A World Economic Forum report finds competitive advantage is shifting from owning key technologies to combining them across data, people and ecosystems.
The biggest barriers to scaling innovative solutions are no longer individual breakthroughs but connecting a combination of AI and digital tools with real-world operations.
The research shows how technology convergence is already reshaping value chains in healthcare, manufacturing, energy, life sciences, wearable electronics and more.
Geneva, Switzerland, April 2026 – The next wave of competitive advantage will come not from individual breakthrough technologies but from the ability to combine and scale multiple technologies across entire operating systems, according to a World Economic Forum report released today. As artificial intelligence, robotics, advanced materials, spatial computing and next-generation energy systems mature simultaneously, the organizations and countries moving fastest to apply these technologies together in intelligent systems are already pulling ahead.
The report, Technology Convergence: The New Logic for Competitive Advantage, produced in collaboration with Capgemini, draws on cross-industry research and real-world case studies in 12 sectors, identifying recurring patterns, including the blending of mature and experimental technologies and the blurring of industry boundaries, that determine whether convergence scales or stalls.
“Breakthrough technologies are advancing rapidly, and value is created when they are applied together,” said Cathy Li, Head of the Centre for AI Excellence and Member of the Executive Committee, World Economic Forum. “The real differentiator is not who owns the most advanced tools, but who can combine them across systems and applications at scale.”
As advanced technologies scale, the main bottlenecks to competitive advantage are no longer time or materials but how well organizations connect digital tools with physical operations. This is already playing out across sectors and geographies. From operating rooms to factory floors, power grids to research labs, converging technologies are reshaping how systems perform worldwide.
In the United Kingdom, novel surgical robots are extending clinician capacity (this article’s feature image shows robots with surgeons at West Hertfordshire Teaching Hospitals NHS Trust first used in 2022) while preserving workflow continuity across care teams. In China, automated labs are linking robotics, AI and data platforms to accelerate discovery while coordinating workflows across research networks.
“Technology convergence has evolved from a technical discussion into a strategic leadership mandate with direct operational impact,” said Aiman Ezzat, CEO of Capgemini Group. “Competitive advantage increasingly depends on an organization’s ability to integrate technologies, teams, partners and operating processes into coherent systems that deliver value at scale. Leaders who master orchestration, not just adoption, are the ones translating convergence into sustained performance and growth.”
“This shift has implications not only for companies but also for national growth strategies and industrial policy,” said Jeremy Jurgens, Managing Director, World Economic Forum. “Economies that align talent, infrastructure, data and policy will be better positioned to capture the benefits of converging technologies amid a fast-shifting global landscape.”
The report is part of the World Economic Forum’s Technology Convergence Initiative, launched in 2024, and builds on the first edition published in 2025. It draws on two years of cross-industry research, including expert interviews, workshops and case studies in healthcare, manufacturing, energy, life sciences and emerging fields such as brain-computer interfaces. The analysis examines how eight advanced technology domains interact, using the Forum’s 3C framework (combination, convergence and compounding) and the Technology Maturity Index to track how technologies move from experimentation to real-world impact.
AI generated headshots are rapidly becoming the default across LinkedIn, company websites, and professional profiles. The appeal is obvious. Faster, cheaper, and visually flawless. But a growing backlash is emerging across industries where trust is not optional.
Visual Handshake
Professionals are discovering that the more polished and “perfect” their image appears, the less credible it may feel to the people viewing it. Subtle inconsistencies in lighting, facial structure, and expression are triggering skepticism, even when the viewer cannot immediately identify why.
This is the Authenticity Paradox.
How I see it (pun intended) is that the issue is not whether AI can create a better image. It is whether that image creates a believable human connection. The goal of a professional headshot is not perfection. It is trust. The moment something feels off, even slightly, people question the person behind the image.
I’ve worked helping executives, founders, and professionals develop what I call a “visual handshake,” because the rise of AI imagery is creating a new layer of risk in how professionals present themselves. The face is often the first point of contact in business. If that moment creates doubt instead of connection, you have already lost ground before a conversation even begins.
Drawing from a broader conversation around AI generated imagery and professional perception , the issue is not simply visual quality. It is psychological response. When people sense artificiality, even subconsciously, it can disrupt the trust signals that strong first impressions rely on.
For the Silo, Famed headshot photographer and expression coach Chris Gillett .
New Report Charts Key Strategies and Trade-Offs for Long-Term Growth The World Economic Forum report outlines key “no-regret” strategies and unresolved dilemmas shaping economic growth in the long-term.
The new economy will be shaped by accelerating AI adoption, geostrategic competition, high public and private debt, and environmental and demographic challenges. Energy costs and political instability currently prevent accelerating growth, while the expansion of IT services, advanced manufacturing, health and leisure will drive output in the future. Read more about the Future of Growth Initiative.
Geneva, Switzerland, April 2026 – As the growth strategies that powered the global economy over the past three decades lose relevance, a new World Economic Forum report calls for a renewed blueprint to navigate a rapidly evolving landscape shaped by AI, geostrategic competition, rising debt and inequality, and mounting environmental and demographic pressures. The report draws on two years of dialogue with nearly 200 global business leaders, policy-makers and experts, and a survey of more than 11,000 executives worldwide.
Four Areas Of Economic Policy
Across four major areas of economic policy, Growth in the New Economy: Towards a Blueprint identifies key “no-regret” strategies and open dilemmas for governments and businesses that will define economic policy in the coming decade:
Technology, productivity and human capital: Sustained growth in the new economy will depend on strengthening productivity and human capital as technology and knowledge become central to value creation. Governments and businesses must navigate between different approaches to translating innovation into new sources of growth and ensuring its benefits are widely shared, pursuing coordinated or competition-led approaches to harness technology and prioritize redistribution or mobility-based strategies for economic inclusion.
Global cooperation and domestic capacity: Leveraging comparative advantage and diversification remain “no-regret” strategies that may enable expansion of economic opportunity and resilience. Yet, governments and businesses will need to balance global engagement with stronger domestic capacity, navigating between self-reliance and global integration strategies.
Business environment and the role of government: In the new economy, reinforcing the fundamentals of economic policy – including credible institutions, high-quality infrastructure and macroeconomic stability – and strengthening multistakeholder alignment continue to be winning strategies. The role of government in economic transformation can range from minimal to more expansive, while policy-makers face hard choices to manage debt levels, shifting between greater fiscal prudence and forms of financial repression.
Sustainability and economic policy: Focusing on the economic and societal benefits of green transition strategies is essential to unlocking long-term prosperity and resilience. Critical dilemmas around how to manage the costs and trade-offs of greener growth persist, with decision-makers navigating a range of investment-led and cost-led strategies.”
The current context demands bold choices and trade-offs from government and businesses. Investing in productivity, talent and reinforcing the fundamentals of economic policy are clear winning strategies that hold across every country and income level,” said Attilio Di Battista, Head of Economic Growth and Transformation, World Economic Forum. “Yet, leaders will need to navigate complex dilemmas while managing record levels of debt and inequalities, rising geostrategic competition, a persisting climate crisis and the fastest technological shift in a generation.”
Shifting engines of global growth
Amid disruptions brought by the current conflict in the Middle East, the report points to long-term shifts in the composition and drivers of economic growth. Middle-income economies are expected to account for nearly two-thirds of global GDP growth through 2030. Regionally, Asia will continue to be the main driver of growth, accounting for more than 50% of global growth. Despite registering the fastest growth rates, low-income economies are projected to contribute just 1% of global growth over the same period.
Information technology services, advanced manufacturing, health and healthcare, and accommodation and leisure sectors are expected to drive growth over the next five years, with Asia, Europe and North America as key hotspots. Latin America and the Caribbean will see opportunities in the agriculture, mining and metals sectors.
Opportunities and challenges
Based on the results of the recent survey of 11,000 business leaders, the report highlights high energy costs and policy instability as the two barriers that are constraining an acceleration of economic growth across various geographies and income levels.
Other barriers vary by country income level. In high-income economies, skill shortages and rigid regulations are seen as the top barriers, while in low-income economies, limited access to finance and inadequate infrastructure were top concerns.
In the long-term, frontier technologies and the green and energy transition are identified as trends that will drive growth and investment, while high debt, societal polarization and climate change are seen as potential headwinds across regions and income levels.
Demographic shifts and geoeconomic fragmentation are expected to create divergent growth trajectories, with ageing populations slowing growth in Eastern Asia and Europe, and younger populations supporting growth in the Middle East and North Africa, and Sub-Saharan Africa. Geoeconomic fragmentation is seen as a drag on growth in most countries, though executives expect South-East Asia to benefit from shifting supply chains and trade patterns.
In addition, domestic corporate investment and foreign demand are seen as the main drivers of growth over the next five years. Domestic investment is especially important in low- and middle-income economies, while advanced economies look to foreign markets. Domestic consumption and public spending are expected to play a smaller role due to high public debt and stagnant real incomes.
About Growth in the New Economy: Towards a Blueprint The report draws on two years of dialogues held as part of the World Economic Forum’s Future of Growth Initiative, with policy-makers, business leaders and economists convening in Davos-Klosters, Dubai, New York, Riyadh, Tianjin and Washington DC between 2024 and 2026, and integrates inputs from the Global Future Councils on the Future of Growth and the Business of Economic Growth. It also consolidates insights from more than 11,000 business leaders in 118 countries participating in the World Economic Forum Executive Opinion Survey 2025. Read the full report here.
Throughout 2026, the Future of Growth Dialogue Series will continue exploring the emerging frontiers of the new economy, as well as new sources and pathways to growth, productivity and innovation. The Future of Growth Initiative is complemented by the World Economic Forum’s Scenarios for the Global Economy Dialogue Series, leveraging foresight to explore scenarios for the future of growth and their implications for strategy, investment decisions and resilience across industries.
April, 2026 – Capital spending on AI has surged into the hundreds of billions, yet the economic payoff remains elusive. Despite rapid investment and widespread experimentation with generative AI tools, measurable productivity gains have yet to show up clearly in aggregate data across advanced economies – raising an important question: who will be first to turn this momentum into measurable gains?
In “From Hype to Output: How AI Investment Translates to Real Productivity Gains,” I discovered that while AI offers a path to stronger economic performance and could help address Canada’s long-standing productivity challenges, realizing these gains will require policies that accelerate business adoption and diffusion, which remain uneven across most industries and regions. This includes removing barriers, ensuring access to data resources and encouraging integration across firms and sectors.
Artificial intelligence has renewed optimism about improving Canada’s long-standing productivity problem, but history shows that general-purpose technologies often take decades to produce measurable economy-wide gains. Early adoption typically produces modest improvements as firms experiment and invest in complementary assets such as data, skills, and organizational changes.
Canada performs reasonably well in international AI rankings and produces strong research output, but it lags leading countries in computing capacity and commercialization. Canadians are among the most frequent users of generative AI tools globally, yet business adoption remains uneven and relatively limited.
Canadian data show that about 12 percent of businesses currently use AI, and most report little change in employment following adoption. Many firms say AI is not relevant to their operations or lack the knowledge needed to implement it effectively, suggesting adoption barriers remain significant.
Policy should therefore prioritize accelerating AI adoption and diffusion across sectors while maintaining Canada’s research and infrastructure capacity. Governments can support this by improving regulatory clarity, expanding data access, strengthening AI literacy, and encouraging firms to experiment and integrate AI into their operations.
Introduction
Canada faces a persistent productivity challenge.
Growth in output per work-hour has stagnated for decades, and the gap with the United States continues to widen.1 Policymakers have long sought levers to reverse this trend, and the rapid emergence of artificial intelligence (AI) – particularly generative AI since late 2022 – has prompted renewed optimism that a transformative technology might finally deliver the productivity gains that have proved elusive.
This Commentary discusses the impacts of AI technologies, with a focus on more recent generative AI. In particular, these technologies are unique in their low barriers to adoption and use in terms of both skill and cost as generalized tools. With such low barriers to entry, there is potential for rapid adoption and scaling of AI technologies across many sectors of the economy. Yet enthusiasm must be tempered by evidence: general-purpose technologies (such as AI) historically take decades to reshape economies, and the path from adoption to measurable productivity growth is neither linear nor guaranteed.
This Commentary examines AI’s potential contribution to Canadian productivity growth through an evidence-based policy lens. It begins by establishing what AI technologies are and how technological change translates into economic output, drawing on the concept of the “productivity J-curve” to explain why early-stage adoption often fails to register in macroeconomic statistics. The paper then situates the current AI investment boom in a historical and international context and summarizes research quantifying both the infrastructure-driven and adoption-driven channels through which AI may affect GDP. A comparative analysis positions Canada among its international peers, revealing a paradox: world-class research output coexists with middling commercial translation and infrastructure capacity. Detailed examination of Canadian business adoption data shows significant variation across industries and provinces, with early signs that initial experimentation may be giving way to more selective, sustained implementation.
The central argument is that maximizing the likelihood that AI technologies will boost Canada’s productivity growth requires policy focused on accelerating AI adoption and diffusion across sectors. While government investment in computing capacity supports the domestic development of AI technologies (and democratizes access to computing power), government resource constraints, significant uncertainty about AI development trajectories, and the relatively smaller size of the Canadian AI economy suggest that AI infrastructure policies should focus on maintaining and improving Canada’s relative international competitiveness. Policies that encourage firms to experiment, learn, and eventually reimagine their operations around AI capabilities can position Canada to capture productivity gains as the technology matures. AI, as a generalized technology, can be deployed across many industries and is linked to other economic development and industrial policy priorities, including small- and medium-sized business growth and international trade diversification. This paper concludes with policy recommendations that balance the uncertain timeline of AI’s macroeconomic impact against the risk of falling further behind more aggressive international competitors.
The Current AI Boom in Context
The emergence of generative AI since late 2022 has triggered an unprecedented wave of capital investment and rapid enterprise adoption. This technological shift is reshaping economic growth dynamics through two channels: the direct contribution of infrastructure investment to GDP and longer-term productivity gains from AI adoption across industries. Understanding the magnitude, timing, and distribution of these effects is essential for policymakers seeking to position their economies to benefit from AI’s transformative potential. Much of this section refers to the US economy, where more data on AI investment and GDP effects are available.
Capital expenditure on AI infrastructure has reached historic proportions. Hyperscaler companies – Amazon, Google, Meta, Microsoft, and Oracle – allocated about $342 billion to capital expenditures in 2025, a 62 percent increase from the previous year (Aliaga 2025). Estimates suggest AI-related capital expenditures could reach US$527 billion in 2026 (Goldman Sachs 2025). This spending encompasses semiconductors, data centres, networking equipment, and the power infrastructure required to support “compute”-intensive AI workloads.
In Canada, the federal government announced $2 billion over five years starting in 2024-25 for the Canadian Sovereign AI Strategy, including $705 million for “compute” infrastructure. Microsoft has also announced it is spending $19 billion on AI infrastructure investment in Canada between 2023 and 2027, with $7.5 billion of that over the next two years (Smith 2025). Industry categories related to AI2 accounted for $195 billion in 2021 and grew to $229 billion in 2024, representing 9.3 to 10 percent of Canada’s GDP, respectively.
The impact of AI investment on the US economy is significant, though estimates vary widely. In the first nine months of 2025, GDP product categories related to AI investment (such as computing and communications equipment, data centre structures, software, and research and development) represented 37 percent of real GDP growth and 8 percent of real GDP (Levine 2025).3 Other estimates suggest AI contributed roughly 1 percentage point to US GDP growth in 2025 (Boussour 2025; Goldman Sachs 2025; Aliaga 2025), making it the second-largest contributor to growth after consumption (Singh 2026).4 The scale of investment in AI infrastructure in the US has led to debate about whether it is fueling an equity market bubble (BNP Paribas 2025; Barnette and Peterson 2025). There have been multiple “AI winters” in the past – periods of significant decline in enthusiasm, funding, and progress in the field of AI.5
However, much of the spending on AI infrastructure does not translate directly into domestic GDP because many inputs are imported – for example, semiconductors manufactured in Taiwan. This is particularly important for Canada, since both infrastructure inputs and the outputs of dominant AI companies (AI products and software) are predominantly located abroad. Secondary economic effects and labour dynamics associated with the expansion of infrastructure investment are also important to consider. Data centre construction has had significant impacts on the construction industry in the United States. The top 50 contractors by size have doubled revenues within a year, and salaries for trades workers are 32 percent higher for data centre projects compared to other construction (Paoli 2025). Once built, however, data centres require relatively few permanent workers.
There are also counter-balancing factors to consider. Corrado et al. (2025) show that while investment in data as an intangible asset can improve efficiency, the growing importance of proprietary datasets slows the diffusion of innovation. So far, the negative impact on diffusion outweighs the efficiency gains from intangible data capital.
Currently, the main channel through which AI is measurably increasing US GDP is capital expenditures. Over the longer term, however, these investments must translate to products and services that meaningfully increase productivity to have a sustained effect on growth. So far, the acceleration of AI development and diffusion has not been associated with higher productivity growth at the macroeconomic level across G7 countries (Filippucci et al. 2024; Andre and Gal 2024; Goldin et al. 2024). As with earlier general-purpose technologies, widespread productivity gains may take years to materialize.
Estimates of AI’s long-term productivity impact for the US vary widely – from 1.5 percent labour-productivity growth annually (Goldman Sachs 2025) to less than 1 percent cumulatively over a decade (Acemoglu 2024), as shown in Figure 1. Some researchers argue that the long-run impact may instead arise from persistently faster growth rather than a one-time productivity shift (Baily, Brynjolfsson, and Korinek 2023). The timeline and magnitude of significant macroeconomic growth related to AI adoption depend on continued improvements in AI capabilities, widespread adoption, and complementarities with human skills and other technologies (Filippucci et al. 2024).
AI Already Affecting Labour Market
There is some evidence that AI is already having labour market effects: Brynjolfsson, Chander, and Chen (2025) estimate that early career workers in AI-exposed occupations experience a 16 percent relative employment decline while employment remained stable for more experienced workers. Across occupational categories in the US, 2.6-75.5 percent of occupations could have at least 50 percent of their tasks affected by GenAI (Arnon 2025). Research on the UK labour market finds that 11 percent of occupational tasks are exposed to generative AI automation/augmentation as part of “low-hanging fruit” implementation cases, and up to 59 percent of tasks will be exposed to Gen-AI as it develops into more integrated systems (Jung and Desikan, 2024). Heneseke et al. (2025) find that the price premium for AI-exposed tasks declined by 12 percent from 2017 to 2023/24 and that job postings were 5.5 percent lower in 2025-Q2 than if pre-GPT hiring patterns had persisted.
As AI technology is adopted, some labour market disruption is inevitable, but that does not mean that AI necessarily leads to widespread unemployment. Job losses can occur only if innovation outstrips growth in demand for new products and services. Further, the potential for automation does not necessarily translate into actual automation: the decision to automate depends on factors such as firm size, competitive pressure, and the cost of a machine versus the cost of human labour. As technology diffuses and improves productivity, GDP and wages increase, which increases demand for goods and services, creating new employment. Rapid technological progress is regularly accompanied by fears of widespread unemployment. In reality, however, markets adjust dynamically to technology adoption over time, and widespread unemployment is unlikely (Oschinski and Wyonch 2017).6
At the firm level, growing evidence suggests AI can improve labour productivity in many contexts (Figure 2, Table A1). Meta-analysis of research measuring the productivity effects of generative AI shows mixed results across studies and applications. But on average, the use of GenAI tools increased labour productivity (in particular, tasks or departments) by 17 percent (Coupe and Wu 2025).
Despite these gains, there is a notable divide in the wide adoption of tools like ChatGPT and enterprise-grade AI systems (Challapally et al. 2025). Nearly 80 percent of organizations report exploring or piloting large language model (LLM) products, and 40 percent report deployment. These tools primarily enhance individual worker productivity but are not deployed for core business functions.7 By contrast, 60 percent of companies have evaluated enterprise or custom systems, yet only 5 percent reach production.
Tools that succeeded had low configuration barriers and immediately noticeable value, while those that require extensive upfront customization often stalled in the pilot stage. The core barrier to scaling is not infrastructure, regulation, or talent. It is GenAI’s lack of capacity to learn or remember over time (particularly static enterprise tools relative to rapidly evolving consumer-facing tools).8 Aside from enterprise-level adoption and development, there is significant adoption of AI technologies by individual workers to enhance personal productivity. In 2024, 40 percent of the working-age population in the US was using GenAI, 23 percent used it for work, and 9 percent used it every workday (Bick, Blandin, and Deming 2025). The high failure rate of AI pilots can be explained by the productivity J-curve and the current stage of development and adoption (intangible capital accumulation that leads to future productivity improvement). A high failure rate of pilots shows experimentation and contributes to ongoing development on the one hand. On the other, companies that experiment with AI and abandon it might be less likely to adopt it in the future, despite rapid evolution.
Uneven Gains
Productivity gains from new technologies are uneven across sectors and difficult to predict because they depend on where technological change occurs.9 Adoption is also shaped by market competition, regulation, and the current distribution of technology within industries (Howitt 2015). Given the uncertainty around AI’s trajectory and the sectors where it may generate the largest gains, productivity improvements are possible, but expectations may also exceed outcomes. Bridging from the initial development and experimentation phase to widespread adoption, eventual integration and process changes can be accelerated by addressing technical, institutional, and bureaucratic barriers (Ouimetter, Teather, and Allison 2024). Empirical modelling suggests that people, process, and data readiness are required in addition to technology/capital investment to achieve long term operational success (Uren and Edwards 2023). Government policy should be economically broad-based and not narrowly focused on particular industries or applications. It should also be cautious and balance uncertain long-run productivity gains with maintaining competitiveness in a rapidly developing global technology market.
How Canada Compares Internationally
Canada performs reasonably well in international AI rankings, placing eighth overall across five different international AI rankings (Figure 3).10 Rankings vary widely depending on their focus and data sources (Table A3). For example, China’s rank ranges from second in the Global AI Index ranking but 32nd in the Global Index on Responsible AI (GCG 2024). Canada ranks highest in the Global AI Index (eighth) and lowest on the IMF’s AI Preparedness Index (18th). Canada has also fallen in the rankings over time – from fourth in 2021 to eighth in 2025 on the Global AI Index (White and Cesareo 2025) and from fifth in 2022 to 12th in 2025 on the Government AI Readiness ranking (Rogerson et al. 2023; Iida et al. 2026). Notably, Canada’s score improved across categories measured in the Oxford AI Readiness Index, suggesting that advancements in AI adoption, development, and public policy have been made, but they have not kept pace with some international peers.
In terms of development and infrastructure, Canada is a high performer, but not a clear leader among the metrics tracked by the OECD’s AI Policy Observatory. More AI research is produced in Canada than in the US or UK (relative to population size). Canada has higher venture capital investment (as a share of GDP) in AI startups than many peer countries, but falls far behind the US and Israel, the clear leaders in the category (OECD.AI 2026a,b). Canada also maintains a respectable presence in computing infrastructure, with 19 of the world’s top 500 supercomputers (Top500 2025) and public cloud regions (Lehdonvirta et al. 2025). Unfortunately, the processing power of those computers ranks only 18th out of 20 comparator countries (Top500 2025).
Strong Research But Weak Commercialization For Canada
Taken together, these indicators reflect a familiar pattern in Canada’s innovation system: strong research output but weaker commercialization. Continued investment in data and processing infrastructure will help maintain competitiveness, but public policy should also encourage private investment.11 Given the scale of investment in countries such as the United States and China, Canada is unlikely to match their computing capacity regardless of policy choices.
Evaluating the usage of major LLMs by country shows that the US and India account for the largest proportions of total users and interactions. After accounting for population size, however, Canada, Australia, and France show the highest usage rates among individuals (Figure 4).12 While individual use does not directly translate to higher productivity in the production of goods and services, familiarity and comfort with AI tools among the general labour force will improve enterprise adoption prospects. A labour force with AI skills reduces enterprise training costs related to adoption and improves the likelihood of employees generating ideas for reorganizing business processes. Conversely, differences between consumer and enterprise applications can create barriers to workplace adoption (Challapally et al. 2025). Even so, if individual workers are using AI tools to increase their personal productivity, there will be marginal productivity effects from using publicly available tools for work tasks.
High individual adoption in Canada contrasts with lower rates of business adoption.
Based on the most recent comparable data, Canada falls below the OECD average overall and for the proportion of medium and large businesses adopting AI (Figure 5). In Korea, Denmark, and Finland, more than half of large companies are using AI.
International data on the businesses’ adoption of AI is highly variable. The OECD estimates 20.2 percent of businesses (with more than 10 employees) use AI,13 while the McKinsey Global Survey finds that 88 percent of businesses experiment with AI in at least one function (McKinsey 2025; OECD 2025). However, only 10 percent of businesses in G7 countries were using AI for core business functions in 2024. Only 2 to 6 percent of firms across G7 countries have high intensity adoption related to core business functions (Fillipucci et al. 2025). High-intensity adoption is highest in the US, followed by Canada, the UK, and Germany.
There are common features of AI adoption across countries:
Firm size: larger businesses are using AI at higher rates than medium or small businesses.14 This could be related to factors including fixed costs of adoption, larger data resources, lower financing constraints, and complementary intangible assets such as information and communication technology (ICT) skills and research capacity (Calvino and Fontanelli 2023).
Industry concentration: adoption is highest in ICT, followed by professional, scientific and technical services, and communications and marketing (OECD 2025).15
Firm characteristics: higher productivity and younger enterprises are more likely to adopt AI (Calvino and Fontanelli 2023; Calvino, Costa, and Haerie, forthcoming).16
Modelled projections suggest that high-intensity AI adoption among enterprises could reach 13 to 63 percent across G7 countries over the next decade, depending on adoption rates and technological progress (Fillipucci et al. 2025). The US is projected to have the highest adoption rates and the largest annual productivity growth related to AI, while Japan is projected to see the lowest. Canada ranks second in projected adoption across scenarios, but third or fourth in projected labour productivity growth, meaning that productivity gains will likely be relatively modest compared to international peers with similar (and slightly lower) adoption rates (Figure 6).17 Estimates suggest that Canada will reach 32-62 percent enterprise adoption in the next 10 years, contributing 0.35 to 1.13 percentage points to annual labour productivity growth.
Overall, available data show that Canada is about average in terms of adoption and development capacity, and the US is a clear leader. Canada has a lower capacity for development related to AI infrastructure than some peer countries, but remains above average. Canadian individuals have higher adoption rates than those in most other countries. Business adoption is less clear-cut. Some indicators place Canada below the international average, while others rank Canada second in the G7 for high-intensity AI adoption in core business functions. For Canada to close the productivity gap, it will need sufficient AI infrastructure to remain competitive, broad adoption, but most importantly, more rapid or effective AI-enabled innovation to develop adaptations of business processes and new products and services.
Canada’s AI Adoption Landscape
Having compared Canada with international peers, this section examines business adoption within Canada.
In the third quarter of 2025, 14.5 percent of businesses were planning to use AI within the next 12 months, a 3.9 percentage point increase from the previous year. As of the second quarter of 2025, 12.2 percent were already using AI. But two-thirds of businesses report no plans to adopt AI, and 18.9 percent were uncertain (Bryan, Sood, and Johnston 2025a). About six in 10 businesses think that AI investment is not important or not relevant to their business.
Planned adoption is highest in Ontario, British Columbia, Nova Scotia, and New Brunswick, with New Brunswick seeing the largest growth in the proportion of businesses planning to use AI compared to the previous year (Figure 7). Adoption is highest in information and cultural industries; finance and insurance; professional, scientific, and technical services; and healthcare and social services. It is lowest in mining and resources extraction, transportation and warehousing, and construction (Table A1).
Comparing previous and planned AI use shows that the share of businesses planning to adopt AI over the next 12 months is only 2.3 percentage points higher, compared with a 6.1 percentage-point increase from 2024 to 2025. In several sectors – including manufacturing, resource extraction, wholesale and retail trade, and professional services – planned adoption is lower than current usage levels in Q2 2025 (Table 1). Adoption is most likely to increase in accommodation, food services, and real estate over the next year.
Provincial patterns are also uneven. Planned AI use in the information and cultural industries declines in five provinces (including a 39.7-percentage-point drop in New Brunswick) but rises in Ontario, Alberta, and Saskatchewan. In Q2 2025, businesses in Ontario, Quebec, and Manitoba were using AI at higher rates than the national average. By Q3 2026, however, only businesses in Ontario intend to use AI at higher rates than the national average. Businesses in Quebec and Alberta show little appetite for further AI adoption, while those in Prince Edward Island report plans to reduce AI use in 2026 compared with 2025.
Businesses engaging in some international activities are adopting AI at higher rates. Across all provinces, businesses that export services were using AI at higher rates than average. The same is true for importing services in all provinces except Nova Scotia. Businesses that relocated activities outside Canada or made investments outside Canada are also more likely to be using AI at the national level (though these same businesses show the largest declines in intended use over the coming year). Importing and exporting goods has no clear association with AI adoption across provinces.
Overall, businesses involved in international services trade or cross-border investment show higher rates of AI use. This gap will likely close slightly over the year: firms without international activities report higher intentions to adopt AI than the overall business average (3.6 percent compared with 2.3 percent). Meanwhile, firms relocating employees or operations abroad, or investing internationally, report declining intentions to expand AI use.
Canada’s AI adoption landscape (as of Q2 2025) generally mirrors international trends. Younger businesses (10 years or less) adopt AI more frequently than older firms, and large firms adopt more than small ones. The highest adoption rates are concentrated in ICT, finance, and professional, scientific, and technical services. Private enterprises had significantly higher adoption than government agencies and non-profits.
Planned adoption over the next year (reported in Q3 2025) suggests both continuation of these trends and broader diffusion. Government agencies and non-profits serving businesses plan to increase the use of AI at higher proportions than private enterprises. Only 0.1 percent of government agencies reported using AI in early 2025, but 24.3 percent plan on using it within the next year. Younger firms plan to continue to adopt AI at higher rates than older firms.
Planned adoption by firm size varies across provinces. In New Brunswick and Saskatchewan, small (5-19 employees) and medium-sized (20-99 employees) companies report adoption intentions above the national average, while larger firms show declining adoption. In Quebec, by contrast, larger firms continue to adopt AI more rapidly than smaller firms, widening the adoption gap.
Turning to employment effects, most businesses using AI report little change in staffing levels. Among firms that adopted AI in the previous 12 months (12.2 percent of businesses in Q2 2025), 89.4 percent reported no related change in employment (Bryan Sood and Johnston 2025b). Similarly, about 70 percent of firms planning to adopt AI expected no employment change, while another 10 percent are uncertain (Bryan Sood and Johnston 2025a). These results suggest firms may overestimate potential labour savings before implementation. After adoption, 15.1 percent of firms reported no reduction in employee tasks, while 47.2 percent reported only small impacts.
For businesses that do not plan to use AI in the next year, the most common reason is that the technology is not relevant to their products or services (78.1 percent). Only 1.5 percent of businesses said that previous use of AI did not meet expectations. Other common barriers include limited knowledge of AI, privacy, or security concerns, and the technology’s perceived immaturity. Industries with fewer firms reporting AI as irrelevant tend to report lack of knowledge as the primary barrier to adoption.18
Government agencies report the highest rates of disappointment with AI performance (14.8 percent citing unmet expectations as the reason for non-use).19 Regionally, businesses in British Columbia, Alberta, and Ontario report the lowest rates of saying AI is irrelevant to their activities (74.1-76.7 percent), while New Brunswick reports the highest share (88.3 percent). Notably, laws and regulations preventing or restricting the use of AI are one of the least commonly reported reasons for not using AI. This shows that existing industry regulations are not playing a strong role in prohibiting adoption in most industries.
Overall, Canadian data suggest that AI adoption continues to grow but at a slower pace than in 2024. In some sectors, AI use is likely to decline, suggesting that some businesses piloted or implemented AI and did not realize sufficient benefits to continue using the technology. While individuals in Canada use generative AI tools more frequently than those in many peer countries, business adoption remains comparatively limited, and most firms report little measurable labour savings from AI use so far.
Although these trends could indicate declining enthusiasm or unmet expectations, they are also consistent with the early stages of adoption for past general-purpose technologies, where experimentation precedes widespread productivity gains.20
Policy Discussion
Policymakers must recognize that maintaining Canada’s competitive standing requires deliberate, differentiated strategies for AI infrastructure, development, and adoption – objectives that demand distinct policy approaches, skill sets, and resources. While the country currently performs respectably in international rankings, declining relative performance across multiple indices signals cause for concern.
There is significant uncertainty in the trend observations within Canada and across countries – from the beginning of 2024 to the present, we have only two observations of business AI adoption and intended use (from the Canadian Survey of Business Conditions). The OECD international comparisons also depend on this singular survey, and the most recent publicly available Canadian data is for 2023. Given the rapid changes to AI adoption and development, and its potential to have significant growth and productivity effects, we have shockingly little information about how AI is being deployed over time. Statistics Canada is ending the Canadian Survey on Business Conditions (CSBC), with the last scheduled release in August 2026. Budget 2025 allocated $25 million over six years and $4.5 million ongoing for Statistics Canada to implement the AI and Technology Measurement Program (TechStat) to support data and insights to measure how AI is used by organizations and understand its impact on Canadians, the labour force, and the economy. There is significant value and need for national statistics agencies to develop a standardized approach to measuring AI adoption to compare rates across countries, sectors, and over time. This expansion in data monitoring of technology adoption will enable social and economic policy research and inform evidence-based policymaking.21
Temporal considerations must also inform policy design. The substantial productivity gains that AI promises remain, by most credible estimates, a decade or more away. Current measurable benefits, while real, derive primarily from infrastructure buildouts and the “replace” phase of technology adoption, wherein AI-enabled tools substitute for existing technologies within established workflows and processes. These incremental improvements, averaging around 17 percent in labour productivity across various studies, represent meaningful but modest gains. The transformative potential of AI will materialize only when industries advance to the “reimagine” phase, restructuring business models, production processes, and organizational architectures around AI capabilities. History suggests this transition requires substantial complementary investments in training, organizational restructuring, and supporting infrastructure – investments whose returns may not register in productivity statistics for years.
Infrastructure and Data
Data centres have near-immediate positive impacts on GDP and employment while they are being built, but then become an input to production themselves, while requiring significant energy inputs and minimal labour.22 This means that government expenditure directed toward AI development and infrastructure faces inherent limitations as an ongoing economic stimulus. This reality does not argue against infrastructure investment, but rather for calibrated expectations about its short and long term macroeconomic impact and for complementary policies that maximize domestic value creation where possible. Infrastructure represents an investment in the foundational inputs that enable AI and future AI development, with direct GDP impacts occurring in the near term. Data centres themselves do not directly contribute to ongoing productivity growth; they increase the total raw computing capacity. How that capacity is used and deployed determines the longer-term growth and productivity impacts.
The government has a significant role to play in developing its own internal capacity for AI development and adoption, as well as ensuring reasonable access to computing and data resources across economic sectors and business sizes (in particular, for non-profit social enterprises and academic research). The latter objective will be achieved through a combination of policies, including direct spending on infrastructure, funding for low-cost and/or targeted borrowing programs to encourage business investment, and accessibility for small, social and non-profit enterprises. In addition, complementary development of open data assets and investment in initiatives that aggregate high-value administrative and public sector data assets while maintaining appropriate privacy and quality controls would enable AI development activities and improve accessibility for smaller- and lower-resource enterprises.23
Since large companies are already making significant investments in AI infrastructure around the globe, government policy should target improving Canada’s attractiveness for that investment and development. For example, in November 2025, the government tabled Bill C-15. It includes accelerated capital cost allowance and expensing measures. The proposed measures are time-limited and include immediate capital cost deductions for certain productivity-enhancing assets.24 In addition to investment, data centres require land, building/permit approvals, and potentially environmental, energy, and Indigenous impact assessments (and the skilled labour to construct them after approvals). Once they are built, they require significant energy resources for ongoing operations.
A comprehensive strategy will require all levels of government to streamline regulatory approvals and, where possible, implement parallel instead of sequential regulatory steps to speed development timelines. Reducing administrative development costs and shortening approval timelines would benefit general economic development, but could take significant time to implement. In the short term, a targeted strategy could involve identifying suitable development sites at the local or regional level and pre-screening them for energy capacity, required environmental impact assessments, and other considerations.
Adoption and Diffusion
Adoption-focused policy, by contrast, may offer marginal immediate benefits but has higher potential to generate productivity growth in the long term.25 The analysis of business intentions to use AI in the future shows that the majority do not think it is currently relevant to their products or services. Further, the limited data available shows AI adoption across industries is slowing and could possibly decline in five industries over the year.
For companies that do think AI could be relevant to their business but do not plan to use AI, the most commonly cited reasons are a lack of knowledge about the technology, concerns about privacy and security, and that the technology is not yet mature enough. Manufacturing and wholesale trade, in particular, show a lower proportion of firms thinking that AI is not relevant to their products/services, and over 10 percent of firms are not planning to use AI due to a lack of skilled labour.
Given the relatively early stage of adoption and development, these barriers provide a target for government policy intervention. In particular, the government has signalled that it plans to update Canada’s privacy legislation for AI, and it should do so sooner rather than later. An updated privacy policy could have the dual benefit of reducing the uncertainty of potential adopters and providing clarity for companies planning to develop AI technologies using Canadian data.26 Similarly, governments have a role to play in increasing AI literacy and knowledge about the technology (see C.D. Howe Institute, forthcoming). Accelerating the adoption and development of AI in Canadian businesses is also supported by various general and specific tax subsidies, including the Scientific Research and Experimental Development (SR&ED) credit and capital cost allowances discussed above. While employee-training costs are generally tax-deductible, there is a need for a more comprehensive skills investment strategy, particularly in content development and availability for mid-career professionals, with a focus on practical applications. Increasing the labour force capable of implementing AI solutions across different industries is a critical complement to infrastructure investment and to encourage broad adoption and process innovation.
The evidence presented in this analysis shows that adoption in Canada generally follows international patterns – larger and younger firms tend to adopt AI at higher rates. This highlights that targeted policies encouraging technology uptake and growth in small and medium-sized enterprises could speed diffusion across sectors. In addition, it appears that engaging in international trade is associated with higher use of AI, though the intended future use is increasing for companies with no international business activities. In November of 2025, Canada, Australia, and India agreed to enter into a trilateral technology and innovation partnership that includes green energy, resilient supply chains for critical minerals, and the development and mass adoption of AI to improve welfare. Canada should continue to collaboratively secure critical input supply chains and enhance international cooperation on AI development and adoption.
The findings in this Commentary suggest that policies promoting trade diversification and encouraging a broader base of Canadian businesses to engage internationally are interrelated with AI adoption goals. Rather than viewing AI policy in isolation, governments should recognize how existing priorities around business growth, trade promotion, and export development can reinforce technology adoption objectives.
Conclusion
The uncertainty about AI’s development trajectory must be balanced with its potential to be revolutionary and a significant source of ever-elusive productivity growth. The country cannot afford to miss an opportunity that may substantially improve living standards and economic competitiveness over time. On the other hand, the uncertainty surrounding AI’s ultimate productivity impact – credible estimates range from less than 1 percent to 15 percent cumulative GDP gains over the next decade – counsels against excessive concentration of public resources. This suggests a multi-pronged strategic approach that includes investment in infrastructure, enhancing research and development policy,27 and updating privacy legislation. In addition, the government needs to rapidly implement the TechStat initiative to improve monitoring of the diffusion of AI and the associated economic effects. Many policies supporting AI infrastructure, development, and adoption are in the works, while data to monitor their effects is highly limited. The evidence base needs to expand to inform comprehensive and coordinated policy.
Policy should continue to pursue a balanced approach: support for infrastructure development and research capacity to maintain Canada’s standing as a credible AI nation, combined with robust efforts to accelerate adoption across sectors and firm sizes. However, governments should resist the temptation to overemphasize AI at the expense of broader economic priorities.28 Government policy can support AI development and diffusion without direct spending by focusing on a supportive regulatory environment based on principles to manage risk and potential harms, and some stimulus in the form of demand-side policies focusing on potential adopters.29 AI represents one important element of Canada’s productivity agenda, but not its entirety. Maintaining perspective on AI’s current limitations and its uncertain trajectory will help ensure that policy balances risks and limited government resources while remaining responsive to emerging evidence.
The author extends gratitude to Peter MacKenzie, Anindya Sen, Daniel Schwanen, Andrew Sharpe, Tingting Zhang, and several anonymous referees for valuable comments and suggestions. The author retains responsibility for any errors and the views expressed.
Acemoglu, D., and P. Restrepo. 2019. “Automation and New Tasks: How Technology Displaces and Reinstates Labor.” Journal of Economic Perspectives 33(2): 3–30.
Agrawal, A., J. Gans, and A. Goldfarb. 2023. “Similarities and Differences in the Adoption of General Purpose Technologies.” NBER Working Paper 30976.
Blit, J. 2025. Replace, Reimagine, Recombine: Building an AI Nation to Fix Canada’s Productivity Crisis. https://www.uwaterloo.ca/scholar/sites/ca.scholar/files/jblit/files/blit_ai_3rs.pdf
Boussour, L. 2025. “AI-Powered Growth: GenAI as a New Engine of U.S. Economic Performance.” EY Parthenon. November 17. https://www.ey.com/en_us/insights/ai/ai-powered-growth
Brynjolfsson, Erik, Daniel Rock, and Chad Syverson. 2021. “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies.” American Economic Journal: Macroeconomics 13(1): 333–372.
Chatterji, Aaron, Thomas Cunningham, David Deming, Zoe Hitzig, Christopher Ong, Carl Yan Shan, and Kevin Wadman. 2025. “How People Use ChatGPT.” NBER Working Paper 34255. September. https://www.nber.org/papers/w34255
Coupé, Tom, and Weilun Wu. 2025. “The Impact of Generative AI on Productivity: Results of an Early Meta-Analysis.” Working Papers in Economics 25/09. University of Canterbury, Department of Economics and Finance. https://ideas.repec.org/p/cbt/econwp/25-09.html
Eloundou, Tyna, Sam Manning, Pamela Mishkin, and Daniel Rock. 2023. “GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models.” arXiv.
Filippucci, Francesco et al. 2024. “The Impact of Artificial Intelligence on Productivity, Distribution and Growth: Key Mechanisms, Initial Evidence and Policy Challenges.” OECD Artificial Intelligence Papers No. 15. Paris: OECD.
Filippucci, F., P. Gal, K. Laengle, M. Schief, and F. Unsal. 2025. “Opportunities and Risks of Artificial Intelligence for Productivity.” International Productivity Monitor 48. https://www.csls.ca/ipm/48/OECD_Final.pdf
Harberger, A.C. 1998. “A Vision of the Growth Process.” American Economic Review 88(1): 1–32.
Heneseke, G., R. Davies, A. Felstead, D. Gallie, F. Green, and Y. Zhou. 2025. “How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index.” https://arxiv.org/pdf/2507.22748
Maslej, Nestor, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kariuki, Emily Capstick, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, Tobi Walsh, Armin Hamrah, Lapo Santarlasci, Julia Betts Lotufo, Alexandra Rome, Andrew Shi, and Sukrut Oak. 2025. The AI Index 2025 Annual Report. Stanford, CA: Institute for Human-Centered AI, Stanford University. April.
McElheran, K. et al. 2023. “AI Adoption in America: Who, What, and Where.” NBER Working Paper 31788.
OECD.AI. 2026a. Data from Preqin, last updated January 5, 2026. https://oecd.ai/
————. 2026b. Data from OpenAlex, last updated September 30, 2025. https://oecd.ai/
OECD/BCG/INSEAD. 2025. The Adoption of Artificial Intelligence in Firms: New Evidence for Policymaking. Paris: OECD Publishing. https://doi.org/10.1787/f9ef33c3-en
OECD. 2026. ICT Access and Usage Database. January.
Yotzov, Ivan, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, Steven J. Davis, Kevin M. Foster, Aaron Jalca, Brent H. Meyer, Paul Mizen, Michael A. Navarrete, Pawel Smietanka, Gregory Thwaites, and Ben Zhe Wang. 2026. “Firm Data on AI.” NBER Working Paper 34836. https://www.nber.org/papers/w34836
Avril 2026 – Les investissements en IA ont atteint plusieurs centaines de milliards, mais les retombées économiques se font encore attendre. Malgré des investissements rapides et une expérimentation répandue des outils d’IA générative, les gains de productivité mesurables ne se reflètent pas encore clairement dans les données agrégées des économies avancées, ce qui soulève une question importante : qui sera le premier à transformer cette dynamique en gains tangibles?
Dans « From Hype to Output: How AI Investment Translates to Real Productivity Gains » (De l’engouement à la production : comment les investissements dans l’IA se traduisent par de réels gains de productivité), l’auteure Rosalie Wyonch constate que si l’IA offre une voie vers de meilleures performances économiques et pourrait contribuer à relever les défis de productivité auxquels le Canada est confronté depuis longtemps, la concrétisation de ces gains nécessitera des politiques favorisant son adoption et sa diffusion par les entreprises, lesquelles restent inégales dans la plupart des secteurs et des régions. Cela implique notamment de supprimer les obstacles, de garantir l’accès aux données et d’encourager l’intégration de l’IA au sein des entreprises et par tous les secteurs.
A feature about the evolution of AI written and composed by AI.
AI
Artificial Intelligence (AI) has become an integral part of our daily lives, influencing everything from how we interact with technology to how businesses operate. But where did it all begin? Let’s take a journey through the early days of AI, exploring the key milestones that have shaped this fascinating field.
Today, AI is a rapidly evolving field with applications in various domains, including healthcare, finance, transportation, and entertainment. From virtual assistants like me, Microsoft Copilot, to autonomous vehicles and systems, AI continues to transform our world in profound ways.
A Copilot self generated image when queried “Show me what you look like”. CP
Conclusion
The journey of AI from its early conceptual stages to its current state is a testament to human ingenuity and perseverance. While the field has faced numerous challenges and setbacks, the progress made over the past few decades has been remarkable. As we look to the future, the potential for AI to further revolutionize our lives remains immense.
This article is via friends at share.america.gov. As artificial intelligence fuels a 21st-century industrial revolution, the United States and partner nations are working together to advance technological progress and mutual prosperity.
In December, the U.S. Department of State launched the Pax Silica initiative , inviting countries that are home to advanced technology companies to adopt AI standards and governance models that will foster trusted digital infrastructure and secure supply chains while unleashing the full economic potential of AI.
Pax Silica is a new US and UK and Australia led tech and critical minerals security coalition.
Where is Canada? We have US-adjacent supply chains and vast critical mineral deposits. Why are we missing – as we also are in AUKUS – again?
“This is an economic security coalition built on the reality that our security is inseparable from our technological edge,” Under Secretary of State for Economic Affairs Jacob Helberg said of Pax Silica, named for the Latin words for peace and the element central to modern technologies.
So far, 11 countries have signed on to the Pax Silica declaration along with the United States: Australia, Greece, India, Israel, Japan, Qatar, Republic of Korea, Singapore, Sweden, United Arab Emirates and the United Kingdom. Taiwan has endorsed the Pax Silica principles.
Under Secretary of State Jacob Helberg, far left, joins international partners at the Pax Silica Summit in Washington December 12. (State Dept.)
A strategy to lead the AI race
Advancing diplomacy and security around future technologies is a pillar of the Trump administration’s AI strategy, Winning the AI Race: America’s AI Action Plan (PDF, 509KB below). The strategy also aims to build U.S. AI infrastructure and accelerate innovation.
In the 21st century, Helberg says, “power is measured by technology. The ability to design and manufacture critical components … The ability to secure and scale advanced systems. The ability to turn knowledge into production.”
(State Dept./M. Gregory)
Countries that have joined Pax Silica are among those already reaping the benefits of innovation. Since November 2022, when U.S.-based OpenAI released ChatGPT, countries that have now joined Pax Silica have averaged 3% to 4% economic growth , three times that of similarly developed nations, Helberg said in congressional testimony in February.
Opportunity for streamlined access
Pax Silica seeks to ensure signatory nations enjoy the benefits of future progress. The State Department is piloting a “concierge” service to streamline partner nations’ access to American-made AI products — including power, cooling, software and hardware.
In February, the U.S. State Department announced foreign assistance to deliver affordable, high-quality smartphones across the Indo-Pacific. This Edge AI Package will bring AI innovations to millions of people while ensuring future users run trusted operating systems.
“When it comes to AI, we will partner with our friends to ensure they take part in this unprecedented economic boom,” Helberg says.
Whistleblowers from Meta and TikTok revealed that both companies knowingly allowed more harmful content, including violence, extremism, and exploitation of minors, on their platforms to win the algorithm-driven engagement race, prioritizing stock prices and political relationships over user safety.
Disclaimer- According to Kate Miller at The Fastest Media, the original source for this story, Cybernews, has been caught in significant inaccuracies.
Cyberbullying Enabled
These platforms also prioritize resolving complaints from politicians over those from vulnerable people, such as minors experiencing cyberbullying.
“While platforms and lawmakers take their sweet time debating what borderline content is, people are left to deal with the psychological fallout of social media addiction. From the inability to tell right from wrong or fake from real, loss of concentration, sleep, and even sense of self, to radicalization, depression, and self harm – the consequences of companies toying with their algorithms to meet business goals are dire for humanity,” writes Jurgita Lapienytė, Editor-in-Chief at Cybernews.
Profit Over Safety?
A new BBC report revealed what we suspected all along – big tech platforms turn a blind eye to harmful content for the sake of profit. Platforms allow so-called borderline content – misogynistic, sexist, racist, conspiracy-driven – that is harmful yet legal.
According to the report, based on accounts from a dozen whistleblowers and insiders, Meta engineers were instructed to allow more borderline content to compete with TikTok. Meanwhile, TikTok is said to have prioritized several user complaints involving politicians to “avoid threats of regulation or bans.”
Unsurprisingly, big tech platforms denied any wrongdoing, insisting that they do not amplify harmful content.
Algorithms are allegedly designed to better understand user interests and needs, and cater to them accordingly. Unfortunately, most of what a user “wants” turns out to be conspiracy theories, AI slop, deepfakes, and pro-Nazi content. Or at least the algorithm seems to think so – because most of this is so-called ragebait content, designed to provoke a strong response from the user.
And since users engage with it, the algorithm is tricked into “thinking” this is what people want. Humans behind the algorithm must clearly understand this is not the case, but clicks translate to cash. So why would Big Tech cut the branch it’s sitting on?
In 2024, Meta earned $16 billion, or 10% of its annual revenue, from scam ads and banned goods. The information comes not from a third-party analytics firm but from Meta’s own documents, proving that the tech giant is well aware of how much harm it can spread – and how much money it can make along the way.
While platforms and lawmakers take their sweet time debating what borderline content is, people are left to deal with the psychological fallout of social media addiction. From the inability to tell right from wrong or fake from real, loss of concentration, sleep, and even sense of self, to radicalization, depression, and self harm – the consequences of companies toying with their algorithms to meet business goals are dire for humanity.
It’s not only our mental health that’s at stake. Adversaries, well aware of algorithmic logic, abuse it to spread misinformation and straightforward lies, sowing division to influence elections all over the world – making us wonder just how much harm performative compliance has already done to democracy.
Cybernews is a globally recognized independent media outlet where journalists and security experts debunk cyber by research, testing, and data.
Cybernews has earned worldwide attention for its high-impact research and discoveries, which have uncovered some of the internet’s most significant security exposures and data leaks. Notable ones include:
Cybernews researchers found that Android AI apps leak Google secrets the most, 700TB of files already exposed.
Cybernews researchers discovered multiple open datasets comprising 16 billion login credentials from infostealer malware, social media, developer portals, and corporate networks – highlighting the unprecedented risks of account takeovers, phishing, and business email compromise.
The research team also studies over 19 billion newly exposed passwords, and found that most people use 8–10 character passwords (42%).
Cybernews researchers analyzed 156,080 randomly selected iOS apps – around 8% of the apps present on the App Store – and uncovered a massive oversight: 71% of them expose sensitive data.
Recently, Bob Dyachenko, a cybersecurity researcher and owner of SecurityDiscovery.com, and the Cybernews security research team discovered an unprotected Elasticsearch index, which contained a wide range of sensitive personal details related to the entire population of Georgia.
The team analyzed the new Pixel 9 Pro XL smartphone’s web traffic, and found that Google’s latest flagship smartphone frequently transmits private user data to the tech giant before any app is installed.
The team revealed that a massive data leak at MC2 Data, a background check firm, affects one-third of the US population.
The Cybernews security research team discovered that 50 most popular Android apps require 11 dangerous permissions on average.
An analysis by Cybernews research discovered over a million publicly exposed secrets from over 58 thousand websites’ exposed environment (.env) files.
The team revealed that Australia’s football governing body, Football Australia, has leaked secret keys potentially opening access to 127 buckets of data, including ticket buyers’ personal data and players’ contracts and documents.
The Cybernews research team, in collaboration with cybersecurity researcher Bob Dyachenko, discovered a massive data leak containing information from numerous past breaches, comprising 12 terabytes of data and spanning over 26 billion records.
The team analyzed NASA’s website, and discovered an open redirect vulnerability plaguing NASA’s Astrobiology website.
Featured image via Cybernews- Elon Musk’s artificial intelligence (AI) firm xAI has said it is working to remove posts by its chatbot Grok that praised Adolf Hitler as the best person to deal with “vile anti-white hate.”
Fake Photo? Manipulated Video? How to Spot Sham AI
This to preserve the credibility of digital media and safeguard users from falling victim to scams. As synthetic media becomes more sophisticated, identifying AI-generated manipulations presents a unique challenge, but numerous free apps and toolsare readily available allowing users to validate photo and video authenticity with ease—a major step forward in safeguarding trust in a world increasingly influenced by AI-generated visuals, ensuring transparency and security in the digital age. More below.
How AI Drives Misinformation
Amid the onslaught of highly concerning news headlines spotlighting how deepfake AI-generated photo and video scams are driving rampant misinformation and wreaking havoc across digital, cultural, workplace, political and other societal frameworks, solutions are emerging combat AI-driven misinformation and fraud before people fall victim to scams.
One AI disruptor transforming the fight against AI fraud is BitMind—an AI deepfake detection authority that offers a suite of free apps and tools that instantly identify and flag AI-generated images before you fall victim.
Built by AI Engineers
Built by a team of AI engineers hailing from leading tech companies like Amazon, Poshmark, NEAR, and Ledgersafe, BitMind’s instant detection of deepfakes helps uphold the credibility of the media, guaranteeing the authenticity of the information we use. A strong deepfake detection enhances digital interactions, supports better decision making and strengthens the integrity of the modern digital world—serving to protect reputations, shield finances and maintain trust for celebrities, politicians, public figures … and everyone else.
For both B2C and B2B use, these 5 BitMind tools are free and accessible to anyone:
AI Detector App: A simple web page where users can drag-and-drop suspicious images for fast deepfake detection results;
Chrome Extension: Flags AI-created content in real-time, while browsing.
X Bot: Verifies if images on X/Twitter are real or AI-generated;
Discord Bot: Verifies if images are real or AI-generated via its Discord Integration;
AI or Not Game: Fun Telegram bot that tests your ability to distinguish between AI-generated and human-created images.
“Recognizing the need to integrate deepfake detection into everyday technology use, our applications fit seamlessly into users’ lives,” notes Ken Miyachi, BitMind CEO. “For example, the BitMind Detection App is a user-friendly application that allows individuals to upload images and quickly assess the likelihood of them being real or synthetic. Additionally, the Browser Extension enhances online security by analyzing images on web pages in real time and providing immediate feedback on their authenticity through our subnet validators. These tools are designed to empower users, enabling them to navigate digital spaces with confidence and security.”
As the world’s first decentralized Deepfake Detection System, BitMind is an open-source technology that enables developers to easily integrate the technology into their existing platforms to provide accurate real-time detection of deepfakes.
“Deepfake technology has emerged as both a marvel and a menace,” continued Miyachi. “With the capacity to create synthetic media that closely mimics reality, deepfakes present unprecedented challenges in privacy, security, and information integrity. Responding to these challenges, we introduced the BitMind Subnet, a breakthrough on the Bittensor network, dedicated to the detection and mitigation of deepfakes.”
According to Miyachi, here are key reasons why BitMind technology is a game changer:
The BitMind Subnet, which represents a pivotal advancement in the fight against AI-generated misinformation. Operating on a decentralized AI platform, this deepfake detection system employs sophisticated AI models to accurately distinguish between real and manipulated content. This not only enhances the security of digital media but also preserves the essential trust in digital interactions.
The BitMind Subnet is equipped with advanced detection algorithms that utilize both generative and discriminative AI technologies to provide a robust mechanism for identifying deepfakes.
BitMind employs cutting-edge techniques, including Neighborhood Pixel Relationships, ensuring competitive accuracy in detection. The operation of the subnet is decentralized, with miners across the network running binary classifiers. This setup ensures that the detection processes are widespread and not confined to any centralized repository, enhancing both the reliability and integrity of the detection results.
Community collaboration is a cornerstone of the BitMind Subnet, actively encouraging the community to contribute to our evolving codebase, and by engaging with developers and researchers, the subnet is continuously improved and updated with the latest advancements in AI.
BitMind combines its extensive industry expertise, cutting-edge academic research, and a deep passion for technology. The team has a proven track record in AI, blockchain, and systems architecture, successfully leading tech projects and founding innovative companies.
What truly sets BitMind apart is their commitment to creating a safer, more transparent digital world where AI benefits humanity, driven by their passion for innovation, security and community engagement. Their technologies are expressly designed to safeguard the integrity of digital media and foster a trustworthy digital ecosystem.
In the modern world full of fake news and increasing cyber threats, BitMind’s innovations are paving the way for a future in which digital trust is not an option, but a necessity. As the threats increase, the global community must be equipped with the means to ingest digital information in a reliable and authentic in order to realize AI’s true potential safely and efficiently. For the Silo, Marsha Zorn.
A growing paradox is reshaping the 2026 workforce: strong performers are still losing their jobs. According to a January 2026 HR Dive survey, nearly 50 percent of companies expect layoffs in Q1, even as most plan to hire selectively for growth roles, exposing a market where competence alone no longer protects careers. Strategic growth advisor and ‘The CodeBreaker Mindset‘ author Chitra Nawbatt warns this moment marks the rise of a “competence trap,” where professionals optimize output while organizations quietly reprice value around speed, adaptability, and influence. The result is a workforce operating by outdated rules in a system that has already moved on. Below are more of her insights.
How to Stay Relevant in 2026
Across industries, a growing number of professionals share the same uneasy feeling: despite strong performance and proven competence, job security feels increasingly fragile. That anxiety is not imagined. The rules of work are shifting in plain sight, and the changes are cutting through roles that once felt insulated from disruption.
Layoffs
Layoffs are no longer limited to underperformers or redundant teams. They are appearing in the middle of organizational charts, within core functions, and among employees who were recently labeled essential. According to strategic growth advisor Chitra Nawbatt, author of “The CodeBreaker Mindset: The Unwritten Rules for Success,” this signals a deeper structural change in how companies define value.
“Competence used to buy you time,” Nawbatt explains. “In 2026, competence is table stakes. The market is rewarding a different set of behaviors, and many professionals are still playing by the old rules.”
This shift is often mischaracterized as a simple story about machines replacing people. In reality, the more immediate force is organizational redesign. Companies are flattening decision layers, reducing bureaucracy, and repricing labor around speed and adaptability.
Reuters already reported that Amazon was preparing additional corporate job cuts as part of an effort to streamline its structure and remove management layers, even as it continues to invest selectively in priority roles tied to long term strategy.
“The narrative is convenient,” says Nawbatt. “Blaming technology masks the harder truth. Many organizations are still figuring out how to operate efficiently in a volatile environment, and people get caught in that recalibration.”
Data shows a contradiction
Data from HR leaders underscores the contradiction. A January 2026 survey cited by HR Dive found that nearly half of companies expect layoffs will likely occur in the first quarter, while most also plan to hire selectively for roles tied to growth initiatives.
This dual track of hiring and cutting reveals why performance reviews alone no longer predict job security. The system itself is changing faster than individual output can keep up.
Rise of the CodeBreaker
Nawbatt describes the professionals who thrive in this environment as CodeBreakers. The term does not refer to rule breakers for their own sake, but to people who understand that success is governed by both written rules and unwritten ones.
“Written rules tell you how things are supposed to work,” she says. “Unwritten rules tell you how decisions actually get made when pressure hits. In periods of reorganization, the unwritten rules are what determine who stays and who goes.”
Based on her work advising leaders and teams across multiple industries, Nawbatt outlines five shifts that separate those who remain relevant from those who become interchangeable.
1. Stop optimizing and start reading patterns
Efficiency can feel reassuring in unstable times, but it can also be misleading. Nawbatt emphasizes that productivity without direction often leads professionals deeper into roles that are quietly being deprioritized.
“The winners are not the busiest people,” she notes. “They are the ones who can see where budgets are tightening, where automation is accelerating, and where their work is becoming easier to replace.”
2. Treat unwritten rules as the real operating system
Most professionals are trained to follow job descriptions and formal processes. During restructurings, however, informal dynamics take over. Who is protected, which narratives leadership repeats, and how risk is managed become far more important than stated policies.
“When written and unwritten rules diverge,” Nawbatt says, “the people who notice early have options. Everyone else is reacting.”
3. Build a nonlinear value stack
The traditional career ladder assumed stability and long time horizons. In today’s environment, resilience comes from a portfolio of relevance that spans skills, relationships, and credibility across contexts.
“You are not competing for a seat anymore,” Nawbatt explains. “You are trying to become a node in an ecosystem. The goal is to create value that travels with you when structures change.”
4. Focus on information quality, not quantity
Modern organizations are saturated with dashboards, metrics, and opinions. According to Nawbatt, the ability to distinguish data driven insight from perception driven or manipulation driven narratives is becoming a defining leadership skill.
“Clarity is power,” she says. “The person who can say what is true, what is assumed, and what is being spun becomes indispensable when decisions must be made under uncertainty.”
5. Replace ladders with loops
Career progress in 2026 is less linear and more iterative. Learning, testing, building proof, and compounding impact now matter more than waiting for titles or recognition.
“High performers often get stuck waiting to be noticed,” Nawbatt observes. “CodeBreakers build evidence. They create work that can be demonstrated, taught, and scaled.”
A Market That No Longer Rewards Comfort
If this moment feels uncomfortable, that discomfort may be the point. The market has stopped rewarding stability for its own sake. The professionals most likely to thrive are those who confront change early and adjust with intention.
AI will continue to improve. Organizations will continue to thin. The defining question is not whether people can outwork machines, but whether they can outgrow outdated playbooks.
As Nawbatt puts it, “The CodeBreaker mindset is not about fear. It is about clarity. It is about understanding how systems really work and moving with discernment when those systems shift.”
Moltbook, an AI-exclusive social media platform launched just days ago and dubbed the “Reddit for AI agents,” has exploded in popularity online. Within its first week, Moltbook attracted over 1.5 million registered AI agents and more than a million human spectators watching the agents interact with each other, sparking countless posts across human social networks.
The project originated with OpenClaw, an open-source AI agent created by Peter Steinberger that runs locally on a user’s machine. The software allows bots to use a computer and internet services just as a human would. Building on this, entrepreneur Matt Schlicht developed his own OpenClaw agent, named Clawd Clawderberg, and tasked it with coding, moderating, and managing the entire Moltbook platform. Now most moltbots on the platform run on OpenClaw.
Vulnerability of Moltbook
Cybersecurity professionals warn that this setup is terribly insecure and creates massive security vulnerabilities. However, most agree that it’s impossible to suppress public curiosity and discourage experimentation. Instead, they are calling for caution and offering some safety tips.
Karolis Arbaciauskas, head of product at the cybersecurity company NordPass, comments:
“Moltbook and OpenClaw have attracted tech-savvy tinkerers with unprecedented opportunities for experimentation because these tools have virtually no built-in security restrictions but have broad access to users’ computers, apps, and accounts. For example, you can connect to your OpenClaw bot through a messaging app to interact with it while you’re away. It can remember your conversations, read and write files on your computer, browse the web, build applications, and even consult other bots on Moltbook for advice on how to do it best.”
Curiosity Killed The Cat
“While it’s exciting and curious to see what an AI agent can do without any security guardrails, this level of access is also extremely insecure. Therefore, please run Moltbook and your personal bots only in secure, isolated environments.
“Do not give your AI agents access to your real accounts. Instead, create disposable alternatives for them to use. Do not let them use your main browser, especially if you store passwords on it. You should also be cautious with enabling autofill because it creates the risk of the agent having permanent remote access to your credentials. If you want an agent to build something autonomously and anticipate it may need to purchase software or rent server space, link it to a disposable payment card.
“Avoid running Moltbook or OpenClaw agents on your personal or work computers. These AI agents are unpredictable and highly vulnerable to prompt injection attacks. This means if your agent processes an email, document, or webpage containing a hidden malicious instruction, it will likely execute that command in addition to its original task. For example, it could be instructed to send all the credentials, personal data, and payment card information it has access to directly to an attacker.
“The risk isn’t limited to hackers with malicious intent. AI agents could leak users’ data unintentionally. And this is just the tip of the iceberg. Cybersecurity researchers have already identified critical flaws in Moltbook, including an unsecured database that could allow unauthorized users to take control of any AI agent on the site.
Launching Bots That Con?
“It would not be surprising if threat actors, trolls, and scammers have already found their way onto Moltbook and launched bots tasked with conning other AI agents into cryptocurrency schemes or luring them into hidden prompt injections.
“That’s why it is best to buy a separate, dedicated machine and use disposable accounts for any experimentation. It is also advisable to use encryption and a private mesh network as well as to try to harden your bot against prompt injections.”
Chief Economists Perceive Relative Resilience but Remain Concerned about Asset Prices, Debt and Geoeconomic Tensions
Acknowledging the relative resilience of the global economy amid turbulence, 53% of chief economists surveyed expect global economic conditions to weaken in the year ahead, down from 72% in September 2025.Uncertainty around technology remains high, with 52% expecting AI-related stocks to decline and 40% expecting gains. On growth, expectations diverge by region, with economists expecting strong momentum in South Asia and East Asia and weak to moderate growth in Europe.
On macroeconomics, nearly a third of respondents are concerned about sovereign debt crises in advanced economies and nearly half in emerging economies; over 60% expect governments to rely on higher inflation and tax revenues to manage elevated debt.Learn more about the Chief Economists’ Outlook here.
Follow the Annual Meeting 2026 here and on social media using #WEF26.
Geneva, Switzerland, January 2026 – The global economic outlook has improved modestly but remains uncertain, with asset valuations, mounting debt, geoeconomic realignment and rapid artificial intelligence deployment creating both opportunities and risks, according to the World Economic Forum’s latest Chief Economists’ Outlook, published today. Although 53% of chief economists expect global economic conditions to weaken in the year ahead, this marks a significant improvement from the 72% who held this view in September 2025.
“The Chief Economists survey reveals three defining trends for 2026: surging AI investment and its implications for the global economy; debt approaching critical thresholds with unprecedented shifts in fiscal and monetary policies; and trade realignments,” said Saadia Zahidi, Managing Director, World Economic Forum. “Governments and companies will have to navigate an uncertain near-term environment with agility while continuing to build resilience and invest in the long-term fundamentals of growth.”
AI and other asset valuations are under scrutiny Concentrated AI stock gains are splitting the views of the chief economists. A narrow majority (52%) are expecting AI-related US stocks to decline over the next year, but 40% foresee further increases. Should values fall sharply, 74% believe impacts would spread across the global economy. Cryptocurrencies face bleaker prospects, with 62% anticipating further declines following market turbulence, while 54% believe gold has peaked after recent rallies.
When it comes to the potential expected returns from AI, there is wide variation across regions and sectors. Roughly four in five chief economists expect productivity gains within two years in the US and China. Chief economists expect the information technology sector to adopt AI fastest, with nearly three-quarters anticipating imminent productivity gains. Financial services, supply chain, healthcare, engineering and retail follow as “fast-movers”, with one to two-year timelines. By firm size, the chief economists expect companies with 1,000+ employees to see gains earlier than others: 77% of chief economists expect meaningful productivity gains within two years.
The employment picture in relation to AI is expected to evolve over time: two thirds expect modest job losses over the next two years, but views diverge sharply over the longer term: 57% anticipate net losses over 10 years, while 32% foresee gains as new occupations emerge.
Debt may drive difficult trade-offs Managing elevated debt levels has become a central challenge for policy-makers, particularly as spending pressures rise. Defence spending is almost unanimously expected to increase, with 97% of chief economists anticipating rises in advanced economies and 74% in emerging markets. Digital infrastructure and energy spending are also expected to rise. Most other sectors are expected to see stable levels of spending, while a majority of surveyed economists anticipate spending on environmental protection to decline in both advanced (59%) and emerging economies (61%).
Views are split equally on the likelihood of sovereign debt crises in advanced economies, while nearly half (47%) see them as likely in the year ahead in emerging economies. A large majority of chief economists expect governments to rely on higher inflation to reduce burdens (67% in advanced economies, 61% in emerging markets). Tax increases are also viewed as likely by 62% for advanced economies and 53% for emerging markets. Some 53% of chief economists anticipate seeing debt restructuring or default as a debt management strategy in emerging markets over five years, compared to just 6% for advanced economies.
Trade flows and regional growth outlooks are realigning Global trade and investment are adjusting to a new, competitive reality. Chief economists expect import tariffs between the US and China to remain mostly stable, though competition could intensify in other domains. Some 91% expect US tech export restrictions to China to remain or increase; 84% anticipate the same for Chinese critical mineral restrictions.
In this new context, 94% of chief economists expect more bilateral trade deals and 69% anticipate growth in regional trade agreements. Some 89% expect Chinese exports into non-US markets to further increase, while surveyed economists are split on the future of global trade volumes. Meanwhile, almost half of them foresee the continued rise of international investment flows, and 57% expect FDI into the US to increase compared to 9% who expect increased inflows to China.
When it comes to growth expectation among the chief economists surveyed, South Asia leads with 66% anticipating strong or very strong performance, driven by robust growth in India. Some 45% expect strong growth and 55% moderate growth in East Asia and the Pacific. Some 36% expect strong growth and 64% moderate growth in the MENA region. The US outlook improved notably, with 69% expecting moderate growth versus 49% in September 2025, but only 11% expecting strong growth. China faces mixed prospects, with 47% expecting moderate growth and 24% strong growth and nearly an equal number – 29% – expecting weak growth. Europe confronts the weakest outlook, with 53% expecting weak growth, 44% moderate growth, and only 3% anticipating strong growth.
About the Chief Economists’ Outlook The report builds on extensive consultations and surveys with chief economists from the public and private sectors, organized by the World Economic Forum’s Centre for the New Economy and Society. The report supports the Forum’s Future of Growth Initiative, aiming to foster dialogue and actionable pathways to sustainable and inclusive economic growth. The Chief Economists’ Outlook is complemented by other recent publications with economic foresight. Four Futures for the New Economy and Four Futures for Jobs in the New Economy explore strategic implications for businesses navigating geopolitical shifts, technology disruption and workforce transformation through 2030, offering indicators to track and strategies to prepare for multiple scenarios.
About the Annual Meeting 2026 The World Economic Forum’s 56th Annual Meeting, taking place today the 19th and running until 23 January 2026 in Davos-Klosters, Switzerland, will convene leaders from business, government, international organizations, civil society and academia under the theme, A Spirit of Dialogue. Click here to learn more.
A Spirit of Dialogue Brings Record Numbers of World Leaders to Davos for World Economic Forum Annual Meeting 2026
A record 400 top political leaders, including close to 65 heads of state and government – with six G7 leaders expected – nearly 850 of the world’s top CEOs and chairs, and almost 100 leading unicorns and technology pioneers will convene in Davos-Klosters for one of the highest-level gatherings in the Annual Meeting’s history. Held under the theme of A Spirit of Dialogue, the 56th Annual Meeting will provide an impartial platform for close to 3,000 participants from over 130 countries to navigate the major economic, geopolitical and technological forces reshaping the global landscape.
A major focus will be on the unprecedented speed of innovation and technological advancement with key voices from industry and academia present.– At a pivotal moment for global cooperation, the World Economic Forum will convene its 56th Annual Meeting today in Davos-Klosters, Switzerland, bringing together close to 3,000 cross-sector leaders from over 130 countries under the theme A Spirit of Dialogue. Marking record levels of governmental participation, 400 top political leaders – including close to 65 heads of state and government and six of the G7’s leaders – are expected to take part, alongside nearly 850 of the world’s top CEOs and chairpersons, and almost 100 leading unicorns and technology pioneers.
Amid the most complex geopolitical backdrop in decades – marked by rising fragmentation and rapid technological change – the need for an impartial platform that brings together diverse and sometimes diverging voices across industries, regions, and generations is urgent. Building on the Forum’s long-standing tradition of providing a trusted space for dialogue and public-private collaboration, the Annual Meeting 2026 will enable an open exchange of ideas and perspectives on the issues that matter most to people, economies and the planet, turning shared understanding into action.
“Dialogue is not a luxury in times of uncertainty; it is an urgent necessity,” said Børge Brende, President and CEO, World Economic Forum. “At a critical juncture for international cooperation – marked by profound geoeconomic and technological transformation – this year’s Annual Meeting will be one of our most consequential. With historic levels of participation, it will provide a space for an unparalleled mix of global leaders and innovators to work through and look beyond divisions, gain insight into a fast-shifting global landscape, and advance solutions to today’s and tomorrow’s biggest and most pressing challenges.”
“As the World Economic Forum enters its next chapter, this year’s Annual Meeting is bringing together a record number of global leaders from government, business, and non-governmental organizations at a moment when dialogue matters more than ever,” said Larry Fink, Interim Co-Chair, World Economic Forum. “Understanding different perspectives is essential to driving economic progress and ensuring prosperity is more broadly shared.”
“At a moment when cooperation matters more than ever, the Annual Meeting provides a unique space to turn dialogue into meaningful progress,” said André Hoffmann, Interim Co-Chair, World Economic Forum. “By bringing together leaders across regions and sectors, it creates the conditions to rebuild trust, align priorities and advance solutions that support long-term, sustainable growth for all, within planetary boundaries.”
Switzerland is the host country for the meeting. 400 government leaders are expected to attend this year, representing the highest level of government participation in the Annual Meeting’s history, including close to 65 heads of state and government, 55 ministers for economy and finance, 33 ministers for foreign affairs, 34 ministers for trade, commerce and industry, and 11 Governors of Central Banks. High-level government representation is expected from all key regions, including six G7 leaders and heads of state from countries central to dialogue on critical global situations – from Ukraine to Gaza and the broader Middle East, and beyond.
Top political leaders taking part include:
Top political leaders taking part include: Donald Trump, President of the United States of America; Mark Carney, Prime Minister of Canada; Friedrich Merz, Federal Chancellor of Germany; Ursula von der Leyen, President of the European Commission; He Lifeng, Vice-Premier of the People’s Republic of China; Javier Milei, President of Argentina; Prabowo Subianto, President of Indonesia; Pedro Sánchez, Prime Minister of Spain; Guy Parmelin, President of the Swiss Confederation 2026; Vahagn Khachaturyan, President of the Republic of Armenia; Ilham Aliyev, President of the Republic of Azerbaijan; Bart De Wever, Prime Minister of Belgium; Gustavo Petro, President of Colombia; Félix-Antoine Tshisekedi Tshilombo, President of the Democratic Republic of the Congo; Daniel Noboa Azín, President of Ecuador; Alexander Stubb, President of Finland; Kyriakos Mitsotakis, Prime Minister of Greece; Micheál Martin, Taoiseach, Ireland; Aziz Akhannouch, Head of Government, Kingdom of Morocco; Daniel Francisco Chapo, President of Mozambique; Dick Schoof, Prime Minister of the Netherlands; Mian Muhammad Shehbaz Sharif, Prime Minister of Pakistan; Mohammed Mustafa, Prime Minister of the Palestinian National Authority; Karol Nawrocki, President of Poland; Mohammed Bin Abdulrahman Al Thani, Prime Minister and Minister of Foreign Affairs of the State of Qatar; Aleksandar Vučić, President of Serbia; Tharman Shanmugaratnam, President of Singapore; Isaac Herzog, President of the State of Israel; Ahmad Al Sharaa, President of Syria; Volodymyr Zelenskyy, President of Ukraine.
Heads of international organizations taking part include:
António Guterres, Secretary-General of the United Nations; Ngozi Okonjo-Iweala, Director-General of the World Trade Organization; Ajay S. Banga, President of the World Bank Group; Kristalina Georgieva, Managing Director of the International Monetary Fund; Mark Rutte, Secretary-General of the North Atlantic Treaty Organization; Tedros Adhanom Ghebreyesus, Director-General of the World Health Organization; Alexander De Croo, Administrator of the United Nations Development Programme; Mathias Cormann, Secretary-General of the Organisation for Economic Co-operation and Development; Doreen Bogdan-Martin, Secretary-General of the International Telecommunication Union; Barham Salih, UN High Commissioner for Refugees; Jasem Al Budaiwi, Secretary-General of the Gulf Cooperation Council.
Around 1,700 business leaders, including close 850 of the world’s top CEOs and chairpersons from the World Economic Forum’s Members and Partners, will also participate, alongside almost 100 CEOs and chairpersons of Unicorn companies and Tech Pioneers who are transforming industries and shaping the future or technology worldwide.
Some of the top voices in technology and innovation taking part include:
JensenHuang, NVIDIA; Satya Nadella, Microsoft; Dario Amodei, Anthropic; Dina Powell McCormick, Meta; Demis Hassabis, Google DeepMind; YoshuaBengio, Université de Montréal; Alex Karp, Palantir Technologies; SarahFriar, OpenAI; YuvalHarari, Centre for the Study of Existential Risk; Khaldoon Khalifa Al Mubarak, Mubadala; PeggyJohnson, Agility Robotics; Arthur Mensch, Mistral AI; BretTaylor, Sierra; PengXiao, G42; EricXing, Mohamed bin Zayed University of Artificial Intelligence.
“In an era where exponential technological innovation and geopolitical disruption are deeply intertwined, the need for constructive dialogue between policy-makers and industry is clear,” said Mirek Dušek, Managing Director, World Economic Forum. “Leaders will share views from across sectors to help build the understanding needed to balance short-term priorities and immediate challenges with long-term value creation.”
Close to 200 leaders from civil society and the social sector – including labour unions, non-governmental and faith-based organizations, as well as experts and heads of the world’s leading universities, research institutions and think tanks – will also participate in the meeting. Heads of civil society organizations participating include:
David Miliband, President and CEO, International Rescue Committee; Sania Nishtar, CEO, Gavi, The Vaccine Alliance; Luc Triangle, General Secretary, International Trade Union Confederation; Kirsten Schuijt, Secretary General, WWF International; Mohammad Al-Issa, Secretary General, Muslim World League; Comfort Ero, President and CEO, International Crisis Group; Pinchas Goldschmidt, Chief Rabbi and President, Conference of European Rabbis; Oleksandra Matviichuk, Nobel Peace Laureate and Chair, Ukraine Center for Civil Liberties; Peter Sands, Executive Director, The Global Fund; Amitabh Behar, Executive Director, Oxfam International; Aulani Wilhelm, President and Executive Director, Nia Tero.
The 2026 programme is centred around five pressing global challenges where public-private dialogue and cooperation, involving all stakeholders, are critical for collective progress:How can we cooperate in a more contested world?How can we unlock new sources of growth?How can we better invest in people?How can we deploy innovation at scale and responsibly?How can we build prosperity within planetary boundaries? “In a global economy shaped by technology, geoeconomics, and demographics, the defining challenge will be whether opportunity is broadly shared or if growth remains sluggish and uneven,” said Saadia Zahidi, Managing Director, World Economic Forum. “The meeting will connect leaders to discuss how to unlock growth, jobs and economic transformation that translate into progress for communities everywhere. “The meeting’s Arts and Culture Programme will further amplify the diversity of voices and perspectives needed to advance impact, while showcasing the power of art, influence, and culture to drive change and create unique space for dialogue. Renowned artistic and cultural leaders in attendance include:
Marina Abramović, Jon Batiste, Thijs Biersteker, Sabrina Elba, Renaud Capuçon, Hiro Iwamoto, Suleika Jaouad, Sir David Beckham, Ahmad Joudeh, Yo-Yo Ma, Emi Kusano, Harvey Mason Jr, Hans Ulrich Obrist, Katie Piper, Ronen Tanchum, JR and will.i.am.
The Open Forum, now in its 23rd year, will host public panel discussions for the local community and participants from around the world, encouraging wider participation and open dialogue on key global issues.
Modern conveniences many take for granted — cell phones, laptops, GPS devices, even coffee makers — run on computer chips introduced by U.S. firms that established America’s leading role in technology. Trace the digital revolution, from its beginnings to the present day, with each groundbreaking advance.
How did these gains happen? Today’s technology emerged from U.S. support for research and development combined with America’s robust private sector, its scientific community, and its innovative spirit.
Bell Labs, a legendary research hub in New Jersey, began as a branch of the Western Electric Company, a subsidiary of the American Telephone and Telegraph Company (AT&T).
Founded in 1925 to meet a growing need for masscommunications, Bell Labs hired top engineers, physicists, chemists, and mathematicians to design and patent equipment (including a high-vacuum tube that transmitted telephone signals across North America).
Bell Labs encouraged interdisciplinary collaboration that produced groundbreaking discoveries. The labs were driven by scientific curiosity, flexible deadlines, and — thanks to AT&T’s budget — stable funding. Lab directors adopted a hands-off management style, and innovation flourished.
DID YOU KNOW?
In 1932, Bell Labs physicist Karl Jansky discovered radio waves coming from outer space. He’s known as the father of radio astronomy.
In the post-World War II period, Bell Labs’ Mervin Kelly assembled an all-star team of scientists to develop a replacement for the vacuum tube, which was bulky, fragile, and prone to burning out.
In 1947, John Bardeen and Walter Brattain — supervised by fellow physicist William Shockley — invented the point-contact transistor, a semiconductor device that amplifies sound and switches electrical currents on and off.
In 1948, Shockley designed the junction transistor, a more robust and reliable transistor. Its small size, low power consumption, and durability paved the way for computers, portable radios, cell phones, and other devices.
Eight years later, Bardeen, Brattain, and Shockley would be awarded the Nobel Prize in physics for this breakthrough.
DID YOU KNOW?
Bell Labs researchers have been awarded 10 Nobel Prizes in physics and chemistry, spanning from 1937 to 2023. While Bell Labs was at its most productive from the 1940s to the 1970s, important research continues today at its New Jersey headquarters.
Bell Labs continued to improve transistor technology during the 1950s, developing the silicon transistor and the metal-oxide-semiconductor field-effect transistor (MOSFET).
The MOSFET proved crucial for building high-density integrated circuits (ICs), or microchips, in the 1960s. Microchips — consisting of billions of tiny transistors crafted from semiconductor materials, commonly silicon — work together to power electronics.
Recognizing the potential for widespread impact and profits, Bell Labs created licensing agreements to share transistor technology with other companies.
In 1955, William Shockley left Bell Labs to establish Shockley Semiconductor Laboratory in Mountain View, California. Within a couple of years, some of his employees — engineers and scientists — formed their own company, Fairchild Semiconductor.
Fairchild is credited with the birth of Silicon Valley. The company became a major player in the growing semiconductor industry, and many Silicon Valley firms — including Intel (founded in 1968) and Apple (in 1976) — have ties to Fairchild alumni to this day.
As demand for semiconductors grew, so did the need for manufacturing capabilities.
Throughout the 1980s and 1990s, Japan, South Korea, and Taiwan became players in the industry, with Japanese companies like Toshiba and NEC influencing the data-storage market and South Korea’s Samsung and SK Hynix focusing on memory-chip production.
Meanwhile, the Taiwan Semiconductor Manufacturing Company (TSMC) upended a traditional business model of integrating chip design and manufacturing. It introduced the fabless-foundry model, encouraging firms to specialize in either design (fabless) or fabrication/manufacturing (foundry).
This increased efficiency. What’s more, it allowed many small firms — those lacking resources to open manufacturing plants — to design chips.
DID YOU KNOW?
The fabless-foundry business model democratized chip production, allowing startups to enter the market without the need for expensive manufacturing facilities.
Experts predict that quantum computing — with its ability to accelerate AI by overcoming limitations on data size, complexity, and processing speeds — will shape the future.
Quantum AI will develop algorithms that could advance pharmaceutical discoveries, predict financial outcomes, improve manufacturing, and bolster cybersecurity. Quantum/AI partnerships already comprise an active and developing market, with U.S. tech giants like IBM and Nvidia investing in both domains.
Industry leaders point to many factors that shape U.S. technological innovation. One such factor is the U.S. system of intellectual property protection, which fosters the spirit of risk-taking, says Walter Copan. (That system is enshrined in the U.S. Constitution, thanks to the foresight of America’s Founding Fathers.)
Sanjay Mehrotra cites the U.S. business culture of “openly, freely being able to debate ideas,” adding, “The best ideas win.”
Thomas Caulfield says, “This is where you can work hard, live your dream, become an entrepreneur, start a company.”
And Jon Gertner notes that key people at Bell Labs came from humble beginnings: “To me, that feels uniquely American — the idea that talent could rise from almost anywhere and shape the future of communications.”
It’s part of Silicon Valley lore that massive tech empires often sprouted from humble roots. As quantum computing and AI herald the next seismic shifts in technology, innovation hubs could emerge in unlikely places. Who knows? The next great U.S. tech companies might now be incubating in a town anywhere in America.
Ottawa’s forthcoming AI strategy needs to walk a tightrope between two equally important principles: safeguarding Canadians from possible misuses of AI but also giving our private and academic sectors the leeway to use Canada’s AI strengths to develop and commercialize new technologies and products.
Planned AI adoption rose sharply between Q3 2024 and Q3 2025, but progress remains highly uneven across industries. Knowledge-intensive sectors – such as information and cultural industries, finance and insurance, and healthcare – show the strongest gains, while several goods-producing and operational sectors, including manufacturing, wholesale trade, and mining, show stagnant or declining expectations.
The federal government must clearly define a framework for responsible, widespread AI innovation – one that encourages beneficial development and adoption while setting firm expectations about the harms innovators must avoid.
Canada’s competition reforms must keep pace with data-driven business models by empowering authorities with modern tools to detect, assess, and stop conduct that genuinely harms competition, innovation, or consumers.
The explosive growth of online shopping is reshaping Canadian retail by empowering consumers with unprecedented choice, driving omnichannel innovation, and intensifying competition.
AI is fundamentally redefining leadership by providing new tools, frameworks, and systems that allow leaders not just to manage complexity, but to see, challenge, and reshape their organizations in ways never before possible. The competitive mandate for leaders is clear: harness AI not merely for efficiency, but as an engine for deeper self-awareness, structured dissent, and proactive sensing that unlocks true organizational agility and resilience.
Strategic Frameworks for Next-Gen AI Leadership
Forward-thinking leaders are moving beyond pilot projects and isolated automation to experiment with new, holistic approaches—many inspired by concepts like the Leadership Mirror, Red-Team Loop, and Organization Pulse Monitor. These paradigms operationalize AI in ways that directly address the perennial blind spots, biases, and inertia that often undermine executive decision-making.
George Yang- helping organizations and executives embrace AI.
The Leadership Mirror: Cultivating Radical Self-Awareness
The Leadership Mirror uses AI to continuously analyze leadership communication, decision rationale, and team interactions, surfacing insights that are often overlooked or difficult for humans to acknowledge. For example, Microsoft has begun leveraging AI tools to track who dominates meetings, which voices get systematically dismissed, and when evidence is overridden by intuition—creating dashboards that encourage leaders to confront uncomfortable patterns.
This approach helps leaders challenge their own narrative, improve inclusiveness, and drive more thoughtful debate.
With AI’s ability to process language in real time, leaders can receive feedback loops and “reflections” that support a culture of deliberate, transparent leadership.
The Leadership Mirror is also a vehicle for mitigating the “competence penalty,” where women and older workers face skepticism for using AI—even when it enhances productivity. By surfacing evidence of expertise and impact, it reduces bias and builds psychological safety.
There are different types of AI including less sophisticated models such as Generative AI. To decide whether to use generative artificial intelligence for a task, ask yourself whether it matters if the output is true and you have the expertise to verify the tool’s output. (Adapted from Aleksandr Tiulkanov‘s LinkedIn post)
The Red-Team Loop: Embedding Structured Dissent
To counter groupthink and executive overconfidence, Red-Team Loop systems employ AI to automate adversarial reviews of strategy and operational decisions. Verizon, for instance, uses an AI framework that captures assumptions, risks, and anticipated outcomes for major decisions, then generates simulated critiques and alternative scenarios—sometimes challenging senior executives on blind spots they themselves hadn’t recognized.
By proactively “red-teaming” their own decisions, leaders foster a culture where dissent is routine, rational, and data-driven—not ad hoc or punitive.
The approach is especially valuable in M&A, crisis management, and product launches, where high-stakes, high-ambiguity decisions benefit from rigorous challenge.
Leading boards now expect Red-Team Loops as part of their fiduciary duty, recognizing that the cost of missed risks is measured not just in dollars, but reputation and long-term viability.
Organization Pulse Monitor: Proactive Sensing for Culture and Risk
The Organization Pulse Monitor uses AI to detect weak signals in organization culture, ethical risk, and operational friction long before traditional metrics or surveys would register them. Some organizations have begun linking AI-powered sentiment analysis of internal communications, workflow behaviors, and network interactions to predict where a culture may be straining, where compliance risks are emerging, or where silent dissent is brewing.
When Pulse Monitors flagged drops in engagement and early warning signs of burnout, one multinational fast-tracked well-being interventions, pre-empting attrition.
AI-driven pulse scans also help surface ethical risks—such as exclusionary behaviors or data privacy concerns—enabling leaders to respond immediately, not months later.
Actionable Strategies: Bringing AI Experiments to Leadership
How can senior leaders experiment and innovate with these systems while maximizing value and minimizing risk?
Map Adoption Hotspots and Blind Spots: Use mirror and pulse data to identify where AI is catalyzing positive behaviors—and where competence penalties or shadow AI usage may be undermining equity or performance. Target interventions accordingly.
Mobilize Role Model Leaders: Encourage respected senior leaders, particularly those from underrepresented demographics, to visibly experiment with and champion AI tools. Research shows that when these role models use AI openly, adoption gaps shrink, and psychological safety rises.
Redesign Evaluation and Disclosure Policies: Shift performance metrics from subjective ratings of proficiency to objective impact, cycle time, accuracy, and innovation. Blind reviews and private feedback mechanisms can reduce bias against AI users and drive fairer rewards.
Embed Structured Red-Teaming in Decision Flows: Institutionalize adversarial testing of key decisions, making AI-enabled dissent a standard step—not a threat or afterthought. Leaders should receive regular “contrarian” insights, not just consensus-building reports.
Common Pitfalls and Human Impact
Despite rising investment, less than one-third of US employers believe staff are equipped for critical thinking in the AI era, and only 16% of American workers use AI on the job despite widespread availability. The main barriers are not just technical, but social: competence penalties, fear of reputation loss, and resistance among influential skeptics.
Competence Penalty: AI users, especially women and older employees, may face a perception of diminished competence. This undermines adoption and can exacerbate workplace inequality.
Shadow AI and Hidden Risks: Employees sometimes use unauthorized tools to bypass bias, exposing the organization to compliance, reputational, and security risk.
Skill Gaps vs. Work Context: Traditional training falls short without tailored, role-specific feedback loops—AI tutors offer scalable, personal learning but must be embedded in daily workflow, not delivered in isolation.
Governance, Ethics, and Sustainable Change
Human-centered leadership isn’t optional—it’s a strategic imperative. Boards and executives must be proactive in:
Instituting transparent governance for all AI systems (mirrors, loops, monitors), with clear oversight on privacy, fairness, and impact.
Ensuring structured role-modeling and psychological safety—particularly for vulnerable groups confronting competence penalties.
Making change management a continuous process, with AI as both coach and sentinel, not just a dashboard.
The call to action for C-suite leaders is urgent and profound: treat responsible, experimental, and self-critical AI adoption as the core discipline of next-generation leadership. Not just for efficiency, but for building organizations where insight, challenge, and well-being are sustainably enabled. Those who master the trifecta of mirror, loop, and pulse will set the new standard for profitable, human-centered growth in the age of AI.
More about:
George Yang is a Toronto-based digital innovator and AI adoption strategist with over 15 years of experience in marketing and digital transformation. As Chair of the AI Working Group at the National Payroll Institute, he helps organizations translate AI strategy into measurable business outcomes. George is passionate about making AI adoption ethical, practical, and impactful, bridging the gap between innovation and implementation across industries. georgeyang.ca
“Pay attention students, write this down for memorization.” The Trivium and Quadrivium, medieval revival of classical Greek education theories, defined the seven liberal arts necessary as preparation for entering higher education: grammar, logic, rhetoric, astronomy, geometry, arithmetic, and music. Even today, the education disciplines identified since Greek times are still reflected in many education systems. Numerous disciplines and branches have since emerged, ranging from history to computer science…
Now comes the Information Age, bringing with it Big Data, cloud computing, artificial intelligence as well as visualization techniques that facilitate the learning of knowledge.
All this technology dramatically increased the amount of knowledge we could access and the speed at which we could generate answers to our questions.
“New and more innovative knowledge maps are now needed to help us navigate the complexities of our expanding landscape of knowledge,” says Charles Fadel. Fadel is the founder of the Center for Curriculum Redesign, which has been producing new knowledge maps that redesign knowledge standards from the ground up. “Understanding the interrelatedness of knowledge areas will help to uncover a logical and effective progression for learning that achieves deep understanding.”
Joining us in The Global Search for Education to talk about what students should learn in the age of AI is Charles Fadel, author of Four-Dimensional Education: The Competencies Learners Need to Succeed.
“We need to identify the Essential Content and Core Concepts for each discipline – that’s what the curation effort must achieve so as to leave time and space for deepening the disciplines’ understanding and developing competencies.” — Charles Fadel
Charles, today students have the ability to look up anything. Technology that enables them to do this is also improving all the time. If I want to solve a math problem, I use my calculator, and if I want to write a report on the global effects of climate change, I pull out my mobile. How much of the data kids are being forced to memorize in school is now a waste of time?
The Greeks bemoaned the invention of the alphabet because people did not have to memorize the Iliad anymore. Anthropologists tell us that memorization is far more trained in populations that are illiterate or do not have access to books. So needing to memorize even less in an age of Search is a natural evolution.
However, there are also valid reasons for why some carefully curated content will always be necessary.
Firstly, Automaticity. It would be implausible for anyone to constantly look up words or simple multiplications – it just takes too long and breaks the thought process, very inefficiently. Secondly, Learning Progressions. A number of disciplines need a gradual progression towards expertise, and again, one cannot constantly look things up, this would be completely unworkable. Finally, Competencies (Skills, Character, Meta-Learning). Those cannot be developed in thin air as they need a base of (modernized, curated) knowledge to leverage.
Sometimes people will say “Google knows everything” or “ask AI” and it is striking, but the reality is that for now, Google stores everything. Of course, with AI, what is emerging now is the ability to analyze a large number of specific problems and make predictions, so eventually, Google and similar companies will know a lot more than humans can about themselves!
Closeup of mobile phone with language learning application in jeans pocket. focus on screen
“What we need to test for is Transfer – the ability to use something we have learned in a completely different context. This has always been the goal of an Education, but now algorithms will allow us to focus on that goal even more, by ‘flipping the curriculum’.” — Charles Fadel
If Child A has memorized the data in her head while Child B has to look up the answers, some might argue that Child A is smarter than Child B. I would argue that AI has leveled the playing field for Child A and Child B, particularly if Child B is digitally literate, creative and passionate about learning. What are your thoughts?
First, let’s not conflate memory with intelligence, which games like Jeopardy implicitly do. The fact that Child A memorized data does not mean they are “smarter” than Child B, even though memory implies a modicum of intelligence. Second, even Child B will need some level of content knowledge to be creative, etc. Again, this is not developed in thin air, per the conversation above.
So it is a false dichotomy to talk about Knowledge or Competencies (Skills/Character/Meta-learning), it has to be Knowledge (modernized, curated) and Competencies. We’d want children to both Know and Do, with creativity and curiosity.
Lastly, we need to identify the Essential Content and Core Concepts for each discipline – that’s what the curation effort must achieve so as to leave time and space for deepening the disciplines’ understanding and developing competencies.
Given the impact of AI today and the advancements we expect each year, when should (all) school districts introduce open laptop examinations to allow students equal access to information and place emphasis on their thinkingskills?
The question has more to do with Search algorithms than with AI, but regardless, real-life is open-book, and so should exams be alike. And yes, this will force students to actually understand their materials, provided the tests do more than multiple-choice trivialities, which by the way we find even at college levels for the sake of ease of grading.
What we need to test for is Transfer – the ability to use something we have learned in a completely different context. This has always been the goal of an Education, but now algorithms (search, AI) will allow us to focus on that goal even more, by “flipping the curriculum”.
Today, if a learner wants to do a deep dive into any specific subject, AI search allows them to do this outside of classroom time. What do you say to a history teacher who argues there’s no need to revise subject content in his classroom?
For all disciplines, not just History, we must strike the careful balance between “just-in-time, in context” vs “just-in-case”. Context matters to anchor the learning: in other words, real-world projects give immediate relevance for the learning, which helps it to be absorbed. And yet projects can also be time-inefficient, so a healthy balance of didactic methods like lectures are still necessary. McKinsey has recently shown that today that ratio is about 25% projects, which should grow a bit more over time as education systems embed them better, with better teacher training.
Second, it should be perfectly fine for any student to do deep dives as they see fit, but again in balance: there are other competencies needed to becoming a more complete individual, and if one is ahead of the curve in a specific topic, it is of course very tempting to follow one’s passion. And at the same time, it is important to make sure that other competencies get developed too. So, balance and a discriminating mind matter.
Employers consider ethics, leadership, resilience, curiosity,mindfulness and courage as being of “very high” importance to preparing students for the workplace. How does your curriculum satisfy employers’ demands today and in the years ahead?
These Character qualities are essential for employers and life needs alike, and they have converged away from the false dichotomy of “employability or psycho-social needs.” A modern curriculum ensures that these qualities are developed deliberately, systematically, comprehensively, and demonstrably. This is achieved by matrixing them with the Knowledge dimension, meaning teaching Resilience via Mathematics, Mindfulness via History, etc. Employers have a mixed view and success as to how to assess these qualities, so it is a bit unfair that they would demand specificity they do not have. And it is also unfitting of school systems to lose relevance.
people, education, technology and exam concept – close up of students with smartphones taking picture of books page and making cheat sheet in school library
“Educators have been tone-deaf to the needs of employers and society to educate broad and deep individuals, not merely ones that may go to college. The anchoring of this problem comes from university entrance requirements.” — Charles Fadel
There is a significant gap between employers’ view of the preparation levels of students and the views of students and educators. The problem likely exists partly because of incorrect assumptions on both sides, but there are also valid deficiencies. What specific inadequacies are behind this gap? What system or process can be devised to resolve this issue?
On one side, employers are expecting too much and shirking their responsibility to bring up the level of their employees, expecting them to graduate 100% “ready to work” and having to spend nothing more than job-specific training at best. On the other side, educators have been tone-deaf to the needs of employers and society to educate broad and deep individuals, not merely ones that may go to college.
The anchoring of this problem comes from university entrance requirements (in the US, AP classes, etc.) and their associated assessments (SAT/ACT scores). They have for decades back-biased what is taught in schools, in a very self-serving manner – narrowly as a test of whether a student will succeed at university. It is time to deconstruct the requirements to broaden/deepen them to serve multiple stakeholders. For the Silo, C.M. Rubin.
(All photos are courtesy of our friends at CMRubinWorld)
C. M. Rubin and Charles Fadel
Join me and globally renowned thought leaders including Sir Michael Barber (UK), Dr. Michael Block (U.S.), Dr. Leon Botstein (U.S.), Professor Clay Christensen (U.S.), Dr. Linda Darling-Hammond (U.S.), Dr. MadhavChavan (India), Charles Fadel (U.S.), Professor Michael Fullan (Canada), Professor Howard Gardner (U.S.), Professor Andy Hargreaves (U.S.), Professor Yvonne Hellman (The Netherlands), Professor Kristin Helstad (Norway), Jean Hendrickson (U.S.), Professor Rose Hipkins (New Zealand), Professor Cornelia Hoogland (Canada), Honourable Jeff Johnson (Canada), Mme. Chantal Kaufmann (Belgium), Dr. EijaKauppinen (Finland), State Secretary TapioKosunen (Finland), Professor Dominique Lafontaine (Belgium), Professor Hugh Lauder (UK), Lord Ken Macdonald (UK), Professor Geoff Masters (Australia), Professor Barry McGaw (Australia), Shiv Nadar (India), Professor R. Natarajan (India), Dr. Pak Tee Ng (Singapore), Dr. Denise Pope (US), Sridhar Rajagopalan (India), Dr. Diane Ravitch (U.S.), Richard Wilson Riley (U.S.), Sir Ken Robinson (UK), Professor Pasi Sahlberg (Finland), Professor Manabu Sato (Japan), Andreas Schleicher (PISA, OECD), Dr. Anthony Seldon (UK), Dr. David Shaffer (U.S.), Dr. Kirsten Sivesind (Norway), Chancellor Stephen Spahn (U.S.), Yves Theze (LyceeFrancais U.S.), Professor Charles Ungerleider (Canada), Professor Tony Wagner (U.S.), Sir David Watson (UK), Professor Dylan Wiliam (UK), Dr. Mark Wormald (UK), Professor Theo Wubbels (The Netherlands), Professor Michael Young (UK), and Professor Minxuan Zhang (China) as they explore the big picture education questions that all nations face today.
C. M. Rubin is the author of two widely read online series for which she received a 2011 Upton Sinclair award, “The Global Search for Education” and “How Will We Read?” She is also the author of three bestselling books, including The Real Alice in Wonderland, is the publisher of CMRubinWorld and is a Disruptor Foundation Fellow.
A recent consumer survey backed by similar results from Elon University reveals that AI adoption for car shopping is skyrocketing, rapidly becoming a standard part of the automobile buying process. This as fully one in four buyers have already used AI tools this year to research, compare prices, negotiate and otherwise outsmart dealerships, and an overwhelming 88% found it helpful. Signaling a seismic shift in the way North Americans are now shopping for cars, nearly half of consumers indicated plans to use AI in their next purchase. Not just for buyer benefits, dealerships are gleaning critical business intelligence from AI to inform sales strategies, train staff and elevate customer engagement. The below report from our friends at CarEdge, which offers its own AI Negotiator car buying tool saving shoppers thousands, details the first data-backed look at how AI tools are reshaping the car buying experience.
Mornine- AI powered car dealership robot.
Study: 1 in 4 Car Buyers Tap AI for Better Deals
Artificial intelligence is changing the way North Americans buy cars, and it’s a transition that is happening quickly. In the first-ever survey of its kind, CarEdge asked 500 car shoppers if they’re using AI tools like ChatGPT to research, compare, and negotiate during the car buying process. The results confirm a major shift is underway. One in four car buyers in 2025 are already using AI tools to gain an edge, and future buyers are even more likely to embrace these technologies.
Car buyers are finding AI to be a valuable tool. Among those who used tools like ChatGPT, Perplexity, Google Gemini, and others, 88% said it was helpful. AI is quickly becoming a trusted co-pilot for car buyers.
Key Findings: Car Buying Is Changing
The 2025 CarEdge AI & Car Buying Survey reveals a clear and growing trend: AI tools are quickly becoming part of the car buying process for a significant portion of consumers. Here are the standout findings:
1 in 4 Car Buyers Use AI
25% of car buyers in 2025 say they used or plan to use AI tools like ChatGPT during the shopping or buying process. This contrasts with a recent survey by Elon University that found 52% of Americans now use AI large language models. While signs point towards increased adoption of AI tools, the CarEdge survey found that most car buyers are still in the early stages of integrating these tools into high-stakes decisions like vehicle purchases. This suggests there’s still significant room for growth in AI adoption amongst car buyers.
AI Use Is Accelerating
Among those who haven’t bought a car yet this year, 40% say they are using or plan to use AI tools during their search or deal-making. This is nearly 3x higher than the 14% seen among those who already bought a car earlier in the year.
AI Tools Deliver Results
Among those who used AI:
88% say the tools were helpful
32% found them very helpful
60% used them “a lot” during the process
The AI Holdouts: Drivers Who Lease
Of the respondents who had already leased a car in 2025, none reported using any AI tools.
The AI-Adopting Buyer: Who’s Using It, and How?
AI adoption among car buyers is still in its early stages, but clear trends are beginning to emerge.
Among Buyers Who Already Purchased in 2025:
Just 14% of those who already bought a vehicle this year used AI tools during the process. Adoption rates were nearly identical across new and used buyers, with 14% in each group saying they used AI tools.
Among Future Car Buyers:
The numbers jump significantly when looking at those who haven’t yet bought in 2025. Among this group — who represent 39% of total respondents — 40% say they either already use or plan to use AI tools during their car search and buying process.
That’s more than triple the current usage rate among recent buyers, suggesting AI adoption is accelerating as awareness grows and tools become easier to use.
This group also appears to be more proactive: 60% of those who used AI tools during their buying journey said they used them “a lot,” while 40% used them only occasionally.
What Car Buyers Are Using AI Tools
AI tools are quickly becoming essential research companions for car shoppers looking to make more informed, confident decisions. After all, why go it alone when a wealth of automotive knowledge powered by large language models (LLMs) is right in your pocket?
Among buyers who used AI tools during their car purchase or lease process, here’s how they put them to work:
88% — Researching Vehicles
The most common use by far, AI tools helped buyers learn about different models, trims, features, and reliability. For many, it was like having an always-available expert to explain the pros and cons of their options.
64% — Comparing Prices and Market Values
Buyers used AI to better understand fair pricing, from invoice pricing to out-the-door.
44% — Learning Negotiation Strategies
Nearly half of AI users leaned on these tools to prepare for conversations with salespeople. Whether role-playing negotiation scenarios or asking how to spot add-on fees, this group used AI to level the playing field at the dealership.
11% — Exploring Finance and Lease Options
A much smaller portion of buyers used these tools to become familiar with leasing vs. financing, how to calculate payments, and similar queries.
Industry Implications
Car buying has always been tilted in favor of the dealership. Information asymmetry — what the dealer knows versus what the customer knows — has long been the source of consumer frustration, confusion, and overpayment.
That dynamic is beginning to shift.
This survey confirms what many in the industry are only starting to realize: AI is giving car buyers the upper hand. Tools like ChatGPT are helping consumers cut through the noise, ask smarter questions, and avoid common dealership traps. Instead of relying on guesswork or scattered advice, buyers are turning to AI for fast, personalized guidance at every step.
But one auto industry veteran has words of caution for buyers relying heavily on AI tools.
“It’s both surprising and a little scary to see how quickly people are turning to AI to guide such a major financial decision,” said Ray Shefska, Co-Founder of CarEdge. “While tools like ChatGPT can be powerful, they’re only as good as the data behind them. AI should complement your research, not replace your own critical thinking.“
That perspective underscores the real takeaway of this report: AI works best when it’s used thoughtfully as a tool, not as a crutch. In an age where automation raises fears of job loss or decision-making without human oversight, this survey offers a more optimistic view — one where technology helps everyday consumers make smarter choices. Used wisely, AI can help level the playing field and bring more transparency and fairness to the car buying experience.
Methodology
This survey was conducted by CarEdge between June 19 and June 24, 2025. A total of 500 U.S. respondents participated, recruited through the CarEdge email newsletter and social media channels. Questions were tailored based on buying status to better understand how and when AI tools were used in the car shopping process.
For the Silo, Karen Hayhurst.
About CarEdge Founded in 2019 by father-and-son team Ray and Zach Shefska, CarEdge is a leading platform dedicated to empowering car shoppers with free expert advice, in-depth market insights, and tools to navigate every step of the car-buying journey. From researching vehicles to negotiating deals, CarEdge helps consumers save money, time, and hassle, hundreds of thousands of happy consumers have used CarEdge to buy their car with confidence. With trusted resources like the CarEdge AI Negotiator tool, Research Center, Vehicle Rankings and Reviews, and hundreds of guides on YouTube, CarEdge is redefining transparency and fairness in the automotive industry. Follow them on YouTube, TikTok, X, Facebook, and Instagram for actionable car-buying tips and market insights. Learn more at www.CarEdge.com.
October, 2025 – Canada has world-class strength in AI research but continues to fall short in widespread adoption, according to a new report from the C.D. Howe Institute. On the heels of the federal government’s announcement of a new AI Strategy Task Force, the report highlights the urgent need to bridge the gap between research excellence and real-world adoption.
In “AI Is Not Rocket Science: Ideas for Achieving Liftoff in Canadian AI Adoption,” Kevin Leyton-Brown, Cinda Heeren, Joanna McGrenere, Raymond Ng, Margo Seltzer, Leonid Sigal, and Michiel van de Panne note that while Canada ranks second globally in top-tier AI researchers and first in the G7 for per capita publications, it is only 20th in AI adoption among OECD countries. “This matters for the economy as a whole, because such knowledge translation is a key vehicle for productivity growth,” the authors say. “It is terrible news, then, that Canada experienced almost no productivity growth in the last decade, compared with a rate 15 times higher in the United States.”
The authors argue that new approaches to knowledge translation are needed because AI is not “rocket science”: instead of focusing on a single industry sector, the discipline develops general-purpose technology that can be applied to almost anything. This makes it harder for Canadian firms to find the right expertise and for academics to sustain ties with industry. Existing approaches – funding academic research, directly subsidizing industry efforts through measures such as SR&ED and superclusters, and promoting partnerships through programs like Mitacs and NSERC Alliance – have not solved the problem.
Four ideas to help firms leverage Canadian academic strength to fuel their AI adoption include: a concierge service to match companies with experts, consulting tied to graduate student scholarships, “research trios” that link AI specialists with domain experts and industry, and a major expansion of AI training from basic literacy to dedicated degrees and continuing education. Drawing on their experiences at the University of British Columbia, the authors show how local initiatives are already bridging gaps between academia and industry – and argue these models should be scaled nationally.
“Canada’s unusual strength in AI research is an enormous asset, but it’s not going to translate into real-world productivity gains unless we find better ways to connect AI researchers and industrial players,” says Kevin Leyton-Brown, professor of computer science at the University of British Columbia and report co-author. “The challenge is not that AI is too complicated – it’s that it touches everything. That means new models of partnership, new incentives, and new approaches to education.”
AI Is Not Rocket Science- 4 Ideas in Detail
Idea 1: A Concierge Service for Matchmaking
We have seen that it is hard for industry partners to know who to contact when they want to learn more about AI. Conversely, it is at least as hard for AI experts to develop a broad enough understanding of the industry landscape to identify applications that would most benefit from their expertise. Given the potential gains to be had from increasing AI adoption across Canadian industry, nobody should be satisfied with the status quo.
We argue that this issue is best addressed by a “concierge service” that industry could contact when seeking AI expertise. While matchmaking would still be challenging for the service itself, it could meet this challenge by employing staff who are trained in eliciting the AI needs of industry partners, who understand enough about AI research to navigate the jargon, and who proactively keep track of the specific expertise of AI researchers across a given jurisdiction. This is specialized work that not everyone could perform! However, many qualified candidates do exist (e.g., PhDs in the mathematical sciences or engineering). Such staff could be funded in a variety of different ways: for example, by an AI institute; a virtual national institute focused on a given application area; a university-level centre like UBC’s Centre for Artificial Intelligence Decision-making and Action (CAIDA); a nonprofit like Mitacs; a provincial ministry for jobs and economic growth; or the new federal ministry of Artificial Intelligence and Digital Innovation.
Having set up an organization that facilitates matchmaking, it could make sense for the same office to provide additional services that speed AI adoption, but that are not core strengths of academics. Some examples include project management, programming, AI-specific skills training and recruitment, and so on. Overall, such an organization could be funded by some combination of direct government support, direct cost recovery, and an overhead model that reinvests revenue from successful projects into new initiatives.
Idea 2: Consultancy in Exchange for Student Scholarships
Many businesses that would benefit from adopting AI do not need custom research projects and do not want to wait a year or more to solve their problems. The lowest-hanging fruit for Canadian AI adoption is ensuring that industry is well informed about potentially useful, off-the-shelf AI technologies. We thus propose a mechanism under which AI experts would provide limited, free consulting to local industry. AI experts would opt in to being on a list of available consultants. A few hours of advice would be free to each company, which would then have the option of co-paying for a limited amount of additional consulting, after which it would pay full freight if both parties wanted to continue. The company would own any intellectual property arising from these conversations, which would thus focus on ideas in the public domain. If the company wanted to access university-owned IP, it could shift to a different arrangement, such as a research contract. This system would work best given a concierge service like the one we just described. The value offered per consulting hour clearly depends on the quality of the academic–industry match, and some kind of vetting system would be needed to ensure the eligibility of industry participants.
Why would an AI expert sign up to give advice to industry? All but the best-funded Canadian faculty working in AI report that obtaining enough funding to support their graduate students is a major stressor. Attempting to establish connections with industry is hard work, and such efforts pay off only if the industry partner signs on the dotted line and matching funds are approved. There is thus space to appeal to faculty with a model in which they “earn” student scholarships for a fixed amount of consulting work. For example, faculty could be offered a one- semester scholarship for every eight hours set aside for meetings with industry, meaning that one weekly “industry office hour” would indefinitely fund two graduate students. Consulting opportunities could also be offered directly to postdoctoral fellows or senior (e.g., post-candidacy) PhD students in exchange for fellowships. In such cases, trainees should be required to pass an interview, certifying that they have both the technical and soft skills necessary to succeed in the consulting role. The concierge service could help decide which industry partners could be routed to PhD students and which need the scarcer consulting slots staffed by faculty members.
The system would offer many benefits. From the industry perspective, it would make it straightforward to get just an hour or two of advice. This might often be enough to allow the company to start taking action towards AI adoption: there is a rich ecosystem of high-performance, reliable, and open-source AI tools; often, the hard part is knowing what tool to use in what way. Beyond the value of the advice itself, consulting meetings offer a strong basis for building relationships between academics and industry representatives, in which the academic plays the role of a useful problem solver rather than of a cold-calling salesperson. These relationships could thus help to incubate Mitacs/Alliance-style projects when research problems of mutual interest emerge (though also see our idea below about how restructuring such projects could help further).
For academics, the system would constitute a new avenue for student funding that would reward each hour spent with a predictable amount of student support. Furthermore, it would offer scaffolded opportunities to deepen connections with industry. The system would come with no reporting requirements beyond logging the time spent on consulting. The faculty member would be free to use earned scholarships to support any student (regardless, for example, of the overlap between the student’s research and the topics of interest to companies), increasing flexibility over the Mitacs/Alliance system, in which specific students work with industry partners. Students who self-funded via consulting would learn valuable skills and would expand their professional networks, improving prospects for post-graduation employment.
Finally, the system would also offer multiple benefits from the government’s perspective. It would generate unusually high levels of industrial impact per dollar spent (consider the number of contact hours between academia and industry achieved per dollar under the funding models mentioned in Section 3). All money would furthermore go towards student training. The system would automatically allocate money where it is most useful, directing student funding to faculty who are both eager to take on students and relevant to industry, all without the overhead of a peer-review process. And it would generate detailed impact reports as a side effect of its operations, since each hour of industry–academia contact would need to be logged to count towards student funding.
Idea 3: Grants for Research Trios
Our third proposal is an approach for expanding the Mitacs/Alliance model to make it work better for AI. Industry–academia partnerships leverage two key kinds of expertise from the academic side: methodological know-how for solving problems and knowledge about the application domain used for formulating such problems in the first place. In fields for which the set of industry partners is relatively small and relatively stable, it makes sense to ask the same academics to develop both kinds of expertise. In very general-purpose domains like AI, it holds back progress to ask AI experts to become domain experts, too. Instead, it makes sense to seek domain knowledge from other academics who already have it. We thus propose a mechanism that would fund “research trios” rather than bilateral research pairings. Each trio would contain an AI expert, an academic domain expert, and an industry partner. This approach capitalizes on the fact that there is a huge pool of academic talent outside core AI with deep disciplinary knowledge and a passion for applying AI. While such researchers are typically not in a position to deeply understand cutting-edge AI methodologies, they are ideally suited to serve as a bridge between researchers focused on AI methodologies and Canadian industrial players seeking to achieve real-world productivity gains. In our experience at UBC, the pool of non-AI domain experts with an interest in applying AI is considerably larger than the pool of AI experts. One advantage of this model is that projects can be initiated by the larger population of domain experts, who are also more likely to have appropriate connections to industry. Beyond this, involving domain experts increases the likelihood that a project will succeed and gives industry partners more reason to trust the process while a solution is being developed. The model meets a growing need for funding researchers outside computer science for projects that involve AI, rather than concentrating AI funding within a group of specialists. At the same time, it avoids the pitfall of encouraging bandwagon-jumping “applied AI” projects that lack adequate grounding in modern AI practices. Finally, it not only transfers AI knowledge to industry, but also does the same to both the domain expert and their students.
Idea 4: Greatly Expanded AI Training
As AI permeates the economy, Canada will face an increasing need for AI expertise. Today, that training comes mostly in the form of computer science degrees. Just as computer science split off from mathematics in the 1960s, AI is emerging today as a discipline distinct from computer science. In part, this shift is taking the form of recognizing that not every AI graduate needs to learn topics that computer science rightly considers part of its core, such as software engineering, operating systems, computer architecture, user interface design, computer graphics, and so on. Conversely, the shift sees new topics as core to the discipline. Most fundamental is machine learning. Dedicated training in AI will require a deeper focus on the mathematical foundations of probability and statistics, building to advanced topics such as deep learning, reinforcement learning, machine learning theory, and so on. Various AI modalities also deserve separate study, such as computer vision, natural language processing, multiagent systems, robotics, and reasoning. Training in ethics, optional in most computer science programs, will become essential.
Beyond dedicated training in the core discipline, we anticipate huge demand for broad-audience AI literacy training; for AI minors to complement other disciplinary specializations; for continuing education and “micro-credential” programs; and for executive education in AI. There is also a growing need for “AI Adoption Facilitators”: bridge-builders who can help established workers in medium-to-large organizations understand how data-driven tools could offer value in solving the problems they face. Training for this role would emphasize business principles and domain expertise, but would also require firmer foundations in machine learning and data science than are currently typical in those disciplines.
Did you know that, every year, home renovation projects are derailed by hidden costs, vague language, and inconsistent contractor bids—pushing 78% of jobs over budget and forcing two-thirds of homeowners into debt? It’s not just homeowners who feel the pain: contractors, property managers, real estate agents, investors, and flippers all struggle to assess and compare bids quickly and accurately.
The problem is that contractor quotes are rarely “apples to apples,” often missing critical details or disguising inflated charges—making it hard to identify true scope, cost, and risk. Now, the free-to-use and industry first BidCompareAI tool analyzes and compares multiple contractor bids, instantly identifying missing scope items, unrealistic allowances and other red flags before any work begins … often with tens of thousands of dollars on the line. In minutes, the AI generates a clear, line-by-line report that standardizes bids into transparent, actionable insights—helping homeowners avoid costly overruns, while enabling industry pros to quote with confidence, negotiate smarter, close deals faster, and protect ROI. Interest in this innovation raising industry transparency standards?
AI Reveals These Top 10 Home Renovation Bid Red Flags
First-of-its-kind free AI tool turns confusing, inconsistent contractor bids into clear, side-by-side insights—helping homeowners avoid costly overruns and enabling industry pros to quote, negotiate and close with confidence
Renovations are one of the most expensive and stressful decisions a homeowner makes. Yet 78% of projects blow their budgets, and 2 in 3 homeowners go into debt just to pay for them. Why? Because contractor bids are often riddled with hidden costs, vague language, and missing work that leave you paying more than you bargained for. Thankfully, new AI technology is now making these red flags impossible to ignore—saving homeowners thousands before a hammer is even swung. BidCompareAI is the first-ever AI tool that lets homeowners upload multiple bids and get a fast, detailed report comparing scope, pricing, and red flags—no construction expertise needed and no signup or payment required.
“Homeowners have been forced to make major financial decisions based on unclear or incomplete bids,” says GreatBuildz Co-CEO Jon Grishpul. “BidCompareAI adds instant transparency and clarity—saving people from costly mistakes before a project even starts. For contractors, property managers, and real estate professionals, it’s a credibility and efficiency tool that streamlines communication, builds trust and helps win more business.”
Here are the top 10 red flags often hiding in contractor bids, and how the BidCompareAI tool reveals them instantly:
1. Missing Scope Items — “Surprise” Costs Waiting to Blow Your Budget
Your contractor’s quote doesn’t include demolition, cleanup, or critical tasks? That’s a ticking time bomb. Now, homeowners can catch these omissions so you never get hit with surprise charges.
2. Vague Allowances — The Fine Print That Drains Your Wallet
Ambiguous line items like “fixtures” or “materials” can mean anything. The AI tool flags vague terms so you can demand specifics upfront.
3. Unrealistically Low Bids — Too Good to Be True? Usually Are
Low-ball bids often mean corners will be cut or costs will balloon later. This AI exposes these dangerously low estimates before you get stuck with change orders.
4. Pricing Inconsistencies — Comparing Apples to Oranges?
Quotes come in all formats with wildly different terminology. This advanced technology standardizes and compares them side-by-side, so you’re not left guessing.
5. Hidden Fees — The Black Box of Renovation Budgets
Permits, procurement, and labor fees sometimes get lumped in mysteriously. The AI reveals these “hidden” charges clearly in its summary report.
6. Overlapping or Duplicate Charges — Paying Twice Without Knowing It
Some bids unknowingly charge for the same work twice. The AI delivers a line-by-line analysis that spots these costly errors fast.
7. Unclear Project Timelines — When Delays Lead to Extra Costs
Vague or missing timelines can spiral into costly delays. While timelines aren’t priced, spotting missing info helps you demand accountability.
8. Missing Cleanup and Disposal — Don’t Get Stuck with the Mess
Quotes that don’t include cleanup leave you responsible for hauling debris and disposing of waste. This AI highlights these crucial omissions.
9. Discrepancies in Material Quality — Low-Quality Where You Expected Premium
One bid may specify high-end fixtures while another hides “allowances” that could mean anything. The AI tool flags these differences so you know exactly what you’re paying for.
10. Inconsistent Labor Charges — Watch for Inflated or Unexplained Fees
Labor costs vary widely, and some bids overcharge or include unnecessary markups. This user-friendly technology points out these red flags clearly.
“This is about more than just tech,” added Paul Dashevsky, Co-CEO of GreatBuildz. “It’s about empowering homeowners to feel confident and in control of their renovation projects—and helping contractors better serve their clients.”
Renovations don’t have to be a financial nightmare. As consumer-facing AI tools proliferate across industries, the BidCompareAI innovation demonstrates how artificial intelligence can bring real-world value by making complex, high-stakes decisions—like selecting the right contractor—faster, clearer and far less stressful. For the Silo, Marsha Zorn.
Artificial Intelligence (AI) has infiltrated our lives for decades, but since the public launch of ChatGPT showcasing generative AI in 2022, society has faced unprecedented technological evolution.
With digital technology already a constant part of our lives, AI has the potential to alter the way we live, work, and play – but exponentially faster than conventional computers have. With AI comes staggering possibilities for both advancement and threat.
The AI industry creates unique and dangerous opportunities and challenges. AI can do amazing things humans can’t, but in many situations, referred to as the black box problem, experts cannot explain why particular decisions or sources of information are created. These outcomes can, sometimes, be inaccurate because of flawed data, bad decisions or infamous AI hallucinations. There is little regulation or guidance in software and effectively no regulations or guidelines in AI.
How do researchers find a way to build and deploy valuable, trusted AI when there are so many concerns about the technology’s reliability, accuracy and security?
That was the subject of a recent C.D. Howe Institute conference. In my keynote address, I commented that it all comes down to software. Software is already deeply intertwined in our lives, from health, banking, and communications to transportation and entertainment. Along with its benefits, there is huge potential for the disruption and tampering of societal structures: Power grids, airports, hospital systems, private data, trusted sources of information, and more.
Consumers might not incur great consequences if a shopping application goes awry, but our transportation, financial or medical transactions demand rock-solid technology.
The good news is that experts have the knowledge and expertise to build reliable, secure, high-quality software, as demonstrated across Class A medical devices, airplanes, surgical robots, and more. The bad news is this is rarely standard practice.
As a society, we have often tolerated compromised software for the sake of convenience. We trade privacy, security, and reliability for ease of use and corporate profitability. We have come to view software crashes, identity theft, cybersecurity breaches and the spread of misinformation as everyday occurrences. We are so used to these trade-offs with software that most users don’t even realize that reliable, secure solutions are possible.
With the expected potential of AI, creating trusted technology becomes ever more crucial. Allowing unverifiable AI in our frameworks is akin to building skyscrapers on silt. Security and functionality by design trump whack-a-mole retrofitting. Data must be accurate, protected, and used in the way it’s intended.
Striking a balance between security, quality, functionality, and profit is a complex dance. The BlackBerry phone, for example, set a standard for secure, trusted devices. Data was kept private, activities and information were secure, and operations were never hacked. Devices were used and trusted by prime ministers, CEOs and presidents worldwide. The security features it pioneered live on and are widely used in the devices that outcompeted Blackberry.
Innovators have the know-how and expertise to create quality products. But often the drive for profits takes precedence over painstaking design. In the AI universe, however, where issues of data privacy, inaccuracies, generation of harmful content and exposure of vulnerabilities have far-reaching effects, trust is easily lost.
So, how do we build and maintain trust? Educating end-users and leaders is an excellent place to start. They need to be informed enough to demand better, and corporations need to strike a balance between caution and innovation.
Companies can build trust through a strong adherence to safe software practices, education in AI evolution and adherence to evolving regulations. Governments and corporate leaders can keep abreast of how other organizations and countries are enacting policies that support technological evolution, institute accreditation, and financial incentives that support best practices. Across the globe, countries and regions are already developing strategies and laws to encourage responsible use of AI.
Recent years have seen the creation of codes of conduct and regulatory initiatives such as:
The Bletchley Declaration, Nov. 2023, an international agreement to cooperate on the development of safe AI, has been signed by 28 countries;
US President Biden’s 2023 executive order on the safe, secure and trustworthy development and use of AI; and
Governing AI for Humanity, UN Advisory Body Report, September 2024.
We have the expertise to build solid foundations for AI. It’s now up to leaders and corporations to ensure that much-needed practices, guidelines, policies and regulations are in place and followed. It is also up to end-users to demand quality and accountability.
Now is the time to take steps to mitigate AI’s potential perils so we can build the trust that is needed to harness AI’s extraordinary potential. For the Silo, Charles Eagan. Charles Eagan is the former CTO of Blackberry and a technical advisor to AIE Inc.
Online casinos actively use bonuses and promotions to attract and retain players. For both beginners and experienced users, understanding how these offers work is important not only to increase the chances of winning but also to ensure safe and controlled gameplay. In this article, we will explore the types of bonuses, how to use them effectively, and what to pay attention to.
Types of Bonuses in Online Casinos
Modern online platforms offer a wide range of casino bonuses. The main types include:
Welcome bonuses – given to new players upon registration and first deposit.
Free spins – free spins on online slots, often included in welcome or seasonal promotions.
Cashback – a partial return of lost funds over a certain period.
Loyalty programs – points awarded for activity, which can be exchanged for bonuses or gifts.
Every bonus comes with wagering requirements, expiration dates, and bet limits. Ignoring these rules can nullify the value of the offer.
How to Use Bonuses Effectively
To get the most out of bonuses, follow a few simple guidelines:
Read the terms carefully – before activating a bonus, pay close attention to the rules, especially wagering requirements and game restrictions.
Plan your budget – use bonuses as an additional resource without exceeding your personal betting limits.
Combine with strategies – bonuses can be applied to online slots and table games, considering your chosen betting strategy.
For example, platforms like Martin Casino offer free spins and cashback systems with clear terms, helping both beginners and experienced players use bonuses safely and efficiently.
What to Watch for When Choosing a Bonus
Not all offers are equally profitable. When selecting a promotion, consider:
Bonus size – very large bonuses may come with complex wagering requirements.
Expiration date – always check how much time you have to meet the conditions.
Game restrictions – some bonuses apply only to specific online slots or types of bets.
Withdrawal conditions – make sure the wagering requirements are realistic and achievable.
Common Mistakes When Using Bonuses
Beginners often make typical mistakes, such as:
Activating all bonuses at once and losing control over their bankroll.
Not reading the terms and not knowing how to meet the wagering requirements.
Trying to use bonuses for “quick profit,” forgetting that gambling should be entertainment, not a guaranteed income source.
A responsible approach, budget and time management help players enjoy bonuses while minimizing risks.
The Future of Casino Bonuses and Promotions
With technological advancement, bonus offers are becoming more personalized. Artificial intelligence analyzes player behavior and suggests the most suitable promotions. Mobile casino bonuses and crypto-platform promotions are also gaining popularity, allowing fast and secure transactions.
Online casinos continue to use bonuses as a tool to increase player engagement. However, the key to successful play is understanding the rules, having a conscious approach, and managing your bankroll wisely.
FAQ
What is wagering a bonus?It is a condition requiring a certain number of bets before bonus funds can be withdrawn.
Can I use multiple bonuses at the same time? Usually not – most platforms allow only one bonus to be activated at a time to prevent abuse.
Which bonuses are best for beginners?Welcome bonuses and free spins are ideal because they let players try games without significant investments.
Can a bonus be lost? Yes, if wagering requirements are not met or betting limits are exceeded. How to choose a profitable bonus? Look at the size, expiration date, game restrictions, and realism of the wagering requirements.
With AI reshaping everything from finance to fast food, the $1.5T auto retail industry is finally facing its overdue disruption. The typical car-buying experience—riddled with hidden fees, lead bloat, pricing games and low trust—has remained stubbornly analog. But now, with 90% of dealerships in America (and growing % in Canada and Mexico) experimenting with AI tools and 1 in 4 buyers already using AI to shop, the tide is turning. Agentic AI technology is fundamentally reshaping one of the most significant purchases in a person’s life.
Zach Shefska, Co-Founder and CEO of CarEdge, asserts that agentic AI is the key to rebuilding trust, removing friction and leveling the playing field for both buyers and sellers. From AI-powered shopping assistants that negotiate on your behalf, to data tools that reveal deceptive dealership practices, Shefska is a pioneer in “agentic AI” — a new form of artificial intelligence bringing much-needed transparency to the industry.
The Broken Status Quo: Car buying is frustrating and inefficient for both consumers and dealerships—highlighting key stats like 72% sales staff turnover and 2% lead conversion from third-party platforms.
Lead Generation Platforms Are Failing: Legacy systems flood dealers with unqualified leads, drain resources, and deliver minimal value to consumers.
The Rise of Agentic AI in Auto Retail: Consumers are turning to tools like ChatGPT and CarEdge’s AI agent to navigate purchases with more confidence, speed, and clarity—25% are already doing it.
From Friction to Fluidity: Agentic AI replaces quantity with quality—streamlining the buyer’s journey, reducing information overload, and improving dealer efficiency.
The End of Pricing Games: AI tools now collect and publish out-the-door pricing from thousands of dealerships, exposing hidden fees and rewarding transparent sellers.
The Future of Negotiation: AI agents can negotiate on behalf of both buyers and sellers—minimizing stress, cutting transaction times from days to hours, and removing the adversarial edge.
Real-World Impact Stories: One buyer saved $1,280 and hours of back-and-forth using CarEdge’s agentic AI—illustrating AI’s practical value in real-life scenarios.
AI Helps Honest Dealers Win: In a trust-starved industry, AI gives reputable dealers a new way to stand out by offering full transparency and faster deals.
What’s Next for AI in Auto Retail: The emerging frontier: AI agents dynamically collecting and updating real-time pricing and inventory data across markets to offer true market intelligence.
For the Silo, Zach Shefska. Zach is CEO of CarEdge, a leading platform—founded by father-and-son team Ray and Zach Shefska—dedicated to empowering car shoppers with free expert advice, in-depth market insights and tools to navigate every step of the car-buying journey. From researching vehicles to negotiating deals, CarEdge helps consumers save money, time and hassle. Alsop with trusted resources like the CarEdge Research Center, Vehicle Rankings and Reviews, and hundreds of guides on YouTube, CarEdge is redefining transparency and fairness in the automotive industry. Connect with Shefska at www.CarEdge.com or on social media on YouTube, TikTok, X, Facebook, and Instagram.
Canada is great at AI development, but what should the country’s first Minister for Artificial Intelligence make his key priorities? University of Waterloo’s Anindya Sen and the C.D Howe Institute’s Rosalie Wyonch offer strong insight — and geek out a bit about the economics-oriented nature of machine learning algorithms.
Hello AI Tinkerers and welcome to the latest Sci-Tech article here at The Silo. Get ready, You will want to pay attention because the spotlight is on this Dude because he knows how to get around ‘bad ai prompting’. Just recently, he has helped spin out 40 startups using one core skill. Can you guess which one? Yep. Prompting.
In the One-Shot video below, Kevin Leneway breaks down his real workflow for shipping AI products fast — using markdown checklists, agent coding, rubric-based UI design, and zero Figma.
“I don’t need Figma. I just prompt my way to a working front end.” — Kevin Leneway
While most people are still asking ChatGPT to write code snippets, Kevin is building full-stack products using nothing but prompts. In this One-Shot episode, he reveals the exact system he’s used to launch over 40 startups at Pioneer Square Labs. We break down:
How he writes BRDs and PRDs that don’t suck
Why vibe coding fails and how to actually use AI agents
The markdown checklist that replaces a product team
How to go from idea to working app with zero context switching
His open-source starter kit that makes Cursor and Claude 3.5 feel like magic
“I’ve helped launch six startups including Singlefile (singlefile.io, $24M raised), Recurrent (recurrentauto.com, $24M raised), Joon (joon.com, $9.5M raised), Gradient (gradient.io, $3.5M raised), Genba (genba.ai, acquired May 2022) and Enzzo (enzzo.ai, $3M raised).”
If you’re a builder, this will change how you work. No gimmicks. Just a ruthless focus on speed, clarity, and shipping. Watch now. Learn the system. Steal it. For the Silo, Joe at aitinkerers.org
This post is a response to the comic book article found at popuniverse which begins like this:
“The comic book industry is the launchpad for one of the most unique and innovative storytelling mediums ever created. Powered by imaginative creators highly skilled in the written and visual arts. Forged by businesspersons who recognize the power of ideas to make an iconic impression on a global scale. Propelled by readers and fans who support the industry and the people who make the stories. The comic book industry is the source of multimedia interpretations of mythic and personal stories that inspire people, entertain the world, and ignite lifelong careers.
It is the adventure of a lifetime.
The comic book industry is a ruthless Darwinian landscape of cronyism, narcissism, and power moves. Its main fodder is the creators who are the engines of its continued existence. Full of flair and pomp, colors and characters both fictional and real-life. A road to hell paved with landmines, bear traps, and the opportunity to work on high-profile, profitable media while living on the precipice of poverty. The industry is fueled by organizations with finite funds and infinite hubris.“
“The comics industry is the illusory world of grenades disguised as dreams.“
The issue I see (and our comic illustrator household has personally experienced) in the comics and illustration / publishing industry is that the original contract terms were never set up fairly to compensate the artists and illustrators. While photographers and videographers retain the rights to their original images, and someone must pay them usage rights fees based on the size of the audience per usage, the artists are never granted that same fair compensation.
While actors get residuals when their TV shows play on in perpetuity, and musicians earn their royalty checks with every needle drop, the comics publishers can repurpose an illustrator’s iconic cover art in perpetuity and make millions from the image—on puzzles, lunch boxes, hoodies, sweatpants, and pajamas in my husband’s particular case—while the artist never sees a dime beyond the initial ANEMIC work-for-hire fee in these insanely unfair, one-sided deals. And if the artist DARES to complain? The smear merchants are only too happy to start their whisper campaigns, blackballing the artist as “too difficult to work with” and completely destroying their already financially challenged lives with nuisance law suits.
When I think back on how Ghost Rider co-creator Gary Friedrich was made the industry scarecrow in the last years of his life as greedy lawyers descended upon him like buzzards picking the last flecks of flesh from his bones, it sickens me.
This impoverished, unwell, elderly man was just trying to eke out the last days of his hard-scrabble life by selling sketches of his OWN co-creation at comic-cons. There’s nothing I despise more than anyone preying on the vulnerable. It’s appalling how Gary was treated.
And then we have AI “art” apps exploiting my husband’s already way underpaid art to create new, derivative works, but only GETTY Images can afford to lawyer up and go after these apps…because the photography world always negotiated image usage the CORRECT and fair way from the start.
The sobering truth is that if illustrators (and line artists, colorists, and letterers) were paid as well as photographers, every comic would sell for $100 per floppy and that would be the final nail in the #comics industry’s coffin.
…DAVE DORMAN… told me at dinner tonight that someone was selling AI art at SDCC last week and was summarily kicked out of Artists Alley. It gave me a brief glimmer of hope…I imagined a deafening crescendo of cheering as the non-talent skulked away, tail between his/her legs. That takes some gall to occupy the highly competitive table space of an ACTUAL hard-working artist (who’s paying off about $100k in art school student loans) with some Mid-Journey derivative crap. Wowzers. For the Silo, Denise Dorman.
While generative AI transforms how Americans shop, it’s also quietly powering a counterfeit crisis now spiraling out of control. A groundbreaking new report from Red Points and OnePoll, The Counterfeit Buyer Teardown, reveals that AI is no longer just helping consumers find the best deals—it’s helping them find fakes. From influencer-driven “dupe culture” to hyper-realistic fake storefronts, the study exposes a booming underground economy that’s been supercharged by technology. With 28% of counterfeit buyers now using AI tools to seek out knock-offs, and fraudulent social media ads spiking 179% in just one year, the findings deliver a wake-up call for brands, regulators, and shoppers alike. Red Points execs are available to break down the data, discuss solutions, and explain why this rapidly evolving trend is both a technological and ethical crisis for the digital marketplace. Interest here as we hope?
AI Supercharging U.S.and Other E-Commerce Counterfeit Crisis
An explosive new report, “The Counterfeit Buyer Teardown, ” paints a concerning picture of a rapidly evolving and increasingly sophisticated counterfeit goods market, driven by a new factor: Artificial Intelligence. Forget the back alleys; findings from the research—conducted by market research firm OnePoll and AI company Red Points in February 2025—highlight that the future of fakes is digital, AI-assisted, and alarmingly mainstream.
The convergence of technology, social media, and shifting consumer mindsets is reshaping e-commerce—and not always for the better. As AI accelerates both the spread and appeal of counterfeit goods, the challenge is no longer just spotting fakes—it’s confronting a counterfeit economy that’s growing smarter, faster, and harder to contain.
“As counterfeiters adopt advanced tools like AI, the fight against fakes is becoming more complex and more urgent,” said Laura Urquizu, CEO & President of Red Points. “We’re now seeing AI shape both the threat and the solution. In 2024 alone, our firm detected 4.3 million counterfeit infringements online—an alarming 15% increase year-over-year.”
Alarming indeed. Here are 5 key revelations from the study.
1. AI is the New Enabler of Counterfeiting – A Two-Sided Threat:
The Counterfeiters’ Edge: AI is dramatically lowering the barrier to entry for bad actors. They can now mimic brand listings, and impersonate social media accounts with unprecedented ease and speed. They can also effortlessly create professional-looking fake websites—a situation that, according to Red Points’ data, is projected to surge 70% in 2025.This isn’t just about cheap knock-offs anymore; it’s about sophisticated deception at scale.
The Consumers’ Assistant: Shockingly, 28% of online shoppers who bought fake goods used AI tools to find them. This isn’t a fringe behavior; it’s a growing trend, especially among Gen X, suggesting consumers are actively leveraging AI in their pursuit of cheaper alternatives. This fundamentally shifts the narrative – it’s not just about being tricked; some are actively seeking fakes with AI’s help.
2. Accidental Counterfeiting is a Major Problem – Trust Signals are Being Hijacked:
1 in 4 luxury counterfeit purchases are unintentional. This shatters the perception that buyers knowingly seek out high-end fakes. Realistic pricing, secure payment promises, and active (but fake) social media presence are successfully deceiving consumers. AI-generated legitimacy cues are becoming indistinguishable from the real deal.
Brands are Paying the Price for These Mistakes: A staggering one in three shoppers stop buying from the genuine brand after an accidental counterfeit experience. This highlights the significant damage to brand loyalty and future sales, even when the brand isn’t directly selling the fake. High-trust categories like luxury and toys are particularly vulnerable.
3. The “Dupe Economy” is Real and Influencer-Driven:
Nearly a third (31%) of intentional counterfeit buyers were swayed by influencer promotions. Social media is driving the demand for “dupes” – budget-friendly replicas. Authenticity is taking a backseat to price and perceived identical appearance, especially among younger demographics.
This isn’t just about saving money; it’s a shift in consumer mindset. The report suggests a growing acceptance of fakes as clever alternatives, fueled by social validation and influencer endorsements.
4. Marketplaces Remain Key, But Social Media and Fake Websites are Surging:
Marketplaces (both US and China-based) are still the primary channels for counterfeit purchases. However, fake websites (accounting for 34% of unintentional purchases) and social media are rapidly gaining ground as sophisticated avenues for distribution, amplified by AI’s ability to create convincing facades.
Social media ads redirecting to infringing websites saw a massive 179% year-over-year growth. This highlights the increasing sophistication of counterfeiters in leveraging advertising platforms to drive traffic to their fake storefronts.
5. Younger Generations are More Vulnerable in Key Categories:
Millennials are significantly more likely to have their personal data stolen after purchasing from fake websites (44% vs. 34% average). This suggests a higher susceptibility to sophisticated phishing scams disguised as legitimate e-commerce sites.
Gen Z and Millennials are 2-4 times more likely to accidentally purchase counterfeit luxury goods and toys compared to Baby Boomers. Their online savviness might be a double-edged sword, making them more exposed to deceptive listings.
This study serves as both a consumer alert and a brand wake-up call. The rise of AI as a tool for both counterfeiters and consumers is a seismic shift that demands urgent attention. With compelling data and a clear-eyed look at accidental purchases, influencer-driven “dupe culture,” and the growing sophistication of fake storefronts, the findings paint a stark warning for the future of online shopping.
“Counterfeiting poses a serious and evolving threat to innovative businesses and consumer safety,” notes Piotr Stryszowski, Senior Economist at the Organization for Economic Co-operation and Development (OECD). “Criminals constantly adapt, exploiting new technologies and shifting market trends—particularly in the online environment. To effectively counter this threat, policymakers need detailed, up-to-date information. This study makes an important contribution to our understanding of how counterfeiters operate and how consumers behave online.” Ultimately, The Counterfeit Buyer Teardown report underscores a new reality: counterfeiting is no longer confined to shady sellers or easily spotted scams—it’s embedded in the very technologies shaping modern commerce. As AI continues to blur the lines between real and fake, the pressure is on for brands, platforms, and policymakers to respond with equal speed and sophistication. Combating this growing threat will require more than just awareness—it demands collaboration, innovation, and a commitment to restoring trust in the digital marketplace before the counterfeit economy becomes the new normal. For the Silo, Merilee Kern.
Merilee Kern, MBA is a brand strategist and analyst who reports on industry change makers, movers, shakers and innovators: field experts and thought leaders, brands, products, services, destinations and events. Merilee is a regular contributor to the Silo. Connect with her at www.TheLuxeList.com and LinkedIN www.LinkedIn.com/in/MerileeKern.
The Global Cooperation Barometer indicates that international cooperation has “flatlined”, driven by heightened geopolitical tensions and instability, but positive momentum in climate finance, health and innovation offers hope. In an era of heightened volatility, leaders will need to embrace “disordered” cooperation and dynamic, solutions-driven decision-making to deliver tangible results and build trust. AI and other emerging technologies are reshaping the global landscape and driving upheaval. Concerted cooperation will be critical to harness benefits and minimize risks.
Geneva, Switzerland, January 2025 – The World Economic Forum’s Global Cooperation Barometer offers a critical assessment of the state of global cooperation, showing a world grappling with heightened competition and conflict, while also identifying various areas where leaders can drive progress through innovative collaboration. Released amid geopolitical, technological and sociopolitical upheaval, the Forum’s flagship annual report underscores the urgency of addressing shared challenges and offers leaders guidance on what cooperation can look like in a shifting world.
The Global Cooperation Barometer 2025, developed in collaboration with McKinsey & Company, uses 41 indicators to measure the current state of global cooperation. The aim is to offer leaders a tool to better understand the contours of cooperation broadly and along five pillars: trade and capital flows, innovation and technology, climate and natural capital, health and wellness, and peace and security. Now in its second edition, the Barometer draws on new data to provide an updated picture of the global cooperation landscape, with a particular focus on the impact of the new technological age.
“The Barometer is being released at a moment of great global instability and at a time when many new governments are developing agendas for the year, and their terms, ahead,” said Børge Brende, President and CEO of the World Economic Forum. “What the Barometer shows is that cooperation is not only essential to address crucial economic, environmental and technological challenges, it is possible within today’s more turbulent context.”
“This second edition of the Global Cooperation Barometer focuses on where cooperation stands today and what it can look like in the new technological age,” said Bob Sternfels, Global Managing Partner, McKinsey & Company. “Advancing global innovation, health, prosperity and resilience cannot be done alone. Leaders will need new mechanisms for working together on key priorities, even as they disagree on others, and the past several years have shown this balance is possible.”
The latest edition of the Barometer highlights that global cooperation is at a critical juncture. The report’s analysis reveals that after trending positively for a decade and surpassing pre-pandemic levels, overall cooperation has stagnated.
This has been driven by a sharp decline of the peace and security pillar of the Barometer over the past seven years, caused by mounting geopolitical tensions and competition which have significantly eroded global collective security. Levels of conflict and attendant humanitarian crises have increased in the past year to record levels, driven by crises including, but not limited to, the Middle East, Ukraine and Sudan.
As the largely stable cooperative order that defined the post-Cold War period is giving way to a more fragmented landscape, solutions to pressing challenges – from climate action to technological governance – require collaboration. And despite the global security crises, the new findings indicate that collaboration has continued in various areas including vaccine distribution, scientific research, renewable energy development, and more – offering models for future cooperation.
Notably, peace and security have declined sharply in recent years, but other pillars of the Barometer have remained resilient and reveal emerging opportunities for international cooperation,
Innovation and technology. While geopolitical competition is rising in regard to certain frontier technologies such as semiconductors, overall global cooperation on technology and innovation advanced in 2023, in part due to digitization of the global economy. This helped drive the adoption of new technologies, a strong ramp-up in the supply of critical minerals – and a related drop in price of lithium batteries – and a rebound in student mobility. However, rapid disruption from emerging technologies such as AI is reshaping the global landscape, raising the possibility of a new frontline of geostrategic competition or even an “AI arms race”. Cooperative leadership and inclusive strategies will be key to harness its vast potential while tackling risks.
Climate and natural capital: Cooperation on climate goals improved over the past year, with increased finance flows and higher trade in low-carbon technologies such as solar, wind and electric vehicles. Yet, urgent action is required to meet net-zero targets as global emissions continue to rise. Greater global cooperation will be essential to scale up technologies and secure the financing needed to meet climate goals by 2030.
Health and wellness: Some health outcomes, including life expectancy, continued to improve post-pandemic, but overall progress is slowing compared to pre-2020. While cross-border assistance and pharmaceutical R&D have declined, and cooperation on trade in health goods and international regulations stalled, various health metrics including child and maternal mortality remain strong. Given rising health risks and ageing populations, leaders should invest in global cooperation to bolster public health and sustainable health systems.
Trade and capital flows: Metrics related to the flow of goods and services, trade, capital and people had mixed outcomes in 2023. Goods trade declined by 5%, driven largely by slower growth in China and other developing economies, while global fragmentation continued to reduce trade between Western and Eastern-aligned blocs. Despite this, global flows of services, capital and people showed resilience. Foreign direct investment surged, particularly in strategic sectors like semiconductors and green energy, while labour migration and remittances rebounded strongly, surpassing pre-pandemic levels.Looking ahead, leaders will need to find ways to work together, even as competition increases, as tangible results will be crucial to maintain public trust and support. The report concludes by underscoring the urgent need for adaptive, solutions-driven leadership to navigate a turbulent global landscape. By pivoting towards cooperative solutions, leaders can rebuild trust, drive meaningful change and unlock new opportunities for shared progress and resilience in the complex years ahead.
About the Global Cooperation Barometer Methodology
The Global Cooperation Barometer – first launched in 2024 – evaluates global collaboration across five interconnected dimensions: trade and capital, innovation and technology, climate and natural capital, health and wellness, and peace and security. The Barometer is built on 41 indicators, categorized as cooperative action metrics (evidence of tangible cooperation, such as trade volumes, capital flows, or intellectual property exchanges) and outcome metrics (broader measures of progress like reductions in greenhouse gas emissions or improvements in life expectancy). Spanning 2012–2023 and indexed to 2020 to reflect pandemic-era shifts, the Barometer normalizes data for comparability (e.g., financial metrics relative to global GDP and migration metrics to population levels) and weights it equally within and across pillars.
About the Annual Meeting 2025
The World Economic Forum Annual Meeting 2025, taking place in Davos-Klosters from 20 to 24 January, convenes global leaders under the theme, Collaboration for the Intelligent Age. The meeting will foster new partnerships and insights to shape a more sustainable, inclusive future in an era of rapidly advancing technology, focusing on five key areas: Reimagining Growth, Industries in the Intelligent Age, Investing in People, Safeguarding the Planet, and Rebuilding Trust. Click here to learn more.
The rise of AI is truly remarkable. It is transforming the way we work, live, and interact with each other, and with so many other touchpoints of our lives. However, while AI aggregates, dyslexic thinking skills innovate. If used in the right way, AI could be the perfect co-pilot for dyslexics to really move the world forward. In light of this, Virgin and Made By Dyslexia have launched a brilliant campaign to show what is possible if AI and dyslexic thinking come together. The film below says it all.
As the film shows, AI can’t replace the soft skills that index high in dyslexics – such as innovating, lateral thinking, complex problem solving, and communicating.
If you ask AI for advice on how to scale a brand that has a record company – it offers valuable insights, but the solution lacks creative instinct and spontaneous decision making. If I hadn’t relied on my intuition, lateral thinking and willingness to take a risk, I would have never jumped from scaling a record company to launching an airline – which was a move that scaled Virgin into the brand it is today.
Together, dyslexic thinkers and AI are an unstoppable force, so it’s great to see that 72% of dyslexics see AI tools (like ChatGPT) as a vital starting point for their projects and ideas – according to new research by Made By Dyslexia and Randstad Enterprise. With help from AI, dyslexics have limitless power to change the world, but we need everyone to welcome our dyslexic minds. If businesses fail to do this, they risk being left behind. As the Value of Dyslexia report highlighted, dyslexic skillsets will mirror the World Economic Forum’s future skills needs by end of this year (2025). Given the speed at which technology and AI have progressed, this cross-over has arrived two years earlier than predicted.
Image: Sarah Rogers/MITTR
With all of this in mind, it’s concerning to see a big difference between how HR departments think they understand and support dyslexia in the workplace, versus the experience of dyslexic people themselves.
The new research also shows that 66% of HR professionals believe they have support structures in place for dyslexia, yet only 16% of dyslexics feel supported in the workplace. It’s even sadder to see that only 14% of dyslexic employees believe their workplace understands the value of dyslexic thinking. There is clearly work to be done here.
To empower dyslexic thinking in the workplace (which has the two-fold benefit of bringing out the best in your people and in your business), you need to understand dyslexic thinking skills. To help with this, Made By Dyslexia is launching a workplace training course later this year on LinkedIn Learning – and you can sign up for it now. The course will be free to access, and I’m delighted that Virgin companies from all across the world have signed up for it – from Virgin Australia, to Virgin Active Singapore, to Virgin Plus Canada and Virgin Voyages. It’s such an insightful course, designed by experts at Made By Dyslexia to educate people on how to understand, support, and empower dyslexic thinking in the workplace, and make sure businesses are ready for the future.
It’s always inspiring to see how Made By Dyslexia empowers dyslexics, and shows the world the limitless power of dyslexic thinking. If businesses can harness this power, and if dyslexics can harness the power of AI – we can really drive the future forward. Richard Branson, Founder at Virgin Group.
World Economic Forum’s EDISON Alliance Impacts Over 1 Billion Lives, Accelerating Global Digital Inclusion.
The EDISON Alliance has connected over 1 billion people globally to essential digital services like healthcare, education and finance through a network of 200+ partners in over 100 countries.
Investments in bridging the universal digital divide could bring $8.7 trillion usd/ $11.7 trillion cad in benefits to developing countries, home to more than 70% of the Alliance’s beneficiaries.
The Alliance’s 300+ partner initiatives, including digital dispensaries in India, economy digitalization programmes in Rwanda and blended learning in Bangladesh, continue to shape a digitally equitable society.
Follow the Sustainable Development Impact Meetings 2024 here and on social media using #SDIM24.
New York, USA, September 2024 – The EDISON Alliance, a World Economic Forum initiative, has successfully connected over 1 billion people globally – ahead of its initial 2025 target – to essential digital services in healthcare, education and finance in over 100 countries. Since its launch in 2021, the Alliance has united a diverse network of 200+ partners from the public and private sectors, academia and civil society to create innovative solutions for digital inclusion.
Despite living in a digitally connected world, 2.6 billion people are currently not connected to the internet.
This digital exclusion impacts access to healthcare, financial services and education, contributing to significant economic costs for both the individuals involved and their countries’ economies.
Klaus Schwab- German mechanical engineer, economist and founder of the World Economic Forum.
“Ensuring universal access to the digital world is not merely about connectivity, but a fundamental pillar of equality and opportunity,” said Klaus Schwab, Founder and Chairman of the World Economic Forum. “Let us reaffirm our commitment to ensuring that every individual, regardless of their geographic or socioeconomic status, has access to meaningful connectivity.”
The Alliance has made substantial progress in South Asia and Africa.
In Madya Pradesh, India, The EDISON Alliance fostered the Digital Dispensaries initiative, a collaboration between the Apollo Hospitals Group and a US telecom infrastructure provider. This partnership has successfully delivered quality and affordable healthcare, improving patient engagement, addressing gender health disparities and optimizing patient convenience, and making it a scalable model for delivering patient-centric healthcare through digital solutions. Other partner projects improved digital access through economy digitalization programmes in Rwanda, provided solutions for bridging the education gap in Bangladesh with blended learning techniques and explored solutions to reduce financial exclusion in Pakistan.
“Everybody, no matter where they were born or where they live, should have access to the digital services that are essential for life in the 21st century,” said Hans Vestberg, Chair of the EDISON Alliance, Chairman and CEO of Verizon. “Making sure that everybody can get online is too big a challenge for any one company or government, so the EDISON Alliance brings people together to find practical, community-based solutions that can scale globally.”
By driving digital inclusion through its 300+ partner initiatives, the Alliance contributes to unlocking the immense potential of the digital economy. Achieving universal internet access by 2030 could require $446 billion usd/ $600 billion cad, but would yield $8.7 trillion usd/ $11.7 trillion cad in benefits for developing countries. This highlights the significant potential of digital inclusion to drive economic growth and improve lives. The EDISON Alliance has made substantial contributions to this goal, with over 70% of its impact concentrated in developing nations.
The milestone of connecting 1 billion lives was initially targeted for 2025.
Achieving this ahead of schedule demonstrates the effectiveness of its partners, through collaboration and targeted projects, in bridging the digital divide and providing access to critical services to underserved communities.
Beyond digital access, the rapidly evolving technological landscape – marked by such advancements as artificial intelligence, presents opportunities and challenges. The EDISON Alliance remains committed to ensuring that marginalized communities can fully benefit from these developments and avoid being left behind. As technology continues to advance, the Alliance will focus on expanding digital access, fostering innovation and addressing the digital gender gap to create a more inclusive digital future.
About the Sustainable Impact Meetings 2024
The Sustainable Development Impact Meetings 2024 are being held this week in New York. Over 1,000 global leaders from diverse sectors and geographies will come together to assess and renew global action around the United Nations Sustainable Development Goals (SDGs) through a series of impact-oriented multistakeholder dialogues. The meetings are an integral part of the Forum’s year-round work on sustainable development and its progress.
