For the past two years, AI has been framed as an inevitability, something societies must adopt quickly or risk being left behind. 2026 feels like a turning point, says Taylor Owen — one of the world’s leading experts on how artificial intelligence and digital platforms are reshaping democracy, information, and society — as AI begins to move from abstract promise to lived reality.
Taylor is the founding director of the Centre for Media, Technology, and Democracy at McGill University’s Max Bell School of Public Policy and the current Beaverbrook Chair in Media, Ethics, and Communications. He also hosts the Globe and Mail‘s influential Machines Like Us podcast and is an advisor on Canada’s National AI Task Force, created in September 2025 by the Government of Canada.
At the forefront of shaping Canada’s AI future, Taylor shared insight into the five AI trends shaping the year ahead and what they signal for the future of technology, governance, and society. Here’s what he wrote.
Five AI Trends to Watch in 2026
Recently, questions of AI legitimacy have emerged alongside the push for rapid AI adoption, driven in part by gains in productivity, scientific discovery, and institutional capacity. Governments, institutions, and organizations are now being forced to confront not only what these systems can do, but whether they can be deployed in ways that sustain public trust, accountability, and democratic consent.
These are the five dynamics that will shape how that balance between opportunity and risk plays out this year.
1. AI Will Get Politicized
For much of the last several years, governments framed AI adoption as an economic necessity, positioning speed and scale as virtues in themselves. That approach generated momentum, investment, and optimism, particularly around potential productivity gains, economic growth, public-sector modernization, and the hope that AI could help institutions do more with constrained resources. But it also deferred difficult political questions about distribution, accountability, and public consent.
As AI moves from pilot projects into core economic and administrative functions of both private and public sectors, those questions can no longer be postponed. If AI delivers productivity gains, governments may claim credit for modernization while facing pressure over job displacement and the uneven distribution of those gains. If AI underperforms, those same governments risk being held responsible for speculative bets that favoured a narrow set of firms and investors. In either case, AI adoption represents a genuine political gamble.
This, in turn, creates an opening for those willing to hedge the AI bet governments are currently making. As adoption accelerates, there is increasing space for leaders to argue not against technology itself, but against the assumption that faster, larger, and less constrained deployment is always in the public interest.
For example, in the United States, this creates a window for figures such as Alexandria Ocasio-Cortez, especially as divisions sharpen within the Democratic Party between progressive skeptics of concentrated technological power and the so-called “abundance Democrats,” who frame AI primarily as a growth and supply-side solution. In Canada, a similar opportunity exists for the country’s new NDP leader, who can position caution, worker protection, and public accountability not as anti-innovation, but as a corrective to a governing consensus that has largely treated AI adoption as an unquestioned good.
As AI becomes more visible in everyday life, some of the parties that have most strongly championed innovation-first agendas may find themselves under growing pressure to explain who bears the costs of adoption and who captures its gains. In that context, centrist liberal parties shaped by decades of market-led growth and pro-innovation policy may face new competition from movements that seek not to slow technological change, but to rebalance its social and economic consequences.
The most significant changes ahead may be neither technical nor regulatory, but cultural. As AI becomes commonplace, societies will begin negotiating new norms around its appropriate use — an essential step in moving from experimentation to confident, productive integration.
2. Governments Will Re-Enter AI Governance
For several years, many governments hesitated to regulate AI, concerned that premature governance would slow innovation or place them at a competitive disadvantage. That hesitancy was reinforced by the risk of political and economic retaliation from a US administration that framed regulation as a form of protectionism and, at times, an attack on free speech. This caution helped accelerate experimentation and private-sector investment, but it also left a governance gap that is becoming harder to sustain.
This year, three forces will pull governments back toward more active AI policy. Geopolitical competition is pushing states to think more seriously about strategic dependence and digital sovereignty. At the same time, national AI strategies promoting rapid, cross-economy adoption are colliding with low public trust, exposing a legitimacy gap between official productivity and growth narratives and concerns about fairness, accountability, and safety. Finally, safety risks, particularly those affecting children, are becoming concrete enough to demand political response even in jurisdictions historically resistant to regulation.
The opportunity for governments lies in rebuilding public confidence and aligning AI deployment with democratic expectations in ways that make large-scale adoption durable, trusted, and economically productive over the long term. The risk is that policy arrives reactively, fragmented, and under pressure, shaped more by crisis than by strategy.
3. Parents Will Turn Their Attention from Social Media to AI
The backlash against social media did not arrive suddenly. It built gradually, then accelerated, before translating into a growing political movement to restrict children’s access to platforms, from Australia to parts of Europe and beyond. Whether those bans ultimately prove effective is beside the point. What matters is that protecting children online has become a powerful and durable political issue.
AI is now moving into that same line of sight, as parents become increasingly aware of how generative systems are being introduced into classrooms, embedded in educational tools, and marketed as companions. That awareness is likely to translate into concentrated pressure around schools and child-facing AI products, where concerns about dependency, developmental harm, surveillance, and safety are beginning to converge.
Unlike social media, however, AI is not confined to discrete platforms that can be banned or age-gated. It is becoming infrastructural, woven into operating systems, productivity software, and institutional workflows in ways that are far harder to disentangle.
The result will likely be a widening gap between the speed of AI integration and the capacity of institutions to explain, justify, and govern its presence in children’s lives. That gap, rather than the technology itself, is what is likely to give this issue real political force over the coming year.
4. AI Will Be Both Increasingly Capable and Structurally Unreliable
This year will bring visible improvements in AI-generated content and task performance. Music, images, video, and text will continue to improve in quality and scale, and AI-assisted research and scientific discovery will deliver real advances, including faster scientific progress, improved decision-support, and meaningful augmentation of knowledge work across sectors. For many organizations and individuals, these capabilities will offer substantial productivity gains and creative expansion.
Yet core limitations will persist. Hallucinations, confident errors, and unpredictable failure modes are unlikely to disappear, because they are not simply bugs but may be structural features of current model architectures. As AI becomes more deeply integrated into workflows, these flaws will become harder to manage and more costly to ignore.
The result is a paradox in which AI becomes both indispensable and untrustworthy. It will offer extraordinary gains in efficiency and capability while remaining fundamentally unreliable in ways that institutions cannot wish away. Learning where and how to rely on these systems, rather than assuming they can be trusted by default, will be one of the defining organizational challenges of the year.
5. New Social Norms Will Begin to Take Shape
The most significant changes ahead may be neither technical nor regulatory, but cultural. As AI becomes commonplace, societies will begin negotiating new norms around its appropriate use — an essential step in moving from experimentation to confident, productive integration.
Individuals will reassess when AI assistance feels reasonable and when it feels like a shortcut that undermines trust. Organizations will quietly establish new expectations around AI-assisted writing, analysis, and decision-making, often before formal policies are in place. Universities will struggle to reconcile AI use with existing models of learning, assessment, and academic integrity. Creative platforms and audiences will renegotiate ideas of authorship, authenticity, and disclosure. Questions of liability, responsibility, and copyright will not be resolved quickly, but they will increasingly move from abstract debate into practical negotiation.
From AI Promise to Practice
All of this is to say that the defining AI story this year will not be about technological possibility alone, nor about the pace of innovation for its own sake. It will be about whether governments, organizations, and institutions can capture the genuine benefits of AI adoption, from innovation to improved services and scientific progress, while managing its political, social, and economic risks in ways that preserve public trust, accountability, and democratic legitimacy.
Those that treat AI as an inevitable force to be adopted without question will find themselves reacting to political backlash, and public loss of confidence. Those that recognize adoption as a strategic choice, shaped by governance, norms, and consent, will be far better positioned to lead through what is likely to be a contested phase of the AI transition.
Taylor Owen’s far-reaching, thought-provoking keynotes draw on his work with global AI developers, entrepreneurs, and policymakers, as well as his hands-on experience shaping national regulation, to explore what the AI revolution means for your organization and society at large. He leaves audiences with actionable insights and strategies to better navigate the opportunities and risks ahead in our digital future.
Contact us to learn more about Taylor and how he can help your organization navigate the AI Revolution with confidence.
