The Future of Artificial Intelligence and AGI

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.

𝗠𝗬 𝗪𝗘𝗘𝗞 𝗜𝗡 𝗔𝗜: 𝘼𝙂𝙄 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

New on AI Snake Oil: Arvind Narayanan and I argue that AGI will not lead to rapid economic effects, the race to AGI is not relevant for great power competition, we won't know AGI when we have built it, and AGI does not imply impending superintelligence. In other words, AGI is not a milestone: https://lnkd.in/exDQbafU 1) Even if general-purpose AI systems reach some agreed-upon capability threshold, we will need many complementary innovations that allow AI to diffuse across industries to realize its productive impact. Diffusion occurs at human (and societal) timescales, not at the speed of tech development. 2) Worries about AGI and catastrophic risk often conflate capabilities with power. Once we distinguish between the two, we can reject the idea of a critical point in AI development at which it becomes infeasible for humanity to remain in control. 3) The proliferation of AGI definitions is a symptom, not the disease. AGI is significant because of its presumed impacts but must be defined based on properties of the AI system itself. But the link between system properties and impacts is tenuous, and greatly depends on how we design the environment in which AI systems operate. Thus, whether or not a given AI system will go on to have transformative impacts is yet to be determined at the moment the system is released. So a determination that an AI system constitutes AGI can only meaningfully be made retrospectively. 4) Businesses and policy makers should take a long-term view. Businesses should not rush to adopt half-baked AI products. Rapid progress in AI methods and capabilities does not automatically translate to better products. Building products on top of inherently stochastic models is challenging, and businesses should adopt AI products cautiously, conducting careful experiments to determine the impact of using AI to automate key business processes. A “Manhattan Project for AGI” is misguided on many levels. Since AGI is not a milestone, there is no way to know when the goal has been reached or how much more needs to be invested. And accelerating AI capabilities does nothing to address the real bottlenecks to realizing its economic benefits. We plan to keep writing on this topic, and have a series of essay planned on the theme of AI as Normal Technology. Follow the AI Snake Oil substack for more.

New essay by Sayash Kapoor and me: AGI is not a milestone. It does not represent a discontinuity in the properties or impacts of AI systems. If a company declares that it has built AGI, based on whatever definition, it is not an actionable event. It will have no implications for businesses, developers, policymakers, or safety. Specifically: * Even if general-purpose AI systems reach some agreed-upon capability threshold, we will need many complementary innovations that allow AI to diffuse across industries to realize its productive impact. Diffusion occurs at human (and societal) timescales, not at the speed of tech development. Worries about AGI and catastrophic risk often conflate capabilities with power. * Once we distinguish between the two, we can reject the idea of a critical point in AI development at which it becomes infeasible for humanity to remain in control. * The proliferation of AGI definitions is a symptom, not the disease. AGI is significant because of its presumed impacts but must be defined based on properties of the AI system itself. But the link between system properties and impacts is tenuous, and greatly depends on how we design the environment in which AI systems operate. Thus, whether or not a given AI system will go on to have transformative impacts is yet to be determined at the moment the system is released. So a determination that an AI system constitutes AGI can only meaningfully be made retrospectively. The essay has 9 sections: 1. Nuclear weapons as an anti-analogy for AGI 2. It isn’t crazy to think that o3 is AGI, but this says more about AGI than o3 3. AGI won't be a shock to the economy because diffusion takes decades 4. AGI will not lead to a rapid change in the world order 5. The long-term economic implications of AGI are uncertain 6. Misalignment risks of AGI conflate power and capability 7. AGI does not imply impending superintelligence 8. We won’t know when AGI has been built 9. Businesses and policy makers should take a long-term view Read it here (about 5k words): https://lnkd.in/eh8dnUQU This is the first of many follow-up essays to the AI as Normal Technology thesis. More soon!