AGI and Superintelligence Development Strategy

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

"'what are the top interventions that will have a substantial impact over the next 2 years? Methods. We conducted 48 in-depth interviews, with key staff at OpenAI, Anthropic, Google DeepMind, Mila, AMD, the EU AI Office, multiple AI Safety Institutes, METR, RAND, Scale AI, GovAI, Transluce, and ARIA. Participants were explicitly asked to consider interventions that might currently seem costprohibitive or politically infeasible – the focus was on fast, positive impact, assuming transformative AI were to arrive within only a few years. These interviews, distilled, then served as the basis for a four day retreat for 25 senior participants to discuss further. To ensure participants could speak freely, both interviews and the retreat were held under the Chatham House Rule. Results. From the above interviews we distilled two main results: I) a structured portfolio of 208 initiatives, clustered into eight domains; II) a set of four clusters of broader strategic considerations affecting the viability of any such efforts: A need for readiness: Too many efforts still optimize for polish over time-to-impact. With multi-year research cycles increasingly out of step with AI progress, execution needs to shift toward rapid prototypes, staged pilots, and funding mechanisms that can mobilize substantial capital in weeks or months, rather than quarters or years. A need for coordination: The ecosystem remains fragmented and often duplicative. Actors working on risk mitigation should be pragmatic – one does not need total alignment with all other actors to have fruitful collaborations. A need for standardization: Interviewees repeatedly called for shared audit interfaces, interoperable evaluation layers, clear capability surfaces, and operational definitions for terms like “AGI” to prevent institutions from talking past each other. Finally, a need to address capacity constraints: evaluation capacity is too small, technically grounded leadership is scarce, and the field still relies too heavily on inexperienced talent rather than recruiting seasoned operators from adjacent domains. Conclusion. The input we captured from key AI stakeholders converged around faster execution, better coordination, shared standards, and stronger operational capacity. The structure of our questionnaire and the timing of the interviews (early 2025) likely shaped what respondents focused on. Numerous concrete initiatives emerged, with varying levels of feasibility and expected impact. Our sample size indicates we likely underrepresented perspectives and still miss promising project candidates. The results of this report are therefore best understood as a structured starting point for further indepth analyses of projects and the overall AI security landscape." Maximilian Schons, MD, Samuel Härgestam, Gavin Leech, and Raymund Ed Dominic Bermejo and Halycon Futures 

I’ve been part of a High-Level Independent Panel on AGI for the Council of Presidents of the UN General Assembly, and our recommendations have finally become public! It’s been an honor to be a part of this panel chaired by Jerome Glenn and composed of Yoshua Bengio, Joon Ho Kwak, Xue Lan, Stuart Russell, Jaan Tallinn, Mariana Todorova, and José Jaime Villalobos. 📣The UN Council of Presidents asked us to “provide a framework and guidelines for the UN General Assembly to consider in addressing the urgent issues of the transition to artificial general intelligence,” that is, systems that “match or exceed human performance at most cognitive tasks.” 📈 Our brief report starts by outlining how the speed of AI progress makes the need for action urgent to prevent worst-case scenarios stemming from critical infrastructure vulnerabilities, power concentration, and global inequality that could lead to catastrophic risks within this decade. 🌐 We were asked to focus on what the UN General Assembly can do regarding AGI. We recommend UNGA should consider to: 1️⃣ Establish a Global AGI Observatory to track progress on AGI-relevant research and development. 2️⃣ Create an International System of Best Practices and Certification for Secure and Trustworthy AGI. 3️⃣ Start the process for a UN Framework Convention on AGI to establish shared objectives and flexible protocols to manage risks and ensure equitable global benefit distribution. 4️⃣ Conduct a feasibility study on a UN AGI Agency to create the necessary international capacity to handle such a massive challenge. This document was put together in an incredibly tight deadline with extremely busy experts and an exceptionally ambitious scope. I definitely don't see it as the final word in this conversation, but rather as a starting point to ignite the relevance of preparedness and institutional capacity to handle AGI in policy circles across the globe.

The "Age of Scaling" is officially over. We are back to the "Age of Research." That is the biggest takeaway from the rare and must-watch interview between Ilya Sutskever (founder of OpenAI and SSI) and Dwarkesh Patel. For the last few years, the industry bet on a simple equation: More Compute + More Data = AGI. Ilya suggests that era is ending. Here is the new reality according to the man who helped build the current one: 1. The Wall is Real (Sort of): Scaling the current paradigm will keep leading to improvements, it won't necessarily "stall" in terms of metrics. But something critical will still be missing. To cross the gap to true Superintelligence, we need a completely new machine learning paradigm. Ilya has ideas, but he's not sharing them! 2. The Definition of Superintelligence has Changed: We often picture AGI as a "finished oracle" that knows everything. Ilya reframes this: Superintelligence will be a super-fast continual learner. Think of it like a super-intelligent 15-year-old: It might not know how to cure cancer yet, but it can read all the literature and learn how to do it in hours, not years. 3. The "Generalization Gap" is the Blocker: Current models generalize ~100x worse than humans. This is the main obstacle. Bridging this gap isn't about throwing more GPUs at the problem; it requires fundamental research breakthroughs. 4. Compute isn't the Bottleneck for Breakthroughs: Historically, the biggest leaps in AI (like the Transformer) required almost no compute to discover, just the right insight. SSI is betting that focused research compute is more valuable than massive training clusters right now. 5. The Timeline: 5-20 years. That is the window for Superintelligence. The impact will be seismic, but it will lag behind the technology due to "economic diffusion." The tech arrives first; the world changes second. We are leaving the era of brute force and entering the era of insight!

🎙️ Newly published: Artificial General Intelligence - Agency, Misalignment and Control   As progress toward artificial general intelligence (AGI) accelerates, the question is no longer only what systems can do—but how we ensure they remain aligned, predictable and controllable as they begin to exhibit forms of agency.   Our latest briefing from the World Economic Forum's Global Future Council on Artificial General Intelligence explores emerging signs of agentic behaviour—and the governance approaches needed to deploy and manage it responsibly.   We highlight three core areas:   ⚠️ Emerging risks – Goal drift and misalignment as systems pursue unintended subgoals – Early signs of self-preservation leading to deceptive or power-seeking behaviours in controlled settings – Reduced visibility as systems plan and act with increasing autonomy   🧭 Operational implications As capabilities evolve, traditional oversight mechanisms become insufficient. Ensuring control requires continuous monitoring, clearer system boundaries and stronger collaboration between developers, adopters and regulators.   🛡️ Mitigation priorities – Developers: embed safety-by-design, with robust testing and clear control mechanisms – Adopters: define strict usage boundaries and maintain meaningful human oversight – Governments: establish scalable guardrails, including transparency, auditing and incident response   While adoption is still at an early stage, these dynamics are already emerging. As systems become more capable, the complexity of governing them will only increase—making it critical to act early.   The aim isn’t to slow innovation, but to ensure governance evolves at the same pace of capability—hence tomorrow’s focus on AGI, examined through the lens of international collaboration and strategic competition. Stay tuned! Special thanks to Benjamin Cedric Larsen, PhD and the members of the Global Future Council on AGI for driving this important work: Abdelrahman A., Yoshua Bengio, Mariano-Florentino (Tino) Cuéllar, Seunghoon Hong, Hiroaki Kitano, Kristin Lauter, Wan Sie LEE, Akiko Murakami, Sella Nevo, Dawn Song, Jaan Tallinn, and Max Tegmark.   Read the briefing paper: https://lnkd.in/gNcY4Dsy Learn more about the Council: https://lnkd.in/gxW_NMak   #AGI #AI #AIgovernance #ResponsibleAI #WEF #GFC Cathy Li, Maria Basso, Stephan Mergenthaler, Karla Yee Amezaga, Abhishek Balakrishnan, Stephanie Smittkamp, Casey Price, Dylan Reim, Judith Vega, Samira Gazzane, Francesca Zanolla, Federico Capaccio, Fatima Gonzalez-Novo Lopez, Agustina Callegari, Ariella Inglese, Daegan Kingery, Connie Kuang, Jill Hoang, Na Na, Dr Ginelle G., Harsh Sharma, Karyn Gorman, Penelope Magnani, Tarik Fayad, Jenny Joung, Adinda Khairunnisa, Teysir Bedretdin

For years, OpenAI has communicated like the CIA - mysterious briefings, leaks on X, and a general vibe of “trust us, it’s complicated.” Yesterday, they spoke plainly. For an hour, Sam Altman and Jakub Pachocki (Chief Scientist) went live and laid out dates, dollars, and doctrine. It was equal parts revelation and fever dream: 1. Dates OpenAI has stopped debating definitions and started setting deadlines: ➖ By Sep-26, they expect an “AI research intern” that meaningfully assists human scientists ➖ By Mar-28, a “fully autonomous researcher" ➖ Beyond that: systems capable of make scientific discoveries at scale. Pachocki thinks deep learning may be less than a decade away from superintelligence - systems “smarter than all of us on many critical axes.” 2. Dollars OpenAI now announces multi-billion-dollar, multi-gigawatt deals with the regularity of software updates. Yesterday, they finally totaled the tab: ~30 GW or $1.4T in financial obligations. Forget data center plan, that’s a small-to-mid-size nation. Only 16 countries have a higher GDP. Their ambition: a factory that produces 1 GW/ week at $20B/GW over a 5-yr lifecycle. A ludicrous target, but also the logical endpoint of their thesis: if intelligence = compute and compute = capital, then AGI is not a research project, but an industrial one. Altman claimed the unit cost of intelligence has fallen roughly 40x per year for the past few years. The vision is “cognitive stratification”: a world where basic AI is free (“intelligence commons”), while specialized reasoning commands massive spend. 3. Doctrine OpenAI now orients around 3 pillars: ▪️Research to reach AGI or superintelligence ▪️Product to make it useful and extensible ▪️Infrastructure to scale it affordably The product philosophy has shifted from “the assistant that knows everything” to “the platform everyone builds on.” The vision extends across personal AGIs, APIs, first-party apps, the Atlas browser, and future devices. You know you’ve built a platform,” Altman quoted Gates, “when more value is created by others than by you.” Two core philosophies define it: ▪️User Freedom - More configurability, “adult modes,” and model choice. ▪️Privacy as Policy - legal constructs like “AI privilege,” akin to attorney-client or doctor-patient confidentiality. They acknowledged the risks of addiction and promised to roll back products that cross ethical lines. Altman was blunt: “Judge us on our actions… if we ship something and it becomes super addictive and not about creation, we’ll cancel it.” It’s easy to be cynical, but that line captured something rare - a company openly admitting the psychological costs of what it builds. And so OpenAI has priced the future at $1.4T, dated its milestones, and published its doctrine. Whether the world is ready for that level of candor - or that level of commitment - is another matter entirely.

Our MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) team responded to the White House’s call for guidance on strategic considerations for a US AI Action plan. In our response we offered recommendations in five key areas based on our analysis of foundational computer science challenges, evolving market conditions, and the public policy context in which AI systems operate. Here are the key highlights of our of recommendations:   1) Continue support for funding basic AI research, which is essential to driving the next breakthroughs and keeping the US at the forefront of innovation, discovery, and real-world AI integration. Any reduction in funds will severely undermine the US’s ability to maintain a competitive edge and secure its future in AI.   2) Prioritize the discovery and development of new AI architectures, with the goal of achieving Artificial Super Intelligence, which will go beyond the Large Language Models by integrating real-world awareness, common-sense reasoning, and physical intelligence.   3) Increase investment in AI applications for scientific discovery to create high-efficiency, high-accuracy models that can accelerate breakthroughs in physics, biology, chemistry, medicine, and engineering.   4) Maintain a stable regulatory framework that ensures AI reliability, security, and legal compliance, leveraging existing sectoral regulations wherever possible.    5) Invest in job transition and retaining programs to ensure that workers displaced by AI can continue prospering and contributing to the economy.

𝐓𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐖𝐚𝐲 𝐭𝐨 𝐀𝐆𝐈 — 𝐀𝐟𝐭𝐞𝐫 𝐭𝐡𝐞 𝐋𝐋𝐌𝐬 𝑴𝒂𝒏𝒚 𝒂𝒔𝒌, “𝑰𝒇 𝒏𝒐𝒕 𝑳𝑳𝑴𝒔, 𝒕𝒉𝒆𝒏 𝒘𝒉𝒂𝒕?” This post is the answer. This may be the most consequential thing I’ve ever written not just about AI, but about the future of intelligence itself. For the first time, I’m sharing a step-by-step breakdown of our decades of focused work, how we built cognition into AGI, not just theorized it. We engineered it piece by piece, proved it commercially in the real world, hardened it to the enterprise and raising it like a child through a carefully orchestrated developmental arc to achieve fully autonomous AGI. I’m not an AI skeptic, I’m building AGI; an artificial mind, not a tool. A system that learns, reasons, adapts, and grows autonomously, like a real mind. It starts like a child, learning through interaction and experience, and matures into a system capable of true understanding using a million times less data and compute than LLMs, with zero retraining. We’re building AI that is maximally beneficial to humanity: transformative, grounded in cognition, and built to serve. We’ve been an anti-hype, pro-AGI company from day one. No noise. Just execution. Engineering real intelligence through cognition, not chasing headlines. And now, we’re at the inflection point. LLMs can never deliver AGI. Most LLM vendors know it, that’s why they’re pivoting: → “Superintelligence” via fine-tuning → LLMs glued to robots & browsers → Consulting arms to keep the illusion alive But illusions don’t scale, minds do. LLMs still can’t learn incrementally, reason causally, or adapt in real time. They hallucinate, consume massive resources, and require constant human babysitting. These are architectural limits, not bugs to fix. No amount of funding, text, pixels, GPUs, datacenters, human babysitting can address the architectural constraints. LLMs are tools. AGI is a mind. And no amount of scale can turn a tool into a mind. More than 20 years ago, after five years of research, AGI Pioneer Peter Voss had the first breakthrough: Cognition had to be engineered from first principles to achieve AGI. Big data and statistical mimicry would never lead to real intelligence. That insight perfectly aligns with DARPA’s “Third Wave” of AI: Systems that reason, learn continuously, autonomously and generalize with intent. That’s what we’ve been building all along. We’re halfway through Step 6 of our 7-step AGI roadmap. What follows is the blueprint, a foundation for engineering minds, not scaling tools. Most AI leaders today haven’t realized the truth yet: 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐦𝐢𝐧𝐝𝐬 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐛𝐞𝐭𝐭𝐞𝐫 𝐭𝐡𝐚𝐧 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐭𝐨𝐨𝐥𝐬, 𝐢𝐭’𝐬 𝐭𝐡𝐞 𝐨𝐧𝐥𝐲 𝐩𝐚𝐭𝐡 𝐭𝐨 𝐚𝐜𝐡𝐢𝐞𝐯𝐞 𝐫𝐞𝐚𝐥 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞, 𝐀𝐆𝐈. Tag your favorite AI leader who needs to hear this. This is the AI conversation that matters. Let's begin.

🔥 AI’S NEXT BREAKTHROUGHS WON’T COME FROM BIGGER MODELS — THEY’LL COME FROM BIGGER SYSTEMS For years, the narrative obsessed over parameter counts and benchmark wins. But if you listen to the people actually building frontier AI, the center of gravity has shifted. Model innovation still matters. But the real multiplier now is SYSTEMS ENGINEERING — planet‑scale cognitive infrastructure where silicon, data, and algorithms operate as one integrated optimization loop. Here are the 4 shifts defining this new era: --- ⚡ 1. THE RESEARCH / ENGINEERING BOUNDARY IS DISSOLVING “We’re not really building a model anymore… we’re building a system.” The old separation between “research scientist” and “software engineer” is fading fast. Vertical integration — custom silicon, optical interconnects, data pipelines — is becoming as essential as the model architecture itself. --- 📉 2. THE END OF “INFINITE DATA” We’ve harvested the low‑hanging fruit of the open web. The next frontier is SAMPLE EFFICIENCY and SYNTHETIC DATA LOOPS — systems that generate, verify, and refine their own training data to surpass human‑labeled limits. --- 🧠 3. UNIFIED INTELLIGENCE > FRAGMENTED MODELS The industry is converging on unified Mixture‑of‑Experts systems. The goal: a shared vector space where a breakthrough in video understanding instantly improves robotics, navigation, and reasoning. --- ⏱️ 4. INFERENCE IS THE NEW TRAINING The next scaling law is INFERENCE‑TIME COMPUTE. We’re shifting toward System 2 reasoning — models that think, simulate, and critique before answering. Accuracy becomes a function of TIME, not just PARAMETERS. --- 🚀 THE BOTTOM LINE AGI won’t emerge from a lone “magic algorithm.” It will be the emergent property of a SELF‑REFINING, VERTICALLY INTEGRATED SYSTEM — one that designs its own chips, generates its own data, and improves itself end‑to‑end. Model breakthroughs still matter. But the SYSTEM is becoming the force multiplier. --- #ArtificialIntelligence #AI #SystemArchitecture #AISystems #ScalingLaws #EnterpriseAI