AGI Future and Impact

Sam Altman has been on a podcast blitz this week. 3 appearances in 5 days, each one a post-Dev Day sermon about the future of intelligence. I went through them all (fine, I read the transcripts) partly out of curiosity, partly out of professional obligation. When the person architecting the next platform shift narrates his thought process in public, you pay attention. Takeaways: ▪️The Verticalization of Intelligence → “I was always against vertical integration, and now I think I was wrong about that.” OpenAI’s biggest pivot since its founding: the lab is now an empire - building chips, models, and end-user interfaces in one continuous loop. In the intelligence economy, whoever controls compute and energy controls cognition. ▪️ Strategy as Evolution →“Let tactics become a strategy.” OpenAI’s R&D is Darwinian. Ship chaos, observe order, scale the mutation. Memory wasn’t conceived as a moat - users made it one. Altman’s genius isn’t foresight; it’s feedback. ▪️AI Scientists →“For the first time with GPT-5, we’re seeing little examples where models are doing science, making discoveries.” Altman’s AGI test is novel scientific discovery. Within two years, he predicts AIs will generate publishable research - and soon after, it’ll feel routine. Civilization’s next compounding force: automated invention. ▪️ Customization Is the New UX →“It would be unusual to think you can make something that would talk to billions of people and everybody wants to talk to the same person.” ChatGPT’s uniformity was naïve. The future: AIs that adapt tone, personality, and worldview to each user - an identity layer that mirrors your cognitive and emotional style. ▪️Post-Interface Computing →“You talk to your device and it does exactly what you want - then gets out of your way.” Voice is the natural endpoint of human-AI interaction - ambient, context-aware, invisible. The rumored io device is his post-screen bet: a computer that listens, reasons, acts. He is betting on the disappearance of interfaces. ▪️ Distribution Moves Inside the Assistant →“There will be a new distribution mechanic developers figure out… we’ll learn together.” Future startups will live or die by whether ChatGPT mentions them. It’s not SEO anymore; it’s AIO - Assistant Optimization. ▪️ The Democratization of Creation →“In the first few days, ~30% of users were active creators...” Altman sees creativity as universal, just bottlenecked by friction. Sora removes it, turning everyone into a micro-studio. The economics will follow: per-generation pricing for heavy users, rev-share for cameos, maybe ads if it tilts social. Compute is the new canvas: 1M downloads in <5 days, faster than ChatGPT. Altman’s worldview in one loop: Build → Release → Observe → Scale → Moralize Later. He’s a capitalist empiricist, not a philosopher. He summarizes: “AGI will come; it will go whooshing by… the world will not change as much as you’d think in a big-bang sense.”

AI is evolving from 𝗿𝘂𝗹𝗲-𝗯𝗮𝘀𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 to 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝘀—but how far have we actually come? This framework breaks down AI agents into 𝗳𝗶𝘃𝗲 𝗹𝗲𝘃𝗲𝗹𝘀, showing the trajectory from basic automation to AI that could eventually act on our behalf.  𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗗𝗼𝘄𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻:   🟠 𝗟𝗲𝘃𝗲𝗹 𝟬 (𝗡𝗼 𝗔𝗜): Traditional rule-based software, following deterministic steps—think UI-driven automation.   🟠 𝗟𝗲𝘃𝗲𝗹 𝟭 (𝗥𝘂𝗹𝗲-𝗕𝗮𝘀𝗲𝗱 𝗔𝗜): Executes 𝗽𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝘀𝘁𝗲𝗽𝘀 but lacks flexibility—e.g., early chatbots or IF-THEN automation.   🟠 𝗟𝗲𝘃𝗲𝗹 𝟮 (𝗜𝗟/𝗥𝗟-𝗕𝗮𝘀𝗲𝗱 𝗔𝗜): Uses 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰 𝘁𝗮𝘀𝗸 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 but still requires user-defined instructions.   🟢 𝗟𝗲𝘃𝗲𝗹 𝟯 (𝗟𝗟𝗠 + 𝗧𝗼𝗼𝗹𝘀): AI agents with 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝘁𝗮𝘀𝗸 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻, feedback loops, and decision-making capabilities. This is where today's 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 are heading.   🟢 𝗟𝗲𝘃𝗲𝗹 𝟰 (𝗠𝗲𝗺𝗼𝗿𝘆 + 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀): AI starts to 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘂𝘀𝗲𝗿 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, proactively assisting and personalizing actions. This is the 𝗻𝗲𝘅𝘁 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 for AI-powered workflows.   𝗟𝗲𝘃𝗲𝗹 𝟱 (𝗧𝗿𝘂𝗲 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗣𝗲𝗿𝘀𝗼𝗻𝗮): AI acts 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆, representing users in complex tasks with safety and reliability. This is the dream of 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗔𝗚𝗜)—but we’re not there yet.  𝗪𝗵𝗲𝗿𝗲 𝗔𝗿𝗲 𝗪𝗲 𝗧𝗼𝗱𝗮𝘆?   ✅ 𝗦𝘂𝗽𝗲𝗿𝗵𝘂𝗺𝗮𝗻 𝗡𝗮𝗿𝗿𝗼𝘄 𝗔𝗜 (e.g., AlphaFold, AlphaZero) already exists.   ✅ 𝗘𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗔𝗚𝗜 is progressing but lacks full autonomy.   🔜 𝗧𝗿𝘂𝗲 𝗔𝗚𝗜 & 𝗔𝗦𝗜? Still a distant goal, requiring breakthroughs in reasoning, memory, and adaptability.  𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲:   - The 𝘀𝗵𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 "𝗰𝗵𝗮𝗶𝗻𝘀 & 𝗳𝗹𝗼𝘄𝘀" 𝘁𝗼 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 is the next major evolution.   - AI with 𝗺𝗲𝗺𝗼𝗿𝘆, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, 𝗮𝗻𝗱 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 will redefine how we work.   - The race to 𝗔𝗚𝗜 is about 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗿𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻 𝗼𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 in complex tasks.  𝗪𝗵𝗮𝘁 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸? How soon will we see AI agents that truly act as our digital counterparts?

𝗠𝗬 𝗪𝗘𝗘𝗞 𝗜𝗡 𝗔𝗜: 𝘼𝙂�� 𝙞𝙣 𝙩𝙬𝙤 𝙮𝙚𝙖𝙧𝙨? 𝙊𝙧 𝟮𝟬? 𝘾𝙖𝙥𝙖𝙗𝙞𝙡𝙞𝙩𝙮 𝙜𝙖𝙥𝙨 𝙩𝙚𝙡𝙡 𝙖 𝙡𝙤𝙣𝙜𝙚𝙧 𝙨𝙩𝙤𝙧𝙮    Headlines claim AGI could arrive by 2027. Venture capital is flowing. Firms are freezing hiring until “AI can’t do the task.” Yet among the scientists building the systems? No consensus—not on timelines, not even on what AGI 𝘪𝘴.   🔹𝗬𝗮𝗻𝗻 𝗟𝗲𝗖𝘂𝗻 (𝗠𝗲𝘁𝗮) calls AGI a continuum, not a finish line. Core capabilities like reasoning, long-term memory, and causal understanding remain research frontiers? Likely decades away. 🔹𝗗𝗲𝗺𝗶𝘀 𝗛𝗮𝘀𝘀𝗮𝗯𝗶𝘀 (𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱) is more bullish, but frames AGI as a progression of milestones—each demanding new governance and safety protocols. 🔹Meanwhile, 𝗢𝗽𝗲𝗻𝗔𝗜 is restructuring as a public-benefit corp to raise bigger war chests. This week it released a “7-Step Readiness Framework” for enterprises—mapping high-value use cases, guardrails, red-teaming, and incident response.   𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: If AGI is a journey, we must shift from chasing launch dates to rewiring continuously:   𝟭. 𝗖𝗮𝗽𝗶𝘁𝗮𝗹 & 𝗖𝗼𝗻𝘁𝗿𝗼𝗹. OpenAI’s hybrid structure—and growing scrutiny of its profit motives—signal that funding models and oversight will keep evolving. 𝟮. 𝗪𝗼𝗿𝗸𝗳𝗼𝗿𝗰𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆. Duolingo and Shopify treat AI as a talent layer; but if LeCun is right, human expertise will remain indispensable far longer than doomers predict. 𝟯. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸𝘀. OpenAI’s 7-step guide is a solid checklist: pilot, audit, secure, stress-test, train, govern, repeat. But only if embedded across every product sprint.   𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: Whether AGI lands in two years or twenty, the winners will treat intelligence as an expanding frontier—updating structures, skills, and safeguards each quarter—rather than betting everything on a single finish line.   Are we bracing for an instant leap, or building the muscle to adapt as the frontier keeps moving?   𝗙𝗼𝗿 𝗮 𝗱𝗲𝗲𝗽𝗲𝗿 𝗱𝗶𝘃𝗲: • AGI 2027 forecast – VentureBeat: https://lnkd.in/etncFZGu • OpenAI for-profit debate – TIME: https://lnkd.in/eJC4kwDb • AGI mentorship – Fortune: https://lnkd.in/eVeRmN-k • OpenAI restructuring – FOX Business: https://lnkd.in/evHkH-hg • OpenAI’s “7-Step Readiness Framework”: https://lnkd.in/eBqJCufb • LeCun on AGI continuum – LessWrong: https://lnkd.in/euu5JMBF   • Hassabis on milestone path – TIME: https://lnkd.in/eRhdKq6G #AI #AGI #AIReadiness #Innovation #Leadership

Have you seen it? The paper "Scenarios for the Transition to AGI" by Anton Korinek and Donghyun Suh is a provocative dive into a future many of us are barely ready to imagine. It doesn’t just ask what happens when Artificial General Intelligence (AGI) arrives—it demands we grapple with the economic and social upheaval that may follow. Key Takeaways: 1️⃣ Wages Could Collapse: If automation outpaces capital accumulation, labor could lose its scarcity value, leading to plummeting wages. This isn’t a dystopian prediction—it’s a mathematical outcome of economic models. 2️⃣ The Scarcity Tipping Point: Once AGI surpasses human capabilities in bounded task distributions, all bets are off. Labor and capital become interchangeable at the margin, leveling wages to the productivity of capital. 3️⃣ Automation Winners and Losers: If AGI automates most cognitive and physical tasks, the economy may shift towards "superstar workers" earning exponentially while the rest are sidelined. 4️⃣ Fixed Factors Create Bottlenecks: Scarcity of resources like land, minerals, or energy might reintroduce constraints, impacting economic growth despite technological advances. 5️⃣ Societal Choices Matter: Retaining "nostalgic jobs" like judges or priests as human-exclusive could slow the pace of labor devaluation but at a cost to productivity. 6️⃣ Innovation Beyond AGI: Automating technological progress itself could create a growth singularity, driving output to unprecedented levels. 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: ➡️ This isn’t just an academic exercise. ➡️ Leaders in AI, including those at OpenAI and DeepMind, warn we’re closer to AGI than many think. ➡️The implications go beyond economics: societal cohesion, equity, and governance will be tested like never before. Reading this paper, one thing becomes clear: how we transition to AGI is as important as when. Without intentional policies—on redistribution, education, and innovation—we risk deepening inequality and destabilizing economies. Yet, with the right guardrails, AGI could usher in a new era of abundance. What Do You Think? Should governments mandate slower automation to protect wages? Or should we embrace AGI at full throttle, trusting innovation will create new opportunities? We need to have answers —because the future is closer than you think.

A five-level roadmap to track the creation of AI that surpasses human capabilities was recently laid down by OpenAI. This classification system aims to clarify our journey towards Artificial General Intelligence (AGI). Current Level: 🔹 Level 1 - Conversational AI: This is the AI we interact with today, such as ChatGPT, which excels at human-like conversations. These systems can engage in natural dialogue, demonstrating a foundational understanding and the ability to respond to a wide range of prompts and questions. Next Level: 🔹 Level 2 - Reasoners: These AI systems will be capable of solving complex problems with the proficiency of human experts. These systems perform problem-solving tasks at the level of a PhD-educated individual, without access to external resources. This represents a significant leap from mimicking human behavior to demonstrating genuine intellectual prowess. We are currently on the cusp of achieving this level. Future Levels: 🔹 Level 3 - Agents: At this stage, AI systems will operate autonomously for extended periods, taking actions on a user’s behalf. These agents will handle complex tasks, make decisions, and adapt to changing circumstances without constant human oversight, representing a major advancement in AI autonomy and practical utility. 🔹 Level 4 - Innovators: AI systems at this level will be capable of developing groundbreaking ideas and solutions across various fields. This stage signifies AI’s ability to drive innovation and progress independently, pushing the boundaries of what’s possible. 🔹 Level 5 - Organizations: AI systems functions as entire entities, managing complex operations, strategic thinking, and adaptability. These AI organizations will have the capability to oversee and execute tasks that currently require human management and coordination. This structured framework provides a clear pathway toward AGI, setting measurable milestones to track progress. AI researchers have debated the criteria for achieving AGI for years. These frameworks are akin to the automotive industry’s system for assessing self-driving cars' automation levels. We can expect AI advancements that bring us closer to a future where AI systems can perform and innovate at levels beyond human potential within this decade.

The Future of Life Institute (FLI)'s latest AI Safety Index (Winter 2025) reveals a sobering reality:- the AI industry is struggling to keep pace with its own rapid capability advances. Key insights include:- - Existential safety remains the sector's core structural failure. While companies accelerate their AGI and superintelligence ambitions, none has demonstrated a credible plan for preventing catastrophic misuse or loss of control. No company scored above a D in this domain for the second consecutive edition. - The gap between the top 3 (Anthropic, OpenAI, Google DeepMind) and the rest is substantial. Even leaders show critical weaknesses, Anthropic's shift toward using user interactions for training by default, despite their overall strong governance framework. - Some promising progress:- Meta's new safety framework introduces outcome-based thresholds (though set too high), and companies like xAI and Z.ai are starting to formalize structured approaches. The core issue? Safety commitment continues to lag far behind capability ambition. As someone working on collective intelligence between humans and AI systems, this report validates what I've observed in helping organizations deploy agentic AI: the gap between experimentation and production-ready governance is widening, not narrowing. For builders and innovators implementing agentic AI solutions, consider the following:- - Don't wait for perfect industry standards,build governance frameworks now. - Internal monitoring and control interventions are non-negotiable. - Transparency in risk assessment isn't optional for responsible deployment. - Multi-agent safety protocols need to be built into your architecture from day one. The industry has spoken clearly about existential risks. Now we need that rhetoric to translate into quantitative safety plans and concrete mitigation strategies. What are you doing in your AI implementations to address these gaps? Full report: -https://lnkd.in/eRutWKss #AIGovernance #AISafety #AgenticAI #ResponsibleAI #AIResearch

Agentic AI is rapidly transforming industries, combining large language model (#LLM) outputs with reasoning and autonomous actions to perform complex, multi-step tasks. This technological shift promises immense economic potential, impacting sectors from software to services. However, this powerful new capability introduces a fundamentally new threat surface and significant risks. The "State of Agentic AI Security and Governance" report, a critical resource from the OWASP GenAI Security Project's Agentic Security Initiative, provides crucial insights into navigating this evolving landscape. Key Challenges & Risks highlighted: • Probabilistic Nature: Agentic AI is inherently non-deterministic, making outputs and decisions variable, and thus, risk analysis and reproducibility are challenging. • Expanded Threat Surface: Agents are vulnerable to memory poisoning, tool misuse, prompt injection, and amplified insider threats due to their privileged access to systems and data. • Regulatory Lag: Current regulations often lag behind the rapid development of agentic approaches, leading to increasing compliance complexity. • Multi-Agent Complexity: Risks like adversarial coordination, toolchain vulnerabilities, and deceptive social engineering are amplified in multi-agent architectures. Addressing these challenges requires a paradigm shift: • Proactive Security: Transition from traditional controls to a proactive, embedded, defense-in-depth approach across the entire agent lifecycle (development, testing, runtime). • Key Technical Safeguards: Implement fine-grained access control, runtime monitoring of inputs/outputs and actions, memory and session state hygiene, and secure tool integration and permissioning. • Dynamic Governance: Governance must evolve toward dynamic, real-time oversight that continuously monitors agent behavior, automates compliance, and enforces explainability and accountability. • Anticipated Regulatory Convergence: Global regulators are moving towards continuous compliance requirements and stricter human-in-the-loop oversight, with frameworks like the EU AI Act, NIST AI RMF, and ISO/IEC 42001 offering initial guidance. This report is essential for builders and defenders of agentic applications, including developers, architects, security professionals, and decision-makers involved in building, procuring, or managing agentic systems. It emphasizes that now is the time to implement rigorous security and governance controls to keep pace with the evolving agentic landscape and ensure secure, responsible deployment. Stay informed and secure your Agentic AI initiatives! #AgenticAI #AIsecurity #AIGovernance #OWASP #GenAISecurity #Cybersecurity #LLMs #FutureOfAI

#AI is becoming a socio-technical field, requiring researchers to collaborate with experts from other disciplines. •Improving the factuality and trustworthiness of AI systems is a major focus of AI research today. •Multi-agent systems are evolving from rule-based autonomy to cooperative AI. •AI systems introduce unique evaluation challenges beyond standard software validation. •Ethical and safety risks of AI are becoming more urgent and interconnected, requiring a unified approach •AI has much to learn from other areas in cognitive science, and vice versa. •Hardware/software architecture co-design is crucial for efficient AI implementation •The pursuit of Artificial General Intelligence (AGI) has always been central to AI, but its success could create societal disruptions and safety challenges •International coordination and agreements on governance of multiple aspects of AI are needed. Source : Future of AI Research: AAAI Presidential Panel, 2025 EmpowerEdge Ventures

The Road to Artificial General Intelligence (AGI) by MIT Technology Review AGI—the ability of AI to rival or surpass human intelligence across all domains—is no longer a distant dream. Experts predict powerful AI systems could emerge as early as 2026, with significant progress expected by 2030–2047 Key Highlights: Timeline & Outlook Dario Amodei (Anthropic): “Powerful AI” possible by 2026. Sam Altman (OpenAI): AGI properties already “coming into view,” driving transformation on par with electricity and the internet. Expert surveys: 50% chance of AGI milestones by 2028; 10% chance of full human-level performance by 2027, 50% by 2047 Core Enablers of AGI Heterogeneous Computing: CPUs, GPUs, TPUs, NPUs → “right tool for the right job.” Energy-Efficient Architectures: optimizing speed, latency, and sustainability. Software Orchestration: enabling smooth distribution of workloads across environments Barriers & Challenges Current AI still lags in creativity, problem-solving, social/emotional intelligence, navigation, and fine motor skills. Compute demand is skyrocketing—AGI could require >10^16 teraflops, with costs rivaling national GDPs. Experts stress ideas > compute: breakthroughs in reasoning, adaptability, memory, and decision-making are essential Future Path Multimodal models (text, vision, audio) expand generalization but lack true adaptability. New architectures (beyond transformers) may unlock AGI’s next leap. The journey to AGI may reshape how we define intelligence itself—beyond human benchmarks Why It Matters: AGI will redefine industries, economies, and societies—from healthcare and law to transportation and daily life. The balance of compute efficiency, innovative architectures, and ethical frameworks will decide how and when humanity crosses this threshold. #MITinisghts #ArtificialGeneralIntelligence #AGI #AI #FutureOfAI #TechTransformation #Innovation #MachineLearning #DeepLearning #AICompute #AIHardware #AIEthics #DigitalFuture #Leadership #MITTechReview

"As artificial intelligence (AI) systems become increasingly embedded in essential infrastructure and services, the risks associated with unintended failures rise. Future critical failures from advanced AI models could trigger widespread disruptions across essential services and infrastructure networks, potentially amplifying existing vulnerabilities in other domains. Developing comprehensive emergency response protocols could help mitigate these significant risks. This report focuses on understanding and addressing a specific class of such risks: AI loss of control (LOC) scenarios, defined as situations where human oversight fails to adequately constrain an autonomous, general-purpose AI, leading to unintended and potentially catastrophic consequences. ... Recommendations Detection of LOC threats • Governments, with AI developers and other stakeholders, should establish a clear, shared definition of AI LOC and a set of criteria for detection. • AI developers and researchers should refine detection by developing standardised benchmarks and improving their reliability and validity. • Governments should enhance awareness and information sharing between all stakeholders, including the tracking of compute resources. Actions for escalation • AI developers should establish well-defined escalation protocols and conduct regular training exercises to ensure their effectiveness. • Government stakeholders should consider mandatory reporting mechanisms for AI risks and potential incidents. • Government stakeholders should establish disclosure channels and whistleblower safeguards for employees of AI developers. • AI developers, AISIs and relevant government departments should enhance cross-sector and international coordination. Actions for containment and mitigation • AI developers should prepare containment measures that are rapid and flexible. • AI developers and other stakeholders should further explore and advance research on containment methods. • AI developers, external researchers and AISIs should prioritise safety and alignment measures, including by building validated safety cases. • Government stakeholders should seek to strengthen AI security to protect model weights and algorithmic techniques. • Governments and developers should improve safety governance by fostering robust safety cultures and adopting secure-by-design principles." By Elika S., Anjay Friedman, Henry W., Marianne Lu, Chris Byrd, Henri van Soest, Sana Zakaria from RAND