ibl.ai

National Security: Superintelligence Strategy

Summary of https://arxiv.org/pdf/2503.05628
This expert strategy document from Dan Hendrycks, Eric Schmidt and Alexander Wang addresses the national security implications of rapidly advancing AI, particularly the anticipated emergence of superintelligence.
The authors propose a three-pronged framework drawing parallels with Cold War strategies: deterrence through the concept of Mutual Assured AI Malfunction (MAIM), nonproliferation to restrict access for rogue actors, and competitiveness to bolster national strength.
The text examines threats from rival states, terrorists, and uncontrolled AI, arguing for proactive measures like cyber espionage and sabotage for deterrence, export controls and information security for nonproliferation, and domestic AI chip manufacturing and legal frameworks for competitiveness. Ultimately, the document advocates for a risk-conscious, multipolar strategy to navigate the transformative and potentially perilous landscape of advanced artificial intelligence.
Rapid advances in AI, especially the anticipation of superintelligence, present significant national security challenges akin to those posed by nuclear weapons. The dual-use nature of AI means it can be leveraged for both economic and military dominance by states, while also enabling rogue actors to develop bioweapons and launch cyberattacks. The potential for loss of control over advanced AI systems further amplifies these risks.
The concept of Mutual Assured AI Malfunction (MAIM) is introduced as a likely default deterrence regime. This is similar to nuclear Mutual Assured Destruction (MAD), where any aggressive pursuit of unilateral AI dominance by a state would likely be met with preventive sabotage by its rivals, ranging from cyberattacks to potential kinetic strikes on AI infrastructure.
A critical component of a superintelligence strategy is nonproliferation. Drawing from precedents in restricting weapons of mass destruction, this involves three key levers: compute security to track and control the distribution of high-end AI chips, information security to protect sensitive AI research and model weights from falling into the wrong hands, and AI security to implement safeguards that prevent the malicious use and loss of control of AI systems.
Beyond mitigating risks, states must also focus on competitiveness in the age of AI to ensure their national strength. This includes strategically integrating AI into military command and control and securing drone supply chains, guaranteeing access to AI chips through domestic manufacturing and strategic export controls, establishing legal frameworks to govern AI agents, and maintaining political stability in the face of rapid automation and the spread of misinformation.
Existing strategies for dealing with advanced AI, such as a completely hands-off approach, voluntary moratoria, or a unilateral pursuit of a strategic monopoly, are flawed and insufficient to address the multifaceted risks and opportunities presented by AI. The authors propose a multipolar strategy based on the interconnected pillars of deterrence (MAIM), nonproliferation, and competitiveness, drawing lessons from the Cold War framework adapted to the unique challenges of superintelligence.

Monash University: Gen AI in Higher Ed – A Global Perspective of Institutional Adoption Policies and Guidelines

Summary of https://www.sciencedirect.com/science/article/pii/S2666920X24001516
This paper examines how higher education institutions globally are addressing the integration of generative AI by analyzing the adoption policies of 40 universities across six regions through the lens of the Diffusion of Innovations Theory.
The study identifies key themes related to compatibility, trialability, and observability of AI, the communication channels being used, and the defined roles and responsibilities for faculty, students, and administrators.
Findings reveal a widespread emphasis on academic integrity and enhancing learning, but also highlight gaps in comprehensive policies and equitable access, offering insights for policymakers to develop inclusive AI integration strategies.
Universities globally are proactively addressing the integration of generative AI (GAI) in higher education, primarily focusing on academic integrity, enhancing teaching and learning, and promoting AI literacy. This is evidenced by the emphasis on these themes in the analysis of policies across 40 universities from six global regions. The study highlights that institutions recognize the transformative potential of GAI while also being concerned about its ethical implications and impact on traditional educational values.
The study, utilizing the Diffusion of Innovations Theory (DIT), reveals that while universities are exploring GAI's compatibility, trialability, and observability, significant gaps exist in comprehensive policy frameworks, particularly concerning data privacy and equitable access. The research specifically investigated these innovation characteristics in university policies. Although many universities address academic integrity and the potential for enhancing education (compatibility), and are encouraging experimentation (trialability), fewer have robust strategies for evaluating GAI's impact (observability) and clear guidelines for data privacy and equal access.
Communication about GAI adoption is varied, with digital platforms being the most common channel, but less than half of the studied universities demonstrate a comprehensive approach to disseminating information and fostering dialogue among stakeholders. The analysis identified five main communication channels: digital platforms, interactive learning and engagement channels, direct and personalized communication channels, collaborative and social networks, and advisory, monitoring, and feedback channels. The finding that not all universities actively use a range of these channels suggests a need for more focused efforts in this area.
Higher education institutions are establishing clear roles and responsibilities for faculty, students, and administrators in the context of GAI adoption. Faculty are largely tasked with integrating GAI into curricula and ensuring ethical use, students are responsible for ethical use and maintaining academic integrity, and administrators are primarily involved in policy development, implementation, and providing support. This highlights a structured approach to managing the integration of GAI within the educational ecosystem.
Cultural backgrounds may influence the emphasis of GAI adoption policies, with institutions in North America and Europe often prioritizing innovation and critical thinking, while those in Asia emphasize ethical use and compliance, and universities in Africa and Latin America focus on equity and accessibility.This regional variation suggests that while there are common values, the specific challenges and priorities related to GAI adoption can differ based on cultural and socio-economic contexts.

UNESCO: AI Competency Framework for Students

Summary of https://unesdoc.unesco.org/ark:/48223/pf0000391105
This UNESCO publication presents a global framework for AI competency in students. Recognizing the increasing role of AI, it argues for proactive education to prepare responsible users and co-creators.
The framework outlines twelve competencies across four dimensions: human-centered mindset, ethics of AI, AI techniques and applications, and AI system design, each with three progression levels. It aims to guide educators in integrating AI learning objectives into curricula, emphasizing critical judgment, ethical awareness, foundational knowledge, and inclusive design.
The document also discusses implementation strategies, teacher professionalization, pedagogical approaches, and competency-based assessments for AI education.
The UNESCO AI competency framework for students aims to equip students with the values, knowledge, and skills necessary to thrive in the AI era, becoming responsible and creative citizens. It is the first global framework of its kind, intended to support the development of core competencies for students to critically examine and understand AI from holistic perspectives, including ethical, social, and technical dimensions.
The framework is structured around 12 competencies spanning four dimensions: Human-centred mindset, Ethics of AI, AI techniques and applications, and AI system design, across three progression levels: Understand, Apply, and Create. This structure is designed to provide a spiral learning sequence across grade levels, helping students progressively build a systematic and transferable understanding of AI competencies.
The framework is grounded in key principles that include fostering a critical approach to AI, prioritizing human-centred interaction with AI, encouraging environmentally sustainable AI, promoting inclusivity in AI competency development, and building core AI competencies for lifelong learning. It embodies UNESCO's mandate by anchoring its vision of AI and education in principles of human rights, inclusion, and equity.
The primary target audience for the AI CFS includes policy-makers, curriculum developers, providers of education programmes on AI for students, school leaders, teachers, and educational experts. The framework is intended to serve as a guide for public education systems to build the competencies required for the effective implementation of national AI strategies and the creation of inclusive, just, and sustainable futures. It is designed as a global reference that needs to be tailored to the diverse readiness levels of local education systems.
The framework envisions students as active co-creators of AI and responsible citizens. It emphasizes the importance of critical judgment of AI solutions, awareness of citizenship responsibilities in the era of AI, foundational AI knowledge for lifelong learning, and inclusive, sustainable AI design. Ultimately, the AI CFS aims to prepare students to not only use AI effectively and ethically but also to contribute to shaping its future development and relationship with society.

PWC: Agentic AI – An Executive Playbook

Summary of https://media.licdn.com/dms/document/media/v2/D561FAQHEys4iGQj7CA/feedshare-document-pdf-analyzed/B56ZUN7jLFHQAY-/0/1739695481660?e=1743033600&v=beta&t=nLUoVEs06lwzFgHpx8DbIfd6nMyvXem1ZrpqPSChhiA
"Agentic AI – the new frontier in GenAI," explores the transformative potential of agentic artificial intelligence, particularly within the realm of generative AI. It highlights how autonomous AI systems, capable of making decisions and acting with limited human input, are evolving through machine learning and multimodal data processing to automate complex tasks and optimize workflows.
The text emphasizes the strategic imperative for organizations to adopt this technology early to gain competitive advantages, improve efficiency, enhance customer experiences, and drive revenue growth, providing numerous real-world examples across various industries and business functions.
It also discusses key considerations for implementing agentic AI, including strategic planning, technological infrastructure, data readiness, talent acquisition, and ethical implications, alongside a comparison of commercial and open-source tools.
Ultimately, the document positions agentic AI as a crucial element for future business success, requiring a strategic vision and commitment to realize its full potential in an increasingly AI-driven world.
Agentic AI, with its advanced human-like reasoning and interaction capabilities, is transforming various sectors including manufacturing, healthcare, finance, retail, transportation, and energy.
Organisations' AI strategies should leverage multimodal GenAI capabilities while ensuring ethical AI safeguards to drive autonomous process re-engineering and enhanced decision-making across all business areas.
When integrated effectively, agentic AI can enhance efficiency, lower costs, improve customer experience, and drive revenue growth.
Agentic AI systems possess the capacity to make autonomous decisions and take actions to achieve specific goals with limited or no direct human intervention, exhibiting key aspects like autonomy, goal-oriented behaviour, environment interaction, learning capability, workflow optimisation, and multi-agent and system conversation.
The evolution of agentic AI has progressed through the integration of machine learning for data learning and NLP-enabled user interactions, the introduction of multimodality combining various data types for enhanced interactions, and the development of advanced autonomy and real-time interactions enabling human-like reasoning and independent decision-making.

Harvard Business School: Global Evidence on Gender Gaps and Generative AI

Summary of https://www.hbs.edu/ris/Publication%20Files/25-023_8ee1f38f-d949-4b49-80c8-c7a736f2c27b.pdf
Examines the gender gap in the adoption and usage of generative AI tools across the globe.Synthesizing data from 18 studies involving over 140,000 individuals, the authors reveal a consistent pattern: women are less likely than men to use generative AI.
This gap persists even when access to these technologies is equalized, suggesting deeper underlying causes. Analysis of internet traffic data and mobile app downloads further supports these findings, indicating a skewed gender distribution among users of popular AI platforms.
The research explores potential mechanisms behind this disparity, such as differences in knowledge, confidence, and perceptions of AI's ethical implications. The authors caution that this gender gap could lead to biased AI systems and exacerbate existing inequalities, emphasizing the need for targeted interventions.
The most prominent explanations behind the gender gap in generative AI adoption are:
Lower familiarity and knowledge Women consistently report less familiarity with generative AI tools. They are also more likely to report not knowing how to use AI tools.
Lower confidence and persistence Women show less confidence in their ability to use AI tools effectively. They are also less persistent when using generative AI, being less likely to attempt prompting multiple times for desired results.
Perception of unethical use Women are more likely to perceive the use of AI in coursework or assignments as unethical or as cheating.
Mixed perceptions of benefits Studies show mixed results regarding whether men and women equally perceive the benefits and usefulness of generative AI. Some studies indicate women perceive lower productivity benefits and are less likely to see generative AI as useful in job searches or educational settings.
No significant differences in trust or risk perception The study indicates that gender differences in generative AI adoption are likely driven by disparities in knowledge, familiarity, and confidence, rather than differences in trust or risk perceptions. There are no statistically significant differences in men and women trusting the accuracy of generative AI, or in expressing concerns about risks such as data breaches or job redundancy.

UC Berkeley: Responsible Use of Generative AI – A Playbook for Product Managers and Business Leaders

Summary of https://re-ai.berkeley.edu/sites/default/files/responsible_use_of_generative_ai_uc_berkeley_2025.pdf
A playbook for product managers and business leaders seeking to responsibly use generative AI (genAI) in their work and products. It emphasizes proactively addressing risks like data privacy, inaccuracy, and bias to build trust and maintain accountability.
The playbook outlines ten actionable plays for organizational leaders and product managers to integrate responsible AI practices, improve transparency, and mitigate potential harms. It underscores the business benefits of responsible AI, including enhanced brand reputation and regulatory compliance.
Ultimately, the playbook aims to help organizations and individuals capitalize on genAI's potential while ensuring its ethical and sustainable implementation.
GenAI has diverse applications and is used for automating work, generating content, transcribing voice, and powering new products and features.
Organizations can use different genAI models. These include off-the-shelf tools, enterprise solutions, or open models, which can be customized for specific needs and products.
Adoption of genAI can lead to increased productivity and efficiency. Organizations that address the risks associated with genAI are best positioned to capitalize on the benefits. Responsible AI practices can foster a positive brand image and customer loyalty.
There are key risks product managers need to consider when using genAI, especially regarding data privacy, transparency, inaccuracy, bias, safety, and security.
There are several challenges to using genAI responsibly, including a lack of organizational policies and individual education, the immaturity of the industry, and the replication of inequitable patterns that exist in society.

Coursera: 2025 Job Skills Report

Summary of https://assets.ctfassets.net/2pudprfttvy6/5hucYCFs2oKtLHEqGGweZa/cf02ebfc138e4a3f7e54f78d36fc1eef/Job-Skills-Report-2025.pdf
The Coursera Job Skills Report 2025 analyzes the fastest-growing skills for employees, students, and job seekers, highlighting the impact of generative AI. The report draws from data of over five million enterprise learners across thousands of institutions.
Key findings emphasize the surging demand for AI skills like GenAI, computer vision, and machine learning, alongside crucial skills in cybersecurity, data ethics, and risk management. These trends reflect the need for individuals and organizations to adapt to technological advancements and evolving job market demands.
The report also identifies regional differences in skill priorities and provides recommendations for businesses, educational institutions, governments, and learners to foster workforce readiness. Overall, the report underscores the importance of continuous upskilling and reskilling in areas like AI, data, and cybersecurity to thrive in the future of work.
GenAI skills are in high demand and are rapidly growing across all enterprise learners. Course enrollments in GenAI have surged, with a significant portion of learners coming from India, Colombia, and Mexico. This growth highlights the increasing need for individuals to develop AI capabilities to succeed in the workplace.
Cybersecurity and risk management skills are crucial due to the increase in cyberattacks. As cyberattacks become more frequent and sophisticated, there is a growing demand for professionals who can identify, assess, and mitigate risks.
Data ethics and data governance are growing priorities, especially among employees and students. There's an increasing emphasis on responsibly managing and analyzing customer data, driven by the need to ensure "safe and secure" AI use.
Students are focusing on sustainability skills such as waste minimization, business continuity planning, and disaster recovery. This focus aligns with the growing demand for green jobs and reflects concerns about the effects of climate change.
Upskilling and reskilling initiatives are vital for workforce readiness. Businesses, higher education institutions, and governments must work together to equip individuals with essential skills in AI, cybersecurity, and data literacy. These initiatives are crucial for improving employability, productivity, and overall competitiveness in a rapidly evolving job market.

McKinsey: The Critical Role of Strategic Workforce Planning in the Age of AI

Summary of https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-critical-role-of-strategic-workforce-planning-in-the-age-of-ai
McKinsey emphasizes the growing importance of strategic workforce planning (SWP) in the age of rapidly evolving technology, particularly generative AI. It highlights how forward-thinking companies are treating talent management with the same importance as financial capital, using SWP to anticipate future needs and proactively manage their workforce.
The article outlines five best practices, including prioritizing talent investments, considering both capacity and capabilities, planning for multiple scenarios, filling talent gaps innovatively, and embedding SWP into business operations. By adopting these practices, organizations can improve their agility, ensure they have the right people with the right skills, and gain a competitive advantage in a dynamic market.
The authors stress that SWP is crucial for navigating technological changes and ensuring long-term resilience. Ultimately, SWP allows for data-driven talent decisions, resource allocation, and a shift away from reactive hiring practices.
The five best practices for companies preparing for disruptions from technological changes such as generative AI through strategic workforce planning (SWP) are:
Prioritizing talent investments as much as financial investments. Successful organizations understand that their workforce is a strategic asset, and investing in talent development and retention is essential for long-term health. Employees represent both an organization’s largest investment and its deepest source of value.
Considering both capacity and capabilities. Organizations can identify the specific skills and competencies required for critical roles that drive higher performance and create more value.
Planning for multiple business scenarios. By implementing a scenario-based approach, organizations create flexibility for rapidly changing industry conditions.
Taking an innovative approach to filling talent gaps. Weigh the time and cost implications of internal versus external hires, considering internal redeployments, reskilling or upskilling existing talent, acquisitions, and outsourcing.
Embedding SWP into business as usual. Strategic workforce planning should become a business-as-usual process, not just a one-off exercise. By embedding SWP into core business operations, companies can better anticipate workforce needs, respond to changing demands, and ensure long-term agility and resilience.

Open Praxis: The Manifesto for Teaching and Learning in a Time of Generative AI – A Critical Collective Stance to Better Navigate the Future

Summary of https://openpraxis.org/articles/777/files/6749b446d17e9.pdf
This document presents a collaboratively written manifesto offering a critical examination of the integration of Generative AI (GenAI) in higher education. It identifies both the positive and negative aspects of GenAI's influence on teaching and learning, stressing that it is not a neutral tool and risks reinforcing existing biases.
The manifesto calls for research-backed decision-making to ensure GenAI enhances human agency and promotes ethical responsibility in education. It also acknowledges that while GenAI has potential, educators must also think about the deprofessionalization of the education field if AI tools increasingly automate tasks like grading, tutoring, and content delivery, potentially leading to job displacement and reduced opportunities for educators.
The text explores the importance of AI literacy for users and also looks to the risks of human-AI symbiosis, including the erosion of human judgement, autonomy and creative agency. The authors hope to encourage debate and offer insight into the future of GenAI in educational contexts.
Here are the five main takeaways:
GenAI is not a neutral tool. It reflects worldviews and can reinforce biases, potentially marginalizing diverse voices.
GenAI can both enhance and diminish essential human elements in education. While it offers potential for personalized learning and efficiency, it also risks eroding creativity, critical thinking, and empathy.
Ethical considerations are paramount. Issues such as bias, fairness, transparency, and data security must be addressed to ensure responsible deployment of GenAI.
Educators, administrators, and policymakers need to rethink education. Continuing with 'business as usual' is not an option. A shift is needed to emphasize learning processes and adapt assessment methods.
Robust, evidence-based research is crucial. Decisions about integrating GenAI in education should be guided by a deep understanding of its impacts.

Microsoft: The AI Decision Brief – Insights from Microsoft and AI Leaders on Navigating the Generative AI Platform Shift

Summary of https://www.microsoft.com/en-us/microsoft-cloud/blog/2025/02/18/maximizing-ais-potential-insights-from-microsoft-leaders-on-how-to-get-the-most-from-generative-ai/
Microsoft's "The AI Decision Brief" explores the transformative power of generative AI across industries. It offers guidance on navigating the AI platform shift, emphasizing strategies for effective implementation and maximizing opportunities while mitigating risks.
The brief outlines stages of AI readiness, key drivers of value, and examples of successful AI adoption. It addresses challenges such as skill shortages, security concerns, and regulatory compliance, providing insights from industry leaders and customer stories.
Furthermore, it emphasizes building trustworthy AI through security, privacy, and safety measures, underscoring Microsoft's commitment to supporting customers in their AI transformation journey. The document concludes by highlighting the future potential of AI in sustainability and various sectors, emphasizing the importance of collaboration and continuous learning in the age of AI.
Here are five key takeaways:
Generative AI is rapidly transforming industries, presenting opportunities for unprecedented impact and growth for leaders who embrace its potential. Its adoption rate is historically fast, with usage among enterprises jumping from 55% in 2023 to 75% in 2024.
AI is becoming more accessible, and Microsoft is committed to providing broad technology access to empower organizations and individuals worldwide to develop and use AI in ways that serve the public good.
Organizations progress through five stages of AI readiness: exploring, planning, implementing, scaling, and realizing, each with its own strategic priorities. Identifying the correct stage and implementing appropriate strategies is critical for managing generative AI transformation.
Trust is crucial for AI innovation, and organizations should prioritize responsible AI practices and security. Trustworthy AI comprises three pillars: security, privacy, and safety.
AI leaders are seeing greater returns and accelerated innovation, averaging a 370% ROI, with top leaders achieving a 1000% ROI. The highest-performing organizations realize almost four times the value from their AI investments compared to those just getting started.