The Road to AGI and Beyond

Players/Concepts: Alan Turing

Alan Turing publishes "Computing Machinery and Intelligence," proposing the Turing Test as a benchmark for machine intelligence and laying philosophical groundwork for AI.

Players/Concepts: John McCarthy, Marvin Minsky, Claude Shannon, Nathaniel Rochester, etc.

A summer workshop at Dartmouth College coins the term "Artificial Intelligence" and brings together founding researchers, establishing AI as a formal field of study.

Concepts: Overpromising, Computational Limits, Lighthill Report (UK), DARPA funding cuts (US).

A period of reduced funding and interest in AI research due to unmet expectations, limitations in computing power, and critical government reports questioning progress.

Concepts: Expert Systems, Knowledge Engineering, Lisp Machines.

AI finds commercial success with Expert Systems – programs designed to mimic the decision-making ability of a human expert in narrow domains. Specialized hardware emerges.

Concepts: Collapse of Lisp Machine market, High cost/brittleness of Expert Systems.

The hype around Expert Systems fades as they prove difficult and expensive to maintain and scale. The specialized hardware market collapses, leading to another downturn in funding and interest.

Players/Concepts: IBM Deep Blue, Garry Kasparov.

IBM's chess-playing computer, Deep Blue, defeats world chess champion Garry Kasparov in a landmark match, showcasing the power of specialized hardware and brute-force search for specific tasks.

Players/Concepts: IBM Watson, Natural Language Processing (NLP), Question Answering.

IBM's Watson computer defeats human champions on the quiz show Jeopardy!, demonstrating significant advances in NLP, information retrieval, and understanding nuanced language. Paul Roetzer called this his "inflection point" for getting into AI.

Players/Concepts: AlexNet, Geoffrey Hinton, Yann LeCun, Yoshua Bengio, ImageNet Challenge, GPUs, Deep Learning.

AlexNet, a deep convolutional neural network, dramatically wins the ImageNet image recognition competition. This success, enabled by large datasets and GPU acceleration, triggers the modern deep learning revolution.

Players/Concepts: Google DeepMind AlphaGo, Lee Sedol, Reinforcement Learning, Monte Carlo Tree Search.

DeepMind's AlphaGo defeats world Go champion Lee Sedol 4-1. "Move 37" in game 2 becomes famous as a creative, unexpected move by the AI, highlighting the potential beyond human intuition in complex strategy.

Players: Sam Altman (OpenAI)

"AGI is when AI can achieve novel scientific breakthroughs on its own."

Reported from an Oct 2023 interview, highlighting a capability-focused view of AGI.

Players: Sam Altman (OpenAI)

"AGI will be a reality in five years, give or take."

Quote from the Oct 2023 interview widely reported in early 2024, accelerating public discussion on timelines.

Players: OpenAI, DeepMind, Google Cloud, Researchers

Increased focus on defining AGI, often centering on human-level performance across cognitive tasks. DeepMind publishes "Levels of AGI" paper proposing a framework for measurement.

"AGI is an AI system that is at least as capable as a human at most tasks." (DeepMind Paper Definition)

Players: Ilya Sutskever (Formerly OpenAI), Daniel Gross, Daniel Levy

Launch of a new lab explicitly focused on building safe superintelligence, signaling intense focus and competition at the frontier.

"Our singular focus means no distraction by management overhead or product cycles..." - SSI Website

Players: Anthropic (Claude 3.5 Sonnet), OpenAI (O1, GPT-4o), Google DeepMind (Gemini 1.5 Pro updates, Gemini 2.5 Pro)

Significant improvements in model reasoning, planning, and complex problem-solving capabilities ("System 2 thinking"), often utilizing techniques like test-time compute scaling.

"...starting to get to what I would call the PHD or professional level." - Dario Amodei (Anthropic CEO, referring to capabilities)

Players: Sam Altman (OpenAI), other lab leaders

Statements from AI lab leaders express increasing confidence in the *path* to AGI, even if the exact timeline remains debated.

"We are now confident we know how to build AGI as we have traditionally understood it." - Sam Altman (Jan 2025)

Players/Concepts: Leopold Aschenbrenner ("Situational Awareness"), Sam Altman (OpenAI), Dan Hendrycks ("Superintelligence Strategy"), Safe Superintelligence Inc.

Focus shifts beyond AGI towards the potential and perils of Artificial Superintelligence (ASI), with predictions of its arrival shortening.

"It is possible that we will have superintelligence in a few thousand days." - Sam Altman (Sep 2024)

Players: OpenAI, Google, Anthropic, Startups

AI systems capable of taking actions (agents) begin to be integrated into workflows, initially often semi-autonomous or focused on narrow tasks like research or scheduling. Computer Use interfaces emerge.

"In 2025 we may see the first AI agents join the workforce..." - Sam Altman (Jan 2025)

Players: Tech Companies, Enterprises

AI agents become more capable and potentially more autonomous, starting to handle more complex, multi-step tasks across various business domains, though widespread adoption challenges (integration, trust, security) likely remain.

"AI agents... just like digital employees you have to train them... evaluate them... guardrail them..." - Jensen Huang (Nvidia CEO, Nov 2024, describing the process)

Players: TBD – Likely OpenAI, Google, Anthropic, SSI

Based on current trajectories and predictions from figures like Amodei and Aschenbrenner, one or more labs might claim to have achieved AGI (matching average or skilled human performance across most cognitive tasks). Debates over definitions will likely ensue.

"...if we extrapolate the straight curve within a few years we will get to these models being above the highest professional level..." - Dario Amodei (Nov 2024, suggesting rapid progress)

Players: OpenAI, Meta, xAI, Future AI Systems

Following OpenAI's proposed stages, AI systems might begin coordinating complex tasks at the scale of an organization, potentially operating with significant autonomy.

(Based on OpenAI's "Level 5: Organizations" concept)

Players: Safe Superintelligence Inc., OpenAI, Google DeepMind

Some predictions place the potential arrival of ASI (outperforming the smartest humans across virtually all domains) around this timeframe, marking a profound shift.

(Aligned with "end of decade" predictions like Aschenbrenner's)

Players: Governments, AI labs, Corporations, Society

Significant societal adaptation becomes necessary to handle the productivity gains, job displacement, ethical challenges, and opportunities presented by advanced AGI or early ASI systems.

"Economic systems shift to accommodate AGI-era productivity and disruption." (Projected outcome)