🧭 Tracking the Path to AGI

The State of AGI Research, Mapped and Explained

Real-time analysis of the labs, researchers, and breakthroughs racing toward artificial general intelligence — investigations, comparative research maps, and founder playbooks, grounded in sourced 2026 reporting, not hype.

AGI Capability Pulse — 2026 Snapshot Tracking weekly
Reasoning & Math 92%
Coding & Tool Use 88%
Long-Horizon Agents 61%
Multimodal Grounding 74%
Continual Learning 34%
Alignment & Interpretability 41%
Directional estimates synthesized from our lab & benchmark coverage — see the Research Frontier Map for sourcing.
Inside the Machine

How Multimodal Signal Becomes an Artificial Mind

Image, language, speech, video, and real-world simulation converge into a single network — which this project visualizes here as a rotatable, benchmark-annotated brain. Drag it, or let it spin on its own.

🖼️Image 💬Language 🎙️Speech 🎬Video 🌐Real-World Sim
Reasoning & Math
92%
Coding & Tool Use
88%
Long-Horizon Agents
61%
Multimodal Grounding
74%
Continual Learning
34%
Alignment & Interpretability
41%
Reasoning & Math
Frontier models now clear graduate-level math and multi-step logic benchmarks at 92% — the region of the network with the most consistent, well-saturated benchmark performance today.
01 · Follow the Frontier

Where AGI Research Is Heading

Long-form, fully sourced series on recursive self-improvement and the capabilities that still separate today’s models from AGI.

✅ Complete · 7 of 7 Parts + 1 Bonus Report + 3 Research Updates
The Recursive Self-Improvement Series
A seven-part deep dive into whether AI can meaningfully accelerate the process that builds better AI — from the 1965 origin of the "intelligence explosion" concept through the real, independently verified systems (Dream-RSI, ModularRSI, ScienceBuddy, AlphaEvolve) attacking pieces of the problem today, with every claim checked against primary sources before publication.
01 · Frontier Recursive Self-Improvement Capability Stack 02 · How LLMs Are Built Architecture & Learning Topic Explainers Model Lineage Model Case Studies Reference Series 03 · Courses Language Modeling from Scratch Self-Improving AI Agents Course 04 · Researcher Researcher Foundations Engineering Practicum Researcher's Path 05 · Company Founding a Startup Startup Lane Deep Dives Economics & Society
02 · Learn How LLMs Work

How LLMs Are Built: From Raw Text to Your Screen

Fourteen steps across four acts — pre-training, alignment, deployment, and what happens the instant a user hits send — with the objective functions behind each one. Take the interactive tour first, then go deeper: every step links to a full article with runnable code.

Act 1 of 4 · Pre-training
Data Collection
🧬 Architecture & Learning Technique · 6 Articles
Architecture & Learning Technique: How the Field Actually Got Here
The history of neural network architectures, the separate thread of learning techniques layered on top, and the live debate over what comes after Transformers.
✅ Complete · 8 Labs, Fully Cross-Referenced
The Model Lineage Series
Eight consolidated comparisons tracing every major model from OpenAI, Anthropic, Google DeepMind, xAI, DeepSeek, Meta, Mistral, and Qwen — architecture, data, safety frameworks, and disclosure philosophy, each lineage cross-linked to the other seven for direct side-by-side reading.
03 · Take a Course

Full University Courses, Lecture by Lecture

Guides to Stanford courses on building language models from scratch and on self-improving agents.

🧱 Lecture Series · Part 4 of 19
Language Modeling from Scratch: A Guide to Stanford's CS336
A lecture-by-lecture deep-dive series on Stanford's from-scratch LLM-building course — tokenization, architectures, systems, scaling laws, data, and alignment, with real citations, original code walkthroughs, and hands-on directions tied to the actual public assignment repos. One full article per lecture, no length cap.
🎓 Course Guide · Handbook + 5 Deep Dives (Complete)
Self-Improving AI Agents: A Guide to Stanford's CS329A
A standalone companion to Stanford's course on the mechanics behind self-improving agents — test-time compute, verification, agentic feedback loops, planning as search, RL for reasoning, and long-horizon evaluation. A different branch of "self-improvement" from this site's RSI series, covered on its own.
04 · Become a Researcher

From Foundations to a Frontier-Lab Career

The curriculum, the engineering practice and the career path.

🧭 The Researcher's Path · 4 Articles
The Researcher's Path: Careers, Labs & Field Notes
What it actually takes to work on AGI — papers to read, labs to target, a real day in the life, and a full investigation into one of the field's most secretive labs.
05 · Build a Company

Three Lanes, Three Very Different Bets

Every AGI-adjacent startup — and increasingly, every researcher's career choice — falls into one of three strategic lanes.

💼 Economics & Society · 2 Articles
Economics & Society: What AI Progress Does to the Rest of the World
Two pieces on the productivity paradox — what happens to jobs, growth, and the shape of the economy as AI adoption accelerates.
Go Deeper

Explore Everything

The full archive, or the interactive course that started it all.