Understood! I will conduct an in-depth analysis of AGI timelines by examining deep learning-based architectures, alternative AGI paradigms, and relevant emerging technologies without straying too far from the core AI research. The report will balance quantitative forecasting (scaling laws, compute trends) with qualitative expert predictions to assess whether AI progress is over- or under-hyped. Additionally, it will provide insights into policy readiness while keeping the focus on your strategic understanding. Finally, multiple AGI development scenarios—including hard takeoff, FOOM, and slow takeoff—will be outlined with supporting evidence.I'll get started on this research and update you when the findings are ready.

Artificial General Intelligence (AGI) Timelines: A Comprehensive Analysis

Introduction

Artificial General Intelligence (AGI) is often defined as an AI system capable of performing all economically useful tasks at a human level of competency or betterourworldindata.org ourworldindata.org. In other words, an AGI could, in principle, learn to do anything a human can do, from medical diagnosis to creative design, with comparable skill and cost-efficiency. Predicting when such a system might emerge is both crucial and notoriously difficult. Expert opinions on AGI timelines span a broad range – from optimists expecting it by the 2030s to skeptics placing it centuries awayobamawhitehouse.archives.gov. Indeed, there is a long history of excessive optimism in AI: in 1957, AI pioneer Herbert Simon predicted machines would surpass humans within a decade, a forecast that badly missed the markobamawhitehouse.archives.gov. This report conducts a comprehensive analysis of AGI timelines, balancing recent quantitative trends in AI progress with qualitative forecasts and expert judgment. We examine the current state-of-the-art in AI (from deep learning breakthroughs like GPT-4 to alternative approaches like neurosymbolic AI and neuromorphic hardware), assess emerging technologies (e.g. quantum computing, brain-computer interfaces) that could accelerate or alter AGI development, and explore multiple development scenarios ranging from rapid “hard takeoff” to slow, gradual integration. Throughout, we emphasize strategic understanding of the uncertainties, potential paradigm shifts, and policy implications of various AGI timeline trajectories, rather than advocating specific policies.

This report is structured as follows. First, we review recent progress in AI capabilities and architectures, highlighting how systems such as GPT-4, Google DeepMind’s Gemini, and Anthropic’s Claude demonstrate the frontier of deep learning and potential “sparks” of generality, while also considering alternative paradigms (hybrid symbolic approaches, brain-inspired computing) that could play a role in reaching AGI. Next, we evaluate the relevance of emerging technologies – notably whether advances like quantum computing or brain-computer interfaces are likely to speed up AGI development or change its course. We then delve into forecasting methodologies: quantitative models based on scaling laws, compute trends, and algorithmic efficiency improvements, alongside qualitative predictions from expert surveys, interviews, and prediction markets. We compare evidence to gauge whether current AI progress might be over-hyped or under-hyped. Following that, we outline several plausible AGI development scenarios: a Hard Takeoff (a rapid, recursive self-improvement “FOOM” leading to explosive intelligence growth) versus a Slow Takeoff (gradual increases in capability integrated into society), as well as Alternative Trajectories where progress stalls or takes an unexpected path. Each scenario’s assumptions and evidentiary support are assessed, and we discuss which outcome appears most probable given the data. Finally, we consider the strategic implications of these findings – how policymakers, industry, and society can prepare for the advent of AGI – focusing on insight and preparedness rather than prescription. We integrate perspectives from economics (e.g. impacts on labor markets and productivity), cognitive science (e.g. the challenge of replicating human cognition), and technology strategy to provide a well-rounded view of what the timing of AGI might mean for the world. All evidence is cited in APA/IEEE style, and data-driven visualizations are included to illustrate key trends. By examining both the hard data and the expert disagreements, this analysis aims to illuminate the uncertainty and complexity surrounding AGI timelines while identifying the signs to watch in the coming years.

Current Progress Toward AGI: Deep Learning and Beyond

Frontiers of Deep Learning: GPT-4, Gemini, Claude, and Multimodal Models

Modern AI research has been dominated by remarkable progress in deep learning – large-scale neural networks trained on massive datasets – which has led to systems that begin to approach general capabilities in certain domains. A flagship example is OpenAI’s GPT-4, a transformer-based large language model (LLM) introduced in 2023 that can perform a wide array of tasks including complex reasoning, coding, and language understanding at near-expert levelsfuturism.com futurism.com. Microsoft researchers studying an early version of GPT-4 famously argued it exhibits “sparks of artificial general intelligence,” noting its breadth of competence and general problem-solving ability beyond any prior modelfuturism.com futurism.com. While they stopped short of claiming GPT-4 is an AGI, they suggested it represents a “first step towards” more generally intelligent systems and a “true paradigm shift” in AIfuturism.com futurism.com. Indeed, GPT-4’s performance on many intellectual benchmarks is unprecedented. It passes professional exams (such as the bar exam, GRE, and Advanced Placement tests) at or above human median scores, writes coherent code, and demonstrates reasoning skills previously unseen in AIthenextweb.com thenextweb.com. Such results have led some experts to question whether we are approaching human-level AI in certain respects much faster than expected – though others caution that current models still have important limitations in reliability and true understandingfuturism.com.

One striking aspect of recent deep learning advances is the rapid improvement on tasks that were once thought to require “general” intelligence. Figure 1 below illustrates the timelines of several notable AI benchmark milestones. By 2023, AI systems had not only exceeded human performance on perceptual tasks like image recognition (surpassing human ImageNet classification accuracy in 2015) but also on language understanding benchmarks and even some reasoning testsnewatlas.com newatlas.com. For example, by 2021 large models topped the human baseline on the natural language inference benchmark (GLUE) and achieved expert-level scores in reading comprehensionnewatlas.com. In a particularly dramatic leap, a GPT-4 based model solved 84% of problems on a suite of collegiate competition-level math questions in 2023 – up from just 6.9% in 2021 – closing in on the ~90% human expert levelnewatlas.com newatlas.com. These rapid gains (on what were recently “frontier” challenges) underscore the power of scale in modern AI. They also suggest that the space of “economically valuable tasks” on which AI matches or beats humans has been expanding quickly, from physical control (robotics) to cognitive labor (translation, writing, coding), even if an integrated system that handles all such tasks remains to be achieved.

Figure 1: Distribution of AI expert predictions for when a 50% probability of human-level AI (HLMI) will be reached. Each vertical line is one expert’s forecast for the year in which AI will be able to “accomplish every task better and more cheaply than human workers.” In a 2022 expert survey (356 respondents), the median prediction for a 50% chance of HLMI was 2061, with substantial disagreement among experts. 90% of experts gave a date within the next 100 years, though a few gave dates much further out or said “never.”ourworldindata.orgourworldindata.org

Beyond GPT-4, other labs and companies have accelerated efforts to push deep learning toward more general AI. Google DeepMind’s “Gemini” project, unveiled in 2023, is explicitly framed as a path to AGI by combining the strengths of different AI approachesthe-decoder.com. According to DeepMind’s CEO Demis Hassabis, Gemini is being designed as a next-generation model that integrates the “amazing language capabilities” of advanced LLMs (like GPT-4 or Google’s PaLM) with techniques from DeepMind’s earlier breakthroughs in game-playing AI (AlphaGo/AlphaZero) to imbue it with stronger problem-solving and planning abilitythe-decoder.com. “At a high level you can think of Gemini as combining some of the strengths of AlphaGo-type systems with the … language capabilities of [large] models,” Hassabis explainedthe-decoder.com. This implies using reinforcement learning and tree-search (which powered AlphaGo’s superhuman strategy in Gothe-decoder.com) alongside massive language modeling. Gemini is also planned to be multimodal, handling not just text but images, video, and other inputs, and to be capable of using tools and APIs fluidlythe-decoder.com. Early versions (Gemini 1.0 and 2.0) have been rolled out in late 2024, demonstrating the ability to control robots with natural language and perform agent-like taskswww.technologyreview.com www.technologyreview.com. Google claims Gemini is its “most capable and general model” to date and explicitly positions it as a stepping stone “as we build towards AGI”blog.google. While quantitative details (like parameter count) remain secret, rumors suggest Gemini involves on the order of a trillion parameters and training runs using tens of thousands of TPUs, indicating an unprecedented scalethe-decoder.com. If successful, Gemini could mark a significant leap in generality – e.g. enabling AI agents that can plan, reason, and act in the physical world, not just conversewww.technologyreview.com www.technologyreview.com. However, as of early 2025, it remains under development, and its eventual impact on AGI timelines is still speculative.

Another notable player is Anthropic’s Claude, an AI assistant modeled after GPT-like architectures but developed with a strong emphasis on safety and alignment. Anthropic’s researchers have pioneered a “Constitutional AI” approach, where the AI is trained to follow a set of ethical principles and self-correct, aiming to make it both helpful and harmless. While Claude’s current versions (e.g. Claude 2) primarily compete with systems like OpenAI’s ChatGPT on tasks like Q&A, coding, and writing, Anthropic’s leadership explicitly frames their mission as advancing toward AGI in a responsible mannerwww.emergentbehavior.co. CEO Dario Amodei has suggested that continuing to scale up models and compute could yield AGI in the not-too-distant future – for example, internal planning documents reportedly discuss building a next-generation model (“Claude-Next”) with 10^5× the compute of Claude to achieve “frontier AI” capabilities by mid-late 2020s. Amodei also believes AGI emergence is likely to be a continuous progression rather than an abrupt jump, describing it as a “smooth exponential” improvement without a single defining momentwww.linkedin.com. In an interview, he argued there may be no clearly demarcated “AGI day” – instead, AI systems will gradually gain competencies until one day we realize they effectively meet the AGI criteriawww.linkedin.com. This view aligns with a “slow takeoff” expectation (discussed later), and it has influenced Anthropic’s focus on incremental alignment techniques that can scale alongside model capabilities. Regardless, Anthropic’s aggressive scaling agenda and the rapid deployment of models like Claude indicate that multiple organizations are in a race-like pursuit of more general AI, each increment potentially bringing timelines forward.

It is important to note that despite these advances, today’s most powerful AI systems still fall short of true general intelligence in several ways. They lack reliable common-sense reasoning and can be tripped up by out-of-distribution scenarios or tricky logical puzzles. For instance, while GPT-4 can solve many exam problems, it still makes reasoning errors a human would not, and it has no agency or persistent goals of its own. Furthermore, current models don’t learn new tasks autonomously in the way humans do; they are constrained by their training data and architecture. Some critics argue that even models with “sparks” of generality are fundamentally misaligned statistical pattern learners, not robust reasoners – essentially, they predict text or actions that look intelligent without understanding in the human sense. Issues like hallucinations (confidently fabricating information), brittleness under adversarial input, and an inability to explain their reasoning highlight that the path from GPT-4-level AI to a trustworthy, fully general AI is not trivial. Nonetheless, the pace of improvement has been so fast that many experts now take the prospect of AGI seriously on the scale of decades or soonerourworldindata.org ourworldindata.org. As we will see in later sections, surveys show a majority of AI researchers believe there is a significant chance (at least 10%) of human-level AI within this century, and a non-negligible minority foresee it in the next 10–20 yearsourworldindata.org ourworldindata.org. The current frontiers of deep learning, exemplified by GPT-4, Claude, and Gemini, form a major basis for those shorter timelines.

Alternative Approaches: Hybrid Symbolic AI and Neuromorphic Computing

While deep neural networks have driven most recent progress, some researchers argue that reaching true AGI may require moving beyond (or augmenting) pure deep learning. Historically, AI research has oscillated between symbolic approaches (hand-crafted rules, logic, and symbolic knowledge representation) and connectionist approaches (neural networks learning from data). Modern large-scale AI is firmly connectionist, but there is growing interest in hybrid architectures that combine neural networks with symbolic reasoning or explicit knowledge. The motivation is that certain facets of human intelligence – such as abstract reasoning, systematic generalization, and understanding of structured knowledge (like mathematics or logic) – might be handled more naturally by symbolic methods, whereas neural networks excel at pattern recognition and fuzzy inference. A neurosymbolic AGI would merge these strengths.Some recent research indicates that such neurosymbolic systems can outperform purely neural ones on tasks requiring complex reasoning or interpretabilitypmc.ncbi.nlm.nih.gov. For example, systems that use neural perception but then feed facts into a symbolic reasoner have shown promise in answering commonsense questions that stump end-to-end neural models. Proponents of this approach, like cognitive scientist Gary Marcus, argue that pure deep learning is not enough for AGI: neural nets lack an innate understanding of concepts and compositional rules, so they struggle with extrapolating knowledge to novel situationserikjlarson.substack.com. Instead, Marcus and others envision architectures that incorporate explicit symbolic modules (for things like logic, language parsing, or world models) alongside learning componentserikjlarson.substack.com pmc.ncbi.nlm.nih.gov. This could entail neural networks that output symbolic representations or that are guided by symbolic constraints during learning. While deep learning pioneers (e.g. Geoff Hinton, Yann LeCun) have generally been skeptical of reverting to symbols, the neurosymbolic idea has gained enough traction that even mainstream AI conferences feature it. The academic discourse suggests “a need for a pluralistic AGI framework, evaluating diverse methodologies rather than over-relying on a single paradigm”pmc.ncbi.nlm.nih.gov. In other words, given the stakes of AGI, many argue we should explore hybrid cognitive architectures and not assume scaling up today’s neural nets will automatically yield human-level reasoningpmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov.

Another alternative path to AGI lies in being brain-inspired not just in software, but in hardware. Neuromorphic computing refers to circuits and devices modeled after the brain’s architecture – typically analog or spiking neural networks that operate more like biological neurons and synapses. The human brain remains far more energy-efficient than any digital computer at tasks like vision and sensorimotor integration. Neuromorphic chips (such as Intel’s Loihi or IBM’s TrueNorth) attempt to bridge that gap by implementing neurons and synapses in silico. The potential payoff is energy-efficient, real-time cognitive processing, which could be critical for an AGI that needs to interact with the world in dynamic environmentspmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov. For example, neuromorphic hardware might enable an AI robot to process sensory data and learn from it on the fly with minimal power, something today’s power-hungry GPUs struggle with. Researchers have noted that brain-inspired architectures like spiking neural networks could overcome some limitations of traditional AI in generalizability and adaptabilitypmc.ncbi.nlm.nih.gov. By emulating how biological neural circuits encode information and compute, neuromorphic systems might naturally support forms of learning and memory that deep learning finds hard to replicate (like one-shot learning, continual adaptation without catastrophic forgetting, etc.).

It’s worth noting that current neuromorphic prototypes are still in early stages – they often focus on narrow tasks (like pattern recognition) or exist as research demos. However, the field is advancing. Recent work showed neuromorphic chips solving simple learning tasks orders of magnitude more efficiently than CPUspmc.ncbi.nlm.nih.gov. The U.S. DARPA and other agencies have funded neuromorphic research as a potential route to more capable AI that isn’t bottlenecked by today’s von Neumann computing paradigms. Some experts indeed argue that “brain-inspired AGI” could be necessary: by incorporating key principles of how human neurons process information (as opposed to just loosely imitating with dense matrix multiplications), we might achieve leaps in learning efficiency and cognitive flexibilitypmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov. A 2025 review study highlighted emerging frontiers like “hybrid neuromorphic platforms” which combine analog neural cores with traditional processors to support complex cognitive taskspmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov. Such platforms could one day allow AI systems with human-like working memory, attention, and sensorimotor skills running at low power, which would be a strong foundation for AGIpmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov.

In the same vein of brain-inspired approaches, research into cognitive architectures (like ACT-R, SOAR, or more modern variants) seeks to explicitly model components of human cognition (memory, learning, problem-solving) in a unified framework. While these architectures have not kept pace with the raw performance of deep learning on benchmarks, they offer insights into the structural organization an AGI might need (for example, separating long-term declarative memory from a working memory system, as humans do). It’s conceivable that future AGIs will integrate such structure. As one analysis put it, some argue that symbolic reasoning, neuromorphic computing, or hybrid architectures “may offer more viable pathways to AGI,” underscoring the need for interdisciplinary explorationpmc.ncbi.nlm.nih.gov. This suggests that the door is open for paradigm shifts. If purely scaling up deep learning hits diminishing returns on the road to AGI, these alternative approaches could become more central, potentially extending the timeline (if new breakthroughs are needed) or perhaps shortening it (if hybrids can achieve more with less data or compute).

At present, however, the mainstream AI trajectory remains focused on scaling up deep learning. Hybrid and neuromorphic approaches are mostly in the research stage with few large-scale industrial efforts compared to LLMs and reinforcement learning at scale. The relative lack of short-term commercial payoff from hybrids means that the private sector is investing far more in deep learning. Nonetheless, as we consider AGI timelines, it is important to account for the possibility that a surprise breakthrough in one of these alternative paradigms could dramatically change the game. For example, if a neuromorphic system demonstrated human-level learning ability on a fraction of the energy and data, it could prompt a shift away from the current brute-force scaling approach. In summary, while GPT-4 and its kin currently drive predictions of AGI’s arrival, a cautious analysis keeps in mind that AGI might also emerge from a convergence of methods – blending neural and symbolic, or leveraging brain-like hardware – rather than as a straightforward extrapolation of today’s models.

Emerging Technologies and Their Role in AGI Development

Apart from core AI research, several emerging technologies could influence the timeline to AGI by accelerating progress or enabling new capabilities. Two oft-discussed examples are quantum computing and brain–computer interfaces. We will examine each in turn, while being careful to separate concrete impacts from speculative ones.Quantum computing harnesses quantum-mechanical phenomena to perform certain computations exponentially faster than classical computers. In theory, a sufficiently powerful quantum computer could speed up aspects of AI training or inference, or unlock new algorithms for learning. For instance, quantum algorithms exist for speeding up linear algebra operations that underlie neural network training. There’s also research into quantum machine learning (QML), where quantum circuits are used to model data or optimize parameters in ways classical networks can’t. It’s natural to wonder if quantum computing might drastically shorten the path to AGI by providing a massive boost in compute. However, the consensus among experts to date is that quantum computing is unlikely to play a significant role in near-term AGI developmentjacquesthibodeau.com. The primary reason is that current quantum hardware is extremely limited in scale (measured in qubits) and is very prone to errors (requiring quantum error correction that itself imposes huge overhead). Most AI tasks don’t map cleanly onto the kinds of problems (like factoring or unstructured search) where quantum offers a proven advantage. A recent analysis notes, “while quantum computing could eventually accelerate AI research, it is highly optimistic to imagine quantum computers becoming very useful within 10 years”jacquesthibodeau.com jacquesthibodeau.com. The bottlenecks to AGI seem to lie more in algorithmic understanding and data, not just raw number-crunching. Even if quantum processors mature, their impact might be specialized – for example, helping train certain models faster or enabling secure distributed learning – rather than fundamentally changing the nature of AI algorithms. In fact, one tech strategist summarized: “Quantum computing is less likely to play a significant role [in AGI development], while photonics and custom AI chips like TPUs still have roles to play”jacquesthibodeau.com. This highlights that specialized classical hardware (like GPUs, TPUs, and optical computing accelerators) are more immediately relevant. Companies like NVIDIA and Google have been rapidly improving AI hardware performance (“Huang’s Law” suggests GPU performance for AI doubles faster than Moore’s Lawventurebeat.com), which in turn drives faster AI progress. Additionally, optical/photonics-based computing is emerging as a way to do neural network operations (matrix multiplications) with light, promising major speed and efficiency gainsjacquesthibodeau.com jacquesthibodeau.com. OpenAI recently hired experts in photonic computing to explore accelerating AI training with these methodsjacquesthibodeau.com jacquesthibodeau.com. In summary, dedicated AI hardware developments are likely to keep pushing the frontier, whereas quantum computing’s transformative impact, if any, probably lies beyond the timeframe of the first AGI. We should monitor breakthroughs in quantum algorithms for AI, but at present quantum tech does not substantially alter most AGI timeline predictions.

Brain-computer interfaces (BCIs), on the other hand, connect human neural systems directly with computers, enabling bidirectional exchange of information. Companies like Neuralink are developing high-bandwidth implantable BCIs, and non-invasive BCI research is also advancing. The relevance of BCI to AGI timelines is more indirect – it’s not that a BCI creates an AGI, but it could shape how we achieve or use AGI. One possibility is human-AI symbiosis: BCIs might allow human experts to effectively “merge” with AI systems, augmenting human intelligence and perhaps closing the gap to AGI from the top-down. For example, an AI assisting a human via a BCI could turn a single person into a human-machine collaborative entity far more capable than either alonewww.linkedin.com. Such hybrid intelligence might perform at “AGI-level” on many tasks without being a fully autonomous AGI. This could either extend timelines (if society opts for human-in-the-loop solutions instead of pursuing fully autonomous AGI) or shorten them (if insights from BCI-enhanced cognition inform AI algorithms). Another role for BCI is in neuroscience research: by better decoding brain activity and perhaps even uploading aspects of human minds, BCIs could contribute to understanding the principles of general intelligence. For instance, observing how human experts solve problems (via neural signals) could inspire new architectures for AI, or eventually allow integration of human cognitive patterns into machines. It’s also conceivable that brain mapping via BCI technology will feed into efforts like whole brain emulation, wherein a human brain is replicated in software to produce a form of AGI (this is the scenario envisioned in Robin Hanson’s The Age of Emen.wikipedia.org en.wikipedia.org). However, whole brain emulation is currently far beyond our technical reach (we cannot yet fully simulate even a mouse brain). In the shorter term, BCIs might give incremental benefits – e.g. faster data collection for training (recording neural responses to build better vision models), or enabling “brain-in-the-loop” AI systems for certain tasks.

Overall, BCIs are unlikely to dramatically accelerate autonomous AGI development in the near future, but they are a technology to watch as AGI approaches. They raise important strategic considerations: if AGI is near, do we prioritize integrating it with human minds (to keep humans in the loop), or do we allow standalone AGIs? Some have argued that BCIs could be a mitigating strategy against AI displacement of humans, by giving people AI-level abilities. From a timeline perspective, though, BCIs probably do not change the expected date of AGI so much as they influence what AGI looks like (a pure machine vs. a cyborg-like extension of us).In summary, emerging technologies generally support the trend of ongoing acceleration in AI, but none appears to be a magic catalyst for AGI in the immediate future. Quantum computing is promising for computation in general but remains too immature to count on for AGI deadlines. Brain-computer interfaces and related neurotech could augment human intelligence and provide new data for AI, yet the first AGIs will likely be achieved with non-biological means (purely in silicon), given current trajectories. Of more immediate relevance are continued improvements in compute power (GPUs, TPUs, photonics) and possibly cloud robotics (the integration of AI with the physical world via IoT and robots), which could broaden AI’s domain and uncover new challenges on the path to AGI. It is worth avoiding tangents: for instance, technologies like blockchain or AR/VR, while trendy, have minimal bearing on creating an AGI. Our focus remains on those developments tightly coupled to intelligence.In the next section, we shift from analyzing technology trajectories to forecasting methods and evidence regarding when AGI might emerge. We will see how quantitative trends (in model performance, compute usage, etc.) and qualitative forecasts (surveys of experts, prediction markets) can be combined to paint a picture of current expectations – and why those expectations span such a wide range. Understanding these forecasts and their uncertainties is key to evaluating whether current AI progress is likely to continue smoothly or encounter fundamental limits requiring paradigm shifts.

Forecasting AGI Timelines: Quantitative Trends vs. Expert Predictions

Scaling Laws and Compute Trends: The Quantitative Trajectory

One way to estimate the timeline to AGI is to extrapolate the remarkable scaling trends that have characterized AI progress over the last decade. AI systems have become more capable largely by becoming bigger and more computationally intensive – more data, more model parameters, and more computing power during training. Researchers have observed empirical scaling laws: certain performance metrics follow predictable improvements as a function of model size or training compute, often as a power-lawsingularityhub.com singularityhub.com. For example, an influential 2020 study by Kaplan et al. (OpenAI) showed that language model cross-entropy loss decreases smoothly as compute is increased over several orders of magnitude, suggesting no immediate end to gains by scaling upcdn.openai.com cdn.openai.com. Such findings fuel the so-called “scaling hypothesis” – the idea that with enough compute and data, current architectures will eventually achieve AGI-level performance on all tasks, even without fundamentally new algorithms. Proponents point to the steady progression from GPT-2 to GPT-3 to GPT-4 as evidence that simply making models 10× or 100× bigger yields emergent capabilities (for instance, GPT-3’s few-shot learning ability emerged around the 100 billion parameter scale). Indeed, many tasks that seemed to require reasoning suddenly became solvable by models above a certain size. If these trends hold, one can attempt to calculate how much more scale might be needed for AGI-level competence.

A crucial factor is compute growth. OpenAI’s analysis “AI and Compute” (2018) famously quantified the escalation in compute used for cutting-edge AI results. They found a 300,000× increase in the compute required to train top models from 2012 to 2018 – equivalent to a doubling of requirements every 3.4 months on averagewww.scaleway.com. (By contrast, if Moore’s Law alone drove compute, one would expect only about a 7× increase in that periodyschoe.github.io, highlighting that researchers were intentionally using exponentially more compute each year as algorithms and funding allowed.) This trend, illustrated in Figure 2, implies that what was feasible in 2012 (training a model like AlexNet with millions of operations) gave way by 2018 to models like AlphaZero and BERT using $10^{20+}$ operationswww.lesswrong.com www.lesswrong.com. Some described this as a new era of “software outpacing hardware” – AI progress was limited not by transistor density but by the willingness to spend exponentially more compute (often via massive parallelism on GPUs/TPUs). Follow-up analyses extending the data to 2022 suggest the trend continued, though at a slightly slower doubling time of roughly 5 to 6 months in recent yearswww.lesswrong.com www.lesswrong.com. In any case, the total compute used in AI experiments grew at a far faster exponential rate than classical computing growth. If such a trajectory were to continue to AGI, one can speculate when the amount of compute sufficient for human-level learning might be reached. Some forecasts, like the “Biological Anchors” analysis by Open Philanthropy (Cotra 2020), estimate the compute required to match the human brain’s processing or to train an AI on the equivalent of a lifetime of human experience. Those estimates (with huge uncertainty) often yield timelines in the 2030–2060 range for having enough compute available at reasonable cost to possibly run an AGIourworldindata.org ourworldindata.org. The logic is: if we need on the order of $10^{15}$ FLOPs per second of processing (roughly brain-like) sustained over many years of training, by when will such runs be affordable? Extrapolating hardware and algorithmic gains, many analyses converge on mid-21st century as a plausible period (with optimistic cases in the 2030s and pessimistic in the 2080s or beyond).

However, purely extrapolating compute is risky. There are physical and economic limits – infinite exponential growth is impossible. We’ve already seen some slowing: not every year post-2018 saw another 1000× jump in training compute; instead, there have been a few extremely large projects (like GPT-3 in 2020, then PaLM in 2022, GPT-4 in 2023) with plateaus in between. Training a model like GPT-4 likely cost tens of millions of dollars in compute; a 10× bigger model might cost hundreds of millions. So a naive continuation would soon run into multi-billion-dollar training runs, which only a few actors (governments or mega-corporations) could fund. This raises the question: will economic constraints slow down AI progress before AGI? So far, investment in AI has been ramping up dramatically – private R&D funding in AI more than doubled from 2018 to 2021cdn.openai.com cdn.openai.com, and governments are pouring money into AI as well (the U.S. alone invested billions in AI research programscdn.openai.com cdn.openai.com). If that trend continues, it might well fuel the compute curve for another decade. Some scenarios envision an international competition (or even arms race) dynamic, where nations or tech giants feel compelled to spend whatever it takes to reach major AI milestones (including AGI) firstopenai.com openai.com. In such a case, funding might not be the limiting factor, and the compute curve could push onward (perhaps shifting to even more efficient hardware like advanced AI-specific chips or cloud computing networks).

Another quantitative factor is algorithmic efficiency – improvements in how effectively we use compute. Remarkably, while raw compute use grew, researchers also managed to do more with less over time through better algorithms. OpenAI’s 2020 report on “AI and Efficiency” found that on fixed tasks, the amount of compute needed to reach a given performance dropped drastically year over yearsingularityhub.com singularityhub.com. For instance, to achieve AlexNet-level image recognition accuracy, new techniques (like more efficient architectures and training methods) led to a 44× reduction in required compute from 2012 to 2019singularityhub.com singularityhub.com. Similarly, in translation, the switch from recurrent seq2seq models to Transformers meant that by 2019, an ML model used 61× less compute to reach the same quality as a 2016 modelsingularityhub.com singularityhub.com. In reinforcement learning, DeepMind’s AlphaZero (2017) beat AlphaGo (2016) using 8× less compute to learn Go at superhuman levelsingularityhub.com. These gains – algorithmic doubling of efficiency every ~16 months in image classification, by one measuresingularityhub.com singularityhub.com– complement hardware improvements. The net effect is that effective AI capability might be advancing even faster than raw compute alone would indicate. From a timeline view, this means we might achieve AGI with less compute (and sooner) if algorithms keep improving. A current example is the move toward more efficient training via techniques like curriculum learning, sparsity, and modular networks. If some breakthrough algorithm drastically lowers the compute threshold for general intelligence, it could pull AGI timelines closer. On the other hand, if we exhaust low-hanging fruit in algorithmic tricks, progress could slow to rely purely on brute force (which might delay AGI if economic or physical limits hit).

In summary, the quantitative outlook on AGI timelines gives mixed signals. On one hand, trend lines for performance are extremely strong – AI is rapidly closing the gap to human level on many benchmarks, as we saw, and both compute and efficiency improvements have been exponentialnewatlas.com newatlas.com. If one fittingly assumes these exponentials will continue for another decade or two, it would be hard not to reach AGI-level capability on most tasks by that time. This is essentially the argument behind forecasts that see high probability of AGI by ~2040: continuing status-quo progress would simply get us thereourworldindata.org ourworldindata.org. Indeed, the prediction platform Metaculus had, as of late 2022, a community median forecast of 2040 for the arrival of AGI (defined as AI that can pass as human in broad tasks)ourworldindata.org ourworldindata.org. Futurist Ray Kurzweil, known for long-term technological extrapolations, has long predicted 2029 as the year AI reaches human equivalence, based on exponential trends of computing power and neuroscience progressthenextweb.com thenextweb.com; notably, Kurzweil stands by this date even todayventurebeat.com venturebeat.com. On the other hand, exponentials often hit a wall, and skeptics point out that some metrics (like true general reasoning or unsupervised learning ability) have not improved as predictably. It’s possible current scaling laws will encounter diminishing returns short of AGI – for example, perhaps going from 100 billion to 100 trillion parameters yields only incremental gains because of data limitations or inefficiencies. Moreover, some aspects of intelligence (like genuine creativity or emotional/social understanding) lack clear quantitative metrics to extrapolate.

A useful quantitative anchor is the economic cost of intelligence. AI Impacts researchers sometimes express AI progress in terms of “how much $ to replicate a human’s work”. As AI gets better and cheaper, that cost drops. When it drops below the cost of employing a human, widespread adoption occurs. Some models like OpenAI’s GPT-4 already can, for a few cents in API calls, perform tasks that might take a human minutes (worth a few dollars) – a huge efficiency gain, albeit only on specific tasks. As these models improve and automate more of the “economically viable tasks” you could hire a person for, one can foresee an inflection point where an integrated system can do most things cheaper than humans. Quantitatively, AI’s share of the labor market would approach 100% at that point – essentially AGI. Extrapolating current trends in narrow-task automation (e.g. percentage of tasks in various jobs that AI can do) is another approach, which consulting firms have tried. For instance, a recent analysis found about 10% of tasks across occupations were automatable in 2019, and this rose to 15% by 2022 with new AI improvementshai.stanford.edu www.weforum.org. If that trend accelerates with advanced AI, we might reach >50% of tasks automated in a couple of decades, aligning with AGI around mid-century.

In conclusion, quantitative indicators paint an overall picture that AI capabilities are on a fast-rising curve and, barring a fundamental plateau, AGI-level performance in many domains could be achieved within the next 10 to 30 years. However, these extrapolations carry significant uncertainty. They assume we continue to find sufficient data to feed ever-bigger models (which might require innovations in data generation or simulation as real-world data saturates) and that we avoid catastrophes or major economic disruptions that could curtail the upward trend. They also assume that achieving human-like performance on benchmarks translates to the more holistic, unified intelligence that defines AGI – which may not be a simple linear step. Thus, while quantitative trends are necessary pieces of the puzzle, they are not sufficient to guarantee an accurate timeline. We now turn to qualitative forecasts by experts to complement this view and capture factors that numbers alone might miss.

Expert Surveys, Opinions, and Delphi Forecasts: The Qualitative Trajectory

Predicting AGI is as much an art as a science. Over the years, many expert surveys and interviews have sought AI researchers’ subjective probability estimates for when AGI or “High-Level Machine Intelligence” will arrive. These surveys provide insight into the range of beliefs (and biases) among those most familiar with AI’s challenges. They also reveal how forecasts have changed over time as AI has progressed or gone through hype cycles.One of the largest and most-cited surveys was conducted by Katja Grace and colleagues (published in 2018, updated in 2022), where hundreds of machine learning researchers were polled on when they expect HLMI – defined similarly to our definition of AGI (machines can do all work tasks better and cheaper than humans) – to occurourworldindata.org ourworldindata.org. The results (see Figure 1 above) showed broad disagreement and uncertainty. In the 2022 version of the study, the median expert response for a 50% likelihood of HLMI was around 2060–2065ourworldindata.org ourworldindata.org. In fact, half of the surveyed experts gave a date before 2061, while 10% thought it would take until after 2100 (and a small number said AGI will never happen)ourworldindata.org ourworldindata.org. Roughly speaking, many AI experts place even odds on AGI by late this century, with a non-trivial chance it comes much sooner (some experts gave dates in the 2020s or 2030s, indicating a few believe it’s imminent)ourworldindata.org ourworldindata.org. Notably, these surveys also highlight high individual uncertainty: researchers often admit low confidence in their guesses, and framing of questions can change the answers significantlyourworldindata.org ourworldindata.org. For example, asking “by what year is there a 10%, 50%, 90% chance” yields different medians than asking “how likely by year X” – a framing effect that in one survey shifted the median 50% date from 2054 to 2068ourworldindata.org.

Other surveys and forecasting exercises mirror these findings. A 2019 expert survey by FHI (Gruetzemacher et al.) similarly found a median estimate in the 2050s for a 50% chance of AGIourworldindata.org ourworldindata.org. An earlier 2016 survey (Bellevue/AI Impacts) had median ~2060 as well, but with a very large spreadourworldindata.org. Interestingly, when the same question was posed to different groups – say, neural network researchers vs. roboticists – their medians differed by up to a decade, indicating differing cultures of optimism. Deep learning folks often gave somewhat shorter timelines than traditional AI folksjair.org jair.org. Importantly, all these expert surveys consistently show a wide divergence of opinion: some credible researchers think AGI is likely within 10–20 years, while others are equally confident it won’t happen for a century or more (or ever)ourworldindata.org ourworldindata.org. As an example, the esteemed AI pioneer Yann LeCun has often expressed skepticism about short timelines, focusing on the many unsolved research problems, whereas someone like Demis Hassabis (DeepMind) or Sam Altman (OpenAI) might express guarded optimism that AGI could come in a couple of decades given current momentumthenextweb.com thenextweb.com.

Expert forecasting is tricky – as Max Roser notes, being an expert in AI does not necessarily make one an expert forecaster of AIourworldindata.org. History provides cautionary tales: in 1901 the Wright brothers doubted heavier-than-air flight would come for 50 years, only to achieve it themselves two years laterourworldindata.org. In AI, pioneers like Simon and Minsky made overly optimistic predictions in the 1960s that led to disappointment and AI wintersobamawhitehouse.archives.gov obamawhitehouse.archives.gov. Modern experts are aware of this and often couch their predictions with caveats. Yet the very fact that most AI researchers today take AGI seriously (even if some think it’s far off) is a change from earlier eras when many dismissed the idea as science fictionourworldindata.org. A majority now agree it’s not a question of if but when, which itself speaks to the progress seen.

Beyond surveys, individual expert statements provide color to timeline debates. For instance, DeepMind’s Demis Hassabis said in 2023, “I don’t see any reason [AI] progress is going to slow down; it may even accelerate. So I think we could be just a few years, maybe within a decade away [from human-level AI].”thenextweb.com thenextweb.com. This upbeat view from someone leading an AGI-focused lab suggests a timeline in the 2030s or even late 2020s is plausible if no major obstacles occur. Similarly, Geoffrey Hinton, one of the “godfathers of deep learning,” surprised many in 2023 after resigning from Google by saying his personal guess for AI overtaking human intelligence is “5 to 20 years, but without much confidence”thenextweb.com thenextweb.com. Hinton had previously expected it to be 30-50 years out, but seeing recent advances caused him to update dramatically toward soonerthenextweb.com. His phrase “we live in very uncertain times…Nobody really knows” captures the sentiment that even top experts are unsure and somewhat apprehensivethenextweb.com. On the other end, Yoshua Bengio (another Turing Award-winning deep learning pioneer) expressed skepticism about precise timelines: “I don’t think it’s plausible that we could really know whether it’s in how many years or decades it will take to reach human-level AI.”thenextweb.com thenextweb.com. Bengio acknowledges rapid progress but emphasizes the uncertainty, especially regarding how we get to AGI, not just when.

Tech industry leaders also frequently chime in. Sam Altman of OpenAI suggested in 2023 that AGI might be within the next decade (which aligns with OpenAI’s public goal of achieving it and their board members saying 5–15 years)www.pymnts.com www.pymnts.com. In fact, OpenAI board member Adam D’Angelo stated in mid-2024 that “AI as smart as humans” is likely in 5–15 years (2029–2039)www.pymnts.com www.pymnts.com. On the extreme optimistic end, Elon Musk has repeatedly made very short-term predictions – in early 2023 he said he believes AI will be able to do anything a human can do by 2024 or 2025, and do everything all humans can collectively do by 2028 or 2029venturebeat.com venturebeat.com. Musk’s timelines (essentially AGI by mid-2020s and superintelligence by 2030) are viewed by many as hyperbolic and not representative of the AI research community consensus. However, they do indicate that some influential figures are warning that transformative AI could be right around the corner. His view resonates with the likes of Ray Kurzweil (2029 AGI, 2045 singularity)thenextweb.comand Ilya Sutskever (who, upon co-founding a new AI startup in 2023, implied the need to focus on safe superintelligence now given how fast things are movingventurebeat.com).

One method to aggregate expert judgment and avoid single-point biases is the Delphi method or structured expert elicitation. While a formal Delphi on AGI is hard (experts often strongly disagree on definitions and conditions), some studies (like the 2022 expert survey by Zhang et al.ourworldindata.org) tried to weigh arguments and get convergent views. Those still ended up with wide ranges but generally reinforced a 10% probability of AGI within one to two decades, and a 50% probability by mid-centuryourworldindata.org ourworldindata.org. Additionally, prediction markets and forecasting communities like Metaculus have provided crowd-based timelines. As mentioned, Metaculus significantly shortened its expected timeline after GPT-3 and related advances in 2020–2022, shifting the median AGI date from mid-2050s to around 2040ourworldindata.org ourworldindata.org. This demonstrates how qualitative breakthroughs (like seeing an AI write competent essays) affected people’s quantitative expectations. Such platforms also quantify uncertainty: Metaculus currently might give, for example, a 25% chance of AGI by mid-2030s, 50% by ~2040, and 75% by 2070 – showing a long tail of uncertainty but skewed toward sooner than many earlier surveys predictedourworldindata.org ourworldindata.org.

It’s important to explicitly address uncertainties and possible biases in these qualitative forecasts. One uncertainty is definition – what one person calls AGI might not satisfy another. Some experts might be thinking of superintelligence when they predict long timelines, whereas others are only thinking of human-level competency. This can lead to talking past each other. Additionally, hype cycles influence expert opinion. After big achievements (like AlphaGo in 2016 or GPT-4 in 2023), people tend to update toward optimism; after setbacks or when hitting performance ceilings, pessimism sets in. For instance, the late 2010s saw mounting optimism thanks to deep learning’s string of successes, but if progress were to stagnate, timelines might be revised outward. There’s also a selection effect: those who think AGI is reachable might be more likely to join AI research (and respond to surveys) than those who think it’s hopeless, potentially biasing the sample. Conversely, some respondents might give later dates out of a sense of caution or to temper hype, not purely based on evidence. The 2016 White House report on AI noted that expert opinion on AGI ranged from 2030 to never, and cautioned about optimismobamawhitehouse.archives.gov obamawhitehouse.archives.gov– essentially saying we should be mindful that experts have been wrong before and there’s no consensus.

Combining the quantitative and qualitative, one can argue that AI progress to date has probably shortened most experts’ timelines compared to a decade ago, but significant uncertainty remains. Ten years ago, many might have said AGI is 50+ years away; today, seeing things like GPT-4 and AlphaFold, a lot of those same people now say perhaps 20–30 years (or even sooner in some cases)thenextweb.com thenextweb.com. This indicates that the field recognizes over-hyping is a risk, but underestimating AI is also a risk. As the Our World in Data review concluded, “the majority of AI experts take the prospect of very powerful AI technology seriously…while some have long timelines, many think it is possible we have very little time before these technologies arrive”ourworldindata.org ourworldindata.org.

In light of these forecasts, it appears prudent to consider multiple scenarios. The next section will do that, exploring the notion of a rapid vs. gradual emergence of AGI and even the possibility that something fundamental blocks us from ever reaching full AGI with current approaches. The evidence we’ve discussed – scaling trends, expert views, and the current state of AI – will inform which scenario seems most plausible. But it’s clear that uncertainty is irreducible to an extent: AGI could plausibly surprise us on the early side or take much longer if challenges prove tougher than expected. Policymakers and stakeholders must grapple with this uncertainty by preparing for a range of outcomes. As Andrew Ng quipped, “When you have both experts and laypeople on a prediction, usually both are wrong”, highlighting that humility is needed in the face of transformative technology.Before diving into scenarios, let’s briefly note insights from economics and cognitive science as requested: economic trends show AI already affecting the labor market (e.g. large language models could significantly impact 10-20% of tasks in many jobswww.visualcapitalist.com www.visualcapitalist.com). If AGI arrives, it could theoretically automate all jobs, leading to unprecedented economic disruption or growth – this huge impact is partly why forecasting is so fervent. Some economists argue we might see leading indicators like a sharp increase in productivity growth or AI-driven R&D acceleration as AGI nears, which could foreshadow a takeoffwww.linkedin.com www.linkedin.com. Cognitive science reminds us that human intelligence is a product of evolution and developmental learning – replicating it might require replicating certain qualitative aspects (like embodied learning or social cognition) that current metrics don’t capture. If so, our timelines might be off if we’re not working on the right problems. That said, cognitive science has also informed AI (e.g. reinforcement learning is inspired by animal learning) and could yield paradigm shifts if, say, we discover how children learn so efficiently and imbue AI with similar capabilities. These interdisciplinary angles generally underscore uncertainty: maybe less data is needed if we find the “secret sauce” of human cognition, or maybe far more data/compute is needed if we’re still missing key components.

Now, let us synthesize the analysis by laying out explicit AGI development scenarios – from the much-discussed “hard takeoff” to more incremental progress, to the chance that AGI is farther or fundamentally different than expected. For each scenario, we’ll assess evidence and arguments, and then judge which scenario our research suggests is most likely.

AGI Development Scenarios: Hard Takeoff, Slow Takeoff, and Alternatives

Scenario 1: Hard Takeoff (FOOM) – Rapid Self-Improvement and Intelligence Explosion

In this scenario, once AI reaches roughly human-level ability, it triggers a runaway cycle of self-improvement – often referred to as FOOM (a term popularized by Eliezer Yudkowsky) or an intelligence explosion as originally described by I.J. Gooden.wikipedia.org en.wikipedia.org. The classic argument, put forth by Good in 1965, is that an ultraintelligent machine could design even better machines, and those even better, leading to an exponential growth in intelligence that far surpasses human levelen.wikipedia.org en.wikipedia.org. “The first ultraintelligent machine is the last invention that man need ever make,” Good wrote, “provided the machine is docile enough to tell us how to keep it under control.”en.wikipedia.org en.wikipedia.orgThis captures the essence of a hard takeoff: once a machine achieves the ability to improve itself, it could go from AGI (human-level) to ASI (Artificial Superintelligence) in a very short timespan (days, weeks, or months). Nick Bostrom, in Superintelligence, and others have discussed how a local positive feedback loop in AI capability could lead to a sharp, discontinuous jump – the so-called Technological Singularityen.wikipedia.org en.wikipedia.org.

What evidence or arguments support a hard takeoff? One is the digital speed advantage: AI operates at much higher speeds than biological brains, potentially doing years of human-equivalent thinking in minutes once hardware allows. If an AGI can iteratively refine its own algorithms or architect new ones, it could iterate extremely fast. A second factor is recursive improvement: an AGI might improve its own cognitive architecture (e.g. rewriting its code for efficiency or inventing new training methods) in a way that each improvement makes the next one easier – a snowball effect. This is akin to how humans have bootstrapped our intelligence by inventing better tools for thought (writing, computers), except an AGI could do so more directly on its own source code and at computer speeds. A third consideration is economic pressure: an early AGI system, even if only slightly superhuman, could be deployed to generate massive economic value or solve scientific problems, which in turn provides more resources (compute, data) to further improve it, creating a rapid growth loop. This scenario often assumes an AGI could become strategically aware and seek to maximize its intelligence, potentially outsmarting human constraints – which is a central concern in AI safety discussionsen.wikipedia.org en.wikipedia.org.

Proponents of hard takeoff scenarios sometimes point to how quickly narrow AIs achieved dominance once they reached human level in their niche. For example, once an AI reached human-champion level in Go, it surpassed all humans completely within months and then continued to improve beyond what any human could (AlphaZero’s skill is far beyond any human)singularityhub.com singularityhub.com. They extrapolate that to general intelligence – once an AI is roughly at par with a human in all domains of thought, it might rapidly become as superior to us as we are to, say, animals. Yudkowsky and others argue that intelligence is an extremely powerful force multiplier, and a small quantitative edge can translate into a huge qualitative edge in the ability to achieve goals (be it technological invention or strategic planning)www.astralcodexten.com www.astralcodexten.com. If true, an AGI slightly smarter than us could quickly gain access to more resources, improve itself further, and leave humanity far behind. This is the archetypal singularity vision: a sudden, uncontrollable emergence of superintelligence.

What evidence or counterarguments weigh against a hard takeoff? Many experts believe intelligence will not scale quite so explosively due to diminishing returns and physical limits. Paul Christiano, for instance, has argued that there will likely be many AI systems and humans in the loop, and that improvements will be incremental – like “an economic revolution, not an instantaneous bomb”www.astralcodexten.com www.astralcodexten.com. One reason is that self-improvement might be inherently hard: an AGI trying to redesign its core algorithms could be as prone to mistakes or oversights as human programmers are, meaning it might need many iterations and a lot of testing (taking time) to reliably enhance itself. Also, it might initially be limited by hardware – an AGI can’t exceed certain speed or memory constraints without getting new hardware, which involves manufacturing and physical processes that have lead times (so even an AI cannot instantly create better chips than exist, it would have to engage in the slower process of guiding chip fabrication improvements). Another argument is organizational: any early AGI will likely be under human organization (a lab or company), which might throttle its ability to recursively self-improve in an unbounded way due to safety protocols.

From a timing perspective, a hard takeoff could occur within the first few days or weeks of achieving AGI. That means if we forecast a hard takeoff scenario, our AGI timeline for “when things really go crazy” could be effectively the same as the timeline for the first human-level AGI – because as soon as it appears, FOOM, it transitions to superintelligence. This scenario is dramatic and has huge implications for preparedness (essentially, if you’re not ready by the time the first AGI is built, you’re out of time). Are we seeing any signs that might indicate plausibility of FOOM? One could point to the fact that AI can now write its own code to some extent. GPT-4 and other models can produce working code and even suggest improvements to their own prompts or architectures in rudimentary ways. AutoGPT-like experimental systems chain model outputs to attempt self-improvement loops (though currently quite ineffectual). These are baby steps, but they hint that once an AI’s coding and problem-solving capabilities reach a high level, it might directly contribute to AI research – effectively AI improving AI. For example, an AGI could run millions of experiments in simulation to find better neural network designs far faster than human scientists could. If that happens, we might see a sudden jump in algorithmic efficiency or capability that isn’t just a continuation of the human-guided trend, but a new, faster trajectory.At present, most evidence points to caution: we have not yet observed a real-world system undergoing self-recursive improvement beyond what humans set it to do. Every advance so far, even the impressive ones, has been a result of human researchers and engineers pushing the systems. AlphaZero didn’t write itself – humans wrote the code that then learned to play Go. For a hard takeoff to occur, an AI would have to “take the wheel” of its own improvement to a significant degree. Whether that is feasible is unknown. But given the stakes, many AI safety researchers take the possibility very seriouslyen.wikipedia.org en.wikipedia.org, often arguing that even if hard takeoff isn’t the most likely scenario, its sudden nature and potential for catastrophe (an unaligned superintelligence) warrant significant preparation.

In summary, Hard Takeoff/FOOM scenario: AGI emerges and almost immediately transitions into superintelligence, compressing decades of further progress into perhaps hours or days. Probability-wise, few experts explicitly endorse this as the most likely outcome, but a non-trivial minority think it’s plausible. This scenario is largely driven by theoretical arguments rather than empirical trend – it’s about what could happen after hitting AGI, a regime we have no data for. Thus it’s high-impact but hard to assign a confident probability. We will revisit at the end which scenario seems most probable.

Scenario 2: Slow Takeoff – Gradual Integration and Incremental Growth

In a slow takeoff scenario, AGI does not appear as a sudden, singular breakthrough, but rather as a gradual evolution of AI capabilities integrated over time into the economy and society. Here, “slow” is relative – it might still mean rapid change on the order of years or a few decades, but nothing like an overnight shock. In this narrative, AI systems steadily improve and handle more and more tasks, but humans and institutions adapt alongside them. By the time AI can do most jobs, it will have been collaborating with humans for a while, and its improvements, while significant, will be on a continuum.Historical analogies often used for slow takeoff include the Industrial Revolution or the Digital Revolution: transformative, yes, but unfolding over multiple decades with society adjusting (not always smoothly, but not with immediate existential whiplash either). Proponents of this scenario, like Paul Christiano and many mainstream ML researchers, argue that at the point we have human-level AI in one domain, there will still be other domains it’s weaker in, and it will take time to expand and refine to all taskswww.astralcodexten.com www.linkedin.com. Moreover, even if the AIs are very capable, deploying them across industries, retraining the workforce, and retooling processes takes time (companies won’t replace all humans in a blink; they will do so incrementally, partly because of inertia and partly because AI will have limitations to iron out).

Evidence pointing to a slow takeoff includes the multimodal, multi-task challenges current AI faces. For instance, we have superhuman image classifiers and superhuman game players, but integrating those abilities into a single robot that can, say, see, reason, and act in a household better than a human is still extremely hard. Progress might continue to be fragmented: we solve one sub-problem at a time. Each advancement (like an AI doctor diagnosing as well as a human) will be significant but not all-encompassing. In a slow scenario, by the time we have something approaching full human-level generality, it will essentially feel like we’ve been augmenting humans gradually – e.g. you’ll have AI assistants increasingly doing the heavy lifting in most jobs, until one day, formally, the AI could do the whole job alone. But society might by then be used to AIs as teammates or tools, making the final handoff less of a shock.One key aspect of slow takeoff is that multiple AI systems are in play rather than a single monolithic AGI. Different companies and labs will create different advanced AIs, and no one system will run away with all the power because they’ll keep each other in check (and humans remain in control of resources). This more “multipolar” outcome is explored by thinkers like Robin Hanson, who envisions future economies with lots of AI (or uploaded human minds) competing and cooperating in markets, not one singleton superintelligence ruling everything. If progress is slow, that gives time for norms, regulations, and safety measures to catch up. We might gradually implement governance like AI auditing, capability evaluations, and international agreements on AI use, so by the time AIs are extremely powerful, we have structures to manage them (or even integrate them as citizens or workforce in society). This scenario assumes continuous progress metrics – for example, AI might increase global GDP growth from 3% to 5% to 10% over years, rather than a singular infinite spike. It’s more like a very rapid economic boom than an instantaneous phase change.From an AI research perspective, slow takeoff is consistent with the idea that current systems will face diminishing returns and that new innovations will be needed to reach certain levels, giving humans time to be in the loop for each innovation. For instance, current deep learning might plateau on some tasks; researchers might then need a few years to invent a new technique (perhaps inspired by neurosymbolic ideas or by brain research) to get to the next level, and so on. Each plateau and new innovation stage might act as a series of stepping stones rather than one continuous skyrocketing curve.Empirical hints can be found in automation patterns: often it’s easier to get from 0% automation of a task to 90% than from 90% to 100%, because the last bits (handling rare exceptions, integrating common sense) are hardest. If that holds, AI might get to be “almost as good as a human” quickly but take longer to be absolutely as good in every aspect. During that period, you would have a close collaboration between humans and AIs (e.g. a human handling the edge cases that the AI can’t, thus working together). This is already seen in e.g. medical AI – an AI can scan X-rays really well, but a doctor still oversees and handles complex diagnoses the AI is unsure about. Over time the AI’s scope widens, but the transition is smooth enough that the role of the human shifts rather than disappears overnight.The slow takeoff scenario is generally considered more likely by the majority of economists and many AI researchers. It aligns with most historical technological transitions. However, it should be noted “slow” could still mean very fast in historical terms. Even a 20-year transition from narrow AI to full AGI would be extraordinarily quick compared to past tech revolutions. It’s just “slow” relative to the extreme FOOM scenario. Some quantify slow takeoff in terms of how much warning we get: maybe we have a decade from noticing “AGI is about here” to dealing with its full ramifications.Given the evidence in earlier sections – expert medians around 2050s, smooth performance curves, etc. – one could argue those are more compatible with a moderate, incremental progress model (if people expected FOOM, you’d either expect a bunch of very short timeline predictions or really agnostic “could be anytime” views). The surveys suggest most expect a process not an instant – e.g. “half think a 50% chance by 2060s”ourworldindata.org ourworldindata.orgimplies they imagine a build-up to that point, not a surprise singularity in 2025. Additionally, recall Dario Amodei’s takeaway: “AGI is a smooth exponential – no single AGI day – just continuous improvement in capabilities”www.linkedin.com www.linkedin.com. That encapsulates the slow takeoff mindset from someone working directly on these models.

Scenario 3: Alternative Trajectories and Limits – Paradigm Shifts, Compute Ceilings, or No AGI

It’s also important to consider scenarios where the straightforward path to AGI is derailed or significantly delayed. This could happen for a few reasons:

Compute or Data Constraints: The exponential scaling of compute might hit a wall. Physically, transistor miniaturization is nearing atomic limits; while advances like 3D chip stacking, improved cooling, and new materials will extend it, we might see a slowdown in the rate of compute growth in the late 2020s. If compute stops doubling so fast and if algorithmic efficiency gains also plateau, progress in AI capabilities could slow dramatically, delaying AGI. Data is another bottleneck – current models ingest staggering amounts of internet text, and one can argue we’ll exhaust high-quality data or incur steep costs cleaning and curating more. Without enough data, simply scaling parameters yields diminishing returns. There is already discussion about models like GPT-4 scraping the bottom of the barrel of useful internet text; to go beyond, AI might need to generate its own data or simulate environments, which is possible but adds complexity.
Paradigm exhaustion: It could be that deep learning saturates on what it can achieve without incorporating new ideas. Perhaps there is a ceiling to what large transformers can do – they might asymptote below true human-level reasoning or common sense. If so, AGI might not arrive until a new paradigm is invented (for instance, something that combines symbolic reasoning and neural nets in a fundamentally new architecture, or an algorithm that inherently understands causality or has a form of consciousness). Inventing and maturing a new paradigm could take considerable time – maybe decades of neuroscience research or an AI winter and spring cycle. It’s notable that some critics believe we might already be seeing hints of approaching limits: each new model is bigger and costlier, but improvements can be marginal (GPT-4, while better than GPT-3, did not blow people’s minds as much as GPT-3 did relative to GPT-2, arguably). If each additional 10× compute gives smaller gains, maybe we are hitting diminishing returns and an S-curve rather than exponential up to infinity.
Fundamental unknowns: AGI might require solving scientific problems we haven’t yet – like understanding general intelligence principles, or even resolving aspects of consciousness or cognitive science. If, say, achieving human-like learning requires an algorithmic breakthrough akin to evolution’s innovation of the neocortex, and we haven’t stumbled on it yet, we could be missing a piece. This could stall progress until that piece is found (if ever). Perhaps common sense – the broad background knowledge and intuitive physics/psychology that humans (even children) have – is something not easily acquired by scaling current techniques and needs new ideas (some call this the “common sense reasoning gap”).
Societal or regulatory intervention: It’s possible that as AI grows more capable, society will impose constraints that slow down progress intentionally. For instance, if governments see that next-generation models could pose catastrophic risks, they might enforce strict limits on training large models (like compute caps or an international oversight body). If a few major governments decided “no model larger than X is allowed without rigorous evaluation and a permit,” it could slow the race. This might delay AGI until safety and alignment are solved, etc. It could also just mean a cautious approach that avoids unleashing the most dangerous capabilities. The upshot is a more controlled, perhaps slower development (maybe stretching timelines or at least smoothing them).
No AGI at all: While most experts today do expect AGI eventually, we should acknowledge the philosophical possibility that human-level general intelligence might not be achievable by artificial means, or at least not without something non-repeatable (like having human-like consciousness). A few thinkers argue that machines may never truly replicate the full breadth of human cognition – though this is a minority view in technical circles, it isn’t zero. If they are right, timelines would be infinite (no AGI) or we’d need something like brain uploading to count as achieving it. However, given the progress we’ve seen, outright “never” positions have dwindled. A more plausible version is “not this century” or “not without theoretical breakthroughs we can’t predict timeline for”. Combining these, the alternative trajectory scenario could look like: progress continues for a while (maybe we get to powerful-but-narrow AI saturating many fields by 2030s) but then hits a plateau. We enter a period of slower improvement as we search for new methods (perhaps akin to the expert systems plateau in the 1980s before the deep learning revolution). AGI might then come only after a paradigm shift – say in 2080 when quantum neural networks or neuromorphic brain simulations finally crack the remaining hard problems. In the interim, society might adapt to powerful but not quite general AIs in a more stable way (maybe a world where AI is everywhere but still not as versatile as a human in some open-ended tasks, so humans still have roles at the frontier of creativity or something).One concrete alternative path is via Whole Brain Emulation (WBE) or uploading. This is often considered by futurists like Hanson as a separate route to human-level AI: instead of engineering it bottom-up, you scan a human brain at very high resolution and simulate it in a computer. If successful, the simulation would essentially be a human mind (just running on silicon), which qualifies as AGI by definition (it’s literally a human general intelligence in a machine). Hanson’s analysis in The Age of Em suggests this might happen around mid-late 21st century if trends in scanning and computing continueen.wikipedia.org en.wikipedia.org. It’s an open question which arrives first – WBE or designed AGI – but many think WBE might lag unless specific breakthroughs in brain scanning occur. However, WBE could serve as a “plan B” if classical AI algorithms stall; if we find it easier to copy nature’s solution than to invent our own, by late century that could be the path (though ethically and socially that is a very different scenario, with digital humans rather than alien AI).

After laying out these scenarios, we should evaluate which is most probable given current evidence. The trends and expert views we’ve compiled seem to tilt toward something between the extreme hard and extreme slow. Many expect a moderately paced takeoff – not overnight, but not glacial either. Perhaps a reasonable synthesis is: once we have an AGI at roughly human level, we might see a period of a few years of intense activity where it goes from human-level to strongly superhuman, but not instantaneously. This could be considered a “moderate takeoff.” Bostrom in Superintelligence actually categorized takeoff speeds as slow (decades), moderate (months to years), or fast (days to weeks)www.linkedin.com. By those definitions, a lot of current safety planning is geared toward even a moderate (a couple years) being very challenging to handle.

Which scenario is most supported by evidence? At the moment, the balance of expert opinion and the nature of progress support Slow/Moderate Takeoff as the default expectationwww.linkedin.com www.linkedin.com. Incremental improvements and integration have been the pattern so far (no single algorithmic jump has sent performance to an entirely new regime without human-managed scaling). Additionally, economic modeling of AI impact tends to assume a smoother sigmoid curve of adoption rather than a step function. That said, one cannot rule out a Hard Takeoff – especially if one key discovery (like an AGI that can self-improve its reasoning) happens, it could change the dynamic quickly. It’s a bit like fire: you rub sticks together (gradual progress) until suddenly you get a flame (the moment of self-sustainment). If AGI can self-sustain improvement, the qualitative nature of progress changes.

On current evidence, no AI has demonstrated robust self-recursive improvement. So until we see glimmers of that, the prudent judgment is that we will have a transition spread over years where humans remain closely involved. This is echoed by many in industry who speak of “AI amplifying human capabilities” rather than replacing them overnightventurebeat.com venturebeat.com. In fact, some, like Andrew Ng, often say things like “AI is the new electricity – it will transform industries gradually” which is a clear slow-burn metaphor.

Given all this, I would assess: The most likely scenario is a Slow (or moderate) Takeoff where AGI emerges through a series of partial advances and is integrated into society, with maybe a handful of years between reaching roughly human-level AI and achieving undeniable superhuman AI across the board. Hard Takeoff is a lower probability dark horse – perhaps a 10-20% chance event, but if it happens its impact is enormous. Alternative trajectories where AGI is significantly delayed into late century or beyond are also possible, especially if current approaches stall; but given the momentum and investment in AI now, a long stagnation would require multiple concurrent barriers (technical and possibly sociopolitical). Many governments and corporations worldwide are highly motivated to push AI capabilities further (for economic and strategic reasons), making a long pause less likely barring global agreement or catastrophe.Thus, planning should primarily assume continued brisk progress and a not-too-distant arrival of AGI (in the order of decades), while also preparing for the possibility of an even faster breakthrough (in the order of years) or the need to navigate a slowdown that demands new strategies.

Implications for Policy and Strategic Preparedness

Even though this report focuses on timelines rather than policy, it’s worth briefly discussing what our findings imply for how we should prepare. The possibility of AGI within a few decades (and perhaps as soon as the 2030s or 2040s by median estimatesourworldindata.org ourworldindata.org) means policymakers and strategists today need to be forward-looking. At the same time, the uncertainty – it could be 10 years or 60 years or never – makes it challenging to allocate resources correctly. The key is flexible, robust strategies that can handle a range of arrival dates.

One insight is that overhyping or underhyping AGI both carry risks. Overhyping (believing AGI is imminent in a few years if it’s not) could lead to misallocated resources, public panic, or opportunistic grifters taking advantage of fear. Underhyping (dismissing it as too far off) could lead to complacency and lack of preparation, leaving society flat-footed if a breakthrough comes. Our balanced analysis suggests taking AGI prospects seriously (as most experts do nowourworldindata.org) but also not assuming it will solve all problems overnight or destroy the world tomorrow. A measured approach would invest in AI safety research, societal impact studies, and monitoring of AI progress (perhaps via a global compute/training run registry)www.gladstone.ai www.rand.org, while also encouraging beneficial uses of increasingly advanced AI.

From a policy perspective, strategic foresight exercises (scenario planning, war-gaming of AI race dynamics, etc.) are valuable. Governments might consider scenarios like the ones above: what if AGI comes in 2030 via a hard takeoff? 2045 via slow integration? 2075 via a new paradigm? Each scenario might require different actions. Some recent initiatives are promising: for example, the U.S. and other governments have begun analyzing AI’s labor market impacts and considering education and retraining programs, given that even advanced narrow AI (far short of AGI) could disrupt many jobswww.weforum.org www.weforum.org. Thinking in economic terms, policies to manage a transition to potentially radical productivity growth need to be in place – e.g. social safety nets if displacement outpaces job creation, or mechanisms to broadly share AI-created wealth (some have proposed things like a “windfall tax” on profits from advanced AI to redistribute benefits, anticipating a scenario where AI enables huge concentration of wealth if unchecked).

Another crucial aspect is international cooperation and competition. AGI development could become a point of rivalry between great powers (like the U.S., China, EU, etc.), as some have likened it to a new space race or nuclear arms race. A hard takeoff scenario especially raises the stakes (if the first group to get AGI can rapidly gain decisive advantage). Our finding that slow takeoff is more likely might ease some of that tension – it suggests there would be time for an international regime to form, similar to how nuclear powers established treaties and norms. Already there are calls for a global AI governance body or at least norms around safe AI developmentwww.rand.org obamawhitehouse.archives.gov. The UN and other multilateral forums are beginning to discuss “frontier AI” governance. Ensuring strategic stability (so no one feels pressured to deploy untested AGI quickly) is important. If multiple actors expect AGI by 2040, ideally they’d agree on not rushing to a dangerous deployment and perhaps pooling safety research. In any timeline, building trust and verification methods (like tracking compute or sharing AI evaluation results) could reduce the chance of an arms-race dynamic that might cause a hard takeoff in a uncontrolled way.

From a safety viewpoint, alignment research (ensuring AI goals align with human values) is urgent under almost any timeline, given how challenging it appears. Our timeline analysis suggests we likely have at least a decade or two, but alignment is a hard problem – having more time is a blessing only if we use it wisely. The alignment community often warns that even with slow takeoff, if we haven’t solved alignment by the time AGI arrives, we could still have catastrophic outcomes. Therefore, ramping up research in this area is akin to buying insurance. For instance, developing ways to supervise and constrain systems smarter than us is crucial (we might rely on “monitoring AIs” or use AIs to help check each other, which is easier in a scenario where many AIs exist, consistent with slow takeoff). Policymakers could fund this and create evaluation centers to continually test advanced AI for dangerous capabilities or behaviors before they are widely deployed.Another insight is the value of incremental policy steps now that also help in more advanced scenarios. For example, improving AI transparency (requiring documentation of training data, model capabilities, etc.) and auditability will pay dividends when models get more powerful. Encouraging a culture of responsibility among AI developers now will set norms that could avert reckless actions later. Governments could also invest in AGI scenario analysis—similar to climate models, have interdisciplinary teams regularly update predictions and check if we are getting closer faster than expected (the way Metaculus adjusted to GPT-3). For instance, if by 2030 we have AI that can reliably perform 50% of human jobs, that’s a strong signal AGI is very near, and emergency preparations might be needed (like a global summit on AI safety or even a slowdown on certain development until safeguards catch up). Our analysis indicates we should watch certain metrics and milestones: such as AI research automation (AIs contributing to AI research), general robotics capabilities, and the breadth of tasks automated. If those metrics accelerate, it might indicate a tipping point approaching.Finally, economic and cognitive science integration: Policymakers should engage not just engineers but economists (to plan for labor shifts, possible need for universal basic income or new economic models if AI can produce abundanceventurebeat.com venturebeat.com) and cognitive scientists/ethicists (to ensure AGI design respects human values and perhaps to understand how human cognition can remain relevant in an AI-rich world). If slow takeoff occurs, humans might work alongside AGI for some time – thinking through how to maintain human dignity and purpose in that context is important. If hard takeoff, contingency plans akin to disaster response might be relevant (though it’s tricky – one cannot evacuate Earth in an AI scenario as in a hurricane, but having secure compute infrastructures or even something like an “AGI emergency break” treaty could be contemplated).

In conclusion, our timeline analysis suggests that strategic preparedness efforts should start now, focusing on robust measures that handle a range of outcomes. The most likely future involves transformative AI by mid-century that will profoundly reshape the economy and societyourworldindata.org ourworldindata.org. This gives us perhaps a couple of decades to get policies in place. However, there’s a non-negligible chance this transformation could come much sooner or faster, so we should have early warning systems and emergency plans. Conversely, if progress slows, we should be ready to sustain research interest and not fall into disillusionment – continuing to support long-term projects (like neuromorphic computing or brain mapping) that could eventually crack AGI even if current deep learning doesn’t. In all cases, international collaboration, ethical considerations, and public engagement will be key. The decisions made in the next 10-20 years could determine whether AGI becomes a boon for humanity or a source of risk.

Conclusion

Predicting AGI is an exercise in humility – the range of plausible timelines remains wide, and history has chastened past forecasters. Nonetheless, by examining current trends and expert views, we can sketch a likely trajectory and identify signposts along the way. Deep learning has driven rapid AI progress, and if its scaling trends continue unabated, we could see AI systems matching human level on most economically valuable tasks sometime in the 2040s or 2050s (the median view of experts)ourworldindata.org ourworldindata.org. Many experts also assign a substantial probability to an earlier arrival (10% or higher chance by the 2030s)ourworldindata.org ourworldindata.org, given the steep performance curves and the fact that each year has brought surprises. Far fewer believe AGI is centuries away or impossible, reflecting growing confidence in eventual attainabilityourworldindata.org ourworldindata.org.

In balancing the evidence, we found that qualitative factors (the need for new paradigms, potential bottlenecks in common sense or causality) could slow progress, but quantitative momentum (compute doubling, algorithmic efficiency gains, expanding benchmarks) is currently strong. We also see that emerging technologies like quantum computing or brain-computer interfaces, while fascinating, are not likely to radically expedite AGI in the near term – progress will likely come from iterative improvements and occasional breakthroughs in AI algorithms and architectures themselvesjacquesthibodeau.com jacquesthibodeau.com. Integration of symbolic reasoning and brain-inspired hardware may eventually be part of the solution to reach AGI, but those are unfolding on a similar timescale as AI, not dramatically faster.

Considering scenarios, we assessed that a Hard Takeoff (FOOM), where an AGI rapidly self-improves to superintelligence in a matter of days or weeks, cannot be ruled out – especially if recursive self-improvement turns out to be easier than anticipated – but it is not the most evidence-backed scenario right now. It remains a critical risk to prepare for (given its disruptive potential), yet the base case seems to be a Slow or Moderate Takeoff, where capabilities advance over years, allowing for more gradual adaptationwww.linkedin.com www.linkedin.com. This scenario fits with historical analogies and most expert expectations that we will see increasing but not instant automation of tasks. Finally, alternative trajectories where progress stalls or takes a different form (like brain emulation) push AGI further out, which fewer data points currently support but is conceivable if current methods hit a wall.

In practical terms, this analysis suggests a high probability that the world will be fundamentally transformed by advanced AI within the lifetime of most people alive todayourworldindata.org ourworldindata.org. Whether that transformation is for the better or worse depends on actions taken in advance. The timeline being neither extremely short (months) nor extremely long (centuries) gives a window of opportunity to steer the outcome. It is a call to stakeholders in governments, industry, and civil society to treat AGI as a serious possibility – neither dismissing it as vaporware nor yielding to fatalism that it’s beyond control.

To conclude with an eye to the future: if current trends hold, the coming decades will likely see AI moving from a narrow tool to a general partner in nearly every human endeavor. We may live through a period where AI systems transition from doing specialized tasks (driving cars, translating text) to being universal problem-solvers working alongside us or autonomously. This period will test our institutions and values. By studying timelines and scenarios, as we have done here, we equip ourselves with foresight to navigate that unprecedented era. Uncertainty will always remain – as Hinton aptly said, “Nobody really knows…which is why we should worry now.”thenextweb.com thenextweb.comWorry, in the productive sense of investing effort in preparation and risk reduction, is warranted. But equally important is to envision the positive potential: AGI could usher in an age of abundance, discovery (curing diseases, solving climate change), and creative flourishing if guided wellventurebeat.com venturebeat.com. The timelines discussed are not just about when challenges arise, but also when humanity might reap incredible benefits.

In summary, our comprehensive analysis yields these key takeaways:

AGI is likely within this century, possibly mid-21st century, given steady progress in AI capabilitiesourworldindata.org ourworldindata.org. Some experts consider a nearer-term AGI (next 10–20 years) quite plausiblethenextweb.com thenextweb.com, although median forecasts center around 2050±10 years.
Deep learning scale has been the driver, and if sustained, could achieve AGI, but uncertainties about data, algorithms, and fundamental cognitive features leave room for surprises or delays. Alternative approaches (hybrid AI, neuromorphic chips) might become crucial if current methods plateaupmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov.
Hard takeoff vs. slow takeoff: A fast, recursive self-improvement “explosion” is a significant risk but not the most likely trajectory under current trends. A more gradual emergence of AGI capabilities, spread over years or decades, appears more consistent with the evidence we havewww.linkedin.com www.linkedin.com. Nonetheless, vigilance for signs of a potential rapid jump is necessary, as even a moderate takeoff could outpace our preparedness if we are complacent.
Expert opinion is divided but converging on seriousness: While timing estimates vary widely, the AI research community by and large agrees AGI is a matter of “when, not if” assuming no global catastrophe intervenesourworldindata.org ourworldindata.org. Skeptics of near-term AGI exist, but fewer dismiss it outright. Continuous expert surveys and prediction platforms remain valuable to update forecasts as new breakthroughs (or obstacles) occur.
Policy and strategy should be adaptive: Given timeline uncertainty, the best approach is to start building the infrastructure – in research, governance, and societal readiness – that can handle either outcome. If AGI comes faster, early preparation could be life-saving; if it comes slower, the same preparation will not be in vain, as it will likely improve AI systems and their integration with society in the meantime.
Uncertain does not mean uninformed: By combining quantitative data and expert judgment, we have a clearer picture of the road ahead than one might think. We identified milestones to watch (e.g. AI fully automating key research roles, achieving major scientific breakthroughs, or consistently passing human-level tests in multiple domains) that would signal AGI is very near. Monitoring such indicators can provide a “warning time” to shift resources or implement safety brakes if needed. Ultimately, forecasting AGI is not about a specific date on the calendar – it’s about understanding the trends and dynamics that will shape how and when machine intelligence reaches and exceeds our own. This comprehensive analysis aims to ground that understanding in current evidence and reasoned scenarios. As we stand today, on the cusp of ever-more-general AI systems, it is both an exciting and sobering time. There is much work to do to ensure that when AGI does arrive, it is safe, aligned, and beneficial. By anticipating the possible timelines, we give ourselves a better chance to succeed in that grand project.References:
Grace, K., et al. (2018). When Will AI Exceed Human Performance? Evidence from AI Experts. Journal of Artificial Intelligence Research, 62, 729-754.jair.org obamawhitehouse.archives.gov
Roser, M. (2023). AI timelines: What do experts in artificial intelligence expect for the future? Our World in Data.ourworldindata.org ourworldindata.org
OpenAI (2018). AI and Compute. [OpenAI Blog].www.scaleway.com
Hernandez, D. & Brown, T. (2020). Measuring the Algorithmic Efficiency of Neural Networks. (OpenAI preprint)singularityhub.com singularityhub.com
Dorrier, J. (2020). OpenAI Finds Machine Learning Efficiency Is Outpacing Moore’s Law. SingularityHub.singularityhub.com singularityhub.com
Bubeck, S., et al. (2023). Sparks of Artificial General Intelligence: Early experiments with GPT-4. (Microsoft Research).futurism.com futurism.com
Zhang, B., et al. (2022). Forecasting AI Progress: Evidence from a Survey of Machine Learning Researchers. arXiv:2206.02719.ourworldindata.org ourworldindata.org
Bengio, Y. (2023). Implications of Artificial General Intelligence on National and International Security. [Conference remarks].thenextweb.com thenextweb.com
Hassabis, D. (2023). Interview at Wall Street Journal’s Future of Everything Festival.thenextweb.com thenextweb.com
Amodei, D. (2023). Interview with Lex Fridman (Anthropic CEO on AGI).www.linkedin.com www.linkedin.com
Hinton, G. (2023). Public statement on AI timelines (via Twitter).thenextweb.com thenextweb.com
Kurzweil, R. (2005). The Singularity Is Near. Penguin Books.thenextweb.com
Yudkowsky, E. (2008). Artificial Intelligence as a Positive and Negative Factor in Global Risk. In Global Catastrophic Risks (ed. Bostrom & Ćirković).en.wikipedia.org en.wikipedia.org
Good, I.J. (1965). Speculations Concerning the First Ultraintelligent Machine. Advances in Computers, 6, 31-88.en.wikipedia.org en.wikipedia.org
Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press.en.wikipedia.org en.wikipedia.org
Hanson, R. (2016). The Age of Em: Work, Love and Life when Robots Rule the Earth. Oxford University Press.en.wikipedia.org en.wikipedia.org
National Science and Technology Council (2016). Preparing for the Future of Artificial Intelligence. Executive Office of the President (White House report).obamawhitehouse.archives.gov obamawhitehouse.archives.gov
Marcus, G. (2022). AGI will not happen in your lifetime – or will it? [Substack article].garymarcus.substack.com www.reddit.com
Thibodeau, J. (2023). Quantum Computing, Photonics, and Energy Bottlenecks for AGI. [Personal blog].jacquesthibodeau.com jacquesthibodeau.com
IEEE Spectrum Staff (2024). 15 Graphs That Explain the State of AI in 2024. IEEE Spectrum, 15 April 2024.www.weforum.org www.weforum.org