial to policymakers by helping to identify opportunities, challenges, risks and disruptions that will affect society into the future. It promotes strategic thinking, adaptability and resilience in the face of uncertainty and change. See Appendix 1 for more details on the approach to futures thinking utilised for this LTIB.

To develop this briefing, Manatū Taonga engaged in a range of futures activities to think about how New Zealand society may change in the leadup to 2040 and beyond. The following sections briefly note key technological trends and drivers of change, and present potential scenarios that imagine what the future could hold. Additional information on the trends and drivers, along with the full future scenarios, is presented in Appendices 3 and 4 respectively.

Trends in digital technology

“By 2040 AI will be more refined and accommodating, funnelling our desires and living inside almost everything—our light switches, vehicles, devices, classrooms and offices. […] In the next 15 years one of the things most likely to be lost due to our fascination with deploying AI is oversight, our meta perspective.” – Barry Chudakov (in Rainie & Anderson, 2024, p. 60)

The increasing proliferation of digital technology is effecting change on a global scale. Key technological advancements that have been identified as being highly relevant by subject matter experts for this LTIB [1] include:

Generative AI – Generative AI (GenAI) refers to artificial intelligence that can create new content—such as text, images, music, video or code—based on patterns in existing data. In New Zealand’s cultural system, the use of GenAI offers new tools for creative expression, translation and content production, but also raises critical issues around originality, copyright, cultural appropriation and trust in the authenticity of stories.

Robotic process automation and agentic AI – Robotic process automation automates routine digital tasks, while agentic AI can independently make decisions, take actions and interact with other systems. These technologies are likely to streamline behind-the-scenes operations in cultural institutions, media production and sports analytics, but may also reshape workforce roles and accountability in content creation and curation.

Web3 decentralised platforms – Web3 refers to an emerging version of the internet that uses blockchain and decentralised systems to shift ownership and control away from centralised platforms. In New Zealand’s cultural system, Web3 may enable more equitable distribution of revenue, improved attribution of indigenous intellectual property (IP), and new models for cultural governance and participation in digital environments.

Extended reality and immersive technology – Extended reality (XR), including augmented reality (AR), virtual reality (VR) and mixed reality (MR), enables immersive digital experiences that blend physical and virtual worlds. These technologies are opening up new frontiers in storytelling, museum exhibitions, performing arts, sports broadcasting and education, offering innovative ways to experience and share culture across generations and locations.

Wearable technology – Wearable devices (e.g., smartwatches, biometric trackers and AR glasses) collect data and enable interactive experiences on the move. Wearables are increasingly used in sports performance tracking, live art and music experiences, and personalised cultural tours, while also raising questions about data ownership and ethical use.

Enhanced connectivity – 5G, 6G and Edge computing – The rollout of faster and more reliable internet through 5G and future 6G networks, alongside edge computing that processes data closer to users, will enable real-time, high-quality digital interactions. This enhanced connectivity may have major impacts for rural inclusion, live-streaming cultural events, immersive experiences and equitable access to digital heritage.

Quantum computing – Quantum computing harnesses quantum mechanics to solve complex problems. In the future, these systems could be commercially available, allowing users to model large cultural datasets, optimise creative processes or develop new forms of encryption to protect Māori and community-held knowledge in secure ways that support data sovereignty.

Digital twins – A digital twin is a virtual replica of a physical object, environment or system updated in real time using data. In New Zealand’s cultural system, digital twins may be used to preserve heritage sites, simulate sport performance, recreate taonga for education or enable remote access to museums and archives—while requiring careful governance to ensure authenticity and cultural integrity.

Taken together, digital technology—and particularly GenAI—is rapidly changing the cultural and creative landscape, assisting in the creation and automation of artistic works and content, increasing productivity and efficiency, and providing new tools for people to create, share and protect their stories. And these changes are only beginning. Some experts anticipate that within a few years, the amount of internet content that is at least partially generated or translated by AI will grow to as much as 90 percent (Nicoletti & Bass, 2023). By 2030, virtual spaces like extended reality (XR) could reshape human interactions, with some people spending “more physical conscious time in the metaverse than the real world” (KPMG, 2023). By the early 2030s, 6G is expected to be commercially available along with wearable technology like brain-computer interfaces (StartUS Insights, 2024), and by 2045, some predict that AI will have surpassed human intelligence (“AI scientist Ray”, 2024).

[1] Parts of this section include input from members of the AI Forum of New Zealand which was received during stakeholder engagement activities.

Other key drivers of change

In futures thinking, the term ‘drivers’ refers to factors or forces that will create change. Looking ahead to 2040, there are many possible drivers that may influence the future. Below, eight broad drivers are briefly highlighted that are highly likely to affect New Zealanders’ use of, and relationship with, digital technology, and how stories are created, shared and protected. See Appendix 3 for further information on these drivers of change.

Shifting demographics toward a more diverse and aged population – Overall New Zealand’s population is projected to increase from its current state (5.27 million in 2024) to just over 6 million people by 2040. The population will continue to increase in age and growth will slow. New Zealand’s population will also become more ethnically diverse with higher populations of Māori, Asian and Pacific peoples (Stats NZ, 2022). Creative and cultural sector impacts may include greater need for diverse content and representation in media, and increased pressure on resources for different population groups.

Increasing geopolitical instability and economic shocks – Heightened geopolitical tensions can disrupt international trade, weaken domestic demand and lead to market volatility (Reserve Bank of New Zealand, 2024). The need to reprioritise government investment to respond to economic shocks may impact investment in parts of the cultural system, as resources become more focused on critical services and infrastructure.

More frequent and intense weather events – According to NIWA, by 2040 average national temperatures will increase by between 0.7°C and 1.0°C relative to the 1986-2005 average, and extreme weather events will occur with increasing severity and frequency, placing large populations of people and buildings at risk (NIWA, n.d.). Marae are central to New Zealand’s unique cultural heritage and are particularly vulnerable. Of the almost 800 marae across New Zealand, 80% are built on low-lying coastal land or flood prone rivers (Kowhai, 2022).

Increasing challenges to social cohesion and declining trust in institutions – Declining social cohesion and trust in institutions, including media and government, pose a threat to New Zealand’s social fabric and democracy. While social cohesion remains relatively high in New Zealand, certain indicators such as loneliness, financial insecurity and food insecurity are increasing, while trust and safety in local communities are decreasing (Helen Clark Foundation, 2025). Challenges, including the rise of mis- and disinformation—which have exponentially increased with GenAI—further threaten to undermine trust. Indeed, 65 percent of New Zealanders are extremely or very concerned about misinformation (InternetNZ, 2023).

Widening digital divide – While AI and other novel and emerging digital technologies have the potential to generate policy interventions to address difficult societal problems, they may also exacerbate existing digital inequalities particularly in relation to workplaces and productivity, education and healthcare, and cultural participation. Benefits (and costs) are likely to be unevenly distributed across businesses, economic sectors and population groups. Barriers to accessing digital cultural experiences could increase as the divide widens.

Rapid expansion of ‘Big Tech’ – With the expansion and dominance of large technology companies or ‘Big Tech,’ both governments and communities are increasingly dependent on a handful of corporations, which hold control over global digital technology and infrastructure. This has led to increasing regulatory scrutiny and policy action to address issues such as data privacy, social harm and security, and monopolising digital media platforms—work that is ongoing.

Increasing cybersecurity risks – Cybersecurity is a critical concern among governments, businesses and communities. Despite protective measures, the landscape of cyber-attacks continues to constantly evolve and become more complex, with increasing threats from both criminals and other countries. As cybercrime increases, new approaches are needed to improve New Zealand’s cyber resilience and ensure adequate protections are in place to safeguard important repositories of information and digital assets, including digital taonga and archives.

Accelerating pace of digital innovation – The pace of technological change experienced within this and the next generation may far outpace previous eras, with AI a significant driver in how quickly innovation will occur in future. This is making it harder for policy and legislation to stay current, and for data, digital infrastructure and assets to keep up to date.

Future scenarios

Based on the emerging trends and drivers of change discussed above, the following four exploratory scenarios imagine how culture and storytelling in New Zealand could evolve under different technological, social and geopolitical conditions. These scenarios are summarised below and provided with additional detail in Appendix 4. The AI-generated images included below were developed using Microsoft Copilot with “New Zealand society in 2040” and the scenario text as a prompt. These images—at once surprising, intriguing and problematic—aim to stimulate thinking, inviting readers to consider the future role of AI in shaping how New Zealand and its future communities could be represented.

Scenario 1: Aotearoa Unfiltered

Image

Continuation scenario: By 2040, digital storytelling is the norm. AI tools support language preservation and translation, and blockchain verification tools support increased data sovereignty. However, continued content moderation challenges and AI-generated and deepfake content mean misinformation proliferates and trust has further declined, and IP legislation cannot be enforced. Big Tech continues to dominate most digital platforms, leading to public backlash and a growing Disconnect movement that emphasises human creation and the formation of ‘unplugged’ communities.

Scenario 2: Hyperlocal Renaissance

Image

Collapse and creation scenario: Global crises have led to a fragmented and fragile digital landscape in New Zealand by 2040. The collapse of Big Tech has left digital platforms outdated and unreliable. With limited access to reliable online spaces, New Zealanders turn back to localised, community-based storytelling and in-person cultural experiences, relying on former means like print media and oral transmission. Frequent infrastructure failures make digital sharing challenging and limit reach. Communities of practice reemerge to share the burden of content creation and dissemination and maintenance of physical technology.

Scenario 3: Digital Guardianship

Image

Constraint scenario: By 2040, the government has taken an active role in regulating digital content, enforcing strict measures to ensure online safety, authenticity and integrity. National fact-checking bodies and a government-owned and curated digital platform provide verified information and preserve New Zealand’s history and cultural assets, in partnership with sovereign Māori-owned platforms. However, there are concerns about transparency, freedom of expression and overregulation. Alternative grey-market digital platforms emerge, where creatives can share works and ideas outside of government oversight.

Scenario 4: A New Digital World

Image

Transformation scenario: By 2040, radical advancements have reshaped society in ways previously unimaginable. Immersive, interactive 3D digital spaces, now integrated with brain-computer interfaces with access to AI, enable live performances to be experienced simultaneously in the physical and digital worlds while exceeding previous human capabilities. While concerns about surveillance and transparency and accountability persist, the fusion of technology and storytelling on connected community-owned platforms provides new means for cultural and civic engagement and governance.

Parts of this section include input from members of the AI Forum of New Zealand which was received during stakeholder engagement activities.

Section 2: Te ao Māori and New Zealand’s unique cultural context
Wāhanga 2: Te ao Māori me te horopaki ahurea ahurei o Aotearoa

Considering the future impacts of digital technology on how New Zealanders create, share and protect their stories begins with an acknowledgment of Aotearoa New Zealand’s unique constitutional landscape, as established by Te Tiriti o Waitangi the Treaty of Waitangi. This section reflects on the intersection between the topic of this LTIB and te ao Māori, and provides essential background for the broader insights and policy options presented in the Create, Share and Protect sections that follow.

“My data is a living essence, it tells the story genetically about where I’ve come from, who I am – that’s telling a story of me, a story about my whakapapa, right down to my belief.” – Dickie Farrar (in Te Mana Raraunga, 2025)

Te Tiriti and culture in the digital age

Te Tiriti o Waitangi the Treaty of Waitangi, signed in 1840, is a foundational document for New Zealand, establishing a partnership between Māori and the British Crown. In the digital age, Te Tiriti remains crucial in framing issues related to culture, creativity and te ao Māori. It provides a framework for addressing historical grievances and ensuring Māori perspectives and rights are respected in digital spaces. This includes the protection of Māori data sovereignty, the promotion of te reo Māori online, and the integration of Māori cultural values into digital policies and practices. Te Tiriti continues to guide the evolution of a bicultural digital landscape, and the unique partnership between Māori and the Crown, in a multicultural New Zealand society.

Protecting cultural intellectual property

Global trends reflect a growing recognition of the importance of protecting indigenous traditional knowledge and traditional cultural expressions as intellectual property, with efforts focused on ensuring fair use, preserving cultural heritage and balancing various interests. In the digital space, work has been underway internationally for over two decades. For example, India’s Traditional Knowledge Digital Library (established in 2001) and, more recently, the global Local Contexts and New Zealand-based Digitaonga initiatives offer different approaches which, collectively, aim to establish the ownership and attribution of digital information, knowledge and taonga, as a means to combat misappropriation and commercial exploitation, and support data sovereignty.

Local Contexts

Digitaonga

The World Intellectual Property Organisation (WIPO) Intergovernmental Committee, a specialised agency of the United Nations, is one of the key international forums to focus on establishing an inclusive, balanced and effective intellectual property system. The WIPO Intergovernmental Committee is undertaking text-based negotiations to finalise one or more international legal instruments that would ensure the balanced and effective protection of Indigenous peoples’ traditional knowledge and traditional cultural expressions as intellectual property. The WIPO Treaty on Intellectual Property, Genetic Resources and Associated Traditional Knowledge, the world’s first treaty of this nature, was adopted in May 2024. Once in force, the WIPO Treaty will require patent applicants to disclose the country of origin of the genetic resources and/or the Indigenous peoples or local community providing the associated traditional knowledge, if the claimed inventions are based on genetic resources and/or associated traditional knowledge.

In New Zealand, Te Puni Kōkiri the Ministry of Māori Development is developing bespoke domestic and international settings to address regulatory barriers that hinder the appropriate use of cultural IP and protect from commercial misappropriation. Addressing the regulatory barriers to cultural IP protection would support artists and creatives to obtain premiums from their work, particularly through the export of authentic goods and services. This work feeds into the government’s economic and trade-related priorities. Research, including by the Ministry of Primary Industries Manatū Ahu Matua, shows that there is a strong international demand for cultural-based products. These settings will also support industry in the domestic economy by strengthening accessibility and market share and enable New Zealand to leverage demand and emerging trends relating to willingness to pay premiums for authentic goods and services. To meet this demand, New Zealand will need to enable businesses to appropriately use cultural intellectual property.

Māori digital innovation and leadership

It is well established that “Te Ao Māori has a history of innovation and embracing technology” (Aiko, 2024, p. 37). Examples abound of Māori leading the way in harnessing digital technology to support cultural aspirations and needs, including:

• using social media such as TikTok to share Māori culture with diasporic and global audiences and foster social connections (Loan et al., 2021)
• digitising sound and visual taonga, using 3D scanning and streaming cultural performances to support intergenerational knowledge transmission (Aiko, 2024)
• working with Big Tech to develop tools like the Microsoft Māori keyboard, Google search in Māori and Māori features in Apple Maps to ensure digital tools include and accurately represent Māori language, place names and cultural sites (Whareaitu, 2025; Yates, 2023)
• creating bespoke education apps such as Ngā Motu, a Minecraft: Education Edition game, immersed completely within te ao Māori to increase all students’ cultural capability (Microsoft, 2021)
• using instrumentation, computational modelling and machine learning to record and analyse large collections of cultural artefacts (Opengov, 2021)
• developing trustworthy AI tools such as Te Hiku Media’s automatic speech recognition model and Rongo app to preserve, transcribe and improve the pronunciation of te reo Māori (Lee, 2024; Smale, 2023).

Taken together, these tools are offering new opportunities to support the transmission, revitalisation, repatriation and preservation of mātauranga and taonga, as well as te reo and tikanga Māori, while increasing access to Māori content, both for Māori communities as well as local and international audiences.

At the same time, it is important to acknowledge the limitations of digital representations as being disconnected from time and space. Some people working in heritage and the performing arts have highlighted the importance of physical presence, and how the unique connection to place, community, whakapapa and wairua embedded in Māori taonga cannot be fully achieved in the digital world (Aiko, 2024).

The risks of ‘digital colonisation’

The advent of GenAI, and specifically large language models has created new risks in the misappropriation and exploitation of Māori cultural intellectual property. Māori AI expert Te Taka Keegan notes that AI tools like ChatGPT are already learning from data they should not necessarily have access to, enabling it to engage in cultural activities such as speaking te reo Māori, composing waiata and writing karakia (Yates, 2023). The rise of AI-generated or synthetic content has taken long-standing issues around misappropriation, representation and the need and ability to maintain cultural authenticity into new realms, raising questions around how communities can preserve and verify their cultural content.

Many people are not aware of the terms and conditions they agree to when signing up to social media platforms, enabling their content to be scraped by AI without informed consent. People may be similarly unaware of the personal data retained through other online services such as whakapapa and DNA information held in the databases of genealogy companies (Yates, 2023).

Reliance on tools like ChatGPT to support te reo language learning could lead to a shift in te reo over time, homogenising te reo and erasing differences in dialects between iwi and hapū (Yates, 2023). The same could happen with other aspects of cultural knowledge, including the mātauranga contained and transmitted through storytelling.

“As Māori, we run with different stories, right? For us from Waikato and Maniapoto, Tāwhaki went up to the heavens to get the baskets of knowledge, but in other iwi, it was Tāne who went up. No one’s ever gonna get up and say: ‘You’re wrong’, because that’s the uniqueness and the beauty of being Māori. Being able to be different, have different stories. But Chat GPT says: ‘Nah, this is the one right answer.’ And that’s a colonising and very Pākehā perspective.” – Te Taka Keegan (in Yates, 2023)

Globally, there are further concerns that AI tools will perpetuate or reinforce inequities more broadly experienced in society—because they are learning from content that has been shaped by human bias and reflects the overrepresentation of certain populations in negative socioeconomic conditions, for example in the criminal justice system. In New Zealand, this could create risks that AI-generated forecasts or facial recognition tools could be inherently biased against Māori, making assumptions based on historical trend data that assumes the same overrepresentation should continue into the future.

There are also some concerns around the potential for digital harm and that digital environments such as social media may be creating new intergenerational trauma. As Fala et al. (2023, p. 6) says, “this includes whānau Māori, and their tamariki and rangatahi, exposure to discrimination and racism, misinformation and the lack of protection of personal and collective mātauranga generated on [social media] platforms. Whānau Māori are grappling with historic and compounding issues […] that increases their vulnerability to these challenges and hinders access to the benefits of digitisation”.

Māori data sovereignty and governance

The landmark findings and recommendations of Ko Aotearoa Tēnei by the Waitangi Tribunal (2011) underscored the importance of a partnership model in the governance of Māori cultural IP, and the need for Māori to retain tino rangatiratanga or self-determination over their taonga. Here, looking towards 2040 and beyond, Māori data sovereignty and governance frameworks provide a way forward.

Māori data sovereignty “recognises that Māori data should be subject to Māori governance,” supporting “tribal sovereignty and the realisation of Māori and Iwi aspirations” (Te Mana Raraunga, 2025). As highlighted in the submissions received during public consultation on the topic of this briefing, in order to ensure New Zealanders stories are protected, it is crucial that future AI and data-driven technologies support Māori data sovereignty principles.

Technologies such as AI need to be carefully managed, deployed and regulated in partnership with Māori. This could include, for example, developing a sovereign Aotearoa New Zealand AI that serves as a national innovation tool, preserves and revitalises te reo Māori, and supports the intergenerational transmission of cultural heritage with Māori serving as guardians of their taonga (Taiuru in Windelov et al., 2025).

“Māori voices and perspectives (and all other parts of our society) must be integral to every stage of AI development and implementation, while advocating for robust regulatory frameworks that protect and promote Māori interests.” – Karaitiana Taiuru (in Public Service Association, 2025, p. 24)

For government agencies, the Māori Data Governance Model (see Figure 1), developed by Te Kāhui Raraunga in collaboration with iwi and Crown stakeholders, provides “a framework to guide the system-wide governance of Māori data, consistent with the government’s responsibilities under Te Tiriti o Waitangi” (Te Kāhui Raraunga, 2023), and provides leadership to consider how data governance can follow a values-based approach. This model can help guide consideration of the insights and policy options ahead.

Māori Data Governance Model (Te Kāhui Raraunga)

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Section 3: Create – Evolving creative tools and cultural expression
Wāhanga 3: Waihanga – Ngā utauta auaha me te whakapuaki ahurea e kukune ana

“Can artificial intelligence be creative? And can it make us more creative? Already AI is changing the landscape of innovation and artistic expression.” – Sachin Dev Duggal (2024)

New Zealanders are increasingly using digital tools to create stories across the cultural system, while tools like GenAI are scraping New Zealand content to learn. Concerns over the lack of regulation, potential negative impacts and the lack of technological skills may be preventing creators from harnessing technologies like GenAI more fully. While creativity and technology have always been linked throughout history, in an era where AI is creating with growing autonomy, technological development is calling into question what it means to be creative, and who has the power to create.

Insights for the future

Insight 1: Digital technology will continue to shape New Zealanders’ future stories

In New Zealand, creators are actively using emerging digital technologies to expedite and generate new aspects of their work across the cultural system. For example, research on AI and the music industry in Australia and New Zealand found that 68 percent and 50 percent of companies, respectively, have already implemented AI technologies, with around AU$7 billion invested in AI technologies in both countries (AU$6.4b in Australia and AU$600m in New Zealand; APRA AMCOS, 2024). In the United Kingdom, 84 percent of all content creators are already leveraging AI-powered tools and applications in their content creation process (Epidemic Sound, 2024). Within the screen industry, by 2030, it is estimated that AI could be creating blockbuster films entirely using text-to-video prompts (Nicoletti & Bass, 2023). Other industries that have been early adopters of technology such as AI are also growing rapidly. In New Zealand, the video game industry has had consistent double digit annual growth, with annual exports now exceeding staples like pharmaceuticals, iron, steel and wood pulp (WeCreate, 2025).

Internationally, government policy and investment underscore the important relationship between digital technology and the cultural and creative industries. The United Kingdom and Northern Ireland have both developed programmes to financially support the integration of AI within their creative industries, and Canada, Finland, the United Kingdom, France and Germany have also developed specific industry policies around the adoption of AI. The Australian Government’s Revive: A National Cultural Policy includes significant investment in digital technologies to enhance the discoverability, access, distribution and consumption of Australian creative content (Australian Government, 2023). In New Zealand, the recent draft strategy Amplify: A Creative and Cultural Strategy for New Zealand (Draft) 2024–2030 outlines how the government plans to prioritise support for the creative and cultural sectors, emphasising the use of digital tools to amplify creativity and culture.

In addition to AI, in 2040 and beyond, other emerging and future digital technologies could radically alter how New Zealanders stories are created. Immersive storytelling could transform the experience of history and culture but also risks commercialising or distorting cultural heritage in unintended ways (for example, ways that are contrary to tikanga Māori). Virtual reality could enable fans to ‘participate’ in historic matches or train alongside their favourite athletes or performers. This might enhance engagement but also raises ethical concerns about commercialisation and data privacy.

Creative expression could extend beyond traditional media, with direct brain-to-digital interfaces enabling new forms of performance yet raising bioethical concerns. Advances in genetic and biological computing could enable living art, which breaks the boundary between human and machine.

Athletes could potentially be enhanced to exhibit superior physical abilities. For instance, gene-editing tools such as clustered regularly interspaced short palindromic repeats (CRISPR) technology might be used to enhance physical biological characteristics relating to muscle growth, endurance and recovery. This could lead to athletes who not only perform at unprecedented levels but also embody a form of living art, showcasing the seamless integration of biological enhancements and human capabilities. Such advancements could revolutionise sport, creating performances that are both awe-inspiring and ethically complex (Psatha et al., 2025). The futures scenarios in Appendix 2 explore these possibilities further.

Insight 2: New Zealanders’ stories will continue to shape our future digital tools

Globally, and within New Zealand, cultural and creative content is being used to train AI. GenAI services require large volumes of third-party content to learn, which is ‘scraped’ en masse from internet sources. This includes creative, cultural, media and heritage content—the primary vehicles containing New Zealanders’ stories. Some AI businesses argue that exceptions to copyright protection apply, often through ‘fair use’ principles, though legalities vary between territories and most legislation is not yet equipped to address this kind of use (Farrer&Co, 2024). However, many creators are against this kind of content scraping due to the harms they are experiencing, including reputational damage, economic loss, plagiarism and copyright infringement (Jiang et al., 2023). This conversation is becoming more overt: in February 2025, for example, over 1,000 musicians released the ‘silent album’ Is This What We Want? to protest the UK government’s proposed reform to copyright law, which the artists believe would make it easier for AI companies to scrape copyrighted content without a licence.

There is a risk that AI could privilege certain groups over others due to biases inherent in its training data. Data used by AI systems reflects the cultural, linguistic and social biases of its creators. For example, AI models trained predominantly on Western data may fail to accurately represent indigenous cultures or other diverse perspectives. As a result, AI could contribute to the erasure of these cultures, further reinforcing historical inequalities. AI is also used to digitise and catalogue cultural heritage artifacts. If the training data predominantly represents artifacts from certain cultures, it could similarly lead to biased digitisation efforts. This means that artifacts from underrepresented cultures might not be accurately or comprehensively digitised, leading to a skewed representation of cultural history (Foka & Griffin, 2024).

There are also concerns among New Zealand music creators that AI could lead to cultural appropriation and the marginalisation of indigenous music (APRA AMCOS, 2024). This could result in Māori and Pacific artists being overshadowed by more mainstream genres, perpetuating existing inequalities in the music industry.

AI algorithms used in newsrooms for content creation and curation may also reflect biases present in their training data. For example, if AI systems are trained on data that predominantly features certain viewpoints or demographics, they might prioritise similar content, marginalising diverse perspectives. Some New Zealand news publications have already faced criticism for using AI to create editorials, raising concerns about the potential for biased and homogenised news content (Peacock, 2024).

Insight 3: The use of digital technology will significantly impact the cultural and creative workforce

Creators have mixed views on the rise of AI within the cultural and creative industries. A recent study found that while over half of music creators surveyed agree that AI technology can assist in the human creative process, 82 percent are concerned they will lose the ability to make a living from their work due to AI (APRA AMCOS, 2024).

These fears are not unfounded. The use of AI-generated content is already disrupting creatives’ ability to earn a living wage, with creatives from marginalised communities disproportionately impacted. In the United Kingdom, 58 percent of creators have recently faced challenges monetising their content (Epidemic Sound, 2024). By 2028, it is projected that in the music industry alone, 23 percent of music creators’ revenues will be at risk in Australia and New Zealand due to GenAI, an estimated cumulative total damage of AU$519 million (APRA AMCOS, 2024).

These impacts are likely to occur across the breadth of the cultural system. AI-driven storytelling could automate sports and cultural reporting, potentially replacing human journalists and altering the depth and nuance of storytelling. In media, as AI-generated content increases, legacy media (radio, newspapers and television) may struggle to reach audiences and remain relevant, leading to reduced public interest journalism and local storytelling.

Conversely, GenAI could also have positive impacts through increasing productivity, potentially freeing creators from the more routine and mundane aspects, for example, of formatting, editing, creating virtual environments, model generation, or other business or administration tasks (APRA AMCOS, 2024; Duggal, 2024). Notably, many of the routine tasks that are increasingly being delegated to AI are those that have traditionally been deemed entry level and allocated to early career professionals as they develop skills and learn the industry. While tasks requiring creative decision-making and management are, at least for now, largely remaining human-centred (and allocated to more senior professionals). In future, this too could change. The change calls into question what creative learning and career pathways will look like in 2040, and what career and skills development will be required to succeed in the digital age.

New skills may be needed to support creators to harness the power of emerging digital technologies. Creators currently have varying degrees of technological capability. According to research on the creative workforce in Australia, more than half of organisations in the performing arts, visual arts, music and publishing report increased needs for skills relating to digitalisation, social media, digital marketing, web development, data analytics and cybersecurity, privacy and data handling. In contrast, “in the digital games sector, where technology is central to business, three quarters of businesses surveyed reported that they do not have a need for new skills relating to digitalisation or AI (75 percent)” (Creative Australia, 2025, p. 67).

New skills are required for supporting young New Zealanders in particular. Programmes such as Tech21-23 and Digital Passport are providing experiences and training in digital technologies, preparing students for future careers in technology (Digital Passport, 2025; Technz, n.d.). The integration of AI and digital storytelling tools in education could help students develop the skills needed to navigate and contribute to a decentralised storytelling landscape. Future challenges include ensuring equitable access to these technologies and fostering creativity, critical thinking, and digital ethics among young New Zealanders.

AI is disrupting news media by diverting traffic, replicating journalistic content and accelerating the decline of an already fragile industry. Traditional news media business models are under intense pressure as GenAI tools and search engines increasingly summarise news directly within search results, reducing click-throughs to original news sites. For example, Google’s AI Overviews and other large language models like ChatGPT are now providing instant, paraphrased answers drawn from journalistic content, often without adequate attribution or revenue sharing. This shift has contributed to a sharp drop in site traffic and advertising income for news outlets. Industry leaders and press freedom advocates have warned that this pattern amounts to systemic content appropriation, undermining the economic sustainability of public interest journalism (Spyridou & Ioannou, 2025).

During engagement with sector stakeholders for this LTIB, one New Zealand news publication noted it had lost 200,000 website hits per day over an eight-week period in early 2025 as internet search engines such as Google started providing AI-generated answers to search questions. This has seen people no longer clicking through links to content and accepting the AI-generated answer at face value. The same publication anticipates losing all website and social media traffic directed from internet search queries in the next six to 18 months, while at the same time large language models continue to learn from scaping their content.

Insight 4: By 2040, the concept of creativity will have changed

Digital tools are changing access to the creative process, enabling high quality independent content creation and production. These tools can help individuals and communities to assert control over their cultural narratives, knowledge and artistic expressions (Handzic & Ismajloska, 2019; Shepherd, 2024). Over time, these tools have become more accessible, at lower costs and requiring fewer specialised skills to operate. By enabling creatives to complete business, design and production tasks that traditionally would have required collaboration with other specialist professionals, digital tools have democratised the production process, and supported the rise of self-publishing in music, literature and audiovisual content. However, some people also argue that rather than democratising the creative industries, digital technology is ‘plutocratising’ them—creating an imbalance of power and widening digital disparities.

GenAI offers even greater opportunities, further making the concept of creativity accessible by enabling people who have not previously viewed themselves as ‘creative’ to generate creative works by harnessing technological as opposed to technical skills.

In other applications, GenAI tools could allow fans to create personalised sports highlights or fictionalised accounts of famous games, gigs or cultural performances. While this facilitates storytelling, it risks blurring the lines between fact and fiction, potentially undermining trust in official sports and cultural narratives.

While GenAI offers new opportunities, it is also calling into question what it means to be a creator. Projects like The Last Screenwriter, the first feature film written entirely by AI, and the commercial success of artworks by Ai-Da, the world’s first ultra-realistic humanoid robot artist (Pope, 2024), are calling into question what it means to be a creator, and the boundaries between human and technological creativity. Some argue that art is a uniquely human activity that requires the capacity for human emotion and experience, and that “at best, the output of image generators is aesthetic, in that it can be appreciated or enjoyed, but it is not artistic or art itself” (Jiang et al., 2023, p. 365). On the other hand, in Ai-Da’s case for example, her creators argue Ai-Da’s work is art, reflecting the integration of technology in today’s society, and ‘creative’ under academic definitions which require works to be “new, surprising and of cultural value” (University of Plymouth, n.d., para 13).

The Last Screenwriter

Potential policy levers

To address the risks and opportunities explored above, future policy professionals may want to consider a range of policy approaches spanning across various government levers including:

• digital infrastructure and data innovation enablers
• workforce transformation initiatives
• legislative and regulatory actions
• funding and incentives
• public-private partnerships and industry collaboration
• international cooperation and cultural diplomacy.

The following section provides a range of ideas for potential options or tools that could be considered within these levers, along with their strengths and weaknesses based on the evaluation criteria (see Appendix 2), and current international examples.

Note in keeping with guidance for the LTIB, this section does not prioritise or recommend which actions the current or future government should take, or allocate responsibilities based on existing government portfolios or structures.

Digital infrastructure and data innovation enablers

Option 1: Creative and cultural data sandboxes where creators, technologists, iwi, archivists and cultural institutions could safely access and experiment with curated cultural, heritage and creative data to develop, test and prototype AI, immersive and interactive tools

Effectiveness: Could create a network of secure, ethical sandbox environments (safe, exploratory spaces where people could test and experiment in a controlled and low-risk way). This could directly enable the ethical development of AI and digital tools using real cultural datasets, while safeguarding rights and encouraging innovation.

Equity: If co-designed with iwi, disability advocates and underrepresented communities, it would empower those groups to shape how their stories and data are used.

Feasibility: Moderate feasibility – would require technical infrastructure, cross-agency coordination, and strong governance frameworks, but would align well with existing digital archiving efforts.

Cost vs. Benefit: High cost – initial investment in infrastructure and data stewardship would be significant but would yield long-term gains in innovation, trust and sector capacity-building.

Risks & Mitigation: Risk of cultural misappropriation or data misuse; could be mitigated through Te Tiriti-based co-design, indigenous data sovereignty principles and robust access controls.

International examples: 

• Scotland – Creative Informantics
  Data-Driven Innovation Hubs are a Scottish Government and University of Edinburgh initiative to provide innovation sandboxes and funding for creatives to access datasets from cultural institutions and experiment with AI, machine learning and immersive technologies.
• Europe – Europeana is digital research and development community that allows technologists, developers and researchers to access cultural heritage data (images, metadata, documents, audiovisual material) from across Europe to experiment, prototype and build digital tools.

Option 2: A sovereign New Zealand AI system could provide publicly governed digital infrastructure designed to serve national innovation goals while embedding equity, Te Tiriti obligations and cultural values at its core

Effectiveness: Could support inclusive digital innovation, strengthen national capacity and help counter harmful biases in imported systems. Could be integrated into public services, cultural institutions and education, helping to create equitable digital systems by design.

Equity: Could support the preservation and revitalisation of te reo Māori through high-quality, Māori-led natural language processing models. Would provide a trusted national AI foundation model as an alternative to extractive or inappropriate foreign technologies.

Feasibility: Moderate feasibility – technically complex and resource intensive, requiring sustained investment, capability building, and access to infrastructure.

Cost vs. Benefit: High up-front costs but long-term national value through AI sovereignty, capability development, cultural transmission and trusted public services.

Risks & Mitigation: Risk of under-delivery without clear leadership or Māori co-design; could mitigate through Te Tiriti-based governance, staged implementation and partnerships with local AI talent.

International examples: 

• Norway – Sámi Language Technology Programme (Giellatekno) at the University of Tromsø is a long-standing project creating AI-supported tools to support Sámi autonomy in digital systems.
• Finland – Finland has invested in developing sovereign, open-source language models such as FinBERT and FinGPT to ensure that Finnish and Swedish languages—and culturally specific nuances—are represented in digital systems. These models are publicly funded and used in government, research and education.
• Taiwan– Taiwan has invested in building its own AI cloud infrastructure to support sovereign, ethical AI development. In 2023, the government initiated the Trustworthy AI Dialogue Engine (TAIDE) project to produce a GenAI chatbot with Taiwanese features and traditional Chinese character support.

Workforce transformation and innovation initiatives

Option 3: A future technology apprenticeship programme could invest in hands-on AI, blockchain and Web3 training, including for young creatives, media and sports professionals

Effectiveness: Could serve to futureproof the workforce in AI-driven and emerging technology-impacted industries, boosting skills development and fostering innovation.

Equity: Apprenticeships can help provide equitable access to high-demand technology skills, especially for underrepresented groups, and paid training could reduce the financial barriers to education. May help to expand access to digital careers, particularly for young and emerging talent.

Feasibility: High feasibility with industry and education partnerships. Apprenticeship programmes have been successfully implemented in various industries and could be adapted for technology. Existing frameworks and support from industry partners could facilitate their establishment.

Cost vs. Benefit: High initial cost, but long-term economic and social gains. Investing in apprenticeships could lead to a highly skilled workforce, reducing long-term recruitment and training costs for employers, and stimulating wider economic growth.

Risks & Mitigation: Risk of exclusion of small businesses, and rapid changes in technology could render some skills obsolete quickly; could mitigate by ensuring funding support for subject matter experts and regional hubs, and regularly updating the curriculum to reflect the latest technological advancements.

International examples: 

• Germany – AI workforce development strategy includes AI training programmes for creative and media professionals.
• The Republic of Korea – The Republic of Korea's approach to future technology apprenticeships combines university and industry collaboration, vocational training and government support to prepare the workforce for the digital age.

Option 4: A national ‘connecting creators and coders’ innovation initiative could match creatives and cultural practitioners with AI researchers, technologists and data scientists to develop culturally and commercially valuable digital and AI tools

Effectiveness: Would directly support creative innovation using AI, build cross-sector capability and generate culturally resonant tools that reflect New Zealand’s unique identity. A programme could grow short-term, high-impact collaborations (for example, three to 12 months), support Māori-led digital innovation, and incubate scalable prototypes with potential public benefit and/or commercial applications.

Equity: Has the potential to empower Māori, Pacific peoples and underrepresented creatives if programme design includes culturally driven practices and targeted outreach.

Feasibility: Moderate feasibility – would require strong cross-sector coordination, but could leverage existing institutions (for example, universities, creative technology incubators, Te Māngai Pāho).

Cost vs. Benefit: High cost – moderate public investment could yield valuable IP, cultural tools and new business models for the creative sector; potential to attract private funding.

Risks & Mitigation: Risk of extractive dynamics or loss of cultural IP; could mitigate through robust governance frameworks, Te Tiriti compliance and indigenous data sovereignty policies.

International examples: 

• Singapore – AI Singapore 100 Experiments (100E) programme is a national initiative that funds and supports collaborations between industry and AI researchers to co-develop AI-powered solutions for real-world problems, accelerating innovation and commercialisation across sectors.

Option 5: A national information campaign on AI and cultural storytelling could educate artists, media professionals and communities on digital rights, privacy and ethical AI use

Effectiveness: Would enhance digital literacy and support informed participation in AI-driven cultural production.

Equity: Would benefit all creatives, especially those unaware of AI risks and rights.

Feasibility: High feasibility – could be integrated into existing media and arts funding and education programmes.

Cost vs. Benefit: Low-cost relative to long-term benefits in AI ethics awareness and digital literacy.

Risks & Mitigation: Risk of low engagement; could mitigate by using interactive formats and partnering with iwi and local media for targeted outreach.

International examples: 

• Canada – Digital Literacy and Indigenous Storytelling Initiatives – the Canadian Government has funded multiple digital literacy and indigenous cultural projects e.g., initiatives under Canada’s Digital Technology Cluster, Digital Literacy Exchange Program and Indigenous media program
• Australia – Australia’s “Artificial Intelligence: Australia’s Ethics Framework” provides guidance on public communication and ethical use of AI.

Legislative and regulatory actions

Option 6: A new regulatory regime could explicitly regulate AI-generated cultural and creative content, Web3-authenticated creative works and cultural IP protection in the virtual environments

Effectiveness: Could protect cultural creators in the AI-driven digital economy, enabling authorship attribution and fair use protections.

Equity: May help facilitate fair compensation for artists and creatives, and that creative and cultural professionals retain rights over AI-generated adaptations of their work.

Feasibility: Moderate feasibility – legislative amendments may be manageable, but copyright and IP law updates would be complex.

Cost vs. Benefit: Legal costs but long-term economic protection for creators and cultural industries.

Risks & Mitigation: Risk of enforcement challenges and AI industry pushback; could mitigate by developing smart contract-based copyright tracking and/or AI licensing models that balance innovation with creator rights.

International examples: 

• United States – The U.S. Copyright Office is considering new regulations for AI-generated content and digital likeness protections.
• Republic of Korea – ‘Cultural Korea 2035’ establishes new copyright protection and utilisation standards for AI-generated works and guarantees fair compensation for original creators.
• Canada – Canada is moving towards recognising indigenous data and cultural IP under co-developed laws.
• WIPO (World Intellectual Property Organisation) – WIPO (World Intellectual Property Organisation) is actively exploring models for AI and traditional knowledge protection.

Option 7: Expanding privacy regulation could cover quantum-safe encryption for digital assets, AI-driven fraud and deepfake detection systems

Effectiveness: Expanded provisions could strengthen digital resilience of cultural and sports data increasing long-term security.

Equity: Would protect individuals and cultural institutions from cyber threats. By addressing AI-driven fraud and deepfake detection, the measure may protect vulnerable groups from exploitation.

Feasibility: Moderate feasibility – quantum encryption is emerging but expensive. AI-driven fraud detection is rapidly advancing, making implementation increasingly feasible.

Cost vs. Benefit: High cost, but important for cybersecurity in the digital age. May prevent costly data breaches and fraud incidents, leading to long-term savings.

Risks & Mitigation: Risk of slow adoption and resistance from digital platforms; could mitigate through mandating compliance for government contracts or provide incentives for early adoption.

International examples: 

• United States –United States regulators have been looking at developing stronger protections against AI-driven fraud and deepfake misinformation.
• The United Kingdom – The United Kingdom is preparing quantum-safe encryption protocols to protect digital assets.

Funding and incentives

Option 8: A cultural technology investment fund could prioritise community-led technology projects, and new and emerging storytelling platforms

Effectiveness: Would stimulate cultural technology innovation and community-led projects. Prioritising locally led initiatives could help ensure that projects are relevant to local communities.

Equity: Could benefit local, regional and small creative enterprises, and empower communities to take charge of their cultural and creative narratives.

Feasibility: High feasibility – aligns with global trends in creative technology funding. Strong community interest and support could facilitate the establishment and success of the fund.

Cost vs. Benefit: High potential return on investment but would require long-term funding commitments. Successful projects may stimulate local economies, create job opportunities and increase tourism and cultural participation.

Risks & Mitigation: Risk of corporate capture, or that some projects may fail to achieve their objectives, leading to wasted resources; could mitigate by setting quotas for regional and small-scale participation, and implementing a rigorous evaluation process for project proposals to help ensure that only viable and impactful projects receive funding.

International examples: 

• Singapore –Singapore’s AI & Digital Innovation Grants fund digital innovation projects across sectors, including for projects that demonstrate adoption of AI.
• The European Union – Funded European Institute of Innovation and Technology for the Cultural and Creative Sectors and Industries co-finances long-term innovation projects with the aim of accelerating competitive and resilient entrepreneurship.

Option 9: Tax incentives for private investment in sustainable technology, including AI-assisted sports analytics, immersive museum experiences and virtual and augmented reality-based arts and cultural exhibitions could encourage growth and innovation

Effectiveness: Would encourage AI innovation in creative and sports industries while supporting sustainability.

Equity: Would benefit investors and industry players; may not directly benefit grassroots creatives or small businesses.

Feasibility: High feasibility – tax incentives are a common policy tool with broad political support.

Cost vs. Benefit: Reduces government tax revenue but may attract private investment and economic growth.

Risks & Mitigation: Risk of large corporations dominating the funding; could mitigate by ensuring tax incentives prioritise locally owned and/or small and diverse businesses.

International examples: 

• The United Kingdom – Creative Industry Tax Reliefs provide tax incentives for film, TV, theatre, and games, and is often cited as a model for supporting creative technology investment.
• Singapore – Singapore’s Government Grants for Digital Innovation. Singapore has an active policy environment for AI and digital innovation, with tax incentives and grants that support sustainable technology in culture and sports.

Public-private partnerships and industry collaboration

Option 10: Public-private partnerships for smart cities and districts could integrate wireless connectivity, digital innovation hubs and blockchain-secured public records for digital heritage

Effectiveness: Would create digital hubs for cultural innovation and immersive storytelling. Public–private partnerships would leverage private sector expertise and resources to develop advanced infrastructure.

Equity: Would help to ensure urban and rural communities benefit from digital infrastructure. Engaging local communities in the development process would help projects to meet diverse needs and priorities.

Feasibility: High feasibility with private sector backing. Public–private partnerships have been successfully implemented in various cities worldwide, providing a feasible model for smart city development.

Cost vs. Benefit: Requires investment but could attract tourism and technology partnerships. Would allow for the sharing of costs between public and private entities, reducing the financial burden on the public sector.

Risks & Mitigation: Unequal access and privacy risks; could mitigate by ensuring regional distribution of funding and transparent data handling practices.

International examples: Public–private partnerships are used to develop smart urban districts with advanced digital infrastructure in various jurisdictions including Finland, the United States and Canada.

Option 11: An ‘AI for success’ initiative could fund ethical AI applications (for example in audience engagement and performance analytics) in line with human rights and privacy laws

Effectiveness: Could encourage responsible AI adoption in sports and performing arts and provide guidance on ethical safeguards.

Equity: Would benefit elite athletes and performers but may not immediately benefit community-level sports and cultural groups.

Feasibility: High feasibility with government and industry collaboration.

Cost vs. Benefit: High research and development costs but long-term benefits in sports and performing arts innovation.

Risks & Mitigation: Privacy risks with AI-driven athlete and performer tracking; could mitigate by mandating athlete and artist consent and transparency in AI analytics.

International examples: 

• The United States – United States Senate hearings have featured discussions on AI, biometric data and privacy (Hendrix, 2024).
• European Union – The European Union AI Act establishes a comprehensive regulatory framework across sectors, including provisions relevant to sports analytics and performance monitoring.

International cooperation and cultural diplomacy

Option 12: A creative export strategy could guide investment in creative trade and help to forge cooperation agreements with global partners and platforms to boost New Zealand’s creative exports

Effectiveness: Would support innovation across the creative sector; a focus on “weightless” digital exports may help to grow the economy and international trade.

Equity: Investment in cultural and creative exports would showcase the uniqueness and global appeal of New Zealand culture, and particularly the contribution of Māori culture.

Feasibility: High feasibility – Boosting exports could generate inward investment, increasing international recognition and soft power.

Cost vs. Benefit: The creative sector already attracts some private investment; government co-ordination in investment could unlock a range of additional creative exports.

Risks & Mitigation: Risks of some firms not developing viable creative products; global downturn would lead to fewer buyers for creative exports; could mitigate by a multi-year commitment to investment in the creative sector that is periodically reviewed against success criteria.

International examples: 

• The United Kingdom – The United Kingdom has developed an International Strategy for the Creative Industries Putting the United Kingdom’s Creative Industries Centre Stage
• Republic of Korea – the Ministry of Culture, Sports and Tourism coordinated government bodies working in the creative industries and invested several trillion won to ensure their success, leading to the worldwide popularity of Korean pop (K-pop) bands such as BTS, TV shows like Squid Game and the Oscar-winning film Parasite (UN Trade & Development, 2024). K-pop blueprint: Drawing inspiration from South Korea's creative industries | UN Trade and Development (UNCTAD)

Section 4: Share – Transforming content distribution and engagement
Wāhanga 4: Tiri – Te whakaumu i te tuaritanga ihirangi me te whai wāhi

New Zealand stands at a pivotal moment as digital technologies transform how stories are shared. While the opportunities for immersive, personalised and widely accessible narratives are immense, the evolving landscape of emerging technologies also poses challenges such as dis- and misinformation, cultural misrepresentation and inequitable access.

By addressing these challenges head-on, New Zealanders can help enable their unique stories to be shared in ways that are both innovative and true to their cultural identity—leading to a vibrant digital future in 2040 and beyond.

Insights for the future

Insight 5: New Zealanders’ future stories will be shared faster and more widely

New technologies for content distribution and engagement are reshaping how stories are shared and consumed, ushering in a new era of digital distribution. The proliferation of portable devices, for example, are making diverse content available at speed and Web3 platforms are redefining how digital cultural and creative content is shared, and who owns and monetises that content. Sporting events, concerts and theatre performances may increasingly shift to digital spaces, creating new possibilities for cultural connection. AI bots and virtual influencers may displace human interaction, altering the nature of fan communities and engagement.

On the other hand, AI-enhanced storytelling could expand reach, making local New Zealand stories more accessible to global audiences. Next-generation digital infrastructure (5G, satellite internet) could also broaden participation in cultural and sporting activities, reaching rural and remote communities.In New Zealand, the adoption of smart streaming devices is on the rise, including video game consoles, smart monitors and projectors. These devices are increasingly being used to access local content, reflecting a growing demand for user-friendly interfaces and high-quality local programming. For instance, platforms like TVNZ OnDemand and Māori Television have developed applications (apps) that are compatible with these devices, allowing users to stream local shows, movies and cultural content directly to their screens (Statista Market Insights, 2024).

As digital technologies revolutionise content distribution and engagement, the importance of training AI with data that reflects New Zealand’s unique culture and stories becomes paramount. However, this task is fraught with complexities and sensitivities. AI models trained on global datasets may not adequately capture the nuances of New Zealand's diverse cultural heritage, including Māori and Pacific narratives. To address this, fine-tuning existing AI models with locally sourced data could help AI systems better reflect and respect New Zealand’s cultural context. This process involves curating datasets that accurately represent the country’s rich cultural tapestry and continuously updating the models to incorporate new cultural insights (Barnes et al., 2024; Kulesz, 2024).

Insight 6: New Zealanders’ ability to share and consume stories in the future will be determined by their access to digital technology

“If people or communities cannot join in digitally, they can lose access to opportunities and services. This can contribute to cycles of exclusion from the economy and society.” – Te Rautaki Matihiko mō Aotearoa The Digital Strategy for Aotearoa (2022, p. 12)

As digital platforms become the primary medium for storytelling, those without adequate access to these technologies risk being left behind. New Zealanders’ varying levels of access to shared content and stories is influenced by factors such as distribution and availability of affordable, high-speed internet, devices and digital literacy. The inclusion and visibility of underrepresented languages and cultures in digital platforms also impacts access and engagement in digital storytelling and global participation (UNESCO, 2024).

Digital literacy and access to technology is essential in ensuring that all New Zealanders can participate in and benefit from these new forms of storytelling, and for preserving and promoting diverse cultural heritage. Initiatives like Digital Story Telling Aotearoa support educators to use digital tools to bring local curricula to life, offering a means to share New Zealand’s rich cultural narratives (Education Gazette, 2022).

Without access to digital technology, many communities—particularly those who have traditionally lacked access, including people in rural or underserved areas, Māori and disabled people—may struggle to maintain and share their cultural stories, leading to a loss of cultural identity and heritage over time.

Insight 7: Algorithms are becoming the content curators of the future

“Today, algorithms influence everything from the news and entertainment we consume on social media to more significant life decisions like job selections and loan approvals. Their reach has extended into every corner of our lives, silently but powerfully shaping our choices, opportunities, and perceptions. The decisions made by these algorithms can determine the information we see online, influence our purchasing behaviours, and even affect our social interactions. The algorithms behind these systems are not mere lines of code but powerful arbiters of content and opportunities.” – Devan J. Walton (2024, p. 134-135)

In an era where an unprecedented amount of content is being generated, AI algorithms act as gatekeepers to the stories being shared. Algorithmic tools are being used to help users navigate digital content online, including news and social media, by filtering and recommending content based on past behaviour. For example, AI software is now being used to forecast ‘hits’ in the music industry, with companies arguing that human brains respond to particular sound patterns. ‘Music Digital Nuance Analysis DNA’ tools claim AI can learn what has succeeded or failed in the past to forecast successful music for the future (AI & Music Survey Results APRA AMCOS 2024, p. 47). There is a risk that these tools could lead to further cultural homogeneity, domination by corporations paying for featured spots in users’ feeds, and the perpetuation of biases against particular groups, including women, who have been traditionally marginalised in the music industry.

Algorithmic tools are also increasingly deployed to moderate content at scale by the major platforms, including to identify copyright infringement and instances of online harm, and to identify or remove content that could be controversial to online advertisers (and thus not commercially viable). People have differing levels of awareness and attitudes towards algorithms, and many do not understand how their choices are being further shaped (Zarouali et al., 2021).

Audiences now encounter news, advertisements and branded content as part of an overwhelming digital mix. The sheer volume of AI-generated content makes it challenging to distinguish between fact and fabrication, undermining trust in media. This constant ‘noise’ complicates both the verification of information and the ability for stories to break through and be seen. AI-driven recommendation systems on platforms like Facebook, YouTube, and TikTok—which provide over 50 percent of the news people consume globally (Pew Research, 2024)—may narrow the diversity of content exposure, prioritising mainstream trends over niche or indigenous stories. This means that what is discovered is often not what is the most important, diverse or locally relevant, but what best serves the platform’s engagement goals.

There is a risk that even national-level stories may not be seen, let alone regional or local stories, given New Zealand’s size and scale. AI algorithms tend to prioritise content that generates the most engagement, which often means that smaller, local stories are overshadowed by more sensational or widely appealing content (Caro, 2024; Simon, 2024). Further, with advancements in AI, audiences can now choose their storytellers, such as AI bots, virtual influencers or commentators. This personalisation allows users to consistently hear from preferred sources, but it also risks creating echo chambers where only certain perspectives are amplified (Du, 2024).

Within New Zealand, there is a risk that the narratives of groups who have traditionally faced exclusion, including Māori, ethnic minorities and people with disabilities, may be misrepresented or lost amid AI-generated and moderated content.There is a need to balance commercial pressures with the preservation of diverse cultural narratives, ensuring that New Zealand local content is prominent and independent creators are not overshadowed by corporate interests or generic content algorithms.

Ensuring transparency in how content is produced and curated is crucial for building trust in the stories being shared. Recent initiatives attempting to improve algorithmic transparency, such as AI content labels and explainability audits, could help consumers discern genuine storytelling from manipulated content. Without deliberate interventions, algorithmic prominence risks reinforcing existing inequalities and narrowing the range and reliability of stories that shape public life.

Insight 8: By 2040, we won’t be able to tell which stories are real

“What happens when someone, somewhere, for some reason, chooses to decide what’s real and what isn’t? Who controls the truth? Who owns reality?” – Guido Melo (2025)

Applications such as deepfake technology, and the increasing use of AI-generated content in mis- and disinformation, are making it harder for people to tell what is real and exacerbating existing socioeconomic inequalities (Capraro et al., 2024). At a talk in June 2024, tech billionaire and founder of Twitter Jack Dorsey warned that in the next five to 10 years, people will be unable to distinguish what’s real and what’s fake (Shibu, 2024). Already, recent evidence has shown that people can no longer distinguish AI-generated faces from human faces (Miller et al., 2023).

Deepfakes are also an issue within music and screen production. There are concerns about AI creators using voice and image cloning to mimic performers for financial gain, which puts the actual performers at financial and reputational risk (APRA AMCOS, 2024). Some artists worry they could be displaced by digital replicas generated by AI.

Trusting New Zealanders’ stories is vital to social cohesion and democracy. As the “glue” that binds societies together, trust is an important component of social cohesion in that it provides solidarity and a common shared identity within social, work, community and national networks. A strong cultural identity fosters social cohesion and trust within communities. This social capital can lead to more robust economic networks and opportunities for collaboration and innovation.

Public media institutions play a central role in telling stories about the values and connections within society, especially in a democracy. They also provide a platform for diverse voices, ensuring that national, regional and local stories are heard and valued. By offering unbiased and comprehensive coverage, public media help to build public confidence and trust in democratic institutions (Farrel & James, 2024). This is particularly important in the digital age, where the sheer volume of AI-generated content can make it challenging to distinguish between fact and fabrication. Public media institutions can act as a counterbalance to this ‘noise,’ providing verified information and fostering an informed citizenry.

Trusting New Zealand stories also has economic implications. At an international level, New Zealand’s cultural identity is central to its global brand. This identity is communicated through arts, heritage, sport and media. New Zealand’s cultural identity not only shapes its national character but also drives economic growth and development. A unique cultural identity, including Te Tiriti partnership and te reo and tikanga Māori, and the ability for people to trust New Zealand’s brand, drive tourism and associated employment opportunities, and makes its products and services more attractive on the international market. This can lead to increased exports, foreign investment and soft power. Genuine, diverse narratives will need to prevail over homogenised, algorithm-driven content to maintain a trusted Brand NZ. However, there is a risk that New Zealand’s culture could be diluted by more powerful countries and interests. Determining whether New Zealand stories are authentically from New Zealand and reflect its unique cultural heritage will be an ongoing challenge for consumers.

The credibility of digital narratives involves the interplay between public institutions and private technology companies. In some jurisdictions, approaches that promote fair revenue sharing and digital literacy have been introduced as an attempt to help digital media ecosystem remain both innovative and trustworthy.For example, California’s recent journalism funding initiative allocated US$250 million to support news organisations and launch an AI research programme. Originally intended to force Big Tech to share advertising revenues with media companies, the plan evolved into a state-supported model following significant pushback. While some critics argue for tax incentives to bolster full-time journalism, this initiative demonstrates efforts to preserve media integrity amid AI-driven disruption (Nguyễn, 2024).

Potential policy levers

To address the risks and opportunities explored above, future policy professionals may want to consider a range of policy levers and approaches, such as:

• emerging technology literacy and upskilling
• public awareness initiatives
• digital rights and consumer empowerment
• legislative and regulatory actions
• funding and incentives
• international cooperation and cultural diplomacy.

The following section provides a range of ideas for potential options or tools that could be considered within these levers, along with their strengths and weaknesses based on the evaluation criteria (see Appendix 2).

Note in keeping with guidance for the LTIB, this section does not prioritise or recommend which actions the current or future government should take, or allocate responsibilities based on existing government portfolios or structures.

Emerging technology literacy and upskilling

Option 13: Expanded media and digital literacy education in schools and tertiary education providers could equip the next generation with skills to navigate AI-generated media, deepfake detection and Web3 technologies

Effectiveness: Would build digital resilience and prevent misinformation, equipping students with skills to navigate AI-generated media and the complexities of the digital world.

Equity: Media and digital literacy education would help to bridge the digital divide by providing all students, regardless of their background, with essential digital skills.

Feasibility: Moderate/ high feasibility – could be integrated into existing education frameworks.

Cost vs. Benefit: Moderate cost, but long-term gains in digital security and literacy and reduced costs associated with misinformation.

Risks & Mitigation: Risk of outdated curricula and resistance from educators and institutions to changing existing curricula; could mitigate by ensuring ongoing updates with emerging technology trends and engaging stakeholders, including educators, parents and policymakers.

International examples: 

• Estonia is often referred to as a leader in digital literacy and e-education.
• The United Kingdom’s AI in Schools & Digital Literacy Strategy includes AI literacy modules in schools, deepfake awareness education and media verification training.
• The European Commission’s Digital Education Action Plan includes deepfake detection training and digital resilience education for youth.

Option 14: Intergenerational digital storytelling programmes could strengthen community connectedness by linking older generations with younger people through digital media to preserve cultural heritage and foster intergenerational bonds

Effectiveness: Would help to preserve and share New Zealand’s cultural heritage, enhance social cohesion and bridges generational divides. High scalability and strong alignment with New Zealand’s cultural and Te Tiriti obligations. Could also be used as a tool to showcase New Zealand cultural heritage to wider audiences.

Equity: Would create a sense of belonging and shared identity while empowering communities to take ownership of their narratives. Could support marginalised groups by highlighting their unique stories and promoting cultural pride.

Feasibility: Moderate feasibility – depends on availability of technical expertise, partnerships with schools, libraries and cultural organisations, and availability of user-friendly technology platforms. Would rely on leveraging emerging technologies (for example, AI for transcription, virtual reality for immersive storytelling) while allowing room for future innovation.

Cost vs. Benefit: Initial setup costs for technology, training and programme facilitation. However, social and cultural benefits would make the investment cost effective long-term. Leveraging existing community spaces (for example, marae, libraries) could reduce operational costs.

Risks & Mitigation: Low digital literacy, particularly among older adults, and potential technological barriers, for example, software or platform usability issues; could mitigate through comprehensive training and support to participants and collaboration with trusted community leaders and organisations to increase participation; and securing long-term funding to ensure programme continuity.

International examples: There are related initiatives operating internationally, for example StoryCenter (United Sates), Memory Lab (European Union), Generations Working Together (Scotland), and a range of indigenous intergenerational digital storytelling projects in Canada.

Option 15: AI and digital technology training for all New Zealanders could support equitable access to digital transformation opportunities

Effectiveness: Would help people to adapt to AI-driven cultural production and address the digital skills gap, preparing professionals in traditionally non-technology-driven sectors to incorporate emerging technologies as well as the next generation.

Equity: Could benefit traditional artists, journalists, and heritage and cultural professionals at all career stages and their wider industries, and promote equitable access to digital transformation.

Feasibility: High feasibility – could be integrated into existing workforce development programmes. Training could be piloted and scaled based on demand and outcomes.

Cost vs. Benefit: High initial cost but long-term workforce sustainability. Upskilling professionals in emerging technologies may lead to new job creation, digital exports and cultural innovation.

Risks & Mitigation: Low uptake and limited reach to rural communities; could mitigate by including regional funding streams. Collaboration with universities, polytechnics, iwi and cultural institutions could also enhance feasibility and reach. Involving target communities and in programme design could help ensure relevance and buy-in.

International examples: 

• Canada - Canadian Media & Digital Skills Funding: initiatives include funds for training indigenous arts and media professionals in digital technologies.
• Oman - Oman’s National Program for AI and advanced digital technologies aims to promote the adoption and localisation of AI, and advance digital technologies with a human-centred vision.

Public awareness and trust-building initiatives

Option 16: A digital technology transparency index could track the role of AI, blockchain and quantum computing, including in arts, sports and media

Effectiveness: A transparency index could track AI’s role in shaping cultural and sports narratives, enhance accountability and inform policy adjustments in AI use. May provide valuable insights into the use and impact of AI and emerging technologies, helping stakeholders make informed decisions.

Equity: Would provide insights on technology’s impacts across different communities. Could highlight disparities in technology access and usage, promoting more equitable distribution and application.

Feasibility: Moderate feasibility – would require coordination between regulators and industry, including partnerships with AI firms. There are existing transparency indices and frameworks that could be adapted for this purpose, providing a foundation to build upon.

Cost vs. Benefit: High cost for data collection, but strong benefits for governance and public trust. The benefits of increased transparency, such as improved decision-making and trust may outweigh the initial development costs.

Risks & Mitigation: Risk of limited enforcement and non-compliance, and collecting and publishing data on technology use could raise privacy concerns; could mitigate by incentivising participation via funding access, implementing strong data privacy measures and anonymising sensitive information.

International examples: 

• Australia - Australia has introduced a transparency framework for AI with mandatory AI transparency statements for all non-corporate Commonwealth entities.
• Nine cities collaborating through the Eurocities network have set standards for the transparent use of AI, via an open-source data schema for algorithm registers.

Option 17: ‘Human-made content’ labels could help consumers to differentiate between human-created, AI-assisted and fully AI-generated content

Effectiveness: Content labels would assist audiences to make informed choices about AI-generated content, including in arts, media and electronic sports (esports). Would be effective in other contexts where artificial inputs may not be desirable for all consumers e.g., pesticide-free, additive-free, New Zealand made.

Equity: Would ensure transparency for diverse audiences and empower all users to make informed decisions about the content they consume. Labels may make digital content more accessible by providing clear information about its creation.

Feasibility: Moderate feasibility – would require adoption by media companies and content platforms. Advances in AI and machine learning could facilitate the automated labelling of content.

Cost vs. Benefit: Low cost, with significant consumer trust benefits. Clear labelling could enhance consumer confidence, potentially boosting engagement and economic activity.

Risks & Mitigation: Risk of companies mislabelling AI content or that users may misinterpret labels; could mitigate by developing independent auditing mechanisms and providing guidelines on how to interpret labels.

International examples: 

• European Union - The European Union AI Act requires platforms to label AI-generated content, including deepfake media and automated journalism.
• China - China requires digital watermarking on AI-generated images, videos, and text-based content.

Digital rights and consumer empowerment

Option 18: A ‘right to reset or retrain algorithms’ could allow New Zealanders to understand, influence and adjust the way AI-driven content recommendation systems shape the information, media and cultural content they receive

Effectiveness: Would empower people to exercise agency in algorithmic environments, enhancing their ability to discover diverse, trusted and relevant content. Inspired by principles in the Consumer Data Right and international developments in AI transparency, this right could include tools to:

• View how personalisation algorithms are influencing content delivery.
• Reset or retrain the algorithm to reflect updated user preferences.
• Opt into alternative recommendation models (for example, chronological, community-curated or culturally anchored feeds).
• Require platforms to explain in plain terms how personalisation decisions are made.

Equity: Could increase transparency and reduce algorithmic bias, though success would depend on how accessible and understandable the tools are for diverse users.

Feasibility: Moderate feasibility – would require negotiation with digital platforms and alignment with privacy and data protection laws but could start as a voluntary code or regulatory trial.

Cost vs. Benefit: High – modest government investment could yield significant public benefit in terms of civic engagement, public trust and cohesion, and cultural pluralism.

Risks & Mitigation: Risk of industry resistance or token compliance; could mitigate through transparency standards, public reporting and regulatory incentives for platforms to comply.

International examples: Consumer Data Rights (for example, Australia, United Kingdom, NZ) offer precedent for empowering people with data portability and control, which could evolve into content personalisation rights.

Legislative and regulatory actions

Option 19: Responsible AI and algorithmic bias standards could focus on preventing algorithmic discrimination and racial or cultural bias in AI-driven hiring

Effectiveness: Would aim to promote fair AI systems and content curation, and preventing racial or cultural bias, for example in AI-driven hiring for creative and sports industries

Equity: May help to protect marginalised groups from AI discrimination, including in digital storytelling and esports.

Feasibility: Moderate feasibility – would require industry compliance and independent AI audits.

Cost vs. Benefit: May be costly to enforce, but strong social equity benefits.

Risks & Mitigation: Risk of regulatory loopholes and ineffective enforcement; could mitigate via regular AI bias audits and transparency mandates.

International examples: 

• European Union - An objective of the European Commission’s AI Act is to mitigate discrimination and bias in the development, deployment and use of AI systems.
• Brazil - Brazil policy discussions on algorithmic transparency and bias indicate a move towards enforcing algorithmic fairness.

Option 20: Explainability audits for AI-powered journalism, virtual reality storytelling and automated sports decision-making could help users to understand how AI influences content creation and analysis

Effectiveness: Explainability audits would improve transparency in AI-driven cultural and sports reporting and identify and mitigate biases in AI systems, to support fair and accurate content creation.

Equity: May help to protect consumers from misinformation and bias.

Feasibility: Moderate feasibility – would require AI auditing infrastructure and industry buy-in. Established methodologies for conducting explainability audits exist that could be adapted for this purpose.

Cost vs. Benefit: Compliance costs for AI developers, but enhanced trust and accountability may enhance consumer confidence.

Risks & Mitigation: Risk of resistance from technology companies due to perceived complexity or cost; could mitigate by providing incentives for compliance and engaging stakeholders in the development process.

International examples: 

• European Union - The European Union AI Act (Article 86) requires AI systems in high-risk areas (e.g., automated journalism and sports analytics) to provide explainable decision-making.
• United States - The United States AI Risk Management Framework outlines principles for AI transparency and accountability in automated processes and analytics.

Funding and incentives

Option 21: A unified government pipeline for digital innovation investment could increase strategic clarity, reduce duplication of effort and enable more impactful investment in technologies that reflect New Zealand’s values

Effectiveness: Could strategically coordinate public funding for digital technology development across sectors, including arts, culture, heritage, education and science. Rather than siloed and bespoke investments by individual agencies, this pipeline would streamline decision-making, align investments with national priorities and ensure coherence in how Aotearoa tells its digital story.

Equity: Strong potential to prioritise underserved communities through shared funding principles but would depend on governance design.

Feasibility: Moderate feasibility – would require significant interagency coordination and strong cross-ministerial leadership but could build on existing mechanisms.

Cost vs. Benefit: Consolidation would improve efficiency, reduce duplicated funding processes, and amplify long-term social and economic returns from public investment.

Risks & Mitigation: Risk of over-centralisation or slow responsiveness to innovation; could mitigate through flexible co-investment models, public accountability and Te Tiriti-based oversight.

International examples: The United Kingdom established the Government Digital Service (GDS) and later the Central Digital and Data Office (CDDO) to foster a unified approach to digital transformation and investment across government departments.

International cooperation and cultural diplomacy

Option 22: Multilateral and regional agreements with partner countries and global cultural institutions could provide a means to share expertise on AI-driven cultural preservation and ethical digital storytelling

Effectiveness: High quality agreements would help build international partnerships for responsible AI use in culture. Would facilitate the sharing of best practices, tools and methodologies, and encourage the development of shared ethical frameworks and technical standards. This may improve consistency and quality across borders.

Equity: Would strengthen indigenous and regional collaboration. Smaller or less-resourced countries and institutions may benefit from the expertise and infrastructure of more advanced partners.

Feasibility: High feasibility – would leverage existing diplomatic channels. New Zealand has a strong track record of international cooperation, especially in cultural and digital diplomacy.

Cost vs. Benefit: Moderate cost, strong cultural diplomacy benefits. Costs could be distributed among participating countries and institutions, reducing the financial burden on any one party.

Risks & Mitigation: Risk of slow policy adoption; could mitigate by prioritising quick wins through pilot projects. Without effective safeguards, shared technologies or methods could be misused or misrepresent cultural narratives; could mitigate by co-developing ethical guidelines and consent protocols to protect cultural integrity and support indigenous and local communities to have a role in decision-making processes.

International examples: 

• United Nations - United Nations Educational, Scientific and Cultural Organization (UNESCO) adopted the first-ever global agreement on the ethics of artificial intelligence, which includes a chapter on culture and aims to promote human rights and sustainable development through digital transformation.
• The African Union - The African Union’s Continental AI Strategy promotes AI’s potential as a driving force for cultural renaissance, and the preservation of Africa’s cultural heritage.

Section 5: Protect – Safeguarding culture and heritage
Wāhanga 5: Tiaki – Te tiaki i te ahurea me te tuku ihotanga

The rise of AI and digital technologies brings both significant risks and exciting opportunities for the protection of New Zealanders’ stories. Many nations are grappling with how to balance encouraging technological innovation and protecting the rights of creators and communities. While AI can facilitate the preservation and sharing of cultural knowledge, it also presents challenges related to cultural misappropriation and control over data. As these technologies evolve, New Zealand faces the delicate task of balancing innovation with the need to safeguard intellectual and cultural property.

Ensuring that New Zealanders’ stories are protected requires addressing the risks associated with digital infrastructure and AI’s potential to reshape or erase cultural narratives. At the same time, the opportunities for data sovereignty and AI-powered preservation offer new ways to ensure that New Zealand’s rich cultural heritage is protected for future generations.

Insights for the future

Insight 9: How we protect New Zealanders’ stories will shape our future history

AI and digital technologies present opportunities for preserving and sharing cultural knowledge that may otherwise be lost, and work in this space is already underway in New Zealand. Some of New Zealand’s libraries, museums and archives, including Te Papa Tongarewa and Ngā Taonga Sound & Vision, are actively developing and using AI tools to catalogue collections into te reo and other underrepresented languages (Chumko, 2023). Te Hiku Media, recognised by Time magazine for their work on preserving te reo through AI, is using AI to transcribe and document oral histories that are at risk of being forgotten as older generations pass away. With the right frameworks in place, AI can be a tool for enhancing cultural knowledge and passing that knowledge down to future generations, and for the Crown to uphold its Te Tiriti obligation to protect and promote te reo and tikanga Māori.

In an era where AI systems can generate content that mirrors human creativity, concerns arise about the risk of AI reshaping history or erasing significant cultural stories. As previously noted, AI could create alternative versions of historical events or perpetuate biases that favour certain perspectives, while ignoring others. As AI begins to play an increasing role in content creation, this poses a risk to cultural integrity and accuracy (Melo, 2025).

AI may appropriate and commercialise cultural knowledge and stories without permission or proper context. Communities whose stories have historically been excluded from dominant narratives are particularly vulnerable to this risk. There is some concern that AI systems, which are primarily trained on data scraped from the internet, might perpetuate cultural stereotypes or reinforce dominant narratives—effectively erasing or distorting some aspects of history. Without adequate recognition of tikanga and Māori intellectual property over traditional knowledge, the integration of Māori cultural values into AI regulations is limited, increasing the risk of cultural misappropriation.

Insight 10: Protecting New Zealanders’ stories will require stronger digital infrastructure

Protecting New Zealanders’ stories requires ensuring adequate and safe long-term storage. One key issue is the culture of obsolescence, where data stored on outdated hardware or software becomes inaccessible as technology evolves (Finance Magnates, 2023). This can lead to the loss of valuable cultural narratives if data is not regularly migrated to current formats and systems. Unlike paper, which can last for centuries if properly preserved, digital data has a much shorter lifecycle and requires ongoing maintenance to remain accessible.

The sheer volume of data generated necessitates robust cybersecurity measures to protect against data breaches and loss. Ensuring that digital storage solutions are both scalable and secure is essential for safeguarding New Zealand’s cultural heritage in the digital age (Stackpole, 2024). Indeed, many of New Zealand’s museums, libraries and archives are grappling with growing data storage requirements relating to digitisation programmes, which have no sign of abating. The storage demands at Auckland War Memorial Museum have increased eight to 10 percent annually (Withers, 2024).

Technological progress had made it easier for New Zealanders to document their stories, but as technology has progressed, it has increasingly led previous technologies and formats to become obsolete, from wax cylinders, to vinyl records, to magnetic tape and older storage systems. It is also increasingly costly to access and maintain access to older, legacy systems of information storage due to technologies and equipment being no longer produced or supported. While cloud storage has made it easier to store and access information, it has also led to ongoing subscription costs and introduced additional new critical dependencies and vulnerabilities. Cloud storage requires fast internet infrastructure, consistent server uptime and ethical vendor practices to ensure that cloud storage remains affordable and available for New Zealand communities in the long-term.

The centralisation of data storage with international Big Tech companies, which increasingly control digital infrastructure, raises concerns. Many New Zealanders’ stories are currently stored in databases and digital archives managed by large multinational corporations, raising questions about who controls access to this data, where it is held, who profits from it, and what will happen if that platform becomes disestablished or obsolete.

Once stories are stored digitally, they may become susceptible to breaches of privacy, misconfiguration, or loss due to poor data management practices. The rise of streaming services has highlighted how content can be removed or altered after release, raising concerns about disappearing cultural content. Brody (2023) notes that digital platforms like Netflix not only choose which content is made available but also have the power to remove or edit it, limiting access to locally produced stories. This practice highlights the risks associated with placing valuable cultural content in the hands of large, international corporations.

The metadata associated with these stories could also be exposed, revealing private information that was not meant to be shared. As digital archives continue to grow, ensuring that they are secure, accessible and protected from misuse becomes increasingly important. There is also the increasing risk of cyberattacks on cultural institutions like museums, archives and sports organisations, which could erase or manipulate digital cultural assets.

Bifet et al. (2021) emphasise that New Zealand must invest in AI research to ensure technological independence and data sovereignty, warning that relying on overseas companies could undermine the country’s control over its own technological future. They stress the need for a strong local AI ecosystem to avoid New Zealand becoming dependent on foreign AI infrastructure. Australia’s Long-term Insights Briefing (Department of the Prime Minister and Cabinet, 2023) highlights the challenges of regulating AI due to the borderless nature of digital platforms, which complicates efforts to control data storage and usage, especially when large international technology companies dominate the sector.

The shift to digital platforms provides an opportunity for New Zealand to assert greater control over how its cultural stories are stored and shared. There is potential for local institutions, such as museums and galleries, to work together to build digital infrastructure that prioritises New Zealand’s data sovereignty. This could involve creating national digital repositories that store cultural data within New Zealand, reducing reliance on international technology companies and enhancing New Zealand data sovereignty.

AI and machine learning could also play a role in improving how cultural stories are indexed, categorised and made accessible to the public. New Zealand’s museums, libraries and archives could use AI to streamline the cataloguing of their collections, improving access while maintaining the integrity of cultural knowledge. However, as the use of AI grows, it is critical that these systems are designed in a way that respects the cultural significance of the material they store and that they do not inadvertently erode local control.

The sustainability of data storage on digital platforms must also be considered. Digital technologies, particularly AI training and immersive technologies, are known to consume vast amounts of energy, raising significant concerns about their environmental sustainability. This creates a dilemma for New Zealand as it seeks to balance the benefits of technological advancement with the need for sustainability. The environmental footprint associated with data storage and AI infrastructure highlights the importance of considering the environmental impact of these technologies when planning for the future of New Zealand’s digital culture and data sovereignty.

Insight 11: Policy and legislation will need to be flexible and adaptive to keep up with the pace of technological change

There are some concerns about the risks of AI for New Zealand and global society. A 2023 survey by Verian revealed that over 66 percent of New Zealanders are highly concerned about the malicious use, lack of regulation and unintended consequences of AI (InternetNZ, 2023). New Zealanders’ trust in AI appears especially low. The Ipsos AI Monitor 2024 found that of 32 countries, New Zealand ranked second highest on levels of concern about AI products and services (Ipsos, 2024). A 2024 survey in Australia found that while people see AI’s potential for improving lives, 80 percent of Australians believe preventing catastrophic risks from advanced AI systems should be a global priority, with an overwhelming majority (86 percent) supporting the development of a new dedicated AI regulation and governance body (Noetel et al., 2024).

Existing intellectual property laws may not adequately protect New Zealanders' stories, particularly in the context of GenAI. For instance, current laws do not necessarily account for collective cultural knowledge, such as that of Māori communities. Copyright legislation currently favours individual rather than collective rights and responsibilities. Māori communities are concerned that their cultural data, including traditional stories, whakapapa and taonga are vulnerable to exploitation and misuse without proper safeguards in place. Technological advancements operate within an economically driven ecosystem that often favours multinational corporations.

To safeguard creators’ rights, Jiang et al. (2023) recommend regulations that prevent unauthorised use of content by AI, funding for AI research independent of corporate interests, and an education system that addresses the power dynamics influencing technological development. A fit-for-purpose regulatory environment and responsible uptake should lead to creative work opportunities evolving, rather than disappearing, and respond to concerns about work being misused.

In recent years, the global community has been grappling with how intellectual property, copyright and other legislation can keep up with developments in AI. In 2021, UNESCO adopted the first global standard on AI ethics—the Recommendation on the Ethics of Artificial Intelligence. Since then, international lawmakers have developed various AI regulations to address ethical concerns about data privacy, bias and transparency. A key concern for the creative and cultural sectors is ensuring copyright and intellectual property law keeps pace with advances in AI technology.

In 2023 the U.S. Senate Judiciary Subcommittee on Intellectual Property held hearings on AI and intellectual property, discussing issues such as licencing for AI training, fair use of creators’ works in data training and outputs, the challenge to creatives to be able to opt in or out of their work being used for AI training, and transparency for consumers regarding AI-generated and human-generated content (Cromwell Insights 2023). The same year, the Japanese Government declared that using datasets for training AI models did not violate copyright law, allowing those training AI models to amass openly accessible data without licencing or consent from data owners (Lanz, 2023).

In 2024 a cross-party group of UK Ministers of Parliament (MPs) called on the United Kingdom’s Government to create “a pro-creative industries AI bill”, setting out the copyright and transparency obligations of AI companies, and introducing “a specific personality right to protect creators and artists from misappropriation and false endorsement”. It stemmed from a report on music and AI published by the All-Party Parliamentary Group on Music, which is chaired by Kevin Brennan MP. Launching the report, he said, “The United Kingdom must grasp the transformative potential of AI in shaping the future of music if it is to retain its role as a powerhouse in exporting music and nurturing world-class talent. We must also confront the danger that unfettered developments in AI could pose to the United Kingdom’s musicians and music businesses”.

In New Zealand, new legislative measures and funding are needed to better protect creatives. New Zealand is currently exploring how it can protect its stories from misuse or misappropriation in an environment where the boundaries of copyright and intellectual property law are blurred by AI technologies. The Ministry for Business, Innovation and Employment Hīkina Whakatutuki is currently leading work on developing a national AI Strategy and reviewing the settings underpinning copyright to ensure that it remains relevant and robust for the future digital environment.

Insight 12: Data sovereignty principles and cultural values may help to safeguard future stories in New Zealand’s unique context

There are opportunities to use emerging data sovereignty frameworks to protect New Zealanders’ stories more effectively. As discussed in Section 2, these frameworks provide leadership for data to be governed responsibly and for communities to assert greater control over their data and cultural knowledge. This includes the right to determine how their stories are used and shared, as well as the ability to consent to the use of their data in AI training models. Decentralised platforms and blockchain offer the possibility of creating more robust and dynamic systems for cataloguing, preserving and sharing cultural knowledge in ways that align with local values and cultural practices.

The development of systems that are informed by Māori values could ensure that New Zealand’s cultural narratives are respected and protected—such as those that recognise the importance of whakapapa and the integrity of taonga. Additionally, the rise of Māori data sovereignty networks such as Te Mana Rauranga and Te Kāhui Rauranga, as well as other indigenous data sovereignty movements around the world, offers New Zealand an opportunity to participate in global conversations on how to protect indigenous stories through the use of emerging technologies.

Potential policy levers

To address the risks and opportunities explored above, future policy professionals may want to consider a range of policy levers and approaches such as:

• legislative and regulatory actions
• public awareness and inclusion initiatives
• emerging technology literacy
• funding and innovation for digital resilience
• oversight and accountability mechanisms and
• international cooperation and diplomacy.

The following section provides a range of ideas for potential options or tools that could be considered within these levers, along with their strengths and weaknesses based on the evaluation criteria (see Appendix 2), and current international examples.

Note in keeping with guidance for the LTIB, this section does not prioritise or recommend which actions the current or future government should take, or allocate responsibilities based on existing government portfolios or structures.

Legislative and regulatory actions

Option 23: A legally binding digital sovereignty framework could support Māori data sovereignty, AI-generated Māori content protections and blockchain-based governance for taonga

Effectiveness: A clear legal framework could help support Māori digital sovereignty and prevent data exploitation. Would align with Te Tiriti o Waitangi and help protect whakapapa, tikanga and mātauranga Māori from misappropriation.

Equity: Could benefit iwi and hapū by supporting rangatiratanga, including over AI-generated cultural content. Would have a strong focus on Māori cultural protection, with scope to expand the framework to include data sovereignty protections for New Zealand communities more broadly.

Feasibility: Moderate feasibility – would require legal clarity, iwi engagement, cross-sector coordination and government commitment.

Cost vs. Benefit: High initial cost but long-term cultural and economic gains.

Risks & Mitigation: Risk of industry resistance and challenges with enforcement; could mitigate by working with industry partners to encourage compliance, embedding in cultural funding and investment policies, and establishing an oversight body with Māori leadership.

International examples: 

• Canada - Indigenous Data Sovereignty in Canada uses First Nations Principles of Ownership, Control, Access, and Possession (OCAP).
• Australia - Australia has a framework for working with indigenous data, co-designed with Aboriginal and Torres Strait Islander partners, to improve access to government-held data and support Indigenous Data Sovereignty.

Option 24: Expanding privacy regulation could directly address the regulation of biometric data, AI-driven sports monitoring and digital identity protection in cultural and media sectors

Effectiveness: Could help protect individuals, including athletes and cultural performers, from AI-driven surveillance, and help legislation to keep pace with real-world applications and risks.

Equity: Could reduce privacy risks for athletes, including population groups that are over-represented in New Zealand professional sports.

Feasibility: Medium/high feasibility – privacy regulations are evolving globally, and amendments to the current Act in New Zealand are already being considered. Regulating AI and biometric systems would require specialised knowledge and may necessitate new compliance infrastructure.

Cost vs. Benefit: Compliance costs for AI firms, but strong privacy and data ethics protection, which may reduce the risk of data breaches, reputational damage and legal liability.

Risks & Mitigation: Potential resistance from sports technology companies and, if not carefully scoped, new rules could stifle innovation or impose unnecessary burdens; could mitigate via industry consultation and phased rollout to ease transition.

International examples: The UK Information Commissioner’s Office provides detailed guidelines on managing biometric data under United Kingdom General Data Protection Regulation.

Option 25: Updating public records regulation could require AI-generated historical archives, digital museums and cultural datasets to meet transparency and authenticity standards

Effectiveness: Could enable historical and cultural AI-generated records to be verified. Mandating transparency and provenance standards for AI-generated archives may help maintain public trust in digital heritage. As digital museums and AI-curated collections grow, legal clarity may be increasingly important to ensure their long-term integrity and accessibility.

Equity: Would protect heritage integrity, benefiting Māori and other communities and enabling diverse narratives to be reliably and responsibly preserved in digital formats. Clear standards could support access to and understanding of digital heritage for a wide range of users, not just technology experts.

Feasibility: High feasibility – legislative updates would be manageable. The Act already includes provisions for standards, audits and inspections, which could be extended to cover AI-generated content.

Cost vs. Benefit: Moderate costs, strong cultural preservation benefits. The long-term benefits of preserving authentic, transparent digital records may feasibly outweigh the costs, especially for national identity and education.

Risks & Mitigation: Risk of inadequate enforcement; could mitigate by establishing AI record-keeping compliance bodies. Excessive requirements could discourage innovation in digital storytelling and museum curation; could mitigate by involving archivists, technologists and cultural practitioners in shaping amendments to ensure relevance and practicality.

International examples: Risk of inadequate enforcement; could mitigate by establishing AI record-keeping compliance bodies. Excessive requirements could discourage innovation in digital storytelling and museum curation; could mitigate by involving archivists, technologists and cultural practitioners in shaping amendments to ensure relevance and practicality.

Option 26: A legal framework for digital twin heritage sites could provide AI-driven reconstructions of historical Māori and Pacific landmarks, respecting ownership and governance structures

Effectiveness: “Digital twins” of heritage sites—especially those created using AI—respecting the cultural, spiritual and historical significance of Māori and Pacific landmarks and protecting indigenous and community ownership of digital heritage could be developed.

Equity: Could define who has the right to create, manage and share digital replicas, reducing disputes and misuse. Embedding Māori leadership and data sovereignty principles may help ensure that iwi and hapū retain control over their cultural heritage.

Feasibility: Moderate feasibility – would require legal updates and technical infrastructure. The government’s AI Framework and Māori data governance guidance already emphasise indigenous rights and ethical AI use, increasing potential policy alignment.

Cost vs. Benefit: Medium cost, but strong cultural preservation benefits. Protecting digital representations of heritage sites would preserve national identity and intergenerational knowledge transmission.

Risks & Mitigation: Risk of commercial exploitation – without clear protections, digital twins could be used inappropriately in games, tourism or commercial media; could mitigate by requiring Māori and community co-governance in digital twin projects.

International examples: 

• France - France is using new technologies to create digital twins of historical sites, integrating cultural governance protections.
• Japan - Japan integrates digital twins into cultural heritage preservation and urban planning.

Emerging technology literacy and upskilling

Option 27: Ethical AI and indigenous knowledge training modules could help AI developers working on Māori content to understand tikanga Māori and indigenous intellectual property rights

Effectiveness: Could prevent AI misuse in Māori cultural storytelling and support ethical AI design and active protection and appropriate use of mātauranga Māori.

Equity: Would support Māori data sovereignty and enable Māori communities to influence how their knowledge and stories are represented in digital systems.

Feasibility: Moderate feasibility – would require collaboration between AI developers, Māori experts and policymakers. Ensuring consistent delivery across the technology sector, especially for international developers, may be challenging.

Cost vs. Benefit: Low to medium cost; strong long-term benefits in culturally appropriate AI. Companies and institutions that adopt culturally grounded practices may gain public trust and international recognition.

Risks & Mitigation: Risk of superficial implementation could undermine the intent and fail to change practice; could mitigate by supporting Māori-led AI training in technology companies, and linking training to funding eligibility, procurement standards or certification schemes to encourage uptake.

International examples: 

• Canada - Canada’s "Indigenous Protocols for AI" is a project led by indigenous researchers to develop ethical AI frameworks aligned with indigenous knowledge and cultural values.
• Finland - Finland’s AI ethics training initiative educates AI developers on ethical considerations in AI development.

Public awareness and inclusion initiatives

Option 28: Community-led digital heritage trusts could enable local iwi and community groups to secure their historical records using blockchain and decentralised storage

Effectiveness: Could strengthen Māori and community-led sovereignty over local heritage.

Equity: Could enhance indigenous decentralised governance over cultural archives.

Feasibility: Moderate feasibility – would require engagement, collaboration and technical support for iwi and local communities.

Cost vs. Benefit: Blockchain storage could be costly, but the long-term cultural preservation benefits may be substantial.

Risks & Mitigation: Risk of barriers to accessing technology; could mitigate by providing training and co-funding models.

International examples: 

• Estonia - Estonia’s KSI (Keyless Signature Infrastructure) blockchain system is used for securing government records and can been extended to digital heritage applications.
• China - Policy reports indicate that China is increasingly using blockchain technology for securing and monetising content and processes across a range of sectors.

Funding and innovation for digital resilience

Option 29: A digital safety fund investing in blockchain authentication, decentralised digital asset storage and cyber-resilience for AI-generated content could strengthen long-term resilience

Effectiveness: Could strengthen cybersecurity and protect digital sports, media and cultural content, increasing long-term resilience.

Equity: Decentralised systems could empower communities to manage and protect their own digital content without relying on central authorities.

Feasibility: High feasibility – could be incorporated into cybersecurity initiatives. The fund could be implemented in phases, starting with pilot projects in high-risk or high-value sectors (for example, journalism, cultural heritage).

Cost vs. Benefit: High cost but beneficial for long-term digital infrastructure resilience. Preventing digital asset loss or manipulation may save significant costs in legal, reputational and operational terms. Could also stimulate local innovation in secure content creation and storage technologies.

Risks & Mitigation: Risk of high operational costs and misuse or overhype; could mitigate through partnering with industry and universities for co-funding, and establishing clear criteria for funding, with regular audits and impact assessments.

International examples: 

• The United Kingdom - The United Kingdom’s Towards a National Collection programme includes several projects exploring secure digital platforms, blockchain verification and innovative AI.
• Germany - Germany’s Federal Blockchain Strategy, which includes the use of blockchain technology to secure digital cultural assets and intellectual property, explores decentralised infrastructure for public digital services, and supports R&D in areas like secure digital archiving.

Oversight and accountability

Option 30: An ‘emerging tech’ regulatory authority could oversee AI, blockchain and XR applications

Effectiveness: A dedicated authority could provide focused, expert oversight and enhance responsible governance of rapidly evolving emerging technologies, including in cultural and sports sectors. A single authority could harmonise standards across sectors, reducing fragmentation.

Equity: Would provide a means to monitor Te Tiriti compliance, Māori data sovereignty and digital rights. Effective oversight could help prevent discriminatory or exclusionary uses of emerging technology, especially in public services and media.

Feasibility: Moderate feasibility – would require interagency coordination and political will.

Cost vs. Benefit: High operational costs but would be useful for regulatory clarity. Proactive regulation could prevent costly harms (for example, data breaches, algorithmic bias, misinformation) and reduce legal uncertainty for innovators.

Risks & Mitigation: Risk of bureaucracy and regulatory capture; could mitigate by adopting an agile regulatory model with industry co-governance, and using adaptive tools and iterative rule-making.

International examples: 

• The European Union - The European Union AI Act establishes an oversight body to regulate AI across multiple sectors, including creative industries and sports.
• Canada - Canada’s Artificial Intelligence and Data Act and framework for responsible use of AI in government aim to ensure transparency, fairness, and human rights compliance in AI-powered decision-making.

Option 31: AI and sports integrity protections could set ethical and privacy standards relating to the use of athlete biometric data, AI-driven referee decisions and predictive sports analytics

Effectiveness: Stronger protections could reduce AI-related ethical concerns, misuse and error in sports decision-making and increase transparency and trust.

Equity: Could help protect athlete privacy from surveillance overreach and data exploitation—especially for youth, amateur and minority groups—and encourage fair competition.

Feasibility: Moderate feasibility – would require global alignment on sports AI ethics. New Zealand’s Sport Integrity Commission and Sport New Zealand are already exploring AI’s role in sport and have frameworks that could support expanded protections.

Cost vs. Benefit: Compliance costs for sports organisations, but important for fair play. Ethical AI use may enhance the credibility of New Zealand’s sports sector, both domestically and internationally. May also prevent costly legal or reputational fallout from data breaches or biased AI decisions.

Risks & Mitigation: Risk of AI bias in officiating, overregulation and data misuse; could mitigate through regular AI audits and explainability requirements, applying different standards based on the level of competition (e.g., elite vs. community sport), or requiring informed, revocable consent for biometric data collection and use.

International examples: 

• The European Union - The European Union, through its AI Act, is introducing a risk classification system for AI applications, including those in sports, and requiring stringent impact assessments for high-risk uses.
• The International Olympic Committee - The International Olympic Committee has been investigating how AI can improve safeguarding in sports, including monitoring online abuse and ensuring safe online spaces for athletes and officials.

International cooperation and cultural diplomacy

Option 32: AI and emerging technology diplomacy and regional cooperation could strengthen New Zealand’s role in protecting digital cultural heritage in the Pacific and beyond

Effectiveness: Strengthening AI diplomacy could position New Zealand as a regional leader in shaping ethical, inclusive and culturally appropriate norms for digital cultural and heritage assets.

Equity: Could benefit small island nations and enhance regional cooperation. Could facilitate equitable access to AI and digital tools across Pacific nations, many of which face infrastructure and capacity challenges.

Feasibility: High feasibility – aligns with New Zealand’s regional leadership role. New Zealand’s AI Blueprint 2025 and public service AI framework already emphasise international cooperation, ethics and Te Tiriti commitments.

Cost vs. Benefit: Diplomatic effort required, but strong long-term cultural security. Multilateral cooperation could reduce costs by pooling expertise, infrastructure and policy development.

Risks & Mitigation: Risk of slow negotiations, geopolitical tensions and tokenism; could mitigate by aligning with existing trade and cultural agreements, promoting shared values like transparency, data sovereignty and cultural respect as the foundation of cooperation, and ensuring Pacific and indigenous voices are central to agenda-setting and decision-making.

International examples: The European Union is working on international agreements to ensure AI systems respect human rights and cultural diversity. For example, the European Union-Japan Digital Partnership is advancing cooperation on digital issues to foster economic growth, facilitate digital trade, and a human-centric digital transformation based on common values.

Summary of future policy options
Whakarāpopoto o ngā kōwhiringa kaupapahere anamata

The following table provides a snapshot of the ideas for policy options or tools presented in the Create, Share and Protect chapters.

Table: Summary of potential government levers and policy options presented in this LTIB

Final thoughts
Ngā whakaaro whakamutunga

Based on the analysis presented in this LTIB, Manatū Taonga concludes that regardless of which future scenario transpires, any policy approach to address the risks and opportunities of AI and emerging technology in New Zealand’s arts, heritage, media and sport sectors will need to take account of a range of key factors, chiefly:

• Balancing regulation with innovation – Supporting AI and digital technology oversight without stifling culture and creativity.
• Increasing equity and inclusion – Investing in reaching underrepresented or vulnerable communities (e.g., youth, older people, people with disabilities, Māori, Pacific peoples, migrants, rural and LGBTQ+ communities) to ensure all parts of society benefit from technology-driven opportunities.
• Leveraging private investment – Using government investment to attract industry co-investment and enable limited public funds to be maximised and meet New Zealand’s emerging technology aspirations.
• Strengthening cyber and privacy protections – Safeguarding New Zealand’s digital cultural knowledge systems, data, infrastructure and taonga.
• Future-proofing the workforce – Helping AI and digital technologies to enhance— not replace—cultural, creative and sports professionals and practitioners.

Policymakers could consider several next steps for strengthening policy analysis and moving to design and implementation:

• Deeper sector-specific impact analysis (for example, digital technology’s effects on Māori broadcasting, esports, or community arts and heritage organisations).
• Detailed cost-benefit modelling to weigh economic, cultural and legal trade-offs of AI and emerging technology regulations. This may include balancing, for example, technological innovation versus consumer protection; taking a light-touch enabling approach versus proactively managing ethical risks and bias; and/or transparency measures versus compliance burdens on creative industries.
• Continuous tracking of international AI and emerging technology regulations to ensure New Zealand policies are aligned with global best practices.
• Further and ongoing targeted engagement with experts and sector stakeholders to refine policy trade-offs, incorporate local and sector-specific insights, and ensure best practice international approaches are suitably adapted to New Zealand’s unique cultural context.

References
Ngā tohutoro

Accenture (2025). Empowering film creatives with digital twins.

AI scientist Ray Kurzweil: ‘We are going to expand intelligence a millionfold by 2045’. (2024, June 29). The Guardian.

Aiko (2024). Mātauranga Māori Te Awe Kōtuku Covid Cultural Recovery Programme: Evaluation report.

Ajani, Y., Oladokun, B., Olarongbe, S., Amaechi, M., Rabiu, N. & Bashorun, M. (2024). Revitalizing indigenous knowledge systems via digital media technologies for sustainability of indigenous languages. Preservation, Digital Technology & Culture, 53(1), 35-44.

Allen Yarnell Center for Student Success (n.d.). Quantum technologies in arts and architecture – Shaping the future of creativity and design.

Andhov, A. (2025). New Zealand’s big tech dilemma.

Anwar, M. S., Yang, J., Frnda, J., Choi, A., Baghaei, N., & Ali, M. (2025). Metaverse and XR for cultural heritage education: applications, standards, architecture, and technological insights for enhanced immersive experience. Virtual Reality, 29(2), 29-51.

APRA AMCOS (2024). AI and music report.

Australian Government (2023). Revive: A place for every story, a story for every place.

Barnes, A. J., Zhang, Y., & Valenzuela, A. (2024). AI and culture: Culturally dependent responses to AI systems. Current Opinion in Psychology 58.

Bifet, A., Green, R., Wilson, D., Zhang, M.(2021). Aotearoa New Zealand artificial intelligence. A strategic approach.

Bolgar, C. (2025). Microsoft’s Majorana 1 chip carves new path for quantum computing.

Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., ... & Viale, R. (2024). The impact of generative artificial intelligence on socioeconomic inequalities and policy making. PNAS nexus, 3(6), 1-18.

Caro, M. (2024). Impact of AI on local news models.

Clearmatic (2024). Exploring the benefits of robotic process automation in entertainment and media.

Click here: The TikTok ban, China, and national security. (2025, March 24).

Creative Australia (2025). Creative Workforce Scoping Study Report.

Creative Industries Trade & Investment Board (2022). Putting the UK’s creative industries centre stage. An international strategy for the creative Industries 2022–2025.

Cromwell Insights (2023). The U.S. Senate hearing on artificial intelligence and intellectual property.

Cullen, J., Marsh, S., Simmonds, L., & Duncan, S. (2024). The impact of digital technologies on children and adolescents in Aotearoa New Zealand: A case for the development of best-practice recommendations for schools. Waikato Journal of Education.

Dale, J. (2025). Ethical considerations in RPA implementation: Navigating the moral landscape of automation.

DATACOM (2025). State of Cybersecurity Index 2025.

Dator, J. (2009). Alternative futures at the Manoa school. Journal of Futures Studies 14(2), 1-18.

Department of Internal Affairs (2023). Frequently Asked Questions.

Department of the Prime Minister and Cabinet (2023). How might artificial intelligence affect the trustworthiness of public service delivery? The first Long-term Insights Briefing report.

Digital Govt NZ (2021). Digital inclusion user insights — Māori.

Digital Govt NZ (2022). Te Rautaki Matihiko mō Aotearoa The Digital Strategy for Aotearoa.

Digital Passport (2025). Get digital skills. Go places.

DiMaggio, P., & Garip, F. (2012). Network effects and social inequality. Annual review of sociology, 38(1), 93-118.

Du, R. (2024). News Recommendation and Information Cocoons: The Impact of Algorithms on News Consumption. In: Barkho, L., Lugo-Ocando, J.A., Jamil, S. (eds) Handbook of Applied Journalism. Springer Handbooks of Political Science and International Relations. Springer, Cham.

Duggal, S. D. (2024). Democratized creativity: The evolution and impact of AI.

Easton, B (2023). Learnings from the New Zealand economic history of shocks.

Education Gazette (2022). Digital storytelling engaging and inspiring ākonga in STEM.

Epidemic Sound (2024). The 2024 Future of the Creator Economy Report.

Ericsson (n.d.). 6G networks.

Farrel, C. & James, L. (2024). The role of the media in democracies: What is it, and why does it matter?

Farrer&Co (2024). AI, the creative industries and content scraping: battleground or marketplace?

Finance Magnates (2023). Big data and the risk of digital obsolescence.

Flew, T., Bruns, A., Burgess, J., Crawford, K., & Shaw, F. (2014). Social media and its impact on crisis communication: Case studies of Twitter use in emergency management in Australia and New Zealand. In 2013 ICA Shanghai Regional Conference: Communication and Social Transformation, 2013-11-08 - 2013-11-10.

Foka, A., & Griffin, G. (2024). AI, cultural heritage, and bias: Some key queries that arise from the use of GenAI. Heritage, 7(11), 6125-6136.

Ford, D. (2025). Decentralized collaboration for art and culture preservation.

Gartner (2025). Gartner forecasts worldwide IT spending to grow 9.8% in 2025.

Government Communications Security Bureau (n.d.). Zero trust.

Green, N., Tappin, D., & Bentley, T. (2020). Working from home before, during and after the Covid-19 pandemic: Implications for workers and organisations. New Zealand Journal of Employment Relations, 45(2), 5–16.

Grimes, A. & White, D. (2019). Digital inclusion and wellbeing in New Zealand.

Handzic, M., Ismajloska, M. (2019). Transferring Cultural Knowledge Through Arts: Two Digital Stories. In: Handzic, M., Carlucci, D. (eds) Knowledge Management, Arts, and Humanities. Knowledge Management and Organizational Learning, vol 7. Springer, Cham.

Hafizov, H. (2023). The AI Revolution: Is it a Game Changer for Disability Inclusion?

Hedges, M., Pacheco, G. & Turcu, A. (2023). The World Internet Project 2023. New Zealand Work Research Institute, Auckland.

Helen Clark Foundation (2025). Social cohesion in New Zealand. Is New Zealand's social fabric fraying?

Hendawy, M. (2024). The intensified digital divide: Comprehending GenAI. Internet Policy Review.

Hendrix, J. (2024). Transcript: US Senate Subcommittee hearing on "Strengthening Data Security to Protect Consumers".

Hliš, T., Fister, I., & Fister Jr, I. (2024). Digital twins in sport: Concepts, taxonomies, challenges and practical potentials. Expert Systems with Applications, 258, 1-15.

IBM (n.d.). What is robotic process automation (RPA)?

IBM (n.d.). What is age computing?

InternetNZ (2023). New Zealand's Internet Insights 2023.

Ipsos (2024). Ipsos NZ AI Monitor 2024.

Jiang, H. H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D., ... & Gebru, T. (2023). AI art and its impact on artists. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (pp. 363-374).

Khanal, S., Zhang, H., & Taeihagh, A. (2025). Why and how is the power of Big Tech increasing in the policy process? The case of generative AI. Policy and Society, 44(1), 52-69.

Komianos, V., Tsipis, A., & Kontopanagou, K. (2024). Introducing Digitized Cultural Heritage to Wider Audiences by Employing Virtual and Augmented Reality Experiences: The Case of the v-Corfu Project. Technologies, 12(10), 1-31.

Kowhai (2022). Māori cultural sites among most vulnerable to climate change, rising sea levels.

KPMG (2023). Future of extended reality.

Kulesz, O. (2024). Artificial Intelligence and international cultural relations. Challenges and opportunities for cross-sector collaboration.

Lai, Y., Yang, J., Liu, M., Li, Y., & Li, S. (2023). Web3: Exploring decentralized technologies and applications for the future of empowerment and ownership. Blockchains, 1(2), 111-131.

Lanz, J. A. (2023). AI art wars: Japan says AI model training doesn’t violate copyright.

Lee, A. (2024). Māori speech AI model helps preserve and promote New Zealand indigenous language.

Liew, C. L., Yeates, J., & Lilley, S. C. (2021). Digitized indigenous knowledge collections: Impact on cultural knowledge transmission, social connections, and cultural identity. Journal of the Association for Information Science and Technology, 72(12), 1575-1592.

Lindemulder, G. & Kosinski, M. (2024). What is cybersecurity?

Masenya, T. M. (2023). Revitalization and digital preservation of indigenous knowledge systems for sustainable development of indigenous communities in South Africa. The Serials Librarian, 84(5-8), 86-102.

McMullan, D. (2025). New INMA report tackles what’s next in a post-search world.

Melo, G. (2025). AI erasure – how AI could reshape our understanding of history and identity.

Mercier, K. (2024). Premiums Under Pressure. How climate change is reshaping residential property insurance and what to do about it.

Microsoft (2021). Bringing Te Ao Māori to life in a digital classroom.

Miller, E. J., Steward, B. A., Witkower, Z., Sutherland, C. A. M., Krumhuber, E. G., & Dawel, A. (2023). AI hyperrealism: Why AI faces are perceived as more real than human ones. Psychological Science, 34(12), 1390-1403.

Ministry for Culture & Heritage (2024). Strategic Intentions Koromakinga Rautaki 2025–29.

Ministry for the Environment (n.d.). Extreme weather events. Climate change is making extreme weather events more powerful and more frequent.

Miranda, E. R. (2022). Quantum Computing in the Arts and Humanities. Springer: Cham, Switzerland.

Murray, A., Kim, D., & Combs, J. (2023). The promise of a decentralized internet: What is Web3 and how can firms prepare? Business Horizons, 66(2), 191-202.

Nguyễn, T. (2024). Google agreed to pay millions for California news. Journalists call it a bad deal.

National Cyber Security Centre (2024). Cyber Threat Report 2023/2024.

Niccolucci, F., Felicetti, A., & Hermon, S. (2022). Populating the data space for cultural heritage with heritage digital twins. Data, 7(8), 1-28.

Nicoletti, L. & Bass, D. (2023). Humans are biased. Generative AI is even worse.

NIWA (n.d.). Climate change scenarios for New Zealand.

Noetel, M., Saeri, A., & Graham, J. (2024). 80% of Australians think AI risk is a global priority. The government needs to step up.

NZ On Air (2023). Where are the audiences 2023.

OECD (2024). Strategic Foresight.

Oh, J., Goldberg, K., Maiolino, P., Bern, J., Rodrigue, H., & Hughes, J. (2025). Special issue on the arts and robotics. Aim and scope.

OpenAI (2025). Introducing Operator. A research preview of an agent that can use its own browser to perform tasks for you. Available to Pro users in the U.S.

Opengov (2021). Modern Tech for New Zealand’s Cultural Heritage.

Pacheco, E. (2024). Older adults' safety and security online: A post-pandemic exploration of attitudes and behaviors. Journal of Digital Media & Interaction 7(17), 107-126.

Pacheco, E., & Burgess, H. (2024). The divide between us: Internet access among people with and without disabilities in the post-pandemic era. Disability & Society, 1-22.

Pasikowska-Schnass, M. & Lim, Y. (2023). Artificial intelligence in the context of cultural heritage and museums. Complex challenges and new opportunities.

Peacock, C. (2024, August 18). The good, bad and ugly of AI for news. RNZ.

Pope, A. (2024). AI artwork of Alan Turing sells for $1m.

Psatha, A., Mitropoulou, C. & Patrinos, G.P. (2025). Genomics and athletic performance: An emerging discipline that is not yet ready for society. Human Genomics 19(40).

Public Service Association (2025). A.I for good. 10 perspectives on artificial intelligence.

Purdy, M. (2024). What is agentic AI, and how it will change work?

Rainie, L. & Anderson, J. (2024). Experts imagine the impact of artificial intelligence by 2040.

Reid, B. (2024). Fast Forward Aotearoa: How exponential technology is defining the future of New Zealand...and what we can do about it. University Book Shop.

Reserve Bank of New Zealand (2020). Cyber incident cost estimates and the importance of building resilience.

Reserve Bank of New Zealand (2024). Geopolitical tensions highlighted as risk to financial stability.

Saura García, C. (2024). Digital expansionism and big tech companies: Consequences in democracies of the European Union. Humanities and Social Sciences Communications, 1-8.

Scheider, J. & Smalley, I. (2023). What is quantum cryptography?

Shepherd, T. (2024). How democratizing access to innovative AI tools is revolutionizing creativity.

Shibu, S. (2024). Jack Dorsey says it will soon be ‘impossible to tell’ if depfakes are real: ‘Like you’re in a simulation’. Entrepreneur, 24 June 2024.

Siegel, J., Morris, D. (2020). Robotics, automation, and the future of sports. In: Schmidt, S.L. (eds) 21st Century Sports. Future of Business and Finance. Springer, Cham.

Simon, F. (2024). Artificial intelligence in the news: How AI retools, rationalizes, and reshapes journalism and the public arena.

Smale, A. (2023). Artificial intelligence revives Māori dialects.

Spyridou, P., & Ioannou, M. (2025). Exploring AI Amid the Hype: A Critical Reflection Around the Applications and Implications of AI in Journalism. Societies, 15(2).

Stackpole, B. (2024). MIT report details new cybersecurity risks.

StartUS Insights (2024). What’s currently happening in brain-computer interfaces? (Q2 2024).

Statista Market Insights (2024). Smart streaming devices - New Zealand.

Stats NZ (2022). National population projections: 2022(base)–2073.

Stats NZ (2023). Estimated resident population (2023-base): At 30 June 2023.

Stats NZ (2024). New Zealanders’ trust in key institutions declines.

Stevens, M., Kraaijeveld, S. R., & Sharon, T. (2024). Sphere transgressions: Reflecting on the risks of big tech expansionism. Information, Communication & Society, 27(15), 2587-2599.

Technz (n.d.). Information for teachers, careers advisors, ākonga and their whānau.

Te Mana Raraunga (2020). Māori Data Governance Model.

Te Mana Raraunga (2025). What is Māori Data Sovereignty?

The Big Idea (2024). Success In Self-Publishing.

The Treasury (2021). Statement on the long term fiscal position He Tirohanga Mokopuna 2021.

The Treasury (2022). Social cohesion in New Zealand. Background paper to Te Tai Waiora: Wellbeing in Aotearoa New Zealand 2022.

Thompson, B., Dhaliwal, M., Frisch, P., Domhan, T., & Federico, M. (2024). A shocking amount of the web is machine translated: Insights from multi-way parallelism. In Findings of the Association for Computational Linguistics ACL 2024 (pp. 1763-1775).

Toner, H. (2023). What are Generative AI, Large Language Models, and Foundation Models?

Torkington, S. (2024). These are the 3 biggest emerging risks the world is facing.

UN Environment Programme (n.d.). AI has an environmental problem. Here’s what the world can do about that.

UN Trade & Development (2024). K-pop blueprint: Drawing inspiration from South Korea's creative industries.

University of Plymouth (n.d.). Is AI-generated art actually art?

Van Deursen, A. J., & Van Dijk, J. A. (2015). Toward a multifaceted model of internet access for understanding digital divides: An empirical investigation. The information Society, 31(5), 379-391.

Verhulst, I., Woods, A., Whittaker, L., Bennett, J., & Dalton, P. (2021). Do VR and AR versions of an immersive cultural experience engender different user experiences? Computers in Human Behavior, 125, 1-10.

Vick, A. (2023). Web3: A new paradigm for creativity & ownership in the artistic world.

Waitangi Tribunal (2011). Ko Aotearoa Tēnei. A Report into claims concerning New Zealand law and policy affecting Māori culture and identity.

Walton, D. J. (2024). Culturally responsive computing: An introduction into computer science, security, and technology.

WeCreate (2025). Creative Export Explainer.

Whareaitu, M. (2025). Apple teams up with iwi to add new Māori features in Maps.

Withers, S. (2024). Auckland museum enhances digitisation efforts with storage upgrade.

World Bank (2024). Individuals using the internet (% of population) – New Zealand.

World Economic Forum (2024). Global Risks Report 2024.

World Economic Forum (2025). Global Cybersecurity Outlook 2025.

Worrell, T. (2024). AI affects everyone – including Indigenous people. It’s time we have a say in how it’s built.

Yates, S. (2023). The dangers of digital colonisation.

Zarouali, B., Boerman, S. C., & de Vreese, C. H. (2021). Is this recommended by an algorithm? The development and validation of the algorithmic media content awareness scale (AMCA-scale). Telematics and Informatics 62.

Appendix 1: Methodology and limitations

As per the Department of the Prime Minister and Cabinet (DPMC) guidance, the Long-term insights briefing (LTIB) process (below) has eight steps. While these steps are presented in a linear fashion below, in practice these steps overlapped. This appendix outlines the methodology applied for the first four steps of the LTIB process between July 2024 and June 2025.

Steps of the LTIB process as set out in DPMC guidance.

1. Gather information about the future
2. Consider topic and potential for joint Briefings
3. Engagement on proposed topic
4. Develop draft Briefing content
5. Engagement on draft Briefing
6. Present final Briefing to the House
7. Select committee examination
8. Conduct review activity

Methodology

Gather intelligence about the future

Evidence review

To gather relevant intelligence and insights, Manatū Taonga engaged in a range of research activities including:

• reviewing national and international research on the use of digital technologies in the cultural and creative industries, arts, culture and heritage, media, and sport and recreation
• reviewing evidence on key drivers of change and emerging trends in technology, with a focus on their application to the cultural system
• analysing and synthesising the evidence to identify key themes
• speaking with subject matter experts to identify emerging trends.

Futures thinking activities

A range of techniques can be used to support futures thinking. Following guidance from futures strategists, after conducting research on drivers and trends, the following techniques were undertaken:

• Horizon scanning – systematically looking for early signs of change that could disrupt the cultural system
• Three horizons – considering how dominant a way of working is and how that may change over time
• Futures scenarios – considering how key drivers and trends are likely to impact the cultural system and could create different futures, and how different futures would impact diverse personas
• Wind tunnelling – testing potential policy options against different future scenarios.

See DPMC’s website for more information on futures thinking and the techniques above

DMPC

Consider topic and potential for joint briefings

Manatū Taonga engaged with the following agencies who were interested in exploring digital technology for their LTIB and/ or who hold responsibilities relating to digital technology:

• Department of Internal Affairs Te Tari Taiwhenua
• Ministry for Business, Innovation and Employment Hīkina Whakatutuki
• Ministry for Pacific Peoples Te Manatū mō ngā Iwi ō te Moana-nui-ā-Kiwa
• Ministry for Women Manatū Wāhine

Following engagement, it was determined it would not be appropriate or feasible to undertake a joint LTIB.

Engagement on the proposed subject matter

Topic consultation was held from 16 September to 7 October.Manatū Taonga received a total of 49 submissions. Respondents represented individuals and organisations in the arts, culture, heritage, media and broadcasting, and sport sectors, as well as across government. Following consultation, the proposed LTIB topic wording and scope for the LTIB were revised.

Read a summary of topic consultation submissions here

Develop the Briefing content

Content development workshops and subject matter expert engagement

The Ministry conducted two internal and eight external workshops with stakeholders from across the cultural system and government, and further engaged with digital technology subject matter experts. These workshops provided a forum to test the draft futures scenarios and gather Aotearoa New Zealand and cultural system perspectives to augment those obtained from the evidence review. These discussions also helped to shape the themes for the briefing and further identify future risks and opportunities, which informed the development of policy options.

Policy analysis

The policy analysis process included the following steps:

1. Define scope – Define and clearly articulate the boundaries of the policy analysis based on identifying specific issues to be explored within the briefing.
2. Review evidence – Review the evidence collected during the Gather phase, including written materials/ grey literature and information gathered from other sources such as meetings and workshops.
3. Identify trends, risks and opportunities – Analyse current trends and anticipated future developments in relation to each of the identified focus areas and draw out key insights.
4. Apply analytical frameworks – Identify the analytical frameworks that will be used to underpin policy analysis and their relevance (i.e., New Zealand Public Service Artificial Intelligence [AI] framework based on Organisation for Economic Co-operation and Development [OECD] AI principles framework and Manatū Taonga strategic outcomes). Make the underlying assumptions and rationale for the chosen frameworks clear.
5. Incorporate Te Tiriti analysis – Given the implications of digital technology for te ao Māori is a cross-cutting theme for our LTIB, the analysis considered how the risks, opportunities and policy options could affect Māori and uphold the Te Tiriti o Waitangi Treaty of Waitangi.
6. Develop policy options – Formulate multiple policy options to address the identified trends, risks and opportunities, so that a range of potential policy levers and options are articulated for key issues explored in the briefing.
7. Evaluate policy options – Assess the strengths and weaknesses of each policy option in relation to the identified criteria and alignment with the strategic framework.
8. Outline limitations – Discuss potential limitations or uncertainties in the data, information and analysis, and state the implications of these limitations or constraints.

Limitations of the data, information and policy analysis

Limited robust evidence on distribution of technological impacts

Limitations:

• AI’s role in cultural heritage, media, sports and digital storytelling is still new and evolving, meaning there is a lack of empirical studies on its effects over time.
• While economic modelling and forecasts exist, real-world evidence on the impacts of emerging technologies on job creation, creative diversity and cultural expression remains speculative or anecdotal.
• Digital technology disrupts industries at different speeds. While some creative jobs have been heavily affected (for example, digital artists and musicians), other cultural and creative applications (for example, AI-generated literature) remain niche.

Implications for future policy design:

• Policy assumptions may not fully account for long-term consequences—some technology applications may be overestimated or hyped, while others may be underestimated.
• The analysis relies on current research, future projections, expert and stakeholder opinions, and international comparisons, which may not fully reflect New Zealand’s unique cultural and regulatory context.
• Gaps in real-world data mean that some policy options would likely need to be tested through pilots before full-scale implementation.

Fast-moving nature of new and emerging technologies

Limitations:

• AI, blockchain, Web3 and immersive digital technologies are evolving rapidly, making static policy analysis quickly outdated.
• AI and digital technology regulations worldwide are still developing—what is considered “best practice” today may change dramatically within months or years.
• Many governments and international bodies (for example, the European Union, United Nations Educational, Scientific and Cultural Organisation [UNESCO] World Intellectual Property Organisation [WIPO] and the OECD) are still debating AI and digital technology governance models, meaning that the global regulatory environment is in flux.

Implications for future policy design:

• Some policy options may need frequent updates to remain relevant- policymakers should build in mechanisms for regular review.
• Uncertainty in international AI governance means New Zealand’s alignment with global AI laws could shift depending on how leading economies (including the United States and China) regulate AI.
• Adoption rates for digital technologies in creative, cultural and sports sectors are unpredictable—some applications (for example, AI-assisted refereeing) may be widely adopted, while others (for example, AI-generated theatre performances) may remain niche.

High-level nature of the policy evaluation

Limitation:

• The policy evaluation is broad and strategic, rather than a detailed regulatory impact assessment or cost-benefit analysis.
• Many policy options are conceptual and have not undergone detailed feasibility studies.
• Sector-specific considerations (for example, the impact of emerging technologies on Māori broadcasting or on community sports funding) are not explored in depth.

Implications for future policy design:

• Some policy options would require further feasibility assessments (for example, piloting AI-assisted training programmes in schools or polytechs before nationwide rollout).
• The policy analysis does not quantify costs and benefits, meaning budgetary and economic trade-offs remain unclear.
• Policymakers will need additional sector-specific engagement (for example, consulting sports bodies, cultural institutions, Māori digital leaders) to refine and tailor policy interventions.

Uncertainty in international comparisons

Limitation:

• The international examples used in the analysis reflect recent policy trends, but some are still in experimental or pilot stages, or are subject to change due to changes in political context.
• AI and emerging technology policies in the European Union, United States and Asia-Pacific are highly context-dependent—what works in one jurisdiction may not translate directly to New Zealand due to differences in legal, cultural and economic factors.
• Some international digital technology regulations remain proposed rather than enacted, making it difficult to predict their full impact or effectiveness.

Implications for future policy design:

• New Zealand specific trials or engagement may be needed before adopting certain international approaches.
• Ongoing monitoring of AI and digital technology policy developments globally is essential to ensure New Zealand remains aligned with best practice.
• Some international policies may not be directly applicable due to New Zealand’s unique Te Tiriti obligations and bicultural governance framework.

Regulatory trade-offs not fully explored

Limitation:

• Some policies (for example, human-made content labelling standards) may increase transparency but also impose compliance burdens on creative industries.
• Stricter AI and emerging tech regulations could slow down innovation, while a light-touch approach could lead to ethical risks and bias.
• The analysis does not fully quantify regulatory trade-offs (for example, balancing technological innovation vs. consumer protection).

Implications for future policy design:

• Policymakers may need to conduct cost-benefit analyses to weigh the economic, cultural and legal impacts of new and emerging technology regulations.
• More stakeholder engagement with technology developers, cultural leaders, legal experts and regulators is needed to refine policy trade-offs.
• Some governance models may need phased implementation, balancing short-term innovation with long-term accountability.

Appendix 2: Policy framework rationale and evaluation criteria

The Department of the Prime Minister and Cabinet (DPMC) identified several best practice examples from the last LTIB cycle that applied a strategic framework to policy analysis. These include the Ministry of Transport’s 2022 LTIB, which used the Transport Outcomes Framework as its strategic lens, and the Treasury’s 2021 combined LTIB and Long-term Fiscal Position, which used the Living Standards Framework and He Ara Waiora as frameworks for considering the wider wellbeing implications and distributional impacts of fiscal objectives and the means of achieving them.

To apply a strategic framework for the policy analysis in this LTIB, Manatū Taonga has drawn on the OECD AI Principles and the Manatū Taonga strategic outcomes and priorities for the cultural system. This appendix provides additional detail on the rationale for using these, along with the evaluation criteria used to assess the policy options presented in the briefing.

Rationale for using OECD AI Principles

This section identifies different attributes of the OECD AI Principles and explains how they are useful for analysing potential policy options.

Rationale for using Manatū Taonga strategic outcomes and priorities

This section highlights attributes of the Manatū Taonga strategic outcomes and priorities, and shows how they are useful for analysing potential policy options.

Policyevaluation criteria

The potential policy options were assessed to identify strengths and weaknesses based on the following criteria:

1. Effectiveness and strategic alignment
   • Does it support New Zealand’s cultural system goals?
   • Does it align with OECD AI Principles?
   • Does it uphold Te Tiriti o Waitangi obligations?
   • Are there international precedents or similar policy examples in other jurisdictions?
2. Impact and equity
   • Who benefits? (Māori, Pacific peoples, creatives, elite sports, private interests?)
   • Does it reduce inequities or risk widening them?
   • Does it address environmental, cultural and social sustainability?
3. Feasibility and cost
   • What are the upfront vs. long-term costs?
   • Are there regulatory, technological or social barriers to adoption?
   • Does it require government funding, or can it leverage private investment?
4. Risk and trade-offs
   • Does it create unintended consequences?
   • Does it require balancing privacy, transparency or innovation?
   • Does it introduce compliance burdens on industry or communities?

Appendix 3: Trends and drivers of change

This appendix provides additional detail on the emerging trends in digital technology and key and emerging broad drivers of change highlighted in Section 1.

Trends in digital technology

With the increasing proliferation of digital technology, there is a range of technologies that are effecting change and have the potential to reshape how New Zealanders create, share and protect their stories. This appendix discusses key technological trends that have been identified as being highly relevant by subject matter experts for this LTIB. (Parts of this section include input from members of the AI Forum of New Zealand which was received during stakeholder engagement activities.)

Generative AI

Generative AI (GenAI) is a “broad term that is used for any AI system whose primary function is to generate content” (Toner, 2023, para 4). This contrasts with other AI systems that serve other functions, such as classifying and grouping data, or choosing actions. GenAI encompasses a range of tools including image generators, large language models, code generation tools and audio generation tools. According to one study, in 2024 57 percent of internet content was at least partly generated or translated by AI (Thompson et al., 2024, p. 2). Some experts anticipate that this figure will grow to as much as 90 percent of internet content within a few years (Nicoletti & Bass, 2023). It is projected that AI could potentially surpass human intelligence by 2045 (“AI scientist Ray”, 2024).

GenAI can also be used to “super-charge” other forms of technology and AI, for example speeding up testing or running ‘what if’ scenarios and reducing the need to design a specific interface in order to create or change a digital product based on user criteria. New opportunities will also emerge through technological convergence, where the merging of previously distinct technologies may lead to new functionalities and capabilities, for example with AI and robotics.

Developments in GenAI have unlimited application across New Zealand’s cultural system, assisting in the creation and automation of artistic works, creative and media content; language transcription, translation and preservation; and sports analysis. This may help to increase productivity and efficiency, freeing up time for creative activities. For example, creative toolsets now allow creatives to story board and create realistic impressions of what a final photo or film may become before they go on location, removing unnecessary delays and costs from productions, and allowing that time and budget to be used to create a better product.

A range of AI tools can also be used to support increased access to and participation in cultural experiences, especially for people with disabilities (Hafizov, 2023). In New Zealand, these tools are rapidly evolving and growing in capability, for example New Zealand start-up Kara Technologies’ new applied AI technology to provide sign language interpretation by digital humans is currently nearing release (https://www.kara.tech/).

Robotic process automation and agentic AI

Robotic process automation (RPA), also known as software robotics, uses intelligent automation tools or ‘bots’ to perform repetitive enterprise and administration tasks on behalf of human workers, such as extracting data and completing forms (IBM, n.d.). RPA can significantly improve productivity beyond human capabilities, reducing the risks of human error and speeding up processing times, for example in data entry, and improving business results. However, ethical concerns have arisen in the implementation of RPA, for example regarding impacts on job displacement, data privacy and security, and transparency and accountability (Dale, 2025).

RPA is being used to automate the documentation and cataloguing of art collections and cultural artifacts. For instance, museums and galleries can use RPA to streamline the process of recording details about new acquisitions, updating inventory records and managing loan agreements. This not only saves time but also reduces the risk of human error, ensuring that records are accurate and up to date (Oh et al., 2025).

In the media sector, some news organisations use RPA to automate the more mechanical tasks involved in the creation of news articles, social media posts and video content. This allows journalists to focus on more complex tasks, such as investigative reporting, while RPA handles routine content production and distribution (Clearmatic, 2024).

RPA is also being used to automate the collection and analysis of sports data. For example, sports organisations can use RPA to gather data from various sources, analyse player performance and generate detailed reports. This helps coaches and analysts make informed decisions and enhances the overall efficiency of sports management (Siegel & Morris, 2020).

Recently, major RPA providers have started to move towards agentic AI. Agentic AI or AI agents, a further step in the evolution of AI, are reshaping how humans and AI interact and collaborate. Where AI assistants are reactive, performing programmed tasks, Agentic AI is AI that is empowered to act proactively and make decisions on behalf of their user. AI agents can realise particular goals or objectives and carry out complex sequences of activities independently (Purdy, 2024).

New tools for building agents have emerged with increasing capability, including the launch of OpenAI’s Operator in January 2025 (OpenAI, 2025). Further platforms like Vertex AI Agent Builder and Copilot Studio provide low-code or no-code options for building agents, particularly within specific ecosystems like Google Cloud and Microsoft 365, removing the need for a programmer to build an agent. Agentic AI technologies are already being used to restore and conserve artworks, as well as being employed in the preservation of cultural heritage sites, and in archaeological and historical research (Pasikowska-Schnass & Lim, 2023).

Web3 decentralised platforms

As a concept, Web3 refers to the next evolutionary stage of the internet, which aims to reshape the current Web2.0 architecture into a more autonomous or user-empowered digital experience. Where Web2.0 provided read-write capabilities, allowing users to interact and share information with an online community, Web3 offers read-write-execute capabilities, enabling intelligent and decentralised web experiences. In terms of the technology itself, Web3 includes a range of emerging technologies for blockchain technology, including fungible tokens (cryptocurrencies), nonfungible tokens (NFTs), decentralised autonomous organisations (DAOs) and metaverses (Murray et al., 2023).

Artists are using blockchain technology to create and sell NFTs, which are unique digital assets that represent ownership of a specific piece of art. This not only provides artists with a new revenue stream but also ensures the authenticity and provenance of digital artworks. Platforms like OpenSea enable artists to sell their NFT art directly to collectors, bypassing traditional galleries and auction houses (Ford, 2025). In New Zealand’s Heritage sector, projects such as Digitaonga are using blockchain to enable new methods of repatriation and preservation of taonga.

In the sports sector, Web3 platforms are being used to enhance fan engagement through tokenisation. For example, Socios.com allows fans to purchase tokens that give them voting rights on certain club decisions, creating a more interactive and participatory experience, while monetising engagement to generate revenue. This model is being adopted by various sports teams around the world, including football clubs and electronic sports (esports) organisations.

Web3 platforms offer increased privacy and data sovereignty compared to current digital infrastructure by empowering locally-controlled data storage and management (Lay et al., 2023). With applications across heritage, media and the wider cultural system, harnessing blockchain’s ability to bring together decentralised data for specific purposes is one of the key ways that data sovereignty and data ownership issues can be addressed, as it allows data to stay where it originates and to be shared by permission. For example, platforms like Mirror.xyz allow writers and content creators to publish their work on a decentralised network, where they can retain full ownership and control over their content. This can reduce reliance on traditional media outlets and provide creators with more direct access to their audience (Vick, 2023).

Extended reality and immersive technology

Extended reality (XR) technology, including augmented reality (AR), mixed reality (MR), and virtual reality (VR) are creating new ways to interact with and through digital content, impacting social connection, cultural participation, education and remote work. However, high costs, complex usability and the need for staff training have limited their adoption across the cultural system to date. New training offered by companies in New Zealand such as StaplesVR for people in the entertainment industry may help address these challenges.

Virtual spaces enable new social and economic opportunities. In a report on the future of extended reality (KPMG, 2023), technology experts predicted that by 2030, XR will have reshaped human interactions, with some people spending “more physical conscious time in the metaverse than the real world”, and higher education delivered on “a virtual campus with digitised assets such as museums as a service”.

The research above further predicted that “the flatscreen, keyboard and a mouse will disappear and be replaced by wafer-like glasses and contact lenses for spatial collaboration across multi-disciplines” (KPMG, 2023, p. 4). This immersive technology offers further opportunities to alter and shape participation in cultural experiences, for example attending a virtual concert, exhibition or heritage site (Anwar et al., 2025; Komianos et al., 2024; Verhulst, 2021).

Wearable technology

Wearable technology such as fitness trackers, VR headsets, wearable cameras, GPS trackers and an array of smart devices are already creating new possibilities to understand, monitor and enhance physical or biological performance. Brain-computer interfaces, which take the next step in integrating technology with the human body, offer further promise and have recently reached new milestones towards blending AI with human cognition. By 2032, brain-computer interfaces are projected to have a market value of US$3.26 billion, with the potential to drastically alter the global economy (StartUS Insights, 2024). These technologies—especially when combined with AI— hold applications across the arts and sport and recreation, both in terms of enhancing performance of artists and athletes, and for communities, such as people with disabilities, that face challenges participating in cultural activities.

Enhanced connectivity – 5G, 6G and Edge computing

Enhanced connectivity through advanced network technologies and internet-enabled devices will enable new services and business models with 6G, the sixth generation of fast-speed wireless networks, expected to be commercially available in the early 2030s (Ericsson, n.d.).

Edge computing brings enterprise applications closer to data sources, enabling faster and local processing of real-time data, and improving bandwidth availability (IBM, n.d.). It can also help to increase privacy and data sovereignty, allowing data processing to be done without sharing data offshore, and support data processing in remote locations.

In the cultural system, this holds applications for example in screen production, when filming in difficult or remote locations, such as under sea ice. In the future, enhanced connectivity could enable hyper-realistic remote participation in cultural and sporting events and further enable large amounts of data—including arts and cultural content—to be created, shared and consumed.

Quantum computing

Quantum computing is expected to further boost computing power, accelerating the adoption of emerging technologies, and further advancing cybersecurity and encryption. While IBM, Google and D-Wave have been running quantum processors for some time, recent advancements such as Microsoft’s Majorana 1 chip unveiled in February 2025 signal progress towards making the technology commercially available. Microsoft anticipates this technology will “realise quantum computers capable of solving meaningful, industrial-scale problems in years, not decades” (Bolgar, 2025, para. 1).

In the future, quantum computing could open new frontiers in digital art creation. Artists could leverage quantum algorithms to generate intricate and data-heavy visual media, creating highly interactive and dynamic experiences. For instance, quantum-enhanced algorithms could help artists push the boundaries of creativity by crafting complex digital artworks that were previously impossible with classical computing (Miranda, 2022). Quantum technologies could also be used to analyse and preserve cultural artifacts. Quantum sensors and imaging techniques, already in use, can provide deeper insights into the molecular structure of historical artifacts, enabling more accurate restoration processes. This ensures that cultural heritage is preserved with minimal damage, enhancing long-term conservation efforts (Allen Yarnell Center for Student Success, n.d.).

Quantum computing could also be applied in sports analytics to optimise performance and strategy. By processing and analysing large datasets more efficiently, quantum algorithms could provide deeper insights into player performance, game strategies and injury prevention.

Digital twins

Digital twin technology, where data is used to create virtual replicas of a physical object, system or process, enables predictive maintenance, real-time monitoring and performance optimisation. This could have applications for example in sport and recreation (Hliš et al., 2024) as well the protection and monitoring of cultural heritage (Niccolucci et al., 2022).

Digital twins are also transforming media production by enabling virtual sets and environments, for example with virtual 3D modelling technology such as photogrammetry, which enables filmmakers and content creators to work together on and around a potential shooting location, without having to be at a physical site together in person (Accenture, 2025).

Key drivers of change

Shifting demographics toward a more diverse and aged population

Overall, New Zealand’s population will age, and growth will slow. According to Stats NZ’s (2022) national population projections, New Zealand’s population growth is expected to slow as the population ages and the gap between the number of births and deaths narrows. There is a roughly 25 percent chance the population will stop growing in the 2050s and start to decline by the 2060s. In 2024, New Zealand’s population was almost 5.27 million. By 2033, the total population is projected to reach between 5.28 and 5.85 million, and by 2073, it could be as high as 7.86 million.

New Zealand’s population on average is expected to age, with the proportion of the population aged 65+ increasing to between 21 to 25 percent by 2048, and 24 to 32 percent by 2073. Migration remains a volatile and uncertain factor, and while it is the only population component directly controllable by the government, high migration rates will not offset the ageing population (Stats NZ, 2022).

New Zealand will become more ethnically diverse, with higher populations of Māori, Asian and Pacific peoples. According to Stats NZ’s (2022) Ethnic population projections, if current growth rates continue, the Māori population is expected to reach one million by 2030, and by 2043:

• 21 percent of all New Zealanders and 32 percent of children (under age 14) will identify as Māori
• 24 percent of all New Zealanders and 27 percent of children (under age 14) will identify as Asian
• 11 percent of all New Zealanders and 18 percent of children (under age 14) will identify as Pacific.

A greater number of New Zealanders will also hold linguistic, cultural and physical connections to other home nations and diasporic communities.

Increasing geopolitical instability and economic shocks

Heightened geopolitical tensions can disrupt international trade, weaken domestic demand and lead to market volatility (Reserve Bank of New Zealand, 2024). Historically, large, disruptive economic shocks have occurred at least once every decade (Easton, 2023). This means that by 2040, we can expect one to two more significant economic shocks.

The need to respond nationally to significant economic shocks will place greater demands on government on top of existing challenges, leading to the prioritisation of investment in critical services and infrastructure. For example, New Zealand is currently facing infrastructure challenges, with up to $185 billion of investment needed for water infrastructure alone over the next 30 years (Department of Internal Affairs, 2023). The Treasury (2021) projects that by 2061, if expenditure and revenue follow historical trends, net debt will be unsustainable.

More frequent and intense weather events

New Zealand will experience severe weather events with increasing frequency and intensity. NIWA projects that by 2040, temperatures will increase by between 0.7°C and 1.0°C nationally. By 2090, this increase is between 0.7°C and 3.0°C. Very extreme precipitation events, reoccurring every 2 years or greater, will increase per degree of warming by five percent for five-day duration events and 14 percent for one-hour duration events. According to NIWA, extreme weather events could expose 700,000 people and 411,516 buildings to river flooding alone, and up to 10,000 houses could be uninsurable by 2050 (Mercier, 2024).

Māori cultural sites, including burial sites and plantations or food sources, are particularly at risk—80 percent of the almost 800 marae in Aotearoa are built on low-lying coastal land or flood-prone rivers. Within this, 191 marae are situated within one kilometre of the coastline, which may experience a 30-centimeter sea level rise in only 10 to 20 years (Kowhai, 2022).

Weather events are increasingly linked to how people use digital technology. This works both ways. Unforeseen events can lead to greater adoption and use of digital technology and digital innovation. For example:

• social media plays a critical role in providing emergency response, information dissemination and community support after extreme events (Flew et al., 2014).
• the COVID-19 pandemic accelerated the adoption of digital technology such as broadband internet, social media, cloud computing and networking tools to enable flexible working arrangements and help people to maintain social connections (Green et al., 2020)
• digital tools (for example, GPS tracking, drones, satellite imaginary) are being used to help understand and reduce the impact of climate change (Ministry for the Environment, n.d.).

At the same time, the increasing demands of digital technology on energy and natural resources may further impact climate change. As the UN Environment Programme (2024) has recently highlighted, data centres housing large-scale AI deployments, including those operated by cloud service providers, take a heavy toll on the environment. For example:

• Making a 2kg computer requires 800kg of raw materials
• The microchips that power AI require rare earth elements, which are often mined in environmentally destructive ways
• Data centres produce electronic waste, which often contains hazardous substances
• A request made by ChatGPT consumes 10 times the electricity of a Google search.

In countries such as Ireland, a technology hub, AI data centres could account for almost 35 percent of the country’s energy use by 2026.

Increasing challenges to social cohesion and declining trust in institutions

“The opposite of social cohesion is polarisation. Social cohesion can be understood as the glue that holds our communities and society at large together. Without social cohesion, societies become increasingly unstable – from politics to business to civil society to day-to-day life in our communities. This is a pattern increasingly seen around the world, and New Zealand is not immune” (Helen Clark Foundation, 2025, p. 6).

Declining social cohesion and trust is a threat to democracy and democratic institutions. Social cohesion in New Zealand communities is still relatively high, however there are significant threats on the horizon. While many New Zealanders have a strong sense of belonging and social connection, this has been declining since 2016 (Stats NZ, 2024). Recent research has found that other indicators such as loneliness, financial insecurity and food insecurity are increasing, while trust and safety in local communities are decreasing (Helen Clark Foundation, 2025). More New Zealanders trust central government (63 percent) than the OECD average (51 percent), however, trust in key institutions (Parliament, media, health system, education system, court system, police) is at its lowest since 2014 (Stats NZ, 2024). When compared internationally, New Zealand’s social cohesion is eight percentage points lower than Australia (49 percent vs. 56 percent), with New Zealand lagging on every dimension: social and political participation, sense of belonging, sense of worth, social inclusion and justice, and acceptance and rejection (Helen Clark Foundation, 2025).

A key factor in declining trust in institutions, including government and media, is the rise of mis- and disinformation, which the World Economic Forum (2024) ranks among the top global risks for the next decade.It is more difficult for people to tell what is true or not due to increasing mis/disinformation, and the mitigation of mis/disinformation’s negative effects can create additional resource and social burdens on government, businesses and the public. In New Zealand, the number of people who are extremely or very concerned about misinformation has increased significantly from 48 percent in 2019 to 65 percent in 2023 (InternetNZ, 2023).

The volume, reach and effectiveness of misinformation is increasing with AI, which has made it easier to create and amplify malicious content. Such synthetic content includes deepfake videos, cloned voices and websites that look like legitimate news outlets; content that challenges the credibility of digital narratives. The impacts of AI-driven misinformation include disruption of electoral process, civil unrest and confrontation, increased polarisation and distrust of media and government (Torkington, 2024). With social media and large media companies pulling back from fact-checking, there is an increased risk that more New Zealanders will become susceptible to mis- and disinformation and that social cohesion, trust and confidence in institutions will be further affected.

Widening digital divide

There are continued disparities in people’s access to digital technology. Digital disparities are reproducing and reflecting existing inequalities in society (DiMaggio & Garip, 2012). Digital disparities include lacking material access to digital tools as well as people’s skills or technological competence, usage, motivations and attitudes (Van Deursen & Van Dijk, 2015).

Digital inequity has traditionally impacted population groups such as people with disabilities, Māori, Pacific peoples, people in social housing, older adults, the unemployed and underemployed, and remote communities (Digital Govt NZ, 2021; Pacheco, 2024; Pacheco & Burgess, 2024). In New Zealand, people who are a part of several of these groups face even greater challenges with digital inclusion (Grimes & White, 2019). For example, the introduction of fibre has provided opportunities for faster internet connections, benefiting activities such as remote work and telehealth services. However, a broadband digital divide has emerged, as people in rural areas, older adults and individuals with disabilities are less likely to have access to fibre at home (Pacheco & Burgess, 2024).

AI is further widening the digital divide and inequity more broadly. With the recent advent of GenAI, the gap between who can and cannot understand, develop or manage AI technologies is widening (Hendawy, 2024). While AI has the potential to generate policy interventions to address difficult societal problems, it may also exacerbate existing digital inequalities particularly in relation to workplaces and productivity, education and healthcare. Where benefits (and costs) are likely to be unevenly distributed across businesses, economic sectors and population groups as well as under-resourced and marginalised communities (Capraro et al., 2024).

Rapid expansion of ‘Big Tech’

Large technology ‘Big Tech’ companies are dominating markets across societal domains. This expansion is not limited to the traditional ‘Big Five’ (i.e., Alphabet, Apple, Meta, Amazon and Microsoft) or the United States, but is a global phenomenon also taking place in Europe, South America, Africa and Asia (Khanal, 2025; Saura García, 2024; Stevens et al., 2024).

There are concerns about the concentration of influence in the hands of large technological companies, as they often control the key resources for innovation, research and development (for example, talent, data and computing power). Governments are reliant on technology companies’ digital infrastructure for data storage and operations, the majority of which—especially in New Zealand’s case—is offshore. Additionally, Big Tech firms are becoming pivotal actors across different stages of the policy cycle (Andhov, 2025).

Global and national regulatory mechanisms have yet to resolve how to manage or mitigate the influence of Big Tech. The expansion and influence of Big Tech has led to increasing regulatory scrutiny and policy action to address issues including data privacy and security risks and monopolising digital media platforms. For example, several countries have imposed and upheld national bans of the social media platform TikTok, with varying reasons including its role in disrupting social harmony, circulating inappropriate content and posing national security concerns (“Click here: The Tiktok Ban”, 2025).

Increasing cybersecurity risks

New Zealand is facing increasingly complex cybersecurity threats. Cybersecurity, defined as “any technologies, practices and policies for preventing cyberattacks or mitigating their impact” (Lindemulder & Kosinski, 2024, para. 1), has been a critical concern among governments, businesses and communities since the inception of digital technologies. Despite protective measures, the landscape of cyber-attacks continues to constantly evolve and become more complex, with increasing threats from both criminals and other countries (National Cyber Security Centre, 2024). This is due to several factors including the growth and increasing capability of emerging technologies such as GenAI, shifting political tensions, the lack of national and international regulation and enforcement mechanisms, increasing cybercrime sophistication, supply chain interdependencies and cyber skills gap (World Economic Forum, 2025). In 2020, it was estimated that within New Zealand, the costs of cyber-attacks could be more than $100 million a year on average for the financial sector alone (Reserve Bank of New Zealand, 2020).

As cybercrime escalates, new approaches are needed to ensure New Zealand is cyber resilient. New cybersecurity tools, such as zero-trust security (Government Communications Security Bureau, n.d.) and quantum cryptography (Scheider & Smalley, 2023), are also emerging to protect critical infrastructure and data. However, within New Zealand, research has shown New Zealand businesses overestimate their cybersecurity and resilience, while many employees lack the skills and awareness to mitigate cybersecurity risks (DATACOM, 2025). This is in keeping with broader global trends (World Economic Forum, 2025). More broadly, countries vary greatly in their capacity to be ‘cyber resilient’ in the face of cyber-attacks—only 25 percent of people in Oceania are confident that the country in which their organisation is based is well prepared to respond to major cyber incidents targeting critical infrastructure, compared to 37 percent for Europe and 45 percent for North America (DATACOM, 2025).

Accelerating pace of digital innovation

Technology is developing at a faster rate than ever before, making it increasingly hard for policy and legislation to stay current, and for data and digital assets to be kept up to date. As Our World in Data’s long-term timeline of technological development illustrates, the pace of technological change experienced within this and the next generation may far outpace previous historical eras, with increasingly sophisticated AI being a significant driver in how quickly innovation may occur in future. Other emerging trends (see Section 1), such as augmented and virtual reality, robotics, advanced network technologies and quantum computing may further increase the speed of change. The focus on rapid technological development is being reflected in global investment, with spending on information technology projected to rise significantly worldwide in 2024/25 and beyond (Gartner, 2025).

Appendix 4: Four future scenarios

Following a process of horizon scanning and identifying key drivers of change, Manatū Taonga developed future scenarios using The Four Futures analytical framework developed by scholar Jim Dator and adapted by futurist John Smart.

The Four Futures model sees the future not as a passive target that will be reached at some point in time, but as a series of possible scenarios representing four ways in which societies and systems grow, evolve and respond to threats, shocks and opportunities.No scenario is considered an inherently good or bad future; a best-case or a worst-case scenario. Or, as Jim Dator puts it, “in the long run, all four generic forms have equal probabilities of happening, and thus all need to be considered in equal measure and sincerity”. (Dator, 2009, p. 7).

The model proposes that the main images (depictions, stories, scenarios) of societal futures can be classified into four recurring groups: Continuation, Collapse, Constraint and Transformation: 

Continuation – Broadly, this refers to the continuation of the status quo; the future is simply whatever is happening now, extended and perhaps amplified. This scenario envisions a future where the current trajectory of societal and economic development continues, with ongoing growth and technological progress.

Collapse (and New Beginning or Creation) – The Collapse scenario represents an inverse to the Continuation scenario and can be described as a movement from a higher stage of development to a lower one. This scenario depicts a catastrophic event, such as a global crisis or financial disaster, that disrupts life as we know it.

Constraint (also sometimes called Limits or Disciplined Society) – This future focuses on adapting to growing internal or external constraints, potentially through new ways of working, resource management or government regulations. Once a constraint has been reached, what does the new equilibrium look like? How are goods and services distributed? What are the impacts, trade-offs and implications? Who are the winners and losers?

Transformation – The Transformation scenario represents rapid, super-exponential growth, usually through some technologically enabled means. This could be, for example, a new industrial revolution, artificial intelligence (AI), genetic engineering, time travel or spacefaring technology.

The following scenarios creatively imagine different futures that could unfold when considering the topic of this LTIB. The AI-generated images included in the scenarios were developed using Microsoft Copilot with “New Zealand society in 2040” and the scenario text as a prompt. These images—at once surprising, intriguing and problematic—aim to stimulate thinking, inviting readers to consider the future role of AI in shaping how New Zealand and its future communities will be represented.

Scenario 1: Aotearoa Unfiltered

Continuation scenario: A booming but largely unregulated digital creative economy, leading to unprecedented storytelling opportunities and increasing misinformation challenges.

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By 2040, digital storytelling in New Zealand is thriving, driven by AI-assisted content creation and immersive virtual reality platforms. With Māori making up a larger share of the population, te reo Māori and tikanga Māori are increasingly embedded in digital storytelling. However, a lack of robust content moderation means misinformation and deepfake narratives flourish. Copyright laws, and frameworks to protect intellectual property rights, have become too difficult to enforce leading to a lack of revenue, funding and investment for creatives and cultural products. The decline of fact-checking, combined with an increase in cyber-crime, erodes trust in digital content. AI-generated athletes and deepfake performances blur the lines between real and virtual competition in sports and the performing arts, leading to controversy over the legitimacy of records and talent. Tech giants dominate digital platforms, shaping public discourse and global geopolitics, and monetising user-generated stories. Free access to digital stories is limited and increasingly hard to come by.

Despite this, new opportunities emerge: AI tools widely translate and help preserve indigenous languages and provide accessibility features for cultural experiences. Blockchain helps to verify “what’s real” and decentralised platforms allow content creators to bypass corporate gatekeepers. Personalised sports broadcasting allows fans to insert themselves into All Blacks games or experience events from an athlete’s point of view through neural playback. Digital sovereignty becomes a major issue as Māori leaders, and cultural and creative practitioners, push for greater control over data and storytelling platforms. There is also a growing Disconnect movement, that emphasises human creation and connection in the face of rapidly developing “noisy” digital technologies, leading to ‘unplugged’ alternative communities.

Key Events Leading to 2040:

2026 – The AI Media Boom

• A major New Zealand media company replaces 50 percent of its journalists with AI-generated news and storytelling tools.
• Deepfake Māori and Pacific historical reenactments cause outrage, leading to the first major debate on AI ethics in cultural storytelling.

2028 – Decentralised Content Platforms Rise

• Aotearoa launches its first fully decentralised storytelling platform, allowing artists to bypass traditional publishers.
• Māori content creators adopt blockchain-powered sovereignty tools to assert digital ownership over cultural narratives.

2031 – The Fact-Checking Collapse

• Leading media organisations close fact-checking divisions due to cost-cutting, increasing the spread of misinformation.
• An AI-generated false news story about a climate refugee crisis in New Zealand goes viral, affecting national elections.

2035 – "The Algorithmic Divide"

• New Zealand’s digital media is controlled by two major international AI-driven platforms, leaving small, independent storytellers struggling to be heard.
• Virtual influencers, indistinguishable from humans, dominate the entertainment industry, leading to debates on what constitutes "authentic" storytelling.
• New Zealand’s top-earning athlete is no longer a rugby player but an AI-enhanced esports competitor with a neural interface.

2040 – "Aotearoa Unfiltered" Becomes Reality

• AI tools create personalised historical narratives, meaning every individual can experience tailored versions of New Zealand’s past, further eroding a shared national identity.
• A government inquiry reveals that over 90 percent of digital content consumed by New Zealanders in 2040 is AI-generated, prompting urgent regulatory discussions.
• A New Zealand AI-coached and genetically optimised “perfect athlete” wins Olympic gold in track and field, sparking a global debate over human vs. AI-assisted sports performance.

Scenario 2: Hyperlocal Renaissance

Collapse and creation scenario: Infrastructure failures, cyber-attacks, and environmental crises make digital storytelling unreliable, forcing a return to localised and traditional knowledge-sharing

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A series of cascading crises—including cyber-attacks on infrastructure, extreme weather events, and democratic erosion—have led to a fragmented and fragile digital landscape in New Zealand by 2040. The collapse of global technology companies has left storytelling platforms outdated and unreliable, while misinformation and state-sponsored disinformation campaigns manipulate public perception. With limited access to reliable online spaces, many New Zealanders turn back to localised, community-based storytelling. Oral traditions, print media, and in-person gatherings become more trusted sources of information and cultural experience. However, with frequent infrastructure failures and an overburdened energy grid, preserving and sharing stories digitally becomes a challenge. A return to handheld digital music recorders allow for musicians to quickly record and physically share their music, but with limited reach. Creatives form organisations with like-minded people to share, create and protect their works, organisations like the Aotearoa Writers Circle emerge to collectively collate, curate and publish works and maintain outdated printing machines.

The decline of digital entertainment also leads to a resurgence of in-person, local sports as a primary form of recreation, and Māori and community-led sports flourish as people return to traditional activities like waka ama and ki-o-rahi, which require fewer resources than professional leagues. For Māori and other indigenous communities, the decline of centralised technology platforms means a resurgence in traditional knowledge-sharing practices but also risks further marginalisation from the global digital conversation and diasporic communities, particularly as international travel becomes more costly and restrictive.

Key Events Leading to 2040:

2027 – The Great Cyber Blackout

• A nationwide cyber-attack cripples digital infrastructure, wiping out years of archived cultural content.
• Heritage organisations scramble to retrieve lost digital collections, sparking renewed interest in physical archives.

2030 – The AI Disillusionment Crisis

• New Zealanders experience mass digital fatigue as AI-generated content becomes repetitive, shallow and prone to bias.
• A surge in demand for authentic, human-made cultural experiences leads to a revival of oral storytelling traditions in Māori and Pacific communities.
• Sports sponsorship collapses as global economic crises force corporations to withdraw from sports investments.

2033 – Climate Catastrophe and the Fall of Streaming

• Extreme weather events destroy critical undersea internet cables, cutting New Zealand off from global content platforms for months.
• The streaming industry collapses as power shortages make large-scale digital content distribution unsustainable.
• With digital infrastructure failing, kapa haka, waka ama, and community-run sports tournaments become the dominant forms of competition.

2037 – The Last Digital Archive Breach

• A major cyber-attack permanently erases New Zealand’s largest digital heritage database, reinforcing the need for physical cultural preservation.
• The government mandates print and analogue backups of all essential cultural materials.

2040 – "Hyperlocal Renaissance" Becomes Reality

• New Zealanders primarily rely on local libraries, museums and community elders for storytelling, history, and cultural education
• The final professional rugby match is held in New Zealand as centralised leagues dissolve; the focus shifts to local inter-iwi and inter-community competitions, ensuring that sports survive as a cultural practice rather than a commercial industry.
• Traditional Māori and Pacific navigation techniques, arts and storytelling are fully revitalised, playing a central role in national identity.

Scenario 3: Digital Guardianship

Constraint scenario: Strict government oversight ensures cultural protection and misinformation control, but raises concerns over censorship and creative freedom

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By 2040, the New Zealand government has taken an active role in regulating digital content, enforcing strict measures to ensure online safety, authenticity and cultural integrity. All digital stories must pass through national fact-checking bodies, and AI-driven monitoring systems flag potential misinformation. New Zealand’s world-leading Te Tiriti-based digital governance model and new data sovereignty laws are implemented, providing a pathway to decolonise the ownership of Māori cultural intellectual property. A government-owned and curated digital platform provides verified news and public information, and preserves New Zealand’s national data, history, cultural memory and assets, including digital taonga, in partnership with sovereign Māori-owned platforms. Māori sports heritage is legally protected, with government oversight ensuring haka and other cultural traditions are not misused by commercial entities. Biometric security measures and quantum encryption safeguard digital archives against cyber threats. New Zealand cultural works have achieved protected global status through trade agreements and international treaties and help to build the brand and reputation of New Zealand and its creative industries. Government-certified AI referees in rugby, cricket and football eliminate human error but spark debate over whether sports should retain an element of human judgement.

However, while these protections help maintain cultural authenticity and social cohesion, they also limit free expression. There are concerns about transparency and freedom of expression and this has an unintended chilling effect on the freedom of individuals, creatives and media operators over what they can publish in the digital space. Some digital creators feel restricted by overregulation, and underground networks emerge to share alternative narratives and grey-market digital zines emerge where creatives can share works and ideas free of government filters. The tension between digital safety and creative freedom remains an ongoing debate.

Key Events Leading to 2040:

2027 – The National Digital Ethics Council Forms

• The New Zealand Government establishes a Digital Kaitiakitanga Council to regulate AI-generated content, ensuring it aligns with national values and Treaty of Waitangi principles.

2029 – AI Storytelling Licenses Introduced

• Creators must apply for government approval before distributing AI-generated historical content.
• AI-generated misinformation is criminalised, with severe penalties for companies that distribute false narratives.

2032 – "Truth by Design" Initiative Launches

• New Zealand mandates AI-powered truth verification for all online stories and media, making it one of the most digitally regulated countries in the world.
• Artists and journalists face creative restrictions, as AI tools block any content deemed “culturally or politically sensitive”.
• A new law bans deepfake sports content, requiring all AI-generated sports media to be clearly labelled.

2035 – The Great Digital Protest

• A New Zealand influencer is convicted for spreading AI-generated false cricket highlights, marking the first-ever legal case of sports misinformation.
• A group of artists, historians and activists resist strict digital controls, arguing that government oversight is limiting creative expression.
• Māori and Pacific leaders demand greater community control over AI-driven storytelling rather than state governance.

2040 – "Digital Guardianship" Becomes Reality

• All digital stories are tagged with a trust score based on AI-powered fact-checking and cultural oversight.
• While misinformation is eliminated, many lament the loss of creative spontaneity and alternative perspectives in storytelling.

Scenario 4: A New Digital World

Transformation scenario: Radical new technologies reshape storytelling, erasing traditional boundaries between memory, identity and virtual experience

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By 2040, radical technological advancements have reshaped storytelling in ways previously unimaginable. Neural interfaces allow direct brain-to-digital storytelling, enabling New Zealanders to share experiences and memories as immersive sensory data. Technologies are open by design so that collectives, nations and individuals are able to tailor and customise software, hardware and their frameworks based on their own needs and context. Holographic archives preserve cultural narratives and data in interactive 3D spaces, and AI curators help personalise storytelling experiences. AI-driven sports psychologists and personalised biomechanical coaches replace human trainers, leading to concerns about athletes losing cultural and personal agency in their training. Enhanced athletes, performers and artists become the norm – genetic modifications and AI-optimised training are used to push human limits beyond anything seen before, and for live events, holographic players and performers add a new dimension, allowing events to be simultaneously experienced physically and virtually.

New Zealand’s digital landscape has shared governance with Māori and the wider public, reflecting a blend of indigenous knowledge systems and cutting-edge technology. Storytelling platforms are decentralised and community-owned, ensuring that digital heritage is protected while still allowing for creative innovation. While concerns about surveillance, transparency, accountability and ethical AI persist, New Zealanders embrace the fusion of tradition and technology, using digital storytelling not just as a tool for entertainment, but as a means of deepening cultural connections, fostering democratic resilience and safeguarding historical truths against manipulation.

Key Events Leading to 2040:

2026 – First Neural Storytelling Trials Begin

• Researchers develop memory-sharing technology, allowing people to experience stories through others' lived experiences.
• Iwi express concerns over the ethical implications of storing and sharing whakapapa through these digital interfaces.

2029 – AI Becomes the Primary Creator

• More than 80 percent of New Zealand’s new films, books and music are AI-assisted, with human input limited to curation and direction.

2032 – Interactive Holographic Heritage is Mainstream

• Museums begin replacing exhibits with fully interactive, AI-driven historical reconstructions, allowing visitors to "witness" major historical events in immersive environments.
• The first controversy arises when AI-generated haka performances are criticised for misrepresenting tikanga.

2036 – Neural Storytelling Becomes Global Standard

• People can now "experience" news, sports and history through neural downloads, allowing full emotional and sensory immersion in current and past events.
• The government struggles with regulating which memories are shared, as private companies profit from selling virtual "lived experiences"’

2040 – "A New Digital World" Becomes Reality

• The boundary between human memory and AI-generated experience blurs, raising fundamental questions about what is "real" history.
• Indigenous groups push for sovereign control over Māori and Pacific digital memory banks, ensuring traditional knowledge is not rewritten or commodified by AI corporations.
• Traditionalists call for a separation between enhanced and non-enhanced sports competitions, leading to the creation of two parallel sports categories: “Pure Human” and “Enhanced”.